EEG May 2019 Volume 25, Number 2 by Association of Environmental & Engineering Geologists (AEG)

ENVIRONMENTAL & ENGINEERING GEOSCIENCE

Environmental & Engineering Geoscience MAY 2019

VOLUME XXV, NUMBER 2

Volume XXV, Number 2, May 2019

THE JOINT PUBLICATION OF THE ASSOCIATION OF ENVIRONMENTAL AND ENGINEERING GEOLOGISTS AND THE GEOLOGICAL SOCIETY OF AMERICA SERVING PROFESSIONALS IN ENGINEERING GEOLOGY, ENVIRONMENTAL GEOLOGY, AND HYDROGEOLOGY

Environmental & Engineering Geoscience (ISSN 1078-7275) is published quarterly by the Association of Environmental & Engineering Geologists (AEG) and the Geological Society of America (GSA). Periodicals postage paid at AEG, 201 East Main St., Suite 1405, Lexington, KY 40507 and additional mailing offices. EDITORIAL OFFICE: Environmental & Engineering Geoscience journal, Department of Geology, Kent State University, Kent, OH 44242, U.S.A. phone: 330-672-2968, fax: 330-672-7949, ashakoor@kent.edu. CLAIMS: Claims for damaged or not received issues will be honored for 6 months from date of publication. AEG members should contact AEG, 201 East Main St., Suite 1405, Lexington, KY 40507. Phone: 844-331-7867. GSA members who are not members of AEG should contact the GSA Member Service center. All claims must be submitted in writing. POSTMASTER: Send address changes to AEG, 201 East Main St., Suite 1405, Lexington, KY 40507. Phone: 844-331-7867. Include both old and new addresses, with ZIP code. Canada agreement number PM40063731. Return undeliverable Canadian addresses to Station A P.O. Box 54, Windsor, ON N9A 6J5 Email: returnsil@imexpb.com. DISCLAIMER NOTICE: Authors alone are responsible for views expressed in articles. Advertisers and their agencies are solely responsible for the content of all advertisements printed and also assume responsibility for any claims arising therefrom against the publisher. AEG and Environmental & Engineering Geoscience reserve the right to reject any advertising copy. SUBSCRIPTIONS: Member subscriptions: AEG members automatically receive digital access to the journal as part of their AEG membership dues. Members may order print subscriptions for $60 per year. GSA members who are not members of AEG may order for $60 per year on their annual GSA dues statement or by contacting GSA. Nonmember subscriptions are $295 and may be ordered from the subscription department of either organization. A postage differential of $10 may apply to nonmember subscribers outside the United States, Canada, and Pan America. Contact AEG at 844-331-7867; contact GSA Subscription Services, Geological Society of America, P.O. Box 9140, Boulder, CO 80301. Single copies are $75.00 each. Requests for single copies should be sent to AEG, 201 East Main St., Suite 1405, Lexington, KY 40507. © 2019 by the Association of Environmental and Engineering Geologists All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from AEG. THIS PUBLICATION IS PRINTED ON ACID-FREE PAPER Abdul ShAkoor Department of Geology Kent State University Kent, OH 44242 330-672-2968 ashakoor@kent.edu

EDITORS

briAn G. kAtz Florida Department of Environmental Protection 2600 Blair Stone Rd. Tallahassee, FL 32399 850-245-8233 eegeditorbkatz@gmail.com

EDITORIAL BOARD Watts, Chester “skip” F. Radford University, Chair hasan, syed University of Missouri, Kansas City nandi, arpita East Tennessee State University OOmmen, thOmas Michigan Technological University

sasOWsky, ira d. University of Akron katz, Brian G. Florida Department of Environmental Protection shakOOr, aBdul Kent State University

ASSOCIATE EDITORS Brankman, Charles, COnsultant Boston MA BruCknO, Brian Virginia Department of Transportation ClaGue, JOhn J. Simon Fraser University, Canada de GraFF, JerOme V. California State University, Fresno Fryar, alan University of Kentucky hauser, ernest Wright State University hutChinsOn, Jean Queens University, Canada keatOn, JeFF AMEC Americas marinOs, Vassillis Aristotle University of Thessaloniki, Greece

mCBride, JOhn Brigham Young University mWakanyamale, kisa Illinois State Geological Survey santi, paul Colorado School of Mines dee, seth University of Nevada, Reno shlemOn, rOy R.J. Shlemon & Associates, Inc. stephensOn, William U.S. Geological Survey stOCk, GreG National Park Service sukOp, miChael Florida International University ulusay, resat Hacettepe University, Turkey Watts, Chester F. “skip,” Radford University West, terry Purdue University

SUBMISSION OF MANUSCRIPTS Environmental & Engineering Geoscience (E&EG), is a quarterly journal devoted to the publication of original papers that are of potential interest to hydrogeologists, environmental and engineering geologists, and geological engineers working in site selection, feasibility studies, investigations, design or construction of civil engineering projects or in waste management, groundwater, and related environmental fields. All papers are peer reviewed. The editors invite contributions concerning all aspects of environmental and engineering geology and related disciplines. Recent abstracts can be viewed under “Archive” at the web site, “http://eeg.geoscienceworld.org”. Articles that report on research, case histories and new methods, and book reviews are welcome. Discussion papers, which are critiques of printed articles and are technical in nature, may be published with replies from the original author(s). Discussion papers and replies should be concise. To submit a manuscript go to http://eeg.allentrack.net. If you have not used the system before, follow the link at the bottom of the page that says New users should register for an account. Choose your own login and password. Further instructions will be available upon logging into the system. Please carefully read the “Instructions for Authors”. Authors do not pay any charge for color figures that are essential to the manuscript. Manuscripts of fewer than 10 pages may be published as Technical Notes. For further information, you may contact Dr. Abdul Shakoor at the editorial office.

Cover photo Emergency aerial damage survey of the lower Layour River Valley, Dominica, West Indies in the aftermath of the Matthieu landslide dam. The dam collapsed about midnight on July 27, 2011 causing drainage of the entire impounded lake by the following day (A. James and De Graff, J.V., 2012, Landslides 9:529–537). The extent of the flood-water affected area was fully discernible on July 29th. Extensive deposition of sediment surrounds the Layou River Hotel. This included completely filling the hotel pool. The river is visibly downcutting into the initial flood-sediment filled channel. The initial emergency when the landslide dam was formed is described in De Graff, J.V., James A., and Breheny, P., 2010, Environmental & Engineering Geoscieces 16:73-89. (Photo credit: Government of the Commonwealth of Dominica, W.I.). See article on p. 138.

Environmental & Engineering Geoscience

Volume 25, Number 2, May 2019

Table of Contents

103

Stabilization Behavior and Performance of Loess Using a Novel Biomass-based Polymeric Soil Stabilizer Shengyan Pu, Yaqi Hou, Jin Ma, Yan Zou, Liu Xu, Qingqing Shi, Sijia Qian, and Xiangjun Pei

103

Stabilization Behavior and Performance of Loess Using a Novel Biomass-based Polymeric Soil Stabilizer Shengyan Pu, Yaqi Hou, Jin Ma, Yan Zou, Liu Xu, Qingqing Shi, Sijia Qian, and Xiangjun Pei

115

Proposed Improvements to Analytical Models of Anchored Retaining Walls Benamara Fatima Zohra, Belabed Lazhar, and Rouaiguia Ammar

115

Proposed Improvements to Analytical Models of Anchored Retaining Walls Benamara Fatima Zohra, Belabed Lazhar, and Rouaiguia Ammar

127

Lateritic Soil Treated with Waste Wood Ash as Liner in Landfill Construction Johnson R. Oluremi, Adrian O. Eberemu, Stephen T. Ijimdiya, and Kolawole J. Osinubi

127

Lateritic Soil Treated with Waste Wood Ash as Liner in Landfill Construction Johnson R. Oluremi, Adrian O. Eberemu, Stephen T. Ijimdiya, and Kolawole J. Osinubi

141

Ensuring Successful Landslide Investigation during an Emergency Response Jerome V. De Graff

141

Ensuring Successful Landslide Investigation during an Emergency Response Jerome V. De Graff

155

Extraction and Comparison of Spatial Statistics for Geometric Parameters of Sedimentary Layers from Static and Mobile Terrestrial Laser Scanning Data Gabriel Walton, Georgia Fotopoulos, and Robert Radovanovic

155

169

Environmental Availability of Potentially Toxic Elements in an Agricultural Mediterranean Site Dimitrios Alexakis, Dimitra Gamvroula, and Eleni Theofili

169

Environmental Availability of Potentially Toxic Elements in an Agricultural Mediterranean Site Dimitrios Alexakis, Dimitra Gamvroula, and Eleni Theofili

179

The Heavy Metals Pollution Index and Water Quality Monitoring of the Zarrineh River, Iran Maryam Khalilzadeh Poshtegal and Seyed Ahmad Mirbagheri

179

The Heavy Metals Pollution Index and Water Quality Monitoring of the Zarrineh River, Iran Maryam Khalilzadeh Poshtegal and Seyed Ahmad Mirbagheri

Stabilization Behavior and Performance of Loess Using a Novel Biomass-based Polymeric Soil Stabilizer

SHENGYAN PU* YAQI HOU JIN MA YAN ZOU LIU XU QINGQING SHI SIJIA QIAN XIANGJUN PEI

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, 1#, Dongsanlu, Erxianqiao, Chengdu 610059, Sichuan, P.R. China

Key Terms: Loess, Stabilization, Soil Erosion, Biomass Polymer, Soil Stabilizer ABSTRACT Serious soil erosion can endanger human survival and sustainable development. Therefore, simple and highly efficient soil stabilizers that can be used to treat loess soil, which has poor water stability and easily disintegrates, are a topic of concern for researchers. In this work, a biomass-based polymeric soil stabilizer (CXZ) was prepared using a “green” strategy with polymerization of carboxymethyl cellulose and xanthan gum. A direct shear test, unconfined compressive strength properties, water stability, and erosion resistance were systematically investigated to test the stabilization performance. The stabilizer agglomerated small loess particles into large aggregates through “coating” and “weaving” effects to increase the cohesion, water stability, and erosion resistance significantly, as demonstrated by Fourier transform infrared spectroscopy and scanning electron microscope. Furthermore, in a 30-day growth experiment, the number of alfalfa plants and the plant height in stabilized loess both increased with the increase in CXZ stabilizer concentration. This work provides insight into a novel biomass-based soil-curing agent, broadening its applications in loess remediation and soil erosion control. INTRODUCTION Loess is a special soil widely distributed in the Loess Plateau of China, including in Shanxi, Shaanxi, and Gansu provinces. Because of its easy collapsibility, dis*Corresponding author email: pushengyan@gmail.com; pushengyan13@cdut.cn

persiveness, high compressibility, and low strength, geological disasters occur frequently, such as soil erosion, foundation collapsibility, reservoir slope cutting, and landslides and collapses. Untreated loess often causes serious harm to industrial and agricultural construction (Zou et al., 2007) and great damage to the ecological environment (Pei et al., 2015). Two primary categories of processing methods are used for loess, densification and reinforcement (Dahale et al., 2012). The densification are aimed at improving the density of loess and include hammer surface compaction, dynamic compaction, soil compaction pile, and prewetting. With methods of this type, loess can be compacted through physical means, whereas soil properties are not modified. The reinforcement method aims to reduce the collapsibility and improve the bearing capacity by changing the microstructure of loess (Kozubal and Steshenko, 2015). In recent years, soil remediation technologies based on soil stabilizers have shown good results in practical engineering applications. Commonly, soil stabilizers are divided into five categories: grappler cement, slag silicate, ions, biological enzymes, and polymers. Traditional stabilizers such as lime cement (Asgari et al., 2015), fly ash (Vukicevic et al., 2015), and slag (Kaya, 2016) have been studied intensively. Their performance in soil reinforcement has been tested, and their stabilizing reactions with soils are reasonably well understood. These types of stabilizers have good stabilization performance and low cost. However, they also have the disadvantages of high incorporation and secondary pollution, which affect the growth of vegetation and fail to meet the requirement of ecological protection. During the last decade, ions and biological enzymes have been developed extensively. Compared with grappler cement and slag silicate, these two types of stabilizers have many advantages, such as lower transportation cost, less raw material use,

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

103

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

103

Pu, Hou, Ma, Zou, Xu, Shi, Qian, and Pei

Figure 1. The structural formula of (a) CMC and (b) XG.

and little ecological destruction. The finished products include ISS (Q.B. Liu et al., 2009) stabilizer, EN1 (Liu and Zhang, 2014) stabilizer, and immobilized enzyme (Lahalih and Ahmed, 1998). However, these types of stabilizers can only be applied to limited soil types, and their stabilizing effects are variable. Compared with the previously mentioned stabilizers, polymer soil stabilizers, such as polyvinyl alcohol (Tumsavas and Tumsavas, 2011), polyacrylamide (Mamedov et al., 2009), polyacrylic acid (Tyliszczak et al., 2009), and SH soil stabilizer (Cheng et al., 2014), are receiving much attention because of their low dosing amount, easy transportation, convenient production, and stable effects. When mixed into soil, these stabilizers function in several ways. They assist in alignment of loess particles, which improves compactibility and reduces sensitivity to water (Onyejekwe and Ghataora, 2015). However, some polymer soil stabilizers have poor water resistance and cause secondary pollution. Therefore, the selection of an appropriate polymer has been the focus of scholars in related fields (J. Liu et al., 2009). Biomass-based polymer materials are a type of new polymer made from renewable biomass through biological, physical, and chemical means (Pu et al., 2017). These polymers have the characteristics of low environmental impact and renewability (Llevot et al., 2016). However, few biomass-based soil stabilizers are currently on the market (Benyounes et al., 2010); therefore, the time is ideal for the selection of biomass materials for application to soil stabilization. In this study, a new type of polymer soil stabilizer was successfully developed using the polymerization of carboxymethyl cellulose and xanthan gum (CXZ). Indirect shear testing, unconfined compressive strength (UCS), water stability, and erosion resistance were used to evaluate the stabilization properties. Additionally, a growth experiment was conducted to investigate the effect of this stabilizer on plant growth. Microstructures of the loess before and after stabilization were compared using a scanning electron microscope (SEM) to document the stabilization mechanism and 104

to provide a theoretical basis for practical engineering applications. MATERIALS AND METHODS Materials Materials included carboxymethyl cellulose sodium (CMC, FVH9), sodium trimetaphosphate (Na3 [PO3 ]3 , STMP), and xanthan gum (XG), all of which can be bought from commercial suppliers. The structural formulas of CMC and XG are shown in Figure 1. Deionized (D.I.) water was produced using a Ulupure Milli-Q water puriﬁcation system. The loess used for this study was acquired from Xianyang, Shanxi Province, China. The soil sample had a liquid limit of 23.3, a plastic limit of 12.6, a plasticity index of 10.7, a natural moisture content of 8.66 percent, a designed dry density of 1.6 g/cm3 , and a natural density of 1.4 g/cm3 . The loess was classiﬁed as silty clay, with particles composed of 46.3 percent sand, 45.5 percent silt, and 8.2 percent clay. Preparation of CXZ Soil Stabilizer First, 3.0 g of CMC, 1.0 g of XG, and 0.1 g of STMP were dissolved in 150, 40, and 10 mL of ultrapure water, respectively. After solutions dissolved, CMC and XG solutions were mixed. The STMP solution was then dripped into the mixed solution slowly. Finally, the resultant solution was stirred fully for 1 hour to ensure complete mixing. Performance Test of the CXZ Soil Stabilizer We tested the direct shear, UCS, water stability, and erosion resistance in a slope simulation, and we conducted a growth experiment. The Fourier transform infrared spectrum (FTIR) was also used to identify the functional groups of the CXZ soil stabilizer. SEM was used to characterize the micromorphology of the unaltered and stabilized loess to provide

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Stabilization and Performance of Loess with Polymeric Soil Stabilizer

theoretical guidance for practical engineering applications in the future. The direct shear test and UCS test were performed according to the Standard for Soil Test Method (GB/T50123-1999), a national criterion for geotechnical tests in China, which was set based on ASTM standards. The water stability test was done by the hydrostatic disintegration method. The erosion resistance test was performed using a rainfall simulation on a constructed slope, with the soil erosion susceptibility recorded. The growth experiment investigated the influence of the CXZ soil stabilizer on plant growth by recording the germination rate and growth status of alfalfa. A certain volume of CXZ with different concentrations was added to 96 g of dry loess, and the mixtures were put into a test model with a diameter of 61.8 mm, a height of 20 mm, and a volume of 60 mm3 . Then, a jack was used to shape the mixtures with static pressure. Direct Shear Test of the Stabilized Loess The direct shear tests were conducted at a strain rate of 0.8 mm/min under pressures of 50, 100, 200, and 300 kPa to define the shear strength parameters. First, the loess was dried and sieved through a 2 mm screen. Second, different concentrations (0 percent, 25 percent, 50 percent, 75 percent, and 100 percent) of the CXZ soil stabilizer were mixed with the loess samples. Third, each loess sample was filled into a direct shear ring cutter with a diameter of 61.5 mm and a height of 20 mm according to a dry density of 1.6 g/cm3 and a designed moisture content of 19.4 percent. Fourth, samples were unloaded from the mold after curing for 72 hours at room temperature. Finally, a direct shear apparatus was used to determine the shear strength of the stabilized loess. Unconfined Compressive Strength Test of the Stabilized Loess The UCS test was performed according to the Standard for Soil Test Method (GB/T50123-1999). The specific steps were as follows: (i) The CXZ soil stabilizer was diluted to concentrations of 0 percent, 25 percent, 50 percent, 75 percent, and 100 percent; (ii) the solutions were mixed with dry loess screened with a 2 mm sieve; (iii) the well-mixed soil samples were put into a test model with a diameter of 39.1 cm and a height of 80 mm according to the designed moisture content of 19.4 percent and dry density of 1.6 g/cm3 at room temperature to shape them with static pressure; and (iv) samples were unloaded from the mold after curing for 0, 24, 48, 72, and 96 hours, and the test was performed.

Stabilization and Performance of Loess with Polymeric Soil Stabilizer

Water Stability Test of the Stabilized Loess Water stability, also called water resistance, is the extent to which soil is affected by water. Specifically, water stability is characterized by a soil surface that resists deformation when exposed to water. No relevant test specifications are available for the water stability test. Thus, the hydrostatic disintegration method was used in the experiment. In the test, immerse the soil with water in a 1,000 mL clear glass beaker. A digital camera was used to record the macroscopic disintegration of the stabilized soil. Different amounts of CXZ were added to the loess, and the mixtures were put into a test model with a diameter of 61.8 mm, a height of 20 mm, and a volume of 60 mm3 , according to the dry density value. Then, a jack was used to shape the mixtures with static pressure. Third, the compacted samples were placed in the standard curing room to air dry for 3 days. After form stripping, a sample was submerged in water inside a 1,000 mL transparent glass beaker and the disintegration rate of the sample was recorded. A digital camera was used to record the macroscopic disintegration of the stabilized soil.

Erosion Resistance Test of the Stabilized Loess The erosion resistance test was performed to test the effect of the CXZ soil stabilizer on loess under simulated rainfall, and the loess erosion rate on the slope was calculated. The frame was constructed with a gradient of 30 degrees and a size of 30 cm × 20 cm × 15 cm. The frame (20 cm × 15 cm × 3 cm) was filled with sample material, and the loess mass was calculated before and after erosion. The rainfall simulator was set to simulate rainfall by regulating the amount of water applied to the loess, and a collection box (20 cm × 20 cm × 20 cm) was used to collect the eroded loess from the simulated rainfall erosion. All test models were constructed by the research group (Figure 2). The experimental procedures were as follows: (i) 1,000 g sample of dry loess was mixed well with different concentrations of the CXZ soil stabilizer (0 percent, 25 percent, 50 percent, 75 percent, and 100 percent), with an initial water content of 19.4 percent; (ii) the mixture was evenly spread in the test tray, with the weight recorded as M0 , and then cured for 3 days; (iii) the tray was placed on the simulated slope at 30° for the erosion test, followed by the simulated rainfall at the intensity and duration of 3 L/min and 1 hour, respectively; and (iv) the eroded loess was collected with the collection box and dried for 24 hours at 105°C, and the weight was recorded as m1 . Dry soil weight m was equal to m1 minus m0 , and the erosion rate was equal

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

105

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

105

Pu, Hou, Ma, Zou, Xu, Shi, Qian, and Pei

Figure 2. (a) Side view of the simulation equipment. (b) Front view of the simulation equipment.

to m/1,000 g. The erosion rate indicated the severity of loess erosion. Plant Growth Experiment The growth experiment was conducted to test the influence of the CXZ soil stabilizer on the growth of alfalfa. The stabilizer was diluted to 0 percent, 25 percent, 50 percent, 75 percent, and 100 percent and mixed with loess thoroughly. Then, a 1000 g sample of stabilized loess with a moisture content of 19.4 percent was placed in a 15 cm × 10 cm flowerpot. After 3 days of curing, alfalfa seeds were evenly spread on the soil surface, covered with 1 cm of fine sand, and watered every 2 days. Samples in triplicate were established in each group, and each sample contained 30 seeds. The germination rate and plant growth status were recorded after 30 days. SEM Characterization Scanning electron microscopy (SEM, Hitachi S3000N, Japan) was used to analyze the changes in loess structure before and after stabilization. Sample preparation was performed as follows: First, stabilized loess clods that had solidified for 4 days with two concentrations of stabilized loess, 0 percent and 75 percent, both of which had 1.6 g/cm3 of dry density and 20 percent initial soil moisture content, were broken. Second, a broken clod was sampled at the fracture to maintain the integrity of the fracture surface, and the sample

was air-dried at room temperature. Third, the rest of the surface was ground smooth to obtain a fracture surface with 1 cm2 and 3 mm for the size and thickness, respectively. Fourth, the loess was attached to the sample stage and sprayed with platinum under vacuum, and images were recorded. RESULTS AND DISCUSSION Shear Strength Parameters Cohesion and internal friction angle are important parameters for evaluating soil shear strength. Cohesion is mainly caused by the joint interaction of attraction forces between soil particles, viscous forces between hydration ﬁlms, and the surface tension of capillary water. However, the internal friction angle is mainly related to the sliding friction and the bite force between the soil particles. The disconnected relationship between cohesion and internal friction angle of stabilized and untreated soil is presented in Table 1 and Figure 3. The cohesion of the reference sample was 22.8 kPa without curing, and the cohesion of samples remained almost constant with increasing CXZ concentration (Figure 3a). This may be due to the stabilizer, which does not change the viscous force between the hydration membrane and the surface tension of the capillary water in the absence of curing, but which enhances the contact force between the soil particles to a certain extent, as manifested by a slight increase in cohesion. Soil water content, hydration ﬁlm thickness, and interparticle distance decreased with increasing

Table 1. Direct shear test results of specimens under diﬀerent curing times. Cohesion (kPa) Sample Number Z1 Z2 Z3 Z4 Z5

106

Table 1. Direct shear test results of specimens under diﬀerent curing times. Internal Friction Angle (°)

Concentration (%)

Moisture Content (%)

72 hours

0 25 50 75 100

19.4 19.4 19.4 19.4 19.4

22.8 28.8 32.7 29.3 34.7

144 162.5 217.7 304.2 321.2

30.4 27.7 29.9 30.2 31.8

52.7 57.5 52.9 64.5 51.3

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Cohesion (kPa) Sample Number Z1 Z2 Z3 Z4 Z5

106

Internal Friction Angle (°)

Concentration (%)

Moisture Content (%)

72 hours

0 25 50 75 100

19.4 19.4 19.4 19.4 19.4

22.8 28.8 32.7 29.3 34.7

144 162.5 217.7 304.2 321.2

30.4 27.7 29.9 30.2 31.8

52.7 57.5 52.9 64.5 51.3

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Stabilization and Performance of Loess with Polymeric Soil Stabilizer

Figure 3. (a) Eﬀect of soil stabilizer concentration on cohesion of samples. (b) Variation in internal friction angle of samples.

Figure 4. (a) Relationship between unconfined compressive strength of different samples and curing time. (b) Relative increment of unconfined compressive strength with stabilization time.

curing time, resulting in a signiﬁcant increase in capillary water surface tension and contact area between soil particles. In addition, the cohesion increased with the stabilizer concentration, which may be attributed to the enhanced interaction between soil particles and augmented formation of reticulated membrane structure at higher CXZ concentrations. Speciﬁcally, a rapid increase in cohesion occurred at CXZ concentrations of 25 percent to 75 percent, while a slower increase

occurred at CXZ concentrations of 0 percent to 25 percent and 75 percent to 100 percent. The internal friction angle of samples increased drastically after curing for 72 hours compared with

Table 2. Unconﬁned compressive strength of soil. Sample Number Z6 Z7 Z8 Z9 Z10

Concentration (%)

Moisture Content (%)

0 25 50 75 100

19.4 19.4 19.4 19.4 19.4

0.182 0.18 0.179 0.19 0.189

± ± ± ± ±

1d 0.004b 0.006b 0.006b 0.003a 0.006ab

0.307 0.217 0.229 0.237 0.256

± ± ± ± ±

0.008a 0.016c 0.036bc 0.017bc 0.022b

2d 0.308 0.313 0.342 0.368 0.639

± ± ± ± ±

0.014c 0.013bc 0.027bc 0.028b 0.045a

occurred at CXZ concentrations of 0 percent to 25 percent and 75 percent to 100 percent. The internal friction angle of samples increased drastically after curing for 72 hours compared with

Table 2. Unconﬁned compressive strength of soil.

Unconﬁned Compressive Strength (MPa) 0

Figure 4. (a) Relationship between unconfined compressive strength of different samples and curing time. (b) Relative increment of unconfined compressive strength with stabilization time.

3d 1.118 1.153 1.304 1.488 1.581

± ± ± ± ±

0.090b 0.108b 0.149ab 0.140ac 0.104a

Sample Number

4d 1.956 2.094 2.128 2.236 2.330

± ± ± ± ±

0.126b 0.123ab 0.153ab 0.204ab 0.148a

The data in the table are the mean ± SD (n = 3); a, b, c, d, and e indicate significant differences at the p = 0.05 level between different concentrations of CXZ at a specific cure time.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

107

Z6 Z7 Z8 Z9 Z10

Concentration (%)

Moisture Content (%)

0 25 50 75 100

19.4 19.4 19.4 19.4 19.4

Unconﬁned Compressive Strength (MPa) 0 0.182 0.18 0.179 0.19 0.189

± ± ± ± ±

1d 0.004b 0.006b 0.006b 0.003a 0.006ab

0.307 0.217 0.229 0.237 0.256

± ± ± ± ±

0.008a 0.016c 0.036bc 0.017bc 0.022b

2d 0.308 0.313 0.342 0.368 0.639

± ± ± ± ±

0.014c 0.013bc 0.027bc 0.028b 0.045a

3d 1.118 1.153 1.304 1.488 1.581

± ± ± ± ±

0.090b 0.108b 0.149ab 0.140ac 0.104a

4d 1.956 2.094 2.128 2.236 2.330

± ± ± ± ±

0.126b 0.123ab 0.153ab 0.204ab 0.148a

The data in the table are the mean ± SD (n = 3); a, b, c, d, and e indicate significant differences at the p = 0.05 level between different concentrations of CXZ at a specific cure time.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

107

Pu, Hou, Ma, Zou, Xu, Shi, Qian, and Pei

Figure 5. Dissolving patterns of unstabilized (a, b, and c, 0 percent) and stabilized (d, e, and f, 100 percent) soils.

the uncured samples, which may be due to the energy required to cross the barrier during the soil sample shearing process, which gradually increases with an increase in the sliding friction resistance. However, under the same curing conditions, the internal friction angle remained almost constant in untreated soil and stabilized soil for the different CXZ concentrations (Figure 3b), because this force is mainly related to the strength of soil particles rather than the concentration of stabilizers. The addition of stabilizers effectively improved the shear strength of the loess after curing for a certain period of time, but the effect of curing time on the internal friction angle was greater than the effect of the concentration of CXZ. Unconfined Compressive Strength The UCS values of samples with different concentrations and curing times are presented in Table 2. As indicated in Table 2 and Figure 4a, the UCS of stabilized soil increased continuously with curing time; however, the concentration effect was only reflected in the curing time of more than 3 days. In the first 2 days, the strength of untreated soil increased rapidly and exceeded that of stabilized soil due to rapid moisture loss. However, the unconfined compressive strength of stabilized soil increased significantly after 2 days, particularly from the second day to the third day, which indicated that some time was required for the CXZ to stabilize the soil. Furthermore, Figure 4b shows the relative increment of UCS values. The increase in strength of stabilized soil also primarily occurred during a curing time from 2 to 3 days. For example, the relative increment of UCS tested at 75 percent concentration after 1, 2, 3, and 4 days was 0.25-, 0.44-, 3.35-, and 0.5-fold, respectively. A paired t-test was also used to select the UCS of each concentration of CXZ sample at a steady state. The results showed that most pairs 108

evaluated according to concentrations were not significant. By conventional criteria, the weak difference is considered to be not statistically significant. Therefore, the concentration of CXZ stabilizer does not enhance the UCS of loess to a large extent, and a certain period of time is required to fully react the stabilizer with the loess to improve the UCS of the loess. Water Stability As shown in Figure 5, the untreated loess (Figure 5a, b, c) disintegrated completely after 30 seconds, and the water stability of the stabilized loess with a concentration of 100 percent stabilizer (Figure 5d, e, f) improved significantly. In detail, when untreated soil encountered water, moisture penetrated into the soil quickly. Then, many bubbles caused the soil to disintegrate rapidly because of the weak adhesion among soil particles, and many fine particles were dispersed, making the water turbid. Comparatively, the performance of disintegration resistance was better in stabilized soil. When the stabilized soil was immersed into water, the voids were filled with water, and massive bubbles overflowed from the soil. After the voids near the wetted surface were completely filled with water, only a few scattered bubbles still adhered. The stabilized soil remained cohesive and minimally deformed over a short time. After the sample was saturated with water for a longer period of time, micro-cracks appeared on the surface, and the water infiltrated along the cracks, which became longer and wider until they penetrated fully. The time of first crack and disintegration time for different samples are shown in Table 3. This table clearly shows that the time of first crack and time of complete disintegration were both reduced with an increase in CXZ stabilizer concentration. Particularly, the time of first crack was delayed from 0 to 140 hours

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Sample Number Z11 Z12 Z13 Z14 Z15

Stabilization and Performance of Loess with Polymeric Soil Stabilizer

Table 3. Disintegration time under diﬀerent stabilizer concentrations.

Concentration (%)

Moisture Content (%)

Dry Density (g/cm3 )

Time of First Crack (h)

0 25 50 75 100

19.4 19.4 19.4 19.4 19.4

1.6 1.6 1.6 1.6 1.6

— 1 20 48 140

with an increase in the concentration from 0 percent to 100 percent. Additionally, although the untreated loess disintegrated completely after 30 seconds, no disintegration of the sample occurred with a concentration of 100 percent, which was attributed to the agglomeration of soil particles into large units in the stabilized samples. Consequently, the time of soaking and softening of the soil agglomerate was prolonged, and the disintegration rate of the soil was slowed (Lv et al., 2014). The numbers of soil fragments deposited on the bottom of the beaker and the particles scattered in the water were reduced after stabilization of the soil. The water in the beaker was clearer, and the degree of disintegration of soil decreased with an increase in stabilizer concentration. These results indicate that the water stability of the treated specimens improved. Erosion Resistance As shown in Table 4 and Figure 6, the surface erosion of untreated soil was noticeable, and the erosion rate reached 100 percent after 30 minutes of simulated rainfall because of the easy disintegration of loess in water. However, the erosion rate of stabilized soil was reduced signiﬁcantly, and it decreased continuously as the concentration of the CXZ stabilizer increased. In particular„ when stabilizer concentrations reached 75 percent and 100 percent, the erosion rate of specimens was only 1.8 percent and 0.7 percent, respectively. Consequently, the CXZ soil stabilizer improved the erosion resistance of the loess.

Disintegration Time 30 seconds 48 hours 144 hours 300 hours —

Growth Experiment The seed germination rates after planting 30 seeds in different CXZ concentrations are shown in Table 5 and Figure 7. The CXZ concentration influenced the germination rate of seeds under certain conditions (Figure 7). With an increase in concentration, the germination rate increased gradually. The maximum germination rate of 73.3 percent was observed at the 75 percent concentration, but the germination began to decrease at CXZ concentrations of 100 percent. However, the germination rate in the untreated loess was low, at 23.3 percent, which might be primarily due to the lack of soil aggregate structure and consequently poor soil water retention properties, resulting in insufficient water for seeds during the germination stage. As shown in Figure 8 (parts a, b, c, d, e), after 30 days of growth, compared with untreated loess, the CXZ soil stabilizer obviously promoted the growth of alfalfa, and the number of alfalfa plants and the plant height increased obviously with an increase in CXZ stabilizer concentration. It was well known that soil water contributes to the migration of inorganic nutrients such as N, P, and K to increase root system development, because water is the solvent by which plants utilize trace elements in the soil, and the water retention efficiency of soil increases with the concentration of stabilizer (Liu et al., 2011). However, in this experiment, the best growth of plants was observed at the 75 percent concentration of CXZ (Figure 8d and Table 5). This was because at the high concentration of stabilizer, the gaps among the aggregates might be reduced, eventually affecting the respiration of roots

Sample Number Z11 Z12 Z13 Z14 Z15

Concentration (%)

Moisture Content (%)

Dry Density (g/cm3 )

Time of First Crack (h)

0 25 50 75 100

19.4 19.4 19.4 19.4 19.4

1.6 1.6 1.6 1.6 1.6

— 1 20 48 140

Table 4. The loess erosion rate of the unstabilized and stabilized soils. Sample Number Z16 Z17 Z18 Z19 Z20

30 seconds 48 hours 144 hours 300 hours —

Table 4. The loess erosion rate of the unstabilized and stabilized soils.

Concentration (%)

Dry Density (g/cm3 )

Weight of Loess (g)

Amount of Scouring Loess (g)

Loess Erosion Rate (%)

0 25 50 75 100

1.6 1.6 1.6 1.6 1.6

1,000 1,000 1,000 1,000 1,000

1,000 154.3 48.2 18.0 7.0

100 15.4 4.8 1.8 0.7

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Disintegration Time

Sample Number Z16 Z17 Z18 Z19 Z20

109

Concentration (%)

Dry Density (g/cm3 )

Weight of Loess (g)

Amount of Scouring Loess (g)

Loess Erosion Rate (%)

0 25 50 75 100

1.6 1.6 1.6 1.6 1.6

1,000 1,000 1,000 1,000 1,000

1,000 154.3 48.2 18.0 7.0

100 15.4 4.8 1.8 0.7

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

109

Sample Number Z21 Z22 Z23 Z24 Z25

Pu, Hou, Ma, Zou, Xu, Shi, Qian, and Pei

Table 5. Seed germination in stabilized soil.

Weight of Loess (g)

Moisture Content (%)

Concentration (%)

Number of Seeds Germinated

Seed Germination Rate (%)

1,000 1,000 1,000 1,000 1,000

19.4 19.4 19.4 19.4 19.4

0 25 50 75 100

7 15 21 22 18

23.3 50 70 73.3 60

The maintenance time was 30 days.

of the plants. Considering the economic rationale and the mechanical properties of soil, the optimal concentration of the CXZ stabilizer is 75 percent. As crop roots grow and develop in the soil environment, the soil supplies not only the nutrients required for the growth of crops, but also the water and air necessary for their physiological functions. Figure 8f shows the root systems of alfalfa plants 30 days after planting in the untreated loess and loess stabilized with the CXZ stabilizer at 75 percent concentration. A more developed root system was observed in the stabilized loess, and the root system was thicker, with a length of 12.6 cm, which was approximately 3-fold greater than that of the 4.4 cm root length when grown in untreated loess. In conclusion, the stabilizer improved the soil properties and increased soil water content, which are necessary for plant growth and which greatly improve the yield and water utilization eﬃciency of plants. Mechanisms of Stabilization The CXZ soil stabilizer had a notable eﬀect on soil improvement after application (Figure 9). More soil

Figure 6. Eﬀect of soil stabilizer concentration on erosion resistance.

110

Sample Number Z21 Z22 Z23 Z24 Z25

Weight of Loess (g)

Moisture Content (%)

Concentration (%)

Number of Seeds Germinated

Seed Germination Rate (%)

1,000 1,000 1,000 1,000 1,000

19.4 19.4 19.4 19.4 19.4

0 25 50 75 100

7 15 21 22 18

23.3 50 70 73.3 60

The maintenance time was 30 days.

aggregate structures formed because of the reaction between the CXZ stabilizer and loess (Keiblinger et al., 2016). In this type of soil with good aggregate structures, precipitation infiltrates rapidly through the noncapillary pores within aggregates and is stored within them immediately to avoid the moisture loss caused by slow infiltration. Aggregates can block the capillary pores through which the soil communicates directly with the atmosphere, slowing the movement of deep soil moisture to the surface and thereby reducing water evaporation. Moreover, more air is within aggregates, and aerobic micro-organisms are vigorous in the soil; thus, the organic matter on the surface of aggregates is rapidly mineralized. More moisture and less air exist in the aggregates, in which organic matter decomposes slowly, to the benefit of soil fertility conservation (Vergani and Graf, 2016). Figure 10 shows the SEM images of the untreated loess and the stabilized loess. Clearly, the particle structures of untreated loess were primarily granular overhead and contact, and the particles, pores, and boundaries were more obvious (Figure 10a). In Figure 10b, much white material, most likely the

Figure 7. Eﬀect of soil stabilizer concentration on the rate of seed germination.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Figure 6. Eﬀect of soil stabilizer concentration on erosion resistance.

110

Figure 7. Eﬀect of soil stabilizer concentration on the rate of seed germination.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Stabilization and Performance of Loess with Polymeric Soil Stabilizer

Figure 8. Plant growth status in stabilized soil at diﬀerent concentrations: (a) 0 percent; (b) 25 percent; (c) 50 percent; (d) 75 percent; (e) 100 percent; and (f) root comparison (root in untreated loess on the left and in stabilized loess with 75 percent concentration on the right; the maintenance time was 30 days).

CXZ soil stabilizer, was attached to the surfaces and edges of soil particles. Microscopic compact, multipledimensional net-based structures were formed among particles, and a few filamentous cement bonds formed in the voids to wrap each skeleton tissue together. Thus, the soil particles increased in size with agglomeration, and their edges became rough. The new relations between the CXZ stabilizer and soil particles are described as “coating” and “weaving” effects (Figure 10c, d). These linkages might strengthen the compressive resistance among soil particles, narrowing the voids among particles and significantly decreasing the permeability. For loess, the adsorption of the CXZ soil stabilizer possesses obvious advantages, including increasing the strength of stabilized soil, decreasing permeability, and improving water stability. However, a few days were required for the physical and chemical linkages between the CXZ stabilizer and loess to form (J. Liu et al., 2009), which could be attributed to CXZ improving the thixotropy of water. To identify the functional groups of the curing materials, FTIR spectra of (a) CMC, (b) XG, and (c) CXZ were analyzed (Figure 11). The band at 1,645 cm−1 was assigned to the C=O bond in -COONa groups, and the broad absorption peak at 3,413 cm−1 corresponded to -OH stretching vibrations (Sadeghi and Yarahmadi, 2011). The symmetrical and asymmetri-

cal vibrational absorption peaks of -CO-C are located at 1,160 cm−1 and 1,020 cm−1 . The FTIR spectra of XG shows that the peaks at 3,445 cm−1 and 1,266 cm−1 are attributed to the stretching vibration and bending vibration of -OH, respectively. The peak at 2,905 cm−1 is derived from the -CH2 stretching vibration, and the peak at 1,728 cm−1 is the stretching vibration of the ester carbonyl group. The peaks at 1,648 cm−1 and 1,415 cm−1 are attributed to the asymmetry and symmetric stretching vibration of -COO− , respectively, and the peak at 1,033 cm−1 corresponds to -C-O-C-O-C- vibration (Mundargi et al., 2007). In addition, the main characteristic peaks of CMC and XG all appeared in CXZ, and the corresponding OH bending vibration peak at 1,266 cm−1 was weakened, which indicated that the group that cross-links on the CXZ molecule may be -OH (Shalviri et al., 2010). All functional groups in the curing material are hydrophilic (You et al., 2011), with molecular structures that have chemical reactions with positive ions of clay grains and that create physico-chemical bonds between molecules and soil aggregates with ionic, hydrogen, or Van der Waals bonds (Jin et al., 2009; and Liu et al., 2011). Moreover, this novel biomass-based soil stabilizer had strong water binding capacity (Rahmat and Ismail, 2011). As a new type of soil stabilizer, CXZ has the following particular advantages: (i) It is synthesized by micro-structural cross-linking of

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

111

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

111

Pu, Hou, Ma, Zou, Xu, Shi, Qian, and Pei

Figure 9. Schematic illustration of aggregation process by biomass-based polymeric soil stabilizer.

Figure 10. SEM images of (a) untreated loess, (b)stabilized loess, (c) “coating” eﬀect in stabilized soil, and (d) “weaving” eﬀect in stabilized soil.

112

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Stabilization and Performance of Loess with Polymeric Soil Stabilizer

Figure 11. FTIR spectra of (a) CMC, (b) XG, and (c) CXZ.

biomass materials, and, therefore, this stabilizer is suitable for environmental protection and contributes no secondary pollution; (ii) CXZ is a water-soluble curing agent, which can be diluted to diﬀerent concentrations; and (iii) CXZ can be prepared conveniently and is therefore available at construction sites.

CONCLUSIONS In this work, a biomass-based polymer soil stabilizer (CXZ) with low cost and convenient production was developed from carboxymethyl cellulose and xanthan gum. The laboratory tests on the UCS, shear strength, water stability, and erosion resistance of the stabilized loess were conducted systematically. The results showed that after curing for 72 hours, the addition of increasing concentrations of the CXZ stabilizer enhanced the UCS, cohesion, water stability, and erosion resistance of the loess, but the internal friction angle did not obviously vary. Growth experiments indicated that the soil stabilizer CXZ significantly promoted the germination and growth of plants. Additionally, FTIR and TEM analyses indicated that CMC has a strong water-binding capacity due to hydrophilic groups such as -OH and -COO, which can be agglomerated into large aggregates through “coating” and “weaving” and applied for ecological protection and soil and water conservation in loess areas. Therefore, this compound can be used as a stabilizer to create optimal soil conditions on different types of land, including for agriculture, animal husbandry, and forestry, with different mechanical strengths and water contents.

Stabilization and Performance of Loess with Polymeric Soil Stabilizer

ACKNOWLEDGMENTS

The National Natural Science Foundation of China (41772264) and the Research Fund of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (No. SKLGP2018Z006) supported this work.

REFERENCES

Asgari, M. R.; Dezfuli, A. B.; and Bayat, M., 2015, Experimental study on stabilization of a low plasticity clayey soil with cement/lime: Arabian Journal of Geosciences, Vol. 8, No. 3, pp. 1439–1452. Benyounes, K.; Mellak, A.; and Benchabane, A., 2010, The effect of carboxymethylcellulose and xanthan on the rheology of bentonite suspensions: Energy Sources, Vol. 32, No. 17, pp. 1634–1643. Cheng J. M.; Wang Y. M.; and Miao S. C., 2014, Property study of solidified loess under wet dry cycles: Journal of Engineering Geology, Vol. 22, No. 2, pp. 226–232. Dahale, P. P.; Nagarnaik, P. B.; and Gajbhiye, A. R., 2012, Utilization of solid waste for soil stabilization: A review: Electronic Journal of Geotechnical Engineering, Vol. 17, pp. 2443–2461. Jin, L.; Shi, B.; Jiang, H. T.; Bae, S. Y.; and He, H., 2009, Improvement of water-stability of clay aggregates admixed with aqueous polymer soil stabilizers: Catena, Vol. 77, No. 3, pp. 175–179. Kaya, Z., 2016, Effect of slag on stabilization of sewage sludge and organic soil: Geomechanics and Engineering, Vol. 10, No. 5, pp. 689–707. Keiblinger, K. M.; Bauer, L. M.; Deltedesco, E.; Holawe, F.; Unterfrauner, H.; Zehetner, F.; and Peticzka, R., 2016, Quicklime application instantly increases soil aggregate stability: International Agrophysics, Vol. 30, No. 1, pp. 123–128. Kozubal, J. and Steshenko, D., 2015, The complex compaction method of an unstable loess substrate: Arabian Journal of Geosciences, Vol. 8, No. 8, pp. 6189–6198. Lahalih, S. M. and Ahmed, N., 1998, Effect of new soil stabilizers on the compressive strength of dune sand: Construction and Building Materials, Vol. 12, pp. 321–328. Liu, J.; Shi, B.; Jiang, H. T.; Bae, S. Y.; and Huang, H., 2009, Improvement of water-stability of clay aggregates admixed with aqueous polymer soil stabilizers: Catena, Vol. 77, No. 3, pp. 175–179. Liu, J.; Shi, B.; Jiang, H.; Huang, H.; Wang, G.; and Kamai, T., 2011, Research on the stabilization treatment of clay slope topsoil by organic polymer soil stabilizer: Engineering Geology, Vol. 117, No. 1–2, pp. 114–120. Liu, Q. B.; Xiang, W.; Zhang, W. F.; and Cui, D. S., 2009, Experimental study of ionic soil stabilizer—Improves expansive soil: Rock and Soil Mechanics, Vol. 30, pp. 2286–2290. Liu, Y. M. and Zhang, X. C., 2014, Effect of EN-1 ionic stabilizer on moisture characteristics of loess soil: Chinese Journal of Soil Science, Vol. 45, pp. 24–31. Llevot, A.; Dannecker, P. K.; von Czapiewski, M.; Over, L. C.; Soyler, Z.; and Meier, M. A. R., 2016, Renewability is not enough: Recent advances in the sustainable synthesis of biomass-derived monomers and polymers: Chemistry—A European Journal, Vol. 22, No. 33, pp. 11509–11520. Lv, Q. F.; Wang, S. X.; Wang, D. K.; and Wu, Z. M., 2014, Water stability mechanism of silicification grouted loess:

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

113

Figure 11. FTIR spectra of (a) CMC, (b) XG, and (c) CXZ.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

113

Pu, Hou, Ma, Zou, Xu, Shi, Qian, and Pei Bulletin of Engineering Geology and Environment, Vol. 73, No. 4, pp. 1025–1035. Mamedov, A. I.; Shainberg, I.; Wagner, L. E.; Warrington, D. N.; and Levy, G. J., 2009, Infiltration and erosion in soils treated with dry PAM, of two molecular weights, and phosphogypsum: Australian Journal of Soil Research, Vol. 47, No. 8, pp. 788–795. Mundargi, R. C.; Patil, S. A.; and Aminabhavi, T. M., 2007, Evaluation of acrylamide-grafted-xanthan gum copolymer matrix tablets for oral controlled delivery of antihypertensive drugs: Carbohydrate Polymers, Vol. 69, No. 1, pp. 130–141. Onyejekwe, S. and Ghataora, G. S., 2015, Soil stabilization using proprietary liquid chemical stabilizers: Sulphonated oil and a polymer: Bulletin of Engineering Geology and Environment, Vol. 74, No. 2, pp. 651–665. Pei, X. J.; Zhang, F. Y.; Wu, W. J.; and Liang, S. Y., 2015, Physicochemical and index properties of loess stabilized with lime and fly ash piles: Applied Clay Science, Vol. 114, pp. 77–84. Pu, S.; Ma, H.; Zinchenko, A.; and Chu, W., 2017, Novel highly porous magnetic hydrogel beads composed of chitosan and sodium citrate: An effective adsorbent for the removal of heavy metals from aqueous solutions: Environmental Science and Pollution Research, Vol. 24, No. 19, pp. 16520–16530. Rahmat, M. N. and Ismail, N., 2011, Sustainable stabilisation of the Lower Oxford Clay by non-traditional binder: Applied Clay Science, Vol. 52, No. 3, pp. 199–208. Sadeghi, M. and Yarahmadi, M., 2011, Synthesis and properties of carboxymethylcellulose (CMC) graft copolymer with on-

114

off switching properties for controlled release of drug: African Journal of Biotechnology, Vol. 10, No. 56, pp. 12085–12093. Shalviri, A.; Liu, Q.; Abdekhodaie, M. J.; and Wu, X. Y., 2010, Novel modified starch–xanthan gum hydrogels for controlled drug delivery: Synthesis and characterization: Carbohydrate Polymers, Vol. 79, No. 4, pp. 898–907. Tumsavas, Z. and Tumsavas, F., 2011, The effect of polyvinyl alcohol (PVA) application on runoff, soil loss and drainage water under simulated rainfall conditions: Journal of Food Agriculture & Environment, Vol. 9, No. 2, pp. 757–762. Tyliszczak, B.; Polaczek, J.; and Pielichowski, K., 2009, PAAbased hybrid organic-inorganic fertilizers with controlled release: Polish Journal of Environmental Studies, Vol. 18, No. 3, pp. 475–479. Vergani, C. and Graf, F., 2016, Soil permeability, aggregate stability and root growth: A pot experiment from a soil bioengineering perspective: Ecohydrology, Vol. 9, No. 5, pp. 830–842. Vukicevic, M.; Pujevic, V.; Marianovic, M.; Jockovic, S.; and Maras-Dragojevic, S., 2015, Stabilization of fine-grained soils with fly ash: Gradevinar, Vol. 67, No. 8, pp. 761–770. You, Y.; Zhou, M.; Hou, H.; Wei, N.; and Shen, R., 2011, Research on improvement of the sludge odor by using HAS soil stabilizer and mechanism. In Proceedings of the International Conference on Mechanic Automation & Control Engineering: IEEE, New York, NY, USA. pp. 2481–2484. Zou, Y. J.; Ma, F. W.; Han, M. Y.; and Wu, Y. W., 2007, Advance in research of effect and mechanism of higher soil compaction on plant growth: Agricultural Research in the Arid Areas. Vol. 25, No. 6, pp. 212–215.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

114

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 103–114

Proposed Improvements to Analytical Models of Anchored Retaining Walls

BENAMARA FATIMA ZOHRA* BELABED LAZHAR ROUAIGUIA AMMAR

Laboratory of Civil Engineering and Hydraulic (LGCH), Université 8 Mai 1945, Guelma, Algeria

Key Terms: Anchor Length, Anchored Retaining Walls, Modified Models, Failure Surface, Modeling, Geo4 and Plaxis 8.6 ABSTRACT Anchored retaining walls are restraining walls constructed using anchors to achieve the highest stiffness possible within economic considerations. Anchor length estimation has an important role in the study of the stability of anchored retaining walls. The purpose of this study was to investigate the anchor length using different proposed failure models and Kranz’s classic model. In addition, several parametric studies were conducted to find reliable results in the stability analysis of retaining anchored walls with the aim of obtaining stable and shorter anchor lengths. A numerical study was carried out using Geo4 and Plaxis 8.6 software on the same standard profile as analytically studied so as to validate the proposed mechanical models. The failure models proposed in this work are shown to be a useful tool for obtaining stable and shorter anchor lengths for anchored retaining walls. INTRODUCTION Anchored retaining walls are used to stabilize excavations and slopes (Wenping et al., 2015). The overall generally stability of anchored retaining walls depend on the anchor length. The equilibrium method is generally used to evaluate the stability of an anchored wall system, and to determine the requirements for ground anchor geometry and capacity. The assessment of overall stability is the objective of a stability analysis, based on the location of the potential failure surface: passing through the anchor wall at the level of the excavation (internal cut) or just below the excavation between the wall tip and the bottom of the excavation (external cut). In models with an internal *Corresponding authors’ email: benamara_fati2003@yahoo.fr

cut, the forces initiating failure are the active earth pressure force (Pa ), which acts behind the wall and the active earth pressure force applied on vertical secondary slip surface (Pa1 ). The forces preventing failure are the reaction force (Q) and the anchor force (A) (Sabatini and Pass, 1999). The models included in this case are the Kranz model, Ranke and Ostermayer model, and Heibaum model. In models involving external cuts, known as the Schulz model and Broms model, the passive earth pressure force (Pp ) in front of the wall is considered. However, the active earth pressure force (Pa ) and the anchor force (A) are not considered in the limit equilibrium analysis for external cuts. Analysis of the stability of anchored retaining walls is generally performed using the Kranz model (Kranz, 1953), which was developed for an anchored bulkhead with deadman anchors (Corfdir, 2008). This model is defined by a deep slide surface inclined at an angle (θ), which starts from the bottom of the wall to the base of the anchor plate and extends at (π/4 + ϕ/2) to the ground (Figure 1a) (William et al., 1983). The Kranz method was extended by Ranke and Ostermayer (1968) to anchored walls with single and multiple rows of anchors, as shown in Figure 1b. According to their method, the failure surface passes through the middle of the bond length. A list of notations and symbols is defined at the end of this paper. Franke and Heibaum (1988) developed a model characterized by a deep sliding surface inclined at an angle (θ) from the horizontal, which intersects the bond length at any distance (Lc ) measured from the tip of the bond as shown in Figure 1c. He assumed a fictitious vertical wall on which a lateral active earth pressure (Pa1 ) is applied. Furthermore, Heibaum (1987) reported the effects of the cohesion force (C) on the main deep slip line and an internal cut, which eventually led to the development of active earth pressures acting on the wall (Pa ) and the force (A) applied by the anchor to the free body (Benamara and Belabed, 2011).

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

115

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

115

Benamara, Belabed, and Rouaiguia

Figure 1. Models with internal cut: (a) Kranz model, (b) Ranke and Ostermayer model, and (c) Heibaum model.

Schulz’s model was developed from the Kranz model (Dennis, 1975; Schulz, 1977), which is differentiated by having an external cut. The ground reaction (Pp ) acting on the wall is defined as the resultant passive earth pressure. Schulz considered soil cohesion acting on the main slip surface, as shown in Figure 2a (Frazier et al., 1997). Broms (1988) also considered an external cut to evaluate the overall stability of a sheet pile anchored with a single row of anchors. The potential failure surface for a sheet pile is shown in Figure 2b by the line EDC. The failure plane passes through the toe at point E. This failure plane passes through a point D located at a distance of half the bond length (s/2) from the back of the anchor, where there is horizontal spacing between anchors, as shown in Figure 2b. Nakai et al. (2014) studied two-dimensional model tests, and the corresponding numerical analyses were carried out on an anchor-type braced excavation. That study revealed that the supporting effect of anchor in a braced excavation can be achieved only if the anchor block is set up outside the assumed slip surface that develops during excavation.

In this study, we propose a new mechanical model, based on the models proposed by Kranz (1953), Schulz (1977), and Heibaum (1987), by making some modifications on each model by varying the inclination of the secondary slip surface (ρ) and considering the effect of the cohesive force (C2 ) on this slip surface. This model is based on the experimental observations in laboratory model tests of Masrouri and Kastner (1991), who showed there are three different zones around the anchored sheet pile—two zones in the active state behind the wall, and one zone in the passive state in front of the wall. To justify these changes, we used a series of finite element calculations performed using two software packages: Plaxis 8.6 and Geo4. These results are presented and compared with the results of the analytical approach explained in the following paragraphs. ANALYTICAL STUDY OF MECHANICAL MODELS The trial double-wedge method comes before the planar single-wedge method for an anchored

Figure 2. Models with external cut: (a) Schulz model and (b) Broms model.

116

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

Figure 2. Models with external cut: (a) Schulz model and (b) Broms model.

116

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

Improved Analytical Models for Retaining Walls

Table 1. Embedded length and anchor force.

ϕ (°)/c (kN/m²)

f (m) A (kN)

Figure 3. Standard wall proﬁle.

retaining wall. The double-wedge analysis works on the premise that the two wedges are rigid blocks that are in equilibrium with themselves and the external system (Dobie and McCombie, 2015). The two wedges are separated by an interwedge boundary (Gäβler and Gudehus, 1989). Clayton et al. (2013) simpliﬁed the procedure by assuming the interwedge failure to be vertical and ignoring interwedge forces. The critical inclination of the deep slip surface (θ) is found iteratively until the required horizontal anchor length (X) is determined to verify equilibrium of the system (Belabed, 2007). The forces applied to the models are calculated according to the anchor length (X) given by Eq. 1. The available anchor length (L) is then calculated by Eq. 2. 3H/4 + f , X = (tanε + tanθ)

(1)

ϕ (°)/c (kN/m²)

40/0

35/5

30/10

25/20

20/30

1.79 110.87

1.97 76.55

2.52 69.23

2.36 31.95

2.71 20.62

(Whitlow, 1995). To design an anchored retaining wall, Coulomb’s earth pressure theory was applied, which considers the equilibrium of wedges at failure (Salençon, 1983). Additionally, various calculations were performed with the same standard profile using the geometric and geotechnical characteristics defined in Figure 3 and Table 1, and substituting them in the models equations defined in the next section to estimate anchor length. The model equations are improved by taking into account the effects of internal forces such as cohesion and friction between wedges, as well as the inclined sliding surface. The consideration of the internal force (C2 ) and the inclination of the slip surface adds high precision on the verification of the stability of the anchored walls. Mechanisms with Internal Cut In these mechanisms, the angle of friction between the wall and soil is taken into account (δa = 2/3ϕ) for the calculation of active earth pressures (Costet and Sanglerat, 1988). Modified Kranz Model We propose a new model, called the modified Kranz model, based on the Kranz mechanism (Figure 4). The

f (m) A (kN)

Figure 3. Standard wall proﬁle.

(1)

where H = height of retaining wall, ε = inclination of the anchor with a horizontal axis, θ = critical inclination of the deep slip surface, f = the wall embedment depth.

X . (2) tanε The variation of the angles and positions of the failure planes control the critical slip surface for the studied models leading to:

mechanisms with an external cut (modiﬁed Schulz

35/5

30/10

25/20

20/30

1.79 110.87

1.97 76.55

2.52 69.23

2.36 31.95

2.71 20.62

mechanisms with an internal cut (modiﬁed Kranz

model and modiﬁed Heibaum model), and

model and new model).

In this study, we adopted the free earth support method for deducing the embedment depth (f) and the anchor force (A) as presented in Table 1

40/0

model and new model).

Figure 4. The applied forces on the rigid body in the modiﬁed Kranz model.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

117

In this study, we adopted the free earth support method for deducing the embedment depth (f) and the anchor force (A) as presented in Table 1

Figure 4. The applied forces on the rigid body in the modiﬁed Kranz model.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

117

Benamara, Belabed, and Rouaiguia

Figure 5. The applied forces on the rigid body for: (a) the Heibaum model and (b) the modiﬁed Heibaum model.

failure mechanism is deﬁned by the principal failure line that cuts the bond length (L0 ) at the center of bond length and divides it into two secondary sliding surfaces. This model is characterized by a rigid wedge (1) on which the following forces are applied: the reaction of the anchor (A), the reaction of the wall on that wedge (Pa ), the friction of the soil on the main failure line (Q), the cohesion force (C1 ), the overload (P), and the weight of the wedge (1) (G) (Belabed, 2000). The active earth pressure (Pa1 ) replaces the wedge action (2) on the wedge (1) through the secondary slip surface considered as a ﬁctitious vertical wall (Gäβler, 1987). However, we take various values of inclination of the secondary slip surface (ρ) with respect to the vertical one. The interwedge friction angle δf = ϕ is used, and the cohesion force (C2 ) is applied on the secondary slip surface (Figure 4). The equilibrium between the vertical and horizontal forces gives us, after algebraic transformation (Eq. 3), [(G + P + Pa1h tan(δf + ρ) + C2v )tan(θ − ϕ) + Ah + Pa1h ] − [(Pah tanδa + Ah tanε + C1h tanθ)tan(θ−ϕ) + Pah + C1h + C2v tanρ] = 0,

(3)

where C2v and C1h are the vertical component of the cohesion force (C2 ) and the horizontal component of the cohesion force (C1 ), respectively, Pah is the horizontal component of the earth pressure (Pa ), and Pa1h is the horizontal component of the earth pressure (Pa1 ). Modiﬁed Heibaum Model This model is characterized by a main failure line cutting the bond length at a residual length (Lc ), which is behind the failure line, with the development of ground anchor capacity (A1 ). The active earth pressure (Pa1 ) is inclined at an angle (δf + ρ), and then the cohe118

sion force (C2 ) is applied on the secondary failure surface shown in Figure 5. The force equilibrium is used to determine the failure equation corresponding to this model (Eq. 4): [(G + P + Pa1h tan(δf + ρ) + C2v + A1h tanε)tan(θ − ϕ) + Ah + Pa1h ] − [(Pah tanδa + Ah tanε + C1h tanθ) × tan(θ − ϕ) + Pah + C1h + C2v tanρ + A1h ] = 0. (4) A1 = κLc. κ= T = a=

(5)

T ; a

(6)

Arup ; L0

(7)

As , A

(8)

where κ and T are the index transformation of the force per meter and the limit value of the anchor’s friction, respectively; As is the service anchor force, Arup is the rupture anchor force, a is the horizontal anchor spacing, and A is the anchor force calculated previously. Arup ≥ η. As

(9)

For anchorage safety factor of η = 1.5 (according to German Standard; DIN 4125, 1990), and Arup = 1.5As , after algebraic transformation, we get: κ=

1.35 A . L0

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

(10)

(3)

(5)

T ; a

(6)

Arup ; L0

(7)

As , A

(8)

(9)

For anchorage safety factor of η = 1.5 (according to German Standard; DIN 4125, 1990), and Arup = 1.5As , after algebraic transformation, we get: κ=

1.35 A . L0

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

(10)

Improved Analytical Models for Retaining Walls

Figure 6. The applied forces on the rigid body for: (a) the Schulz model and (b) the modiﬁed Schulz model.

A1 =

1.35 ALc . L0

(11)

The residual length (Lc ) varies in the interval [0,L0 ]. For Lc = 0.9L0 , we get Eq. 12, giving the minimum values of the anchor length: A1 = 1.35 A.

(12)

Mechanisms with an External Cut In the external cut model, the potential plane of sliding passes behind the anchor and below the wall. The soil mass under consideration is limited in the left side of the soil mass by the anchored wall and on the right side by the vertical boundary, which extends from the back of the anchor. In front of the wall, passive earth pressure (Pp ) acts at an angle δp = –1/2ϕ, and active earth pressure (Pa1 ) is assumed to be on the right vertical face.

model) characterized by an external cut. The important elements combined are: the passive earth pressure (Pp ) provided by the soil in front of the wall, cutting the bond length by a failure surface at a residual length (Lc ) with the development of ground anchor capacity (A1 ), and application of the active earth pressure (Pa1 ) and the cohesion force (C2 ), respectively, on the secondary failure, which is inclined at an angle (ρ) relative to the vertical. Equation 14 is developed by the equilibrium study of forces applied on the rigid body as shown in Figure 7. [(G + P + Pa1h (δf + ρ) + C2v + A1h tanε)tan(θ − ϕ) + Pa1h ] − [(PPh tanδP + C1h tanθ)tan(θ − ϕ) + PPh + C1h + A1h + C2v tanρ] = 0.

(14)

A1 =

1.35 ALc . L0

(11)

The residual length (Lc ) varies in the interval [0,L0 ]. For Lc = 0.9L0 , we get Eq. 12, giving the minimum values of the anchor length: A1 = 1.35 A.

(12)

Modiﬁed Schulz Model

Forces exerted on this model are: the passive earth pressure (Pp ) inclined at an angle (δ p ), the cohesion force (C2 ), and the active earth pressure (Pa1 ) inclined at an angle (δf + ρ). Equation 13 is developed by the study of equilibrium forces shown in Figure 6 (Schulz, 1977).

[(G + P + Pa1h (δf + ρ) + C2v )tan(θ − ϕ) + Pa1h ] − [(PPh tanδP + C1h tanθ)tan(θ − ϕ) + PPh + C1h + C2v tanρ] = 0. (13)

The New Proposed Model This model is a combination of two models (the modiﬁed Heibaum model and the modiﬁed Schulz

(14)

The New Proposed Model Figure 7. The applied forces on the rigid body for the new proposed model.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

119

This model is a combination of two models (the modiﬁed Heibaum model and the modiﬁed Schulz

Figure 7. The applied forces on the rigid body for the new proposed model.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

119

Benamara, Belabed, and Rouaiguia

Figure 8. The effect of variation of the secondary failure surface and the active earth pressure Pa1h on the anchor length (a, b) for modified Kranz and modified Heibaum models.

CALCULATION OF MINIMUM ANCHOR LENGTH The Effect of Inclination on the Secondary Failure Surface and the Anchor Length The general form of structural limiting states tend to fall into two major categories: The loads are described by the solicitations (S), and the strength is described by the resistance (R). The probability of the considered failure mechanism is quantified by the probability that the solicitation (S) is higher than or equal to the resistance (R). The equations of the failure models can be written as (Resistance forces − Driving forces = 0). A Turbo Pascal computer program was developed to determine the anchor length. The flowchart in Appendix A represents the sequences of the program. We also used a combination of soil parameters, including friction angle (ϕ) and cohesion (c) as presented in Table 1. The comparative results of studies for different inclination angles (ρ = 0°, −5°, +5°), surcharge loads (q = 10 kN/m2 ), and slope angle of backfill behind wall β = 10° are shown in Figures 8 and 9. We observed that for the ratio (ϕ/c) = (30/10) using the modified Kranz model, the anchor length decreases from 15.20 m for ρ = 0 to 14.70 m for ρ = 5°. 120

Similarly, the earth pressure Pa1h decreases from 276.1 to 210.74 kN/m2 for the same values of ρ. This decrease was observed for all modified models. It was observed that the anchor length increased with increasing cohesion. Concerning the modified Heibaum model, the anchor length increased from 8.80 m for (ϕ/c) = (40/0) to 14 m for (ϕ/c) = (20/30) for the value of ρ = 5°. Similar observations occurred with the other modified models. Therefore, the value of ρ = 5° provides the minimum anchor length, as shown in Figures 8 and 9. Comparison of Failure Models For different soil types and different active earth pressures behind the secondary failure surface, we determined the anchor lengths for all the models, and these results are presented in Figure 10. With regard to the internal cut mechanisms, as shown in Figure 11, the Kranz and Heibaum models suggested larger anchor lengths (from 17.2 m to 14.4 m). The modified models suggested shorter anchor lengths (from 16.1 m to 14 m) for cohesive soils (ϕ/c) = (20/30). With regard to the external cut mechanisms, the new model suggested shorter anchor lengths (from 3.6 m to 10.4 m) compared to the Schulz and modified Schulz models for different values of (ϕ/c).

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

Improved Analytical Models for Retaining Walls

Figure 9. The eﬀect of variation of the inclination of the secondary failure surface and the active earth pressure Pa1h on the anchor length (a,b) for modiﬁed Schulz and New proposed models.

From Figure 11, we observe that the earth pressure (Pa1h ) acting on the secondary sliding surface was increased for cohesive soil, while it was decreased in the modiﬁed models for the same soil. The anchor lengths were even shorter when the earth pressure decreased behind a secondary failure surface that was inclined by an angle of ρ = 5 and a cohesion force (C2 ) was applied on the same sliding surface with the development

of the ground anchor capacity A1 behind the main failure line. Table 2 summarizes the reduction in anchor length as a percentage of the modiﬁed models compared to other models (Kranz model, Heibaum model, and Schulz model). This reduction can be translated into approximate savings in practice.

Figure 10. Comparison of anchor lengths between models with internal and external failure cuts.

Figure 11. Comparison of active earth pressures behind the secondary failure surface.

Figure 10. Comparison of anchor lengths between models with internal and external failure cuts.

Figure 11. Comparison of active earth pressures behind the secondary failure surface.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

121

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

121

Benamara, Belabed, and Rouaiguia Table 2. The reduction of anchor length compared to other methods as a percentage. ϕ (°)/c (kN/m²) L (%)

40/0

35/5

30/10

25/20

20/30

Modified Kranz model Modified Heibaum model Modified Schulz model Proposed new model

11.52 8.78 26.12 76.78

11.44 8.89 22.72 41.07

9.12 8.98 21.66 26.74

8.71 8.05 13.82 10.66

6.77 2.49 6.64 1.10

NUMERICAL STUDY We carried out a study based on the finite element method to validate the improved mechanical models. This analysis was carried out using GeoFem in Geo4 (Bures et al., 2003) and Plaxis Professional Version 8.6 (Brinkgreve, 2003). Numerical analyses were performed under drained conditions using geometric and geotechnical parameters for the standard profile used in the analytical procedure, as described in Table 1 (Wen et al., 2018). In both software suites, the size of the model was 35 m × 20 m. Horizontal movement was restrained for the left and right boundaries. Figure 12 shows the mesh of the numerical model. Two-dimensional plane strain triangular mesh elements with six displacement nodes were used for the soil body. The profile was modelled with a simple linear elastic, perfectly plastic Mohr Coulomb model. We considered an anchor head of 2.5 m depth, inclined at 20°. The free anchor length was 7 m, and the bond length was 4 m. The anchors, which were defined by their starting and end points and by their stiffness, were modelled with GeoFem software by means of an elastic tensilecompressive bar element. Another important parameter was the tensile strength, because a sufficiently large

Benamara, Belabed, and Rouaiguia

Table 2. The reduction of anchor length compared to other methods as a percentage.

Results

We carried out a study based on the finite element method to validate the improved mechanical models. This analysis was carried out using GeoFem in Geo4 (Bures et al., 2003) and Plaxis Professional Version 8.6 (Brinkgreve, 2003). Numerical analyses were performed under drained conditions using geometric and geotechnical parameters for the standard profile used in the analytical procedure, as described in Table 1 (Wen et al., 2018). In both software suites, the size of the model was 35 m × 20 m. Horizontal movement was restrained for the left and right boundaries. Figure 12 shows the mesh of the numerical model. Two-dimensional plane strain triangular mesh elements with six displacement nodes were used for the soil body. The profile was modelled with a simple linear elastic, perfectly plastic Mohr Coulomb model. We considered an anchor head of 2.5 m depth, inclined at 20°. The free anchor length was 7 m, and the bond length was 4 m. The anchors, which were defined by their starting and end points and by their stiffness, were modelled with GeoFem software by means of an elastic tensilecompressive bar element. Another important parameter was the tensile strength, because a sufficiently large

This analysis showed horizontal displacements that suggested a failure mechanism similar to the modiﬁed failure mechanisms, as shown in Figures 13 and 14. In Figure 13, a failure mechanism is induced by displacements of the wall and the anchored groundmass. It was characterized by sliding lines starting from the bottom of the anchored wall, forming an acute angle with the horizontal plane, which crossed the bond length at a distance (Lc ). This failure mechanism was conducted using GeoFem program; it can be divided into two wedges separated by sliding surfaces inclined at the same angle, which implies that the secondary sliding surface is an inclined plane. The presence of cohesive force on the failure line causes sliding in translation between the wedges, and as a result, the failure line will be an inclined plane as shown in Figure 14a. However, when the cohesive force is assumed to be zero, the translation does not take place between the wedges, but it moves in opposite directions and perpendicular to the plane of the failure line, which results in the formation of a vertical failure line as shown in Figure 14b.

ϕ (°)/c (kN/m²) L (%)

40/0

35/5

30/10

25/20

20/30

Modified Kranz model Modified Heibaum model Modified Schulz model Proposed new model

11.52 8.78 26.12 76.78

11.44 8.89 22.72 41.07

9.12 8.98 21.66 26.74

8.71 8.05 13.82 10.66

6.77 2.49 6.64 1.10

NUMERICAL STUDY

Figure 12. Initial mesh of numerical model in: (a) GeoFem and (b) Plaxis.

122

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

value may be specified to avoid anchor failure. For both software applications, the wall was modelled using plate elements, considering elastic behavior, with a bending stiffness, EI, of 0.3 × 105 kNm2 /m and zero Poisson ratio. The anchor level was modeled by a combination of a node-to-node anchor (free length) and a concrete beam (bond length). The material of the node-to-node anchor was assumed to be linear elastic with a stiffness, EA, equal to 6.6 × 104 kN/m. The bond length was considered to be linear elastoplastic with a stiffness equal to 2.61 × 106 kN/m. Results This analysis showed horizontal displacements that suggested a failure mechanism similar to the modified failure mechanisms, as shown in Figures 13 and 14. In Figure 13, a failure mechanism is induced by displacements of the wall and the anchored groundmass. It was characterized by sliding lines starting from the bottom of the anchored wall, forming an acute angle with the horizontal plane, which crossed the bond length at a distance (Lc ). This failure mechanism was conducted using GeoFem program; it can be divided into two wedges separated by sliding surfaces inclined at the same angle, which implies that the secondary sliding surface is an inclined plane. The presence of cohesive force on the failure line causes sliding in translation between the wedges, and as a result, the failure line will be an inclined plane as shown in Figure 14a. However, when the cohesive force is assumed to be zero, the translation does not take place between the wedges, but it moves in opposite directions and perpendicular to the plane of the failure line, which results in the formation of a vertical failure line as shown in Figure 14b.

Figure 12. Initial mesh of numerical model in: (a) GeoFem and (b) Plaxis.

122

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

Improved Analytical Models for Retaining Walls

Figure 13. Failure mechanism deduced from the representation of the total displacements carried out with GeoFem: (a) deformed construction; (b) undeformed construction. Case ϕ (°)/C (kN/m2 ) = 35/5.

As shown in Figures 13 and 14, the main failure surface cuts the bond length not in the middle but at a length of Lc > L0 /2 behind the main failure surface, which conﬁrms the improved models previously presented in the analytical study. The analysis performed by Plaxis, as illustrated in Figure 15, presents the failure mechanisms composed of two wedges with a steeper secondary failure surface, which is observed for the cohesive soil (ϕ/c) = (25/20; 20/30), and which becomes vertical for the friction soil (ϕ/c) = (40/0; 35/5). With increasing cohesion, most of the failure lines pass just below the excavation between the wall tip and the bottom of the excavation. We note also the failure lines are more distinguished in front of the wall, and the potential slip line changes position along the bond length to its end.

The deformation contours of the soils observed in front of the wall show that the ground collapse in this region is in the form of a rigid wedge. The development of a rigid wedge downstream of the wall corresponding to the passive earth pressure is shown in Figure 14, which is considered only in the new improved model but not in the classical models (Kranz, Heibaum, and Broms models).

CONCLUSIONS In this study, the failure models of anchored walls were presented. These models can be divided into two groups: models with an internal cut (Kranz Model and Heibaum model) and models with an external cut

Figure 14. Failure mechanism deduced from the representation of the total displacements carried out with Plaxis program: (a) ϕ (°)/C (kN/m2 ) = 25/20; (b) ϕ (°)/C (kN/m2 ) = 40/0.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

123

Figure 14. Failure mechanism deduced from the representation of the total displacements carried out with Plaxis program: (a) ϕ (°)/C (kN/m2 ) = 25/20; (b) ϕ (°)/C (kN/m2 ) = 40/0.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

123

Benamara, Belabed, and Rouaiguia

Figure 15. Failure mechanisms obtained from the analysis performed with Plaxis and GeoFem software applications.

(Schulz model and Broms model). The difference between the two groups has been studied. The modifications on the failure models were performed to estimate the minimum anchor lengths for the anchored walls. We propose a new mechanical model based on the models proposed by Kranz, Schulz, and Heibaum by making some modifications on each of the models. The model equations were improved by taking into account the effects of internal forces such as cohesion and friction between wedges, as well as the inclined sliding surface. The variation of inclination of the secondary slip surface (ρ) and considering the effect of the cohesive force C2 on this slip surface led to a change of the active earth pressure Pa1h , which influenced the estimation of the anchor length. Series of finite element calculations were performed to validate the modifications made to the failure models. The resulting reduction in anchor length could be useful for economical or technical reasons, particularly for urban environments, where the use of long anchors might be limited. NOTATION ϕ a, s Pp Pa L0 Lc c 124

: the friction angle for the soil; : the horizontal anchor spacing; : resultant passive earth pressure; : active earth pressures acting on the retaining wall; : bond length; : residual (cut) bond length behind the main failure surface; : cohesion;

A Ah Pa1 θ X L ε ρ ϕ G P q Pah P a1h C1h Q C1 C2 C2v C1h Pah Pa1h κ

: anchor force; : horizontal component of A; : active earth pressure applied on vertical secondary slip surface; : critical inclination of a deep slip surface; : necessary horizontal anchor length; : minimum anchor length; : inclination of anchor relative to the horizontal; : inclination of the secondary slip surface relative to the vertical; : friction angle of soil; : weight of the rigid wedge (1); : surcharge per meter; : surcharge; : horizontal component of the active earth pressure applied on the retaining wall; : horizontal component of the active earth pressure applied on the vertical secondary slip surface; : horizontal component of the cohesion force on the principal failure surface; : soil reaction force on the main slip surface; : cohesion force on the main slip surface; : cohesion force on the secondary slip surface; : vertical components of the cohesion force C2 ; : horizontal component of the cohesion force C1 ; : horizontal component of the active earth pressure Pa ; : horizontal component of the active earth pressure applied to the inclination secondary slip surface; : index transformation of the force per meter;

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

A Ah Pa1 θ X L ε ρ ϕ G P q Pah P a1h C1h Q C1 C2 C2v C1h Pah Pa1h κ

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

Improved Analytical Models for Retaining Walls

T As Arup η δp δa δf Pph B A1 A1h γ f

: limit value of the anchor’s friction; : service anchor force; : rupture anchor force; : anchorage safety factor; : passive angle wall friction; : active angle wall friction; : active angle friction on the secondary slip surface; : horizontal component of the passive earth pressure applied to the retaining wall; : angle of surface ground slope; : ground anchor capacity; : horizontal component of ground anchor capacity; : soil unit weight; : the wall embedment depth. ACKNOWLEDGMENTS

This research work is supported by the Ministry of Higher Education and Research of Algeria. APPENDIX A Flowchart of the Steps for Calculating the Anchors Lengths

Improved Analytical Models for Retaining Walls

REFERENCES Belabed, L., 2000, Anchored retaining walls: Modelling the failure along the deep slip surface: Revue Française de Géotechnique, Vol. 92, No. 3, pp. 49–55, https://doi.org/10.1051/ geotech/2000092049 Belabed, L., 2007, New method for the determination of the anchor lengths for double-propped retaining walls: Bautechnik, Vol. 84, No. 11, pp. 803–815, http://onlinelibrary. wiley.com/doi/10.1002/bate.200710068/abstract. Benamara, F. Z. and Belabed, L., 2011, The analysis stability of anchor retaining wall: Advanced Materials Research, Vol. 324, pp. 376–379, https://www.scientific.net/AMR.324.376. Brinkgreve, R. B. J., 2003, Manuel de Référence de PLAXIS Version 8.6 : Delft University of Technology, Delft, Netherlands, and PLAXIS BV, Pays-Bas, Netherlands. Broms, B. B., 1988, Design and construction of anchored and strutted sheet pile walls in soft clay. (Editor) © 1988 University of Missouri—Rolla, All rights reserved. In International Conference on Case Histories in Geotechnical Engineering 20 (Editors), © 1988 University of Missouri—Rolla, All rights reserved.: University of Missouri, Rolla, MO, pp. 1515–1550. Electronic document, available at: https://scholarsmine.mst.edu/icchge/2icchge/ 2icchge-session6/20 Bures, P.; Blas, P.; and Jiri, L., 2003, GEO4 FEM User’s Guide, GeoFem Version 4.0.7.62: Fine company lader ship Milos Vodolan and Jiri Laurin, Prague, Czech Republic. Clayton, C. R. I.; Woods, R. I.; Bond, A. J.; and Milititsky, J., 2013, Earth Pressure and Earth-Retaining Structures, 3rd ed.: Taylor & Francis Group, CRC Press, Boca Raton, FL. Corfdir, A., 2008, Kranz’s method from yesterday to day: A critical review: Revue Française de Géotechnique, Vol. 124, No. 3, pp. 19–30. Electronic document, available from: http:// www.geotech-fr.org/sites/default/files/rfg/article/124-2.pdf Costet, J. and Sanglerat, G., 1988, Cours Pratique de Mécanique des Sol 2 Calcul des Ouvrages, 3rd ed.: Dunod, Paris, France, 447 p. Dennis, W. K., 1975, Design of Tied-Back Retaining Walls: Report submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirement for the degree of master of engineering, Alberta University, Edmonton, Canada, 64 p. https://doi.org/10.7939/R3R20S631 Deutsches Institut für Normung (DIN) 4125, 1990, Ground Anchorages: Design Construction and Testing: Institut Allemand de Normalisation, Berlin, Germany. Dobie, M. J. D. and McCombie, P. F., 2015, Reinforced soil design using a two-part wedge mechanism: Justification and evidence. In Proceedings of the XVI ECSMGE Geotechnical Engineering for Infrastructure and Development: ICE Publishing, Edinburg, United Kingdom, pp. 1409–1414, https://doi.org/10.1680/ecsmge.60678 Franke, E. and Heibaum, M., 1988, Overall stability of anchored retaining walls: Bauingenieur, Vol. 63, No. 9, pp. 391–398. Frazier, J. R.; David Elton, P. E. J.; and James, E. W., 1997, Tieback Wall Design and Construction: Final report to the Alabama Highway Research Center, Auburn University, Auburn, AL, 72 p. Electronic document, available at: http://www.eng.auburn.edu/files/centers/hrc/IR-97-03.pdf Gäβler, G., 1987, Vernagelte Geländesprüge Tragverhalten und standsichereit Veröffentlli-chungen: Unpublished thesis, Ridericiana Universität, Karlsruhe, Germany. Gäβler, G. and Gudehus, G., 1989, Anchored wall: Model tests and statistical design. In Publications committee

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

125

T As Arup η δp δa δf Pph B A1 A1h γ f

This research work is supported by the Ministry of Higher Education and Research of Algeria. APPENDIX A Flowchart of the Steps for Calculating the Anchors Lengths

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

125

Benamara, Belabed, and Rouaiguia of XIII CSMFE. Opus: University of Bath Online Publication Store (Editors), 12th International Conference on Soil Mechanics and Foundation Engineering: Rio de Janeiro, Brazil, A.A. Balkema/Rotterdam/Brookﬁeld Publishing, pp. 829–832. Electronic document, available from: http://mie.umass.edu/sites/default/ﬁles/cee/ issmfe_proceedings_toc_1989-1994.pdf Heibaum, M., 1987, Zur Frage der standseicherheit verankerter stüdzwände auf der tiefen gleitfuge: Unpublished M.S. Thesis, Institute for Foundation Engineering Soil and Rock Mechanics, University of Technology Darmastadt, Germany, 177 p. Kranz, E., 1953, Über die Verankerung von Spundwänden. Mitteilungen aus dem Gebiet des Wasserbaues und der Baugrundforschung, Vol. 11: Ernst & Sohn, Berlin, Germany. Electronic document, available at: https://repository.tudelft.nl/ islandora/object/uuid:1a596cdc-6cd9-40d0-a651-4a22784d8 8db/datastream/OBJ/download Masrouri, F. and Kastner, R., 1991, Essai sur modèle de rideaux de soutènement: Confrontation à diverses méthode de calcul: Revue Française de Géotechnique, Vol. 55, pp. 17–33. Nakai, T.; Shahin, H. M.; and Okuda, K., 2014, Reinforcing mechanism of anchor type retaining wall model test and numerical analysis. In Yoo, C.; Park, S.-W.; Kim, B.; and Ban, H. (Editors), The 8th International Symposium on Geotechnical Aspects of Underground Construction in Soft Ground: Korean Geotechnical Society, Seoul, Korea, pp. 73–78. Ranke, A. and Ostermayer, H., 1968, Beitrag zur Stabilitätsuntersuchung mehrfach verankerter Baugruben umschließungen: Bautechnik, Vol. 45, No. 10, pp. 341–350.

126

Electronic document, available at: https://www.ernst-undsohn.de/app/artikelrecherche/artikel.php?lang=de&ID=445 Sabatini, P. J. and Pass, D. G., 1999, Ground Anchors and Anchored System: Geotechnical Engineering Circular No. 4: Office of Bridge Technology, Federal Highway Administration, Report No. FHWA-IF-99-015, Washington, D.C., 281 p. Electronic document, available at: https://www.fhwa. dot.gov/engineering/geotech/pubs/if99015.pdf Salençon, J., 1983, Failure Calcul and Analyzis Limit: Press ENPC, Paris, France, 366 p. Schulz, H., 1977, Überlegungen zur führung des mecheises der standsicherheit in der tiefen gleitfuge: Mitt.Bl.d.BAW, Vol. 41, pp. 156–170. Electronic document, available at: https://izw. baw.de/publikationen/mitteilungsblaetter/0/mb41_Schulz. pdf Wen, Z.; Jian, Y. H.; Yang, C.; Peng, J. J.; Shen, G. L.; and Zhen, Z., 2018, A numerical study on the influence of anchorage failure for a deep excavation retained by anchored pile walls: Advances in Mechanical Engineering, Vol. 10, No. 2, pp. 1–17, https://doi.org/10.1177/1687814018756775. Wenping, G.; Hongwei, H.; Hsein Juang, C.; and Wang, L., 2015, Simplified-robust geotechnical design of soldier pile–anchor tieback shoring system for deep excavation: Marine Georesources & Geotechnology, Vol. 35, pp. 157–169, https://doi.org/10.1080/1064119X.2015.1120369. Whitlow, R., 1995, Basic Soil Mechanics, 3rd ed.: Longman Group Limited, Essex, U.K., 577 p. William, F. A.; Thomas, H. H.; and Magued, N. A., 1983, Overall stability of anchored retaining walls: International Journal of Geomechanics, Vol. 109, No. 11, pp. 1416–1433, https:// doi.org/10.1061/(ASCE)0733-9410(1983)109:11(1416).

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

126

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 115–126

Lateritic Soil Treated with Waste Wood Ash as Liner in Landﬁll Construction

JOHNSON R. OLUREMI*

Civil Engineering Department, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria; Geotechnical Group, Civil Engineering Department, Ahmadu Bello University, Zaria, Kaduna State, Nigeria

ADRIAN O. EBEREMU STEPHEN T. IJIMDIYA KOLAWOLE J. OSINUBI

Civil Engineering Department, Ahmadu Bello University, Zaria, Kaduna State, Nigeria

Key Terms: Lateritic Soil, Waste Wood Ash, Stabilizers, Geotechnical and Geoenvironmental, Liner Material, Municipal Solid Waste Management, Hydraulic Conductivity, Acceptable Zones

rily as liner and covers in waste containment application and will minimize the pollution and environmental impact of wood waste disposal.

Key Terms: Lateritic Soil, Waste Wood Ash, Stabilizers, Geotechnical and Geoenvironmental, Liner Material, Municipal Solid Waste Management, Hydraulic Conductivity, Acceptable Zones

rily as liner and covers in waste containment application and will minimize the pollution and environmental impact of wood waste disposal.

ABSTRACT

INTRODUCTION

ABSTRACT

INTRODUCTION

Inherent variability in engineering properties of lateritic soil in relation to its plasticity, permeability, strength, workability, and natural moisture content, has made it an unpredictable material for use in civil engineering works, resulting in the need for its treatment by stabilization. A lateritic soil classified as A-6(6) and CL, according to American Association of State Highway and Transportation Officials and Unified Soil Classification System of ASTM (2011), was treated with up to 10 percent waste wood ash (WWA). Compaction was carried out using four energies, namely, reduced British Standard light, British Standard light (BSL), West African Standard, and British Standard heavy, on samples, which were then examined for hydraulic conductivity, volumetric shrinkage, and unconfined compressive strength as major criteria for use as liner and for the development of acceptable zones. Specimens with 4 percent WWA content compacted with a minimum BSL energy satisfied the maximum hydraulic conductivity (k) value of 1 × 10−9 m/s, maximum volumetric shrinkage strain of 4 percent, and minimum unconfined compressive strength value of 200 kN/m2 required for use as liner in engineered landfills. The overall acceptable zone was enlarged for up to 4 percent WWA content, thereby accommodating higher moulding water content, but the minimum compactive effort required to achieve it became reduced. The beneficial treatment of lateritic soil with up to 4 percent WWA will perform satisfacto-

Tropical laterites, products of weathering under intense temperature, humidity, and alternating wet and dry conditions, have found extensive use in numerous construction activities, such as sub-grade material for road construction and brick production material (Goswami and Mahanta, 2007), liner material in the construction of landﬁll (because of their low hydraulic conductivity; Anderson and Hee, 2000), and base material in hydraulic structures such as dams. This allows them to be categorized as natural resources of importance in geo-environmental applications (Gabas et al., 2007; Frempong and Yanful, 2008; and Osinubi and Nwaiwu, 2008). They comprise material that is adequate enough in chemical resistance and of low desiccation-induced shrinkage potential (Osinubi and Nwaiwu, 2008). However, the predominant presence of kaolinite, a non-swelling, non-expanding 1:1 clay mineral (Osinubi et al., 2009b), together with swelling 2:1 clay mineral, has caused inherent variability of the soil in terms of engineering properties in relation to its plasticity, permeability, strength, workability, and natural moisture content, and so it is considered an unpredictable material for use in civil engineering works (Elarabi et al., 2013; Salahudeen and Ochepo, 2015). Soil treatment for geotechnical purposes is usually considered a means of improving some desirable engineering properties. Engineering properties of soil, such as compressibility, strength, permeability, swell potential, and workability, are greatly inﬂuenced by the grain size distribution and consistency characteristics

*Corresponding author email: jroluremi@lautech.edu.ng

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

127

*Corresponding author email: jroluremi@lautech.edu.ng

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

127

Oluremi, Eberemu, Ijimdiya, and Osinubi

of the soil. These properties are of importance to engineers because they depict the behavior of the soil and thereby serve as a performance indicator for lateritic soil. It is therefore pertinent to sometimes treat lateritic soil so as to improve its quality and serviceability under adverse environmental conditions (Adeyeri, 2015). This purpose has rendered conventional stabilizers— in particular cement and lime—of great importance in the stabilization of lateritic soil. However, the high cost and energy-intensive production of these stabilizers, which result in a high cost of construction, coupled with their propensity for environmental pollution due to greenhouse gas emission associated with their production and usage (Mousavi and Wong, 2015) has led to a paradigm shift away from the use of these materials, even though they are highly potent compared to the pozzolanic ash produced from either industrial or agricultural sources such as fly ash, baggash ash, waste wood ash, etc. The quest to overcome these challenges has led researchers (Medjo and Riskowiski, 2004; Osinubi and Stephen, 2006; Alhassan and Mustapha, 2007; Moses, 2008; Osinubi and Nwaiwa, 2008; Osinubi et al., 2009a; Osinubi and Eberemu, 2009b; Oriola and Moses, 2010; and Oluremi et al., 2012, 2013, 2016b, 2016c) to explore the use of locally available agricultural and industrial waste that can be used to improve deficient soil for the attainment of requirements for geotechnical engineering design. Wood waste, such as sawdust, wood chips, wood barks, sander-dust, or shavings, constitutes a potential environmental hazard because of the disposal problem associated with it. Even though it has found alternative use as poultry litter and in the growing of mushrooms, the heap of wood waste in sawmills, especially in the developing countries, reveals its disposal problem. Waste wood ash (WWA) is the grayish-white to black powdery inorganic residue left when wood wastes—sawdust, wood chips, wood barks, sanderdust, or shavings—are burnt. It has been confirmed to be a good source of potassium, phosphorus, magnesium, and calcium (Demeyer et al., 2001; Saarsalmi et al., 2001). Previous studies (Awodun et al., 2007) have shown that waste wood ash has the same liming effect as commercial lime. In fact, just like cement, it can cause flocculation and agglomeration of the clay minerals resulting from isomorphous substitution on the soil surface and can thereby improve the engineering properties of the soil, especially with regard to its durability and workability. Therefore, modification and stabilization of soil materials with the aim of improving their properties for the construction of engineering appurtenances such as landfill liners and covers might be achieved with the use of WWA, thereby serving as a 128

Oluremi, Eberemu, Ijimdiya, and Osinubi

means of disposing it and mitigating its environmental pollution. Meanwhile, quality control in the construction of compacted clayey liners is basically concerned with the appropriation of the correct moulding water content– dry unit weight under which the compacted soil will satisfy the desirable threshold values for potential properties necessary for constructing liner, as required by environmental regulatory agencies. This range of correct moulding water content–dry unit weight is known as the “Acceptable Zone.” According to Daniel and Benson (1990), Daniel and Wu (1993), and Albrecht and Benson (2001), the geotechnical properties of the materials for liner should possess the following permissible values: 1. Coefficient of permeability (hydraulic conductivity) of 1 × 10−9 m/s; 2. Minimum unconfined compressive strength (UCS) of 200 kN/m2 ; 3. Maximum allowable value of 4 percent volumetric shrinkage strain; 4. Minimum clay content 10 percent; 5. Plasticity index of >10 percent but <65 percent; 6. Liquid limit of <90 percent; 7. Minimum fines (clay and silt) content of >30 percent; and 8. Maximum particle size of 75 mm. Therefore, this study was aimed at improving engineering properties of lateritic soil with the use of WWA as stabilizer in terms of its use as liner material and at developing the acceptable range of moulding water content–dry unit weight at which the optimally treated soil can be effectively compacted to achieve effective liner. MATERIALS AND METHODS Materials Lateritic Soil A reddish brown lateritic soil with inclusions of white mottles was collected by disturbed sampling from a studied borrow pit located in Shika, Zaria (latitude 11°15� N and longitude 7°45� E), Nigeria. It belongs to the group of ferruginous tropical soils derived from igneous and metamorphic rocks. Waste Wood Ash This was produced by open burning of the wood wastes derived from hardwoods obtained from a section of new saw mill in the Abogunde Area of the Ogbomoso North Local Government Area, Oyo State,

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Potential of Waste Wood Ash Treated Lateritic Soil as Liner

Figure 1. (A) Open burning of the wood waste. (B) Appearance of the waste wood ash after sieving.

Nigeria. The wood waste was heaped in a large quantity on a galvanized rooﬁng sheet inside a concrete tank (see Figure 1A) to prevent soil contamination. The residue formed after complete burning was collected and sieved through sieve No. 200 (75 μm) to obtain a material of ﬁneness similar to that of cement (Figure 1B) and was then kept in an airtight container to prevent pre-hydration and caking. METHODOLOGY All specimens were prepared from air-dried lateritic soil passing British Standard Institute (BS) sieve 4.75 mm (Sieve No. 4) and thoroughly mixed with up to 10 percent WWA in a stepped concentration of 2 percent (by dry weight of soil) until a uniform color was obtained. The following tests were carried out on both the natural and stabilized samples based on the procedures outlined in BS 1377 (1990) and BS 1924 (1990), respectively: sieve analysis, Atterberg limits,

Potential of Waste Wood Ash Treated Lateritic Soil as Liner

compaction, UCS, hydraulic conductivity, and volumetric shrinkage.

Characterization of Waste Wood Ash

Waste wood ash was characterized by determining its oxide composition using X-ray ﬂuorescence. This was carried out in two diﬀerent places in order to validate the results.

Index Properties

The speciﬁc gravity, particle size distributions, the Atterberg limits (i.e., liquid limit [LL], plastic limit [PL], plasticity index [PI]) and linear shrinkage of the natural lateritic soil and the various samples formed by mixing varying percentage of WWA with lateritic soil were determined in accordance with procedures outlined in BS 1377 (1990) and Head (1994a).

Compaction

The moisture–density relationships of the prepared samples were determined for the four compactive efforts used in the study (i.e., Reduced British Standard light [RBSL], British Standard light [BSL], West African Standard [WAS], or ‘Intermediate’ and British Standard heavy [BSH]) according to the data given in Table 1. The process was conducted in accordance with the procedure outlined in BS 1377 (1990), BS 1924 (1990), and Head (1994a).

Hydraulic Conductivity, Volumetric Shrinkage, and Unconfined Compressive Strength Six sample specimens were prepared at optimum water content for each of the four compactive efforts used, resulting in 24 specimens for each of the three major geotechnical tests conducted. There were 72 sample specimens in all. Hydraulic Conductivity The first set of 24 compacted samples was saturated for 48 hours to minimize advection by suction and permeated using a falling head permeameter, as recommended by Head (1994b) under 24-hour measurement. Volumetric Shrinkage Another set of 24 compacted samples was extruded and allowed to dry under laboratory atmospheric condition for 30 days while the height, diameter, and mass of the samples were measured at 5-day interval using

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

129

Figure 1. (A) Open burning of the wood waste. (B) Appearance of the waste wood ash after sieving.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

129

Compactive Eﬀort Reduced British Standard Light (RBSL) British Standard Light (BSL) West African Standard (WAS) British Standard Heavy (BSH)

Oluremi, Eberemu, Ijimdiya, and Osinubi

Table 1. Description of compactive eﬀorts used.

No. of Blows

Weight of Rammer (kg)

Height of Dropping Dropping (mm)

No. of Layers of Soil

Volume of Mould (cm3 )

15 27 10 27

2.5 2.5 4.5 4.5

300 300 450 450

3 3 5 5

1,000 1,000 1,000 1,000

a digital vernier calliper and a weigh balance, respectively. The volume and change in volume and, hence, volumetric shrinkage were determined from the average height and diameter computed for each of the samples according to the methods of Berndt and Coughlan (1976), Schafer and Singer (1976), and Tariq and Durnford (1993). Unconﬁned Compressive Strength The third set of 24 compacted samples was used to core test specimens of 38 mm by 76 mm, which were cured for a minimum of 48 hours to allow for uniform moisture distribution, according to BS 1924 (1990) for stabilized soil. The samples were then crushed under the axial stress of the compression machine until failure occurred. Development of Overall Acceptable Zones and Delineation of Compaction Moulding Water Content The paramount design parameters among geotechnical properties for which acceptable zones should be developed are coeﬃcient of permeability (hydraulic conductivity), UCS, and volumetric shrinkage strain. The acceptable zone for each of the three design parameters is developed by relating the acceptable limits of each of the design parameters with the dry density and moulding water content on the compaction plane formed, using the values of average dry densities of hydraulic conductivity, UCS, and volumetric shrinkage. Consequently, the overall acceptable zones for each of the designed mixes were produced by superimposing the results of the acceptable zones for each of the design parameters on one another on the compaction plane. DISCUSSION Oxide Composition Table 2 shows the oxide composition of the lateritic soil. The hydration modulus (HM), according to AlJabri et al. (2002) Udoeyo and Hyee (2002), and Naik 130

Reduced British Standard Light (RBSL) British Standard Light (BSL) West African Standard (WAS) British Standard Heavy (BSH)

et al. (2003), as determined from Eq. 1 for the WWA is 0.023, which is extremely low and outside the range for the HM of 1.7 (Alite) to 2.4 (Belite) for active cementitious material such as cement and cement kiln dust. The low HM value reveals the non-cementitious property of WWA. The total reactive oxide content (TROC) given by Eq. 2 (Al-Jabri et al., 2002; Udoeyo and Hyee, 2002; and Naik et al., 2003) is −11.94, which further buttresses its non-cementitious characteristics: CaO ; HM = (SiO2 + Al2 O3 + Fe2 O3 )

Compactive Eﬀort

(1)

TROC = (CaO + MgO − LOI) − (Na2 O + K2 O) , (2)

Composition by Weight, Waste Wood Ash 1a

11.6 2.53 — 0.31 0.87 0.28 0.06 10.67 13.26 — 0.06 47.94 0.44 0.04 0.05 0.06 14.6

4.17 0.07 1.62 3.86 — — 0.07 9.26 14.51 2.22 0.05 48.17 0.47 — — — 13.18

1 = Nigeria Geological Survey Laboratory, Kaduna, Kaduna State; 2 = West African Portland Cement Organisation Analytical Laboratory, Ewekoro, Ogun State.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Height of Dropping Dropping (mm)

No. of Layers of Soil

Volume of Mould (cm3 )

15 27 10 27

2.5 2.5 4.5 4.5

300 300 450 450

3 3 5 5

1,000 1,000 1,000 1,000

Unconﬁned Compressive Strength The third set of 24 compacted samples was used to core test specimens of 38 mm by 76 mm, which were cured for a minimum of 48 hours to allow for uniform moisture distribution, according to BS 1924 (1990) for stabilized soil. The samples were then crushed under the axial stress of the compression machine until failure occurred. Development of Overall Acceptable Zones and Delineation of Compaction Moulding Water Content

Table 2. Oxide composition of waste wood ash used in the study.

K2 O Na2 O CaO MgO BaO ZnO TiO Fe2 O3 Al2 O3 P2 O3 MnO SiO2 SO3 CeO2 Eu2 O3 Lu2 O3 Loss on ignition (LOI)

Weight of Rammer (kg)

where LOI indicates loss on ignition.

Oxide

No. of Blows

The paramount design parameters among geotechnical properties for which acceptable zones should be developed are coeﬃcient of permeability (hydraulic conductivity), UCS, and volumetric shrinkage strain. The acceptable zone for each of the three design parameters is developed by relating the acceptable limits of each of the design parameters with the dry density and moulding water content on the compaction plane formed, using the values of average dry densities of hydraulic conductivity, UCS, and volumetric shrinkage. Consequently, the overall acceptable zones for each of the designed mixes were produced by superimposing the results of the acceptable zones for each of the design parameters on one another on the compaction plane. DISCUSSION Oxide Composition Table 2 shows the oxide composition of the lateritic soil. The hydration modulus (HM), according to AlJabri et al. (2002) Udoeyo and Hyee (2002), and Naik 130

CaO ; (SiO2 + Al2 O3 + Fe2 O3 )

(1)

TROC = (CaO + MgO − LOI) − (Na2 O + K2 O) , (2) where LOI indicates loss on ignition.

Table 2. Oxide composition of waste wood ash used in the study. Composition by Weight, Waste Wood Ash Oxide K2 O Na2 O CaO MgO BaO ZnO TiO Fe2 O3 Al2 O3 P2 O3 MnO SiO2 SO3 CeO2 Eu2 O3 Lu2 O3 Loss on ignition (LOI)

11.6 2.53 — 0.31 0.87 0.28 0.06 10.67 13.26 — 0.06 47.94 0.44 0.04 0.05 0.06 14.6

4.17 0.07 1.62 3.86 — — 0.07 9.26 14.51 2.22 0.05 48.17 0.47 — — — 13.18

1 = Nigeria Geological Survey Laboratory, Kaduna, Kaduna State; 2 = West African Portland Cement Organisation Analytical Laboratory, Ewekoro, Ogun State.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Potential of Waste Wood Ash Treated Lateritic Soil as Liner

Speciﬁc Gravity

Consistency Limits

Speciﬁc Gravity

Consistency Limits

The specific gravity of the treated soil increased to 2.63 for 2 percent WWA treatment and then decreased with an increase in the WWA content. The values ranged from 2.61 for 0 percent WWA (natural soil) to 2.55 for 10 percent WWA. The increase in specific gravity might have resulted from the agglomeration of the soil particles under the pozzolanic effect of WWA, while the presence of the lightweight unreactive WWA in the mixture might lead to the reduction in the specific gravity, as WWA increased (Figure 2).

The variation of Atterberg limits with WWA content is shown in Figure 5. The Atterberg limits of the natural and treated lateritic soil specimens are in the ranges of 34.2 to 41.2 percent, 24.8 to 30.5 percent, and 7.9 to 11.7 percent for LL, PL, and PI, respectively. The PI on which the potential for shrinkage is dependent initially decreased from 11.6 percent for the natural soil to 7.9 percent at 4 percent WWA content. and thereafter it slightly and continuously increased to 10.8 percent for 10 percent WWA content. The reduction in the plasticity might be due to cation exchange reaction between the soil minerals and the WWA, which enhanced attraction and bonding between the particles of the soil-ash mixture. Although Albrecht and Cartwright (1989) reported that soils with a PI as low as 7 percent have been successfully used to build soil liners with extremely low in situ hydraulic conductivity, Daniel (1990) recommended that soil with PI 10 percent should be used. The Atterberg limits of the WWA-treated lateritic soil, as discussed in Oluremi et al. (2017), satisfy the condition reported by Benson et al. (1994) for soils to be used for construction of liners and covers.

Particle Size Distribution Soils with inadequate fines typically will have too little silt- and clay-sized material to produce suitably low hydraulic conductivity. The clay and silt size materials represented by the percentage passing sieve No. 200 decreased for 2 percent WWA treatment relative to natural soil (0 percent WWA), while from 4 percent WWA it continued to increase with the increasing percentage of the treatment (Figures 3 and 4). This reduction in the fine content at 2 percent addition of WWA resulted from the pozzolanic reaction of WWA with soil, which produced cementitious material that binds fine particles together and the flocculation and agglomeration of fine particles in the presence of calcium ion (Ca2+ ) to form larger size particles (Amadi and Eberemu, 2012a). Daniel (1990) recommended that the soil liner materials contain at least 30 percent fines, and it also satisfied the suggestion made by Benson et al. (1999) that a minimum of 50 percent fines might be an appropriate requirement for many soils to be used as liner material.

Compaction Characteristics The variations of compaction characteristics with WWA contents are shown in Figure 6. Generally, the trends observed for maximum dry density (MDD) and optimum moisture content (OMC) for the four compactive eﬀorts considered are those of increasing MDD/decreasing OMC and vice versa. Peak and minimum MDD and OMC values were obtained at

Figure 2. Variation of speciﬁc gravity with waste wood ash content.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Figure 2. Variation of speciﬁc gravity with waste wood ash content.

131

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

131

132

Oluremi, Eberemu, Ijimdiya, and Osinubi

Figure 3. Particle size distribution curves for lateritic soil–waste wood ash mixtures.

Figure 4. Relationship between percentage passing sieve No. 200 and waste wood ash content.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

132

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Potential of Waste Wood Ash Treated Lateritic Soil as Liner

Figure 5. Variation of Atterberg limits with waste wood ash content.

2 percent WWA content. The maximum dry density of the lateritic soil samples initially increased to peak values at 2 percent WWA treatment and thereafter gradually decreased with higher WWA treatment. The increase in the MDD up to 2 percent WWA is likely due to the flocculation and agglomeration of the clay particles as the percentage fines decreased and coarser particles formed, thereby occupying a larger space (Osinubi, 2006). In addition, according to Osinubi and Stephen (2006) and Oriola and Moses (2010), the filling of the void within the coarse aggregate with WWA particles

might result in a decrease in the MDD. This trend was observed for all the compactive eﬀorts used. On the other hand, the OMC values initially decreased to a minimum at 2 percent WWA content and thereafter increased with higher WWA treatment. The initial increase in OMC was likely due to the extra moisture required to complete the hydration reaction within the lateritic soil–WWA mixture (Osinubi and Oyelakin, 2012). The summary of the preliminary tests on the natural soil, which encapsulates the properties of the natural lateritic soil used for this work, is shown in Table 3.

Figure 6. Variations of maximum dry density and optimum moisture content of lateritic soil with waste wood ash content.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Figure 6. Variations of maximum dry density and optimum moisture content of lateritic soil with waste wood ash content.

133

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

133

Oluremi, Eberemu, Ijimdiya, and Osinubi Table 3. Properties of the natural lateritic soil. Property

Value

Natural moisture content (percent) Percentage Passing BS No. 200 Sieve Liquid limit (percent) Plastic limit (percent) Plasticity index (percent) Linear shrinkage (percent) Speciﬁc gravity USCS AASHTO Classiﬁcation pH Color * Dominant clay mineral

17.0 59.1 39.6 28.0 11.6 6.3 2.61 CL A – 6(6) 5.72 Reddish brown Kaolinite

AASHTO = American Association of State Highway and Transportation Officials; USCS = Unified Soil Classification System. Source: Osinubi et al. (2015); * Osinubi (1998).

UNCONFINED COMPRESSIVE STRENGTH The variation of UCS with WWA content is shown in Figure 7. The general trend shows that the UCS values initially increased to peak values at 2 percent WWA content and thereafter decreased to minimum values at 6 percent WWA content before increasing to 10 percent WWA content. Peak UCS values of 410.82, 490.15, 608.67, and 751.88 kN/m2 were obtained at 2 percent WWA treatment for specimens prepared at OMC and compacted with RBSL, BSL, WAS, and BSH energies, respectively. This improvement in UCS is likely a function of the formation of cementitious products such as hydrated calcium silicate gel and calcium aluminate gel through pozzolanic reactions and cementitious material hydrations that coat and bind the soil particles (Amadi and Eberemu, 2012a, 2013).

The reduction in UCS values recorded is in agreement with the ﬁndings reported in (Osinubi et al., 2007a, 2012) and might be due to the presence of the residual WWA, which required more water to complete its hydration process. All of the compacted treated samples satisﬁed the minimum compressive strength of 200 kN/m2 according to Daniel and Wu (1993).

HYDRAULIC CONDUCTIVITY The variation of hydraulic conductivity and WWA content for specimens prepared at OMC treatment is shown in Figure 8. Satisfactory results were obtained for the compactive efforts used at 2 percent and 4 percent WWA treatment for all the prepared samples except for the sample compacted with RBSL. All samples met the maximum specified value of 1 × 10−9 m/s except for samples compacted with RBSL and BSL for all treatments above 4 percent WWA content. This is an indication that the WWA occupied the voids present in the coarser particles of lateritic soil, thereby interfering and reducing the interconnectivity of the pores, which accumulate to effect a reduction in hydraulic conductivity. Similar results were observed by Goswami and Mahanta (2006). This could also possibly be due to increased cation exchange reaction that enhanced the development of an expansive diffuse double layer and, hence, deflocculated soil matrix, which resulted in reduction of hydraulic conductivity. However, as WWA increases, there might be replacement of the Na+ ions from the soil by more highly charged cations and/or the increase of ionic concentration, which usually decreases the doublelayer thickness and resulted in an increase in hydraulic

Oluremi, Eberemu, Ijimdiya, and Osinubi Table 3. Properties of the natural lateritic soil. Property Natural moisture content (percent) Percentage Passing BS No. 200 Sieve Liquid limit (percent) Plastic limit (percent) Plasticity index (percent) Linear shrinkage (percent) Speciﬁc gravity USCS AASHTO Classiﬁcation pH Color * Dominant clay mineral

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

17.0 59.1 39.6 28.0 11.6 6.3 2.61 CL A – 6(6) 5.72 Reddish brown Kaolinite

AASHTO = American Association of State Highway and Transportation Officials; USCS = Unified Soil Classification System. Source: Osinubi et al. (2015); * Osinubi (1998).

Figure 7. Variations of unconﬁned compressive strength lateritic soil with waste wood ash content.

134

Value

Figure 7. Variations of unconﬁned compressive strength lateritic soil with waste wood ash content.

134

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Potential of Waste Wood Ash Treated Lateritic Soil as Liner

Figure 8. Variations of hydraulic conductivity of lateritic soil with waste wood ash content.

conductivity (Schmitz, 2006; Francisca and Glatstein, 2010; and Oluremi et al., 2016a). Although a trend of results similar to that of 2 percent treatment was obtained for 6 percent WWA treatment, hydraulic conductivity increased beyond 4 percent WWA treatment. This may be due to the presence of excessive WWA in the soil-WWA mixture leading to increased ﬂocculation. Similar results were reported by Osinubi et al. (2009a), Osinubi and Eberemu (2009b), and Amadi and Osinubi (2010). VOLUMETRIC SHRINKAGE The variation in the volumetric shrinkage with the WWA contents for the compacted soil is shown in

Figure 9. For all of the compactive efforts considered, the maximum 4 percent volumetric shrinkage value criterion was satisfied with higher WWA content, such that as the compactive effort increased, the volumetric shrinkage strain decreased. This is consistent with the results of Osinubi and Eberemu (2009a, 2009b). Clay content is regarded as one of the parameters that affects the volumetric shrinkage of compacted lateritic soil. According to Blotz et al. (1998), volumetric shrinkage strain increases with increase in clay content because higher clay content or PI in soil promotes greater affinity for water, which is reflected in compaction, with an increase in optimum water content and a decrease in maximum dry unit weight as the

Figure 9. Variation of volumetric shrinkage of lateritic soil with wood waste content.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Figure 9. Variation of volumetric shrinkage of lateritic soil with wood waste content.

135

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

135

Oluremi, Eberemu, Ijimdiya, and Osinubi

Figure 10. Overall acceptable zone for (A) natural lateritic soil, (B) lateritic soil—2 percent waste wood ash mixture, (C) lateritic soil—4 percent waste wood ash mixture, (D) lateritic soil—6 percent waste wood ash mixture, (E) lateritic soil—8 percent waste wood ash mixture, and (F) lateritic soil—10 percent waste wood ash mixture.

PI increases. This water is made available during primary drying of the compacted samples. It is noteworthy that substantial volumetric shrinkage strains of between 5 and 10 percent were obtained for clay contents less than 30 percent (Albrecht and Benson, 2001). This was not supported by this work, since as the particle content of clay size or PI increased beyond 4 percent WWA content, the volumetric shrinkage strain decreased. This may imply that the percentage ﬁnes cannot be directly used to predict the changes in volumetric shrinkage of soil; in addition, the ﬁne particle might just be the residual WWA that fails to undergo pozzolanic reaction and therefore possesses no cementation properties. 136

BEHAVIOR OF OVERALL ACCEPTABLE ZONES The developed overall acceptable zones from the superimposition of the acceptable zones for UCS, hydraulic conductivity, and volumetric shrinkage on the compaction plane are shown by the hatched area in Figure 10A–F. The acceptable zone is the water content–dry unit weight bounded region at which compacted soil will achieved the desired properties for hydraulic conductivity, UCS, and volumetric shrinkage. The overall acceptable zones become enlarged from 0 to 4 percent WWA treatment, thereby accommodat-

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Potential of Waste Wood Ash Treated Lateritic Soil as Liner

ing more moulding water content and shrinkage as the WWA increased beyond 4 percent. The best overall acceptable zone was obtained at 4 percent WWA treatment, and the ranges of moulding water content were 19.3 percent, 14.0–18.1 percent, 13.8–20.0 percent, and 12.7–18.7 percent for RBSL, BSL, WAS, and BSH, respectively. Selection of any combination of dry unit weight and moulding water content from those overall acceptable zones (Figure 10A–F) for the various mixtures will assist in complying with and adhering to the required specifications during field sample preparation and the actual construction process (Daniel and Benson, 1990; Osinubi et al., 2009a; and Amadi and Eberemu, 2012b). CONCLUSION A kaolinite-dominated lateritic soil treated with WWA was evaluated for use as liner material in engineered landfills and investigated for Atterberg limits, compaction characteristics, hydraulic conductivity, UCS, and volumetric shrinkage, which were used to evaluate the compliance of the treated sample with set regulated threshold values. Basically the study has demonstrated the beneficial use of WWA for the improvement of lateritic soil, with the following conclusions: 1. All of the treated samples meet the acceptable maximum volumetric shrinkage value of 4 percent; 2. All of the compacted treated samples satisfied the minimum compressive strength of 200 kN/m2 ; 3. All of the treated samples compacted with WAS as minimum compactive effort satisfied the maximum hydraulic conductivity of 1 × 10−9 m/s; 4. However, all samples treated with up to 4 percent WWA met the acceptable hydraulic conductivity of 1 × 10−9 m/s, minimum prescribed UCS value of 200 kN/m2 , and maximum specified value of 4 percent for volumetric shrinkage when compacted with BSL as minimum compactive effort. 5. The best overall acceptable zone of moulding water content was obtained at 4 percent WWA treatment; 6. The optimal content of 4 percent WWA (by dry weight of soil) could best be used in treating this lateritic soil compacted with BSL as minimum compactive effort for waste containment application. REFERENCES Adeyeri, J. B., 2015, Soil improvement and stabilization. In Technology and Practice in Geotechnical Engineering: IGI Global, USA, pp. 589–647. doi:10.4018/978-1-4666-6505-7.ch010.

Potential of Waste Wood Ash Treated Lateritic Soil as Liner

Albrecht, B. A. and Benson, C. H., 2001, Effect of desiccation on compacted natural clay: Journal Geotechnical Geoenvironmental Engineering, ASCE, Vol. 127, No. 1, pp. 67–75. Albrecht, K. A. and Cartwright, K., 1989, Infiltration and hydraulic conductivity of a compacted earthen liner: Groundwater, Vol. 27 pp. 14–19. Alhassan, M. and Mustapha, A. M., 2007, Effect of rice husk ash on cement stabilized laterite: Leonardo Electronic Journal Practice Technology, Vol. 6, No. 11, pp. 47–58. Al-Jabri, K.; Taha, R.; Al-Harthy, A.; Al-Oraimi, S.; and AlNuaim, A., 2002, Use of cement by-pass dust in flowable fill mixtures: Cement, Concrete, Aggregates, Vol. 24, No. 2, pp. 53– 57. Amadi, A. A. and Eberemu, A. O., 2012a, Performance of cement kiln dust in stabilizing lateritic soil contaminated with organic chemicals: Advanced Material Research, Vol. 367, pp. 41–47. doi:10.4028/www.scientific.net/AMR367.41. Amadi, A. A. and Eberemu, A. O., 2012b, Delineation of compaction criteria for acceptable hydraulic conductivity of lateritic soil–bentonite mixtures designed as landfill liners: Journal of Environment Earth Science, Vol. 67, No. 4, pp. 999–1006. doi:10.1007/s12665-012-1544-z. Amadi, A. A. and Eberemu, A. O., 2013, Potential application of lateritic soil stabilized with cement kiln dust (CKD) as liner in waste containment structures: Geotechnical Geological Engineering, doi:10.1007/s10706-013-9645-3. Amadi, A. A.; Eberemu, A. O.; and Osinubi, K. J., 2012, Strength consideration in the use of lateritic soil stabilized with fly ash as liners and covers in waste landfills. In Hryciw, R. D.; Athanasopoulos-Zekkos, A.; and Yesiller, N. (Editors), State-of-the-Art and Practice in Geotechnical Engineering: Geotechnical Special Publication, ASCE, Vol. 225, pp. 3835–3844. Amadi, A. A. and Osinubi, K. J., 2010, Assessment of bentonite influence on hydraulic conductivity of lateritic soil: International Journal Engineering Research Africa, Vol. 3, pp. 84–93. Anderson, S. A. and Hee, B., 2000, Lateritic soil in landfill and covers. In Acar, Y. B. et al. (Editors), Geoenvironment 2000: Characterization, Containment, Remediation and Performance in Environmental Geotechnics, ASCE Special Publication: pp. 936–947. ASTM International, 2011, ASTM D2487-11: Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System), West Conshohocken, Pa, USA. Awodun, M. A.; Ojeniyi, S. O.; Adeboye, A.; and Odedina, S. A., 2007, Effect of oil palm bunch refuse ash on soil and plant nutrient composition and yield of maize: American-Eurasian Journal Sustainable Agriculture, Vol. 1, No. 1, pp. 50–54. Benson, C. H.; Daniel, D. E.; and Butwell, G. P., 1999, Field performance of compacted clay lines: Journal Geotechnical and Geoenvironmental Engineering, Vol. 125, No. 105, pp. 390–403. Benson, C. H.; Zhai, H.; and Wang, X., 1994, Estimating hydraulic conductivity of compacted clay liners: Journal Geotechnical Engineering, ASCE, Vol. 120, No. 2, pp. 366–387. Berndt, L. D. and Coughlan, K. J., 1976, The nature of changes in bulk density with water content in a cracking clay: Australian Journal Soil Resources, Vol. 15, pp. 27–37. Blotz, L. R.; Benson, C. H.; and Boutwell, G. P., 1998, Estimating optimum water content and maximum dry unit weight for compacted clays: Journal Geotechnical Geoenvironmental Engineering, ASCE, Vol. 124, No. 9, pp. 907–912. British Standards Institution (BSI), 1990, BS 1377: Methods of Testing Soils for Civil Engineering Purposes: BSI, London, U.K.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

137

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

137

Oluremi, Eberemu, Ijimdiya, and Osinubi BSI, 1990, BS 1924: Methods of Test for Stabilized Soils: BSI, London, U.K. Daniel, D. E., 1990, Summary review of construction quality control for earthen liners. In Bonaparte, R. (Editor), Waste Containment Systems: Construction, Regulation, and Performance: ASCE GSP, Vol. 26, pp. 175–189. Daniel, D. and Benson, C., 1990, Water content-density criteria for compacted soil liners: Journal Geotechnical Engineering, Vol. 116, No. 12, pp. 1811–1830. Daniel, D. E. and Wu, Y. K., 1993, Compacted clay liners and covers for arid sites: Journal Geotechnical Engineering, ASCE, Vol. 119, No. 2, pp. 223–237. Demeyer, A.; Voundi, J. C. N.; and Verloo, M. G., 2001, Characteristics of wood ash and influence on soil properties and nutrient uptake: An overview: Bioresource Technology, Vol. 77, No. 3, pp. 287–295. Elarabi, H.; Taha, M.; and Elkhawad, T., 2013, Some geological and geotechnical properties of lateritic soils from Muglad Basin located in the south-western part of Sudan: Research Journal Environment Earth Science, Vol. 5, No. 6, pp. 291–294. Francisca, F. M. and Glatstein, D. A., 2010, Long term hydraulic conductivity of compacted soils permeated with landfill leachate: Applied Clay Science, Vol. 49, pp. 187–193. Frempong, E. M. and Yanful, E. K., 2008, Interaction between three tropical soils and municipal solid waste landfill leachate: Journal Geotechnical Geoenvironmental Engineering, ASCE, Vol. 134, No. 3, pp. 379–396. Gabas, S. G.; Boscov, M. E. G.; and Sarkis, J. E. S., 2007, Cadmium and lead adsorption in a compacted lateritic soil. CDROM of presentations at the First International Conference on Environmental Research, Technology Policy ERTEP 2007, Session E3.18: State of- the-Art Technologies for Environmental Performance and Protection, July 16–19, 2007, Ghana. pp. 1– 12. Goswami, R. K. and Mahanta, C., 2007, Leaching characteristics of residual lateritic soils stabilised with fly ash and lime for geotechnical applications: Waste Management, Vol. 27, pp. 466–481. Head, K. H., 1994a, Manual for Soil Laboratory Testing, Soil Classification and Compaction Tests: Halsted Press, New York. Head, K. H., 1994b, Manual of Soil Laboratory Testing. Permeability, Shear Strength and Compressibility Tests: 2nd ed., Pentech Press, London, U.K. Medjo, E., and Riskowiski, G., 2004, A procedure for processing mixtures of soil, cement, and sugar cane bagasse: Journal Scientific Research Development, Agricultural Engineering International, Vol. 990, No. 3, pp. 1–5. Moses, G., 2008, Stabilization of black cotton soil with ordinary portland cement using bagasse ash as admixture: IRJI Journal Research Engineering, Vol. 5, No. 3, pp. 107–115. Mousavi, S. and Wong, L. S., 2015, Performance of compacted and stabilized clay with cement, peat ash and silica sand: Jordan Journal Civil Engineering, Vol. 9, No. 1, pp. 20–32. Naik, T. R.; Canpolat, F.; and Chun, Y., 2003, Uses Of CKD other than for Flue Gas Desulfurization, CBU Report No. CBU-2003-35 REP-529: Holcim (U.S.), Department of Civil Engineering and Mechanics College of Engineering and Applied Science, The University of Wisconsin–Milwaukee. Oluremi, J. R.; Adedokun, S. I.; and Osuolale, O. M., 2012, Stabilization of poor lateritic soils with coconut husk ash: International Journal Engineering Research Technology (IJERT), Vol. 1, No. 8, pp. 1–9. Oluremi, J. R.; Eberemu, A. O.; Ijimdiya, T. S.; and Osinubi, K. J., 2016a, Absorption and diffusion potential of waste wood

138

ash-treated lateritic soil. In Yesiller, N. (Editor), Geo-Chicago 2016: Sustainable Waste Management and Remediation, ASCE Geotechnical Special Publication (GSP), Vol. 273, pp. 98–107. http://dx.doi.org/10.1061/9780784480168.011. Oluremi, J. R.; Eberemu, A. O.; and Osinubi, K. J., 2013, Compaction behaviour of waste wood ash treated lateritic soil: Proceedings of the 2nd International Conference on Engineering and Technology Research, Theme: Revamping the Industrialization of the Nigerian Economy–The Role of Science, Engineering and Technology, Faculty of Engineering; LAUTECH, Ogbomoso, Oyo State, March 26–28, pp. 16–22. Oluremi, J. R.; Osuolale, O. M.; Adeoye, T. T.; and Akingbade, A. A., 2016b, Strength development in lateritic soil stabilised with coconut shell ash for highway pavement construction: Innovative Systems Design and Engineering, Vol. 7, No. 11, pp. 49–56. Oluremi, J. R.; Rafat, S.; and Adeboje, E. P., 2016c, Stabilization potential of cement kiln dust treated lateritic soil: International Journal Engineering Research Africa, Vol. 23, pp. 52–63. doi:10.4028/www.scientific.net/JERA.23.52. Oluremi, J. R.; Yohanna, P.; Ishola, K.; Yisa, G. L.; Eberemu, A. O.; Ijimdiya, S. T.; and Osinubi, K. J., 2017, Plasticity of lateritic soil admixed with selected admixtures: Environmental Geotechnics, http://dx.doi.org/10.1680/jenge.15. 00085. Oriola, F. and Moses, G., 2010, Groundnut shell ash stabilization of black cotton soil: Electronic Journal Geotechnical Engineering, Vol. 15, Bund. E, pp. 415–428. http://www.ejge.com/2010/Ppr10.036.pdf Osinubi, K. J., 2006, Influence of compactive efforts on lime-slag treated tropical black clay: Journal Materials Civil Engineering, ASCE. Vol. 18, No 2, pp. 175–181. Osinubi, K. J. and Eberemu, A. O., 2009a, Compatibility and attenuative properties of laterite-blast furnace slag mixtures: Journal Waste Technology Management, Vol. 35, No. 1, pp. 7–16. Osinubi, K. J. and Eberemu, A. O., 2009b, Desiccation-induced shrinkage of compacted lateritic soil treated with bagasse ash: The Twenty-Fourth International Conference on Solid Waste Technology and Management, Session 5C: Bio-reactors and Innovative Landfills, CD-ROM, March 15–18, Philadelphia, PA,. pp. 856–867. Osinubi, K. J.; Eberemu, A. O.; and Amadi, A. A., 2009a, Compacted lateritic soil treated with blast furnace slag (BES) as hydraulic barrier in waste containment systems: International Journal Risk Assessment Management, Vol. 13, No. 2, pp. 102– 120. Osinubi, K. J.; Ijimdiya, T. S.; and Nmadu, I., 2009b, Lime stabilization of black cotton soil using bagasse ash as admixture. In Advanced Materials Research, Vol. 62–64: 3–10. Trans Tech. Publications, Zurich. http://www.scientific net. Osinubi, K. J. and Nwaiwu, C. M. O., 2008, Desiccation induced shrinkage in compacted lateritic soil: Journal Geotechnical Geological Engineering, Vol. 26, No. 5, pp. 603–611. Osinubi, K. J.; Oluremi, J. R.; and Eberemu, A. O., 2015, Unsaturated behaviour of waste wood ash treated lateritic soil: Journal Solid Waste Technology Management, Vol. 41, No. 4, pp. 36–49. Osinubi, K. J. and Oyelakin, M. A., 2012, Optimising soilcement-ash stabilisation mix for maximum compressive strength: A case study of the tropical clay sub-base material stabilised with cement-locus bean waste ash. In Laryea, S.; Agyepong, S. A.; Leiringer, R.; and Hughes, W. (Editors), Proceedings 4th West Africa Built Environment Research

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

138

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

Potential of Waste Wood Ash Treated Lateritic Soil as Liner (WABER) Conference, July 24–26, 2012, Abuja, Nigeria, pp. 1207–1218. Osinubi, K. J. and Stephen, T. A., 2006, Effect of curing period on bagasse ash stabilized black cotton soil: Book Proceedings Bi-monthly Meetings/Workshop, Material Society of Nigeria, Zaria, pp. 1–8. Saarsalmi, A.; Malkonen, E.; and Piirainen, S., 2001, Effects of wood ash fertilization on forest soil chemical properties: Silva Fennica, Vol. 35, No. 3, pp. 355–368. Salahudeen, A. B. and Ochepo, J., 2015, Effect of bagasse ash on some engineering properties of lateritic soil: Jordan Journal Civil Engineering, Vol. 9, No. 4, pp. 468–476.

Potential of Waste Wood Ash Treated Lateritic Soil as Liner

Schafer, W. M. and Singer, M. J., 1976, A new method of measuring shrink-swell potential using soil pastes: Soil Science Society: American Journal, Vol. 40, pp. 805–806. Schmitz, R. M., 2006, Can the diﬀuse double layer theory describe changes in hydraulic conductivity of compacted clay?: Geotechnical Geological Engineering, Vol. 24, pp. 1835–1844. Tariq, A. and Durnford, D. S., 1993, Soil volumetric shrinkage measurements: A simple method: Soil Science, Vol. 155, No. 5, pp. 325–330. Udoeyo, F. F. and Hyee, A., 2002, Strengths of cement kiln dust concrete: Journal Materials Civil Engineering, Vol. 14, No. 6, pp. 524–526.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

139

(WABER) Conference, July 24–26, 2012, Abuja, Nigeria, pp. 1207–1218. Osinubi, K. J. and Stephen, T. A., 2006, Effect of curing period on bagasse ash stabilized black cotton soil: Book Proceedings Bi-monthly Meetings/Workshop, Material Society of Nigeria, Zaria, pp. 1–8. Saarsalmi, A.; Malkonen, E.; and Piirainen, S., 2001, Effects of wood ash fertilization on forest soil chemical properties: Silva Fennica, Vol. 35, No. 3, pp. 355–368. Salahudeen, A. B. and Ochepo, J., 2015, Effect of bagasse ash on some engineering properties of lateritic soil: Jordan Journal Civil Engineering, Vol. 9, No. 4, pp. 468–476.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 127–139

139

Ensuring Successful Landslide Investigation during an Emergency Response

JEROME V. DE GRAFF*

Department of Earth & Environmental Sciences, California State University–Fresno, 2576 East San Ramon Avenue, Mail Stop ST-24, Fresno, CA 93740

Key Terms: Emergency Response, Geologic Investigation, Landslides, Emergency Management, Natural Disasters ABSTRACT When a destructive landslide happens, geologists may be recruited to be part of the team carrying out the emergency response. An emergency response situation requires geologists to quickly acquire needed geologic information during an intense and stressful assignment. There are five significant operational approaches that are essential to ensure success in this situation. First, the geologists should fully understand and remain focused on the objectives of the response mission. Second, the landslide area must be accessed safely when collecting needed data. From a team standpoint, an injury negatively affects available data and time. Third, the landslide information that is developed must be reliable within the context of the mission and be obtainable within a limited time. Fourth, given the constraints on data collection imposed by an emergency response situation, the degree of uncertainty associated with the findings will need to be explained to ensure subsequent decision-making is done on a sound basis. Fifth, the information needs to be communicated to different audiences, who will range from individual team members to groups of people affected by the landslide. Whether providing documentation or making a presentation, the geologist will need to engage them by explaining the landslide information so it speaks to their needs. Experience gained serving on teams for a huge landslide damming a river in Dominica, West Indies, in 1997 and a large rock slide that buried a major highway in California in 2006 illustrate these important aspects for ensuring success when investigating landslides during an emergency response. INTRODUCTION We are accustomed to the idea that firefighters and emergency medical responders are hired specifically to *Corresponding author email: degraff@csufresno.edu

be on call for responding to fire and medical emergency situations. In contrast, a geologist providing technical information as part of an emergency response is much more likely to be on a temporary, and possibility unexpected, assignment from their normal geologic endeavors. Consequently, geologists employed by government agencies, land development or management entities, engineering geology consulting firms, and academic institutions should anticipate the possibility of finding themselves participating in an emergency response at some point in their career. While emergency response teams for most natural disasters might profit from involvement of one or more geologic professionals, their participation when a team is investigating a landslide is absolutely crucial. Landslides are recognized as occurring in many parts of the world. However, many individual landslides produce only minor or localized impacts, resulting in only their combined effect being economically significant (Brabb and Harrod, 1989; Schuster, 1996; and Highland, 2012). So, it is reasonable to consider the circumstances that might produce a landslide or landslides needing an emergency response and, in turn, how geologic participation might be part of the response effort. For example, some landslides may only necessitate scientific examination rather than an emergency response. The December 1991 Mount Cook rockfall was a spectacular and catastrophic event within Mount Cook National Park in New Zealand. Despite lowering the peak by 10 m and sending a mass of rock debris downslope for a distance of 7.5 km, this individual event was not a landslide disaster due to the absence of property damage, human injury, or casualties (McSaveney, 2002). Additionally, the likelihood of future landslides in this environment is widely recognized among those mountaineering individuals likely to be present when such events take place. Landslides with the capability of causing injuries, fatalities, property damage and destruction, and economic loss can occur due to a number of triggering mechanisms. Widespread intense landslide occurrence in response to a major precipitation event (Coe et al., 2014), an earthquake (Wang et al., 2009), or a volcanic eruption (Cummans, 1981) could certainly produce a situation where geologists would be part of the

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

141

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

141

De Graﬀ

emergency response efforts. Landslides triggered during these geologic disasters will often be in association with other damaging occurrences such as flooding, ground shaking, and ash fall, which would also require geologic data to limit their adverse impacts. The potential for destructive debris-flow occurrence is now being recognized as a consequence of another natural disaster, wildfires. This potential requires a rapid emergency response action in order to anticipate and mitigate post-fire debris flows (De Graff et al., 2007, 2011, 2015a). It might be assumed that the need for a geologist on an emergency response team is contingent on the size of the event, the type of landslide, or the number of people killed or injured. However, published literature seems to reflect that geologic information during emergency response is necessary for landslides exhibiting a wide range of sizes, based on either volume or areal extent, involves most landslide types (Cruden and Varnes, 1996; Hungr et al., 2001, 2014), and can include the injury or death of a few people or many. For example, a mass of nearly 25,000 m3 was involved in the movement of the 1985 Mameyes rock-block slide in Puerto Rico. The Mameyes landslide killed 129 people, which is considered the largest death toll for any single landslide in North American history (Jibson, 1986). The 2014 Oso debris flow near Oso, WA, is estimated to have mobilized 7.6 × 106 m3 and resulted in the deaths of 43 individuals (Wartman et al., 2016). In contrast, the 2013 rock-fall event in Rockville, UT, involved 1,070 m3 and was responsible for the death of two individuals (Lund et al., 2014). The need for a geologist to provide technical input during an emergency is a function of multiple factors rather than simply being a function of involving a landslide occurrence of a particular character. Landslides requiring an emergency response mostly result when there are impacts to people, property, and infrastructure. A primary component of the emergency response team will be first responders to address the immediate needs of rescue, medical, and safety issues. Other team members will be specialists from affected jurisdictions as well as technical personnel from utilities and agencies. One or more geologic professionals may need to be dispatched as part of an emergency response team responding to an individual landslide or a widespread landslide event. Unlike first responders, these geologists may not routinely do this type of assignment. Consequently, geologists must adapt to the unique aspects of emergency operations to be effective. The organizational structure of the emergency response team will generally define how the geologist is expected to carry out their role as a team member (OFDA, 1994; De Graff et al., 2007; and Lancaster et al., 2014). It is important for the geologist to recog142

nize that the focus and information needs of an emergency response team will often differ from a typical geologic investigation. The organizational structure of the emergency response team includes the authorities it functions under and associated operational procedures. Commonly, these operational procedures are derived from the principles of the incident command system (ICS). ICS was originally developed to facilitate cooperative and efficient emergency operations between agencies with differing mission and authorities (Bigley and Roberts, 2001). While initially developed to improve wildland firefighting, it is now applied to a wide variety of hazards by a broad group of emergency service entities within and outside the United States (Bigley and Roberts, 2001; Zhang and She, 2014). Participating in an emergency response is an intense experience. There is some degree of initial confusion, situational changes happen in both the operational and field environment, and there is often a limited period of time to implement mitigating actions. This paper will describe five approaches to ensure success when participating in one of these stressful endeavors: 1. satisfying the objectives of the mission, 2. accessing the landslide area safely during data collection, 3. developing reliable information with limited time, 4. explaining the uncertainty associated with the findings, and 5. communicating the information needed by different audiences. Typically, emergency response teams are dealing with a large landslide or widespread landslides within a defined area. Case studies illustrating how these five operational approaches guide geologic input are provided by emergency response team efforts related to the 1997 landslide that dammed a major tributary of the Layou River on the island nation of Dominica, West Indies (W.I.), and the 2006 Ferguson rock slide that buried a part of California Highway 140 in Merced Canyon, CA. The 1997 Matthieu landslide took place at the junction of the Matthieu River with the Layou River in the Commonwealth of Dominica. The Layou River is the largest river on this island nation in the Windward Islands of the eastern Caribbean. About 8 km upstream from the mouth of the Layou River, it is joined by the Matthieu River, a major tributary, where both channels are enclosed by nearly vertical bedrock walls. On November 18, 1997, a debris flow took place near the mouth of the Matthieu River, which then flowed into the Layou River, blocking its flow (De Graff et al., 2010). Three days later, the landslide dam was breached by overtopping and released impounded water, causing flooding within the lower Layou River

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

De Graﬀ

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

Landslide Investigation during an Emergency

Figure 1. (A) A view north of the downstream face of the Matthieu landslide dam as seen from the southern bank of the Layou River. In the foreground, the Layou River channel is seen flowing through the remnants of two earlier debris flows, which had dammed that river. The vertical distance from the water in the Layou River channel to the crest of the landslide dam is approximately 119 m. The Matthieu landslide dam abuts vertical exposures of the Layou Tuff. (Photo by Jerome De Graff.) (B) An aerial view of the Matthieu landslide dam looking north-northeast. The star (orange) identifies where the author was standing when taking the photo in 1A. Note the large mass of vegetation on the Layou Tuff circled (gold) in both photos. (Photo by Jerome De Graff.)

valley to its junction with the Caribbean Sea. On November 25th, a larger landslide movement at the mouth of the Mathieu River added debris-flow material to the remnants of the earlier debris flow, again damming the Layou River. However, the bulk of the slump-earthflow blocked the mouth of the Matthieu River, forming a much higher landslide dam than the earlier ones in the Layou River (Figure 1). The second Layou River landslide dam was breeched by overtopping on November 28, 1997. The resulting flood through the lower Layou River valley was greater than the one on November 21st (De Graff et al., 2010).

Landslide Investigation during an Emergency

The Office of Foreign Disaster Assistance (OFDA) dispatched a two-person Disaster Assistance Response Team (DART), which arrived in the country on December 5, 1997. The team departed three days later having completed their assessment and having briefed officials of the government of the Commonwealth of Dominica (GCD) on their findings and recommendations. The 2006 Ferguson rock slide manifested itself in late April as an unremarkable, but noticeable, number of rocks accumulated on and next to California Highway 140. This occurrence was at the base of Ferguson Ridge, about 10 km west of the El Portal entrance to Yosemite National Park. By the end of May 2006, accumulated rock falling from the toe of a reactivated 800,000 m3 rock slide overwhelmed the protective structures installed by the California Department of Transportation (CalTrans), buried the twolane highway, and extended 10 m into the river channel in the Merced Canyon (Figure 2) (Harp et al., 2008). On June 5, 2006, the U.S. Forest Service (USFS) activated a Federal Incident Management Team to coordinate emergency efforts by local, state, and federal agencies involved in responding to the Ferguson rock slide. The incident commander and the leaders of various sub-units such as operations, communications, and logistics comprised an existing leadership team. The other team members included local USFS geologists and representatives from agencies responding to this disaster. Geologists from the US Geological Survey (USGS) were brought in to the team for an initial consultation. Over the next 15 days, the team completed a report summarizing emergency actions completed during this time and giving recommendations provided by agency team members for additional actions to be undertaken. BACKGROUND The destructive actions of a landslide occurrence and its potential for additional movement are important factors for planning and implementing emergency response actions. Determining if there is a basis for evacuating people within a threat zone and defining that threat zone are also factors of paramount importance. The overall emergency operation will need an assessment of the people, property, and infrastructure that are impacted or potentially impacted. Such an assessment is not limited to the area immediately impacted by the landslide. Consequently, geologic assessment for an ongoing emergency response should include any associated secondary impacts such as landslide dams (Meyer et al., 1986; Govi et al., 2002) or tsunamis (Bonaccorso et al., 2003). These secondary effects of a landslide occurrence can expand the area

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

143

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

143

De Graﬀ

Figure 2. A view looking downstream at the Ferguson rock slide. The channel of the Merced River is seen in the foreground, with the unburied part of California Highway 140 on the left and the one-lane road for the detour on the right. The lighter brown strip along the base of the headscarp was created by the 2006 reactivated movement. The accumulated talus extends from within the Merced River channel to the exposed toe of the Ferguson slide mass, which serves as the source of individual rocks and small rockfall events. (Photo by Jerome De Graﬀ.)

where emergency actions are needed well beyond the immediate locality of the landslide. A crucial part of assessing movement is identifying where such movement might interfere with ongoing emergency actions. For example, continued movement on the 1983 Thistle landslide was partly responsible for the failure of the initial effort to excavate a channel to prevent impounding water behind the landslide. Later, an attempt using piping to siphon rising water over the landslide was disrupted by the continuing movement (Hansen and Morgan, 1986). There can also be significant economic disruption or loss well beyond the location of the actual landslide when linear infrastructure elements such as pipelines (Baum et al., 2008), canals (De Graff et al., 2017), and railroads (Clague and Evans, 2003) are affected. Roads are the most common linear infrastructure impacted by landslides, creating a physical impediment 144

De Graﬀ

to traffic flow and causing economic and other societal impacts at significant distances from the landslide site (Kuehn and Bedrossian, 1987; Schuster, 1996; and Harp et al., 2008). Landslides that block roads can be especially disruptive where alternative routes require long detours or, for practical purposes, are unavailable (Hansen and Morgan, 1986; Harp et al., 2008; Koch et al, 2014; and Tizzano et al., 2016). The approaches described in this paper for successfully responding to these and other geologic information needs during an emergency response draw upon the author’s experience, between 1981 and 2013, serving as a geologist on: (1) 36 Burned Area Emergency Response (BAER) teams for wildland fires, including six of the 20 largest in California as of 2017, (b) two International Agency for International Development (USAID) post-disaster landslide assessment teams (Thailand, and St. Lucia, W.I.), (c) a 2003 federal review team of post-fire debris flows in southern California, (d) a DART in Dominica, W.I., for the OFDA, and (e) a 2006 Federal Incident Management Team in California.

SATISFYING THE OBJECTIVES OF THE MISSION

An active landslide that is causing, or threatening to cause, harm will generate a multitude of questions for geologists reaching the scene. The answers to some of these questions will be useful to the emergency response. The answers to others will contribute to longterm solutions to problems attributable to the landslide event. Still others will simply satisfy professional interest and curiosity. Of these questions, answers that serve the needs of the emergency response are the most important. The geologist on the emergency response team is making an initial assessment that must provide information and recommendations for timely decisions, limiting further loss from the landslide and promoting a resumption of normal activity by affected communities (OFDA, 1994). Consequently, the geologist should seek direction from the emergency response team leadership to establish the mission objectives for their work. It is entirely possible that the geologist may need to draft objectives as part of this process. Considerations in such a formulation should include: (1) developing information on the magnitude and dimension of the landslide and the area it is affecting, (2) generating a first approximation of the extent of its impact on both the local population and infrastructure, and (3) identifying baseline data for effectively monitoring this ongoing disaster (OFDA, 1994). Interaction with other technical and emergency management members of the team may help to define the geologic objectives

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

Landslide Investigation during an Emergency

by the type of information they may need to satisfy their mission objectives. Matthieu Landslide Dam In the case of the Matthieu landslide dam, the earlier two landslide dams on the Layou River and subsequent flooding after overtopping made obvious the threat posed by the rising water impounded behind the Matthieu landslide (De Graff et al., 2010). The slumpearthflow forming the Matthieu landslide dam was dramatically higher than the lower-lying debris flows that had dammed the Layou River. This was the impetus for the GCD requesting U.S. government technical guidance about possible means for limiting the landslide-dam lake’s growth. The two-person DART team was able to make observations as the threat of future flooding from the Matthieu landslide dam was increasing. There was information available on the debris flows responsible for damming the Layou River and their downstream flood effects based on local observations by GCD emergency management and technical specialists. Based on this government-to-government request and direction from the headquarters staff of OFDA, the geologist had two distinct mission objectives: 1. Identify the specific hazards posed by the Matthieu landslide dam. 2. Identify what technical assistance might be suitable for mitigating any of these hazards. Any release of water impounded by the Matthieu landslide dam would flood the same valley where the waters from the breached Layou River landslide dams had passed. The recent nature of those floods left deposits and damage that simplified identification of potentially affected areas, property, and infrastructure. The GCD estimates of maximum flood flows for both Layou river flood events could be compared to any anticipated release from the Matthieu landslide dam. This was an important component to achieving mission objective #1. Other important factors were the observations that could be made to more fully describe the Matthieu landslide dam and its character (De Graff et al., 2010). This was not an easy or simple task within the time available, owing to both the steep terrain and the extensive vegetative cover present. Data already gathered by GCD technical personal and ongoing monitoring of the impounded water were invaluable in achieving mission objective #1. Assessment for mission objective #2 benefited from field observations of existing roads and potential access for the types of heavy equipment needed to artificially breach the Matthieu landslide dam or install other measures to limit the amount of water that might

Landslide Investigation during an Emergency

form a lake behind the landslide. Given the equipment resources on this island nation, it was clear that most heavy equipment would need to be brought by barge from Puerto Rico or some other nearby island source. The steepness of the terrain upstream from the landslide dam posed an extreme impediment to equipment access, as did the downstream access, which required bridging the biggest river in this country. These considerations were used in satisfying mission objective #2. Ferguson Rock Slide At the Ferguson rock slide, the mission was more typical of what many geologists on an emergency response team would encounter. Broadly, the mission goals were to collect information to clarify the situation in terms of the unfolding disaster and its immediate impacts on the locality where it was happening (OFDA, 1994). The mission of the geologists on the Federal Incident Management Team was defined by three objectives: 1. Provide a situational analysis of the Ferguson rock slide by describing the nature of the landslide, defining its movement, examining probable triggering mechanisms, and characterizing its existing and possible future impact to California Highway 140 and the adjacent Merced River. 2. Contribute to identifying and formulating remedies for mitigating any of the identified hazards. 3. Serve as the primary source of geologic information for inquires being made by news media outlets. The Ferguson rock slide was found to be a reactivation of an older rock slide, a fact that was visually evident from weathering of the exposed head scarp, except for the light brown, unweathered 9–12 m basal exposure attributable to the current movement (Harp et al., 2008). Taking into account the planar dimensions of the Ferguson rock slide and estimating a thickness of 30–40 m, the volume of the new displaced landslide mass was determined to be 800,000 m3 (Harp et al., 2008). The recent reactivation had disrupted the rock-block slide into three morphologically distinguishable sections. The toe of the slide mass was exposed on the steep canyon wall tens of meters above the Merced River. The exposed unstable toe was the source for the many individual rocks and rock masses falling to form the accumulated talus that buried California Highway 140 and extended 10 m into the channel of the Merced River (Harp et al., 2008). With the drier summer period, the main slide mass slowed, which decreased instability at the toe and produced fewer rockfalls per day than in late spring. It

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

145

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

145

De Graﬀ

was concluded that a very wet 2006 spring happening on the heels of a wet 2005 spring created the conditions leading to reactivation of the Ferguson rock slide (Harp et al., 2008). Because October through May are the primary months when precipitation occurs at this location, an increase in movement was considered likely when the rains began again during the fall season. The blockage of California Highway 140 by the Ferguson rock slide prevented residents in El Portal, CA, and employees at Yosemite National Park from easily reaching Mariposa, CA (Harp et al., 2008). This also affected local school children attending school in Mariposa. Additionally, Mariposa hosted the stores and other resources routinely needed by those residents. Consequently, people were forced to make an extensive commute on other access routes on a daily basis. As a hub for tourist activity related to Yosemite National Park, Mariposa suffered significant economic loss. During the 92 day period from the time when the highway was blocked to opening of the temporary bypass, local businesses lost about 4.8 million USD (Harp et al., 2008). The situational analysis briefly described in the preceding paragraphs achieved mission objective #1. It also constituted the primary data used by the geologists in the multi-disciplinary process called for in mission objective #2. The analysis information was combined with other geologic information in order to respond to inquiries as noted in mission objective #3. ACCESSING THE LANDSLIDE AREA SAFELY DURING DATA COLLECTION Geologists working around active landslides are not immune to the same hazards that might threaten residents, first responders, and other people in the immediate vicinity. Most agencies, and certainly emergency response teams, will make a safety assessment for their employees working in such an environment. Every person in the field around the landslide, geologists included, should have and use appropriate personal protective gear in addition to carrying a firstaid kit and the means for communicating with other personnel should an accident occur. Conducting field work in a group of two or more people is also a prudent practice. Aside from the obvious desire to avoid injury or worse to yourself, a geologist must recognize that such an incident would negatively affect collection of the geologic information needed. Also, dealing with an accident will detract from the overall resources available to address the emergency response situation. Conducting initial field investigation from strategic vantage points is a good practice. Valuable informa146

tion can be acquired with a good set of binoculars at these locations. Photo-documentations with global positioning system (GPS) built into the digital camera or carried separately capture the observations in a manner allowing for later detailed examination and sharing with others who were not present at the same location. Software exists for using GPS and associated camera information to precisely locate observations on a topographic base map. Keeping these data geographically located within a geographic information system (GIS) environment is very beneficial. These initial observations not only provide basic information for the situational analysis of the landslide, but they also help to identify critical places for additional ground investigation and identification of the safest routes for reaching them. One excellent way to collect data about an active landslide occurrence is to use a helicopter as a mobile observation platform. Admittedly, a helicopter flight includes accepting a certain safety risk that can be avoided by using only ground-based transportation. However, helicopter overflight can provide a means to assess possible ground-level routes to features on or near the landslide that appear to be important to developing the situational analysis. Aerial observations also benefit from the same photo-documentation that can be applied to ground observations from different vantage points. Use of a video camera can be advantageous in documenting what was seen during a helicopter overflight. Videos offer a means of sharing aerially observed landslide features with those unable to take the flight. Real-time communication with the pilot during a flight allows additional and different paths or altitudes to be taken over the landslide area to gain just the right angle for seeing salient features. These observations are enhanced by taking flight early in the morning or late in the afternoon, so the low sun angle will enhance more subtle ground irregularities (Keaton and De Graff, 1996). Reflections off helicopter windows can detract from views and photos. These obscuring reflections can be limited by the flight path direction taken relative to the observer’s seat location or the time of day of the flight. Removal of windows or doors can also avoid this reflection problem. However, this may not be permitted by either work safety regulations or helicopter operations requirements. If doors or windows are removed, secure all light-weight materials such as maps, gloves, etc., to avoid the unfortunate exit of those items outside, where they can interfere with operation of the aircraft. It should also be noted that helicopters can drop geologists off at points that might be difficult to reach by ground transport or involve a long, difficult hike. This can be a data collection advantage when time is limited and delivery to

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

De Graﬀ

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

Landslide Investigation during an Emergency

a speciﬁc point could be combined with an observational overﬂight.

Matthieu Landslide Dam The terrain surrounding most of the Matthieu landslide dam and the growing lake behind it was covered by rain forest. Area access to get in close proximity to either feature was limited to single-lane nativesurfaced road best suited for small pickup trucks. Consequently, long hikes were taken along trails leading to vantage points on the rain-forest margin along the Layou River channel, along the shore of the impounded water behind the landslide dam, and on the edge of vertical valley walls or landslide scarps where views were unobscured (see figure 4 in De Graff et al., 2010). A useful hike was between a ridge road upstream from the Matthieu landslide dam down to the edge of the impounded water within the Matthieu River valley. While not a long distance, hiking the rough track proved arduous because it was within the still confines of the rain forest on a steep slope. This terrain combined with exposed roots on a trail underlain by wet, clayey soil made walking difficult. An aerial reconnaissance using a helicopter was soon evident as a necessity for adequately completing the situational analysis. Because this was an official U.S. government mission, the use of a helicopter from a U.S. Coast Guard cutter operating in the vicinity of the island nation of St. Lucia was made available. A pre-flight safety briefing revealed an additional safety risk compared to helicopter use over only land. Because the flight was over open water for part of the time, instruction in underwater exit following a crash was demonstrated and explained during the safety briefing. Because U.S. Coast Guard helicopters are often used for rescues, they are configured for flying safely with the door open. Sitting in the open doorway secured within the helicopter by a gunnery belt facilitated photo-documentation of observations during the flight. By communicating with the pilot via headphones, many different flight paths at varying altitudes were undertaken over a 2 hour period (see figure 5 in De Graff et al., 2010). This provided valuable data for finalizing the situational analysis. In terms of data collection safety, the flight also revealed that one of the previous day’s ground observation vantage points at the end of a farmer’s dirt track in a field of bananas was on an unstable block that was partially displaced by the landslide movement. This fact, which was readily apparent from the large cracks outlining the block as seen from the air, had seemed on the ground the previous day to only involve a small scarp on an otherwise stable slope near the cliff edge.

Landslide Investigation during an Emergency

a speciﬁc point could be combined with an observational overﬂight.

Ferguson Rock Slide The steep canyon slope opposite the Ferguson rock slide provided an open area with an excellent view of the entire feature. Consequently, it was also where CalTrans established a permanent station for taking repetitive measurements to targets set up on the surface of the slide mass. Ground access to the landslide required a circuitous route up the canyon slope on the upstream side of the landslide. Some of this route used an existing hiking trail, and other parts were remnants of a footpath used during installation of the concrete base of an electric high-voltage power line tower. An attempt to access the landslide mass from the downstream side proved steeper with some difficult rocky ledges. While hiking on the slide mass provided valuable information for the situational analysis, areas of tall brush where the ground surface was cracked and heaved partially obscured mapping their extent. The uppermost part of the slide mass was partially covered with large trees. To improve understanding of the Ferguson rock slide, a helicopter overflight was arranged. The helicopter was a contract ship for Yosemite National Park normally used for transporting wildland firefighting crews to hard-to-reach locations. The seating arrangement permitted passengers to be secured with the side door left open. Headsets built into the flight helmets enabled the observers to speak with the pilot. The flight commenced from a Yosemite National Park heliport near El Portal, CA. It followed the river down canyon, which gave a view of residences and other infrastructure potentially affected by any rising water should the landslide restrict the flow of the Merced River. Several different passes up and down the canyon in front of the Ferguson rock slide were made at different altitudes. The flight was in the early afternoon, so the sun angle was beginning to be lower and highlight ground features. A video was made of the entire flight by one geologist. A particularly valuable segment of the video was due largely to the skill of the pilot, who hovered in front of the rock slide. With the video recording, the pilot maneuvered the helicopter to slowly drift toward the landslide while maintaining its altitude. This enabled a broad overview and then an increasingly detailed examination of the upper part of the slide mass and headscarp. Such careful piloting may not always be available, but it is very useful when it is. The video was used repeatedly by the geologists and other team members in their analysis work. Software that enables extraction of individual images from a video for use in documents and presentations is a worthwhile investment for this type of investigation.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

147

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

147

De Graﬀ

DEVELOPING RELIABLE INFORMATION WITH LIMITED TIME The scope of potential field investigation during a landslide emergency is narrowed by the mission objectives. It is further constrained by having limited time in which to gather, analyze, and interpret the landslide data collected. A geologist working in this situation has to conclude that many aspects of the event will remain unknown until some future date, and others will not be fully determined to the level of detail or certainty that would be expected for a research project or professional practice-level study. To aid in gathering reliable information, the geologist can draw upon standard practices (Beukelman and Hylland, 2016) and common field techniques (Keaton and De Graff, 1996). There is certainly a wealth of research on different landslide types and associated factors influencing their occurrence and behavior. The majority of this research is drawn from investigations that are likely to bear some resemblance to the emergency situation the geologist is addressing, e.g., impacting infrastructure like roads (Tizzano et al., 2016) and canals (De Graff et al., 2017) or residential areas (Baum et al., 2005; Lund et al., 2014). Other useful landslide research findings can be drawn from field studies in natural settings (De Graff and Stock, 2015). Searches for relevant literature as background data for the emergency situation at hand can now draw upon internet resources such as the Google Scholar search engine (De Graff et al., 2013). There will also be some observations and data available from first responders and other local resources in the vicinity of the landslide creating the emergency situation. However, assessment questions addressed to the local community and first responders should be structured to avoid raising expectations of specific actions at this early stage in the response (OFDA, 1994). The experience, knowledge, and skill of the geologist or geologists serving on the emergency response team are ultimately the determinants for whether reliable information is developed. As noted earlier, emergency response action is associated with landslide activity that results in adverse impacts to society. So, the need for reliable information should always focus on understanding why and how the landslide is creating destructive effects or other adverse conditions. A common question is usually: Will this landslide continue to move, and, if so, what are any additional undesirable consequences? The geologist trying to answer this question must attempt to forecast expected movement and identify future impacts that may happen at some distance from the actual location of the landslide. Such forecasting will likely also require providing a way to know when to activate measures to protect people and property. In 148

De Graﬀ

some cases, these measures may be protective of people and property initially unaffected by the initial movement. Monitoring is an effective means for determining when an emergency response action should be initiated. The difficulty for the geologist is not only recommending the appropriate monitoring, but also identifying the measured threshold or point at which a response action should be initiated (De Graff et al., 2015b; De Graff, 2018). Matthieu Landslide Dam At the Matthieu landslide dam, the reliable information needed was mainly about the future condition of the landslide dam. A starting point was a landmark publication on the worldwide formation and failure of natural dams, which provided invaluable data on these features, including an analysis of how long they might persist (Costa and Schuster, 1988). The integrity of the landslide dam governed the volume of water that was ultimately impounded, which could be released as a future flood event. Failure of the landslide dam was expected to occur by overtopping or by internal erosion, i.e., piping (Costa and Schuster, 1988). The primary information needed to forecast future conditions was a situational analysis of not only the occurrence of the Matthieu landslide dam, but also the occurrence and failure of the preceding Layou debris-flow dams. The Matthieu landslide dam and the rising river waters behind it were the starting points for forecasting any future catastrophic failure and flood events. Monitoring of the rising water level was already under way. Technical specialists from the GCD were able to provide data about the earlier floods, identify the people and properties at risk, share information from an earlier field visit by a geologist with the Caribbean Disaster Emergency Response Agency (CDERA), and lead field excursions to examine the earlier Layou River valley flooding impacts and various ground-level views of the Matthieu landslide dam and the water being impounded behind it (De Graff and Rogers, 2003; De Graff et al., 2010). Collection of pertinent information for the situational analysis was enhanced by the fact that the team geologist had conducted the first island-wide landslide inventory and landslide susceptibility map in 1987 and returned 3 years later to inventory more recent landslides caused by a subsequent hurricane and several tropical storms. The aerial reconnaissance conducted using a U.S. Coast Guard helicopter temporarily based in nearby St. Lucia was an invaluable source of information on the landslide-dam feature. Field examination was done of the extent of flood damage from the earlier Layou River landslidedam failures. Examination of the face of the landslide dam together with other field observations established

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

Landslide Investigation during an Emergency

that movement of the landslide damming the Matthieu River had ceased at that time. Also found were springs issuing from the downstream face of the dam, consistent with water moving through the mass of the landslide dam. The situational analysis, based on interpretation of the available data, the current understanding of how landslide dams form and fail, and a familiarity with the community at risk from a future flood, was documented in an internal report prepared for GCD and the U.S. Office of Disaster Assistance. The analysis did not identify any clearly feasible means for preventing additional water from being impounded by the landslide dam. The flooding associated with failure of the two smaller Layou landslide dams established the minimum area that would be affected by a flood due to failure of the Matthieu landslide dam. So, the situational analysis did identify the geographic areas where release of this water would put people and property at risk. While GCD monitoring of both the rising water level behind the landslide dam and the water discharging from the springs on its downstream face would continue, insufficient data were developed to identify how the monitoring could be used to trigger an evacuation of people downstream. Ferguson Rock Slide Geologic and geotechnical specialists representing CalTrans on the Federal Incident Management Team were a valuable resource for developing the situational analysis. Their experience covered the period from the initial rock hazard on California Highway 140 up to the point when it clearly was not possible to keep the road from being totally blocked by the continuing rockfall activity. The CalTrans specialists shared a detailed light detection and ranging (LiDAR)–based topographic map of the landslide and the area immediately surrounding it. Local knowledge about landslide activity in Merced Canyon and similar local areas was provided by two USFS geologists, whose many years of dealing with smaller landslides on national forest system land in this area provided context to the situational analysis. Series of historic aerial photographs of Merced Canyon were part of the information provided to the team. The CalTrans geologic and geotechnical specialists and USFS geologists conducted most of the field investigation of the Ferguson rock slide, including documenting the ongoing movement. However, the questions raised about future movement and its impacts initiated a request to the USGS, which sent in several geologists experienced in the study of large landslides and landslide monitoring techniques to participate in the field assessment (Harp et al., 2008). Another team member, the National Park Service, used one of their local helicopters for the team’s aerial re-

Landslide Investigation during an Emergency

Ferguson Rock Slide

Uncertainty will be associated with geologic information gathered about a landslide during an emergency response arising from the limited time available and other constraints on field investigation. Certainly, the type of landslide involved in an emergency situation may be readily determined. The exact volume of the displaced landslide mass is less likely to be known with certainty. This is because the dimensions of length and width may be reasonably measured, but the dimension of thickness will be estimated because the time and difficulty involved in drilling may preclude an accurate measurement. Another uncertainty arises because comparative information about the landslide creating the emergency situation is often statistical in nature. Studies of a particular type of landslide can statistically demonstrate the typical behavior or other considerations important to the emergency response team. However, the landslide being investigated may not be typical. So, the geologist needs to make note in the situational analysis and recommendations about the associated uncertainty for detailed information that is developed (OFDA, 1994). Matthieu Landslide Dam The two debris flow–created landslide dams in the Layou River and their associated down-valley flood

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

149

Geologic and geotechnical specialists representing CalTrans on the Federal Incident Management Team were a valuable resource for developing the situational analysis. Their experience covered the period from the initial rock hazard on California Highway 140 up to the point when it clearly was not possible to keep the road from being totally blocked by the continuing rockfall activity. The CalTrans specialists shared a detailed light detection and ranging (LiDAR)–based topographic map of the landslide and the area immediately surrounding it. Local knowledge about landslide activity in Merced Canyon and similar local areas was provided by two USFS geologists, whose many years of dealing with smaller landslides on national forest system land in this area provided context to the situational analysis. Series of historic aerial photographs of Merced Canyon were part of the information provided to the team. The CalTrans geologic and geotechnical specialists and USFS geologists conducted most of the ﬁeld investigation of the Ferguson rock slide, including documenting the ongoing movement. However, the questions raised about future movement and its impacts initiated a request to the USGS, which sent in several geologists experienced in the study of large landslides and landslide monitoring techniques to participate in the ﬁeld assessment (Harp et al., 2008). Another team member, the National Park Service, used one of their local helicopters for the team’s aerial re-

connaissance and provided the means for improving problematic radio communications in the vicinity of the Ferguson rock slide. The situational analysis resulting from this team effort was compiled as internal team documents and, later, published in a professional journal (Harp et al., 2008). Installation of two temporary bridges connecting to a one-lane bypass road on the opposite side of the river from the Ferguson rock slide restored much of the critical traffic flow between El Portal and Mariposa. Findings in the situational analysis of possible accelerated movement within the main rock slide mass, continuing rockfall activity, and the chance of restriction or damming of the Merced River flow by rock debris resulted in the recognition that people and property upstream and downstream were potentially at risk. Emplacement of several monitoring systems was done as a mitigation measure, including real-time monitoring to ensure first responders could keep local residents safe (Harp et al., 2008; Reid et al., 2012; and De Graff et al., 2015b). Action thresholds for initiating planned emergency actions were identified for the different monitoring methods being used (De Graff, 2018). EXPLAINING THE UNCERTAINTY ASSOCIATED WITH YOUR FINDINGS Uncertainty will be associated with geologic information gathered about a landslide during an emergency response arising from the limited time available and other constraints on field investigation. Certainly, the type of landslide involved in an emergency situation may be readily determined. The exact volume of the displaced landslide mass is less likely to be known with certainty. This is because the dimensions of length and width may be reasonably measured, but the dimension of thickness will be estimated because the time and difficulty involved in drilling may preclude an accurate measurement. Another uncertainty arises because comparative information about the landslide creating the emergency situation is often statistical in nature. Studies of a particular type of landslide can statistically demonstrate the typical behavior or other considerations important to the emergency response team. However, the landslide being investigated may not be typical. So, the geologist needs to make note in the situational analysis and recommendations about the associated uncertainty for detailed information that is developed (OFDA, 1994). Matthieu Landslide Dam The two debris flow–created landslide dams in the Layou River and their associated down-valley flood

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

149

De Graﬀ

impacts gave added importance to the question of future failure by the Matthieu landslide dam and its expected flooding. The Layou landslide dams had existed for three days before failing by overtopping (De Graff et al., 2010). This did not provide any certainty for estimating how long the Matthieu landslide dam would take to overtop. While the GCD data included the typical discharge for the Layou River, no similar information for the inflow on the Matthieu River existed. At best, a proportional estimate could be made based on a comparison of the watershed surface area drained by each river, which was 70 km2 and 3.66 km2 , respectively (De Graff et al., 2010). An additional uncertainty for the possible future failure of the Matthieu landslide dam was the mechanism for failure. Overtopping could reasonably be assumed. However, it was also possible that seepage through the dam would cause internal erosion, leading to a breach. This added uncertainty to the potential flood flow. Overtopping required a full lake behind the landslide dam. Failure by internal erosion (piping) might occur with only a partially full lake. In the end, this uncertainty was dealt with by taking the Layou landslide-dam flooding as a minimum affected area and assuming the Matthieu landslide dam would fail within 6 months of its formation, based on a large-scale study including both types of failure mechanisms (Costa and Schuster, 1988). In terms of recommendations, this uncertainty supported the continued monitoring of water levels in the lake forming behind the Matthieu landslide dam. The uncertainty relating to how long the Matthieu landslide dam and its associated lake would persist provides a perfect example of how far removed an emergency response conclusion can be from reality and the reason why uncertainty needs to be defined in the situational analysis. Eleven years after occurrence of the Matthieu landslide dam, the dam and nearly full lake continued to exist. This was despite the area being subject to many tropical storms and Hurricane Lenny. Miracle Lake, as it was locally known, was now featured on a number of day tours for tourists. Beginning in 2005, the lake’s water level had stabilized at about 19.55 m below the overtopping elevation (De Graff et al., 2010). De Graff et al. (2010) concluded that the landslide dam and lake still posed a hazard to the lower Layou River valley and that monitoring of the water level in the lake and the seepage on the downstream face of the landslide dam should continue. The Matthieu landslide dam did fail 14 years after its initial formation and caused catastrophic flooding (James and De Graff, 2012). Property and infrastructure destruction or damage was significant, with cleanup costs and long-term repairs estimated to be between ECD$9 million and ECD$18 million. Fortunately, local awareness of the hazard contributed to the ability 150

De Graﬀ

of everyone in the flood’s path to escape without injury (James and De Graff, 2012). Ferguson Rock Slide Because the Ferguson rock slide was a reactivation of an existing landslide feature, there was an expectation that past movement might provide insight into future movement for the situational analysis (Harp et al., 2008). Movement was a concern because it might lead to partial or complete blocking of flow on the Merced River. Complete blockage would impound water upstream into areas with residences, close California Highway 140 again, and possibly cause downstream flooding if the landslide dam were to be overtopped later. Lacking data on the time between the initial movement and the 2006 reactivation, it was difficult to determine if evidence of past blockage of the river was lacking or whether such evidence had eroded away. The original movement had caused movement within an intact rock slope. The 2006 movement was along a pre-existing slide surface that may have weathered in a way that reduced strength (Harp et al., 2008). So, this comparison also introduced uncertainty regarding movement to expect in the future. Consequently, the situational analysis and recommendation called for a coordinated monitoring effort (Harp et al., 2008; De Graff et al., 2015b). Given the differing scenarios for future movement, from the entire landslide mass moving into the river to only parts of the landslide mass, it was recommended to model these events to determine which ones would result in a landslide dam occurring and what kind of barrier height to water flow might exist afterwards (De Graff et al., 2015b). Monitoring showed that the Ferguson landslide moved in response to storm events (Reid et al., 2012); however, the magnitude of these movements decreased during each successive rainy season as a prolonged drought began to take hold in California. COMMUNICATING THE INFORMATION NEEDED BY DIFFERENT AUDIENCES Recorded observations, documented situational analysis, and the resulting findings and conclusions will be combined to communicate orally or in written form to different audiences. Foremost among the audiences will be other members of the emergency response team. Daily briefings are not uncommon for conveying the salient observations of the previous days and describing planned activities during the remainder of the day. Development of team recommendations often involves discussions with team members who are familiar with geologic information, such as hydrologists and engineers. Discussions may also

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

Landslide Investigation during an Emergency

include others who may not be familiar with geologic terms, such as emergency management specialists, law enforcement officers, and public affairs personnel. There will be decision-makers ranging from agency managers to elected officials. Public meetings and media interviews will add the general public to the mix of people who will be trying to understand the findings and analysis results. Consequently, careful consideration should be made about how to communicate data to these varied audiences so it will be correctly understood. In addition to team briefings, oral communication in public forums and to news media are commonly part of emergency response team activities. For either circumstance, preparing talking points is a useful exercise. Consider the audience’s primary interests when developing the talking points. They may cover similar information, but a presentation to elected decisionmakers may emphasize different points compared to one for residents directly affected by the landslide event. Preparing talking points helps to distill the findings to key points and enables relevant facts to be in hand during a presentation. Presentations can be designed around these talking points, and use of visuals can promote a fuller understanding of the information. Talking points also are helpful for answering questions asked by print, radio, or television reporters. Answering their questions can allow important information from the talking points to be communicated during an interview. Answers to media question should be similar in structure to writing paragraphs. A response should start with a short and direct answer followed by some elaboration. If the information interests the reporter, a follow-up question can provide a chance to expand on the topical point. Refer questions outside of personal expertise or responsibility to other appropriate team members who may be present. Do not be reluctant to say you do not have information at this time for answering a particular question. This honest response is better than a speculative one that might later require correction. A standard format for all team member reports might be provided to facilitate assembling the overall report for the emergency situation. Lacking such specific direction for writing findings and recommendations, a recognized professional standard for reporting on landslides and other natural hazards involving engineering geology could be used, i.e., guidance from geologic surveys (Bowman and Lund, 2016). It is beneficial to include an executive summary with the report to highlight the key findings and describe the specific recommendations being made. Clearly written findings and recommendations are vital to supporting decision-making and planning for the emergency response (OFDA, 1994).

Landslide Investigation during an Emergency

Matthieu Landslide Dam Media interviews can often happen when they are least expected. Public forums where decision-makers are being briefed or where meetings are held to inform the affected community will include reporters intent on getting an interview. Finding a radio reporter waiting at the airport for the return of the U.S. Coast Guard helicopter’s aerial survey was a surprise. For an island nation that is roughly 47 km long and 29 km wide, radio in Dominica continues to be an important source of news reaching into the most remote localities. The audience could be expected to range from inhabitants of the areas at risk of future flooding to elected officials charged with keeping those inhabitants safe from such a an event. In this case, there was no time to consider possible talking points stimulated by the aerial overview, so care was needed to answer questions in a manner that did not raise expectations of specific actions occurring in the future due to this assessment. Ferguson Rock Slide When preparing and presenting information at public forums, what the public might perceive about the landslide and the impacts they are enduring should be taken into consideration. Public affairs officers on the emergency response team may have insight on these perceptions based on their conversations with local media representatives and from their work responding to inquiries from the public. Perception can be important when communicating to people who are enduring longer-than-normal commutes to work or whose businesses are dependent on tourist traveling to Yosemite National Park along California Highway 140. In the case of the Ferguson rock slide, the public saw the talus-covered slope and the buried highway as being the landslide (Figure 3). This was a reasonable conclusion for people who could only see the landslide from some point along the river at highway level. Consequently, the participants in a public meeting held in the affected community of El Portal, CA, wondered why CalTrans was not mobilizing its heavy earthmoving equipment to clear the road. It was possible to correct this misperception during presentation of the team findings using photographs highlighting the entire rock slide in relation to the talus-covered slope created by multiple rockfalls from the landslide toe. The meeting participants could then see that additional rockfall was very likely to undo such removal efforts and that earthmoving work would pose a risk to CalTrans employees operating the equipment.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

151

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

151

De Graﬀ

Figure 3. The section of California Highway 140 buried by the talus accumulation from the toe of the Ferguson rock slide. The view is from the one-lane detour road looking across the Merced River channel. The larger boulders visible at the toe of the talus are similar in size to refrigerators and washing machines. A plume of dust from recent rock movement is visible near the upper part of the talus. This view was typical of what the public recalled as being the “Ferguson rock slide” prior to the area being closed to public access due to rockfall hazard. (Photo by Jerome De Graﬀ.)

DISCUSSION AND CONCLUSIONS A geologist on a landslide emergency response team is unlikely to be able to fully and successfully follow the suggested approaches described in this paper when suffering from chronic fatigue. Fatigue is a health and safety concern as important to landslide emergency response team members as it is to wildland firefighters (Aisbett and Nichols, 2007). Chronic fatigue results when physical and mental stress associated with an emergency response effort reduces physical and mental work capacity to the extent that normal rest does not produce recovery (https://www. nwcg.gov/committee/6mfs/fatigue-stress). Chronic fatigue can impact any team member on the emergency response team. Avoiding chronic fatigue is important to the emergency response team member to 152

maintain their mental and physical capabilities during their assignment. An emergency response situation requires a prolonged period of work-related stress that can lead to chronic fatigue (Aisbett and Nichols, 2007). Some of this stress will be physical due to the on-the-ground exertion from collecting data, extended travel to reach the emergency location, and work involving long hours daily for many consecutive days. Other sources of work-related stress will be mental, from the concern raised by the actual or potential human toll associated with the landslide event or from experiencing risky situations such as helicopter overflights. The most obvious way to counter chronic fatigue is increasing the amount of rest beyond normal patterns to allow for physical and mental recovery. Organizations involved regularly with responding to natural and human-caused emergencies typically have workrest requirements. The USFS wildland firefighters are allowed to work no more than 16 hours per day and no more than 14 days consecutively without taking an extended number of days off. Eating well-balanced meals and maintaining proper hydration are also important to recovery from fatigue (https://www.nwcg.gov/ committee/6mfs/fatigue-stress). The use of unmanned aerial vehicles (UAVs), sometimes referred to as drones, seems likely to be used more often in the future due to their increasing availability and their flexibility for acquiring data (Farina et al., 2017). Multi-copter versions have many of the benefits of helicopter overflights for viewing and photo-documenting conditions at an active landslide event. This type of vehicle also could provide a more detailed look at possible ground-level routes to features on or near the landslide than might be possible by helicopter overflight. Obviously, UAVs are a safer means for the geologist collecting data to make these initial observations than taking a helicopter flight. UAVs also offer the opportunity to conduct detailed surveys that can generate three-dimensional representations of the landslide (Farina et al., 2017). Producing this type of representation may or may not be possible within the limited time of an emergency response situation. Simpler mapping may be possible to undertake in a short enough time period to provide a common base map for collected data. An image or stitched series of images could serve as an initial base map for documenting observed features (Coe et al., 2016). UVAs are unlikely to completely replace helicopters in the near future in these emergency response situations. For example, medivac and other emergency uses of helicopters mean they may be available on-site sooner than a UVA for the emergency response team to use. Also, helicopters will continue to move geologists and other personnel to different locations—a

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

De Graﬀ

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

Landslide Investigation during an Emergency

capability not currently available in a UVA. In the future, emergency response situations may not only take advantage of UVA technology, but they will also need to address the adverse aspects of non-authorized personnel using UVAs for news or personal interest observations. Because helicopters may also be operating within this same airspace, delivering supplies or people to locations that are difficult to reach on the ground, both authorized and unauthorized UVA operations will need to be a coordinated, and operational consideration will be necessary in order to keep everyone safe. For geologists, this type of assignment may become more common in the coming years. Urban expansion is taking place in many parts of the United States where landslides have occurred in the past. Developmental activities can induce slope instability ranging from reactivation of older landslides to induction of landslides where they previously were not present. Areas with known landslide hazard are being subjected to more intense storms, which have the potential for effectively triggering landslide events. Given these trends, geologists may find themselves increasingly part of emergency response actions for destructive landslides. The five approaches described in this paper are deemed important to completing their emergency response team assignment successfully. ACKNOWLEDGMENTS The author was the 2016 Richard H. Jahns Distinguished Lecturer in Engineering Geology. This lectureship is jointly sponsored by the Association of Environmental and Engineering Geologists (AEG) and the Environmental and Engineering Division of the Geological Society of America (ED-GSA). The subject of this paper was among the presentations offered to universities and colleges during the 2016 series and one that seemed worth sharing in a published version. The paper benefited materially from the comments and suggestions given by Paul Santi, Brian Bruckno, and an anonymous reviewer during the review process. REFERENCES Aisbett, B. and Nichols, D., 2007, Fighting fatigue whilst fighting bushfire: An overview of factors contributing to firefighter fatigue during bushfire suppression: The Australian Journal of Emergency Management, Vol. 22, No. 3, pp. 31–39. Baum, R. L.; Coe, J. A.; Godt, J. W.; Harp E. L.; Reid, M. E.; Savage, W. Z.; Schulz, W. H.; Brien, D. L.; Chleborad, A. F.; McKenna, J. P.; and Michael, J. A., 2005, Regional landslide-hazard assessment for Seattle, Washington, USA: Landslides, Vol. 2, No. 4, pp. 266–279. Baum, R. L.; Galloway, D. L.; and Harp, E. L., 2008, Landslide and Subsidence Hazards to Pipelines: U.S. Geological Survey Open-File Report 2008-1164, 192 p. Beukelman, G. S. and Hylland, M. D., 2016, Guidelines for evaluating landslide hazards in Utah, In Bowman, S. D. and Lund,

Landslide Investigation during an Emergency

W. R. (Editors), Guidelines for Investigating Geologic Hazards and Preparing Engineering-Geology Reports with a Suggested Approach to Geologic-Hazard Ordinances in Utah: Utah Geological Survey Circular 122, pp. 61–73. Bigley, G. A. and Roberts, K. H., 2001, The incident command system: High-reliability organizing for complex and volatile task environments: Academy of Management Journal, Vol. 44, No. 6, pp. 1281–1299. Bonaccorso, A.; Calvari, S.; Garfì, G.; Lodato, L.; Patanè, D., 2003, Dynamics of the December 2002 flank failure and tsunami at Stromboli volcano inferred by volcanological and geophysical observations: Geophysical Research Letters, Vol. 30, No. 18, pp. 6-1 to 6-4. Bowman, S. D. and Lund, W. R., 2016, Guidelines for conducting engineering-geology investigations and preparing engineeringgeology reports in Utah. In Bowman, S. D. and Lund, W. R. (Editors), Guidelines for Investigating Geologic Hazards and Preparing Engineering-Geology Reports with a Suggested Approach to Geologic-Hazard Ordinances in Utah: Utah Geological Survey Circular 122, pp. 17–30. Brabb, E. E. and Harrod, B. L. (Editors), 1989, Landslides: Extent and Economic Significance (28th International Geological Congress, Washington, D.C.): A. A. Balkema, Rotterdam, Netherlands. Clague, J. J. and Evans, S. E., 2003, Geologic framework of large landslides in Thompson River Valley, British Columbia: Environmental and Engineering Geoscience, Vol. 9, No. 3, pp. 201– 212. Coe, J. A.; Baum, R. L.; Allstadt, K. E.; Kochevar, B. F.; Schmitt, R. G.; Morgan, M. L.; White, J. L.; Stratton, B. T.; Hayashi, T. A.; and Kean, J. W., 2016, Map data and unmanned aircraft system imagery from the May 25, 2014, West Salt Creek rock avalanche in western Colorado: U.S. Geological Survey Data Release, http://dx.doi.org/10.5066/F74J0C55. Coe, J. A.; Kean, J. W.; Godt, J. W.; Baum, R. L.; Jones, E. S.; Gochis, D. J.; and Anderson, G. S., 2014, New insights into debris-flow hazards from an extraordinary event in the Colorado Front Range: GSA Today, Vol. 24, No. 10, pp. 4–10. Costa, J. E. and Schuster, R. L., 1988, The formation and failure of natural dams: Geological Society of America Bulletin, Vol. 100, pp. 1054–1068. Cruden, D. M. and Varnes D. J., 1996, Landslide types and processes. In Turner, A. K. and Schuster, R. L. (Editors), Landslides—Investigation and Mitigation: Transportation Research Board Special Report 247, National Academy Press, Washington, D.C., pp. 36–75. Cummans, J., 1981, Mudflows Resulting from the May 18, 1980 Eruption of Mount St. Helens, Washington: U.S. Geological Survey Circular B1-B16. De Graff, J. V., 2018. Action thresholds for landslide emergency response monitoring: AEG News, Vol. 61, No. 2, pp. 16–20. doi:10.13140/RG.2.2.32847.38560. De Graff, J. V.; Cannon, S. H.; and Gallegos, A. J., 2007, Reducing post-wildfire debris flow risk through the Burned Area Emergency Response (BAER) process. In Schaefer, V. R.; Schuster, R. L.; and Turner, A. K. (Editors), Conference Presentations, 1st North American Landslide Conference, Vail, CO: Special Publication 23, Association of Engineering Geologists, Lexington, KY, pp. 1440–1447. De Graff, J. V.; Cannon, S. H.; and Gartner, J. E., 2015a, The timing of susceptibility to post-fire debris flows in the Western United States: Environmental & Engineering Geoscience, Vol. 21, No. 4, pp. 277–292.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

153

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

153

De Graff De Graff, J. V.; DeGraff, N.; and Romesburg, H. C., 2013, Literature searches with Google Scholar: Knowing what you are and are not getting: GSA Today, Vol. 23, No. 10, pp. 44–45. doi:10.1130/GSAT175GW.1. De Graff, J. V.; Gallegos, A. J.; Reid, M. E.; LaHusen, R. G.; and Denlinger, R. P., 2015b, Using monitoring and modeling to define the hazard posed by the reactivated Ferguson rock slide, Merced Canyon, California: Natural Hazards, Vol. 76, No. 2, pp. 769–789. De Graff, J. V.; Giraud, R. E.; and McDonald, G. N., 2017, The adverse impact to canals from landslide activity in California and Utah. In De Graff, J. V. and Shakoor, A. (Editors), Landslides: Putting Experience, Knowledge and Emerging Technologies into Practice: Special Publication 27, Association of Engineering Geologists, Lexington, KY, pp. 340–351. De Graff, J. V.; James, A.; and Breheny, P., 2010, The formation and persistence of the Matthieu landslide-dam lake, Dominica, W.I.: Environmental and Engineering Geoscience, Vol. 16, No. 2, pp. 73–89. De Graff, J. V. and Rogers, C. T., 2003, An unusual landslidedam event in Dominica, West Indies: Landslide News, Vol. 14/15, pp. 8–11. De Graff, J. V. and Stock, G. M., 2015, The importance of protected areas as natural landslide laboratories: AUC Geographica, Vol. 50, No. 2, pp. 165–172. doi:10.14712/23361980.2015.95. De Graff, J. V.; Wagner, D.; Gallegos, A. J.; DeRose, M.; Shannon, C.; and Ellsworth, T., 2011, The remarkable occurrence of large rainfall-induced debris flows at two different locations on July 12, 2008, Sierra Nevada, CA: Landslides, Vol. 8, No. 3, pp. 343–353. Farina, P.; Rossi, G.; Tanteri, L.; Salvatici, T.; and Casigali, N., 2017, The use of multi-copter drones for landslide investigations. In De Graff, J. V. and Shakoor, A. (Editors), Landslides: Putting Experience, Knowledge and Emerging Technologies into Practice: Special Publication 27, Association of Engineering Geologists, Lexington, KY, pp. 978–984. Govi, M.; Gullà, G.; and Nicoletti, P. G., 2002, Val Pola rock avalanche of July 28, 1987, in Valtellina (Central Italian Alps). In Evans, S. G. and DeGraff, J. V. (Editors), Catastrophic Landslides: Effects, Recurrence, and Mechanisms: Reviews in Engineering Geology 15, Geological Society of America, Boulder, CO, pp. 71–90. Hansen, D. C. and Morgan, R. L., 1986, Control of Thistle Lake. In Schuster, R. L. (Editor), Landslide Dams: Processes, Risk, and Mitigation: Geotechnical Special Publication 3, American Society of Civil Engineers, New York, pp. 84–96. Harp, E. L.; Reid, M. E.; Godt, J. W.; DeGraff, J. V.; and Gallegos, A. J., 2008, Ferguson rock slide buries California state highway near Yosemite National Park: Landslides, Vol. 5, No. 3, pp. 331–337. Highland, L. M., 2012, Landslides in Colorado, USA—Impacts and Loss Estimation for 2010: U.S. Geological Survey Open-File Report 2012-1204, 49 p. Hungr, O.; Evans, S. G.; and Hutchinson, I., 2001, A review of the classification of landslides of the flow type: Environmental & Engineering Geoscience, Vol. 7, No. 3, pp. 221–238. Hungr, O.; Leroueil, S.; and Picarelli, L., 2014, The Varnes classification of landslide types, an update: Landslides, Vol. 11, No. 2, pp. 167–194. James, A. and De Graff, J.V., 2012. The draining of Matthieu landslide-dam lake, Dominica, West Indies: Landslides, Vol. 9, No. 4, pp. 529–537.

154

Jibson, R. W., 1986, Evaluation of Landslide Hazards Resulting from the 5–8 October 1985 Storm in Puerto Rico: U.S. Geological Survey Open-File Report 86-26, 40 p. Keaton, J. R. and De Graff, J. V., 1996, Surface observation and geologic mapping. In Turner, A. K. and Schuster, R. L. (Editors), Landslides—Investigation and Mitigation: Transportation Research Board Special Report 247, National Academy Press, Washington, D.C., pp. 178–230. Koch, J.; Clague, J. J.; and Blais-Stevens, A., 2014, Debris flow chronology and potential hazard along the Alaska Highway in southwest Yukon Territory: Environmental and Engineering Geoscience, Vol. 20, No. 1, pp. 25–43. Kuehn, M. H. and Bedrossian, T. L., 1987, 1983 U.S. Highway 50 landslide near Whitehall, California: California Geology, Vol. 30, No. 11, pp. 247–255. Lancaster, J.; McCrea, S. E.; and Short, W. R., 2014, Assessment of Post-Fire Runoff Hazards for Pre-Fire Hazard Mitigation Planning—Southern California: Special Report 234, California Geological Survey, Sacramento, CA, 66 p. Lund, W. R.; Knudsen, T. R.; and Bowman, S. D., 2014, Investigation of the December 12, 2013, Fatal Rock Fall at 368 West Main Street, Rockville, Utah: Report of Investigation 273, Utah Geological Survey, Salt Lake City, UT, 20 p. McSaveney, M. J., 2002, Recent rockfalls and rock avalanches in Mount Cook National Park, New Zealand. In Evans, S. G. and DeGraff, J. V., (Editors), Catastrophic Landslides: Effects, Recurrence, and Mechanisms: Reviews in Engineering Geology 15, Geological Society of America, Boulder, CO, pp. 35–70. Meyer, W.; Sabol, M. A.; and Schuster, R., 1986, Landslide dammed lakes at Mount St. Helens, Washington. In Schuster, R. L., (Editor), Landslide Dams: Processes, Risk, and Mitigation: Geotechnical Special Publication 3, American Society of Civil Engineers, New York, pp. 21–41. Office of Foreign Disaster Assistance (OFDA), 1994, Field Operations Guide for Disaster Assessment and Response: Office of Foreign Disaster Assistance, Bureau of Humanitarian Response, U.S. Agency for International Development, Washington, D.C., Sections I–IV (version 2). Reid, M. E.; LaHusen, R. G.; Baum, R. L.; Kean, J. W.; Schulz, W. H.; and Highland, L. M., 2012, Real-Time Monitoring of Landslides: U.S. Geological Survey Fact Sheet 2012-3008, 4 p. Schuster, R. L., 1996, Socioeconomic significance of landslides. In Turner, A. K. and Schuster, R. L. (Editors), Landslides— Investigation and Mitigation: Transportation Research Board Special Report 247, National Academy Press, Washington, D.C., pp. 12–35. Tizzano, A.; Shakoor, A.; and Lund, W. R., 2016, Characterization of failure parameters and preliminary slope stability analysis of the Cedar Canyon Landslide, Iron County, Utah: Journal of Environmental & Engineering Geoscience, Vol. 22, No. 3, pp. 245–258. Wang, F.; Cheng, Q.; Highland, L.; Miyajima, M.; Wang, H.; and Yan, C., 2009, Preliminary investigation of some large landslides triggered by the 2008 Wenchuan earthquake, Sichuan Province, China: Landslides, Vol. 6, No. 1, pp. 47–54. Wartman, J.; Montgomery, D. R.; Anderson, S. A.; Keaton, J. R.; Benoît, J.; dela Chapelle, J.; and Gilbert, R., 2016, The 22 March 2014 Oso landslide, Washington, USA: Geomorphology, Vol. 253, pp. 275–288. Zhang, M. and She, L., 2014, Incident command system in China: Development and dilemmas evidence from comparison of two cases: Journal of Contingencies and Crisis Management, Vol. 22, No. 1, pp. 52–57.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

154

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 141–154

Extraction and Comparison of Spatial Statistics for Geometric Parameters of Sedimentary Layers from Static and Mobile Terrestrial Laser Scanning Data

GABRIEL WALTON*

Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO 80401

GEORGIA FOTOPOULOS

Department of Geological Science and Geological Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada

ROBERT RADOVANOVIC

McElhanney Geomatics Engineering Ltd. 402 11 Ave SE, Calgary, AB T2G 0Y4, Canada

Key Terms: Remote Sensing, Spatial Statistics, Sedimentary Sequence, Mobile Laser Scanning ABSTRACT Terrestrial laser scanning (TLS) is a surveying technology that has seen increasing use in the field of geosciences in recent years. One potential application for this technology is to aid in quantitative stratigraphy. Given a point cloud containing multiple lithologies, the points associated with a specific lithology can be analyzed to quantify the geometric characteristics of that lithology, such as apparent dip, thickness, and spacing. In this study, a semi-automated work flow to perform such a characterization is presented and applied to a case study from an oil sands pit mine in the Athabasca region of Alberta, Canada. The results obtained using data collected with mobile and static TLS systems are compared to evaluate the effects of the various measurements and resolutions on the resulting stratigraphic statistics. In addition, mobile data collected for a small portion of the pit that was actively being mined are compared over time to evaluate changes in sedimentary layering in the direction perpendicular to the pit face. This component of the study highlights the impact of data quality on the resulting interpretations and represents a potential methodology for enhancing threedimensional quantitative spatial modeling in a sedimentary environment.

*Corresponding author email: gwalton@mines.edu

INTRODUCTION Terrestrial laser scanning (TLS) is a well-known technique for collecting high-density, high-accuracy, three-dimensional point clouds. TLS utilizes groundbased lidar instruments to build three-dimensional data sets from a platform with known location. However, whereas aerial lidar data is typically limited to point densities on the order of 10 points per square meter, TLS allows point densities as high as 1 million points per square meter, with a corresponding increase in the level of detail that can be captured. The past several years have seen the emergence of mobile collection capabilities within TLS systems, where laser ranging data can be collected while the scan platform is in motion. Such mobile terrestrial laser scanning (MTLS) systems allow rapid capture of three-dimensional point clouds to a point density of approximately 2,500 points per square meter, which is signiﬁcantly higher than that achievable from aerial techniques. Due to greater uncertainty in determining a moving platform’s position as compared to a static platform, typical MTLS data can identify the true locations of individual points to within approximately 2 cm, whereas TLS data can be conventionally acquired to identify the true locations of individual points within approximately 5 mm. In this study, mobile scan data were collected using a Riegl VMX-450 mobile scan system receiving real-time kinematic corrections to improve dynamic alignment. Two data sets were collected for the purposes of comparison to ensure that no major errors were present in the data; the two data sets were compared using a mesh-to-point normal distance calculation to ensure that the two scans were within 2 cm (local normal

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

155

*Corresponding author email: gwalton@mines.edu

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

155

Walton, Fotopoulos, and Radovanovic

Figure 1. Example of static lidar data from the case study site; the exposure is composed almost entirely of sandstone, with the exception being the mudstone beds noted in the image.

distance) throughout the scan area. All survey data were georeferenced to the mine control system by establishing a ﬁxed Active Control Station for the duration of the project. The Active Control Station itself was geolocated via GPS ties to surrounding mine control monuments. Static scans were collected utilizing a Z&F Imager 5006 scanner. Individual static scans were georeferenced using cloud-to-cloud registration techniques, with mobile scans acting as the reference cloud. In this way, higher-precision static scans could be directly compared to lower-precision scans, but properly georeferenced data derived from mobile laser scanning. Since this study was conducted in an operating mine, minimization of time spent within the area of operations was critical. As a result, while mobile scan data could be collected simply by driving along the pit face, static data collection time had to be minimized because of the need to avoid disrupting mining operations. For this reason, static scanning was restricted to areas intended for comparison with mobile scan data. In addition to three-dimensional coordinates of scanned points, TLS provides supplementary information in terms of the reﬂectance of the scanned surface.

156

As the scanning beam of the instrument passes over a surface of interest (such as an excavated surface), the amplitude of the return signal varies as a function of the scan distance, incident angle, atmospheric conditions, and reflectance of the target surface at the scanning wavelength (typically in the near infrared). Modern TLS systems have the ability to utilize the return information to separate the reflectance component of the target element (Fowler et al., 2011), which creates a “black-and-white image” effect when viewing scan data (Figure 1). Due to the dual advantages of high data density and high accuracy, TLS systems have been employed in a wide variety of applications, including tunneling (Van Gosliga et al., 2006), natural hazard evaluation (Jaboyedoff et al., 2009; Abellan et al., 2011), and hydrogeological studies (Harpold et al., 2015). The utilization of TLS data within each of these areas is diverse, including surveying, monitoring, and characterization activities. With respect to rock mass characterization, the primary focus to date has been on the manual or semi-automated extraction of fracture orientation (Roncella and Forlani, 2005; Ferrero et al., 2009; Lato et al., 2009; Gigli and Casagli, 2011;

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

156

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

Extraction of Statistics from TLS

Otoo et al., 2011; Pate and Haneberg, 2011; Lato and Vöge, 2012; Maerz et al., 2012, 2013; Gigli et al., 2014; Riquelme et al., 2014; and Maerz et al., 2015). Another emerging application is the semi-automated delineation of distinct lithological (or morphological) units (Burton et al., 2011; Mills and Fotopoulos, 2013; Inocencio et al., 2014; and Walton et al., 2016). Telling et al. (2017) provide a review of earth science–related research using TLS with a comprehensive section on geological studies. In this case study, a semi-automated work flow for determining the apparent geometry of distinct sedimentary units from TLS data is proposed and applied to an exposure data set. The overall goal is to facilitate the analysis of TLS data in a manner that allows for the rapid and reliable characterization of the geometry of sedimentary layers. The current technique for characterizing the geometry of sedimentary layers is heavily dependent on manual interpretation. The area of interest is located in the Athabasca Oil Sands of Alberta, Canada. TLS data were collected at an oil sands pit mine using both static (±5 mm laser strike accuracy and ∼5 mm mean point spacing) and mobile (±2-cm laser strike accuracy and ∼2-cm mean point spacing) TLS systems (Walton et al., 2016). In this case, the MTLS data will be accurate relative to the Active Control Station (and thus relative to mine survey control) to a positional accuracy of 2 cm. The relative point-to-point accuracy of MTLS is also 2 cm. The static data, on the other hand, have a relative accuracy of 5 mm but are limited to an overall accuracy of 2 cm because they are georeferenced to the mobile data. Knowledge of changes in the geometric attributes of sedimentary layers across a study area (e.g., thickening, thinning, and pinch-out) can aid in the prediction of petroleum reservoir locations and characteristics (Nardin et al., 2013). This study measures visible geologic exposures to inform models for forecasting fluid flow. These geologic exposures are examined to allow determination of the number, thickness, and orientation of potential baffles and barriers to fluid flow (e.g., mudstone layers) within petroleum-bearing units (e.g., sandstone), providing inputs for more accurate reservoir models (Labrecque et al., 2011). At the site studied herein, the apparent dip, thickness, and spacing of the mudstone beds interspersed within a bituminous sandstone unit are of interest, as is the variation of these quantities across the study area (Figure 1). Whereas a manual analysis in the field or a fully manual analysis of the TLS data may be relatively time intensive, the semi-automated approach proposed in this article is capable of providing detailed, spatial statistics describing layer geometries within a short time frame that serve as inputs for reservoir models (e.g., Martinius et al., 2017).

Extraction of Statistics from TLS

Due to operational constraints, no manual mapping data were collected for comparison to the TLS and MTLS in this study. However, several previous studies have concluded that point-cloud data collected by TLS systems provide accurate representations of real-world geometry (and reflectance) both in general (see rigorous error analysis by Glennie, 2018) and in rock-focused applications (e.g., Franceschi et al., 2009; Burton et al., 2011; Campos Inocencio et al., 2014; Gomes et al., 2016; and Ge et al., 2018). With that in mind, Fisher et al. (2014) do note that different results may be obtained if manual mapping and TLS cover different areas (e.g., due to occlusion), if the surfaces being imaged are oblique to the scan direction, or if the areas being imaged are small relative to the TLS resolution. In our case study, coverage/occlusion and obliqueness are not major factors since the TLS data collected covered the entire area of interest and the scanned surface was sub-vertical and relatively smooth (i.e., virtually no occlusion). The difference in resolution between TLS and manual mapping results is a topic that merits study in the future, including consideration of what the effective resolution of manual sedimentary layer mapping is (e.g., should a manual mapper treat a series of millimeter-scale beds as discrete beds or combine them into a single feature?). For the purposes of the current study, however, we can conclude that the accuracy of the obtained bed statistics will be slightly skewed toward larger bed spacings and thicknesses than those that exist in reality due to the apparent blending of small beds in the TLS data due to resolution limitations. This phenomenon is subsequently discussed in greater detail in the context of the comparison of results obtained from static and mobile TLS data based on their differing resolutions. DESCRIPTION OF THE SEMI-AUTOMATED WORK FLOW The objective of this study is to demonstrate a novel work flow for the geometrical characteristics of key sedimentary strata in visible exposures. The first step toward characterizing the geometrical attributes of a given set of sedimentary layers (in this case, mudstone) using the proposed work flow is to extract the laser strike points (i.e., point-cloud point coordinates) corresponding to these layers. In theory, this process can be largely automated by using both geometrical and normalized intensity information from the point cloud in conjunction with the use of a machine learning algorithm (Walton et al., 2016). In practice, however, it was found that the resulting classification of the point cloud into mudstone and sandstone categories was ultimately too noisy for direct characterization of the spatial statistics of the mudstone (Walton et al., 2016).

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

157

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

157

Walton, Fotopoulos, and Radovanovic

Figure 2. Work ﬂow for characterization of sedimentary beds.

Instead, the points corresponding to the mudstone layers within the TLS data set were visually identified based on local point intensity contrasts and manually extracted using PolyWorks (Innovmetric, 2015). With the layers of interest extracted from the point cloud, two separate characterization approaches can be applied (Figure 2). Analysis of Full Beds The first approach is to use a clustering algorithm to group (x, y, z) coordinate triplets into distinct beds within the point cloud containing only mudstone points. Following this, principal component analysis (PCA) can be used to determine the apparent dip of each bed within the rock face; given that only a one-dimensional trace of each bed is visible on the approximately two-dimensional surface that was scanned, the apparent dip calculation assumes no outof-plane component of dip. To obtain further information about each distinct mudstone bed, the convex hull of the two-dimensional projection of each bed (onto the best-fit plane for that bed) can be determined. The convex hull of each projected set of points is the smallest convex shape that fully bounds that set of points and can be determined using any number of algorithms (Avis et al., 1997). In this study, convex hulls were computed using an open-source MATLAB func158

tion (Lundgren, 2012). Because the convex hull provides a definite shape to an otherwise diffuse set of points, the points defining the convex hull can be used to calculate the area and perimeter of each bed (the ratio of perimeter to area can be used as an indicator of the complexity or degree of inter-fingering of a given bed). This approach of analyzing full beds poses two limitations: (1) the whole-bed statistics derived may be significantly skewed if portions of the beds are not visible or are misidentified, and (2) the information obtained may not provide significant added value relative to finer-scale assessments and more global assessments (i.e., total apparent area of mudstone). Segmental Analysis of Beds This second characterization approach involves the assessment of layer characteristics variability across the area of interest and represents the main approach taken in this study. Position Parameterization During the analysis of geological characteristics on a nearly planar surface (a gently curved outcrop face), it is valuable to parameterize positions along that surface such that computed statistics can be placed in space relative to an easily interpretable reference. In

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

Extraction of Statistics from TLS

Figure 3. Schematic illustrating the approach for point-cloud parametrization and segmentation used in this study.

this study, given the sub-vertical nature of the pit-wall outcrop and the sub-horizontal nature of the sedimentary layering, analyses were conducted based on local projections of the mudstone beds point cloud onto a series of vertical planes with strikes parallel to the slope. The parameterization and segmentation of the outcrop with respect to the horizontal distance along the slope is illustrated in Figure 3 and performed using the following steps: (1) Project the point cloud onto a horizontal plane. (2) Determine the midpoint along the best-ﬁt (via least-squares adjustment) line for the projection. (3) Split the point cloud based on the derived midpoint. (4) Repeat steps 1 to 3 for each segment in the point cloud up to eight point-cloud segments. (5) Project the mudstone points onto the best-ﬁtting vertical plane within each point-cloud segment.

(6) Segment the vertical projections based on the horizontal distance along the best-fitting vertical plane identified in step 5 such that the full range is covered with a uniform horizontal panel size as close as possible to specific target (in this study, 1 m). Using this approach, the parameterized distance along the outcrop can be calculated as the number of preceding panels multiplied by the panel width added to the local horizontal coordinate in the projection plane of interest. Initial Clustering At any given lateral position along the outcrop, determination of the thickness and spacing of individual beds requires that the points corresponding to different beds be identified as such. For this purpose, the “dbscan” clustering algorithm (Ester et al., 1996) was

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

159

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

159

Walton, Fotopoulos, and Radovanovic

Figure 4. Flowchart representation of the iterative cluster grouping processed used (results of this process are shown in Figure 5).

employed to assign each mudstone point to a given bed segment (Figure 5). Iterative Cluster Grouping Since point clouds collected using the mobile system contained irregular point densities, it was necessary to avoid over-segregation via an iterative cluster grouping process. The overall process used is illustrated as a ﬂowchart in Figure 4 and described here: (1) Identify all clusters that have a diﬀerence between their maximum and minimum horizontal coordi160

nate values of less than 95 percent of the width of two-dimensional projected panel being analyzed (1 m in this case; this is illustrated in Figure 5); these correspond to clusters that are candidates for grouping into other larger clusters. (2) An upper-bound thickness is determined from the diﬀerence between the maximum and minimum vertical coordinates of points within each cluster. (3) The median horizontal and vertical coordinates of each cluster are calculated (referred to here as the cluster centroid). (4) A line of best ﬁt is determined for each cluster of points.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

Extraction of Statistics from TLS

Figure 5. Projected mudstone points within a single vertical panel; the raw data points are illustrated, as are the original clusters determined using a clustering algorithm and the ﬁnal clusters obtained after iterative cluster grouping. Note that the scale bar shown does not apply to the plan view of the data set shown at the top of the ﬁgure.

(5) Repeat the following for each pairing of a cluster that is a candidate for grouping into other larger clusters (cluster 1) and all other larger clusters (cluster 2): project the linear fit to cluster 2 from its cluster centroid to the horizontal coordinate of the cluster centroid of cluster 1; if the vertical distance between the cluster centroid of cluster 1 and the projection of the linear fit to cluster 2 is smaller than half the upper-bound thickness of either cluster 1 or cluster 2, all points within cluster 1 are reassigned to cluster 2. (6) Repeat steps 1 to 5 until no additional matches between clusters are identified and remove remaining clusters containing fewer than 30 total data points or 1 percent of the total number of data points within the local projection plane. This process is valid for cases where the layers of interest are sub-horizontal as in this study. The methodology would have to be modified for highly inclined and more complex strata geometries. A visual inspection of the results reveals the process as being effective in ultimately producing acceptable clustering results (Figure 5). The following analyses are performed

Extraction of Statistics from TLS

on each of the clusters of points within individual projection planes.

Bed Apparent Dip Calculation

Given a set of clustered bed segments, such as the one shown in Figure 5, the apparent dip of each segment can be determined using PCA. A twodimensional PCA of each cluster of points provides the principal component directions, which can then be used to calculate a local apparent dip angle; the apparent dip angle is the inverse tangent of the ratio between the vertical and horizontal components of the principal component eigenvector oriented closest to the horizontal.

Bed Thickness Calculation

To determine the thickness of each bed, the range is computed between the maximum and minimum vertical coordinates for the points in each of the clusters. This in turn represents an upper-bound thickness accounting for the situation where some of the distance between the highest and lowest points in a cluster is due to the eﬀect of a non-zero dip of the layer. A correction is applied by subtracting the expected vertical drop across the projection plane width (e.g., a 1-m horizontal distance in the case of this study) as predicted based on the linear trend of the cluster data across the projection plane width. It should be noted that this will lead to errors if the thickness or apparent dip of a given bed is not constant across an individual projection plane. In this study, the projection plane width is small such that any deviations from constant-dip, constant-thickness conditions for the beds within these planes are considered to be insigniﬁcant.

Bed Spacing Calculation A similar approach can be used to determine bed spacing. First, the clusters of points corresponding to different beds within a given projection plane are sorted based on the vertical position of the cluster centroid. Next, the difference between the maximum and minimum vertical coordinates of points within adjacent clusters is calculated as a preliminary spacing value (the maximum vertical coordinate of the lower cluster and minimum vertical coordinate of the upper cluster). As in the case of the thickness calculation, a non-zero apparent dip has the potential to skew the result of this calculation (in this case, toward smaller spacing values). To compensate for this type of error, a correction is applied by adding the expected vertical drop across the projection plane for a line with a slope equal to the average of the slopes of the linear fits to

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

161

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

161

Walton, Fotopoulos, and Radovanovic

Figure 6. Distribution of apparent dip angles for mudstone beds as determined from analysis of (a) static and (b) mobile data collected on June 27. Note that the static data do not cover the full range of the pit, as the static data set did not fully extend along the pit’s length.

the two clusters being considered. This results in the calculation of an average spacing within the projection plane for the pair of beds being considered.

DISCUSSION OF RESULTS The proposed methodology was implemented on 1,393 distinct mudstone 1-m-wide bed segments over 156 m of pit wall identiﬁed from the static data set and 1,025 1-m-wide distinct mudstone bed segments over 205 m of pit wall identiﬁed from the mobile data set. The greater number of bed segments in the case of the static data is due to its higher resolution. Some mudstone beds were missed because they were not observable in the mobile data due to the lower resolution of the mobile data set. To visualize the resulting statistics, box plots were generated for 10-m-wide segments along the pit wall. The apparent dips found by the analysis (Figure 6) show an overall average dip toward the northeast (left side of the outcrop) of a few degrees. Considering all the static data, the middle two quartiles of apparent dip remain between 5º and −8º. The mobile data show more variability (likely due to the variability in the interpreted mudstone locations) and have an interquartile range of approximately 7º to −12º. In both cases, however, the trends in the data are consistent, with a median apparent dip of approximately −3º; minima in apparent dip occurring around 55, 100, and 150 m; and maxima in apparent dip occurring around 75 and 120 m. The bed thickness statistics are consistent with the hypothesis used to explain the greater number of an162

alyzed bed segments in the static data: that the lower resolution in the mobile data affects the interpretation of mudstone versus sandstone units such that fewer beds that are larger are identified. Note that in this case, where interpretations are made primarily on the basis of lidar intensity values, the effective resolution is a function not only of the point spacing but also of the laser beam size (Lichti and Jamtsho, 2006). Figure 7 illustrates the range of thickness values observed in both data sets, with the median thickness being approximately 10 cm in the static data set and 25 cm in the mobile data set. In the case of bed thickness, the trends in the mobile and static data sets do not match particularly well overall; the mobile thickness values are strongly influenced by the lower-resolution lithological interpretation associated with the mobile data (Figure 8). The areas of missing data visible in the static data do not appear in the mobile data because the mobile scanner utilizes multiple lines of sight. The same resolution issues associated with the identification of mudstone bed thickness from the mobile data apply to the bed spacing statistics. Because of the lower resolution of the mobile data interpretation, the smallest interbed sandstone regions were incorporated into the interpreted mudstone bed, biasing the mobile data slightly toward larger spacings (Figure 9). Also, the inability to identify some of the thinner beds below the main set of mudstone beds (see upper third of Figure 10) in the mobile data set led to the absence of large spacing values that were observed in the static data.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

Extraction of Statistics from TLS

Figure 7. Distributions of mudstone bed thickness as determined from analysis of (a) static and (b) mobile data collected on June 27. Note that the static data do not cover the full range of the pit, as the static data set did not fully extend along the pit’s length.

For the purposes of comparison, histograms showing the variability of apparent dip, thickness, and spacing across the entire pit face (ignoring spatial variability) are shown in Figure 11. This image shows quite clearly that the thickness and spacing values exhibit bimodal lognormal distributions, likely developed as a function of the sedimentary processes that governed the formation of the mudstone beds. The total area of mudstone interpreted within range covered by both the static and the mobile data sets was estimated to be 277 m2 using in the static data

Figure 8. Example showing diﬀerences in apparent bed thickness from (a) static and (b) mobile data.

set and 317 m2 using the mobile data set; over the full range of the mobile data set, 428 m2 of mudstone were mapped. The diﬀerence of approximately 14 percent between the mobile and static data sets can be attributed to the inclusion of small sandstone interlayers in the interpretation of the mudstone beds from the lower-resolution mobile data. It should be noted that this discrepancy would be slightly larger if not for the ability of some smaller-scale features not mapped in the mobile data to be detected within the static data. An additional application of the characterization methodology presented herein is the detection of spatial variability in the bed characteristics over the course of the excavation sequence. In particular, at one edge of the image segment of the pit (imaged only by mobile data), separate data sets were collected on June 27, June 28, and June 30, 2014; these data sets, which show the change in the pit geometry with progressive excavation, were interpreted and mudstone bed points manually extracted (Figure 12). The statistics of each data set were evaluated, and the results are shown in Figure 13. Overall, it appears that the mudstone bed geometrical characteristics remain similar, although a few trends were observed: the beds observed on June 30 tend to have larger thicknesses and smaller spacing than those observed on June 27 or June 28, and the June 28 data appear to have slightly larger spacing overall. Day-to-day changes can also be considered within the context of the overall area of visible beds. In this case, values were normalized and expressed as a percentage of the pit face (in the area being excavated) for comparative purposes. On June 27, ∼6 percent of

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

163

Figure 8. Example showing diﬀerences in apparent bed thickness from (a) static and (b) mobile data.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

163

Walton, Fotopoulos, and Radovanovic

Figure 9. Distributions of vertical spacing between mudstone beds as determined from analysis of (a) static and (b) mobile data collected on June 27; (c) and (d) are enlarged versions of (a) and (b), respectively. Note that the static data do not cover the full range of the pit, as the static data set did not fully extend along the pit’s length.

Figure 10. Illustration of large inter-bed spacings in the static data set that are not present in the mobile data.

164

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

164

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

Extraction of Statistics from TLS

Figure 11. Comparison of mudstone bed statistics across the full pit wall as determined from analysis of static and mobile data collected on June 27.

the face was mapped as mudstone; on June 28, almost 3 percent of the face was mapped as mudstone; and on June 30, ∼7 percent of the face was mapped as mudstone. Although the June 27 and June 30 values are somewhat similar, the June 28 data were interpreted to have substantially less mudstone. An in-

dependent manual interpretation of this data reveals that the data acquired on this day did not allow for the mudstone layers to be accurately extracted, and likely several mudstone layers were missed (Figure 14). This may be attributed to diﬀerences in the drive path used for the mobile surveys on these days or to diﬀerences in

Figure 12. Relative locations of manually mapped beds over 3 days. (a) June 27. (b) June 28. (c) June 30.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

Figure 12. Relative locations of manually mapped beds over 3 days. (a) June 27. (b) June 28. (c) June 30.

165

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

165

Walton, Fotopoulos, and Radovanovic

Figure 13. Bed statistics determined from the pit wall over 3 days.

the excavated profile roughness and reflectance, leading to different intensity values (and lower-intensity contrasts) occurring in the June 28 data as a result. The difference between the mudstone areas mapped on June 27 (∼6 percent) and June 30 (∼7 percent) is statistically insignificant. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE WORK In this study, a methodology for the extraction of spatial statistics for sedimentary layers from static and mobile terrestrial laser scanning data is described and implemented on an actual case study in the Athabasca region in Alberta, Canada. The most critical step in the process is the identification of which components of the point cloud correspond to the lithology of in166

terest and is, at present, performed manually. The static and mobile data sets were interpreted to extract mudstone layers, and the relevant spatial statistics for apparent dip, thickness, spacing, and total area of visible beds were calculated. The discrepancies in the results obtained using the static and mobile data sets are attributed to the diﬀerences in the extraction of the mudstone layers from the original point clouds, which in turn was highly dependent on the resolution and accuracy of the data sets. As such, the static data were found to provide more reliable results than the mobile data, although, depending on the spatial scales of interest for the end use of these results, the mobile data may be invaluable for situations where features less than 10-cm thickness are considered insigniﬁcant and where speed of data collection is essential.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

Extraction of Statistics from TLS

Figure 14. Data discrepancies over the 3-day monitoring period.

ACKNOWLEDGMENTS The authors would like to thank Suncor Energy for supporting this research. Thanks are also due to Dr. Zane Jobe for his input on the relevance of the work to reservoir modeling. REFERENCES Abellán, A.; Vilaplana, J. M.; Calvet, J.; García-Sellés, D.; and Asensio, E., 2011, Rockfall monitoring by terrestrial laser scanning—Case study of the basaltic rock face at Castellfollit de la Roca (Catalonia, Spain): Natural Hazards and Earth System Science, Vol. 11, No. 3, pp. 829–841. Avis, D.; Bremmer, D.; and Seidel, R. 1997. How good are convex hull algorithms?: Computational Geometry, Vol. 7, pp. 265– 301. Burton, D.; Dunlap, D. B.; Wood, L. J.; and Flaig, P. P. 2011. LiDAR intensity as a remote sensor of rock properties: Journal of Sedimentary Research, Vol. 81, No. 5, pp. 339–347. Campos Inocencio, L.; Veronez, M. R.; Wohnrath Tognoli, F. M.; De Souza, M. K.; Da Silva, R. M.; Gonzaga, L.; and Blum Silveira, C. L. 2014. Spectral pattern classiﬁcation in lidar data for rock identiﬁcation in outcrops: Science World Journal. doi:10.1155/2014/539029. Ester, M.; Kriegel, H. P.; Sander, J.; and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise: Proceedings of KDD, Vol. 96, No. 34, pp. 226–231. Ferrero, A. M.; Forlani, G.; Roncella, R.; and Voyat, H. I. 2009. Advanced geostructural survey methods applied to rock mass characterization: Rock Mechanics and Rock Engineering, Vol. 42, No. 4, pp. 631–665. Fisher, J. E.; Shakoor, A.; and Watts, C. F. 2014. Comparing discontinuity orientation data collected by terrestrial LiDAR and transit compass methods: Engineering Geology, Vol. 181, pp. 78–92. Fowler, A.; France, J. I.; and Truong, M. 2011. Applications of advanced laser scanning technology in geology: Available at http://www.rieglusa.com/pdf/applications-

Extraction of Statistics from TLS

ofadvanced-laser-scanning-technology-in-geology-anandafowler-final.pdf Franceschi, M.; Teza, G.; Preto, N.; Pesci, A.; Galgaro, A.; and Girardi, S. 2009. Discrimination between marls and limestones using intensity data from terrestrial laser scanner: ISPRS Journal of Photogrammetry Remote Sensing, Vol. 64, pp. 522–528. Ge, Y.; Tang, H.; Xia, D.; Wang, L.; Zhao, B.; Teaway, J. W.; Chen, H.; and Zhou, T. 2018. Automated measurements of discontinuity geometric properties from a 3D-point cloud based on a modified region growing algorithm: Engineering Geology, Vol. 242, pp. 44–54. Gigli, G. and Casagli, N. 2011. Semi-automatic extraction of rock mass structural data from high resolution LIDAR point clouds: International Journal of Rock Mechanics and Mining Sciences, Vol. 48., No. 2, pp. 187–198. Gigli, G.; Morelli, S.; Fornera, S.; and Casagli, N. 2014. Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios: Landslides, Vol. 11, No. 1, pp. 1–14. Glennie, C. 2018. Rigorous 3D error analysis of kinematic scanning LiDAR systems: Journal of Applied Geodesy, Vol. 1, No. 3, pp. 147–157. Gomes, R. K.; de Oliveira, L. P.; Gonzaga, L., Jr.; Tognoli, F. M.; Veronez, M. R.; and de Souza, M. K. 2016. An algorithm for automatic detection and orientation estimation of planar structures in LiDARscanned outcrops: Computers and Geosciences, Vol. 90, pp. 170–178. Harpold, A. A.; Marshall, J. A.; Lyon, S. W.; Barnhart, T. B.; Fisher, B. A.; Donovan, M.; Brubaker, K. M.; Crosby, C. J.; Glenn, N. F.; Glennie, C. L.; Kirchner, P. B.; Lam, N.; Mankoff, Kenneth D.; McCreight, J. L.; Molotch, N. P.; Musselman, K. N.; Pelletier, J.; Russo, T.; Sangireddy, H.; Sjooberg, Y.; Swetnam, T.; and West, N. 2015. Laser vision: LiDAR as a transformative tool to advance critical zone science: Hydrology and Earth System Sciences, Vol. 19, pp. 2881– 2897. Innovmetric. 2015. Polyworks [computer software]. Innovmetric, Montreal, QC, Canada. Inocencio, L.; Veronez, M. R.; Wohnrath Tognoli, F. M.; de Souza, M. K.; da Silva, R. M.; and Blum Silveira, C. L. 2014. Spectral pattern classification in lidar data for rock identification in outcrops: The Scientific World Journal. Available at http://dx.doi.org/10.1155/2014/539029 Jaboyedoff, M.; Demers, D.; Locat, J.; Locat, A.; Locat, P.; Oppikofer, T.; Robitaille, D.; and Turmel, D. 2009. Use of terrestrial laser scanning for the characterization of retrogressive landslides in sensitive clay and rotational landslides in river banks: Canadian Geotechnical Journal, Vol. 46, No. 12, pp. 1379–1390. Labrecque, P. A.; Jesen, J. L.; and Hubbard, S. M. 2011. Cyclicity in Lower Cretaceous point bar deposits with implications for reservoir characterization, Athabasca Oil Sands, Alberta, Canada: Sedimentary Geology, Vol. 242, pp. 18–33. Lato, M.; Diederichs, M. S.; Hutchinson, D. J.; and Harrap, R. 2009. Optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses: International Journal of Rock Mechanics and Mining Sciences, Vol. 46, No. 1, pp. 194–199. Lato, M. J. and Vöge, M. 2012. Automated mapping of rock discontinuities in 3D LiDAR and photogrammetry models: International Journal of Rock Mechanics and Mining Sciences, Vol. 54, pp. 150–158.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

167

Figure 14. Data discrepancies over the 3-day monitoring period.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

167

Walton, Fotopoulos, and Radovanovic Lichti, D. D. and Jamtsho, S. 2006. Angular resolution of terrestrial laser scanners. The Photogrammetric Record, Vol. 21, No. 114, pp. 141–160. Lundgren, J. 2012. alphavol.m: MATLAB function, available at https://www.mathworks.com/matlabcentral/ﬁleexchange/ 28851-alpha-shapes?focused=5222508&tab=function Maerz, N. H.; Aqeel, A. M.; and Anderson, N. 2015. Measuring orientations of individual concealed sub-vertical discontinuities in sandstone rock cuts integrating ground penetrating radar and terrestrial LIDAR: Environmental and Engineering Geoscience, Vol. 21, No. 4, pp. 293–309. Maerz, N. H.; Otoo, J.; Kassebaum, T.; and Boyko, K. 2012. Using LIDAR in highway rock cuts. In Proceedings of the 63rd Annual Highway Geology Symposium: The California State Department of Transportation and The California Geological Survey, Redding, California, pp. 7–10. Maerz, N. H.; Youssef, A. M.; Otoo, J. N.; Kassebaum, T. J.; and Duan, Y. 2013. A simple method for measuring discontinuity orientations from terrestrial LIDAR data: Environmental and Engineering Geoscience, Vol. 19, No. 2, pp. 185–194. Martinius, A. W.; Fustic, M.; Garner, D. L.; Jablonski, B. V. J.; Strobl, R. S.; MacEachern, J. A.; and Dashtgard, S. E. 2017. Reservoir characterization and multiscale heterogeneity modeling of inclined heterolithic strata for bitumenproduction forecasting, McMurray Formation, Corner, Alberta, Canada. Marine and Petroleum Geology, Vol. 82, pp. 336–361. Mills, G. and Fotopoulos, G. 2013. On the estimation of geological surface roughness from terrestrial laser scanner point clouds. Geosphere, Vol. 9, No. 5, pp. 1410–1416. Nardin, T. R.; Feldman, H. R.; and Carter, B. J. 2013. Stratigraphic architecture of a large-scale point-bar complex in the McMurray Formation: Syncrude’s Mildred Lake Mine, Alberta, Canada. In Hein, F. J.; Leckie, D.; Larter, S.; and Suter, J. R. (Editors), Heavy-oil and oil-sand petroleum systems

168

in Alberta and beyond: AAPG Studies in Geology, Vol. 64, pp. 273–311. Otoo, J. N.; Maerz, N. H.; Xiaoling, L.; and Duan, Y. 2011. 3-D discontinuity orientations using combined optical imaging and LiDAR techniques. In Proceedings of the 45th US Rock Mechanics/Geomechanics Symposium: American Rock Mechanics Association, San Francisco, California. Pate, K. and Haneberg, W. C. 2011. Photogrammetric and LiDAR 3-D rock slope discontinuity mapping and interpretation surveys to improve baseline information for supporting design and construction of capital improvement projects at hydroelectric facilities. In Proceedings of the 45th US Rock Mechanics/Geomechanics Symposium: American Rock Mechanics Association, San Francisco, California. Riquelme, A. J.; Abellán, A.; Tomás, R.; and Jaboyedoff, M. 2014. A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Computers and Geosciences, Vol. 68, pp. 38–52. Roncella, R. and Forlani, G. 2005. Extraction of planar patches from point clouds to retrieve dip and dip direction of rock discontinuities. In G. Vosselman and C. Brenner (Editors), Proceedings of Laser Scanning 2005: ISPRS, Enschede, the Netherlands, pp. 162–167. Telling, J.; Lyda, A.; Hartzell, P.; and Glennie, C. 2017. Review of Earth science research using terrestrial laser scanning. Earth-Science Reviews, Vol. 169, pp. 35–68. Van Gosliga, R.; Lindenbergh, R.; and Pfeifer, N. 2006. Deformation analysis of a bored tunnel by means of terrestrial laser scanning. In H.-G. Maas and D. Schneider (Editors), Proceedings of the IAPRS Volume XXXVI, Part 5, Dresden, Germany, pp. 25–27. Walton, G.; Fotopoulos, G.; Radvanovic, R.; and Stancliffe, R. P. W. 2016. Comparison of static and mobile LiDAR data collection for rock outcrop characterization. Geosphere, Vol. 12, no. 6, pp. 1833–1841.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

168

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 155–168

Environmental Availability of Potentially Toxic Elements in an Agricultural Mediterranean Site

DIMITRIOS ALEXAKIS* DIMITRA GAMVROULA ELENI THEOFILI

Laboratory of Geoenvironmental Science & Environmental Quality Assurance, Department of Civil Engineering, University of West Attica, 250 Thivon & P.Ralli Str, 12244, Athens, Greece

Key Terms: Geochemistry, Contamination, Sequential Extractions, Bioavailability ABSTRACT Total contents of 36 potentially toxic elements are summarized for agricultural topsoil (n = 12; soil depth = 0–20 cm), subsoil (n = 12; soil depth = 20–40 cm), and representative rock samples collected from a Mediterranean site (Megara Plain, Greece). The five-stage sequential extraction procedure for the geochemical partitioning of cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), manganese (Mn), and nickel (Ni), proposed by Tessier, was applied to topsoil and subsoil collected from the study area. Soil Cd was highly associated with exchangeable fraction, illustrating high bioavailability of this element. The order of mobility of the elements was as follows: Cd > Cu > Co > Zn > Ni > Cr > Mn. Results from sequential extraction experiments illustrated that the bioavailability of Cu, Co, and Zn is moderate, while Ni, Cr, and Mn presented low bioavailability, indicating that these elements could pose a limited threat to the quality of crops. Cadmium is the chief contamination controlling factor posing moderate potential ecological risk. The contamination sources of the examined elements are discussed. INTRODUCTION Mobility of potentially toxic elements and contamination of agricultural soils is a very critical concern for mankind. According to Delavar and Safari (2016), the determination of spatial variability of trace element soil contents may provide useful information about how to manage the contaminated land. Natural processes, such as weathering of geological formations, as well as anthropogenic inﬂuences contaminate soils and impair their use for agricultural purposes (Alexakis, 2011; Papadopoulou-Vrynioti et al., 2013). In some ar*Corresponding author email: d.alexakis@uniwa.gr

eas the anthropogenic input of trace elements in agricultural soils has exceeded the input from geochemical processes (Facchinelli et al., 2001; Zhang et al., 2017). Nowadays, contamination of agricultural soil is a sensitive issue in many countries since it is directly related to human health and safety. Globally many research studies were conducted on issues related to soil contamination and contamination sources (Beeler and Mitchell, 2017; Kiracofe et al., 2017) as well as to mobility of trace elements (Kabala and Singh, 2001; Chandra Sekhar et al., 2003). The trace element contents in soils are closely related to human health (Romic and Romic, 2003; Velea et al., 2009; and Ding et al., 2018). Potentially toxic elements in soils are distributed among different phases (clay minerals, carbonates, oxyhydroxides of iron and manganese, organic matter, etc.). The partitioning of elements between the different soil phases controls their mobility, redistribution, and bioavailability. Sequential extraction schemes were widely applied to mimic the fate and transport of elements in the environment as well as their behavior in the plant-soil system (He et al., 2013; Rinklebe and Shaheen, 2014). Exposure to cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), and zinc (Zn) can induce genotoxicity and may cause some health problems (Sun et al., 2018). Contamination, geochemical properties of the soil in which the plant is cultivated, and bioaccumulation ability of the plant are among the primary factors that control the content of elements in plants (Seyfferth et al., 2016; Eduardo Marquez et al. 2018; Sun et al., 2018). Research studies on the environmental availability of trace elements assist in developing strategies to protect human health against long-term accumulation (Guo et al., 2012; Ungureanu et al., 2017). In recent years, trace element contamination in agriculture has become a serious issue as a result of anthropogenic activities, such as intensive use of trace element–enriched agrochemicals, wastewater irrigation, and industrial or mining activities (Islam

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

169

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

169

Alexakis, Gamvroula, and Theoﬁli

Figure 1. A simpliﬁed geological map of the area studied showing sampling sites, modiﬁed from Gaitanakis et al. (1984, 1985): (1) Alluvial deposits; (2) Sand, sandstones, marly limestones, and marls with lignite intercalations; (3) Limestones of the Upper Cretaceous; (4) Bauxites; (5) Ultrabasic rock masses; (6) Schist-chert formation of the Middle-Upper Jurassic; (7) Limestones of the LowerMiddle Jurassic; (8) Limestones, dolomites, marbles, and cipolines of Middle-Upper Triassic; (9) Limestones-dolomites of the MiddleUpper Triassic–Lower Jurassic; (10) Complex of cherts, sandstones, and schists of the Lower-Middle Triassic; (11) Argillaceous shales and sandstones-Permian Upper Carboniferous; and (12) Lenses of limestones.

et al., 2015; Hou et al., 2018). Moreover, Hou et al. (2018) concluded that Cd and Cu–combined pollution deteriorates soil quality, which may be unfavorable to plant growth. The main objectives of this work were: (1) to explore the mobility of potentially toxic elements in agricultural soils and (2) to estimate the potential ecological risk posed by arsenic (As), Cd, cobalt (Co), Cr, Cu, Ni, lead (Pb), and Ni concentrations in agricultural soils. Gamvroula (2013) has investigated the groundwater quality and soil quality of the Megara Plain. Alexakis (2016) has determined the potential health risk for adults and children posed by Co, Cr, Mn, Ni, and vanadium (V) contents in agricultural soils of the Megara Plain. The Megara Plain is among the most productive agricultural areas in Greece (latitudes 37o 57� and 38o 08� , longitudes 23o 10� and 23o 27� ). The plain is bounded to the east by Pateras Mountain, by Geraneia Mountain to the west, by the Saronikos Gulf to the south, and by the Korinthiakos Gulf to the north (Figure 1). Observed outcrops within the area studied include the following geological formations: schists; cherts; argillaceous shales with lenses of limestones; limestones; dolomites; bodies of ultramaﬁc rocks; sandstones alternating with bodies of maﬁc igneous rocks; marls; lignite intercalations; marly formations with manganese oxides; reddish brown clays and sandy clays; and sandstones (Dounas et al., 1971; Aslam, 170

Alexakis, Gamvroula, and Theoﬁli

1982; Bornovas et al., 1984; Gaitanakis et al., 1984, 1985; and Gamvroula et al., 2013).The following types of land use are mainly included in the study area: agricultural, urban, forest, and uncultivated areas. Predominant vegetation in Megara Plain consists of vegetables, olive trees, and vineyards. During the last decades, there has been rapid expansion of agricultural activities in the Megara Plain. In the Megara Plain, excessive amounts of fertilizers have been applied to agricultural soils in recent years.

MATERIALS AND METHODS

Fieldwork The agricultural topsoils from the 0–20-cm depth and subsoils from the 20–40-cm depth were collected from twelve sampling sites (S1 to S12) at the area studied, as presented in Figure 1. Composite soil samples for each sampling sites consisted of four soil samples that were taken from four points over a patch of land of approximately 5 m2 (Figure 1). Any foreign material from the samples was manually discarded during the collection. Soil samples were mixed thoroughly to obtain a composite sample (approximately 2 kg) for each site. One bulk sample was collected from lignite, and one bulk sample was taken from marly formation (sampling sites are not shown in Figure 1). Each rock sample collected (about 3 kg) was composed of five to seven samples from different locations in an area measuring approximately 3 × 3 m. Laboratory Work All soil samples were dried indoors at room temperature (<25°C) and divided into two subsample sets. The first set of soil subsamples was passed through a 0.200-mm nylon sieve and stored in clean plastic bags for the single extraction analysis of trace elements. The second set of soil subsamples was sieved to 2 mm using a nylon screen and stored in clean plastic bags for the measurement of soil properties (grain size, calcium carbonate content, organic matter content, pH, Eh [redox potential], and electrical conductivity) as well as for the determination of sequential extracted elements, including Cd, Co, Cr, Cu, Mn, Ni, and Zn. The bulk lignite and marly formation samples were air-dried, crushed, pulverized to 0.200 mm, and stored for trace element analysis. For the determination of trace elements, the homogenized and pulverized samples were digested with a mixture of HNO3 and HCl. The digested soil samples were analyzed for Cd, Co, Cr, Cu, Mn, Ni, and Zn by atomic absorption spectroscopy method (AAS) using a GBC 908AA model at the Laboratory of Geology of the Agricultural University of Athens, while

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

Environmental Availability of Elements

the digested soil and rock samples were analyzed for silver (Ag), aluminum (Al), arsenic (As), gold (Au), barium (Ba), beryllium (Be), bismuth (Bi), calcium (Ca), iron (Fe), potassium (K), lanthanum (La), magnesium (Mg), molybdenum (Mo), sodium (Na), niobium (Nb), phosphorus (P), lead (Pb), sulfur (S), antimony (Sb), selenium (Se), tin (Sn), strontium (Sr), thorium (Th), titanium (Ti), uranium (U), yttrium (Y), zirconium (Zr), vanadium (V), and tungsten (W) by inductively coupled plasma mass spectrometry at the ACME Analytical Laboratories Ltd., Vancouver, Canada (ISO 9002 Accredited Co). The Bouyoucos hydrometer method (Bouyoucos, 1962) has been applied to determine the clay (<0.002 mm), silt (0.002 to 0.02 mm), and sand (0.02 to 2 mm) contents (in percent). The volumetric method of Bernards’ calcimeter was applied for the determination of calcium carbonate (CaCO3 ) content. The dichromate oxidation method was used for measuring organic matter content (in percent) (Soil Survey Staﬀ, 2004). Electrical conductivity, pH, and Eh in soil samples were measured in a 1:2.5 soil-water suspension. Sequential element extraction in the agricultural soils of the Megara Plain was achieved based on the method proposed by Tessier et al. (1979). The extraction procedure that was applied in this study included the following fractions (Tessier et al., 1979): (a) Fraction A (exchangeable); (b) Fraction B (bound to carbonates); (c) Fraction C (bound to Fe-Mn oxide); (d) Fraction D (bound to organic matter); and (e) Fraction E (residual). Each step of the sequential extraction experiment was carried out in triplicate, using a mass of 1.0 g of dry soil sample. Cadmium, Co, Cr, Cu, Mn, Ni, and Zn contents were determined in the eﬄuent after each experiment using AAS. Standard reference samples (Montana soil SRM 2710) from the National Institute of Standards and Technology were used for quality assurance. For ensuring the precision and accuracy of analysis, three analytical duplicates and three reagent blanks were also included in each batch. Data Analysis The descriptive statistics of the geochemical data set were calculated. The mobility factor (MF) of trace elements in soils is calculated using the following equation (Salbu et al., 1998; Kabala and Singh, 2001; and Qasim and Motelica-Heino, 2014): MF Fraction A + Fraction B = . Fraction A + Fraction B + Fraction C + Fraction D + Fraction D

(1)

According to Qasim and Motelica-Heino (2014), the MF illustrates the bioavailability of trace elements in

Environmental Availability of Elements

the soil. In other words, high values of MF have been interpreted as evidence of high bioavailability of trace elements in the soil-plant system (Karczewska, 1996; Qasim and Motelica-Heino, 2014). Ecological risk indices were calculated using Hakanson’s method (Hakanson, 1980), as follows: Eri = Tr i × RI =

Cs i Cn

; i

Er i ,

(2) (3)

i=1

where Tr i is the toxic response factor of an element; Cs i is the mean content of the element from at least five sampling sites; and Cn i is the background value. Hakanson’s method is widely recognized worldwide for the calculation of ecological risk indices in soils (Alexakis, 2016; Huang et al., 2018; Krčmar et al., 2018; and Zhou et al., 2018). RESULTS AND DISCUSSION Evaluation of Soil Properties The topsoil pH of the Megara Plain was slightly alkaline and varied between 7.80 and 8.70, while the pH values of soil depth of 20–40 cm ranged from 8.24 to 8.80; this could be because of the strong buffering capacity of carbonates. The slightly alkaline soil pH was mainly due to the calcareous nature of the parent material (Table 1). The slightly alkaline pH that is recorded in agricultural soils of the area studied plays a significant role in reducing the element uptake by plants. A negative correlation between element uptake by plants and soil pH has been illustrated by several research studies (Gray et al., 1999; Kabata-Pendias and Mukherjee, 2007). The organic matter varied between 0.54 percent and 6.97 percent and between 2.29 percent and 5.49 percent for soil depths of 0–20 cm and 20–40 cm, respectively. The organic matter contents and low percentages of clay seemed to suggest a high mobility of toxic elements by these components. In order to evaluate the mobility of the potentially toxic elements, further research was performed by applying single and sequential extraction procedures. The median values of soil proportions of sand, clay, and silt were 58.88 percent, 15.12 percent, and 24.00 percent, respectively, for 0–20-cm soil depth, while the values were 63.60 percent, 12.76 percent, and 24.00 percent, respectively, for 20–40-cm soil depth. The median values of calcium carbonate contents (CaCO3 ) were 57.73 percent for the soil depth of 0–20 cm and 53.14 percent for the 20–40-cm soil depth, while the CaCO3 content varied between 29.85 percent and 78.06 percent (0–20-cm soil depth) and between 56.72

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

171

Fraction A + Fraction B . Fraction A + Fraction B + Fraction C + Fraction D + Fraction D

(1)

According to Qasim and Motelica-Heino (2014), the MF illustrates the bioavailability of trace elements in

Cs i Cn i

;

Er i ,

(2) (3)

i=1

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

171

Alexakis, Gamvroula, and Theoﬁli

Table 1. Descriptive statistics of soil properties of the study area.

Topsoil Soil Properties CaCO3 (%) Sand (%) Clay (%) Silt (%) Organic matter (%) pH Eh Electrical conductivity (μS/cm)

Minimum 29.8 29.9 6.8 12.0 0.5 7.8 190.0 172.0

Median 57.7 58.9 15.1 24.0 2.7 8.1 223.0 380.0

Subsoil Maximum 78.1 79.9 30.8 52.0 7.0 8.7 250.0 5,450.0

Minimum 56.7 62.7 14.6 22.6 2.3 8.2 226.9 678.6

Median 53.1 63.6 12.8 24.0 2.0 8.2 230.0 320.0

Topsoil Maximum 93.5 80.9 26.1 32.0 5.5 8.8 253.0 6,005.0

Soil Properties CaCO3 (%) Sand (%) Clay (%) Silt (%) Organic matter (%) pH Eh Electrical conductivity (μS/cm)

Minimum 29.8 29.9 6.8 12.0 0.5 7.8 190.0 172.0

Median 57.7 58.9 15.1 24.0 2.7 8.1 223.0 380.0

Subsoil Maximum 78.1 79.9 30.8 52.0 7.0 8.7 250.0 5,450.0

Minimum 56.7 62.7 14.6 22.6 2.3 8.2 226.9 678.6

Median 53.1 63.6 12.8 24.0 2.0 8.2 230.0 320.0

Maximum 93.5 80.9 26.1 32.0 5.5 8.8 253.0 6,005.0

percent and 93.48 percent (20–40-cm soil depth). These high calcium carbonate contents in the agricultural soil samples of the Megara Plain originate from the calcareous rock masses of the surrounding mountains, which are the primary source of CaCO3 . Electrical conductivity median values were 380 μS cm−1 for the topsoil and 320 μS cm−1 for the deep soils.

The element concentrations of As, Co, Cr, Cu, Mo, Sc, U, and V in a bulk lignite sample of the Megara Plain were higher than those in the World Coal reported by Clarke and Sloss (1992) (Table 2). The element contents of As, Co, Cr, Mo, Ni, Sr, and U of marly formations showed concentrations higher than did the Earth’s crust (Mason and Moore, 1982).

Total Element Contents

Geochemical Partitioning of Cd, Co, Cr, Cu, Mn, Ni, and Zn

Total Element Contents

Geochemical Partitioning of Cd, Co, Cr, Cu, Mn, Ni, and Zn

Table 2 summarizes the results of element contents in samples of the area studied. The median values of Ag, Bi, Ca, Cd, Co, Cr, Cu, Mg, Mn, Mo, Ni, S, Sb, Sr, and U in both topsoil and subsoil of the Megara Plain were generally higher than those given by the Geochemical Atlas of Europe project. The soils of the Megara Plain that are rich in Co, Cr, Cu, and Ni are mainly associated with mafic-ultramafic rocks of ophiolitic affinity, which outcrop in the broader area. Moreover, Gamvroula et al. (2013) reported elevated Cr, Mn, and Ni groundwater contents due to the weathering of mafic-ultramafic rocks. Other studies (e.g., Oropos-Kalamos basin [Stamatis et al., 2011], Susaki area [Kelepertsis et al., 2001], and East Attica region [Alexakis, 2002, 2011]) have also reported elevated Co, Cr, Mn, and Ni contents that are associated with the weathering products of ultramafic rocks. The median values of P in topsoil and As in subsoil are higher than these reported by Salminen et al. (2005) (Table 2). Elevated Co, Cr, Mn, and Ni contents in soil and sediments due to the weathering products of ultramafic rocks were also recorded in the Susaki area (Kelepertsis et al., 2001) and the East Attica region of Greece (Alexakis, 2011). Moreover, Alexakis and Gamvroula (2014) observed elevated Co, Cr, and Mn concentrations in Oropos-Kalamos soils, which are also attributed to the same geogenic source, which is the ultramafic rocks. The median concentrations of Ba, Be, Fe, K, La, Na, Nb, Pb, Sc, Sn, Th, Ti, V, W, Y, Zn, and Zr in agricultural soils of the Megara Plain are lower than the corresponding values given by Salminen et al. (2005). 172

According to Tariq and Bashir (2012) and Kumar et al. (2014), elements associated with exchangeable, carbonate, Fe-Mn, and organic fraction may be available to biota and water columns; the sum of the element contents of these four fractions can be described as the total bioavailable element content. On the other hand, the non-available element includes the residual phase. In this study, the sum of the exchangeable, carbonate, Fe-Mn, and organic geochemical fractions was considered to represent available fraction, while the residual phase was considered to represent the nonavailable fraction. Moreover, according to Chandra Sekhar et al. (2003), high element contents in the nonavailable fraction indicate that soils are relatively unpolluted. The mobility factors of Cd, Co, Cr, Cu, Mn, Ni, and Zn in agricultural soils of the Megara Plain are tabulated on Table 3. The order of mobility in topsoils and subsoils of the Megara Plain was the following: Cd > Cu > Co > Zn > Ni > Cr > Mn (Table 3). The high values of Co, Cr, and Ni in the residual phases of both soil depths shows that there are high threshold values in the area studied. The key factor that controls the variation of Co, Cr, and Ni contents in the Megara basin is the composition of parent material. The fractionation results demonstrate that exchangeable fraction is the predominant Cd fraction in both soil depths, suggesting the anthropogenic source of contamination, which is mainly the intensive application of fertilizers. Agricultural practices applied in the area studied continue to introduce nitrogen and phosphatic fertilizers into the topsoil.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

0.5 0.01 5 4 1 1 5 0.01 0.4 2 2 2 0.01 0.01 2 0.01 5 2 0.01 2 2 0 5 0.1 5 1 2 2 2 0.01 20 2 4 2 2 2

Ag (mg/kg) Al (%) As (mg/kg) Au (mg/kg) Ba (mg/kg) Be (mg/kg) Bi (mg/kg) Ca (%) Cd (mg/kg) Co (mg/kg) Cr (mg/kg) Cu (mg/kg) Fe (%) K (%) La (mg/kg) Mg (%) Mn (mg/kg) Mo (mg/kg) Na (%) Nb (mg/kg) Ni (mg/kg) P (%) Pb (mg/kg) S (%) Sb (mg/kg) Sc (mg/kg) Sn (mg/kg) Sr (mg/kg) Th (mg/kg) Ti (%) U (mg/kg) V (mg/kg) W (mg/kg) Y (mg/kg) Zn (mg/kg) Zr (mg/kg) <0.5 2.62 213 <4 116 1 7 6.21 <0.4 23 142 39 3.97 0.65 14 1.07 56 307 0.20 3 272 0.01 <5 5.5 <5 7 <2 2,270 <2 0.11 86 310 <4 15 30 20

Bulk Lignite <0.5 1.12 29 <4 54 <1 5 13.27 <0.4 41 691 12 3.07 0.22 6 5.77 673 30 0.17 2 857 0.01 <5 2.1 <5 6 <2 390 <2 0.05 59 49 <4 6 26 9

Marly Formation

173 Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

0.5 0.01 5 4 1 1 5 0.01 0.4 2 2 2 0.01 0.01 2 0.01 5 2 0.01 2 2 0 5 0.1 5 1 2 2 2 0.01 20 2 4 2 2 2

<0.5 2.62 213 <4 116 1 7 6.21 <0.4 23 142 39 3.97 0.65 14 1.07 56 307 0.20 3 272 0.01 <5 5.5 <5 7 <2 2,270 <2 0.11 86 310 <4 15 30 20

Bulk Lignite

<0.5 1.12 29 <4 54 <1 5 13.27 <0.4 41 691 12 3.07 0.22 6 5.77 673 30 0.17 2 857 0.01 <5 2.1 <5 6 <2 390 <2 0.05 59 49 <4 6 26 9

Marly Formation

DL = detection limit; Min = minimum; Max = maximum. 1 Clarke and Sloss (1992). 2 Mason and Moore (1982). 3 Salminen et al. (2005).

Element (units)

0.5 1.27 5 4 50 1 5 5.38 0.4 10 40 11 1.36 0.24 6 0.63 300 2 0.03 4 52 0.02 5 0.1 5 4 2 73 2 0.07 20 27 4 5 19 14

Min 0.5 2.72 6 4 139 1 5 17.85 0.5 19 200 19 1.97 0.67 14 1.09 517 2 0.12 7 261 0.06 15 0.1 5 6 2 99 5 0.14 20 44 4 10 42 33

Median 0.5 7.25 18 4 337 3 15 26 1.2 35 549 59 3.87 1.61 39 5.07 845 2 0.63 19 438 0.83 52 0.1 5 13 3 442 12 0.41 42 89 4 27 196 115

Max 0.5 1.23 5 4 52 1 5 10.3 0.4 5 15 5 0.89 0.24 6 0.45 164 2 0.03 4 26 0.01 5 0.1 5 3 2 66 2 0.06 20 23 4 5 15 15

Min 0.5 2.53 9 4 130 1 5 17.94 0.4 19 182 20 2.08 0.6 13 1.08 513 2 0.12 6 240 0.04 11 0.1 5 7 2 98 5 0.14 20 50 4 10 35 29

Median

Subsoil

0.5 5.49 15 4 332 2 14 27.7 0.9 37 602 36 3.2 1.39 29 7.04 796 2 0.66 14 477 0.18 35 0.1 5 11 7 448 9 0.29 43 75 4 23 108 80

Max — — 10 — 200 1–15 — — 1–3 5 20 15 — — — — 70 3 — 1–20 — — — — 1 4 1–10 — 4 — 2 40 — 2–50 50 —

World Coals1 — — 1.8 — 425 3 — — 0.2 25 100 55 — — 30 — 1,000 1.5 — 20 75 — 13 — 0.2 22 2 375 7.2 — 1.8 135 — 33 70 —

Crustal Average2

0.5 1.27 5 4 50 1 5 5.38 0.4 10 40 11 1.36 0.24 6 0.63 300 2 0.03 4 52 0.02 5 0.1 5 4 2 73 2 0.07 20 27 4 5 19 14

Min

0.5 2.72 6 4 139 1 5 17.85 0.5 19 200 19 1.97 0.67 14 1.09 517 2 0.12 7 261 0.06 15 0.1 5 6 2 99 5 0.14 20 44 4 10 42 33

Median

Topsoil

0.5 7.25 18 4 337 3 15 26 1.2 35 549 59 3.87 1.61 39 5.07 845 2 0.63 19 438 0.83 52 0.1 5 13 3 442 12 0.41 42 89 4 27 196 115

Max

0.5 1.23 5 4 52 1 5 10.3 0.4 5 15 5 0.89 0.24 6 0.45 164 2 0.03 4 26 0.01 5 0.1 5 3 2 66 2 0.06 20 23 4 5 15 15

Min

0.5 2.53 9 4 130 1 5 17.94 0.4 19 182 20 2.08 0.6 13 1.08 513 2 0.12 6 240 0.04 11 0.1 5 7 2 98 5 0.14 20 50 4 10 35 29

Median

Subsoil

0.5 5.49 15 4 332 2 14 27.7 0.9 37 602 36 3.2 1.39 29 7.04 796 2 0.66 14 477 0.18 35 0.1 5 11 7 448 9 0.29 43 75 4 23 108 80

Max

— — 10 — 200 1–15 — — 1–3 5 20 15 — — — — 70 3 — 1–20 — — — — 1 4 1–10 — 4 — 2 40 — 2–50 50 —

World Coals1

— — 1.8 — 425 3 — — 0.2 25 100 55 — — 30 — 1,000 1.5 — 20 75 — 13 — 0.2 22 2 375 7.2 — 1.8 135 — 33 70 —

Crustal Average2

Table 2. Summary statistics of the chemical composition of the examined samples in comparison with values given by the literature.

DL = detection limit; Min = minimum; Max = maximum. 1 Clarke and Sloss (1992). 2 Mason and Moore (1982). 3 Salminen et al. (2005).

Element (units)

Topsoil

Table 2. Summary statistics of the chemical composition of the examined samples in comparison with values given by the literature.

0.27 5.82 7.03 — 375 <2 <0.5 0.66 0.15 7.78 60 13 2.45 0.8 23.5 0.46 504 0.62 0.59 9.68 18 0.06 22.6 0.02 0.6 8.21 3 89 7.24 0.34 2 60.4 <5 21 52 231

FOREGS Atlas3

0.27 5.82 7.03 — 375 <2 <0.5 0.66 0.15 7.78 60 13 2.45 0.8 23.5 0.46 504 0.62 0.59 9.68 18 0.06 22.6 0.02 0.6 8.21 3 89 7.24 0.34 2 60.4 <5 21 52 231

FOREGS Atlas3

Environmental Availability of Elements Environmental Availability of Elements

173

Alexakis, Gamvroula, and Theoﬁli Table 3. Statistics of the mobility factors of Cd, Co, Cr, Cu, Mn, Ni, and Zn in the agricultural soils of the area studied. Cd Topsoil Mean Median Standard deviation Minimum Maximum Subsoil Mean Median Standard deviation Minimum Maximum

78.2 17.2 4.8 77.8 15.0 3.9 1.1 8.1 3.6 76.7 6.8 1.0 80.0 30.9 11.7

22.8 24.0 3.5 12.1 25.1

2.7 2.7 0.9 1.4 4.3

6.6 15.5 3.6 15.4 5.9 9.7 1.0 3.6 15.5 33.2

77.5 77.8 2.1 75.0 80.0

23.9 24.2 4.7 11.0 30.1

2.3 2.5 0.8 1.1 3.9

10.1 4.8 11.5 2.0 41.3

17.4 6.5 14.2 4.5 10.8 6.0 6.2 1.0 45.4 18.5

20.2 20.3 12.0 6.5 44.6

According to Alloway (2013), phosphatic fertilizers generally contain the highest contents of most heavy metals, including Cd. Furthermore, the Cd concentration in phosphatic fertilizers around the world ranges from 0.1 to 170 mg kg−1 (Alloway, 2013). Cadmium Cadmium extracted in exchangeable fraction ranged from 53.3 percent to 60.0 percent and from 50.0 percent to 58.3 percent of the total Cd for topsoil and subsoil, respectively (Figure 2), suggesting anthropogenic sources of this element, such as the known intensive application of fertilizers in the Megara Plain. Moreover, according to Nicholson et al. (2003), Cd is present in all phosphate fertilizer materials. It can be seen that the highest percentages of Cd are recorded for the exchangeable of topsoil and subsoil. This was comparable for the two soil depths, demonstrating that exchangeable fractions for topsoil and subsoil, respectively, are the predominant fractions. The greater proportion of Cd that is in the exchangeable fraction in relation to the pH values ranging up to 8.8 can be explained by the concurrent increase in the number of exchangeable sites with pH. The results were in agreement with those of Mann and Ritchie (1993). Mann and Ritchie (1993) observed that the percentage proportion of Cd in the exchangeable fraction at higher pH did not decrease. Moreover, according to many researchers (Forstner, 1985; Ebrahimpour and Mushrifah, 2008), Cd characteristically presented greater proportions in the more mobile fractions, suggesting its high availability. The majority of Cd remaining in the exchangeable fraction was distributed among carbonates and organic matter. Fe-Mn oxides present the lowest ability to bound Cd, compared to the other examined elements, while Cd was the element with the greatest percentage in the exchangeable fraction. According to Qasim et al. (2013) and Dudka and Chlopecha (1990), the exchangeable fraction includes 174

Cd held by electrostatic adsorption; this form could be considered as more mobile. Since Cd is the only element that presents the highest content in exchangeable fraction, it can be considered as the most mobile element in the agricultural soils of the Megara Plain (Table 3). A geochemical study performed by He et al. (2013) yielded similar results, with a high percentage of exchangeable fraction (up to 55.2 percent), indicating its high bioavailability. Moreover, many researchers (Lair et al., 2008; Jalali and Hemati, 2013) concluded that the high potential mobility of Cd might reﬂect not only its high biovailability but also the high level of human-induced contamination. Gray et al. (1999) investigated the soil pH eﬀects on the behavior of Cd, especially on the transferring from soil to plants. Outcomes derived from Gray et al. (1999) illustrated that in general, increasing soil pH from 5.5 to 7.0 reduces dramatically the Cd contents in plants. Increasing soil pH by liming is regarded as a common agricultural practice that reduces Cd phytoavailability. In the study area, this agricultural practice is not required, since weathering of calcareous material acts as a natural factor of liming. In other words, the alkaline conditions of agricultural soil of the study area act as a natural safeguard alleviating Cd toxicity in plants.

Cobalt It can be understood that the majority of Co was mainly associated with residual fraction, varying between 38.0 percent and 53.9 percent (topsoil) and between 40.4 percent and 59.5 percent (subsoil) of total Co content (Figure 2). The high percentages of these fractions indicated that a major part of the cobalt in soil of the Megara Plain is immobile. The next abundant Co fraction of the Megara soils is the organic fraction, demonstrating a potential source of Co contamination through the mineralization of organic matter.

Chromium The majority of Cr was associated with the residual fraction, ranging from 59.9 percent to 83.9 percent and from 56.4 percent to 80.3 percent for topsoil and subsoil, respectively, suggesting lithogenic sources for this element (Figure 2). This fraction represents Cr bound to crystalline phase, suggesting that a major part of Cr is immobile, while less than 3.4 percent and 7.5 percent of Cr for topsoil and deep soil samples of the Megara Plain, respectively, are associated with the exchangeable fractions, suggesting low bioavailability of this element.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

78.2 17.2 4.8 77.8 15.0 3.9 1.1 8.1 3.6 76.7 6.8 1.0 80.0 30.9 11.7

22.8 24.0 3.5 12.1 25.1

2.7 2.7 0.9 1.4 4.3

6.6 15.5 3.6 15.4 5.9 9.7 1.0 3.6 15.5 33.2

77.5 77.8 2.1 75.0 80.0

23.9 24.2 4.7 11.0 30.1

2.3 2.5 0.8 1.1 3.9

10.1 4.8 11.5 2.0 41.3

17.4 6.5 14.2 4.5 10.8 6.0 6.2 1.0 45.4 18.5

20.2 20.3 12.0 6.5 44.6

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

Environmental Availability of Elements

Figure 2. Fractionation of Cd, Co, Cr, Cu, Mn, Ni, and Zn in (a) topsoils and (b) subsoils of the Megara Plain (Fraction A: Exchangeable; Fraction B: Bound to carbonates; Fraction C: Bound to Fe-Mn oxides; Fraction D: Bound to organic matter; and Fraction E: Residual).

Copper

the Megara Plain is the organic matter (10.7–31.3 percent in topsoil; 14.4–28.3 percent in subsoil). The nonresidual fraction of Mn (exchangeable + carbonate + Fe-Mn bound + organic matter bound) was very high compared to the residual fraction in both soil depths (Figure 2). This means that the major proportion of Mn can be solubilized and transferred to the food chain through plant uptake. Manganese is the element with the lowest percentage in the exchangeable fraction. The low contents of Mn in this fraction indicate that this element is derived mainly from natural sources, which are the known manganese oxides within marly formations.

Copper

Nickel

Soil Mn was mainly bound with Fe-Mn fraction, representing 22.2–71.4 percent (topsoil) and 19.7–67.2 percent (subsoil) of total Mn content. The next major leachable pool of Mn in the agricultural soils of

The highest Cu contents were leached from organic matter fraction in topsoil (29.5–56.1 percent), demonstrating an additional source of Cu contamination through the mineralization of organic matter, while the highest Cu contents were bound to crystalline phase in subsoil (21.2–50.3 percent), indicating that a major part of Cu is immobile. Copper is equally bound in the Fe-Mn oxide and hydroxides fraction in both soil depths (Figure 2). Manganese Soil Mn was mainly bound with Fe-Mn fraction, representing 22.2–71.4 percent (topsoil) and 19.7–67.2 percent (subsoil) of total Mn content. The next major leachable pool of Mn in the agricultural soils of

Nickel fractionation is dominated by residual fraction for both soil depths, suggesting that a major part

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

175

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

175

Alexakis, Gamvroula, and Theoﬁli Table 4. The results of potential ecological risk index of eight elements from agricultural soils of the study area.

As Cd Co Cr Cu Ni Pb Zn

Topsoil

Subsoil

6.79 56.67 5.1 4.96 2.56 17.3 4.73 0.69

6.92 47.5 5.0 4.75 2.15 16.2 3.81 0.49

(up to 80.4 percent in topsoil and up to 78.9 percent in subsoil) of Ni is immobile (Figure 2). The Ni in the examined soils is mainly bound in residual fraction, indicating that it is strongly retained in Megara soils, suggesting a natural source of this element, which is the known bodies of ultramaﬁc rocks and their weathering products. In other words, Ni mainly presents low mobility in the Megara soils. Zinc Zinc was found mainly in association with the FeMn fraction, varying between 25.2 percent and 62.7 percent (topsoil) and between 25.9 percent and 62.1 percent (subsoil) (Figure 2). It can be seen that the next abundant Zn fraction of the Megara soils is the residual fraction, suggesting that the above-mentioned Zn contents are not available to plants. Estimation of Potential Ecological Risk Of the investigated elements, a Tr i value was suggested only for As, Cd, Co, Cr, Cu, Ni, Pb, and Zn, so these elements were further evaluated with the Er i method. The ranking order of the potential ecological risk of the examined elements was as follows: Cd > Ni > As > Co > Cr > Pb > Cu > Zn for both soil depths (Table 4). The Potential Ecological Risk Index of Cd was in the class of moderate (40 < Er Cd < 80), while the ecological risk indices of As, Co, Cr, Cu, Ni, Pb, and Zn were lower than 40, which suggested low potential ecological risk. According to Shi et al. (2014) and Hakanson (1980), the Potential Ecological Risk (index RI) describes the sensitivity of the biological areas to the toxic elements. Cadmium is the element that contributed most for the index RI values of the examined soils. These values at 0–20-cm and 20–40-cm soil depth were 98.84 and 86.84, respectively, denoting moderate potential ecological risk. Although Cd presents low contents in agricultural soils of the Megara Plain, moderate ecological risks of Cd were recorded in the 176

Alexakis, Gamvroula, and Theoﬁli

area studied. This can be attributed to the geochemical characteristics of a major geochemical form of Cd in the soils. Similar geochemical characteristics to the easy dissolution and transport of Cd have been also reported for sediments (Yang et al., 2009; Zhang et al., 2018). On the contrary, high contents of Co, Cr, Cu, and Ni were associated with low potential ecological risk. CONCLUSIONS It can be concluded that the fractionation patterns are the same in topsoil and subsoil samples for the elements Cd, Co, Cr, Mn, Ni, and Zn, indicating the homogeneous variation of these elements in both soil depths of the area studied. The calcareous nature of the parent material mainly control the slightly alkaline pH values and the high calcium carbonate contents. The order of bioavailability of trace elements in both soil depths was the following: Cd > Cu > Co > Zn > Ni > Cr > Mn. Extraction studies suggest that the most bioavailable element in both soil depths was Cd, while Cu, Co, and Zn presented moderate percentages in the bioavailable fraction. The sequential extraction results showed that Ni, Cr, and Mn have the highest abundance in the residual fraction, indicating that these elements are rather immobile. This illustrates that these elements could pose a limited threat to the quality of crops. The signiﬁcant amounts of Ni, Cr, and Mn in the least bioavailable fraction are derived from lithogenic sources. Cadmium is the chief contamination controlling factor posing moderate potential ecological risk. Furthermore, sequential extraction results illustrated that exchangeable fraction is the predominant Cd fraction in both soil depths. This is mainly attributed to the intensive agricultural practices in the area studied. REFERENCES Alexakis, D., 2002, The Impact of Geologic and Anthropogenic Factors on the Quality and the Chemical Composition of East Attica Groundwaters: Unpublished Ph.D. Thesis, National and Kapodistrian University of Athens (in Greek with English abstract). Alexakis, D., 2011, Diagnosis of stream sediment quality and assessment of toxic element contamination sources in East Attica, Greece: Environmental Earth Sciences, Vol. 63, pp. 1369– 1383. Alexakis, D., 2016, Human health risk assessment associated with Co, Cr, Mn, Ni and V contents in agricultural soils from a Mediterranean site: Archives Agronomy Soil Science, Vol. 62, No. 3, pp. 359–373. Alexakis, D. and Gamvroula, D., 2014, Arsenic, chromium, and other potentially toxic elements in the rocks and sediments of Oropos-Kalamos basin, Attica, Greece: Applied Environmental Soil Science, Vol. 2014, Article No. 718534.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

Table 4. The results of potential ecological risk index of eight elements from agricultural soils of the study area.

As Cd Co Cr Cu Ni Pb Zn

Topsoil

Subsoil

6.79 56.67 5.1 4.96 2.56 17.3 4.73 0.69

6.92 47.5 5.0 4.75 2.15 16.2 3.81 0.49

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

Environmental Availability of Elements Alloway, B. J., 2013, Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability, Environmental Pollution 22: Springer Science+Business Media, Dordrecht, The Netherlands. Aslam, S., 1982, Geomorphological Study of Megara Basin: Ph.D. Thesis, National and Kapodistrian University of Athens (in Greek). Beeler, K. R. and Mitchell, R. J., 2017, Sediment and phosphorus inputs from perennial streams to Lake Whatcom, Washington State: Environmental Engineering Geoscience, Vol. 30, No. 3, pp. 153–164. Bornovas, J.; Eleftheriou, A.; Gaitanakis, P.; Rondogianni, Th.; Simeakis, C.; and Tsaila-Monopolis, St., 1984, Geological Map of Greece, Scale 1:50.000, Kaparellion Sheet.: Institute of Geology and Mineral Exploration, Greece. Bouyoucos, G. J., 1962, Hydrometer method improved for making particle size analysis of soils: Agronomy Journal, Vol. 54, pp. 464–465. Chandra Sekhar, K.; Chary, N. S.; Kamala, C. T.; Suman Raj, D. S.; and Sreenivasa, R. A., 2003, Fractionation studies and bioaccumulation of sediment-bound heavy meals in Kolleru lake by edible fish: Environmental International, Vol. 29, pp. 1001–1008. Clarke, L. and Sloss, L., 1992, Trace Elements: Emissions from Coal Combustion and Gasification. International Energy Agency Report CR/49, London, U.K. Delavar, M. A. and Safari, Y., 2016, Spatial distribution of heavy metals in soils and plants in Zinc Town, northwest Iran: International Journal Environmental Science Technology, Vol. 13, pp. 297–306. Ding, Z.; Li, Y.; Sun, Q.; and Zhang, H., 2018, Trace elements in soils and selected agricultural plants in the Tongling mining area of China: International Journal Environmental Research Public Health, Vol. 15, p. 202. Dounas, A.; Christodoulou, G.; and Tsaila–Monopolis, St., 1971, Geological Map of Greece, Scale 1:50.000, Erithrai Sheet: Institute of Geology and Subsurface Research, Athens, Greece. Dudka, S. and Chlopecha, A., 1990, Effect of solid-phase speciation on metal mobility and phytoavailability in sludge amended soil: Water, Air Soil Pollution, Vol. 51, pp. 153–160. Ebrahimpour, M. and Mushrifah, I., 2008, Heavy metal concentrations in water and sediments in Tasik Chini, a freshwater lake, Malaysia: Environmental Monitoring Assessment, Vol. 141, pp. 297–307. Eduardo Marquez, J.; Pourret, O.; Faucon, M. P.; Weber, S.; Hòa Hoàng, T. B.; and Martinez, R. E., 2018, Effect of cadmium, copper and lead on the growth of rice in the coal mining region of Quang Ninh, Cam-Pha (Vietnam): Sustainability, Vol. 10, p. 1758. Facchinelli, A.; Sacchi, E.; and Mallen, L., 2001, Multivariate statistical and GIS-based approach to identify heavy metal sources in soils: Environmental Pollution, Vol. 114, pp. 313– 324. Forstner, U., 1985, Chemical forms and reactivity of metals in sediments. In Leschber, R. (Editor), Chemical Methods for Assessing Bioavailable Metals in Sledges and Soils: Elsevier, London, U.K. pp. 1–30. Gaitanakis, P.; Mettos, A.; Fytikas, M.; Tsaila–Monopolis, St.; Tsapralis, V.; and Ioakim, Ch., 1985, Geological Map of Greece, Scale 1:50.000, Sofikon Sheet: Institute of Geology and Mineral Exploration, Athens, Greece. Gaitanakis, P.; Mettos, A.; Koutsouveli, A.; Rondogiani, Th.; Tsaila–Monopolis, St.; Tsapralis, K.; and Chorianopoulou, P., 1984, Geological Map of Greece, Scale

Environmental Availability of Elements

1:50.000, Megara Sheet: Institute of Geology and Mineral Exploration, Athens, Greece. Gamvroula, D., 2013, Environmental and Hydrogeochemical Research Study in the Megara Basin: Unpublished Ph.D. thesis, Agricultural University of Athens (in Greek with English summary). Gamvroula, D.; Alexakis, D.; and Stamatis, G., 2013, Diagnosis of groundwater quality and assessment of contamination sources in the Megara basin (Attica, Greece): Arabian Journal Geosciences, Vol. 6, No. 7, pp. 2367–2381. Gray, C. W.; McLaren, R. G.; Roberts, A. H. C.; and Condron, L. M., 1999, Effect of soil pH on cadmium phytoavailability in some New Zealand soils: New Zealand Journal Crop Horticultural Science, Vol. 27, No. 2, pp. 169–179. Guo, G.; Wu, F.; Xie, F.; and Zhang, R., 2012, Spatial distribution and pollution assessment of heavy metals in urban soils from southwest China: Journal Environmental Sciences, Vol. 24, No. 3, pp. 410–418. Hakanson, L., 1980, An ecological risk index for aquatic pollution control. A sedimentological approach: Water Research, Vol. 14, pp. 975–1001. He, Q.; Ren, Y.; Mohamed, I.; Ali, M.; Hassan, W.; and Zeng, F., 2013, Assessment of trace and heavy metal distribution by four sequential extraction procedures in a contaminated soil: Soil Water Research, Vol. 2, pp. 71–76. Hou, S.; Zheng, N.; Tang, L.; and Ji, X., 2018, Effects of cadmium and copper mixtures to carrot and pakchoi under greenhouse cultivation condition: Ecotoxicology Environmental Safety, Vol. 159, pp. 172–181. Huang, Y.; Chen, Q.; Deng, M.; Japenga, J.; Li, T.; Yang, X.; and He, Z., 2018, Heavy metal pollution and health risk assessment of agricultural soils in a typical peri-urban area in southeast China: Journal Environmental Management, Vol. 207, pp. 159–168. Islam, M. S.; Ahmed, M. K.; Habibullah-Al-Mamun, M.; and Masunaga, S., 2015, Assessment of trace metals in foodstuffs grown around the vicinity of industries in Bangladesh: Journal Food Composition Analysis, Vol. 42, pp. 8–15. Jalali, M. and Hemati, N., 2013, Chemical fractionation of seven heavy metals (Cd, Cu, Fe, Mn, Ni, Pb and Zn) in selected paddy soils of Iran: Paddy Water Environment, Vol. 11, pp. 299–309. Kabala, C. and Singh, B. R., 2001, Fractionation and mobility of copper, lead and zinc in soil profiles in the vicinity of a copper smelter: Journal Environmental Quality, Vol. 30, pp. 485–492. Kabata-Pendias, A. and Mukherjee, A., 2007, Trace Elements from Soil to Human: Springer-Verlag, Berlin, Germany. 551 p. Karczewska, A., 1996, Metal species distribution on top and subsoil on an area affected by smelter emission: Applied Geochemistry, Vol. 11, pp. 35–42. Kelepertsis, A.; Alexakis, D.; and Kita, I., 2001, Environmental geochemistry of soils and waters of Susaki area, Korinthos, Greece: Environmental Geochemistry Health, Vol. 23, pp. 117– 135. Kiracofe, Z. A.; Henika, W. S.; and Schreiber, M. E., 2017, Assessing the geological sources of manganese in the Roanoke River Watershed, Virginia: Environmental Engineering Geoscience, Vol. 23, No. 1, pp. 43–64. Krčmar, D.; Tenodi, S.; Grba, N.; Kerkez, D.; Watson, M.; Rončević, S.; and Dalmacija, B., 2018, Preremedial assessment of the municipal landfill pollution impact on soil and shallow groundwater in Subotica, Serbia: Science Total Environment, Vol. 615, pp. 1341–1354. Kumar, B.; Verma, V. K.; Naskar, A. K.; Sharma, C. S.; and Mukherjee, D. P., 2014, Bioavailability of metals in soil and

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

177

Alloway, B. J., 2013, Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability, Environmental Pollution 22: Springer Science+Business Media, Dordrecht, The Netherlands. Aslam, S., 1982, Geomorphological Study of Megara Basin: Ph.D. Thesis, National and Kapodistrian University of Athens (in Greek). Beeler, K. R. and Mitchell, R. J., 2017, Sediment and phosphorus inputs from perennial streams to Lake Whatcom, Washington State: Environmental Engineering Geoscience, Vol. 30, No. 3, pp. 153–164. Bornovas, J.; Eleftheriou, A.; Gaitanakis, P.; Rondogianni, Th.; Simeakis, C.; and Tsaila-Monopolis, St., 1984, Geological Map of Greece, Scale 1:50.000, Kaparellion Sheet.: Institute of Geology and Mineral Exploration, Greece. Bouyoucos, G. J., 1962, Hydrometer method improved for making particle size analysis of soils: Agronomy Journal, Vol. 54, pp. 464–465. Chandra Sekhar, K.; Chary, N. S.; Kamala, C. T.; Suman Raj, D. S.; and Sreenivasa, R. A., 2003, Fractionation studies and bioaccumulation of sediment-bound heavy meals in Kolleru lake by edible fish: Environmental International, Vol. 29, pp. 1001–1008. Clarke, L. and Sloss, L., 1992, Trace Elements: Emissions from Coal Combustion and Gasification. International Energy Agency Report CR/49, London, U.K. Delavar, M. A. and Safari, Y., 2016, Spatial distribution of heavy metals in soils and plants in Zinc Town, northwest Iran: International Journal Environmental Science Technology, Vol. 13, pp. 297–306. Ding, Z.; Li, Y.; Sun, Q.; and Zhang, H., 2018, Trace elements in soils and selected agricultural plants in the Tongling mining area of China: International Journal Environmental Research Public Health, Vol. 15, p. 202. Dounas, A.; Christodoulou, G.; and Tsaila–Monopolis, St., 1971, Geological Map of Greece, Scale 1:50.000, Erithrai Sheet: Institute of Geology and Subsurface Research, Athens, Greece. Dudka, S. and Chlopecha, A., 1990, Effect of solid-phase speciation on metal mobility and phytoavailability in sludge amended soil: Water, Air Soil Pollution, Vol. 51, pp. 153–160. Ebrahimpour, M. and Mushrifah, I., 2008, Heavy metal concentrations in water and sediments in Tasik Chini, a freshwater lake, Malaysia: Environmental Monitoring Assessment, Vol. 141, pp. 297–307. Eduardo Marquez, J.; Pourret, O.; Faucon, M. P.; Weber, S.; Hòa Hoàng, T. B.; and Martinez, R. E., 2018, Effect of cadmium, copper and lead on the growth of rice in the coal mining region of Quang Ninh, Cam-Pha (Vietnam): Sustainability, Vol. 10, p. 1758. Facchinelli, A.; Sacchi, E.; and Mallen, L., 2001, Multivariate statistical and GIS-based approach to identify heavy metal sources in soils: Environmental Pollution, Vol. 114, pp. 313– 324. Forstner, U., 1985, Chemical forms and reactivity of metals in sediments. In Leschber, R. (Editor), Chemical Methods for Assessing Bioavailable Metals in Sledges and Soils: Elsevier, London, U.K. pp. 1–30. Gaitanakis, P.; Mettos, A.; Fytikas, M.; Tsaila–Monopolis, St.; Tsapralis, V.; and Ioakim, Ch., 1985, Geological Map of Greece, Scale 1:50.000, Sofikon Sheet: Institute of Geology and Mineral Exploration, Athens, Greece. Gaitanakis, P.; Mettos, A.; Koutsouveli, A.; Rondogiani, Th.; Tsaila–Monopolis, St.; Tsapralis, K.; and Chorianopoulou, P., 1984, Geological Map of Greece, Scale

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

177

Alexakis, Gamvroula, and Theofili health risk assessment for population near an Indian chromite mine area: Human Ecological Risk Assessment: International Journal, Vol. 20, No. 4, pp. 917–928. Lair, G. J.; Graf, M.; Zehetner, F.; and Gerzabeck, M. H., 2008, Distribution of cadmium among geochemical fractions in floodplain soils of progressing development: Environmental Pollution, Vol. 156, pp. 207–214. Mann, S. and Ritchie, G., 1993, The influence of pH on the forms of cadmium in four West Australian soils: Australian Journal Soil Research, Vol. 31, No. 3, pp. 255–270. Mason, B. and Moore, C. B., 1982, Principles of Geochemistry: Wiley, New York. Nicholson, F. A.; Smith, S. R.; Alloway, B. J.; Carlton-Smith, C.; and Chambers, B. J., 2003, An inventory of heavy metal inputs to agricultural soils in England and Wales: Science Total Environment, Vol. 311, pp. 205–219. Papadopoulou-Vrynioti, K.; Alexakis, D.; Bathrellos, G. D.; Skilodimou, H. D.; Vryniotis, D.; Vassiliades, E.; and Gamvroula, D., 2013, Distribution of trace elements in stream sediments of Arta plain (western Hellas): The influence of geomorphological parameters: Journal Geochemical Exploration, Vol. 134, pp. 17–26. Qasim, B. and Motelica-Heino, M., 2014, Potentially toxic element fractionation in technosoils using two sequential extraction schemes: Environmental Science Pollution Research, Vol. 21, pp. 5054–5065. Rinklebe, J. and Shaheen, S., 2014, Assessing the mobilization of cadmium, lead and nickel using a seven-step sequential extraction technique in contaminated floodplain soil profiles along the central Elbe river, Germany: Water, Air Soil Pollution, Vol. 225, Article No. 2039. Romic, M. and Romic, D., 2003, Heavy metals, distribution in agricultural top soils in urban area: Environmental Geology, Vol. 43, pp. 795–805. Salbu, B.; Krekling, T.; and Oughton, D. H., 1998, Characterization of radioactive particles in the environment: Analyst, Vol. 123, pp. 843–849. Salminen, R. (Chief-Editor); Batista, M. J.; Bidovec, M.; Demetriades, A.; De Vivo, B.; De Vos, W.; Duris, M.; Gilucis, A.; Gregorauskiene, V.; Halamic, J.; Heitzmann, P.; Lima, A.; Jordan, G.; Klaver, G.; Klein, P.; Lis, J.; Locutora, J.; Marsina, K.; Mazreku, A.; O’Connor, P. J.; Olsson, S. A.; Ottesen, R. T.; Petersell, V.; Plant, J. A.; Reeder, S.; Salpeteur, I.; Sandstrom, H.; Siewers, U.; Steenfelt, A.; and Tarvainen, T., 2005, FOREGS Geochemical Atlas of Europe Part 1.Background Information, Methodology and Maps: Geological Survey of Finland, Espoo, Finland. Seyfferth, A. L.; McClatchy, C.; and Paukett, M., 2016, Arsenic, lead, and cadmium in U.S. mushrooms and substrate in relation to dietary exposure: Environmental Science Technology, Vol. 50, pp. 9661–9670.

178

Shi, P.; Xiao, J.; Wang, Y.; and Chen, L., 2014, Assessment of ecological and human health risks of heavy metal contamination in agriculture soils disturbed by pipeline construction: International Journal Environmental Research Public Health, Vol. 11, pp. 2504–2520. Soil Survey Staff, 2004, Soil Survey Laboratory Methods Manual, Version No. 4.0: USDA NRCS, Soil Survey Investigations Report No. 42. U.S. Government Printing Office. Washington, DC. Stamatis, G.; Alexakis, D.; Gamvroula, D.; and Migiros, G., 2011, Groundwater quality assessment in Oropos-Kalamos basin, Attica, Greece: Environmental Earth Sciences, Vol. 64, pp. 973–988. Sun, H.; Cheng, H.; Lin, L.; Deng, K.; and Cui, X., 2018, Bioaccumulation and sources of metal(loid)s in lilies and their potential health risks: Ecotoxicology Environmental Safety, Vol. 151, pp. 228–235. Tariq, S. R. and Bashir, A., 2012, Speciative distribution and bioavailability of metals in agricultural soils receiving industrial wastewater: Environmental Monitoring Assessment, Vol. 184, pp. 4609–4622. Tessier, A.; Combell, P. G. C.; and Bisson, M., 1979, Sequential extraction procedures for the speciation of particulate trace metals: Analytical Chemistry, Vol. 51, pp. 844–851. Ungureanu, T.; Ovidiu Iancu, G.; Pintilei, M.; and Marian Marius, M., 2017, Chicoş spatial distribution and geochemistry of heavy metals in soils: A case study from the NE area of Vaslui County, Romania: Journal Geochemical Exploration, Vol. 176, pp. 20–32. Velea, T.; Gherghe, L.; Predica, V.; and Krebs, R., 2009, Heavy metal contamination in the vicinity of an industrial area near Bucharest: Environmental Science Pollution Research Vol. 16, pp. 27–32. Yang, Z.; Wang, Y.; Shen, Z.; Niu, J.; and Tang, Z., 2009, Distribution and speciation of heavy metals in sediments from the mainstream, tributaries, and lakes of the Yangtze River catchment of Wuhan, China: Journal Hazardous Materials, Vol. 166, pp. 1186–1194. Zhang, H.; Huang, B.; Dong, L.; Hu, W.; Saleem Akhtar, M.; and Qu, M., 2017, Accumulation, sources and health risks of trace metals in elevated geochemical background soils used for greenhouse vegetable production in southwestern China: Ecotoxicology Environmental Safety, Vol. 137, pp. 233–239. Zhang, Z.; Lu, Y.; Li, H.; Tu, Y.; Liu, B.; and Yang, Z., 2018, Assessment of heavy metal contamination, distribution and source identification in the sediments from the Zijiang River, China: Science Total Environment, Vol. 645, pp. 235–243. Zhou, Y.; Niu, L.; Liu, K.; Yin, S.; and Liu, W., 2018, Arsenic in agricultural soils across China: Distribution pattern, accumulation trend, influencing factors, and risk assessment: Science Total Environment, Vol. 616–617, pp. 156–163.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

178

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 169–178

The Heavy Metals Pollution Index and Water Quality Monitoring of the Zarrineh River, Iran

MARYAM KHALILZADEH POSHTEGAL* SEYED AHMAD MIRBAGHERI

Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran

Key Terms: Metals Pollution, Water Supply, Water Quality, Statistics ABSTRACT Water pollution is the most important reason to monitor and assess water quality. The chemical, physical, and biological quality of natural waters may be aﬀected by anthropogenic activities such as industrial, urban, and agricultural activities. In the present study, the water quality of the Zarrineh River, one of the main rivers supplying water in the northwest of Iran, was investigated. A total of 21 sites were selected for surface water sampling during spring and winter (dry and wet) seasons. The concentrations of the metals aluminum, iron, barium, chromium, arsenic, copper, manganese, nickel, selenium, lead, and zinc were determined for source partition and heavy metal pollution index (HMPI) assessment during both the spring and winter seasons. Four important dominant factors in the principal component analysis depicted, in toto, 76.22 percent of the variance, with an initial eigenvalue greater than 1. The calculated HMPI for all the seasons and sampling stations was 66, ﬂuctuating between 30 and 170 in single water sampling sites. The results showed that almost all locations fall into the high classes of the HMPI, but below the critical pollution index limit of 100. Apart from the gold mining establishments and industrial factories, the reasons for the increased concentrations of metals in the Zarrineh River may be attributed in particular to anthropogenic and mining activities. INTRODUCTION Surface waters have attracted the attention of human societies, especially with respect to drinking water supplies. To take advantage of water resources, most agricultural, urban, and industrial centers have been established near rivers (Sánchez et al., 2007). Therefore, the monitoring of a drinking water source before and after water treatment is crucial, not only for establishing an accurate database with which to mini*Corresponding author email: maryam.khalilzadeh61@gmail.com

mize health hazards but also to provide knowledge of the physical, chemical, and microbial characteristics of water quality parameters. Population and economic growth inevitably lead to environmental pollution, which is caused by the discharge of urban and industrial sewage and agricultural waste, which in turn limits the use of increasingly scarce water resources. Among the materials that may enter the environment are heavy metals. These elements are natural components of the Earth’s crust, but human activities alter natural, geochemical, and biochemical cycles and change the balance of these metals in the environment. Heavy metal pollution in aquatic systems is a growing worldwide problem (Malik et al., 2010). As heavy metals have harmful effects on the riverine ecosystems, point sources of heavy metals should be monitored (Liao et al., 2017). Heavy metals and agricultural toxins were investigated in the Garasou River in Northwestern Iran to show the distribution of pollution (by minerals, organic nutrients, and heavy metals) in this river basin (Tchounwou et al., 2012). Many studies have been conducted on heavy metal pollution entering river resources (Förstner, 1980; Akoto et al., 2008). Since heavy metals combine with sediment and clay minerals, river water quality and river pollution monitoring have recently become important issues (Abdel-Satar et al., 2017). Mining and minerals are two of the most important sources of heavy metal pollution, along with the disposal of untreated effluents that contain toxic metals from different industries. In addition, the use of fertilizers, which include heavy metals, is an important problem in agricultural lands (Martin, 2000; Macklin et al., 2006; Nouri et al., 2008; and Varol and Şen, 2012). River quality assessment has become important because discharges of raw wastewater from industries have polluted water quality, especially in urban areas, where the water supply is a crucial factor (Smith et al., 1987; Sekabira et al., 2010). Changes in river water quality can result from natural processes as well as anthropogenic activities related to the effluents of agricultural drainage and industrial and domestic wastewater. When faced with a large volume of spatial and temporal water quality and

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

179

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

179

Poshtegal and Mirbagheri

ecological assessment data, the multivariable statistical approach can be used from different monitoring sites to detect the geochemical relationship between metals and controller elements that are the sources and origins of the metals. Many different statistical techniques comprising cluster analysis, principal component analysis, and factor analysis can be used to identify sources of pollution and to assess river water quality, as these are useful tools for the effective determination of sources of metal pollution (Li et al., 2008; Phung et al., 2015; Sharma et al., 2015; and Khan et al., 2016). To evaluate the suitability of the data, the Kaiser–Meyer–Olkin test (Li et al., 2013) was applied to the water quality data from this study. The heavy metal pollution index (HMPI) is a method for ranking the quality of water. The index is the single resultant effect of every heavy metal on water quality and is designed to determine the water quality’s effect on human health. If the index is greater than 100, the water is deemed hazardous to human health. The Zarrineh River is one of the largest rivers in the Urmia Lake Basin and one of the largest rivers in northwestern Iran. The source area is the mountains of Chehelcheshmeh in the Kurdistan Province. The river has an average discharge of about 2 × 109 m3 at its reservoir dam. The river provides the water supply to many cities. It is also a source for agricultural and drinking water in several cities in Kurdistan as well as the East and West Azerbaijan Provinces (Ghaheri et al., 1999; Alipour, 2006). Considering the important role of the Zarrineh River in the provision of the water supply, this study has attempted to determine the quality of the Zarrineh River, especially as it relates to metal pollution, in order to detect the origin of contamination. The identification of pollution sources in the river will therefore be useful in the provision of protective water quality control. The implementation of prevention programs will reduce the sources of pollution in the river (Paul, 2017). MATERIALS AND METHODS The Case Study Area The study was performed in the Zarrineh River Basin, one of the most important rivers of the Urmia Lake Basin. The main branches of the river are the Chamghoreh, Chamkhorkhoreh, and Sarough rivers. The Zarrineh River Basin area—from its branches to the entrance of the Urmia Lake—measures about 11,780 km2 . The climate of the study area is arid, but it includes a cold humid climate in the southern parts of the Highlands, with an annual average precipitation of 200 to 300 mm (Eimanifar and Mohebbi, 2007). The scope of the study (21 total sites) was 180

Poshtegal and Mirbagheri

classified into four domains: the Chamsaghez River, with six sampling stations (Stations Ch-1 to Ch-6); the Chamkhorkhoreh River, with three monitoring stations (Stations Kh-1 to Kh-3); the Jaghatooo River, with four sampling stations (Stations J-1 to J-4); and the Sarough River, with eight monitoring stations (Stations Sa-1 to Sa-8). The case study specifications and 21 sampling stations are described in Figure 1 and Table 1, respectively. The sampling stations are located upstream of the Zarrineh Dam because of the key role played by the Zarrineh River in supplying drinking water to several cities and villages in Kurdistan and the East and West Azerbaijan provinces. Agricultural and industrial uses of the river water make the evaluation of water quality and contamination levels more significant. Sampling and Analysis In order to evaluate heavy metal pollution of water resources, the water samples were collected from 21 sites during the dry and wet seasons (spring and winter) in 2015 from the Zarrineh River. Polyethylene 1.5liter bottles were used for water sampling. The samples were covered by a sheet of aluminum foil and placed in plastic containers with a tight lid. Prior to sampling, the bottles were washed with acid, and the samples were rinsed three times with water before sampling. Water samples were stored at 4°C in ice containers until their arrival in the laboratory. Soon after the arrival at the laboratory, the water samples were passed through 0.45-µm filters, and the pH of the samples was reduced to approximately 2 by means of nitric acid (APHA, 1985). Heavy metal concentrations were measured by the coupled plasma–mass spectrometry method in a valid laboratory. Three replicate samples were collected at each site and were subsequently mixed in situ. If necessary, water samples were secured by special stabilizers (such as sulfuric acid and nitric acid) in fixed or hand-held sterile containers at temperatures below 4°C (without freezing), with maintenance and within 4 to 6 hours, and were sent to the laboratory to perform the necessary analysis. The water concentrations of aluminum (Al), iron (Fe), barium (Ba), chromium (Cr), copper (Cu), arsenic (As), manganese (Mn), nickel (Ni), selenium (Se), lead (Pb), and zinc (Zn) were determined. To determine the sources of the heavy metals, factor analysis was used. Factor analysis is a statistical method used to reduce the number of factors to prioritize the data in accordance with their degrees of importance. Principal component analysis (PCA) is a useful statistical technique for finding patterns in a large group of data. In other words, this technique identifies patterns in data collection and displays

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

Heavy Metal Pollution

Figure 1. The locations of the sampling stations.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

181

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

181

Station Ch-1 Ch-2 Ch-3 Ch-4 Ch-5 Ch-6 J-1 J-2 J-3 J-4 Kh-1 Kh-2 Kh-3 Sa-1 Sa-2 Sa-3 Sa-4 Sa-5 Sa-6 Sa-7 Sa-8

Poshtegal and Mirbagheri

Table 1. The case study sampling locations and site speciﬁcations.

Location

Altitude (m)

Geographical Position

Chamsaqez after the Tamugheh Village Ghamsaghez before Kileshin Village Ghamsaghez before the Veis Dam workplace Ghamsaghez before entering the Saqez Ghamsaghez exiting the Saqez Ghamsaghez entering the Shahid Kazemi Dam Jaghatooo after the Bastam Village Jaghatooo Talejar Village Jaghatooo Polgeshlagh Village Jaghatooo entering the Bookan Dam Chamkhorkhoreh Mahidar Olia Village Chamkhorkhoreh Gataloo Village Chamkhorkhoreh entering the Bookan Dam Entering Tekab City Downstream of Tekab City Madanchai upstream of Zarnikh Mine Aqh-Darreh upstream of Pooyazarkan factory Balacholeh Shirmard Village Balacholeh Tekab Road Gugerdchi hydrometric station (right branch Alasaggal) Sarough river before entering Bukan Dam

1,526 1,717 1,540 1,475 1,458 1,441 1,730 1,540 1,475 1,435 1,600 1,530 1,438 1,834 1,757 2,260 1,867 1,837 1,716 1,725 1,442

N:36°12� 03�� , E:46°07� 14�� N:36°05� 52�� , E:46°00� 20�� N:36°11� 09.3�� , E:46°06� 34.1�� N:36°13� 35�� , E:46°15� 52.7�� N:36°15� 18.3�� , E:46°18� 10�� N:36°18� 25.05�� , E:46°23� 05.8�� N:35°48� 02�� , E:46°24� 20�� N:36°00� 53.3�� , E:46°18� 38.1�� N:36°05� 59.7�� , E:46°20� 50.5�� N:36°12� 31.5�� , E:46°25� 53.4�� N:35°59� 27.8�� , E:46°28� 38.5�� N:36°05� 22.1�� , E:46°29� 47.6�� N:36°17� 28�� , E:46°35� 48.7�� N:36°22� 24.7�� , E:46°08� 00.3�� N:36°26� 18.4�� , E:47°05� 25.2�� N:36°43� 21.3�� , E:47°07� 27.8�� N:36°36� 53.8�� , E:47°05� 29.6�� N:36°35� 54.9�� , E:47°05� 32.7�� N:36°28� 13.3�� , E:47°01� 38.1�� N:36°23� 59.7�� , E:47°06� 13.6�� N:36°24� 00.9�� , E:46°39� 18 .7��

data in a way that highlights similarities and diﬀerences (Sharma, 1995; Vega et al., 1998; and Marec et al., 2008). PCA was applied on decreasing standardized data sets to describe information about correlations among variables analyzed in the water samples (Mahlknecht et al., 2004; Srivastava and Ramanathan, 2008). Khan et al. (2016) used PCA to investigate the water quality and spatial variability of the Ramganga River and its tributaries in the Ganga Basin, India. Sharma et al. (2015) studied seasonal variation, by correlation analysis (CA) and PCA and CA components, to assess sources of pollution in the Ganga and Yamuna rivers in Uttarakhand State, India (Sharma et al., 2015). Other researchers have used PCA in order to determine the pollution source and classify the sampling sites (Vega et al., 1998; Alberto et al., 2001; Simeonov et al., 2003; Zhou et al., 2007; Bouza-Deaño et al., 2008; Hai et al., 2009; and Kazi et al., 2009). These researchers showed that the statistical methods, in particular PCA, deﬁne the relationship between water quality parameters and environmental indicators, distinguishing the sources of pollution and grouping monitoring stations into clusters with similar characteristics. Therefore, PCA was selected for the present study to assess the water quality in the Zarrineh River.

Pollution Factor In order to determine river pollution by heavy metals, the contamination index was used. 182

Station

where wi represents the weight ratio of the elements evaluated; Qi represents the sub-index element; and n represents the number of parameters. The subindex Qi is represented as follows: n [Mi − Ii ] × 100. Qi = Si − Ii

(2)

i=1

In this equation, Mi represents the concentration of the measured element, Ii represents the ideal concentration of the element according to the Prasad and Bose (2001), and Si is the standard value taken from the World Health Organization (WHO) guidelines for the assessment of elements. This index predicts the result of the eﬀect of individual heavy metals on water quality and human health. If the index is greater than 100, the water is hazardous to human health. At a value of 100, the threshold of human health relative to water pollution has been reached. If the index is below 100, there is no signiﬁcant heavy metal pollution, and there is much less of a cause of concern for consumers of the water.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

Ch-1 Ch-2 Ch-3 Ch-4 Ch-5 Ch-6 J-1 J-2 J-3 J-4 Kh-1 Kh-2 Kh-3 Sa-1 Sa-2 Sa-3 Sa-4 Sa-5 Sa-6 Sa-7 Sa-8

Location

Altitude (m)

Geographical Position

1,526 1,717 1,540 1,475 1,458 1,441 1,730 1,540 1,475 1,435 1,600 1,530 1,438 1,834 1,757 2,260 1,867 1,837 1,716 1,725 1,442

Pollution Factor In order to determine river pollution by heavy metals, the contamination index was used. 182

The calculation of the HMPI is one of the best methods by which to represent the whole water quality bases on heavy metals (Mohan et al., 1996; Tamasi and Cini, 2004).To measure the pollution index, the following relations are used (Prasad and Bose, 2001): n i=1 wi Qi , (1) HMPI = n i=1 wi where wi represents the weight ratio of the elements evaluated; Qi represents the sub-index element; and n represents the number of parameters. The subindex Qi is represented as follows: n [Mi − Ii ] × 100. Qi = Si − Ii

(2)

i=1

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

Heavy Metal Pollution

Table 2. Concentration of dissolved metals (µg/L) in the Zarrineh River, Iran.

Spring Metal Al Fe Ba Cr Cu As Mn Ni Se Pb Zn

Winter

Spring

Minimum

Maximum

Average

Minimum

Maximum

Average

0.010 0.009 0.029 0.005 0.012 0.005 0.016 0.002 0.094 0.012 0.010

0.980 0.503 0.440 0.028 0.035 0.110 0.599 0.082 0.100 0.020 0.075

0.093 0.202 0.108 0.021 0.019 0.025 0.099 0.022 0.097 0.016 0.027

0.03 0.15 0.025 0.005 0.01 0.004 0.02 0.0023 0.008 0.007 0.009

1.18 1.28 0.195 0.028 0.033 0.096 0.24 0.082 0.01 0.095 0.074

0.368 0.415 0.062 0.020 0.017 0.024 0.098 0.022 0.009 0.015 0.026

RESULTS AND DISCUSSION

and standards, are illustrated in Table 3. The results show that the total concentrations of the heavy metals presented in this research include the mean values of 0.066 and 0.090 µg/L in spring and winter, respectively. Elemental concentrations were higher in spring when compared to winter, regardless of the location.

The accumulated concentrations of heavy metals and the results of the analysis from 21 sampling stations in winter and spring (wet and dry seasons) are presented in Table 2. In the winter months, the concentrations of a number of the metals in river water— Al, Fe, As, Mn, and Pb—were higher than the drinking water standards at several locations. In the spring, concentrations of several heavy metals, such as As, Mn, Se, and Pb, were higher than the drinking water standards. The drinking water guidelines of the WHO (WHO, 2009), U.S. Environmental Protection Agency (USEPA) (USEPA, 2009), and the Institute of Standards and Industrial Research of Iran (ISIRI, 2010) were applied for the purpose of comparison (Table 3). The mean concentrations of Al and Se in spring exceeded the USEPA water quality criteria. In both seasons, the amount of Cu exceeded the criterion of maximum concentrations, and Cr in both seasons exceeded not only the criterion of the continuous concentration range but also the criteria of maximum concentrations. The mean concentrations of heavy metals and variations in the metals of Zarrineh River for spring and winter, and a comparison with the relative guidelines

Water Quality Parameters The physico-chemical parameters of the water, such as dissolved oxygen (DO), pH, specific conductivity (Ec), and temperature, are presented in Table 4. The physico-chemical parameters are important, as they have a significant role with regard to the water quality, which influences the aquatic life through degradation of water quality. Temperature is one of the most important elements that influences the aquatic ecology (Huet and Timmermans, 1986). The average pH values were 8.10 and 7.37 during spring and winter, respectively. The highest value of DO was recorded during winter and may have been because the temperature in this season was low (Williams, 1985), as the solubility of oxygen increases with a decrease in temperature (Singh et al., 1990). The DO ranged between 2.96 and

Table 3. Metal concentrations (µg/L) and variations in the Zarrineh River and comparison with international guidelines and standards.

Metal Al Fe Ba Cr Cu As Mn Ni Se Pb Zn

Winter

Minimum

Maximum

Average

Minimum

Maximum

Average

0.010 0.009 0.029 0.005 0.012 0.005 0.016 0.002 0.094 0.012 0.010

0.980 0.503 0.440 0.028 0.035 0.110 0.599 0.082 0.100 0.020 0.075

0.093 0.202 0.108 0.021 0.019 0.025 0.099 0.022 0.097 0.016 0.027

0.03 0.15 0.025 0.005 0.01 0.004 0.02 0.0023 0.008 0.007 0.009

1.18 1.28 0.195 0.028 0.033 0.096 0.24 0.082 0.01 0.095 0.074

0.368 0.415 0.062 0.020 0.017 0.024 0.098 0.022 0.009 0.015 0.026

RESULTS AND DISCUSSION The accumulated concentrations of heavy metals and the results of the analysis from 21 sampling stations in winter and spring (wet and dry seasons) are presented in Table 2. In the winter months, the concentrations of a number of the metals in river water— Al, Fe, As, Mn, and Pb—were higher than the drinking water standards at several locations. In the spring, concentrations of several heavy metals, such as As, Mn, Se, and Pb, were higher than the drinking water standards. The drinking water guidelines of the WHO (WHO, 2009), U.S. Environmental Protection Agency (USEPA) (USEPA, 2009), and the Institute of Standards and Industrial Research of Iran (ISIRI, 2010) were applied for the purpose of comparison (Table 3). The mean concentrations of Al and Se in spring exceeded the USEPA water quality criteria. In both seasons, the amount of Cu exceeded the criterion of maximum concentrations, and Cr in both seasons exceeded not only the criterion of the continuous concentration range but also the criteria of maximum concentrations. The mean concentrations of heavy metals and variations in the metals of Zarrineh River for spring and winter, and a comparison with the relative guidelines

Mean Spring Winter Drinking water quality guidelines Drinking water quality, ISIRI:1053 Drinking water quality, WHO Drinking water quality, USEPA

0.093 0.368

0.7 0.7 2

0.05 1 0.05 2 0.100 1.3

0.01 0.01 0.10

0.01 0.4 0.50

0.07 0.07 0.10

The physico-chemical parameters of the water, such as dissolved oxygen (DO), pH, specific conductivity (Ec), and temperature, are presented in Table 4. The physico-chemical parameters are important, as they have a significant role with regard to the water quality, which influences the aquatic life through degradation of water quality. Temperature is one of the most important elements that influences the aquatic ecology (Huet and Timmermans, 1986). The average pH values were 8.10 and 7.37 during spring and winter, respectively. The highest value of DO was recorded during winter and may have been because the temperature in this season was low (Williams, 1985), as the solubility of oxygen increases with a decrease in temperature (Singh et al., 1990). The DO ranged between 2.96 and

Metals in Zarrineh River (µg/L) Se

Reference

0.202 0.108 0.021 0.019 0.025 0.099 0.022 0.097 0.016 0.027 0.415 0.062 0.020 0.017 0.024 0.098 0.022 0.009 0.015 0.026

0.2 0.3 0.3 2 0.05–0.2 0.3

Water Quality Parameters

Table 3. Metal concentrations (µg/L) and variations in the Zarrineh River and comparison with international guidelines and standards.

Metals in Zarrineh River (µg/L) Parameter

0.01 0.04 0.50

0.01 0

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

3 3 5

ISIRI (2010) WHO (2006) USEPA (2009)

183

Parameter

Mean Spring Winter Drinking water quality guidelines Drinking water quality, ISIRI:1053 Drinking water quality, WHO Drinking water quality, USEPA

0.093 0.368

Reference

0.202 0.108 0.021 0.019 0.025 0.099 0.022 0.097 0.016 0.027 0.415 0.062 0.020 0.017 0.024 0.098 0.022 0.009 0.015 0.026

0.2 0.3 0.3 2 0.05–0.2 0.3

0.7 0.7 2

0.05 1 0.05 2 0.100 1.3

0.01 0.01 0.10

0.01 0.4 0.50

0.07 0.07 0.10

0.01 0.04 0.50

0.01 0

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

3 3 5

ISIRI (2010) WHO (2006) USEPA (2009)

183

Poshtegal and Mirbagheri

Table 4. Water quality parameters of the Zarrineh River, Iran.

Station

Water Temperature (°C)

Air Temperature (°C)

Ch-1 Ch-2 Ch-3 Ch-4 Ch-5 Ch-6 J-1 J-2 J-3 J-4 Kh-1 Kh-2 Kh-3 Sa-1 Sa-2 Sa-3 Sa-4 Sa-5 Sa-6 Sa-7 Sa-8

Sp 21.0 21.1 19.8 22.3 23.0 22.3 22.0 22.8 23.0 23.7 20.0 22.0 23.0 17 19.2 14.3 18.9 20.5 17.3 22.7

Sp 28 27 27 28 29 32 24 27 28 28 22 24 27 19 23 13.2 19.4 25.1 22.2 25.5

Wi 2.1 1.2 1.5 4.3 4.4 6.8 4.6 3.2 5.3 8 1.4 2.4 10.2 3.5 5.7 2.2 1.5 3 2 2.2

Wi 4 1.6 3 4.6 7.1 6.9 3.8 3.2 1.8 8.9 1.9 3.9 8.8 5.3 4.4 2.7 3.7 4.9 3.9 2

pH Sp 8.6 7.15 9.02 8.45 8.24 7.86 8.23 8.25 8.23 8.51 8.19 8.2 8.12 7.87 7.87 7.99 7.58 7.75 7.89 7.99

DO (mg/L) Wi 7.33 6.9 7.51 7.25 7.2 7.21 7.17 7.15 7.64 7.43 6.87 7.04 7.25 8.36 8.38 6.84 7.23 7.14 7.83 7.63

Sp 8.16 7.51 7.75 7.94 2.96 6.27 8.25 7.37 7.53 8.69 8.77 8.57 8.54 6.9 7.1 7.2 7.3 5.2 6.1 8

Ec (μS/cm)

Wi 10.93 9.66 9.99 8.91 9.07 8.16 10.95 10.71 10.38 10.43 10.33 9.37 9.66 11.9 11.2 10 8.2 10.8 10.3 11.9

Sp 575 1,180 949 868 949 775 756 572 475 947 565 524 496 2,990 2,170 360 747 1,013 698 890

Wi 252 242 275 347 351 400 290 268 279 302 311 385 383 2490 1748 351 1182 961 885 937

Sp = spring season; Wi = winter season.

Station

Water Temperature (°C)

Air Temperature (°C)

Ch-1 Ch-2 Ch-3 Ch-4 Ch-5 Ch-6 J-1 J-2 J-3 J-4 Kh-1 Kh-2 Kh-3 Sa-1 Sa-2 Sa-3 Sa-4 Sa-5 Sa-6 Sa-7 Sa-8

Sp 21.0 21.1 19.8 22.3 23.0 22.3 22.0 22.8 23.0 23.7 20.0 22.0 23.0 17 19.2 14.3 18.9 20.5 17.3 22.7

Sp 28 27 27 28 29 32 24 27 28 28 22 24 27 19 23 13.2 19.4 25.1 22.2 25.5

Pearson correlation coefficient was used. Significant correlations were obtained during the study between Cu and Fe and Ba; Fe and Al; Ni and Al, Fe, Mn, Ba, Pb, and Cu; Cr and Cu; Zn and Cu and Ni; and Fe, Ba, Pb, Cu, and Ni (Table 4). However, it should be noted that in the determination of the metal source or origin, the single correlation analysis is not a sufficient tool; thus, other factors and a method of analysis should be considered. As mentioned earlier, PCA was used to find patterns in a large group of data sets, such as water quality monitoring studies. In order to determine the most

8.77 mg/L during spring and between 8.16 and 11.9 mg/L in winter. The lowest value of DO was observed during spring (dry) season, which is due to less river ﬂow coupled with the increase in temperature that leads to a decrease in DO due to the rate of oxygen consumption from aquatic organisms and the high rate of decomposition of organic matter (Ali et al., 2016). The average speciﬁc conductivity was recorded as 925 (mS) in spring and 632 (mS) in winter, respectively. To ensure the normal distribution of data, the Kolmogorov–Smirnov test was used. To determine the association between concentrations in samples, the

Table 5. Evaluation of the correlation matrix for several heavy metals in water samples of the Zarrineh River.

Sp 8.6 7.15 9.02 8.45 8.24 7.86 8.23 8.25 8.23 8.51 8.19 8.2 8.12 7.87 7.87 7.99 7.58 7.75 7.89 7.99

Wi 7.33 6.9 7.51 7.25 7.2 7.21 7.17 7.15 7.64 7.43 6.87 7.04 7.25 8.36 8.38 6.84 7.23 7.14 7.83 7.63

Sp 8.16 7.51 7.75 7.94 2.96 6.27 8.25 7.37 7.53 8.69 8.77 8.57 8.54 6.9 7.1 7.2 7.3 5.2 6.1 8

Ec (μS/cm)

Wi 10.93 9.66 9.99 8.91 9.07 8.16 10.95 10.71 10.38 10.43 10.33 9.37 9.66 11.9 11.2 10 8.2 10.8 10.3 11.9

Sp 575 1,180 949 868 949 775 756 572 475 947 565 524 496 2,990 2,170 360 747 1,013 698 890

Wi 252 242 275 347 351 400 290 268 279 302 311 385 383 2490 1748 351 1182 961 885 937

0.797** 1

0.029 0.110 1

− 0.176 − .0212 0.247 1

− 0.209 − 0.131 0.185 0.036 1

− 0.267 − 0.315* 0.152 0.389* 0.087 1

− 0.338* − .388* 0.319* 0.332* 0.353* 0.662** 1

− 0.031 − .018 0.118 0.148 − 0.055 0.370* − 0.024 1

− 0.136 − 0.201 − 0.099 0.080 0.120 0.622** 0.561** 0.190 1

− 0.251 − 0.310* 0.282 0.343* 0.511** 0.491** 0.811** 0.069 0.291 1

Correlation is signiﬁcant at the 0.05 level (two-tailed). Correlation is signiﬁcant at the 0.01 level (two-tailed).

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

Table 5. Evaluation of the correlation matrix for several heavy metals in water samples of the Zarrineh River.

184

Wi 4 1.6 3 4.6 7.1 6.9 3.8 3.2 1.8 8.9 1.9 3.9 8.8 5.3 4.4 2.7 3.7 4.9 3.9 2

DO (mg/L)

Sp = spring season; Wi = winter season.

Al Fe Mn Ba Pb Cu Ni Cr Zn As

Wi 2.1 1.2 1.5 4.3 4.4 6.8 4.6 3.2 5.3 8 1.4 2.4 10.2 3.5 5.7 2.2 1.5 3 2 2.2

Al Fe Mn Ba Pb Cu Ni Cr Zn As *

0.797** 1

0.029 0.110 1

− 0.176 − .0212 0.247 1

− 0.209 − 0.131 0.185 0.036 1

− 0.267 − 0.315* 0.152 0.389* 0.087 1

− 0.338* − .388* 0.319* 0.332* 0.353* 0.662** 1

− 0.031 − .018 0.118 0.148 − 0.055 0.370* − 0.024 1

− 0.136 − 0.201 − 0.099 0.080 0.120 0.622** 0.561** 0.190 1

− 0.251 − 0.310* 0.282 0.343* 0.511** 0.491** 0.811** 0.069 0.291 1

Correlation is signiﬁcant at the 0.05 level (two-tailed). Correlation is signiﬁcant at the 0.01 level (two-tailed).

184

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

Elements Al Fe Mn Ba Pb Cu Ni Cr Zn As Eigenvalues % Total variance Cumulative variance

Heavy Metal Pollution

Table 6. The results of a rotation matrix.*

PC1

PC2

PC3

PC4

− 0.554 − 0.592 0.276 0.486 0.429 0.783 0.891 0.210 0.586 0.802 3.592 35.915 35.915

0.691 0.706 0.590 0.196 0.077 0.170 0.141 0.268 0.049 0.208 1.535 15.348 51.264

0.085 0.005 − 0.345 0.061 − 0.597 0.438 − 0.133 0.607 0.465 − 0.314 1.380 13.800 65.064

0.267 0.212 − 0.415 − 0.565 0.259 0.048 0.204 − 0.313 0.537 0.104 1.117 11.165 76.229

For maximizing the variation among the variables under each factor, PCA with varimax rotation of standardized component loadings was conducted, and those PCs with eigenvalue of >1 were retained (Giri and Singh, 2014).

important parameters through which to explain the variance, eigenvalues were selected that were larger than 1, and the varimax rotation matrix analysis was used for the Zarrineh River samples (Table 6). This statistical technique was applied to describe information about correlations among the diﬀerent variables. For maximizing the variation among the variables under each factor, PCA with varimax rotation of standardized component loadings was conducted, and those principal components with eigenvalues of >1 were retained (Giri and Singh, 2014). The results showed that there are four eigenvalues greater than 1, which account for 76.22 percent of the total variance. These four factors were selected after the varimax rotation. Other factors were not considered in the statistical inference, since they include only a small percentage of the variance. Principal component index (PC1) includes 35.91 percent of the total variance that contains numerical value for Ni, As, and Cu—respectively, 0.891, 0.802, and 0.783. This variance is related to the Zarshuran and Aq-Darreh gold deposits in addition to mining activities located on the Sarough River in the present study. The concentration of As in water samples increased downstream because of the inﬂows from the major industrial sources. The second component, PC2, revealed that Fe, Al, and Mn account for 15.34 percent of the metals. The source of Fe can be related to the earth’s crust and the geological formations. The Al portion is also attributed to industrial sources, especially the granite, silica, and sand mining industries in the studied area (Bogradi, 1971). The Mn can be related to industries and fossil fuel electric power generation, heating, and commercial manufacturing (Malm et al., 1988). PC3 contained 13.80 percent of the total variance, including the numerical values 0.607 and 0.597 for Pb and Cr, re-

spectively. The increased concentrations of these metals may be related to anthropogenic sources, possibly plastic and rubber industrial wastes. Environmental pollution of Cr—in different forms—originated from its usage in the chemical industry, such as plastic and rubber production. PC4 explained 11.16 percent of the variance related to the Ba and Zn. To evaluate the quality of drinking water, the HMPI was calculated. Different HMPI calculations are presented in Table 7. The spring metal concentrations were about 71.4 in the water, as compared with those in winter, during which time they measured 64. Taking into consideration the two seasons and sampling sites, the HMPI value for the Zarrineh River was around 67.7, with regard to the mean concentrations of metals from all sample sites, according to Eqs. 1 and 2. Although the calculated HMPI value is below the critical value of 100, according to Prasad, it was higher than the range of Edet and Offiong in 2002. Hence, the quality of the Zarrineh River with respect to metal pollution remains a subject of concern, as its values are more than 30 (Edet and Offiong, 2002). Therefore, these results have delineated an alarming state of the water quality—considering the impact of all the metals on the total quality of the river— attributable to mineralization, mining, and industrial activities. Considering each station, the surface water HMPI for the Zarrineh River showed variation, ranging between 30 and 174. The lowest HMPI value was 30 at the headwaters of the Jaghatoo River at Station J-1, located near the village of Bastam. In comparison, the highest value was 174 for Sarough River at Station Sa-5, at Balacholeh, Shirmard Village, near the gold mining area (Modabberi and Moore, 2004). For all of the sampling sites for the two seasons, the HMPI values were higher than the critical value,

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

185

Elements Al Fe Mn Ba Pb Cu Ni Cr Zn As Eigenvalues % Total variance Cumulative variance

PC1

PC2

PC3

PC4

− 0.554 − 0.592 0.276 0.486 0.429 0.783 0.891 0.210 0.586 0.802 3.592 35.915 35.915

0.691 0.706 0.590 0.196 0.077 0.170 0.141 0.268 0.049 0.208 1.535 15.348 51.264

0.085 0.005 − 0.345 0.061 − 0.597 0.438 − 0.133 0.607 0.465 − 0.314 1.380 13.800 65.064

0.267 0.212 − 0.415 − 0.565 0.259 0.048 0.204 − 0.313 0.537 0.104 1.117 11.165 76.229

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

185

Poshtegal and Mirbagheri

Table 7. Values of heavy metal pollution index in the Zarrineh River.

Station

Location

Spring

Winter

Station

Location

Spring

Winter

Ch-1 Ch-2 Ch-3 Ch-4 Ch-5 Ch-6 J-1 J-2 J-3 J-4 Kh-1 Kh-2 Kh-3 Sa-1 Sa-2 Sa-3 Sa-4 Sa-5 Sa-6 Sa-7 Sa-8 Average

Chamsaqez after the Tamugheh Village Ghamsaghez, before Kileshin Village Ghamsaghez, before the Veis Dam workplace Ghamsaghez before entering the Saqez Ghamsaghez exiting the Saqez Ghamsaghez entering the Shahid Kazemi Dam Jaghatooo after the Bastam Village Jaghatooo, Talejar Village Jaghatooo, Polgeshlagh Village Jaghatooo entering the Bookan Dam Chamkhorkhoreh, Mahidar Olia Village Chamkhorkhoreh, Gataloo Village Chamkhorkhoreh entering the Bookan Dam Entering Tekab City Downstream of Tekab City Madanchai, upstream of Zarnikh Mine Aqh-Darreh, upstream of Pooyazarkan factory Balacholeh, Shirmard Village Balacholeh, Tekab Road Gugerdchi hydrometric station (right branch Alasaggal) Sarough river before entering Bukan Dam

53 51 49 45 50 52 42 37 39 53 50 42 54 67 91 111 209 174 128 55 86 73

45 38 72 40 72 67 30 32 31 49 39 39 53 64 83 114 153 40 151 48 108 65

Ch-1 Ch-2 Ch-3 Ch-4 Ch-5 Ch-6 J-1 J-2 J-3 J-4 Kh-1 Kh-2 Kh-3 Sa-1 Sa-2 Sa-3 Sa-4 Sa-5 Sa-6 Sa-7 Sa-8 Average

53 51 49 45 50 52 42 37 39 53 50 42 54 67 91 111 209 174 128 55 86 73

45 38 72 40 72 67 30 32 31 49 39 39 53 64 83 114 153 40 151 48 108 65

except at three locations on the Jaghatoo River during winter, at which values were below the critical level. It is noteworthy that the HMPIs for all the locations in both spring and winter were below the critical value of 100, except at the stations located on the Sarough River, speciﬁcally Stations Sa-3 and Sa-6 in both seasons, Station Sa-4 in winter, and Station Sa-8 in spring. Station Sa-5, located close to the Aghdareh gold mining site, had values higher than 100. CONCLUSIONS Heavy metal pollution indices depict the contamination of surface water resources. Regardless of the site and locality, the metal concentrations were reduced in winter in comparison to values in the spring because of the dilution eﬀect. The concentrations of metals were compared with the WHO, USEPA, and ISIRI standards. The results showed that metals such as As, Mn, Ni, Se, Pb, Al, and Fe measure higher than the standard limits. Attention should be paid to these metals in comparison with the other remaining metals, the concentrations of which were below the limit. The higher values of metals in the river not only convey the geological sources of metals but also indicate that anthropogenic activity should be considered in the Zarrineh River Basin. A high level of metal pollution was observed in a few locations near manufacturing and gold mining sites. PCA analyses indicated that both anthropogenic and natural sources contribute to the el186

evated concentrations of heavy metals in the Zarriheh River. Thus, it can be concluded that heavy metal concentrations in the river water will be largely due not just to mining activities but also to the eﬄuents of urban and industrial waste into the river. Therefore, regular monitoring of water resources and environmental pollutants—such as the accumulation of heavy metals—is strongly recommended to protect ecological and public health. The results of this study can help environmental and drinking water experts plan ways to reduce the cost of water resource management strategies. These water quality results can guide protection of the future surface water quality in the basin of the Zarrineh River, one of the most important rivers in the Urmia Lake Basin supplying drinking water to many cities in the area. REFERENCES Abdel-Satar, A. M.; Ali, M. H.; and Goher, M. E., 2017, Indices of water quality and metal pollution of Nile River, Egypt: Egyptian Journal Aquatic Research, Vol. 43, No. 1, pp. 21–29. Akoto, O.; Bruce, T. N.; and Darko, D., 2008, Heavy metals pollution proﬁles in streams serving the Owabi reservoir: African Journal Environmental Science Technology, Vol. 2, No. 11, pp. 354–359. Alberto, W. D.; del Pilar, D. M.; Valeria, A. M.; Fabiana, P. S.; Cecilia, H. A.; and de los Ángeles, B. M., 2001, Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía River Basin (Córdoba–Argentina): Water Research, Vol. 35, No. 12, pp. 2881–2894.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

Heavy Metal Pollution Ali, M. M.; Ali, M. L.; Islam, M. S.; and Rahman, M. Z., 2016, Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh: Environmental Nanotechnology, Monitoring Management, Vol. 5, pp. 27–35. Alipour, S., 2006, Hydrogeochemistry of seasonal variation of Urmia salt lake, Iran: Saline Systems, Vol. 2, No. 1, p. 9. American Public Health Association (APHA), American Water Works Association, Water Environment Federation, 1985, Standard Methods for the Examination of Water and Wastewater: APHA, Washington, DC. Bogradi, E., 1971, The vanadium content of Hungarian bauxites and its utilization. In Proceedings of the 2nd International Symposium of ICSOBA: Vol. 3, pp. 349–358. Bouza-Deaño, R.; Ternero-Rodríguez, M.; and FernándezEspinosa, A. J., 2008, Trend study and assessment of surface water quality in the Ebro River (Spain): Journal Hydrology, Vol. 361, No. 3–4, pp. 227–239. Edet, A. E. and Offiong, O. E., 2002, Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, Lower Cross River Basin (southeastern Nigeria): GeoJournal, Vol. 57, No. 4, pp. 295–304. Eimanifar, A. and Mohebbi, F., 2007, Urmia Lake (northwest Iran): A brief review: Saline Systems, Vol. 3, No. 1, p. 5. Förstner, U., 1980, Inorganic pollutants, particularly heavy metals in estuaries. In Olausson, E. and Cato, I. (Editors), Chemistry and Biochemistry of Estuaries: John Wiley & Sons Ltd., Chichester, U.K. pp. 307–348. Ghaheri, M.; Baghal-Vayjooee, M. H.; and Naziri, J., 1999, Lake Urmia, Iran: A summary review: International Journal Salt Lake Research, Vol. 8, No. 1, pp. 19–22. Giri, S. and Singh, A. K., 2014, Assessment of surface water quality using heavy metal pollution index in Subarnarekha River, India: Water Quality, Exposure Health, Vol. 5, No. 4, pp. 173–182. Hai, X. U.; Lin-Zhang, Y. A. N. G.; Geng-Mao, Z. H. A. O.; JiaGuo, J. I. A. O.; Shi-Xue, Y. I. N.; and Zhao-Pu, L. I. U., 2009, Anthropogenic impact on surface water quality in Taihu Lake region, China: Pedosphere, Vol. 19, No. 6, pp. 765–778. Huet, M. and Timmermans, J. A., 1986, Textbook of Fish Culture. Breeding and Cultivation of Fish, 2nd ed.: Fishing News Books Ltd, West Byfleet, Surrey and London. Institute of Standards and Industrial Research of Iran (ISIRI), 2010, Drinking water—physical and chemical specifications, 1053, 5th ed.: Institute of Standards and Industrial Research of Iran, Tehran, Iran. Electronic document, available at http://www.isiri.gov.ir/ Kazi, T. G.; Arain, M. B.; Jamali, M. K.; Jalbani, N.; Afridi, H. I.; Sarfraz, R. A.; and Shah, A. Q., 2009, Assessment of water quality of polluted lake using multivariate statistical techniques: A case study: Ecotoxicology Environmental Safety, Vol. 72, No. 2, pp. 301–309. Khan, M. Y. A.; Gani, K. M.; and Chakrapani, G. J., 2016, Assessment of surface water quality and its spatial variation. A case study of Ramganga River, Ganga Basin, India: Arabian Journal Geosciences, Vol. 9, No. 1, p. 28. Li, C.; Lu, F. Y.; Zhang, Y.; Liu, T. W.; and Hou, W., 2008, Spatial distribution characteristics of heavy metals in street dust in Shenyang city: Ecol Environ, Vol. 17, No. 2, pp. 560–564. Li, F.; Huang, J.; Zeng, G.; Yuan, X.; Li, X.; Liang, J.; and Bai, B., 2013, Spatial risk assessment and sources identification of heavy metals in surface sediments from the Dongting Lake, Middle China: Journal Geochemical Exploration, Vol. 132, pp. 75–83.

Heavy Metal Pollution

Liao, J.; Chen, J.; Ru, X.; Chen, J.; Wu, H.; and Wei, C., 2017, Heavy metals in river surface sediments affected with multiple pollution sources, South China: Distribution, enrichment and source apportionment: Journal Geochemical Exploration, Vol. 176, pp. 9–19. Macklin, M. G.; Brewer, P. A.; Hudson-Edwards, K. A.; Bird, G.; Coulthard, T. J.; Dennis, I. A.; and Turner, J. N., 2006, A geomorphological approach to the management of rivers contaminated by metal mining: Geomorphology, Vol. 79, No. 3–4, pp. 423–447. Mahlknecht, J.; Steinich, B.; and De León, I. N., 2004, Groundwater chemistry and mass transfers in the Independence aquifer, central Mexico, by using multivariate statistics and mass-balance models: Environmental Geology, Vol. 45, No. 6, pp. 781–795. Malik, N.; Biswas, A. K.; Qureshi, T. A.; Borana, K.; and Virha, R., 2010, Bioaccumulation of heavy metals in fish tissues of a freshwater lake of Bhopal: Environmental Monitoring Assessment, Vol. 160, No. 1–4, p. 267. Malm, O.; Pfeiffer, W. C.; Fiszman, M.; and Azcue, J. M., 1988, Transport and availability of heavy metals in the Paraíba do Sul-Guandu River system, Rio de Janeiro state, Brazil: Science Total Environment, Vol. 75, No. 2–3, pp. 201–209. Marec, A.; Thomas, J. H.; and El Guerjouma, R., 2008, Damage characterization of polymer-based composite materials: Multivariable analysis and wavelet transform for clustering acoustic emission data: Mechanical Systems Signal Processing, Vol. 22, No. 6, pp. 1441–1464. Martin, C. W., 2000, Heavy metal trends in floodplain sediments and valley fill, River Lahn, Germany: Catena, Vol. 39, No. 1, pp. 53–68. Modabberi, S. and Moore, F., 2004, Environmental geochemistry of Zarshuran Au-As deposit, NW Iran: Environmental Geology, Vol. 46, No. 6–7, pp. 796–807. Mohan, S. V.; Nithila, P.; and Reddy, S. J., 1996, Estimation of heavy metals in drinking water and development of heavy metal pollution index: Journal Environmental Science Health Part A, Vol. 31, No. 2, pp. 283–289. Nouri, J.; Mahvi, A. H.; Jahed, G. R.; and Babaei, A. A., 2008, Regional distribution pattern of groundwater heavy metals resulting from agricultural activities: Environmental Geology, Vol. 55, No. 6, pp. 1337–1343. Paul, D., 2017, Research on heavy metal pollution of river Ganga: A review: Annals Agrarian Science, Vol. 15, No. 2, pp. 278–286. Phung, D.; Huang, C.; Rutherford, S.; Dwirahmadi, F.; Chu, C.; Wang, X.; and Dinh, T. A. D., 2015, Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: A study in Can Tho City, a Mekong Delta area, Vietnam: Environmental Monitoring Assessment, Vol. 187, No. 5, pp. 229. Prasad, B. and Bose, J., 2001, Evaluation of the heavy metal pollution index for surface and spring water near a limestone mining area of the lower Himalayas: Environmental Geology, Vol. 41, No. 1–2, pp. 183–188. Sánchez, E.; Colmenarejo, M. F.; Vicente, J.; Rubio, A.; García, M. G.; Travieso, L.; and Borja, R., 2007, Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution: Ecological Indicators, Vol. 7, No. 2, pp. 315–328. Sekabira, K.; Origa, H. O.; Basamba, T. A.; Mutumba, G.; and Kakudidi, E., 2010, Assessment of heavy metal pollution in the urban stream sediments and its tributaries: International Journal Environmental Science Technology, Vol. 7, No. 3, pp. 435–446.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

187

Ali, M. M.; Ali, M. L.; Islam, M. S.; and Rahman, M. Z., 2016, Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh: Environmental Nanotechnology, Monitoring Management, Vol. 5, pp. 27–35. Alipour, S., 2006, Hydrogeochemistry of seasonal variation of Urmia salt lake, Iran: Saline Systems, Vol. 2, No. 1, p. 9. American Public Health Association (APHA), American Water Works Association, Water Environment Federation, 1985, Standard Methods for the Examination of Water and Wastewater: APHA, Washington, DC. Bogradi, E., 1971, The vanadium content of Hungarian bauxites and its utilization. In Proceedings of the 2nd International Symposium of ICSOBA: Vol. 3, pp. 349–358. Bouza-Deaño, R.; Ternero-Rodríguez, M.; and FernándezEspinosa, A. J., 2008, Trend study and assessment of surface water quality in the Ebro River (Spain): Journal Hydrology, Vol. 361, No. 3–4, pp. 227–239. Edet, A. E. and Offiong, O. E., 2002, Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, Lower Cross River Basin (southeastern Nigeria): GeoJournal, Vol. 57, No. 4, pp. 295–304. Eimanifar, A. and Mohebbi, F., 2007, Urmia Lake (northwest Iran): A brief review: Saline Systems, Vol. 3, No. 1, p. 5. Förstner, U., 1980, Inorganic pollutants, particularly heavy metals in estuaries. In Olausson, E. and Cato, I. (Editors), Chemistry and Biochemistry of Estuaries: John Wiley & Sons Ltd., Chichester, U.K. pp. 307–348. Ghaheri, M.; Baghal-Vayjooee, M. H.; and Naziri, J., 1999, Lake Urmia, Iran: A summary review: International Journal Salt Lake Research, Vol. 8, No. 1, pp. 19–22. Giri, S. and Singh, A. K., 2014, Assessment of surface water quality using heavy metal pollution index in Subarnarekha River, India: Water Quality, Exposure Health, Vol. 5, No. 4, pp. 173–182. Hai, X. U.; Lin-Zhang, Y. A. N. G.; Geng-Mao, Z. H. A. O.; JiaGuo, J. I. A. O.; Shi-Xue, Y. I. N.; and Zhao-Pu, L. I. U., 2009, Anthropogenic impact on surface water quality in Taihu Lake region, China: Pedosphere, Vol. 19, No. 6, pp. 765–778. Huet, M. and Timmermans, J. A., 1986, Textbook of Fish Culture. Breeding and Cultivation of Fish, 2nd ed.: Fishing News Books Ltd, West Byfleet, Surrey and London. Institute of Standards and Industrial Research of Iran (ISIRI), 2010, Drinking water—physical and chemical specifications, 1053, 5th ed.: Institute of Standards and Industrial Research of Iran, Tehran, Iran. Electronic document, available at http://www.isiri.gov.ir/ Kazi, T. G.; Arain, M. B.; Jamali, M. K.; Jalbani, N.; Afridi, H. I.; Sarfraz, R. A.; and Shah, A. Q., 2009, Assessment of water quality of polluted lake using multivariate statistical techniques: A case study: Ecotoxicology Environmental Safety, Vol. 72, No. 2, pp. 301–309. Khan, M. Y. A.; Gani, K. M.; and Chakrapani, G. J., 2016, Assessment of surface water quality and its spatial variation. A case study of Ramganga River, Ganga Basin, India: Arabian Journal Geosciences, Vol. 9, No. 1, p. 28. Li, C.; Lu, F. Y.; Zhang, Y.; Liu, T. W.; and Hou, W., 2008, Spatial distribution characteristics of heavy metals in street dust in Shenyang city: Ecol Environ, Vol. 17, No. 2, pp. 560–564. Li, F.; Huang, J.; Zeng, G.; Yuan, X.; Li, X.; Liang, J.; and Bai, B., 2013, Spatial risk assessment and sources identification of heavy metals in surface sediments from the Dongting Lake, Middle China: Journal Geochemical Exploration, Vol. 132, pp. 75–83.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

187

Poshtegal and Mirbagheri Sharma, M.; Kansal, A.; Jain, S.; and Sharma, P., 2015, Application of multivariate statistical techniques in determining the spatial temporal water quality variation of Ganga and Yamuna Rivers present in Uttarakhand State, India: Water Quality, Exposure Health, Vol. 7, No. 4, pp. 567–581. Sharma, S., 1995, Applied Multivariate Techniques: John Wiley & Sons, Inc., New York. Simeonov, V.; Stratis, J. A.; Samara, C.; Zachariadis, G.; Voutsa, D.; Anthemidis, A.; and Kouimtzis, T., 2003, Assessment of the surface water quality in Northern Greece: Water Research, Vol. 37, No. 17, pp. 4119–4124. Singh, C. S.; Sharma, A. P.; and Deorani, B. P., 1990, Limnological studies for bioenergetics transformation in a Tarai reservoir, Nanak Sagar (UP): Advances Limnology, pp. 356–362. Smith, R. A.; Alexander, R. B.; and Wolman, M. G., 1987, Water-quality trends in the nation’s rivers: Science, Vol. 235, No. 4796, pp. 1607–1615. Srivastava, S. K. and Ramanathan, A. L., 2008, Geochemical assessment of groundwater quality in vicinity of Bhalswa landﬁll, Delhi, India, using graphical and multivariate statistical methods: Environmental Geology, Vol. 53, No. 7, pp. 1509–1528. Tamasi, G. and Cini, R., 2004, Heavy metals in drinking waters from Mount Amiata (Tuscany, Italy). Possible risks from ar-

188

senic for public health in the Province of Siena: Science Total Environment, Vol. 327, No. 1–3, pp. 41–51. Tchounwou, P. B.; Yedjou, C. G.; Patlolla, A. K.; and Sutton, D. J., 2012, Heavy metal toxicity and the environment. In Molecular, Clinical and Environmental Toxicology: Springer, Basel, Switzerland. pp. 133–164. U.S. Environmental Protection Agency (USEPA), 2009, National Recommended Water Quality Criteria: United States Environmental Protection Agency, Office of Water, Office of Science and Technology. Springfield, VA. Varol, M. and Şen, B., 2012, Assessment of nutrient and heavy metal contamination in surface water and sediments of the upper Tigris River, Turkey: Catena, Vol. 92, pp. 1–10. Vega, M.; Pardo, R.; Barrado, E.; and Debán, L., 1998, Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis: Water Research, Vol. 32, No. 12, pp. 3581–3592. Williams, W. D., 1985, Biotic adaptations in temporary lentic waters, with special reference to those in semi-arid and arid regions: Hydrobiologia, Vol. 125, No. 1, pp. 85–110. Zhou, F.; Liu, Y.; and Guo, H., 2007, Application of multivariate statistical methods to water quality assessment of the watercourses in Northwestern New Territories, Hong Kong: Environmental Monitoring Assessment, Vol. 132, No. 1–3, pp. 1–13.

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188

188

Environmental & Engineering Geoscience, Vol. XXV, No. 2, May 2019, pp. 179–188