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Cover photo Head scarp of an active landslide in colluvium near the Tanana River in Interior Alaska. Photo by Margaret Darrow. See article on page 255.
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Environmental & Engineering Geoscience Volume 28, Number 3, August 2022 Table of Contents 237
Land Subsidence Due to Creep of the Gulf Coast Aquifer System in the Houston-Galveston Region Yi Liu and Jiang Li
255
Loess Is More: Field Investigation and Slope Stability Analysis of the Tanana 440 Landslide, Interior Alaska Jaimy A. Schwarber, Margaret M. Darrow, Ronald P. Daanen, and De Anne S. P. Stevens
275
Factors Affecting Shrinkage Crack Development in Clay Soils: An Experimental Study Chinmay V. Lokre, Abdul Shakoor, and Neil A. Wells
293
Site Selection for Municipal Solid Waste Landfill: Case Study of Artvin, Turkey Halil Akinci and Kazim Onur Demirarslan Technical Note
311
Treatment and Control of Urban Sewage with Excessive Heavy Metals for Ecological Environment Protection Lisi Zhu
317
Landscape and Ecological Foundations for the Organization of Regional Systems of Special Protected Areas Jianmei Wang, Bao Yu, and Lina Niu Book Review
325
Applied Multidimensional Geological Modeling: Informing Sustainable Human Interactions with the Shallow Subsurface Jeffrey R. Keaton
329
Geology of National Parks, Seventh Edition Greg M. Stock
331
Roadside Geology of Northern and Central California, Second Edition Robert Anderson
333
Mass Extinctions, Volcanism, and Impacts: New Developments: Geological Society of America Special Paper 544 Robert Anderson
Open Access Article
Land Subsidence Due to Creep of the Gulf Coast Aquifer System in the Houston-Galveston Region YI LIU* JIANG LI Morgan State University, Department of Civil Engineering, 5201 Perring Pkwy, Baltimore, MD 21251
Key Terms: Subsidence, Coastal Aquifers, Secondary Consolidation/Creep, Groundwater Management, United States ABSTRACT The compaction measurements of Quaternary and Tertiary Gulf Coast aquifer system sediments in the Houston-Galveston region (TX) show spatially variable compression of 0.08 to 8.49 mm/yr because of geohistorical overburden pressure when groundwater levels in the aquifer system were stable after about the year 2000. An aquifer-system creep equation is developed for evaluating this variable compression, with a thicknessweighted average creep coefficient based on Taylor’s (1942) secondary consolidation theory. The temporal variation of aquifer system creep can be neglected in a short-term observation period (such as a decade) after a long-term creep period (such as over 1,000 years) in geohistory. The creep coefficient of the Gulf Coast aquifer system is found to be in a range of 8.74 × 10−5 to 3.94 × 10−3 (dimensionless), with an average of 1.38 × 10−3 . Moreover, for silty clay or clay-dominant aquitards in the Gulf Coast aquifer system the creep coefficient value varies in the range of 2.21 × 10−4 to 3.94 × 10−3 , which is consistent with values found by Mesri (1973) for most soils, which vary in the range of creep coefficient, 1 × 10−4 to 5 × 10−3 . Land subsidence due to secondary consolidation of the Gulf Coast aquifer system is estimated to be 0.04 to 4.33 m in the 20th century and is projected to be 0.01 to 0.64 m in the 21st century at the 13 borehole extensometer locations in the HoustonGalveston region. The significant creep should be considered in the relative sea level rise, in addition to tectonic subsidence and primary consolidation. INTRODUCTION The Houston-Galveston region (HGR) in Texas, comprising Harris, Galveston, Fort Bend, Montgomery, Brazoria, Liberty, San Jacinto, Walker, *Corresponding author email: yi.liu@morgan.edu
Grimes, Waller, and Chambers Counties (Figure 1), has been one of the largest areas of land subsidence (LS) in the United States. It was in the early 1900s that the Houston area began to see the first true signs of human-induced LS–initially attributed to the extraction of oil gas from beneath the surface (Pratt and Johnson, 1926). The Houston area has been subsiding because of the combined effects of groundwater withdrawal, hydrocarbon extraction, salt dome movement, and faulting (Qu et al., 2015). By 1979, 3 m of subsidence had occurred in the HGR, and approximately 8,288 km2 of land had subsided more than 0.3 m (Coplin and Galloway, 1999; Kasmarek et al., 2015). Over southeastern Harris County, the maximum subsidence reached 4 m during the 1915–1917 and 2001 periods (Kasmarek et al., 2009). LS caused by fluid withdrawals was first documented in the HGR in the Goose Creek oil field in southeastern Harris County (Pratt and Johnson, 1926). Subsidence continued throughout the 20th century as a result of groundwater withdrawal that depressurized the major aquifers in this area, thereby resulting in the compaction of the aquifer sediment (Kasmarek et al., 2010). Historically, groundwater has been the primary water source for industrial use, municipal supply, and irrigation, and groundwater use in the HGR had sharply increased for a few decades to meet the needs of the rapidly growing population (Seifert and Drabek, 2006). In addition, the complex geologic setting, laterally diverse subsurface hydrological units, regional faults, hydrocarbon extraction, and salt dome movement in the HGR (Coplin and Galloway, 1999) have made it difficult to characterize the source(s) of the observed LS (Qu et al., 2015). A network of 13 discrete borehole extensometers was installed within the HGR to monitor groundwater-level changes and measure accumulated clay compaction to better understand the extent and magnitude of the regional subsidence in the 1970s (Kasmarek et al., 2010). It was identified that most of the subsidence in the HGR has occurred as a direct result of groundwater withdrawals that depressurized and dewatered the Chicot and Evangeline
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Figure 1. Hydrogeologic section A–A of the Gulf Coast aquifer system in Grimes, Montgomery, Harris, and Galveston Counties, TX (modified from Braun et al. [2019] and Baker [1979, 1986]).
aquifers, thereby causing compaction of the aquifer sediments (Galloway et al., 1999; Kasmarek, 2013; and Kasmarek et al., 2015). Historically, groundwater withdrawn from the Chicot, Evangeline, and Jasper aquifers had been the primary source of water for municipal supply, commercial and industrial use, and irrigation in the HGR since the early 1970s (Kasmarek, 2013; HGSD, 2017). This is the “primary consolidation” of the unconsolidated Quaternary and semiconsolidated Tertiary aquifer systems (Liu et al., 2020) due to groundwater withdrawal. In the study area, sand layers are more transmissive and less compressible than are fine-grained clay and silt layers, and these sand layers depressurized more rapidly than did the clay and silt layers. When groundwater withdrawing rates change, pressure equilibrium is to reestablish more rapidly in the sand layers than that in the clay and silt layers.
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The amount of compaction of the sand layers is usually minor when compared to that of the clay and silt layers (Trahan, 1982; Galloway et al., 1999). Because most compaction of subsurface sediments is inelastic and largely permanent only a small amount of rebound of the land-surface elevation can occur because of unloading or increase of pore water pressure. While the compaction of one thin clay and silt layer typically will not cause a measurable decrease in the land-surface altitude, a measurable amount of subsidence can occur when an aquifer system comprises numerous stratigraphic sequences of sand layers and clay and silt layers (e.g., characteristic of the Gulf Coast aquifer system) that are subjected to depressurizing and compaction (Gabrysch and Bonnett, 1975). Groundwater-level fluctuations are measured by the U.S. Geological Survey (USGS) in over 700 wells in an 11-county area annually in the HGR to develop a re-
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gional depiction of groundwater levels. The cumulative compaction in the Chicot and Evangeline aquifers is measured at the 13 borehole extensometer stations in this region, with data collection extending from 1973 to 1980. Compaction measurements of the 13 borehole extensometers recorded through about the year 2000 corroborate primary consolidation analysis with monitored groundwater-level changes (Liu et al., 2019). However, a nearly constant rate of subsidence with spatial variation of 0.08 to 8.49 mm/yr emerged while the groundwater levels in the two aquifers were being managed to be stable in trend since about 2000 after groundwater-level elevations had recovered to −42.4 m in 2010 (from −97.1 m in 1990) in the Chicot aquifer and to −56.0 m in 2005 (from −125.0 m in 1984) in the Evangeline aquifer, respectively (Liu et al., 2019). This gives rise to the following question: Is the nearly constant rate of consolidation of 0.08 to 8.49 mm/yr still attributed to the primary consolidation due to groundwater withdrawal? The time dependency on the volume change of clay observed in one-dimensional compression has been an active research area for decades. For practical applications, the volume changes over time are arbitrarily divided into “primary consolidation” and “secondary consolidation,” though the latter is widely known to occur in the entire compression process. In the case of primary consolidation, the volume changes are mainly governed by the changes in effective stress (hydrodynamic effect). The volume changes occurring after the primary consolidation or during the secondary consolidation are dominated by the viscous behavior of the clay and silt system. Within a compressible unit during the secondary consolidation the effective stress remains relatively constant after the pore water pressure reaches equilibrium for a given boundary condition. Most research activities have focused on modeling the time-dependent or viscous response during secondary consolidation observed in the laboratory or in the field. Upon loading, the hydrodynamic and viscous effects occur simultaneously. It is very challenging to determine those two separate effects experimentally or analytically. In addition, study on basic mechanisms contributing to the intrinsic viscous behavior that occurs in creep compression is still limited. It was suggested that the recent nearly constant rate of consolidation of 0.08 to 8.49 mm/yr within the Gulf Coast aquifer system could be attributed to secondary consolidation in sedimentation due to geohistorical overburden pressure or self-weight (Liu et al., 2019; Liu, Li, Fasullo, et al., 2020; and Liu, Li, Fang, et al., 2020). The goal of this article is to present an equation with an equivalent creep coefficient and to apply it to quantify creep deformation of the Gulf Coast aquifer system in the 20th and 21st centuries, respectively, based on 13 bore-
hole extensometers’ compaction measurements in the HGR. STUDY SITE From northwest to southeast, the HGR investigated in this article includes Grimes County (with an elevation close to 122 m), Montgomery County, Waller County, Harris County, and Galveston County (with an elevation from 0 to 15 m) along the coast of Gulf of Mexico (Figure 1). The three primary Quaternary and Tertiary aquifers in the Gulf Coast aquifer system in the HGR study area are the Chicot, Evangeline, and Jasper aquifers (Figures 1 and 2), which comprise laterally discontinuous deposits of gravel, sand, silt, and clay. The youngest and uppermost Quaternary Chicot aquifer consists of Holocene- and Pleistocene-age sediments; the underlying Tertiary Evangeline aquifer comprises Pliocene- and Miocene-age sediments; and the oldest and most deeply buried Tertiary Jasper aquifer consists of Miocene-age sediments (Figures 1 and 2) (Baker, 1979, 1986). The Burkeville confining unit between the Evangeline and Jasper aquifers consists of Miocene Fleming Formation Lagarto clay. The Miocene-age Catahoula confining system, which includes the Catahoula Sandstone, is the lowermost unit of the Gulf Coast Tertiary aquifer system. The Catahoula confining system consists of sands in the upper section and clay and tuff interbedded with sand in the lower section. Numerous authors have contributed to the body of knowledge and understanding of the complex stratigraphic and hydrogeologic relations of the Gulf Coast aquifer system in the HGR study area (Figure 2). Using this information, a series of groundwater flow models were created, the most recent being that of Kasmarek (2013) on hydrogeology and simulation of groundwater flow and land-surface subsidence in the Northern part of the Gulf Coast aquifer system, Texas, 1891–2009 (HAGM, 2013). These models provide an evaluative tool that can be used by water-resource managers to help regulate and conserve the important natural water resource of the aquifer system. The percentage of clay and other fine-grained clastic material generally increases with depth downdip (Baker, 1979). Over time, geologic and hydrologic processes created accretionary sediment wedges (stacked sequences of sediments) that are more than 2,318 m thick at the coast (Figure 1) (Chowdhury and Turco, 2006). The sediments composing the Gulf Coast aquifer system were deposited by fluvial-deltaic processes and subsequently were eroded and redeposited (re-worked) by worldwide episodic changes in sea level (eustacy) that occurred because of oscillations between glacial and interglacial climate conditions (Lambeck
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Figure 2. Geologic and hydrogeologic units of the Gulf Coast aquifer system in the Houston-Galveston region study area, TX (modified by Kasmarek et al. [2015] from Sellards et al. [1932]; Baker [1979]; Meyer and Carr [1979]).
et al., 2002). The Gulf Coast aquifer system consists of hydrogeologic units that dip and thicken from northwest to southeast (Figure 1); the aquifers thus crop out in bands inland from and approximately parallel to the coast and become progressively more deeply buried and confined toward the coast (Kasmarek, 2013). The Burkeville confining unit restricts groundwater flow between the Evangeline and Jasper aquifers by being stratigraphically positioned between the Evangeline and Jasper aquifers (Figure 1). There is no significant confining unit between the Chicot and Evangeline aquifers; therefore, the aquifers are hydraulically connected, which allows groundwater flow between the aquifers (Figure 1). Because of this hydraulic connection, water-level changes occurring in one aquifer can affect water levels in the adjoining aquifer (Kasmarek and Robinson, 2004). Supporting evidence for the interaction of groundwater flow
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between the Chicot and Evangeline aquifers is demonstrated by comparing the two long-term (1977–2015) water-level–change maps (Kasmarek et al., 2015), indicating that the areas in which water levels have declined or risen are approximately spatially coincident. Hydraulic properties of the Chicot aquifer do not differ appreciably from those of the hydrogeologically similar Evangeline aquifer but can be differentiated based on hydraulic conductivity (Carr et al., 1985). From aquifer test data, Meyer and Carr (1979) estimated that the transmissivity of the Chicot aquifer ranges from 915 to 7,625 m2 /d and that the transmissivity of the Evangeline aquifer ranges from 915 to 4,575 m2 /d. The Chicot aquifer outcrops and extends inland from the Gulf of Mexico coast and terminates at the most northern updip limit of the aquifer. Proceeding updip and inland of the Chicot aquifer, the older hydrogeologic units of the Evangeline aquifer, the Burkeville
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Land Subsidence Due to Creep Table 1. Estimated porosity, specific compressibility, and specific storage of clay beds as a function of depth of burial (from Kelley and Deeds [2019]). Clay-Specific Storage, Ssk 1/m Depth of Burial (m) 30.5 76.25 152.5 228.75 305 457.5 610 762.5 915
Porosity
Clay Compressibility (M2 /N)
Inelastic Sskv
Elastic Sske
Vertical Hydraulic Conductivity, K (m/d)
0.5 0.4 0.34 0.31 0.29 0.26 0.25 0.23 0.22
1.20E-07 6.20E-08 3.80E-08 2.90E-08 2.30E-08 1.80E-08 1.40E-08 1.20E-08 1.10E-08
1.15E-03 6.23E-04 3.61E-04 2.82E-04 2.30E-04 1.74E-04 1.41E-04 1.21E-04 1.08E-04
1.38E-05 7.87E-06 5.25E-06 4.26E-06 3.61E-06 2.85E-06 2.46E-06 2.20E-06 2.03E-06
1.31E-05 4.36E-06 1.85E-06 1.06E-06 6.89E-07 3.66E-07 2.37E-07 1.72E-07 1.34E-07
confining unit, and the Jasper aquifer sequentially outcrop (Figure 1). The aquifer in the outcrop and updip areas of the Jasper aquifer can be differentiated from the Evangeline aquifer based on the depths to water below land-surface datum, which are shallower (closer to the land surface) in the Jasper aquifer compared to those in the Evangeline aquifer. Additionally, in the downdip parts of the aquifer system, the Jasper aquifer can be differentiated from the Evangeline aquifer on the basis of stratigraphic position relative to the elevation of the Burkeville confining unit (Figure 1). Table 1 illustrates clay vertical hydraulic conductivity and inelastic and elastic-specific storage values with burial depth estimated porosity by Kelley and Deeds (2019). Figure 3 shows the 11 borehole extensometer station locations in the HGR (Kasmarek and Lanning-Rush, 2003). There are two borehole extensometers (shallow and deep) at the Baytown and Clear Lake stations, respectively. Each of the other nine stations has only one borehole extensometer. In total, there are 13 borehole extensometers. Detailed information on the scientific theory, construction, and operation of borehole extensometers is presented in Gabrysch (1984). Five borehole extensometers were installed in Harris (four) and Galveston (one) counties and began recording compaction data in July 1973: East End, Baytown Shallow, Baytown Deep, and Seabrook in Harris County and Texas City in Galveston County (see Figure 3). The borehole extensometer Johnson Space Center was installed in 1962 in Harris County to record compaction. Additional borehole extensometers were added to the network at four locations in Harris County during the 1974–1976 period: Addicks in 1974, Pasadena in 1975, and Clear Lake Shallow and Clear Lake Deep in 1976 (see Figure 3). In 1980, the final three borehole extensometers were installed in Harris County: Lake Houston, Northeast, and Southwest (see Figure 3). Since activation or installation that has taken place be-
tween 1973 and 1980, compaction data have been constantly recorded and periodically collected about every 28 days at the 13 borehole extensometers, thereby providing site-specific rates of compaction that are accurate to within 0.3 mm (Kasmarek et al., 2015). METHODOLOGY Subsidence and its Components In this article, subsidence denotes an observed field cumulative land subsidence, S(t), of a compressible aquifer system with a total thickness of H from one borehole extensometer. S(t) consists of two components: primary and secondary/creep consolidations, Sp (t) and Sc (t), respectively as the below Eq. 1: S(t) = S p (t) + Sc (t)
(1)
Subsidence rate can be written in Eq. 2 from Eq. 1: · · · S(t) = S p (t) +Sc (t)
(2)
· · · where S(t) = dS/dt, Sp (t) = dSp /dt, and Sc (t) = · dSc /dt. The secondary consolidation rate Sc can be identified from a subsidence curve with time when · the primary consolidation rate Sp (t) becomes zero [i.e., · · · · S(t) = Sc (t)]. If Sp−v and Sp−e denote subsidence rates of primary consolidation due to changes in inelastic (virgin) storativity of aquitards and elastic storativity of sand layers and aquitards, respectively, in the aquifer system, Eq. 2 can be further written as the following Eq. 3: · · · · S(t) = S p−v (t) +S p−e (t) +Sc (t)
(3)
Each term in Eq. 3 will be applied to the qualitative analysis or interpretation using the subsidence data measured from fields.
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Figure 3. Location of borehole extensometer sites, Houston Galveston region, TX (modified from Kasmarek and Lanning-Rush [2003]). (Note: The location of wells LJ-65-21-229 and LJ-65-21-227 is at the same location of borehole extensometer Southwest).
Time for Completion of Primary Consolidation Terzaghi (1925) developed an analytical solution to simulate the equilibration of pore water pressure in an individual saturated clay layer (or aquitard), with a uniform initial pore water pressure where only vertical flow is permitted, in response to a specified instantaneous step change (note: practically gradual or stepby-step change) in the hydraulic head at the bottom and top of its surrounding upper and lower aquifers, respectively. This process elucidates the theory of pore water dissipation process (consolidation), which was extended to the analysis of aquitard/confining unit drainage (Riley, 1969) in an aquifer system and subsequently to the simulation of aquitard/confining unit drainage. This concept, which was referred to as “the aquitard drainage model” by Helm (1984), has formed the theoretical basis of many successful subsidence investigations (Holzer, 1998). For the doubly-draining
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clay layer, the same aquifer hydraulic head changes are assumed to occur at the upper and lower surfaces. A time constant (not infinity) for all aquitards in the aquifers system is τ 0 (=S sk (b 0 equiv /2)2 /K , where S sk , b 0 equiv , and K denote vertical skeletal-specific storage (dimensionless), equivalent thickness (L), and vertical hydraulic conductivity (L/T) of the aquitards, respectively) (Riley, 1969). The primary consolidation degree in the aquifer system reaches 93.1 percent, 99.4 percent, and 100 percent when time factor Tv (= t/τ 0 ) (where t is real time or Terzaghi time (T), since pore pressure is immediately reduced to be lower than pre-consolidation pressure for activation of inelastic consolidation) equals 1, 2, and , respectively. The term “no-delay interbeds” in Hoffmann et al. (2003) is used to denote the interbeds for which τ 0 is short compared to the time steps used in the MODFLOW simulation, while the primary consolidation degree is 93.1 percent. The primary consolidation
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is considered fully completed when the time factor Tv reaches 2 with consolidation of 99.4 percent (Liu and · · Li, 2016). S(t) Sc (t) when time is larger than t · and Sp (t) 0 based on Eq. 2. In practice, the in· elastic consolidation rate Sp−v in Eq. 3 is dominant, with delay when pore pressure in aquitard(s) within the aquifer system and in confining bed(s) is kept lower than its/their preconsolidation pressure, since the magnitude of inelastic-specific skeletal storage is about 2 or 3 magnitude orders larger than that of elastic-specific storage. The deformation of the aquifer system performs elastically because the aquifer system’s sand storativity is usually much larger than the aquitard/confining layer’s storativity after the inelastic deformation ends. Therefore, the trend elastic deformation disappears when groundwater level approaches stability for a long-term period in trend after inelastic consolidation ends, while the secondary consolidation (creep) of the aquifer system appears. The time between the historical lowest groundwater level and the end of inelastic compaction of the Gulf Coast aquifer system is denoted by the inelastic delay time td in this article. td can be determined by analyzing the relationship between measured compaction and water level of the Gulf Cost aquifer system. The inelastic delay time factor TV−v can be estimated from td using the following Eq. 4:
TV −v =
td = τ0−v
td K 2 Sskv b0equiv /2
Secondary Consolidation Equation of Gulf Coast Aquifer System As a result of the weight of the overburden and the inelastic compaction characteristics of the clay layers, about 90 percent of the compaction is permanent (Gabrysch and Bonnett, 1975) during loading. Three main sedimentation stages are defined with respect to the concentration degree in self-weight consolidation (Tong et al., 2011): the clarification regime (suspension), zone-settling regime (settling without consolidation), and compression regime (consolidation) (Fitch, 1983). The above Quaternary and Tertiary aquifer systems are still in the third compression stage. This compression was referred to as “secondary consolidation” by Taylor and Merchant (1940) or as “self-weight consolidation” by Been and Sills (1981). Let Hi and Cαi denote the thickness (L) and the secondary consolidation coefficient (dimensionless) of soil layer i, which includes any individual sand, silt, and clay layers within a compressible Gulf Coast aquifer system with a total soil layer number N. The compaction Sci (t) of each individual aquitard layer, i, is assumed to be in a secondary consolidation due to geohistorical overburden pressure and follows Taylor’s (1942) creep equation Sci (t) = Cαi Hi log t/t0 (Taylor, 1942), where t and t0 represent creep time and t t0 . If a bulk creep deformation Sc (t) in Eq. 1 is measured from a borehole extensometer for a compressible aquifer system, Sc can be written as follows:
(4)
Sc (t) =
N i=1
The consolidation degree is larger than 99.4 percent when TV−v is equal to 2 if there are lowering and/or recovery periods of groundwater level before and after the lowest groundwater level. The equivalent aquitard thickness of the Gulf Coast aquifer system in the HGR is estimated to be 3.4 m to 3.7 m in Figure 4 based on the fact that the td values are 27 years and 32 years, respectively. For instance, Figure 4B indicates that if (a) td = 32 years, (b) an aquitard equivalent thickness = 3.7 m, and (c) the values of K and S skv in Table 1 are applied to the burial depth of 0–900 m, TV−v will be larger than 2, which means that the consolidation degree of the aquitard will be larger than 99.4 percent. Furthermore, based on Figure 4B, if the equivalent thickness increases to 6 m, only those aquitards located within 0–300-m burial depth can reach more than 99.4 percent consolidation degree (TV−v > 2) in 32 years. Thus, Eq. 4 can be a tool for the first-hand estimate to evaluate if the process of primary consolidation is completed.
Sci =
N
Cαi Hi log t/t0 = Cα H log t/t0
i=1
= 0.4343Cα H ln t/t0 (5) N and H = N where Cα = i=1 Cαi Hi /H i=1 Hi , which is the aquifer system thickness. So, Cα denotes a secondary consolidation coefficient of an aquifer system. The Cα value of the aquifer system can be dominated by that of aquitards or confining layers when compared to the negligible Cα of sand layers. Pseudo-Constant Rate of Secondary Consolidation · Creep rate Sc (t) = (Cα H/ln10) 1/t follows Eq. 5 by taking the derivative with respect to time t. The de· crease percentage (DS ) of Sc (t) from t to t + t was · · · derived with [Sc (t) − Sc (t + t)]/Sc (t), as follows: t × 100 (6) DS(t) = 1 − t + t DS approaches zero when t t, which implies that · Sc (t) a constant when t t. Put differently, the · changing value of Sc (t) over the t period (such as
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Figure 4. Time factors (TV−v ) for inelastic compaction of aquitards in the Gulf Coast aquifer system estimated by Eq. 4 with K and S skv values in Table 1 and assumed equivalent aquitard thickness b 0 equiv . (A) b 0 equiv of the Gulf Coast aquifer system is probably equal to 3.4 m for td = 27 years, and (B) b 0 equiv is probably equal to 3.7 m for td = 32 years.
10- or 20-year observation period) is difficult to be identified and can be ignored. This negligibly variable rate is referred to as the “pseudo-constant rate of secondary consolidation” (Liu et al., 2019). Figure 5 shows that creep subsidence rate decrease percentage (DS ) changes with the secondary consolidation time (t) for each given time period ( t) of 5, 10, 20, 30, 40, and 50 years. Table 2 displays how many years are needed for specified subsidence rate percentage changes of 1.0 percent, 0.5 percent, and 0.1 percent in one given period. For example, if an observation period ( t) is 10 years, 990; 1,990; and 9,990 years are needed for specified subsidence rate decrease percentages of 1.0 percent, 0.5 percent, and 0.1 percent, respectively. The · secondary consolidation rate Sc (t) during an observation period ( t) is considered a pseudo-constant if t t. For example, over a 14-year observation dur244
ing the 2003–2017 period ( t = 14 year) and 1,000year creep (t = 1,000), based on Eq. 6, DS = 1.38 · percent, which means a value of Sc (t) = 3.87 mm/yr Table 2. Time of the secondary consolidation needed for a specified subsidence rate decrease in given periods (modified from Liu et al. [2019]). Given Time Period, t in Eq. 6 (yr) Ds1 (%) 1.0 0.5 0.1
5 2
495 995 4,995
10
20
30
40
50
990 1,990 9,990
1,980 3,980 19,980
2,970 5,970 29,970
3,960 7,960 39,960
4,950 9,950 49,950
· DS = the decreased percentage of Ss in Eq. 6. 2 The subsidence rate change is 1.0% for a 5-year period when the secondary consolidation elapses 495 years. 1
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0.57 million m3 /d in 1931 to 4.28 million m3 /d in 1976, with an average growth of 0.05 million m3 /d per year; Near Texas City, the withdrawal of groundwater for public supply and industry caused more than 0.5 m of subsidence between 1906 and 1943 (Galloway et al., 1999); (3) decreasing withdrawal rates for 25 years from 4.28 million m3 /d in 1976 to 3.03 million m3 /d in 2001; and (4) roughly steady withdrawal rates of around 3 million m3 /d from 2001 to date for more than 17 years. Groundwater-Level Change with Groundwater Withdrawal
Figure 5. Percentage change of secondary consolidation subsidence rate with time from Eq. 6 for t = 10, 20, 30, 40, and 50 years, respectively.
will drop by 1.38 percent and reduce to 3.82 mm/yr over a 1,000-year creep (see Figure5). If this drop is ig· nored, Sc (t) is approximately considered as a constant. · This approximation is also applied to other Sc (t) values, with a range changing from 0.08 to 8.49 mm/yr in the HGR (Liu et al., 2019). Estimate of Secondary Consolidation Subsidence From Eq. 5, the secondary consolidation subsidence Sc from time t1 to time t2 can be estimated by the following Eq. 7: Sc = Sc (t2 ) − Sc (t1 ) = Cα H log = 0.4343Cα H ln
t2 t1
t2 t1 (7)
With the data from fields, the coefficient Cα can be calibrated using Eq. 7. RESULTS Roughly Steady Groundwater Withdrawal Since 2001 Artificial primary consolidation first occurred in the early 1900s in areas where relatively large volumes of groundwater, oil, and gas were extracted. Primary consolidation continued throughout the 20th century, due primarily to groundwater pumpage (Galloway et al., 1999; Kasmarek, 2013). Groundwater withdrawal in the HGR experienced the following four periods (see Figure 6C): (1) a slight withdrawal rate of about 0.19 million m3 /d from 1891 to 1930 for 40 years; (2) increasing withdrawal rates for 46 years from
The lowering of groundwater level in the HGR due to groundwater withdrawal from 1891 to 1976 (Figure 6C) was observed and simulated by the USGS using steel tape measurements and MODFLOW software. Based on their simulation results in Figure 6A and B, from 1891 to 1900, the groundwater levels were about 21.35 m and 9.15 m in Chicot and Evangeline aquifers, respectively. This would be close to the status observed during the pre-development of groundwater before 1891. From 1901 to 1930, groundwater levels were lowered to 8.2 m and 5.2 m in Chicot and Evangeline aquifers, respectively. Based on measured results in Figure 6A and B, in 1976, the lowest groundwater levels corresponding to the highest regional groundwater pumpage of 4.28 million m3 /d were −82 m and −86 m in Chicot and Evangeline aquifers, respectively; then, groundwater levels were raised about 41.5 m to −40 m in 2008 for Chicot aquifer and about 43 m to −43 m in 2008 for Evangeline aquifer. After 2008, groundwater levels have been roughly stable. The lowest groundwater-level depression cones for Chicot and Evangeline aquifers in 1976 likely are very close to the observed lowest depression cones in Figure 7A and B, respectively, which were found by the USGS Subsidence Reviewer. Through checking groundwaterlevel data monitored and published by USGS, the lowest groundwater levels were −97.1 m on 12 January 1990 (compared to 16 January 1984) at Well LJ-65-21150 for the Chicot aquifer and −125.0 m on 9 January 1984 (compared to 9 January 1978) at Well LJ-65-13904 for the Evangeline Aquifer, respectively. The maximum drawdown caused by groundwater withdrawal in 1976 was estimated to be about 112 m for the Chicot aquifer and 134 m for the Evangeline aquifer, respectively, in the HGR. Based on the two wells, groundwater levels were raised by about 54.6 m to −42.4 m in 2010 for the Chicot aquifer and about 64.4 m to −56.0 m in 2005 for the Evangeline aquifer. At the two wells, these aquifers’ groundwater levels were roughly stable in trend after 2010 and 2005, respectively.
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Figure 6. Groundwater withdrawal and groundwater-level fluctuations in the Houston-Galveston region: (A) Measured and simulated groundwater level in the Chicot aquifer (modified from Kasmarek [2013]); (B) Measured and simulated groundwater level in Evangeline aquifer (modified from Kasmarek [2013]); and (C) Groundwater withdrawal (data from Kasmarek [2013] after Liu et al. [2019]).
Identification of Secondary Consolidation of the Gulf Coast Aquifer System Theoretically, secondary consolidation fully appears in trend when groundwater levels are stable in trend, while inelastic primary consolidation disappears in all compressible aquifer systems. Appendix A (https://www.aegweb.org/e-eg-supplements) shows how an appearance period of the pseudo-constant sec· ondary compaction Sc was identified in the Chicot and Evangeline aquifers from 1980 to 2017 from the total compaction measured in the two Gulf Coast aquifer
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systems at borehole extensometer Southwest (see Figure 3 for location) in the HGR through empir· · ical analysis of Sp−v and Sp−e variations and disappearances in six different periods of groundwaterlevel change (Figure 8). Thereafter, the period of the · pseudo-constant secondary compaction Sc in the Gulf Coast aquifer system for each of other 12 borehole extensometers was identified by application of this same methodology (shown in Figure 9) (Liu et al., 2019). Only the Chicot aquifer and Evangeline aquifers are involved with those 11 borehole extensometers, except Texas City and Baytown Shallow in Figure 3. A total
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Figure 7. Measured groundwater-level depression cones (A) in Chicot aquifer (lowest: −97.14 m on 12 January 1990 compared to 16 January 1984) and (B) in Evangeline aquifer (lowest: −125.04 m on 9 January 1984 compared to 9 January 1978) (from USGS shape files on https://pubs.usgs.gov/ds/793/).
of 19 groundwater-level wells (Table 3) in the two aquifers at or near the 11 locations of the 13 borehole extensometers were employed in the analysis of inelas· · tic and elastic compaction variations Sp−v and Sp−e corresponding to trends of groundwater-level fluctuations for identifying the pseudo-constant periods of
the secondary consolidation based on the methodology in section METHODOLOGY. Columns 5 and 6 in Table 3 illustrate the starting date and the ending date, respectively, of the full appearance of secondary consolidation at each borehole extensometer site at each of the 13 borehole
Figure 8. Primary consolidation due to groundwater withdrawal ended in about 2000, and secondary consolidation became apparent at borehole extensometer Southwest in Houston when groundwater-level trends were stable. (I) Inelastic subsidence dominated by primary consolidation; (II) Subsidence dominated by elastic rebound due to unloading; (III) Subsidence due to primary consolidation (delay compaction) and creep offset by rebound during unloading; (IV) Subsidence caused by both primary consolidation and secondary consolidation, but it is primarily elastic compaction, because reloading stress is less than the historical one; (V) Subsidence due to secondary consolidation offset by rebound during unloading; and (VI) Subsidence dominated by secondary consolidation represented with a red trend line. Slope of the trend · line (Sc ) is 0.0106 mm/d or 3.87 mm/a in a pseudo-constant rate; 427.76 mm in the logarithmically linear equation shows a value for 0.4343 Cb α H in Eq. 5 (modified from Liu et al. [2019]).
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Figure 9. Secondary consolidation (creep) (Sc ) simulated with linear and logarithmically linear trends from observed cumulative compactions at 13 borehole extensometer locations in the Houston-Galveston region (data source: USGS). The secondary consolidation period for each site is given in Table 3. The slope values in the linear and logarithmically linear trend equations are in mm/d. The slope values in the logarithmically linear trend equations are dimensionless, which are employed to compute secondary consolidation (creep) coefficient values (see columns 7 and 8 in Table 4). (A) Extensometers Addicks, Texas City, Seabrook, ClearlakeShallow and Johnson Space Center; (B) Extensometers Northeast, Lake Houston, EastEnd, ClearLakeDeep and Southwest; and (C) Pasadena, BaytownShallow and BaytownDeep.
extensometer sites (Southwest; Texas City; Seabrook; Johnson Space Center; Clear Lake Shallow and Deep; Baytown Shallow and Deep; Addicks; East End; Northeast; Pasadena; and Lake Houston) based on monitored groundwater-level data from wells near each site. The slope of the groundwater-level trend,
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such as from Figure 8, was given in column 7 in m/d and in column 8 in m/yr during the appearance period at each site. All other 10 starting dates (or inelastic delay compaction end dates) from 7 January 2004 through 25 January 2008 commenced after the starting date (18 September 2003) of the appearance
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Land Subsidence Due to Creep Table 3. Secondary consolidation appearance periods based on groundwater-level trend at or near borehole extensometer sites in the HoustonGalveston region (modified from Liu et al. [2019]).
Secondary Consolidation Appearance Period Borehole Extensometer
Depth (m)
Texas City Seabrook
244 421
Space Center Clear Lake Baytown Shallow Baytown Deep Addicks
239 9371 131
East End
304
Northeast
663
Pasadena
864
Lake Houston
591
Southwest
719
450 549
Starting Date
Well No.
Aquifer
Ending Date
KH-64-33-901 LJ-65-32-519 LJ-65-32-630 LJ-65-32-422 LJ-65-32-424 LJ-65-16-933
Chicot Chicot Evangeline Chicot Evangeline Chicot
24 Jan 2008 25 Jan 2008
14 Jan 2017 14 Jan 2017
25 Jan 2007
20 Dec 2017
LJ-65-16-931 LJ-65-12-729 LJ-65-12-726 LJ-65-22-623 LJ-65-22-622 LJ-65-14-745 LJ-65-14-746 LJ-65-23-321 LJ-65-23-326 LJ-65-07-902 LJ-65-07-908 LJ-65-21-229 LJ-65-21-227
Evangeline Chicot Evangeline Chicot Evangeline Chicot Evangeline Chicot Evangeline Chicot Evangeline Chicot Evangeline
11 Jan 2007 1 Oct 2007
15 May 2014
27 Jul 2007
13 Jan 2015
1/4/2008
1 Mar 2011
5 Jan 2007 6 Feb 2007 7 Jan 2004
5 Jan 2011 30 Mar 2010 4 Apr 2007
18 Sep 2003
2 Dec 2017
26 May 2005 28 May 2009
Groundwater-Level Trend
Pseudo-Constant Secondary · Consolidation Rate, Sc
m/d
m/yr
mm/d
mm/yr
− 8.73E-05 − 2.77E-05 − 7.67E-05 9.09E-05 6.79E-05 3.82E-04
− 0.03 − 0.01 − 0.03 0.03 0.02 0.14
2.222E-04 8.317E-03
0.08 3.04
5.062E-03 3.033E-03 3.630E-03
1.85 1.11 1.33
9.21E-05 2.73E-04 0.00E+00 − 2.72E-04 5.64E-04 5.27E-04 1.07E-03 3.76E-06 8.39E-04 8.38E-05 1.24E-04 − 1.13E-04 4.59E-04
0.03 0.10 0.00 − 0.10 0.212 0.19 0.393 0.00 0.314 0.03 0.05 − 0.04 0.17
5.956E-03 2.327E-02
2.17 8.49
4.926E-03
1.80
1.150E-02
4.20
6.032E-03
2.20
3.760E-03
1.37
1.060E-02
3.87
1
Clear Lake Deep is 937 m deep, and Clear Lake Shallow is 530 m deep. The difference of groundwater level between −42.75 m on 7 May 2008 and −42.77 m on 3 November 2014 is 0.02 m. 3 The difference of groundwater level between −54.14 m on 4 January 2008 and −54.18 m on 1 March 2011 is 0.04 m. 4 The difference of groundwater level between −40.03m on 6 February 2007 and −40.17 m on 30 March 2010 is 0.14 m. 2
of secondary consolidation at borehole extensometer site Southwest (Table 3). The inelastic compaction delay time td relative to 1976 (the lowest groundwater time due to groundwater withdrawal) can be determined to be 27–32 years. All the periods are after the inelastic compaction ceased within period (V) from 20 September 2000 to 18 September 2003 identified in Figure 8. The groundwater-level trend values from 0.21 to 0.39 m/yr in column 8 in Table 3 are a bit larger for borehole extensometer sites East End, Northeast, and Pasadena, but the groundwater-level difference between the starting date and the ending date of the corresponding secondary consolidation appearance period is very small (0.02 to .014 m). This small groundwater-level difference signifies that the cumulative elastic deformation approaches zero during that period. Previously, the reason for the huge subsidence fluctuations at Baytown Shallow and Deep and Pasadena from 2010 to 2014 (see Figure 9C) was not well understood. Water usage in the area has not increased. The only other physical factor is reaction along faults in the area, which was detected by mapping ground defor-
mation with multi-temporal InSAR (Qu et al., 2015). However, if this was indeed true, all borehole extensometer locations would be affected similarly (email communication with USGS hydrologist, Jason Ramage, 2018). Secondary compaction at all other 10 borehole extensometer sites seems to have dominated land subsidence without primary inelastic consolidation, occurring since around 2003. The secondary consolidation will continue for a very long period into the future, since inelastic compaction was fully completed in 2003, if the current groundwater management is kept with the stability of groundwater level. Secondary Consolidation Coefficient of the Gulf Coast Aquifer System Logarithmically linear simulations of secondary consolidation at the 13 borehole extensometers in the HGR since 1973 are depicted in Figure 9 with black solid trend lines. The equations for each logarithmically linear trend line simulated using Microsoft Excel show the slope values of the logarithmically linear trend line in millimeters. The slope values of 9.256
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Liu and Li Table 4. Identification of secondary consolidation (creep) coefficient values for the Gulf Coast aquifer system in the Houston-Galveston region.
H (m)
· Sc (mm/yr)
0.4343Cαb H1 (mm)
Cαb 2
· Sc 3 (m) in the 20th Century
341/49
530
0.44
50.76
2.21E-04
0.23
0.03
Silty clay
748/49
937
1.11
125.1
3.07E-04
0.58
0.09
Silty clay
131
1.33
144.8
2.55E-03
0.67
0.10
Clay
Borehole Extensometer
HC /CC (m/%)
HE /CE (m/%)
Clear Lake Shallow Clear Lake Deep Baytown Shallow Baytown Deep Johnson Space Center Texas City East End Lake Houston Northeast Seabrook Southwest Addicks Pasadena Average
189/53 189/53
HB (m)
131/50
· Sc 4 (m) in the 21st Century
Inferred Dominant Aquitard (s)
175/50 189/45
275/59 46/47
450 235
2.17 1.85
233.8 212.0
1.20E-03 2.08E-03
1.08 0.98
0.16 0.15
Clay Clay
244/21 194/54 165/55
110/50 426/59
244 304 591
0.08 1.80 1.37
9.256 200.9 141.5
8.74E-05 1.52E-03 5.51E-04
0.04 0.93 0.65
0.01 0.14 0.10
Clayey silt Clay Silty clay
194/55 187/44 229/61 207/55 195/50
462/25 234/48 472/26 342/29 669/65
663 421 719 549 864
4.20 3.04 3.87 8.49 2.20
460.5 344.2 427.76 939.8 239.3
1.60E-03 1.88E-03 1.37E-03 3.94E-03 6.38E-04 1.38E-03
2.13 1.59 1.97 4.33 1.10
0.32 0.24 0.29 0.64 0.16
Clay Clay Clay Clay Silty clay
7 18
HC = Chicot aquifer thickness; HE = Evangeline aquifer thickness; HB = Burkeville confining layer (clay); CC = clay thickness percentage out · of Chicot aquifer thickness; CE = clay thickness percentage out of Evangeline aquifer thickness. H = HC + HE + HB . Sc = pseudo-constant creep or secondary consolidation rate observed from borehole extensometer. 1 The value for 0.4343Cαb H (see Eq. 5) is from the simulated logarithmically linear trend in Figure 9. 2 b Cα = (0.4343Cαb H)/(0.4343H) (dimensionless). 3 Calculated with Eq. 7 using t1 = 366 days (31 December 1900) and t2 = 36,981 days (31 December 2000) since time in days is used in Figure 9. 4 Calculated with Eq. 7 using t1 = 36,981 days (31 December 2000) and t2 = 73,415 days (31 December 2100).
to 939.8 mm approximately represent 0.4343 Cα H in Eq. 5 and are shown in column 7 in Table 4. Column 6 in Table 4 shows all pseudo-constant secondary consolidation values from Table 3. The Chicot aquifer thickness HC , Evangeline aquifer thickness HE , and Burkeville confining unit (clay) thickness HB involved by borehole extensometer are given in columns 2, 3, and 4, respectively, in Table 4. The total thickness H of a Gulf Coast Aquifer system involved in one borehole extensometer is shown in column 5. Meanwhile, the values of the secondary consolidation coefficient Cα are displayed in column 8, as derived from values in column 7, with total thickness H values in column 5. The creep coefficient Cα value of the Gulf Coast aquifer system is in a range of 8.74 × 10−5 to 3.94 × 10−3 (dimensionless), with an average of 1.38 × 10−3 , at the 13 borehole extensometer locations. The Chicot aquifer Cα value is in the range of 8.74 × 10−5 to 2.55 × 10−3 at borehole extensometers Texas City and Baytown Shallow. The Cα value of the Chicot and Evangeline aquifers in addition to the Burkeville confining unit is in the range of 1.38 × 10−3 to 1.60 × 10−3 , with an average of 1.48 × 10−3 at borehole extensometers 250
Southwest and Northeast. At the other nine borehole extensometer locations, the Cα value of the Chicot and Evangeline aquifers is in the range of 2.21 × 10−5 to 3.94 × 10−3 .
Secondary Consolidation Subsidence in the 20th and 21st Centuries, Respectively The secondary consolidation subsidence of the involved Gulf Coast aquifer system in the 20th century is estimated to be 0.04 to 4.33 m (see column 9 in Table 4) at the 13 borehole extensometers. The highest creep of 4.33 m in the 20th century is at Addicts because the dominant aquitards are clay, with the highest secondary consolidation coefficient of 0.00394, which likely led to the low-lying areas near Addicts with similar geology. The secondary consolidation subsidence of the involved Gulf Coast aquifer system in the 21st century (2001–2100) is estimated to be 0.01 to 0.64 m (see column 10 in Table 4) at the 13 borehole extensometers. The creep will continue with a much smaller rate after 2100.
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DISCUSSION The Period of Primary Inelastic Consolidation From Figure 6A and B, it can be said that the inelastic consolidation started in about 1942 during pumping period 2 (1931–1976), when the groundwater level was lowered below the pre-consolidation pressure level of −21.5 m almost at the same time in both Chicot (Well LJ-65-14-912) and Evangeline (Well LJ-65-22618) aquifers (Kasmarek, 2013). This inelastic compaction was completed before a time from 18 September 2003 to 25 January 2008 (Table 3). Based on groundwater level in the two wells in 2000, the updated lowest pre-consolidation pressure level would be −50.3 m in the Chicot aquifer and −51.2 m in the Evangeline aquifer, respectively. Therefore, the primary consolidation is elastic or recoverable, since inelastic compaction was completed if the groundwater level in the Chicot aquifer and in the Evangeline aquifer here is higher than −50.3 m and −51.2 m, respectively. The period of primary inelastic consolidation period existed for about 61–66 years, approximately from 1942. Dominant Aquitard(s) Inferred from Secondary Consolidation Coefficient Values The values of secondary consolidation coefficient (Cα ) found in Table 4 may be understood to represent characteristics of dominant aquitard(s) rather than sands in the Gulf Coast aquifer system. Magnitude orders of −3, −4, and −5 would apparently represent aquitard(s) dominated by clay, silty clay, and clayey silt, respectively, in the Gulf Coast aquifer system. The inferred dominant aquitard(s) in the Gulf Coast aquifer system at the 13 borehole extensometer locations is/are given in column 11 in Table 4. Most aquitards are clay or silty clay. The Cα value for silty clay or clay-dominant aquitards from column 8 in Table 4 is in the range of 2.21 × 10−4 to 3.94 × 10−3 at 12 out of 13 borehole extensometer sites, which matches a secondary consolidation coefficient Cα range of 1 × 10−4 to 5 × 10−3 found by Mesri (1973) for most soils from laboratory tests. Only one Cα value for clayey silt dominant aquitards is 8.72 × 10−5 at borehole extensometer site Texas City (Table 4), with aquitard(s) thickness percentage of 20.8 percent in the Chicot aquifer (Kasmarek and Robinson, 2004). Significance of Secondary Consolidation in Gauged Sea Level Rise The secondary consolidation existing in the Gulf Coast aquifer system in the HGR is found from the 13 borehole extensometer compaction measurements.
The pseudo-constant creep rate during a very short period (such as a decade) after a long-term geological history of sedimentation (such as 1,000 years or longer) played an important role in this finding (Liu et al., 2019). The secondary consolidation coefficient values found in Table 4 help describe slowly variable · creep rate with equation Sc (t) = (Cα H/ln10) 1/t. However, the secondary consolidation can be concealed by a significant primary inelastic consolidation for about 61 years from 1942 to 2003 due to huge groundwater withdrawals from the aquifer systems in the HGR. The secondary consolidation can fully appear while the inelastic consolidation is completed for the historical maximum pre-consolidation pressure in the Gulf Coast aquifer system, and elastic deformation approximately approaches zero in trend. Therefore, because of groundwater withdrawal, the secondary consolidation of the Gulf aquifer system is one land subsidence component, in addition to tectonic subsidence and primary consolidation subsidence. Tide gauge Galveston Pier 21 (see location in Figure 3) has the longest tide records (for 112 years since 1909) among the 25 gauges along the Gulf Coast of Mexico. Sea level in Galveston Bay along the Gulf Coast of Mexico has risen about 71 cm, with a mean rate of 6.51 mm/yr at tide gauge Galveston Pier 21 since 1909, based on National Oceanic and Atmospheric Administration (NOAA) estimation. This mean sea level rise (SLR) rate is 3.8 times larger than the global mean SLR rate of 1.7 mm/yr (Parris et al., 2012; Walsh et al., 2014). Tebaldi et al. (2012) and Zervas et al. (2013) estimated land subsidence rate values at some NOAA tide gauge stations by using the difference between relative sea level rise rate to global mean sea level rise of 1.70 mm/yr. For example, the land subsidence rate at the tide gauge Galveston Pier 21 (Figure 3) in the HGR was estimated to be 4.72 mm/yr (Zervas et al., 2013), which should include secondary consolidation subsidence of the Gulf Coast aquifer system at Galveston Pier 21 in addition to regional basement rock subsidence due to tectonics and primary consolidation subsidence due to groundwater withdrawal (Liu, Li, Fasullo, et al., 2020).
CONCLUSION In this article, the land subsidence caused by compaction of the Gulf Coast aquifer system and measured by borehole extensometers in the HGR is assumed to be the sum of primary consolidation due to groundwater withdrawal and secondary consolidation due to geo-historical overburden pressure. The primary consolidation comprises inelastic and elastic components caused by groundwater-level or pore
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water pressure-lowering due to groundwater withdrawal from the compressible aquifer systems. The inelastic or non-recoverable consolidation is induced by the lowed pore water pressure head in aquitard(s) or confining units when it is lower than its preconsolidation pressure. Inelastic consolidation dominates land subsidence when it happens because inelastic-specific skeletal storage value is about 2-3 magnitude orders larger than elastic-specific skeletal storage value. The secondary consolidation may exist, at least, for more than 1,000 years in the Gulf Coast aquifer system. In this article, the secondary consolidation coefficient of the Gulf Coast aquifer system is defined with a thickness-weighted average of each individual creep coefficient of aquitards in the aquifer system, based on Taylor’s (1942) secondary consolidation theory. The rate of secondary consolidation behaves as a pseudo-constant characteristic, especially if it has elapsed over 1,000 years since the youngest and uppermost sediments of the Holocene Chicot aquifer were formed in the Greenlandian Age (4,200–8,200 years ago) and the Northgrippian Age (8,200–11,700 years ago), respectively. Significant inelastic primary consolidation likely started around 1942, when the groundwater levels in the Chicot and Evangeline aquifers were below the pre-consolidation pressure of −21.35 m. According to Terzaghi’s theory, any primary consolidation can be more than 99 percent completed when its consolidation time factor reaches 2 if there is no updated pre-consolidation pressure within aquitards or confining units. The inelastic primary consolidation of the Gulf Coast aquifer system in the HGR was completed from 2003 to 2008. Then, the inelastic primary consolidation disappeared or tended to disappear when the aquifer groundwater level exhibited stability, while the pseudo-constant secondary consolidation fully appeared. The equivalent thickness of aquitards in the Gulf Coast aquifer system is estimated to be 3.4 to 3.7 m in this article. Thirteen borehole extensometers in the HGR were built to monitor the compaction of the Quaternary and Tertiary Gulf Coast aquifer since 1973. The pseudo-constant secondary consolidation rate was identified to be 0.08 to 8.49 mm/yr from the borehole extensometer compaction data. The secondary consolidation coefficient of the Gulf Coast aquifer system is found to be in a range of 8.74 × 10−5 to 3.94 × 10−3 , with an average of 1.38 × 10−3 . It is inferred that magnitude orders of −3, −4, and −5 in the secondary consolidation coefficient values would represent aquitard(s) dominated by clay, silty clay, and clayey silt, respectively, in the Gulf Coast aquifer system. The secondary consolidation coefficient value range of 2.21 × 10−4 to 3.94 × 10−3 for silt clay or 252
clay-dominant aquitards in the Gulf Coast aquifer system matches well the individual secondary consolidation coefficient value range of 1 × 10−4 to 5 × 10−3 found by Mesri (1973) for most soils from laboratory tests. The secondary consolidation subsidence of the involved Gulf Coast aquifer system is estimated to be 0.04 to 4.33 m in the 20th century and is projected to be 0.01 to 0.64 m in the 21st century at the 13 borehole extensometers in the HGR. In addition to regional tectonic subsidence and land subsidence due to groundwater withdrawal, the significant creep subsidence of the Gulf Coast aquifer system in the 20th and 21st centuries suggests a need to consider the secondary consolidation subsidence due to geohistorical overburden pressure as a new factor of relative sea-level rise along the Gulf Coast of Mexico. DATA AVAILABILITY The compaction data and groundwater data were provided by USGS (https://txpub.usgs.gov/houston_ subsidence/home/). Direct requests for these materials may be made to the provider, as indicated in the “Acknowledgments” section. ACKNOWLEDGMENTS This research is supported by the National Science Foundation (NSF) grant 1832065 entitled “Identification of urban flood impacts caused by land subsidence and sea-level rise in the Houston-Galveston region” and Maryland Port Administration (MPA) grant 51831 “Use of dredged material to protect lowlying areas in the Chesapeake Bay.” The authors express their gratitude to Jason Ramage (USGS) and John Ellis (USGS) for accessing and explaining data and reviewing an early version of the manuscript and to William Mike Chrismer for his help in data collection, as well as assistance from Tranell Griffin with GIS mapping and Ermei Liu with mapping support. The authors appreciate two journal peer reviewers, J. M. Sharp and an anonymous reviewer, for their insightful and constructive comments that led to an improved manuscript. REFERENCES Baker, E. T., 1979, Stratagraphic and Hydrogeologic Framework of Part of the Texas Coastal Plain: Texas Department of Water Resources, 18 p. Baker, E. T., 1986, Hydrology of the Jasper Aquifer in the Southeast Texas Coastal Plain: Texas Water Development Board, Report 295, 64 p. Been, K. and Sills, G. C., 1981, Self-weight consolidation of soft soils: An experimental and theoretical study: Geotechnique, Vol. 31, No. 4, pp. 519–535.
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and Evangeline Aquifers, Houston-Galveston Region, Texas: U.S. Geological Surgey Open-File Report 03-109, 16 p. Kasmarek, M. C.; Ramage, J. K.; and Johnson, M. R., 2015, Water-Level Altitudes 2016 and Water-Level Changes in the Chicot, Evangeline, and Jasper Aquifers and Compaction 1973– 2015 in the Chicot and Evangeline Aquifers, Houston–Galveston Region, Texas: U.S. Geological Survey Scientific Investigations Map 3365, 17 p. Kasmarek, M. C. and Robinson, J. L., 2004, Hydrogeology and Simulation of Groundwater Flow and Land-Surface Subsidence in the Northern Part of the Gulf Coast Aquifer System, Texas, 2004-5102: U.S. Geological Survey Scientific Investigations Report 2004-5102, 111 p. Kelly, V. and Deeds, N., 2019, Assessment of Subsidence and Regulatory Considerations for Aquifer Storage and Recovery in the Evangeline and Chicot Aquifers: Harris-Galveston Subsidence District, 25 p. Lambeck, K.; Esat, T. M.; and Potter, E. K., 2002, Links between climate and sea levels for the past three million years: Nature, Vol.. 419, No. 6903, pp. 199–206. Liu, Y. and Li, J., 2016, MODFLOW No-delay flow simulation for low permeable confining units. In roceedings of World Environmental & Water Resources Congress 2016, West Palm Beach, FL, pp. 387–393. Liu, Y.; Li, J.; and Fang, Z. N., 2019, Groundwater level change management on control of land subsidence supported by borehole extensometer compaction measurements in the HoustonGalveston Region, Texas: Geosciences, Vol. 9, No. 223, pp. 19. Liu, Y.; Li, J.; Fang, Z. N.; Rashvand, M.; and Griffin, T., 2020, The secondary consolidation (creep) due to geohistorical overburden pressure in the Houston-Galveston Region, Texas. In Proceedings of the Tenth International Symposium on Land Subsidence (TISOLS), pp. 315–320. Liu, Y.; Li, J.; Fasullo, J.; and Galloway, D. L., 2020, Land subsidence contributions to relative sea level rise at tide gauge Galveston Pier 21, Texas: Scientific Reports, Vol. 10, 17905, pp. 1–11. Mesri, G., 1973, Coefficient of secondary compression: Journal Soil Mechanics Foundations Division, ASCE, Vol. 99, No. 1, pp. 123–137. Meyer, W. R. and Carr, J. E., 1979, A Digital Model for Simulation of Ground-Water Hydrology in the Houston Area, Texas: Texas Dapartment of Water Resources, 76 p. Parris, A.; Bromirski, P.; Burkett, V.; Cayan, D.; Culver, M.; Hall, J.; Horton, R.; Knuuti, K.; Moss, R.; Obeysekera, J.; Sallenger, A.; and Weiss, J., 2012, Global Sea Level Rise Scenarios for the United States National Climate Assessment: NOAA Technical Report OAR CPO-1, 37 p. Pratt, W. E. and Johnson, D. W., 1926, Local subsidence of the Goose Creek oil field: Journal Geology, Vol. 34, pp. 577–590. Qu, F.; Lu, Z.; Zhang, Q.; Bawden, G. W.; Kim, J.; Zhao, C.; and Qu, W., 2015, Mapping ground deformation over Houston– Galveston, Texas using multi-temporal InSAR: Remote Sensing Environment, Vol. 169, pp. 290–306. Riley, F. S., 1969, Analysis of borehole extenso meter data from central California. In International Association of Scientific Hydrology Publication 89, pp. 423–431. Seifert, J. and Drabek, C., 2006, History of production and potential future production of the Gulf Coast Aquifer. In Mace, R. E.; Davidson, S. C.; Angle, E. S.; and Mullican, W. F. I. (Editors), Aquifers of the Gulf Coast of Texas: Texas Water Development Board Report 365, pp. 261–267. Sellards, E. H.; Adkins, W. S.; and Plummer, F. B., 1932, The Geology of Texas: Volume 1 Stratigraphy: University of Texas Bulletin, No. 3232, p. 1007.
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Open Access Article
Loess Is More: Field Investigation and Slope Stability Analysis of the Tanana 440 Landslide, Interior Alaska JAIMY A. SCHWARBER MARGARET M. DARROW* Department of Civil, Geological, and Environmental Engineering, University of Alaska Fairbanks, 1764 Tanana Loop, Fairbanks, AK 99775
RONALD P. DAANEN DE ANNE S. P. STEVENS Alaska Division of Geological & Geophysical Surveys, 3354 College Road, Fairbanks, AK 99709
Key Terms: Landslides, Inventory, Slope Stability Model, Loess, Direct Shear Test, Alaska ABSTRACT Landslides are geologic hazards that threaten human life, property, and infrastructure. Proper mitigation requires knowledge of where landslides occurred in the past. Until recently, no landslide inventory maps had been published for any area of Alaska. Here, we present a short overview of landslide mapping within the Fairbanks North Star Borough (FNSB), Alaska, and a focused investigation of the Tanana 440 (T440) landslide. We mapped 1,679 landslides and field-verified 51 landslides within the FNSB. These landslides vary in age, movement type, and material. We present the results of in-depth mapping; subsurface exploration; soil engineering properties, including results of direct shear testing; and slope stability analysis of the T440 landslide, which we determined is a flow slide in loess that occurred during the late Pleistocene to mid-Holocene. We modeled seven slope stability scenarios for the T440 landslide by varying water table position and seismic load. Our modeling results suggest that thawing permafrost and/or seismic loading were likely possible triggers for the T440 landslide. To our knowledge, we present the first comprehensive direct shear testing of non-plastic silt with a variation in moisture content and the first comparison of direct shear and field vane shear measurements of silt. The average cohesion and internal friction angle of the wetted remolded silt were 3.0 kPa and 23.1°, respectively. These values did not significantly change with increasing moisture content. The direct shear and vane shear strengths of silt had low correlation (R2 = 0.20), unlike the strong correlation that is typical of clay soils. *Corresponding author email: mmdarrow@alaska.edu
INTRODUCTION Landslides are geologic hazards that threaten human lives, property, and infrastructure. In the United States alone, landslide damage exceeds $3 billion annually (Spiker and Gori, 2003; Burns, 2007). To mitigate these natural disasters, we must first know where they occurred in the past. Although this task sounds simple, it involves creating a landslide inventory through thorough and systematic mapping of landslides as geomorphic features (Burns and Madin, 2009; Slaughter et al., 2017). Until recently, no published landslide inventory maps existed for any part of Alaska, a state consisting of 172 million hectares. In 2017, the Fairbanks North Star Borough (FNSB) in Interior Alaska publicly released USGS Quality Level (QL) 1 and QL2 (Heidemann, 2014) light detection and ranging (lidar) datasets that cover much of the borough’s populated areas (FNSB, 2020; Figure 1). The development of this dataset is a boon to the landslide mapping effort in Interior Alaska, as previous studies indicate that lidar analysis identifies three to 200 times more landslides than using just aerial imagery or ground-based studies (Schulz, 2004; Slaughter et al., 2017). Supported in part by the U.S. Geological Survey (USGS) EDMAP, a program that funds universities to train the next generation of geologic mappers, we used lidar-derived datasets to develop a landslide inventory map for part of the FNSB. Nearly 1,700 landslides (Figure 2; Schwarber et al., in review) were identified from lidar derivatives, and more than 50 landslides were fieldverified in the FNSB. As part of the field verification, we conducted detailed mapping and a subsurface investigation of one of the larger landslides, the Tanana 440 (T440) landslide (see Figure 2 for location). This dormant landslide is approximately 2-km long by 1-km wide, with
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Figure 1. Location of the study area with available lidar data extent in the FNSB in relation to Alaska, major cities, and transportation systems (inset). Road data from ADOT&PF (2018), hydrology data from AGC (2020), lidar data from FNSB (2020), and ArcticDEM data (uniform gray areas indicate no data) from Porter et al. (2018).
a total areal extent of 1.9 km2 . It occurred on a 12.5° slope in loess (windblown silt) deposits overlying schist bedrock. We interpret the T440 as a flow slide in loess (Hungr et al., 2001) that occurred sometime during the late Pleistocene to mid-Holocene. In reviewing the literature, we found that only a few studies address the strength properties of silt, either through laboratory tests (e.g., Higgins and Fragaszy, 1988; Derbyshire et al., 1994; Harris et al., 2008; and Zhuang et al., 2018) or field tests (e.g., Lu et al., 2013). In this paper, we (1) present an overview of the landslide mapping effort in the FNSB, with a summary of general findings; (2) present the results of our fieldwork and laboratory testing for the T440 landslide, including direct shear tests for silt and a comparison of those results to field vane shear testing; and (3) explore potential triggers for this landslide event using slope stability modeling. Regional Setting The FNSB is located in Interior Alaska, which has a continental sub-Arctic climate (Wendler and Shulski, 2009). Continental climates are characterized by large annual temperature fluctuations, which are reflected by the record high (35.6°C, June 1969) and low (−54.4°C, January 1934) temperatures recorded in
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Fairbanks (ACRC, n.d.(b)). The average annual snowfall and precipitation calculated for Fairbanks in a 30year period (1981–2010) were 165 cm and 27.5 cm, respectively (ACRC, n.d.(a)). The mean annual air temperature in Fairbanks is –2.4°C, which is low enough to support a periglacial environment and permafrost (French, 2007). The FNSB is within the discontinuous permafrost zone, where 50–90 percent of the ground is underlain by permafrost (Jorgenson et al., 2008). Fairbanks is located along the southern edge of the Yukon-Tanana Upland, which is characterized by rounded, even-topped ridges that rise 152–914 m above adjacent valleys and have gentle side slopes (Wahrhaftig, 1965). The bedrock of these uplands surrounding Fairbanks is part of the Yukon-Tanana Terrane unit of Upper Paleozoic and older metamorphic rocks (Newberry et al., 1996). This region is mainly underlain by the heterogeneous Fairbanks Schist unit, which includes a variety of metamorphic rocks (e.g., schist, marble, quartzite, and amphibolite) but is predominantly quartzite and quartz muscovite schist (Newberry et al., 1996). Interior Alaska experienced only localized glaciation of small cirque glaciers in mountainous areas (Péwé, 1975; Briner et al., 2017) during the Pleistocene, and instead much of the region is blanketed by
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Tanana 440 Landslide, Interior Alaska
Figure 2. Landslide distribution within available lidar extent in the FNSB. Dots and triangles represent landslide occurrence and do not reflect landslide size. The Tanana 440 (T440) landslide location is indicated by the arrow and text. Letters (a) through (f) refer to the landslides presented in Figure 3. Road data from ADOT&PF (2018), hydrology data from AGC (2020), lidar data from FNSB (2020), and ArcticDEM data (uniform gray areas indicate no data) from Porter et al. (2018).
Quaternary loess. The wind-blown silt was transported mainly from the outwash plains of the Tanana River (as well as other fluvial sources) by southern katabatic winds and deposited during glacial cycles (Péwé, 1955, 1975). Herb tundra vegetation characterized glacial landscapes in Interior Alaska, and this lack of trees and shrubs may have affected loess entrapment rates (Muhs et al., 2003a) and reduced the overall contribution of root cohesion to slope stability. Loess deposition in Alaska began as far back as 3 million years ago (Westgate et al., 1990), but deposition around Fairbanks may have begun 2 million years ago (Begét et al., 2008). The loess around Fairbanks ranges in thickness from 0.3–24.4 m on hill tops to 3–30.5 m on middle slopes (Péwé, 1955). Retransported silt in the valley bottoms ranges from 30.5- to 91.5-m thick (Péwé, 1955). Interior Alaska is a seismically active region, and the FNSB has a history of moderate- to large-magnitude earthquakes. The three main tectonic structural drivers
in the Interior are dextral strike-slip faults (e.g., the Denali fault), seismic zones (e.g., Minto Flats fault zone), and thrust faults in the foothills of the Alaska Range (Tape et al., 2015; Koehler et al., 2018). The M 7.9 Denali fault earthquake in 2002 was the largest seismic event recorded in Interior Alaska and triggered thousands of landslides in a ∼13–19-km band along the rupture (Fuis et al., 2003). Although the 2002 tremblor and associated landslides occurred to the south of the study area, large earthquake events also were recorded in FNSB, including a M 7.4 event in 1912, M 6.3 and M 6.5 events in 1929, and M 7.3 events in 1937 and 1947 (St. Amand, 1948; Péwé, 1982). Multiple rockslides were reported along the Tanana River after the 1947 earthquake event (St. Amand, 1948). Overview of FNSB Landslide Inventory Mapping Figure 2 includes the mapped landslides in the portion of the FNSB with lidar coverage. We mapped a
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Figure 3. Examples of field-checked landslides of different material types and various ages: landslides in loess that are (a) prehistoric (interpreted landslide extent marked by dashed lines), (b) historic, and (c) currently active; and landslides in bedrock that are (d) prehistoric, (e) historic (scarps of varying ages marked by dashed lines), and (f) currently active. In (a) and (e), there are adjacent landslides that are not delineated. Arrows indicate the direction of landslide movement. The background is a lidar-derived slope map. See Figure 2 for locations of these landslides.
total of 1,679 landslides and field-verified 51 landslides (Schwarber et al., in review). The landslide desktop mapping and fieldwork methodology is explained in detail by Schwarber et al. (in review). Schwarber et al. (2021) presented preliminary qualitative interpretations of the landslides and described the mapping effort. The mapped landslides vary in age, material, and movement type (Figure 3). Based on geomorphic expression, we interpreted that most of the mapped landslides were prehistoric (i.e., older than the approximately 100 years since the community of Fairbanks was established). We assumed that landslides with rougher surface expression are younger than those with relatively smoother surfaces (McCalpin, 1984). There are also historic and currently active landslides, verified by relative age relationships with existing roads or trails and field observations. We identified landslides of varying ages that occurred in bedrock and in surficial deposits. Landslides occurring in bedrock typically featured the characteristics of translational or rotational movement, but it was not always possible to distinguish the exact movement type due to a lack of
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geomorphological expression. The landslides that occurred in silty surface deposits, however, almost always exhibited the characteristics of flow slides, such as a concave head scarp, curved flanks, and a lobate toe (Varnes, 1984). In general, it was more difficult to gage the relative age of prehistoric landslides in silt due to erosion and its impact on geomorphological expression. These prehistoric flow slides typically only had the arcuate toe deposit remaining, with no clearly identifiable head scarps or flanks due to severe gullying. The landslides in bedrock, by contrast, typically had easily identifiable head scarps, flanks, and toe deposit regions. METHODS Fieldwork We conducted two field investigations of the T440 landslide during the summer of 2020. We mapped exposed stratigraphy near the toe and along the right flank and drilled eight vertical test holes at various
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Tanana 440 Landslide, Interior Alaska
Figure 4. Tanana 440 (T440) landslide extent with test hole and stratigraphy locations and lidar-derived slope map background (FNSB, 2020). Radiometric age dates and depths are shown next to the corresponding test holes. The arrow indicates the direction of landslide movement. We used the profile A-A to extract the topography of the landslide cross section for the slope stability modeling.
locations on the landslide (Figure 4). The test holes were drilled using a powerhead and a Snow, Ice, and Permafrost Research Establishment (SIPRE) coring tool, which limited drilling depths to 4 m. We measured peak and remolded shear strength at regular depths in each boring using a field vane shear test kit following the ASTM D2573 standard (ASTM, 2018). Laboratory Testing We performed a suite of geotechnical laboratory tests on 69 samples collected from the test holes and stratigraphy sites (see Table 1 for a summary of the samples tested). All samples were classified using the Unified Soil Classification System (USCS) according to ASTM D2847 (ASTM, 2017a). We selected five organic samples from different boreholes for radiometric date testing (conducted by Beta Analytic, Inc.). Three of the five samples were rootlets (i.e., less than 10 mm
long and less than 0.5 mm in diameter), along with one paleosol and one charcoal sample. The samples were selected based on their location on the landslide and depth in an effort to bracket the age of the landslide movement. A large portion of our laboratory testing program consisted of direct shear tests on silt samples. Most published direct shear testing results are for sandy or clayey soils, with only a few studies on silt or loess using other methods such as ring shear (e.g., Derbyshire et al., 1994; Zhuang et al., 2018). Typical shear rates varied from 0.02 mm/min to 2 mm/min for clayey to sandy soil, respectively, to achieve a shear strain rate of 5 percent per hour following ASTM D6528 (ASTM, 2017c). Based on published rates and the average grain-size distribution for the silt, we ran the tests at a shear speed of 0.5 mm/min. We tested both remolded soil and undisturbed samples collected from the stratigraphy locations. We tested the silt at a vari-
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Schwarber, Darrow, Daanen, and Stevens Table 1. Laboratory testing conducted for the T440 landslide. Test Name Dry sieve analysis (gradation) Sedimentation analysis (hydrometer) Water content Specific gravity Unit weight of soil Soil classification Atterberg limits Direct shear
ASTM Designation
Reference
No. Samples Tested
D6913 D7928 D2216 D854 D7263 D2487 D4318 D6528
ASTM (2017d) ASTM (2021b) ASTM (2019) ASTM (2014) ASTM (2021a) ASTM (2017a) ASTM (2017b) ASTM (2017c)
2 18 22 4 3 21 6 7
ety of moisture contents, including dry (∼0.3 percent), 5 percent, 10 percent, 15 percent, 20 percent, 25 percent, and saturated (∼37 percent) and at the in situ moisture content when sampled. We prepared all remolded samples to the average dry unit weight of the in situ soil as determined from volumetric measurements (which ranged from 11.9 to 12.7 kN/m3 ). All tests were duplicated for repeatability. Slope Stability Modeling We performed slope stability modeling of the T440 landslide using a deterministic approach and SLOPE/W software (GeoStudio, Broomfield, CO). The ground surface profile across the landslide was extracted from the FNSB lidar data (see A-A in Figure 4). The profile line was oriented slightly obliquely to the landslide movement direction to include some of the original slope uphill past the head scarp and downhill past the toe. We reconstructed the possible original slope surface, adding volume to the upper part of the slope and removing the toe deposit (Figure 5a). To simplify the model, we added horizontal ground surfaces above and below the slope (Figure 5b). For the subsurface materials, we assumed silt overlying schist bedrock based on a published geologic map (Newberry et al., 1996) and well data of the local area (WELTS, 2005). The bedrock surface was placed parallel to the horizontal ground surface above and below the slope. Well data indicated a silt thickness of approximately 4.6 m at the top of the slope (WELTS, 2005), which is estimated to thicken to 100 m in the valley bottom (Péwé, 1955). From the surface reconstruction (Figures 4 and 5a), the surface slope angle (in silt) was estimated as 12.5°. The bedrock angle was estimated as 21.6° to account for the silt thicknesses previously described. For the silt, we applied the average saturated (γsat ) and dry unit weights (γdry ) and average cohesion (c) and internal friction angle ( ) obtained from our direct shear testing of the wetted T440 loess. For the schist bedrock, we selected saturated and unsaturated
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unit weights from the average values of laboratorytested schist (Özbek et al., 2018), cohesion from shear strength reported by Wyllie and Norrish (1996), and the average friction angle corresponding to foliation planes paralleling the slope angle from Gonzalez de Vallejo and Ferrer (2011). As no bedrock exposures are present in or near the T440 area, we chose this conservative value to simulate dip slope failure. Table 2 is a summary of the silt and bedrock properties. Using the SLOPE/W module, we modeled four different groundwater cases, each with and without seismic loading (except Case 4), for a total of seven scenarios. For the first case, the water table was set at 124 m, the approximate elevation of the Tanana River, as determined from the lidar data (the Tanana River runs just south of the T440 area; see Figure 2). In the second case, the water table followed the schist bedrock surface down to the elevation of the Tanana River. The third case had the water table at the ground surface elevation to simulate completely saturated conditions. For the fourth case, we began with the Tanana River at the surface elevation to imitate flood conditions and incrementally rose the water table position from Case 2 conditions until slope failure occurred. Case 4 did not have a seismic component, as the purpose of this part of the analysis was to determine the effect of water table position on slope stability. As noted earlier, Interior Alaska is a seismically active region, with a history of M 7.0 earthquake events in the last 100 years (Péwé, 1982). Understanding this potential for seismic activity, we assessed the effect of earthquake events on slope stability and applied a horizontal peak ground acceler-
Table 2. Summary of the soil and rock properties used in the SLOPE/W modeling. Soil Properties
Silt
Schist
γsat (kN/m ) γdry (kN/m3 ) (°) c (kPa)
14.9 12.3 23.1 3.0
24.3 23.7 25 47.9
3
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Figure 5. Schematic of (a) the longitudinal profile A-A (see Figure 4 for location) with reconstructed pre-landslide surface and (b) the Case 1 slope model with surficial loess and schist bedrock layers, piezometric surface, slip surface grid, and defined landslide entry and exit points.
ation of 0.31g to selected scenarios (Boore and Atkinson, 2007). We used the Spencer deterministic analysis as the main method for all iterations of the model conditions and compared the factor of safety (FS) results with those obtained using the Morgenstern-Price and Janbu Generalized methods. These methods were chosen because they satisfy both the moment and force equilibrium equations and account for both shear and normal interslice forces. We chose the piezometric line option to represent pore water pressure conditions and entry and exit with specified radius tangent lines as the slip surface method, which defined failure surface entry and exit points. Figure 5b is an example of the model environment for Case 1. RESULTS Landslide Stratigraphy Figures 6 and 7 illustrate the stratigraphy derived from the eight test holes drilled across the T440
landslide. Sampled landslide material consisted of silt, with rare occurrences of sandy silt or silty sand. All silt became mottled at depth. We also encountered buried tephra and organic layers such as dark brown paleosols, rootlets, wood chunks, and charcoal. Vane shear peak strength results typically increased with depth, with a moderate coefficient of determination (R2 ) of 0.52 (Figure 8a). Although the residual strength also typically increased with sample depth, these data demonstrated more scatter (Figure 8b). Figure 9 contains the stratigraphic columns for the areas mapped along the right flank and at the inner toe. Mapping revealed silt and mottled silt at depth. At no point during the field work did we encounter bedrock or frozen soil on the T440 landslide. Laboratory Testing Table 3 is a summary of soil test results by borehole or stratigraphic column and depth. Overall, the T440 soils classified as silt (ML), with one sample each of
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Figure 6. Stratigraphic columns for test holes (TH) 1 through 4. Vane shear test (VST) results are adjacent to the tested depth, with residual shear values in brackets. Dates in boxes are radiometric dating results for the corresponding organic layer. BOH = bottom of hole.
silty sand (SM) and sandy silt (ML) (see Figure 10 for grain-size distributions). None of the samples tested demonstrated a plastic limit; however, all samples yielded a liquid limit, which ranged from 19 to 28. Specific gravity ranged from 2.68 to 2.75, which is consistent with a previously published value of 2.70 for Fairbanks silt (Haynes et al., 1980; Farouki, 1981). Gravimetric moisture content ranged from 4.6 percent to 25.8 percent, with samples at deeper depths typically demonstrating the lower moisture contents.
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Volumetric moisture content from in situ samples ranged from 15.8 percent to 30.1 percent. Direct Shear Test Results Table 4 is a summary of the cohesion, friction angle, and moisture content results. Figure 11 illustrates how friction angle and cohesion varied with the moisture content of the silt, respectively. The friction angle was highest for the dry samples, but then decreased
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Figure 7. Stratigraphic columns for test holes (TH) 5 through 8. Vane shear test (VST) results are adjacent to the tested depth, with residual shear values in brackets. Dates in boxes are radiometric dating results for the corresponding organic layer. BOH = bottom of hole.
and was relatively consistent regardless of the moisture content (Figure 11a). Cohesion demonstrated more scatter (Figure 11b), with 0 kPa values for one of the dry samples and both saturated samples. One of the undisturbed samples demonstrated a much higher cohesion (16 kPa) than any of the other values. We attribute this to natural variations in the soil structure, consider it an outlier, and do not include it in the average. The average friction angle and cohesion of the
moist remolded and undisturbed samples (with the noted exception) were 23.1° and 3.0 kPa, respectively. Radiocarbon Dates Three of the samples dated were rootlets collected from 2.55 m to 3.00 m below ground surface (bgs). We initially hypothesized that the rootlets collected near the toe were buried by the landslide event. The
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Figure 8. Peak (a) vane shear strength and (b) residual vane shear results with depth. Residual shear strength values from the direct shear tests are plotted at depths corresponding to the applied normal stress.
modern dates from these samples (Table 5) indicate that the root systems of modern trees penetrate the subsurface more deeply than expected, and thus these samples were not used for landslide dating. The remaining two samples yielded older dates. The oldest date (31,210–30,981 year Cal BP) was obtained from a paleosol at 2.37 m bgs in TH06, located on the distal slope of the inner toe. The other date (7,864– 7,700 year Cal BP) was obtained from a charcoal layer at 1.65 m bgs in TH08, downslope of a gully incised through the outer toe. Slope Stability Analysis Figure 12 illustrates the slope model failure surfaces for all seven scenarios, and Table 6 is a summary of the scenarios and resulting FS. The FS results of the Spencer, Morgenstern-Price, and Janbu Generalized methods for all scenarios were comparable, typically varying by an average of 4 percent; thus, only the results from the Spencer method are included in Table 6 for brevity. In Scenarios 1 and 3, the FS remained above 1. Failure occurred in Scenario 5, where the water table was at the ground surface. Slope failure also occurred in Scenario 7, in which the water table depth varied depending on location within the model (see Figure 12g). In contrast, every scenario with applied seismic loading had a FS below 1. Most
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scenarios demonstrated similar shallow failure surfaces in the surficial silt layer only. The one exception to this was Scenario 3, where the failure surface cut into the schist bedrock; however, the slope did not fail (FS = 1.802). DISCUSSION The T440 landslide primarily consists of windblown silt with rare occurrences of sandy soils and with no bedrock exposure. We interpret the silt deposits as massive unstratified loess. The soil properties and our field observations suggest that the T440 landslide is currently unfrozen, although permafrost may exist at depth. Based on its geomorphology, we interpret the T440 landslide as being younger than other paleoslides in loess that we mapped in the western FNSB. For example, the T440 head scarp and flank regions are rougher and more well-defined than the loess landslides near Fairbanks, which are characterized by arcuate toe deposits and severe gullying in the head scarp regions (see Figure 3a as an example). Two out of the five radiocarbon-dated organic samples yielded dates that may help to bracket the age of the T440 landslide. These are the paleosol from TH06 and the buried charcoal layer in TH08 (see Figure 4 for locations). Based on the reported ages, the paleosol and charcoal layers formed during periods of reduced
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Figure 9. Stratigraphic columns for the four mapped silt exposures. BOS = bottom of section.
loess deposition rates due to landscape stability (Muhs et al., 2004; Josephs, 2010). These radiocarbon dates yield two possible interpretations (illustrated in Figure 13) for when the T440 landslide occurred:
1) Young single landslide model. In this model, the ∼31,000-year-old paleosol (TH06) was transported downslope to its current location as part of the inner toe within a mass of soil that
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Schwarber, Darrow, Daanen, and Stevens Table 3. Summary of laboratory test results.
Location and Depth (m) TH01 1.24 2.40 3.60 TH02 0.91 2.95 3.34 TH03 1.33 2.90 3.37 TH04 1.42 2.33 TH05 0.94 2.18 3.52 TH06 1.01 1.73 TH07 1.66 2.34 3.34 TH08 1.11 1.40 2.68 3.43 STRAT2 1.90 3.00 3.80 STRAT3 2.75 STRAT4 1.70 2.30 3.60
Classification
Liquid Limit (%)
Natural Moisture Content (%)
Volumetric Moisture Content (%)
Specific Gravity
— — ML
— — 27.8
20.8 13.3 6.7
— — —
— — —
ML SM —
19.3 — —
12.0 6.3 4.6
— — —
2.73 — —
ML (2 samples) — ML
24.8 — —
15.0 8.0 9.2
— — —
— — —
— ML (2 samples)
— —
9.0 7.4
— —
— —
ML — ML
— — —
— 9.9 9.1
— — —
— — 2.68
— ML
— 27.5
20.1 —
— —
— 2.75
ML — ML
— 26.9 —
13.7 — 7.1
— — —
— — —
— ML ML —
— — — —
20.5 — — 13.7
— — — —
— — — —
ML — ML
— — —
— 24.4, 25.8 —
— — —
— — 2.74
ML
—
23.3, 25.3
30.1, 30.8
—
— — ML
— — 27.8
12.6 13.0 18.0
— 15.8 —
— — —
— Indicates no data. TH = test hole; SM = silty sand; ML = silt or sandy silt; STRAT = stratigraphic column.
failed as a competent block, making the landslide younger than ∼31,000 years (Figure 13a). Hungr et al. (2001) indicated that flow slides in non-plastic soils, such as the silt of the T440 landslide, may be mostly unsaturated with liquefaction constrained to a thin basal layer, resulting in a dry landslide body. Our observations of the T440 surface support this possible interpretation of the landslide event. In this model, we assume that subsequent gullying and erosion through the landslide toe (Figure 13b) occurred shortly after the landslide event when
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a bare loess surface was exposed, producing the sediment that buried the charcoal layer (TH08). Thus, the landslide also may be younger than ∼8,000 years. The inner toe and outer toe deposits could have occurred in two pulses from the same triggering event. 2) Old multiple landslide model. In this model, an initial landslide event occurred prior to ∼31,000 years ago, forming the inner toe (Figure 13c). The paleosol formed on the new landslide surface of the inner toe. That first event was subsequently overrun by a second, younger event,
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Depth
Material
Age
TH01 TH03 TH04 TH06 TH08
3.00 m 2.55 m 2.59 m 2.37 m 1.65 m
Rootlets Rootlets Rootlets Paleosol Charcoal
1991–1994 Cal AD 1994–1998 Cal AD 1979–1981 Cal AD 31,210–30,981 Cal BP 7,864–7,700 Cal BP
TH = test hole.
which formed the outer toe and buried the charcoal downslope of it around ∼8,000 years ago (Figure 13d).
Figure 10. Grain-size distributions for T440 samples. The blue lines show upper and lower boundaries, and the dotted gray line is the approximate average grain-size distribution of all hydrometer-tested samples (N = 18) within the shaded blue region.
We do not have enough information to make a definitive conclusion on the timing of the T440 landslide with only two radiocarbon date results. We believe the young single landslide model is more likely based on our interpretation of the T440 geomorphology. It is improbable that a subsequent landslide event would overrun the inner toe resulting in a deposit with such a similar shape (i.e., the outer toe). Regardless, these dates indicate that the T440 landslide occurred in the late Pleistocene to mid-Holocene. Additionally, the T440 surface supports two dry, vegetated, flat-bottomed gully systems that run the full length of the landslide body, as well as two shorter gullies near the left flank (see Figure 4). None of these gullies demonstrate evidence of modern water flow, such as incision or sediment deposition over vegetation. These gullies most likely formed after the landslide occurred, during the late Pleistocene to mid-Holocene when the landscape was underlain by permafrost and characterized by herb tundra (Muhs et al., 2003a), with no trees to capture water or provide cohesion through their root systems. Permafrost in the Fairbanks region last formed during the Wisconsin glaciation beginning around 150,000 Table 6. Slope stability analysis scenario conditions and factor of safety results.
Table 4. Direct shear testing results. Test 1
Water Table Position and Scenario Test 2
Water Content (%)
(°)
c (kPa)
(°)
c (kPa)
Dry 1 Dry 2 in situ (10.5, 25.1) 4.9 9.6 16.4 20.3 25.3 Saturated (37.2)
32.1 37.7 15.7 22.5 19.2 21.7 25.7 25.6 26.6
2.3 0 15.8 1.0 6.6 3.3 0.6 1.6 0
29.4 34.2 27.0 23.2 21.9 21.5 21.4 25.6 22.3
3.6 1.8 4.6 1.9 5.5 7.8 3.9 3.8 0
Case 1: base elevation 1 2 Case 2: bedrock surface 3 4 Case 3: ground surface 5 6 Case 4: rising water table 7
Seismicity
FS
None 0.31g
2.072 0.815
None 0.31g
1.802 0.821
None 0.31g
0.691 0.266
None
0.993
FS = factor of safety.
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Figure 11. Variation of (a) friction angle and (b) cohesion with moisture content from the direct shear test results. Note that the cohesion of 16 kPa from one of the undisturbed tests is considered an outlier and was not included in the average.
year BP for Alaska (Péwé, 1977). An Arctic climate with windy and arid conditions persisted during the Last Glacial Maximum from 30,700 to 15,700 year BP, and the landscape was characterized by herb tundra with minimal ground roughness (Muhs et al., 2003a; Finkenbinder et al., 2014). Factors that favored loess production (e.g., strong winds) were outweighed by factors that hindered loess deposition (e.g., herb tundra) (Muhs et al., 2003b). These conditions supported periods of paleosol pedogenesis, as reduced surface roughness was insufficient to trap loess. The paleosol
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that we sampled and dated at ∼31,000 year BP from the T440 landslide may correspond to this period of soil formation. It is also likely that the T440 landslide slope was perennially frozen (permafrost) during this period. Starting around 15,700 year BP, the climate shifted to warmer, wetter, and less windy conditions and herb tundra declined (Finkenbinder et al., 2014). The Holocene Thermal Maximum occurred 11,000 to 9,000 year BP in Alaska (Kaufman et al., 2004). The higher-than-current temperatures during that period (Kaufman et al., 2004) may have created favorable con-
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Figure 12. Modeling results with failure surface shapes for Case 1 (a) scenario 1 and (b) scenario 2; Case 2 (c) scenario 3 and (d) scenario 4; Case 3 (e) scenario 5 and (f) scenario 6; and Case 4 (g) scenario 7. The green region is the failure shape and slices; refer to Figure 5b for explanation of the other features. Each figure has 1:1 vertical and horizontal scales, and an indication of scale is provided in (g).
ditions for permafrost thaw. The early Holocene period from 9,400 to 8,700 year BP saw considerable changes as the climate transitioned to stable Holocene conditions, with increases in temperature and precipitation and further decline in herb tundra (Finkenbinder et al., 2014). There was a warming interval during the early to middle Holocene from 8,000 to 5,000 year BP that lowered the permafrost table and melted ice wedges (Péwé, 1977). Alder and spruce also appeared and increased during this period (Finkenbinder et al., 2014), indicating the shift from herb tundra to forest. The T440 charcoal dated at ∼8,000 year Cal BP is consistent with the timing and appearance of forest vegetation. All modeled scenarios with a FS < 1.0 demonstrated surficial failure in the loess. This modeled failure shape and depth agrees with the T440 slope failure A-A pro-
file and the lack of bedrock observed during fieldwork. One possible trigger was thawing permafrost. Results from the slope stability analysis indicate that a high water table at or near the surface (see Figure 12e–g and Table 6) results in slope failure. The dry gullies and dry soils intercepted while drilling and in stratigraphic sections suggest that a fully saturated condition is not probable for current conditions. As permafrost thawed and remained at depth, however, it would have formed an impermeable layer. This, coupled with the pore water pressures modeled in Scenario 7, may have triggered the T440 landslide. The timing of the young, single landslide model fits well with mid-Holocene warming when permafrost started to degrade. Our slope stability analysis results also suggest that a strong seismic event is a possible trigger or
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Figure 13. Schematics illustrating possible T440 landslide event models (not to scale). For the single landslide model, (a) the ∼31,000-year-old paleosol was transported within a competent block that formed the inner toe and then (b) subsequent erosion and gullying deposited sediment that buried the ∼8,000-year-old charcoal. For the multiple landslide model, (c) an older landslide event formed the inner toe prior to ∼31,000 years ago and then (d) Event 1 was overrun by a younger event (Event 2) ∼8,000 years ago that formed the outer toe, followed by erosion and gullying.
contributing factor for the T440 landslide (see Figure 12b, d, and f and Table 6). Future seismic events in this region may cause new landslides, as evidenced by rockslides triggered by a powerful earthquake event in the 20th century (St. Amand, 1948). As part of this study, we present direct shear results for silt with negligible clay and sand content. Published grain-size distributions of Alaska loess indicate the soil typically has sand and clay contents of under 5 percent (Muhs and Budahn, 2006), which agrees with our results. Only a handful of studies have analyzed the strength of silt (e.g., Higgins and Fragaszy, 1988; Derbyshire et al., 1994; Harris et al., 2008; and Zhuang et al., 2018), and the tested soils were silty sand or clayey loess. Test methods varied from ring shear tests (Derbyshire et al., 1994; Zhuang et al., 2018) to consolidated-undrained and unsaturatedconsolidated-undrained triaxial tests (Higgins and Fragaszy, 1988). Our work, however, consisted of a sys-
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tematic program of direct shear testing of non-plastic loess at varying moisture contents. These results represent a novel contribution to the body of literature. The lack of clay content is a distinguishing feature of loess found in Interior Alaska. It also has a low calcite content, which distinguishes it from highly calcareous North American, European, and Chinese loess (Muhs and Budahn, 2006). Our average cohesion value (3.0 kPa) agrees with the values obtained by Higgins and Fragaszy (1998) and Harris et al. (2008) of 2.1 kPa and 3.5 kPa, respectively. Our average friction angle (23.1°) is lower than other recorded values of 34.5° (Higgins and Fragaszy, 1998) and 28°–36° (Harris et al., 2008) of silty soils. The friction angle dropped when the silt was wetted, but then remained consistent with varying moisture content, demonstrating that loess loses shear strength once wetted (Derbyshire et al., 1994). Cohesion demonstrated more scatter around the average value with varying moisture content,
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dropping to 0 kPa at the maximum water content (37.2 percent). Two previous studies demonstrated that vane shear strength and direct shear strength results were nearly identical for clay soils (Lefebvre et al., 1988; Hirabayashi et al., 2016). To our knowledge, no studies exist that compare direct shear and field vane shear measurements of silt. Unlike the clay soils, our direct shear strength results for silt did not demonstrate such a strong correlation to field vane shear test results. Our results indicated that the vane shear strength most closely matched the residual shear strength from laboratory tests (see Figure 8b). This is most likely due to the structure of the loess being disturbed and soil grains, particularly platy micas, being realigned during the shearing process. Although the overall correlation of the data in Figure 8b is low (R2 = 0.20), we attribute these differences to the natural variation with depth in moisture content and structure of the in situ soils as compared to the controlled moisture content and remodeled structure in the laboratory samples. CONCLUSIONS We interpret the T440 landslide as an inactive flow slide in loess that occurred during the late Pleistocene to mid-Holocene based on an analysis of stratigraphy, soil testing, and geomorphology. Two possible interpretations, based on radiocarbon dating, of how the T440 landslide occurred are (1) as one event younger than ∼8,000 years with portions moving as competent blocks or (2) as two events that formed the inner (prior to ∼31,000 years ago) and outer toe deposits (after ∼8,000 years ago). Additional dating is required to determine the landslide age conclusively. Thawing permafrost during early- to mid-Holocene warming may have been a possible trigger. Slope stability modeling indicates that seismic activity also could have been a trigger for this landslide, as every model with an applied seismic load resulted in slope failure. We provide a new research contribution through the direct shear testing of silty soils. Our work systematically tested non-plastic silt with variation in moisture content and recorded the soil strength parameters of internal friction angle and cohesion. The average values of the internal friction angle and cohesion were 23.1° and 3.0 kPa, respectively. Our results are consistent with loess losing shear strength once wetted. These values can be used for engineering design purposes for Interior Alaska silt for any gravimetric water content over 5 percent. Here, we present the results of laboratory and field testing of the strength of silt to support this specific landslide analysis. It was not the purpose of our study to validate these methods against each other. Our re-
sults, however, indicate that a comprehensive study of the strength properties of silt, including the repeatability of field vane shear testing in loess deposits, would be beneficial. The field vane shear test is simple and inexpensive to perform; validating its use in silt against laboratory testing may provide another tool to practicing engineers in Interior Alaska. Additionally, we recommend future work exploring the effect of increasing normal stress and soil density on field vane shear testing of silt. The landslide inventory map was not the focus of this paper; however, prior to conducting the comprehensive analysis to produce the inventory, we were unaware of the presence of the T440 landslide. Therefore, the landslide inventory represents a muchneeded resource that can be used to determine which landslides in the FNSB represent potential risks to infrastructure. Future work should include determining the absolute and relative ages of other landslides in the FNSB. In addition to collecting more organic samples from flow slides, bedrock exposures on rockslides can be radiometrically dated using cosmogenic nuclide methods (Ivy-Ochs and Kober, 2008), and tephra samples should be analyzed to determine their source and age. Identifying clusters of landslides that occurred at the same time could indicate if they were triggered by one major or a series of smaller seismic events or other possible triggers. Suggested fieldwork includes trenching landslide bodies and coring local long-lived lakes to search for seismically disturbed sediments or flood deposits to identify evidence of possible trigger mechanisms. Thermal modeling with paleoclimate scenarios can be used to determine conditions necessary for thawing permafrost underlying south-facing slopes. ACKNOWLEDGMENTS This research was supported by the U.S. Geological Survey, National Cooperative Geologic Mapping Program, under USGS award number G20AC00130. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government. We extend our thanks to the staff of the Alaska Division of Geological & Geophysical Surveys for their support during fieldwork and help with formatting the maps, and we are grateful to the homeowners who allowed us access to their private properties during the T440 fieldwork. REFERENCES Alaska Climate Research Center (ACRC), n.d.(a), Climate Normal, available at http://climate.gi.alaska.edu/Climate/ Normals
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Factors Affecting Shrinkage Crack Development in Clay Soils: An Experimental Study CHINMAY V. LOKRE ABDUL SHAKOOR* NEIL A. WELLS Department of Geology, Kent State University, Kent, OH 44242
Key Terms: Clay, Shrinkage Cracks, Dry Density, Initial Water Content, Drying Temperature, Layer Thickness, Plasticity Index
ABSTRACT Clay soils are widely used in constructing embankment dams, levees, highway embankments, sanitary landfills, and hydraulic barriers where they are compacted at maximum dry density and optimum water content. These structures are exposed to many cycles of wetting and drying during their service life, resulting in volume changes and developing shrinkage cracks. This study investigated the effect of density, water content, drying temperature, layer thickness, plasticity index, and multiple wetting and drying cycles on shrinkage crack parameters (length, aperture, and area). Five samples each of low-plasticity clay, medium-plasticity clay, and high-plasticity clay at water contents on both sides of optimum water content were compacted and then oven dried at temperatures of 10°C, 20°C, 30°C, 40°C, and 50°C. Upon complete drying, crack length, aperture, and area were digitally measured. Additionally, we saturated uncompacted clay layers of varying thicknesses (5, 7, 10, 20, and 30 mm) of the three clay types and oven dried them at the listed temperatures to investigate the effect of layer thickness on shrinkage crack parameters. The length, aperture, and area of the cracks were correlated with the influencing factors. Among the compacted samples, only high-plasticity clay samples developed shrinkage cracks, exhibiting an increase in crack length, aperture, and area with an increase in dry density and water content. For the uncompacted samples, crack length and crack area decreased with increasing layer thickness, whereas crack aperture increased.
*Corresponding author email: ashakoor@kent.edu
INTRODUCTION Background Information Clays are widely used to construct embankment dams, levees, highway embankments, sanitary landfill covers and liners, and other hydraulic barriers. Clays also comprise the foundation soil for numerous buildings. For most of these structures, the clay is compacted at maximum dry density (MDD) and optimum water content (OWC). These structures are often exposed to alternate wetting and drying conditions that can result in the development of shrinkage cracks. Propagation and interconnection of these cracks affect the stability of these structures and result in increased seepage. Clay liners, used to prevent downward migration of leachate from sanitary landfills, may experience shrinkage cracking if their compaction water content dries out during extended dry periods before waste placement. Similarly, sanitary landfill covers, experiencing alternate wetting and drying, may develop shrinkage cracks, resulting in increased percolation of precipitation and increased leachate generation. Engineering literature extensively documents the detrimental effects of shrinkage cracks in structures made of clay soils (Longwell, 1928; Willden and Mabey, 1961; Mitchell, 1986; Morris et al., 1992; Kong, 1994; Rodrigues et al., 2007; Tang et al., 2008; Costa et al., 2013; and Julina and Thyagaraj, 2018). Shrinkage cracks mostly exhibit a uniform polygonal geometry. However, the restriction of deformations by friction or adhesion, heterogeneity of boundary conditions, and variations of local porosity and mineralogy may lead to a non-uniform pattern of cracks (Wei et al., 2016; Banerjee et al., 2021). According to Kodikara et al. (2000), shrinkage cracks can exhibit orthogonal patterns, non-orthogonal patterns, and hexagonal patterns. Shrinkage crack polygons in various sediments can range in size from a few centimeters to a few meters, usually being less than 2 m across (El Maarry et al., 2010). In some cases, they can span tens of meters and, in rare cases, hundreds of meters, such as the giant shrinkage crack polygons found in Utah salt beds that reach up to 300 m in
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diameter (Neal et al., 1968; Lakshmikantha et al., 2012). Shrinkage cracks usually have a V-shaped parting between the two sides of a crack (Anderson, 2014). Mechanisms of Shrinkage Crack Development Wei et al. (2016) state that “cracking occurs when soils are restrained while undergoing volume change produced as a result of the soil suction generated within the desiccating soil matrix.” During shrinkage, the matrix volume decreases due to collapsing layers of clay micelles. The forces acting in the horizontal plane cause shrinkage and produce cracks in the clay layer (Morris et al., 1992; Péron et al., 2009; Anderson, 2014; and Wei et al., 2021). Corte and Higashi (1960) point out that shrinkage macro-cracking is due to the formation of tensile stresses during dehydration that exceed the tensile strength of the clay soil. Shrinkage cracks in muddy sediment form by tension created by a decrease in the volume of water, resulting from salinity variations of the depositing medium, sediment compaction, temperature, or alternating wetting and drying (Plummer and Gostin, 1981). Primary cracks tend to form and propagate along an orthogonal configuration (Morris et al., 1992; Tang et al., 2010, 2010; Costa et al., 2013; and Kodikara and Costa, 2013). With progressive dehydration and cracking, the blocks between primary cracks display a tendency to break into smaller blocks along a secondary network of cracks. Energy optimization in the process causes the secondary cracks to intersect at angles close to 120° (Corte and Higashi, 1960; Péron et al., 2009). Non-orthogonal cracks are the least frequently occurring pattern and tend to occur in soils of low thicknesses, leading to a high concentration of strain energy. The formation of an orthogonal crack pattern depends upon uniform distribution of tensile stress and/or of pores within the material (Costa et al., 2013). Since such uniformity is difficult to maintain even under controlled conditions, the formation of orthogonal cracks is also infrequent. A combination of orthogonal and non-orthogonal cracks is a commonly seen crack pattern. Both vertical and horizontal shrinkage cracks develop due to active tensile forces, with vertical cracks being perpendicular to the horizontal cracks (Wei et al., 2016). Dry density, water content, drying temperature, clay layer thickness, plasticity index, and number of wetting and drying cycles are likely to influence the nature of shrinkage cracks upon dehydration of clays (Khatun et al., 2015; Bamgbopa, 2016; and Lokre, 2019). Generally, one would expect the frequency of shrinkage cracks to be higher for clays with lower dry density, higher initial water content, greater change in temperature, thinner clay layer, higher plasticity index, and
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greater number of wetting and drying cycles (Daniel and Wu, 1993; Albrecht, 1996; and Holtz et al., 2011). Miller et al. (1998) state that the geometric features of cracks, such as aperture, depth, and surface area, are important parameters because they influence both the soil hydraulics and mechanics that are active during the process of dehydration and shrinkage of clay. The length, aperture, and area of these cracks contribute to secondary porosity and permeability and are important parameters to consider in such cases. STUDY OBJECTIVE Despite numerous studies, the mechanisms and principal factors involved in the development of shrinkage cracks cannot be fully explained (Corte and Higashi, 1960; Abu-Hejleh, 1993; Konrad and Ayad, 1997; and Li and Zhang, 2011). Most studies of shrinkage cracks have focused on the angles at which cracks intersect or the number of crack intersections per unit area. There is a need to further investigate the influence of dry density, water content, drying temperature, clay layer thickness, plasticity index, and number of wetting and drying cycles on shrinkage crack parameters such as crack length, crack aperture, and crack area. Thus, the objective of this study is to evaluate the effect of these factors on shrinkage crack parameters for three types of clay soil: low-plasticity clay (CL), medium-plasticity clay (CM), and highplasticity clay (CH). Note that in soil mechanics, CM is a symbol used for silty clay according to the Unified Soil Classification System (USCS) (Casagrande, 1948). However, in this study M designates “medium plasticity.” METHODS Sample Collection and Sample Preparation In this study, we used three different types of clay: CL, CM, and CH. Ninety kilograms of CL was collected from a local site in northeast Ohio. The clay was oven dried at 105°C for 24 hours and pulverized to pass through a #40 (0.042 mm) sieve. The pulverized clay was divided into three portions weighing approximately 36, 27, and 27 kg. We mixed small samples of pulverized clay with varying amounts of commercial bentonite (a very high plasticity clay) and tested the Atterberg limits (described in the “Laboratory Investigations” section) until the amount (percent by weight) of bentonite required to produce CM and CH were determined. Atterberg limits tests indicated that 11 percent and 21 percent of bentonite, by dry weight, were required to change CL to CM and CH, respectively. Mixing of these percentages of bentonite with the two
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27-kg portions of CL provided the CM and CH samples required for laboratory testing. The three types of clay samples were stored in 19-L plastic buckets with airtight lids. It should be noted that mineral composition of the three types of clay was not determined in this study. Clay soils with different clay minerals will have different shrink-swell characteristics. The CLs in northeast Ohio, from where we collected our CL sample, generally contain illite as the primary clay mineral (Wolfe, 2014). Laboratory Investigations Laboratory investigations consisted of performing the Atterberg limits test, compaction test, uncompacted saturated samples test, and multiple wetting and drying cycles test on uncompacted samples. The Atterberg limits test, performed according to American Society for Testing and Materials (ASTM) method D4318 (ASTM, 2010) using tap water, was used to determine liquid limit (LL), plastic limit (PL), and plasticity index (PI). We used the results of the Atterberg limits test to distinguish between CL, CM, and CH, using the USCS (Casagrande, 1948). The standard Proctor compaction test (ASTM method D698; ASTM, 2010) was used to establish the compaction curves for the three types of clay to help determine MDD and OWC values. We compacted samples using 25 blows as well as 10 blows per layer to study the effect of varying density, resulting from two different compactive efforts, on various aspects of shrinkage cracks (crack length, crack aperture, and crack area). Based on the initially established compaction curves, we prepared replicate compacted samples at five different water contents for each type of clay. Care was taken to prepare two samples each on the dry and wet sides of OWC and one sample close to OWC. All samples were dried at temperatures of 10°C, 20°C, 30°C, 40°C, and 50°C until a constant dry weight was obtained for each sample. Photographs of all dried samples were taken and digitized to determine crack parameters. The data from compaction tests were used to investigate the influence of density and initial water content on crack parameters. Circular, aluminum pie plates, 21.5 cm in diameter and 3.5 cm deep, were used to prepare uncompacted saturated layers of the three types of clay with the following thicknesses: 5, 7, 10, 20, and 30 mm. Circular containers were used in this study to reduce the edge effects. The material of the container (aluminum versus ceramic) may also affect crack geometry, but that was not an objective of this study. Before saturation, the clay was placed in the pie plates in the loose state until the layer reached its desired thickness across the entire sample. All samples were dried at temperatures
of 10°C, 20°C, 30°C, 40°C, and 50°C until a constant dry weight was obtained for each sample. Photographs of the dried samples were taken and digitized to determine crack length, crack aperture, and crack area. The data from uncompacted dried samples were used to investigate the effect of layer thickness on crack parameters. To investigate the effect of cyclic wetting and drying on the nature of shrinkage cracks, CL, CM, and CH samples, with a layer thickness of 10 mm, were saturated and dried at 40°C. The dried samples were analyzed for the crack parameters after the first cycle of wetting and drying. The same samples were then subjected to four additional cycles of wetting (saturation) and drying, and crack length, crack aperture, and crack area were measured after each cycle, using photographs taken after each cycle and digitizing them for image analysis. Five samples each of the three types of clay were compacted at OWC. Similarly, five, 10-mm-thick, uncompacted saturated samples of the three clay types were prepared. All samples (compacted and uncompacted) were oven dried at 40°C to investigate the reproducibility of results in terms of the nature of cracks. Measuring Shrinkage Crack Parameters A Samsung SM-G920-A camera was used to take pictures of all compacted and uncompacted dried samples. The purpose was to capture various aspects of shrinkage cracks developed during the drying process. ImageJ software was used to measure the length of shrinkage cracks. Individual sections of developed shrinkage cracks were measured by employing the freehand line drawing tool. The number of pixels spanning each individual section was measured and summed using the measure function. A scale was present in each image to help convert the number of pixels into actual length in centimeters. The crack length was normalized with respect to sample surface area and was designated as “normalized crack length.” The units of this parameter are mm/mm2 , as it is a ratio of length (mm) to area (mm2 ). Crack aperture was measured perpendicular to the length of each large crack at several locations for each sample, and the average of the three highest values was recorded to account for the worst-case scenario. This was done using the software ImageJv.1.51t and employing the freehand drawing tool. The number of pixels spanning each transect was calculated using the measure function, and the procedure was repeated for all dried samples. Crack area is a better indicator of secondary porosity induced by shrinkage because it encompasses both crack length and crack aperture. In case of a hydraulic
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Lokre, Shakoor, and Wells Table 1. Atterberg limits for the three types of clay.
Type of Clay Low-plasticity clay Medium-plasticity clay High-plasticity clay
Liquid Limit
Plastic Limit
Plasticity Index
38 50 80
19 21 29
19 29 51
barrier, the permeability will depend on crack area, in addition to crack connectivity, if shrinkage cracks develop. Previous research on shrinkage cracks focused on crack length and crack aperture but ignored crack area. For measuring crack area, each of the acquired photographic images were processed identically as follows: (1) the edges of the photographs were cropped to the sample boundaries; (2) color photographs were converted into black and white photographs using the “Black and White” feature in Google Picasa software version 3.9 (Google, 2011); (3) a layer of the pencil sketch effect was introduced in order to provide a contrast between the shrunken clay and the cracks in between (the clay exhibited a much lighter shade of grey than the cracks); and (4) each polygon in the photograph, representing a portion of the shrunken clay sample, was replaced with white pixels and the remaining area, covering the cracks, was replaced with black pixels. The images obtained in step 4 were analyzed using a MATLAB program (MATLAB, 2017). The pixel count function was employed to distinguish between the area occupied by the cracks and that occupied by the shrunken clay sample. The ratio of the surface area of the cracks to the total surface area of the shrunken sample, expressed as a percentage, was designated as the “normalized crack area.” Data Analysis The length, aperture, and area of shrinkage cracks for the three types of clay were correlated with dry density and initial water content for compacted samples and clay layer thickness for uncompacted samples. Additionally, the effect of drying temperature, PI, and wetting and drying cycles on crack length, aperture, and area was evaluated. RESULTS OF LABORATORY INVESTIGATIONS Table 1 summarizes the results of Atterberg limits tests for the three types of clay, whereas Figure 1 shows a plot of Atterberg limits on the Casagrande plasticity chart. The plot indicates that, according to the USCS (Casagrande, 1948), the three clays classify as CL, CM, and CH.
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Figure 1. A plot of Atterberg limits on Casagrande plasticity chart. The red, green, and blue circles indicate CL, CM, and CH samples, respectively.
Figure 2 displays the compaction curves for CL, CM, and CH, respectively. The MDD values for CL, CM, and CH are 101.9 lb/ft3 (1.63 Mg/m3 ), 97.0 lb/ft3 (1.56 Mg/m3 ), and 94.2 lb/ft3 (1.51 Mg/m3 ), respectively, whereas the OWC values are 20.7 percent, 25.3 percent, and 40.2 percent, respectively (Figure 2). The compaction test results indicate that MDD decreases and OWC increases with increasing plasticity for the three types of clay. Among the compacted samples of the three types of clay, only the CH samples developed sinkage cracks upon drying. Figures 3 and 4 are composites of images of CH samples compacted using 10 and 25 blows, respectively, at various water contents (by columns) and drying temperatures (by rows). Gaps indicate images lost due to a computer virus. The figures show that drying did not lead to the development of distinct shrinkage cracks in all samples. For drier samples (30 percent or 35 percent water content), the combination of lower compactive effort and low water content resulted in crumbly or clumpy samples with numerous voids (Figure 3). These samples did not develop distinct shrinkage cracks because the presence of voids prevented crack propagation and helped dissipate tensile stresses responsible for crack development. The wetter samples (>35 percent water content), compacted using 10 hammer blows/layer, showed some development of shrinkage cracks (Figure 3). For samples compacted using 25 hammer blows/layer, the higher compactive effort resulted in relatively homogeneous samples conducive to crack formation during oven drying (Figure 4). Generally, crack length and crack aperture increased with increasing water content and increasing temperature. Figures 5, 6, and 7 are composites of images of uncompacted saturated samples of CL, CM, and CH, respectively, of varying thicknesses (rows) and dried at varying temperatures (columns). Gaps indicate where images were lost due to a computer virus. The figures
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Figure 3. A composite of images of CH samples compacted using 10 hammer blows/layer, at specific water contents (columns), and dried at specific temperatures (rows).
increasing plasticity (Figures 6 and 7). Note that in the thicker samples, the combination of larger polygons and increased crack aperture lead to a reduction in total crack length.
Figure 2. Compaction curves for low-plasticity clay (top), mediumplasticity clay (middle), and high-plasticity clay (bottom). Note: 62.4 lb/ft3 = 1 Mg/m3 .
show a gradual trend from long and narrow shrinkage cracks surrounding smaller polygons in the thinner samples (5 and 7 mm) to wide cracks surrounding larger polygons in thicker samples (20 and 30 mm). These differences become more pronounced with
Figure 4. A composite of images of CH samples compacted using 25 hammer blows/layer, at specific water contents (columns), and dried at specific temperatures (rows).
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Figure 5. A composite of images of CL samples of mentioned thicknesses (rows) dried at the specified temperatures (columns).
Figure 7. A composite of images of CH samples of mentioned thicknesses (rows) dried at the specified temperatures (columns).
Reproducibility of Laboratory Test Results
samples. The normalized crack length for the five samples ranges from 0.18 mm/mm2 to 0.31 mm/mm2 , crack aperture ranges from 0.7 mm to 1.4 mm, and normalized crack area ranges from 4.91 percent to 6.37 percent. The data indicate a considerable amount of variability among the five samples. This lack of reproducibility can be attributed to the heterogeneous nature of compacted samples, resulting from nonuniform distribution of water and compactive energy within the samples. Five, 10-mm-thick, uncompacted saturated samples of each of the three types of clay were oven dried at 40°C to check reproducibility. All samples developed cracks upon drying. Tables 3, 4, and 5 list the normalized crack length, crack aperture, and normalized
Five samples each of CL, CM, and CH were compacted at OWC and oven dried at 40°C. Only CH samples exhibited noticeable cracks. Table 2 presents the data regarding normalized crack length, crack aperture, and normalized crack area for the five CH
Table 2. Normalized crack length, crack aperture, and normalized crack area for CH samples compacted at OWC and dried at 40°C.
CH
Figure 6. A composite of images of CM samples of mentioned thicknesses (rows) dried at the specified temperatures (columns).
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Mean Standard deviation Coefficient of variance
Normalized Crack Length (mm/mm2 )
Crack Aperture (mm)
Normalized Crack Area (%)
0.31 0.18 0.27 0.21 0.24 0.24 0.05 0.21
0.8 1.1 1.2 1.4 0.7 1.04 0.29 0.28
5.31 5.89 5.48 6.37 4.91 5.59 0.56 0.10
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Table 5. Normalized crack area for the five uncompacted saturated samples, 10 mm thick, dried at 40°C for each of the three types of clay.
Clay (mm/mm2 )
Mean Standard deviation Coefficient of variance
Clay (%)
CL
CM
CH
CL
CM
CH
0.39 0.44 0.48 0.50 0.52 0.47 0.05 0.11
0.30 0.33 0.38 0.42 0.46 0.38 0.06 0.17
0.30 0.36 0.38 0.41 0.45 0.38 0.06 0.15
3.08 3.11 3.25 3.32 3.4 3.23 0.14 0.04
4.03 4.26 4.56 4.84 5.02 4.54 0.41 0.09
18.64 19.03 20.37 21.69 22.94 20.53 1.80 0.09
crack area for the five samples of each clay type, respectively. The normalized crack length values vary between 0.39 mm/mm2 and 0.52 mm/mm2 for CL, 0.30 mm/mm2 and 0.46 mm/mm2 for CM, and 0.30 mm/mm2 and 0.45 mm/mm2 for CH (Table 3). CM and CH samples show the greatest difference between maximum and minimum values of crack length. The data suggest the results are not very reproducible. However, the extent of variability of the results for uncompacted samples is considerably less than that for compacted samples. The crack aperture varies between 3 mm and 4 mm for CL samples, between 5 mm and 6 mm for CM samples, and between 11 mm and 14 mm for CH samples (Table 4). These data suggest much better reproducibility of aperture results for the uncompacted saturated samples. The normalized crack area varies between 3.1 percent and 3.4 percent for CL, between 4.0 percent and 5.0 percent for CM, and between 18.6 percent and 22.9 percent for CH (Table 5). These results suggest fair to good reproducibility for uncompacted, saturated samples compared to compacted samples. Table 6 shows the comparison of p-values for pairwise comparison of crack measurements for the three clay types, according to the Mann-Whitney-Wilcoxon test. In this test, a p-value less than 0.05 indicates that Table 4. Crack aperture for the five uncompacted saturated samples, 10 mm thick, dried at 40°C for each of the three types of clay. Clay (mm)
Mean Standard deviation Coefficient of variance
CL
CM
CH
0.30 0.32 0.36 0.38 0.42 0.36 0.05 0.13
0.49 0.51 0.52 0.54 0.56 0.52 0.03 0.05
1.11 1.15 1.24 1.31 1.35 1.23 0.10 0.08
Mean Standard deviation Coefficient of variance
the two sets of measurements are likely to be different for normalized crack length, crack aperture, and normalized crack area within the three combinations of clays. The p-values indicate that data for normalized crack length are similar only in the cases of CM and CH. All the other pairwise comparisons for the three crack parameters are not statistically similar. EFFECT OF DENSITY, WATER CONTENT, AND DRYING TEMPERATURE ON SHRINKAGE CRACK PARAMETERS FOR COMPACTED SAMPLES For compacted samples, data regarding crack length, crack aperture, and crack area could be obtained only for CH samples because the CL and CM samples did not develop shrinkage cracks. In the following text, for the sake of brevity, CH samples dried at 40°C are used to show the effect of density and water content on crack parameters, whereas samples showing the effect of temperature had a water content of 40 percent. Plots for all temperatures and all water contents can be found in Lokre (2019). Crack Length Generally, normalized crack length shows little change as dry density increases for samples compacted using 10 blows/layer (Figure 8a). Samples compacted using 25 blows do not show a consistent trend. However, ignoring the data point at a density value of Table 6. p-Values comparing the crack parameters for each of the three types of clay.
p-Values CL vs. CM CL vs. CH CM vs. CH
Normalized Crack Length
Crack Aperture
Normalized Crack Area
0.032 0.984 0.579
<0.004 0.039 <0.004
<0.004 <0.004 <0.004
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94 lb/ft3 (1.51 Mg/m3 ) (Figure 8a). The negligible rate of change for 10 blows/layer compared to 25 blows/layer is due to the presence of more voids in samples compacted using 10 blows/layer (Figure 3) than those compacted using 25 blows per layer (Figure 4). The presence of voids results in dissipation of tensile stresses, thereby decreasing the tendency for crack propagation. Compacted samples with more voids shrink primarily along the pre-existing voids. These results are contrary to the general thinking that low-density samples are likely to develop more cracks upon drying. The normalized crack length increases as the sample water content increases prior to drying (Figure 8b). This makes sense as higher amounts of initial water content mean larger changes in volume upon drying and, therefore, larger crack length. The crack length is lower for 10 blows/layer samples (lower density) than for 25 blows/layer samples (higher density). Furthermore, the rate of increase in crack length with increasing water content is higher for higher-density samples than for lower-density samples (Figure 8b). This is because the higher dry density of compacted samples results in the presence of fewer voids (less dissipation of tensile stresses) and higher water content means enhanced and prolonged desiccation effect. Drying temperature has practically no effect on normalized crack length (Figure 8c). Crack Aperture
Figure 8. The effect of (a) dry density, (b) compaction water content, and (c) drying temperature on normalized crack length for compacted samples of CH. “b” in the legend denotes the number of hammer blows/layer during compaction. Note: 62.4 lb/ft3 = 1 Mg/m3 .
86 lb/ft3 (1.38 Mg/m3 ) as an outlier due to experimental error, a relatively sharp increase in normalized crack length can be seen as density increases from approximately 92 lb/ft3 (1.47 Mg/m3 ) to approximately
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The effects of density, compaction water content, and drying temperature on crack aperture are shown in Figure 9. Despite the significant amount of scatter in the data, the crack aperture generally increases with increasing density (Figure 9a). There is a clear distinction between samples compacted using 10 blows/layer and those compacted using 25 blows/layer, highlighting the effect of density on crack aperture. These results mirror the effect of density on crack length, and the explanations for the observed trends are similar as well. Crack aperture increases with increasing water content of the compacted samples, with the increase in aperture being gradual for samples compacted using 10 blows/layer and sharp for samples compacted using 25 blows/layer (Figure 9b). As for temperature, the aperture first increases with increasing temperature and then decreases, with the change in trends occurring at different temperatures for the two types of samples (Figure 9c). This suggests slow drying at lower temperature allows the aperture to grow more than rapid drying at higher temperature. Crack Area Data regarding the effect of density on normalized crack area are inconsistent, showing a significant
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Figure 9. The effect of (a) dry density, (b) compaction water content, and (c) drying temperature on crack aperture for compacted samples of CH. Note: “b” in the legend denotes the number of hammer blows/layer during compaction. Note: 62.4 lb/ft3 = 1 Mg/m3 .
amount of scatter (Figure 10a). However, the difference between low-density (10 blows/layer) and high-density (25 blows/layer) samples is evident. A weak trend of increasing crack area with increasing
Figure 10. The effect of (a) dry density, (b) compaction water content, and (c) drying temperature on normalized crack area for compacted samples of CH. “b” in the legend denotes the number of hammer blows/layer during compaction. Note: 62.4 lb/ft3 = 1 Mg/m3 .
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density can be observed from Figure 10a. Crack area is equal to crack length multiplied by crack aperture. However, crack length and crack aperture do not follow similar trends and that may be the reason for the substantial amount of scatter in the data in Figure 10a. Normalized crack area clearly increases with increasing water content (Figure 10b). Increasing temperature has no effect on normalized crack area for samples compacted using 25 blows/layer and only a minor effect for samples compacted using 25 blows/layer (Figure 10c). EFFECT OF LAYER THICKNESS, DRYING TEMPERATURE, PLASTICITY INDEX, AND MULTIPLE CYCLES OF WETTING AND DRYING ON SHRINKAGE CRACK PARAMETERS FOR UNCOMPACTED SATURATED SAMPLES Uncompacted layers of CL, CM, and CH with varying thicknesses were saturated and dried at the same temperatures as the compacted samples. Whereas the compacted CL and CM samples did not show any cracking, the uncompacted saturated samples of all three types of clay showed the development of shrinkage cracks upon drying. This can be attributed to the higher water content of the saturated samples. The crack length, crack aperture, and crack area were recorded for each clay. Layer thickness did not exhibit any consistent relationship with normalized crack length for any of the three clays. Furthermore, increasing drying temperature had little to no effect on normalized crack length for layers of different thicknesses (Lokre, 2019). Crack aperture showed a consistent increase with increasing layer thickness for samples dried at 40°C (Figure 11a). This trend is representative of other temperatures, the plots for which are provided in Lokre (2019). Furthermore, for a given layer thickness, the crack aperture increases with increasing clay plasticity. Increasing drying temperature showed no noticeable effect on crack aperture for 20-mm-thick layers of the three clays (Figure 11b). Clay layers of other thicknesses exhibited similar trends with respect to temperature. In general, both CL and CM showed a gradual increase in normalized crack area with an increase in layer thickness. However, CH shows a consistent decrease in normalized crack area with an increase in layer thickness (Figure 12). The decrease in area is sharp for the thickness between 5 mm and 10 mm but becomes gradual for the 20-mm and 30-mm thick layers. It is not clear why CH behaves differently compared to CL and CM. A decrease in crack length with increasing layer thickness may contribute to a decrease
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Figure 11. The effect of (a) layer thickness on crack aperture for the three types of clay dried at 40°C and (b) increasing temperature on 20-mm-thick layers of each clay type.
in normalized crack area for CH. The data in Figure 12 are for samples dried at 40°C and are representative of the trends for other temperatures (Lokre, 2019). Increasing temperature had little effect on crack area for all three types of clay. Crack length increases with increasing PI from CL (low PI) to CM (medium PI) for all layer thicknesses (Figure 13a). However, from CM to CH, the trends are different for layers of different thicknesses (Figure 13a). The most likely reason for the change in trends between CM and CH is that once a crack develops in CH, it keeps increasing in aperture more than in length. CH, with the highest PI value, shows lower crack lengths for thicker layers. Crack aperture generally increases with increasing layer thickness, first gently from CL to CM and then more steeply from CM to CH (Figure 13b). CH shows the largest values of crack aperture, with aperture increasing with increasing layer thickness. Atique and Sanchez (2011)
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Figure 12. The effect of layer thickness on normalized crack area for the three types of clay dried at 40°C.
and Khatun et al. (2015) reported similar behavior regarding the effect of PI on crack aperture. Normalized crack area generally decreases from CL to CM, followed by an increase in area from CM to CH (Figure 13c). Note the crack length is greater for thinner layers (Figure 13a), the crack aperture is greater for thicker layers (Figure 13b), and the crack area is smaller for thinner layers (Figure 13c). This suggests that crack length has a greater influence on crack area than crack aperture. Uncompacted saturated layers of the three types of clay, each 10 mm thick, were subjected to five cycles of wetting and drying at a drying temperature of 40°C. Ignoring the scatter in the data, generally the crack length showed an increase up to the fourth cycle but a slight drop for the fifth cycle (Figure 14a). A possible explanation for this trend is that crack walls start disintegrating after the fourth cycle, thereby decreasing crack length. Overall, the crack aperture increased with the increasing number of cycles, with CH exhibiting the highest vales (Figure 14b). The irregularities in the trends may be due to changes that occurred in samples after multiple wetting-drying cycles (e.g., wall disintegration). The crack area generally increased for the first two or three cycles and then decreased for the fourth and fifth cycles. This suggests that after the first few cycles, either the crack length or crack aperture start decreasing, probably due to crack-wall disintegration or plastic deformation of the soil during the saturation periods. DISCUSSION Compacted Clay Samples In this study, compacted samples of CL and CM did not develop shrinkage cracks upon drying. A possible explanation for cracks not developing in
Figure 13. The effect of plasticity index on (a) normalized crack length, (b) crack aperture, and (c) normalized crack area for clay layers of different thicknesses.
compacted samples of CL and CM is their higher densities, lower PI values, and lower tendency for volume changes compared to CH (Mitchell and Soga, 2005). These results illustrate why CL, in compacted form, is the preferred and frequently used soil for construction of homogeneous embankment dams, levees, and
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have a greater tendency to undergo volume changes upon alternate wetting and drying, resulting in the formation of shrinkage cracks. However, the non-uniform nature of compacted CH samples greatly influenced the length, aperture, and area of the cracks developed. The addition of water to CH for compaction purposes results in the formation of lumps, because of its highly cohesive nature, instead of a uniform soil-water mixture. The higher the PI, the greater the amount of cohesion and the tendency to form lumps. It is usually difficult to break down these lumps, and their presence considerably reduces the homogeneity of the compacted samples. Clay lumps can also result in the presence of voids in the compacted samples. The size and number of these voids varies from sample to sample. Furthermore, in the standard compaction test, the clay is compacted in three layers with each layer receiving 25 blows from a standard hammer. The nonuniform distribution of compaction energy and the nature of contacts between different compacted layers contribute to a lack of uniformity among different samples compacted at the same water content using the same procedure. The degree of uniformity of the compacted clay samples greatly influences the propagation and nature of shrinkage cracks. The presence of voids in a compacted sample does not allow shrinkage cracks to propagate because voids dissipate tensile stresses that cause shrinkage cracks. This explains why the five CH samples, compacted at MDD and OWC, did not exhibit reproducible results upon drying. The extent of voids in compacted clay samples depends on three primary factors: 1. The amount of compactive effort, i.e., whether one compacts the sample using 25 blows of hammer/layer (higher compactive effort) or 10 blows of hammer/layer (lower compactive effort). 2. Compaction water content, i.e., whether the sample is compacted dry or wet of OWC. 3. Type of clay, i.e., whether the sample consists of CL, CM, or CH.
Figure 14. The effect of the number of wetting and drying cycles on (a) normalized crack length, (b) crack aperture, and (c) normalized crack area for the three types of clay.
sanitary landfill liners and covers. Development of shrinkage cracks in these structures would be highly undesirable. The compacted CH samples showed distinct development of shrinkage cracks due to their lower dry density and higher PI values. Clays with higher PI values
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During the laboratory compaction tests, samples compacted using 25 blows/layer had fewer voids than those compacted using 10 blows/layer, particularly when compacted on the dry side of OWC (Figure 15a). This is because a larger compactive effort results in closer packing of soil particles, thereby resulting in fewer voids. On the other hand, using the same compactive effort (say, 25 blows/layer), samples compacted on the dry side of OWC were more homogeneous in nature, with fewer voids than those compacted on the wet side of OWC (Figure 15b). On the dry side of OWC, the clay-water mixture retained its powdered form due to a smaller amount of water, resulting in considerably fewer void spaces. Samples
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compacted on the wet side of OWC contained more voids because of the lumpy nature of soil-water mixtures. In the case of samples compacted using 10 blows of the hammer/layer, voids were more prominent in samples compacted on the dry side of OWC than those compacted on the wet side of OWC (Figure 15c). The presence of these voids results in shrinkage of the clay along voids without the formation of shrinkage cracks. Uncompacted Saturated Samples
Figure 15. (a) Two CH samples compacted using 10 blows/layer (left) and 25 blows/layer (right), both compacted on the dry side of OWC and having the same water content. Notice the greater degree of voids due to lumps in the left sample and fewer voids and more uniform nature of the right sample. The presence of voids in the left sample prevented crack propagation. (b) Two compacted samples of CL, with the left sample compacted on the dry side of OWC and the right sample compacted on the wet side of OWC. Both samples were compacted using 25 blows/layer. Notice the uniform nature of the left sample and the presence of extensive voids in the right sample. (c) CL samples compacted using 10 blows of hammer/layer. The left sample was compacted on the dry side of the OWC, and the right sample was compacted on the wet side of the OWC. Notice the greater number of voids in the left sample compared to the right sample.
To investigate the effect of clay layer thickness and PI on shrinkage crack length, aperture, and area, uncompacted saturated samples of different thicknesses were dried at the same temperatures as the compacted samples. The cracks developed during drying showed linear or curvilinear patterns without being tortuous. All cracks penetrated full thickness of the clay layer. Crack aperture showed an increase with increase in layer thickness. The increase in aperture resulted in a corresponding decrease in crack length. These results corroborate the findings by Atique and Sanchez (2011) and Khatun et al. (2015). Figure 16 shows that the 5-mm-thick layer of CL (left image) exhibited higher crack length, indicative of continual crack development throughout the shrinkage process. On the other hand, the 30-mm-thick layer (right image) resulted in lower crack length and much wider crack aperture, indicating its tendency to shrink along initially formed cracks through the shrinkage process. Both images in Figure 16 have similar scales. These thicknessdependent shrinkage mechanisms may be the reason why a small increase in normalized crack area occurred for CL and CM but a decrease in normalized crack area occurred for CH with increasing layer thickness. The crack length, aperture, and area did not exhibit a consistent relationship with drying temperature for the three clays. This eliminates the scope of using naturally occurring shrinkage cracks as a tool to determine the temperature at which the sediments dried. Additional research is required to check if pans of different shapes and sizes, or made of different materials, will produce different results. Konrad and Ayad (1997) measured shrinkage cracks that propagated to a depth of 70 cm. More field studies need to be conducted on clay layers of considerable thickness to investigate the maximum depth to which shrinkage cracks can propagate. Shrinkage cracks are tension cracks. Theoretically, the depth of a tension crack in a saturated, undrained clay is equal to 2c/γ, where c = soil cohesion and γ = soil density (Peck et al., 1974). Assuming average values of c = 355 kPa and γ = 1.92 Mg/m3 (Holtz et al., 2011) results in a crack depth of 3.6 m. However, clay soil cohesion can have a wide range and, therefore, crack depth
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Figure 16. A comparison of shrinkage crack length and aperture between 5-mm and 30-mm-thick layers of CL (left and right images, respectively). Field of view for each sample is 10 cm across (i.e., the scale is the same for both images). Black represents shrinkage cracks, and white represents clay.
can be quite variable. Furthermore, other factors (density, water content, drying rate, clay layer thickness) can affect crack depth. Groismann and Kaplan (1994) noted trapped air bubbles/voids in saturated clay layers acted as points of nucleation for propagation of shrinkage cracks. Circular aluminum pie pans were used in this study to reduce the edge effects. Miller et al. (1998), Kodikara et al. (2000), Yesiller et al. (2000), Kodikara and Choi (2006), Nahlawi and Kodikara (2006), Péron et al. (2009), Amarasiri and Kodikara (2010), Li and Zhang (2011), and Lakshmikantha et al. (2012) all used rectangular pans to conduct similar research. Baer and Anderson (1997), Rayhani et al. (2008), Khandelwal (2011), Anderson (2014), and Priyankara et al. (2015) used circular pans. These studies indicate that longer cracks developed in the circular samples compared to narrower rectangular samples. Circular samples exhibited curvilinear cracks, whereas rectangular samples developed linear cracks. Samples dried in rectangular pans also exhibited wider apertures than those in circular pans. However, there are no studies relating crack parameters (crack length, crack aperture, and crack area) to the geometry of the pans used for drying clay samples. Effect of Wetting and Drying Cycles Crack length first increased and then decreased with an increasing number of wetting and drying cycles for the three types of clay, with the change in trends
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occurring after a different number of wetting and drying cycles for each clay (Figure 14a). The initial increase in crack length with an increasing number of wetting and drying cycles is understandable as additional wetting and drying cycles tend to cause additional shrinkage and the formation of newer cracks. However, a decrease in crack length after a certain number of wetting and drying cycles is not straightforward to explain. Most likely, after a certain number of wetting and drying cycles, further decrease of already shrunk clay may not be possible because the dry clay particles may have reached their tightest possible configuration. During additional wetting cycles, some of the previously formed, but relatively tight, cracks may close, either because of swelling (in CH) or textural changes. Also, during additional drying cycles, the tensile stresses responsible for shrinkage cracks may dissipate along existing cracks without forming new cracks. Other researchers (Chen, 1965; Chen et al., 1985; Subba Rao and Satyadas, 1987; Dif and Blumel, 1991; Al-Homoud et al., 1995; and Basma et al., 1996) also noted that the shrinkage of swelling clays decreased after a few cycles of wetting and drying because of fabric rearrangement. Crack aperture did not show a significant increase with an increasing number of wetting and drying cycles (Figure 14b). This is because either the pre-existing cracks accommodate additional shrinkage or shrinkage stresses dissipate along these cracks. The effect of multiple wetting and drying cycles on crack area was similar to crack length, i.e., first
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increasing and then decreasing with an increasing number of wetting and drying cycles (Figure 14c). The same explanation as for crack length may be applicable to crack area. Obviously, as the crack length decreases, so does the crack area. The results of wetting and drying cycles discussed above differ from those reported by Yong and Warkentin (1975) and Yesiller et al. (2000). These researchers found that the major irreversible fabric changes occurred in the material during the first cycle of drying, beyond which additional wetting and drying cycles had little effect. This study found a gradual change of crack parameters until the third round of wetting and drying. Experimental studies by Chen (1965), Chen et al. (1985), Subba Rao and Satyadas (1987), Dif and Blumel (1991), AlHomoud et al. (1995), and Basma et al. (1996) suggest that compacted, remolded specimens of clay show sign of fatigue after each cycle of wetting and drying, thus exhibiting lesser expansion and contraction than previous cycles. Thus, the decrease in shrinkage with increasing number of wetting and drying cycles manifests as a decrease in crack length and crack area.
CONCLUSIONS The results of this study lead to the following conclusions: 1. CL and CM samples, compacted at varying dry density values, do not exhibit the development of shrinkage cracks upon drying, whereas compacted samples of CH result in shrinkage cracks of varying lengths and apertures upon drying. 2. Generally, crack length, crack aperture, and crack area increase with increasing density in CH samples. 3. Crack length, crack aperture, and crack area increase with increasing values of initial water content for compacted samples of CH. 4. Drying temperature does not appear to affect crack length, crack aperture, and crack area for compacted samples of CH. 5. Uncompacted saturated samples of CL and CM show a general increase in crack length, crack aperture, and crack area with an increase in clay layer thickness. Uncompacted saturated samples of CH show a decrease in crack length and crack area, but an increase in crack aperture, with an increase in layer thickness. 6. Increasing the number of wetting and drying cycles shows an initial increase in crack length and crack area and then a decrease in these parameters for un-
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Site Selection for Municipal Solid Waste Landfill: Case Study of Artvin, Turkey HALIL AKINCI* Artvin Coruh University, Faculty of Engineering, Department of Geomatics Engineering, 08100, Artvin, Turkey
KAZIM ONUR DEMIRARSLAN Artvin Coruh University, Faculty of Engineering, Department of Environmental Engineering, 08100, Artvin, Turkey
Key Terms: AHP, Artvin, GIS, Fuzzy Membership, Landfill Site Selection ABSTRACT This study aimed to select a landfill site for the disposal of municipal solid waste (MSW) produced in the central district of Artvin Province in the Eastern Black Sea region of Turkey. Although Artvin has a smaller population compared to other provinces in the region, it has become increasingly important as it hosts sensitive ecological areas as well as dams used for energy production. Currently, the MSW collected in the provincial center is disposed through uncontrolled dumping. The topographic structure of the region is rather rough, which makes the selection and application of disposal methods difficult. However, increasing detrimental impact on the environment justifies the immediate necessity for a new landfill site. These considerations necessitate a study for landfill site selection in the region. Many different factors are considered when selecting the site for a landfill, and, thus, the process is not a simple one. In this study, various factors, including geology, slope, land use, distance from settlements, roads, surface waters, faults, and protected sites, as well as the landslide and flood susceptibility of the site, were taken into consideration. These factors were standardized using the fuzzy membership functions and weighted through the analytical hierarchy process. Spatial analysis in the GIS environment revealed that 99.91 percent of the study area was considered unsuitable, 0.08 percent moderately suitable, and only 0.01 percent suitable for landfilling. The study identified two sites that can be used as a landfill.
*Corresponding author email: halil.akinci@artvin.edu.tr
INTRODUCTION Factors such as improvements in living standards, increasing population, and commercial and industrial activities have significantly increased the amount of produced solid waste worldwide (Chabuk et al., 2016; Al-Anbari et al., 2018). Therefore, solid waste management has become one of the major environmental problems in the world (Eskandari et al., 2016). Humans are increasingly responsible for solid wastes generated as a consequence of household, industrial, and construction activities. Especially in developing countries, changing consumption habits, economic and revenue growth, urbanization, and industrialization have contributed to an increase in the quantity and variety of solid wastes, which have reached alarming levels for local governments (Güler and Yomralıoğlu, 2017; Rahmat et al., 2017). For all these reasons, the process—from the collection to the disposal of solid wastes—should be managed well. In many parts of the world, inadequate management of solid wastes has adversely affected humans and the environment (Mahamid and Thawaba, 2010). Despite the development of new treatment technologies, the implementation of cost-effective systems, and the launching of numerous programs for the management of municipal solid waste (MSW), solid waste remains a major problem for municipal environmental management, a point of concern for local governments (Shahabi et al., 2014). The most suitable way to manage solid waste is to reduce its generation (Sener et al., 2006). On the other hand, various waste disposal methods often applied include treating with heat/biological agents, establishing landfills, and recycling (Şener et al., 2011). Even if a combination of these techniques is employed along with waste reduction and reuse policies, landfills are necessary for a sustainable system (Shahabi et al., 2014). A landfill site is defined as an area used for the disposal of solid wastes in a manner that does not harm the environment
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and human health (Mutlutürk and Karagüzel, 2007; Kahraman et al., 2018). Although regular landfill provides a simple and economical approach for waste disposal, if not managed properly, it creates significant environmental problems, such as soil, water, and air pollution, that affect human health. It is reported that solid wastes in the world are responsible for 5 percent of global carbon emission, and burning them increases this percentage even more. Furthermore, leachate, which occurs as a result of the degradation of organic materials in the operation process of landfills and may contain high concentrations of metals and some dangerous organic chemicals, causes pollution of soil and groundwater (Imen et al., 2009; Ferdous et al., 2021; Shi et al., 2021). The site selection process is regarded as one of the most challenging operations among solid waste management stages (Rafiee et al., 2011; Al-Anbari et al., 2018). To maintain sustainability, the selected landfill sites should be environmentally effective, economically and technically feasible, and socially acceptable (Eskandari et al., 2016; Kapilan and Elangovan, 2018). However, the efficiency of a site depends not only on the decision makers but also on the criteria for the use of all candidate sites, including surrounding settlements and transportation facilities, as well as the geomorphological characteristics of the candidate area, the hydrological criteria, and the current legal framework (Miller and Raba, 1981; El Maguiri et al., 2016; Demesouka et al., 2019). Landfills are considered undesired facilities and are generally opposed by the public. It has been stated in the literature (Palmeira Wanderley et al., 2017) that the disposal policies are opposed by the public because of the adverse effects (associated with the disposal methods of urban solid wastes) on the environment and public health. In addition, not informing the public about the pollution risks of landfills also causes the syndrome termed “not in my backyard.” According to this syndrome, the public is aware of the necessity of landfills, but they do not want them to be built near their homes. This situation makes it harder to construct new landfills (Sasao, 2004). Because of the scarcity of land and increasing amount of waste, it is essential to use existing land resources with proper planning. The challenge encountered in the site selection process is to make the project environmentally friendly and cost efficient. It is difficult to find suitable land close to the waste source for the construction of sites. Transportation of wastes to a remote location is not economically viable. Moreover, the candidate land should be accessible through road networks. Landfills too close to existing highways can affect traffic as well as land prices and tourism in the region. Thus, landfills should be located at an adequate distance for
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economic feasibility but sufficiently far from the waste source to avoid social conflict (Kharat et al., 2016). The task of selecting an appropriate landfill site for solid waste involves considering many different environmental, economic, social, and technical factors (Şener et al., 2010), which makes the selection process a multi-criteria decision-making (MCDM) application. Therefore, approaches based on the integration of GIS and MCDM methods are highly effective in the selection of solid waste landfill sites. Rezaeisabzevar et al. (2020) stated that “MCDM techniques were employed to make the solid waste landfill site selection procedure more accurate and convenient.” In many parts of the world, MCDM methods, such as the GIS-based analytical hierarchy process (AHP), were employed in the selection of suitable locations (Şener et al., 2011; Uyan, 2014; Eskandari et al., 2016; Barakat et al., 2017; Güler and Yomralıoğlu, 2017; Al-Anbari et al., 2018; and Kamdar et al., 2019), fuzzy-AHP (Torabi-Kaveh et al., 2016; Ajibade et al., 2019; Karasan et al., 2019; Karimi et al., 2019; and Pasalari et al., 2019), analytical network process (Isalou et al., 2013; Afzali et al., 2014; Jamshidi-Zanjani and Rezaei, 2017; and Eghtesadifard et al., 2020), ordered weighted average (Gorsevski et al., 2012; Motlagh and Sayadi, 2015; and Rezaeisabzevar et al., 2020), and TOPSIS (Yal and Akgün, 2014; Beskese et al., 2015; Kharat et al., 2016; and Yildirim et al., 2018). This study aimed to determine a suitable location for a solid waste landfill in the central district of Artvin Province, which is located in the Eastern Black Sea region of Turkey. There are no solid waste landfills in the central district to date. The objective was to select a landfill site for a 25-year period using ArcGIS 10.5 software. Factors including geology/lithology, slope, land use, distance from settlements, roads, surface waters (streams and dam lakes), protected sites (national parks), and predominant wind direction were taken into account. Artvin is one of the most natural disaster-prone cities in Turkey because of its climatic, topographic, and geological characteristics. Natural disasters, especially those of meteorological nature, such as floods, as well as rainfall-induced landslides frequently occur across the province. Landslides account for a large number of the natural disasters in Artvin. Therefore, parameters related to the natural disasters, such as distance from faults, landslide susceptibility, and flood susceptibility, were also considered in this study. To the best of our knowledge, no previous studies have considered the parameters related to earthquake, flood, and landslide disasters to select landfill areas. The factors used in the study were standardized using the fuzzy membership functions and weighted using the AHP approach. The weighted
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product model was used for suitability mapping of the landfill site. MATERIALS AND METHODS This study comprised five stages. In the first stage, the study area was determined; then, a population estimation was performed for a 25-year period and the expected amount of the waste was calculated. In the second stage, the appropriate factors for the selection of solid waste landfill sites were identified based on the national legislation. In the third stage, available spatial and non-spatial data regarding the selected factors were collected from the relevant institutions or organizations. In the fourth stage, an AHP-based MCDM approach was implemented. The fifth stage consisted of selecting the suitable locations for the landfill sites. A detailed explanation of the methodology followed in this study is given in the subsequent sections. Study Area This study was performed in the central district of Artvin Province, which is located in the eastern part of the Eastern Black Sea region of Turkey. The study area, located between 41°0 0.31 and 41°18 45.45 north latitudes and 41°30 50.76 and 41°59 59.9 east longitudes, has a surface area of 907.41 km2 (Figure 1). The topography of the Artvin Province is generally mountainous and very rough. In the central district, where the study was conducted, the altitude ranges from 125 to 3,195 m and the slope ranges from 0° to 77.66°. There is not a developed industrial sector in the province because of its topographic structure and its distance from the raw material and consumption markets. The manufacturing industry of the province can be said to be way below the Turkish average (Artvin Special Provincial Administration, 2014). Businesses operating in the industrial sector mainly produce food, mineral, and forest products, making use of the province’s natural resources. There are no organized industrial zones in Artvin (Demirarslan et al., 2017). Therefore, only domestic solid wastes are generated in the center of the province. These wastes were traditionally handled using the uncontrolled waste storage method in an area near the provincial center until 2015, causing great environmental damage, especially on the Çoruh River passing through the city center. Since 2015, the wastes have been transferred to a designated area in Ardanuç District, 30 km away from the city center, but a proper landfill is not possible given the poor physical structure of the land. The address-based population registration system (ABPRS), administered by the Turkish Statistical In-
Table 1. The population and waste projection of the study area for the period 2019–2045.
Year
Population
2020 2025 2030 2035 2040 2045
34,007 35,641 36,095 36,549 37,002 37,456
Waste Amount (kg/d)
Waste Aamount (kg/yr)
40,808 42,769 43,314 43,859 44,402 44,947 waste
14,895,066 15,610,758 15,809,610 16,008,462 16,206,876 16,405,728 80,041,434
stitute (TURKSTAT), is a database in which the demographic information is maintained based on the residential locations of the people in the province. According to the ABPRS data, Artvin’s total population in 2019 was 170,875. The total population of the central district where the study was conducted was 35,186; 26,078 of these residents reside in the central district, while 9,108 reside in the villages (TURKSTAT, 2020). Average annual data provided by the General Directorate of Meteorology indicate that in the province (1949–2018), the amount of precipitation is 694.8 mm and temperature is 12.4°C, with the lowest temperature (−0.2°C) occurring in January and the highest (26.3°C) in August. Population Estimation and Solid Waste Volume for the Study Area In order to design the facility, it was necessary to calculate the volume of solid waste generation expected over the next 25 years. Consequently, the rate of population growth for the study area was estimated for that time period. The Arithmetic Increase Method was used to estimate the population; next, the estimated population in the study area after 25 years was estimated (Table 1). According to the data provided from TURKSTAT (2018), the average amount of the waste generated per capita in Artvin Province was found to be 1.2 kg/person/d. In light of this information, the amount of the waste generated by year is given in Table 1. According to Table 1, 80,041,434 kg (80,041.434 tons) of waste will be generated annually by the end of the 25th year. Although solid wastes vary in density, they are reported in the literature (UNEP, 2017) to measure 700–1,000 kg/m3 after being compacted in landfills. When an average value of 850 kg/m3 was taken into account, the volume of the waste in 2045 was estimated to be approximately 94,166 m3 . Typical landfills are large structures, with heights ranging from 10 to 150 m (Gao et al., 2018). When the height was
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Figure 1. Location map of the study area.
selected as 10 m, it was found that a landfill area of 9,417 m2 would be required in the study area. Considering the administrative buildings, on-site roads, parking facilities, daily cover, storage location of the final top cover, and the leachate collection pool, an area of approximately 14,000 m2 will be necessary for the landfill site. Selection of Suitable Factors for Landfill Site and Data Collection This study aimed to select a suitable location for a landfill that would allow for the disposal of municipal waste. When selecting the site, first, the factors necessary to evaluate the suitability of the area for such a facility had to be determined. The factors used in this study were determined with respect to the current regulations in Turkey; these factors are specified in Article 15 of the “Regulation on Regular Storage of Wastes,” published in the Official Gazette No. 27533, dated March 26, 2010 (RRSW, 2010). Accordingly, the main factors were determined, taking into account the
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topographic, geological, and hydrological structure of the study area; land use; susceptibility to floods, landslides, and earthquakes; predominant wind direction; surface water; and protected sites. Air transport safety was ignored, as there are no airports within the study area. Similarly, power transmission lines and groundwater level were ignored in the study, as no related data were obtained. The site selection criteria specified in the “Regulation on Regular Storage of Wastes” and the factors used considering this regulation are given in Table 2. On the contrary, Rezaeisabzevar et al. (2020), stated that “the factors used in the selection of the locations can serve as a constraint or a criterion.” According to Rezaeisabzevar et al. (2020), “a constraint limits alternatives and reclassifies the suitable landfill locations as ‘1’ and those that are unsuitable as ‘0’; these restrictions are shown in the form of Boolean maps.” Again, according to Rezaeisabzevar et al. (2020), “a criterion allocates a value to the factor; this value is determined using a fuzzy membership function that fits the nature of the criterion.” Pasalari et al. (2019) stated that
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Solid Waste Landfill Site Selection Table 2. Site selection criteria used in the study (RRSW, 2010). Site Selection Criteria Specified in the Regulation
Factors Used in the Study
The distance between landfill facility boundaries and residential units should be at least 1 km for Class I (for hazardous wastes) landfill facilities and should be at least 250 m for Class II (for municipal waste) and Class III (for inert waste) landfill facilities.
Distance from settlements
Whether the landfill facility affects air transport security should be considered when selecting the site for landfill facility.
Since there is no airport in Artvin, air transportation safety was not taken into account in the site selection of the landfill facility.
In the site selection of the landfill facility, distance to protected areas for special purposes, such as forest areas, afforestation areas, wildlife and vegetation protection, should be taken into account.
Land use Distance from protected sites
In the site selection of the landfill facility, the condition of underground and surface water resources and protection basins in the region, groundwater level, and groundwater flow directions should be taken into account.
Distance from surface water Groundwater resources and groundwater level were ignored in the study, as no related data were obtained from the relevant public institutions.
In the site selection of the landfill facility, topographic, geological, Geology/lithology geomorphological, geotechnical, and hydrogeological conditions of Slope the field should be taken into account. Distance from roads In the site selection of the landfill facility, flood, landslide, avalanche, erosion and high earthquake risk should be taken into consideration.
Flood susceptibility Landslide susceptibility Distance from faults
In the site selection of the landfill facility, dominant wind direction and precipitation should be taken into account.
Wind direction Since there is only one meteorological station in the study area, it is not possible to produce a proper rainfall distribution map for the area, which is why the precipitation was not considered in this study.
In the site selection of the landfill facility, natural or cultural heritage status should be taken into account.
Distance from protected sites
There should be no high-voltage power transmission lines or pipelines Power transmission lines were ignored in the study as no related data were obtained from the relevant public institutions. that are used in the transportation of fuel, gas, and potable-use water in the field.
“linear or sigmoidal functions are sufficient for location selection.” In this study, factors were standardized using the fuzzy membership functions included in ArcGIS 10.5 software (e.g., fuzzy large, fuzzy linear, and fuzzy small). The resolution/scale of the spatial data for the factors used in the study, the institutions from which they were obtained, and how they were standardized are explained below. Distance from Surface Water Landfills can threaten water bodies by serving as a source of pollution. As also noted by Rezaeisabzevar et al. (2020), “landfills produce leachate and gaseous contaminants, which pose a threat to lakes, wetlands, pools, and rivers.” Therefore, landfills should not be constructed near surface water. To prevent pollution, according to Rahmat et al. (2017) and Kamdar et al. (2019), the distance between a landfill site and a water body should not be less than 300 m, while according to Güler and Yomralıoğlu (2017), Jamshidi-Zanjani and Rezaei (2017), Demesouka et al. (2019), Eghtesadifard et al. (2020), and Rezaeisabzevar et al. (2020), this distance should be 500 m, and according to Rahimi et al.
(2020) and Pasalari et al. (2019), it should be 1,000 m. This study determined that the areas located less than 500 m from water bodies were unsuitable for a landfill. The 26th Regional Directorate of Artvin State Hydraulic Works provided the spatial data pertaining to the surface water of rivers, dam reservoirs, and streams encompassed by the study area. In ArcGIS 10.5 software, the buffers were created using the Euclidean distance function (Figure 2a and b), and the distances from surface water were standardized using the fuzzy linear membership function (Table 3). Geology/Lithology The first step in ensuring the safety of a landfill site is to address the sustainability of the site from a seismic perspective to prevent the emerging leachate from mixing into groundwater (Rahimi et al., 2020). Choosing a location with good impermeability will protect the soil and aquifers from contamination caused by leachate (Maguiri et al., 2016). Thus, when choosing a landfill site, the factors that affect the geological structures and the bedrock permeability in the study area should be considered. Güler and Yomralıoğlu (2017)
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Figure 2. (a) Distance from rivers, (b) distance from dam lakes, and (c) lithological map of the study area. Table 3. Scoring values for factors used in the study. Factors
Membership Function
Control Points
Geology/lithology Distance from faults Flood susceptibility Landslide susceptibility Slope Distance from protected sites
User defined (see Table 4) Fuzzy linear (increasing) User defined User defined Fuzzy linear (decreasing) Constraint
Distance from roads Distance from settlements Distance from surface waters Land use
Fuzzy linear (decreasing) Fuzzy linear (increasing) Fuzzy linear (increasing) User defined
— a = 500 m, b = 1,000 m — — a = 2°, b = 25° <1,000 m unsuitable (0) >1,000 m suitable (1) a = 200 m, b = 1,000 m a = 250 m , b = 1,000 m a = 500 m, b = 2,000 m —
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Solid Waste Landfill Site Selection Table 4. Scoring values for lithological units in the study area. Unit
Score
Tekk: Middle Eocene, Andesite-Agglomerate-Porphyrite, Volcanic Rock Tekb: Ypresian-Lutetian, Basalt, Volcanic Rock Teks: Ypresian-Lutetian, Porphyritic-Andesite-Pyroclastic Rock Tekd: Ypresian-Lutetian, Rhyodacite, Volcanic Rock Ks: Santonian, Dacite, Volcanic Rock KTkg: Upper Cretaceous, Granitoid Jng: Lower Jurassic (Lias)-Middle Jurassic (Dogger), Diorite Ca: Carboniferous, Granitoid Cay: Carboniferous, Dacite Jnh: Lower Jurassic (Lias)-Middle Jurassic (Dogger), Andesite-Basalt-Pyroclastic Rock-Agglomerate Kçay: Campanian-Maastrichtian, Dacite-Rhyolite-Pyroclastic Rock-Ignimbrite-Tuff-Sandstone-Mudstone (Shale)-Limestone Kça: Santonian-Campanian, Basalt-Andesite-Pyroclastic Rock-Agglomerate-Sandstone-Mudstone (Shale)-Limestone Kk: Santonian, Dacite-Rhyolite-Pyroclastic Rock-Ignimbrite-Tuff-Sandstone-Mudstone (Shale)-Limestone Kç: Turonian-Santonian, Basalt-Andesite-Pyroclastic Rock-Agglomerate-Sandstone-Mudstone (Shale)-Limestone Tek: Ypresian-Lutetian, Andesite-Porphyrite-Pyroclastic Rock-Agglomerate-Rhyodacite-Sandstone, Mudstone (Shale)-Limestone Tpee: Monsian-Lower Eocene, Conglomerate-Sandstone-Mudstone (Shale) KTb: Maastrichtian-Lower Paleocene, Limestone-Clayey Limestone, Shelf, Sedimentary Rock Kçayk: Maastrichtian, Clayey limestone Ko: Maastrichtian, Conglomerate-Sandstone-Mudstone (Shale) KTad: Maastrichtian, Clayey Limestone, Shelf, Sedimentary Rock Kçak: Santonian-Campanian, Clayey limestone Kö: Lower Cretaceous, Conglomerate, Terrestrial-Shelf, Sedimentary Rock Jnbk: Lower Jurassic (Lias)-Middle Jurassic (Dogger), Olistostrome-Conglomerate-Sandstone Jnbt: Lower Jurassic (Lias)-Middle Jurassic (Dogger), Conglomerate Jnb: Lower Jurassic (Lias)-Middle Jurassic (Dogger), Conglomerate-Sandstone-Mudstone (Shale) Ka: Lower Cretaceous, Limestone, Shelf, Sedimentary Rock Kçz: Turonian, Conglomerate KTa: Maastrichtian-Lower Paleocene, Limestone, Shelf, Sedimentary Rock Qym: Quaternary, Slope Debris Qa: Quaternary, Alluvium Qt: Quaternary, Old Alluvium Lake
stated that “volcanic terrains are suitable sites for landfills as they are less permeable.” Karimi et al. (2019) also stated that “limestone is not suitable for landfill sites due to their large pores and high permeability.” The General Directorate of Mineral Research and Exploration (GDMRE) provided a 1/25,000-scale digital geological map, which was also used in this study. This map showed the 31 individual lithological units included within the study area (Figure 2c). Lithological units were reclassified by giving weighted scores between 0 and 1, based on their permeability in accordance with the opinions of geological engineers working in universities and public institutions (Table 4). Distance from Faults Landfill sites should be in geologically appropriate locations that are not susceptible to earthquakes, floods, and landslides. Earthquakes and earth movements can cause damage and pollution (Pasalari et al., 2019; Rezaeisabzevar et al., 2020). Moeinaddini et al. (2010) stated that “faults can lead to groundwater
1 1 1 1 1 1 1 1 1 0.7 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0 0 0
pollution as they increase the permeability of rocks.” Therefore, the distance to faults plays an important role in the selection of landfill sites; sites should be located as far away from fault lines as possible to reduce the damage from earthquakes. However, it seems that there is a lack of consensus in the literature on the safe distance to faults. In the studies cited next, locations at the following distances from faults were deemed inappropriate areas for landfills: Yal and Akgün (2014), areas located at a distance of less than 100 m from faults; Ajibade et al. (2019), areas that located at a distance of less than 160 m from faults; Rahimi et al. (2020), Jamshidi-Zanjani and Rezaei (2017), Bahrani et al. (2016), and Eskandari et al. (2016), areas located at a distance of less than 200 m from faults; Kamdar et al. (2019), areas that located at a distance of less than 300 m from faults; and Gorsevski et al. (2012), areas that located at a distance of less than 500 m from faults. The lithology and faults used in the study were obtained using a 1/25,000-scaled digital geological map provided from the GDMRE. The fault buffers were calculated using the Euclidean distance
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Figure 3. Factor maps of the study area: (a) distance from faults, (b) flood susceptibility, (c) landslide susceptibility; and (d) slope.
function of ArcGIS 10.5 software. The maximum distance to faults was calculated as 6,551.50 m. In this study, areas closer than 500 m from faults were also deemed inappropriate for landfill sites (Figure 3a). The fuzzy linear membership function of ArcGIS 10.5 software was used to standardize the distance to fault lines (Table 3). Flood Susceptibility Rezaeisabzevar et al. (2020) stated that “if floods reach a landfill site, it can spread the waste to the environment and cause pollution.” Therefore, it is emphasized that areas susceptible to flooding, areas in which
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frequent or periodic flooding occurs, or areas within a 100-year-old floodplain are not suitable for landfills (Kamdar et al., 2019; Eghtesadifard et al., 2020; and Rezaeisabzevar et al., 2020). In this study, a flood susceptibility map with a scale of 1/25,000 produced by the Natural Hazards Application and Research Center (NHARC) of Artvin Coruh University was used. This map categorized the study area into five floodsusceptibility classes, as follows: 1 = very low; 2 = low; 3 = medium; 4 = high; and 5 = very high. Areas with high or very high flood susceptibility are not suitable for landfills. Therefore, the value of “0” was assigned to these classes. The flood susceptibility map was standardized by assigning a “1” to the areas with very low
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and low flood susceptibility and a “0.5” to the areas with medium flood susceptibility (Figure 3b). Landslide Susceptibility Earth movements such as landslides, especially when they occur mountainous regions with steep slopes, can cause pollution and damage to the landfill sites. Therefore, active landslide areas or areas susceptible to landslides are not suitable for landfills (Motlagh and Sayadi, 2015; Kamdar et al., 2019). Artvin is one of the cities in Turkey in which landslides occur frequently. The GDMRE has mapped a large number of active landslides in the study area, which made it necessary to consider landslide susceptibility, and, consequently, the 1/25,000-scale NHARC landslide-susceptibility map was utilized in this study. This map, similar to the flood-susceptibility map, includes five susceptibility classes: 1 = very low; 2 = low; 3 = medium; 4 = high; and 5 = very high. Since the areas with very high or high landslide susceptibility are not suitable for landfills, the value of “0” was assigned to these areas. On the other hand, the areas with very low or low landslide susceptibility are suitable for landfill sites; therefore, the value of “1” was assigned to these areas. The landslide susceptibility map was standardized by assigning “0.5” to the areas with moderate landslide susceptibility (Figure 3c). Slope Slope is an important factor in the selection of landfill sites, especially in terms of its impact on excavation costs; construction of access roads; collection of surface water and leachate, as well as removal from the landfill site; and erosion potential in the construction phase (Karasan et al., 2019; Karimi et al., 2019; Pasalari et al., 2019; Rahimi et al., 2020; and Rezaeisabzevar et al., 2020). The literature reports different opinions as to which slope value(s) should be considered when selecting the locations. Rezaeisabzevar et al. (2020) suggested that slopes between 8 percent and 12 percent are suitable for landfills. In the study conducted by Rahimi et al. (2020), the highest score among the slope classes was given to the 2° to 8° slope group. Similarly, different slope scores can be found in the applications performed in Turkey. For example, Güler and Yomralıoğlu (2017) stated that areas in which the slope is greater than 25° are not suitable for landfills and gave the highest score to the 0° to 5° slope group. In the study conducted by Yal and Akgün (2014), a 0 percent to 10 percent slope group was accepted as the most suitable one for landfills, and the value of “1” was assigned to this group. On the
contrary, the current regulations in Turkey state that the longitudinal slope of a landfill base must not be less than 3 percent (approximately 2°) (RRSW, 2010). The contours of 1/25,000-scaled topographic maps were used to produce a digital elevation model (DEM) of the study area. A raster version of this DEM, having a spatial resolution of 10 m, was then created. This raster DEM was used to produce the slope map of the study area shown in Figure 3d. The slope in the study area varies between 0° and 77.65°. The slope was standardized in ArcGIS 10.5 software using the decreasing fuzzy linear membership function (Table 3). Distance from Protected Sites This criterion is important for preventing sensitive ecosystems from deterioration by pollutants. According to EU legislation, landfill sites should not deteriorate the natural environment or protected sites (Elahi and Samadyar, 2014). Therefore, protected sites such as national parks and archaeological sites are not suitable for landfills, and landfill sites should not be located near these areas. According to the report (UMT, 2014) on landfill design, site selection, and improvement of wild landfill sites prepared by the Solid Waste Commission of Union of Municipalities of Turkey, landfill sites should be located at a distance of at least 150 m from the protected ecological and historical areas. Hatila Valley National Park, which was declared a national park in 1994 and covers an area of 16,944 ha, is located within the study area. Buffer zones were created according to the national park border using the Euclidean distance function. The maximum distance from the national park was calculated as 24,132.21 m (Figure 4a). In the study, this criterion was considered as a constraint, and the areas closer than 1,000 m to the national park were considered as unsuitable (0), while the areas farther than 1,000 m (1) were considered suitable (Table 3). Distance from Settlements This factor is very important in terms of possible water, soil, air, and noise pollution that may occur during the construction and operation phases as well as potential environmental hazards, such as insect growth, visual pollution, reduced property value, odor, negative aesthetic effects, “Not in My Backyard” (NIMBY) syndrome, and future urban expansion. Hence, landfill sites should not be close to settlements and also should not be built too far away to minimize the waste shipping costs. According to the regulation, facilities required for the storage of municipal and non-hazardous wastes should be at least 250 m away from settlements (RRSW, 2010). However, Güler
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Figure 4. Fuzzy maps of (a) distance from protected sites, (b) distance from settlements, (c) distance from roads, and (d) land use.
and Yomralıoğlu (2017) stated that “taking into account the limit set for hazardous wastes, landfill sites should be at least 1 km away from settlements.” Similarly, Karasan et al. (2019) highlighted that “landfill sites may not be located at a distance of less than 1 km from settlements.” There are one municipality and 27 rural (village) settlements in the study area (Figure 4b). The Euclidean distance function was used to calculate the distances from the settlements, and the maximum distance was calculated as 10,029.21 m. Taking into account the provisions of the regulation, in this study, the areas that are closer than 250 m to the settlements were accepted as unsuitable for landfill sites. The distance to the settlements was standardized using
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the fuzzy linear membership function in ArcGIS 10.5 software (see Table 3). Distance from Roads As transportation costs are affected by the distance from the facility to roads, this is a significant factor for waste transport and, therefore, for landfill site selection. When selecting landfill sites, the locations in which transportation is possible in all weather conditions should be preferred. It may also be necessary to construct access roads to enable transportation to such sites. However, if the distance between the existing road and the landfill site is greater than 1 km,
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the construction of access roads will be costlier (Yesilnacar et al., 2012). Although it is desirable that landfill sites are close to roads, Pasalari et al. (2019) noted that landfill sites that are very close to the road lead to an aesthetically bad view. Rezaeisabzevar et al. (2020) also stated that “landfill sites should be far enough to cause no negative aesthetic effect on the road.” In this study, the areas closer than 200 m to the road were accepted to be unsuitable, while the areas located at a distance of 200–1,000 m from the road were accepted to be suitable to ensure that landfill sites do not create a negative aesthetic view. The digital road data of the study area were obtained from Başarsoft Information Technologies, Inc., which is a company in Turkey that produces navigation maps. The road buffers were produced using the Euclidean distance function of the ArcGIS 10.5 software (Figure 4c). Subsequently, the distances to roads were standardized using the decreasing fuzzy linear membership function (Table 3). Land Use Land use is commonly defined as “a series of operations on land, carried out by humans, with the intention to obtain products and/or benefits through using land resources” (Coffey, 2013). In other words, “land use” refers to how or for what purpose people use the land. In land use, depending on the characteristics of the study area, there are many different categories, such as recreation, conservation, agriculture, and residence. According to the legislation in Turkey, agricultural, forest, and pasture lands cannot be used except for the purposes specified in the land use plans. Therefore, such lands should not be selected as landfill sites. The lands in Turkey are divided into eight classes according to their capability of being used as agricultural lands. The lands in the first four classes are suitable areas for cultivation, while the lands in classes VII and VIII are unfavorable for cultivation (Arkoc, 2014). The lands in classes V and VI are suitable for grazing purposes (as meadows and pastures) rather than mechanized agriculture. Therefore, lands of classes VII and VIII can be used as a landfill site. Rezaeisabzevar et al. (2020) noted that “arid and low-value lands are the most suitable locations for landfill sites.” The land use data used in this study were extracted from the forest management maps, with a scale of 1/25,000, obtained from Artvin Regional Directorate of Forestry. According to the forest management map, there are seven land use classes, as follows: national park, forest, settlement, agricultural land, water bodies, rocky/stony terrain, and sand/quarry (Figure 4d). As rocky/stony terrains and class VII and VIII agricultural lands are suitable for landfills, they were reclassified by assigning them a value of “1,” while the other land use classes,
which were not suitable for landfills, were reclassified by assigning them a value of “0.” Wind Direction Landfills can cause odor and pollutant emissions. Therefore, considering the climatic parameters for landfill sites, wind direction is quite important. When selecting landfill sites, predominant wind directions should be taken into account to prevent the transport of odors and other emissions to settlements by wind (Djokanović et al., 2016; Torabi-Kaveh et al., 2016; and Barakat et al., 2017). Using the wind data measured at the meteorological station within the study area, a graph showing the predominant wind direction was produced (Figure 5). The wind data represent the years 2017–2019 on an hourly basis. The WRPLOT View 7.0.0 program developed by Lakes Environmental Software was used to create the wind rose. With the help of the program, wind direction, frequency analysis, wind class, direction, and speed can be determined according to time and location (Demirarslan and Akinci, 2016). Examining Figure 5, it is understood that the predominant wind direction in the study area is NW. The predominant wind speed was determined to be 0.5 to 1.2 m/s, with a rate of 60.6 percent. This parameter was used to choose between the alternative sites determined as a result of the MCDM analysis. Therefore, it was not included in the MCDM analysis as a direct factor. AHP Approach The AHP is a MCDM approach developed by Saaty (1977). The simplicity and power of AHP have led to its widespread use in different fields worldwide (Bhushan and Rai, 2004). This method allows users to set the weights of the criteria when solving a problem that depends on multiple criteria. Generally, in the AHP method, three stages are involved in problem solving: (1) conversion of the decision-making problem into a hierarchical structure comprising goals, criteria, subcriteria, and alternatives; (2) formation of a pairwise comparison matrix and calculation of the criteria weights, using the opinions of experts in the field; and (3) calculation of the consistency ratio (CR) of the pairwise comparison decisions. After transforming the complex decision problem into a hierarchical structure, pairwise comparisons of the criteria are collected from the experts or decision makers. The study used the preference scale (Table 5) proposed by Saaty (1980) for the pairwise comparison of the criteria. Then, the judgments from the experts are arranged in a square matrix. In the matrix,
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Figure 5. Wind graph of the study area.
Table 5. Scale of relative importances (Saaty, 1980). Intensity of Importance
Definition
Explanation
1 3
Equal importance Weak importance of one over another
5
Essential or strong importance
7
Demonstrated importance
9
Absolute importance
2, 4, 6, 8
Intermediate values between the two adjacent judgments If activity i has one of the above numbers assigned to it when compared with activity j, then j has the reciprocal value when compared with i.
Reciprocals
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Two activities contribute equally to the objective Experience and judgment slightly favor one activity over another Experience and judgment strongly favor one activity over another An activity is strongly favored and its dominance demonstrated in practice The evidence favoring one activity over another is of the highest possible order of affirmation When compromise is needed
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Solid Waste Landfill Site Selection Table 6. Random index (RI) values for different matrix sizes (Saaty, 1980). n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
RI
0
0
0.58
0.90
1.12
1.24
1.32
1.41
1.45
1.49
1.41
1.48
1.56
1.57
1.59
the diagonal elements are represented by “1.” A value greater than “1” for the (i, j)th element of the matrix indicates that the criterion in the ith row is superior to the that in the jth column; if the value is less than “1,” the criterion in the jth column is superior to the criterion in the ith row. The (j, i)th element in the matrix corresponds to the (i, j)th element (Bhushan and Rai, 2004). Determination of the weights of the criteria for which pairwise comparisons are performed is realized by subjecting the pairwise comparison matrix to normalization. Hence, each column sum is divided by the column elements in the matrix, which results in a normalized pairwise comparison matrix. The total value of the row elements in this normalized matrix is calculated and then divided by the number of elements in the row, resulting in the priority vector or weight vector (Tombuş, 2005). The weight values ranged from “0” to “1,” with their sum being “1” (Malczewski, 1999; Öztürk and Batuk, 2010). When the AHP is used, the pairwise comparisons of the criteria may display some inconsistencies. Therefore, checks for logical consistency should be carried out on the pairwise comparisons (Öztürk and Batuk, 2010). Their consistency can be measured by applying the CR proposed by Saaty (1980). Eq. 1 gives the calculation for the CR of the pairwise comparison matrix. CR = CI/RI
(1)
calculating the factor weights. To produce the pairwise comparison matrix, the judgments were created by obtaining 43 expert opinions. These experts are academicians from environmental engineering departments of universities, public institutions (Artvin Provincial Directorate of Environment and Urbanism), and environmental engineers from consultancy firms. All of the experts were selected from Turkey. In addition, about half of the experts were familiar with the study area and its topographic characteristics. The CR of the pairwise comparison matrix was calculated as 0.065. Since the CR value was less than 0.10, the judgments were found to be consistent, and it was decided that the weights would be used. The landfill suitability index (LSI) was calculated using the weighted product model through the raster calculator function of ArcGIS. LSI, which ranged from 0 to 0.535, was categorized into three classes (not suitable, moderately suitable, and suitable) using the natural breaks classification method to produce the suitability map of the study area (Figure 6). In terms of suitability for a landfill site, only 0.01 percent of the study region was found to be “suitable,” 0.08 percent was “moderately suitable,” and the remaining 99.91 percent was “unsuitable.” According to this, five sites in the suitable areas could potentially be used for landfills (Figure 6). Of these, sites 1 through 5 have surface areas of 47,800 m2 ; 12,300 m2 ; 7,600 m2 ;
where CI is the consistency index and RI is the random index. The RI values used for different matrix sizes are given in Table 6. The CI is computed as CI =
λmax − n n−1
(2)
where λmax is the maximum eigenvalue and n is the matrix size in pairwise comparison (n × n). The upper limit recommended by Saaty for CR is 0.10. For CR values of <0.10, judgments are accepted to be consistent, and calculated weights can be used. Otherwise, it is necessary to reevaluate the relative importance values of judgments or criteria in the pairwise comparison matrix. Application of AHP Method and Selection of Alternative Landfill Sites The AHP method was implemented by creating the pairwise comparison matrix shown in Table 7 and
Figure 6. Landfill suitability map of the study area.
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Akinci and Demirarslan Table 7. Pairwise comparison matrix and weights of the factors. Factor
DPS
DSW
DS
FS
LS
LU
S
DR
G
DF
Weights
DPS DSW DS FS LS LU S DR G DF
1 1/2 1/3 1/4 1/4 1/5 1/7 1/8 1/9 1/9
2 1 1/2 1/3 1/3 1/4 1/6 1/7 1/8 1/9
3 2 1 1/3 1/3 1/4 1/5 1/6 1/7 1/8
4 3 3 1 1 1/2 1/3 1/4 1/5 1/6
4 3 3 1 1 1/2 1/3 1/3 1/4 1/5
5 4 4 2 2 1 1/2 1/3 1/4 1/5
7 6 5 3 3 2 1 1/2 1/3 1/4
8 7 6 4 3 3 2 1 1/2 1/3
9 8 7 5 4 4 3 2 1 1/2
9 9 8 6 5 5 4 3 2 1
0.284 0.205 0.166 0.090 0.083 0.062 0.042 0.030 0.021 0.016
DPS = distance from protected sites; DSW = distance from surface water; DS = distance from settlements; FS = flood susceptibility; LS = landslide susceptibility; LU = land use; S = slope; DR = distance from roads; G = geology; DF = distance from faults. Maximum eigenvalue = 10.868; consistency index = 0.096; random index = 1.49; and consistency ratio = 0.065.
5,100 m2 ; and 4,900 m2 , respectively. Despite the fact that the site 1 meets the area criterion, some power transmission lines pass through it, which is why this site was excluded. On the other hand, sites 2 through 5 do not satisfy the area criterion and suffer from transportation problems (as the transportation is mainly through forest roads; the road lengths vary between 27 and 30 km, and the transportation infrastructure needs to be substantially improved), which was also the reason that these sites were excluded. Therefore, the focus was on moderately suitable locations for landfills. The sites indicated by the numbers 6 through 9 (see Figure 7) have surface areas of 4,900 m2 ; 12,600 m2 ; 14,600 m2 ; and 136,600 m2 , respectively. Site 6, located approximately 700 m from the nearby residential buildings and 6.8 km from the Artvin city center, was eliminated because of both its unfavorable predominant wind direction and area
inadequacy. Site 7 was eliminated for the following reasons: (1) its area was smaller than it should be; (2) it can be seen from the city center, which ruins the landscape; and (3) it is a private property, which makes it hard to acquire. Site 8, which is located 13.2 km from the city center, was found to have the characteristics required for use as a landfill site. This site is about 9 km from the national park, about 3 km from surface waters, and about 800 m away from the nearest settlement area (Salkımlı Village) (350 m to the nearest residential building in the village). The average slope of this area, which is included in class VII in terms of land use capability, is 18.67°. Therefore, it would be appropriate to select Site 8 as the landfill site. Although Site 9 was very suitable in terms of surface area, it was determined to be the second alternative, since it is located 27.9 km from the city center. RESULTS AND DISCUSSION
Figure 7. Moderately suitable locations for the landfill site.
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Unlike in many studies in the literature, very few areas were found to be suitable for landfills. The main reasons for this include the fact that forests make up 72.28 percent of the study area, a national park encompasses 18.55 percent of the area, 76 percent of the area is covered by steep slopes (>25°), and, finally, there are problems with transportation. Kamdar et al. (2019) reported that 99.43 percent of the study area of Songkhla City (Thailand) was unsuitable for landfill sites, whereas Karimi et al. (2019) found 87 percent of their research area of Javanrood County (Iran) to be unsuitable. Moreover, as in the present study, Karimi et al. (2019) also ascribed the high rate of unsuitable sites to the location of the study area in a mountainous terrain covered with dense forest and having high slope values. Because of the restrictions stated above, it was possible to determine only two candidate areas to be used
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as a landfill site within the study area. Detailed geological, geotechnical, and economic analysis is required to make the final decision on these areas. A study on landfill site selection in Şanlıurfa Province (Turkey) by Yesilnacar et al. (2011) noted the need for comprehensive hydrogeological and geotechnical analyses. They also stated that political, social, and economic constraints should also be taken into account prior to engineering design within the areas that were found to be suitable for use as landfill sites. Barakat et al. (2017) recommended further evaluation of selected alternative areas based on other local criteria and field studies before making a final decision on site selection. In this study, in which a total of 10 factors were considered, the distances from protected sites, surface water, and settlements, respectively, were the most important factors. Geology and the distance from faults were determined to be the least important factors. Because of its rich biodiversity and unique visual beauty, Hatila Valley National Park was decisive in determining the factor weights. Güler and Yomralıoğlu (2017) stated that the factors and weights used for the selection of a landfill site may vary depending on the study area. For example, in the study conducted by Alavi et al. (2013) in Mahshahr County (Iran), the parameters of distance from sensitive ecosystems and surface water were the most important. In the study conducted by Şener et al. (2011), distance from surface water was determined as the most important parameter, since the Eğridir Lake is located within the study area. The study area is one of the regions in Turkey where natural disasters happen frequently. There are several active landslide areas in Artvin, as stated by GDMRE and the Provincial Disaster and Emergency Directorate of Artvin. Therefore, landslide susceptibility was also used as a site selection factor, as was the case in the work of Eskandari et al. (2016). Ersoy and Bulut (2009), Yildirim (2012), and Ersoy et al. (2013) are other researchers who used landslide factor in landfill site selection studies. However, the most important point that distinguishes this study from the other studies in the literature is that flood susceptibility and distance to faults were also considered in addition to landslide susceptibility. Some studies used two of these parameters concomitantly. For example, Ersoy and Bulut (2009) used landslide susceptibility and distance from faults, Moeinaddini et al. (2010) used distance from faults and flooding over 100 years, Yildirim (2012) used landslide susceptibility and distance from faults, Ersoy et al. (2013) used distance from active fault area and distance from unstable area, and Aksoy and San (2016) combined the parameters of distance from fault lines and distance from landslides. However, according to the resources we could access, no
studies have been performed wherein the parameters for natural disasters, such as earthquakes, floods, and landslides, were evaluated concomitantly in the selection of landfill sites. This study will fill this gap in the literature.
CONCLUSIONS Landfills potentially pose a risk to human health and environment; thus, the location of these landfills must be selected with great care. Many disciplines, methods, and factors must be combined for proper site selection. Choosing the most suitable site will minimize environmental, economic, and social conflicts. Presently, the MSW that is produced in the Artvin city center is stored in an unorganized way, which causes negative impacts on water bodies and sensitive ecosystems. In this study, we aimed to choose the location for a sustainable landfill that is most suitable for the Artvin city center in terms of environmental, economic, and social aspects. When selecting the site, 10 environmental, economic, and social factors were considered in total. These factors included the distance to faults, flood and landslide susceptibility, slope, distance to protected sites and settlements, distance to roads, land use, and distance to water bodies. These factors were standardized using the fuzzy membership functions, and the weights of the factors were determined using the AHP approach. Sensitive ecosystems, such as national parks and dam lakes in the study area, were decisive in factoring weights. The most suitable locations for MSW landfill sites were determined via GIS-based MCDM analysis. Overall, up to 99.91 percent of the study area was found to be “unsuitable” for landfill sites, whereas 0.08 percent was “moderately suitable,” and only 0.01 percent was “suitable.” In conclusion, these results were seen as stemming from the land use and the topographic features of the region chosen for the study. The unsuitable topographical characteristics and land cover features of Artvin make it very difficult to choose a proper landfill site. Therefore, the primary target in the city center should be to reduce waste generation. In addition, it is necessary to benefit from the waste that still possesses economic value as raw material through practices such as reuse and recycling. Moreover, it should be encouraged that the organic part of the wastes be composted for use within the agricultural sector within the province. The main goal should be to reduce the volume of the waste produced as a result of these practices. In this way, the need for landfill facility space can be reduced, which, in turn, creates an advantage in site selection and increases the lifecycle of landfill facilities.
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Technical Note Treatment and Control of Urban Sewage with Excessive Heavy Metals for Ecological Environment Protection LISI ZHU* North Campus of Inner Mongolia Technical College of Construction, South of Youth Ecological Park, Hui People District, Hohhot City, Inner Mongolia, China
Key Terms: Sewage Treatment, Ecological Environment, Heavy Metals, Reverse Osmosis INTRODUCTION The rapid development of the urban economy is inseparable from the support of various heavy industries. Although heavy industry can provide many economic benefits in the process of development, it will inevitably produce a lot of pollution. Water is essential in the process of industrial production. In addition to the waste gas that is discharged into the atmosphere, a lot of wastewater will also be discharged in industrial production (Iurchenko et al., 2016). Heavy industrial production is also related to metals; therefore, there are a lot of heavy metals in wastewater. If left untreated, the wastewater will inevitably cause heavy metal pollution to the environment (Sharma et al., 2016). Heavy metal pollution to the environment will be transferred to the human body through the enrichment of the food chain. Excessive heavy metals will cause huge adverse effects on animals and the human body, and some of these metals are toxic, which will cause direct toxicity to the human body, rather than chronic effects (Chen et al., 2018). Therefore, in industrial production, the sewage needs to be treated before it can be discharged into the natural environment. Kaczor et al. (2015) analyzed the main factors affecting the sewage discharge and water quality of tourism facilities in national parks and other nature reserves. They found that the domestic sewage generated by tourism facilities had high variability, and they proposed a solution based on the soil-plant system or a sand filter. The scheme had advantages in cost but had a long construction cycle. Hegazy et al. (2015) prepared different types of ion-exchange membranes by radiation-initiated graft copolymerization. The experimental results showed that the ion-exchange membranes had high selectivity for ferric ions and could be recycled many times. The scheme was simple to operate but was only specific for trivalent iron ions; *Corresponding author email: si59612859@163.com
therefore, it may not work well when faced with multiple heavy metals in wastewater. Razak et al. (2018) modified kenaf fiber with immune diacetic acid (IDA) and used it to treat heavy metals in wastewater. The research results showed that the modified kenaf fiber could be used for bivalent copper ions in wastewater. This scheme is also specific to a single kind of heavy metal. In order to reduce the environmental pollution caused by sewage discharge, sewage treatment is required prior to discharge. In the study described in this article, pollutants were removed from wastewater using the activated sludge method. To further remove heavy metals from wastewater, reverse osmosis technology was used to remove heavy metals from the activated sludge–treated sewage. A case study was then conducted in a sewage treatment plant. The final results showed that the heavy metal contamination was effectively removed from the sewage using reverse osmosis technology. The novelty of this study lies in its use of reverse osmosis technology to treat heavy metals in sewage. This work provides an effective reference for sewage treatment. TREATMENT OF WASTEWATER WITH EXCESSIVE HEAVY METALS As described in the introduction on heavy metal pollution, industrial wastewater needs to be treated before being discharged into the natural environment (Culhane et al., 2019). In addition to items such as nitrogen, phosphorus, pH, and chemical oxygen demand (Mintenig et al., 2017), heavy metal content is also the key item of detection. Given the problem of excessive heavy metals in sewage, the method of removing heavy metals in sewage is mainly shown in Figure 1. CONTROL TECHNOLOGY OF URBAN SEWAGE TREATMENT Traditional Process The process of traditional urban sewage treatment control is shown in the flow with solid arrows. The basic steps are as follows:
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Flow of the Improved Process
Figure 1. Main methods for removing heavy metals from wastewater.
1. The urban sewage first passes through the coarse and fine screens (Rahayu et al., 2019). The main purpose of this step is to preliminarily remove the water-insoluble pollutant particles or suspended solids in the sewage by physical means, which is conducive to the subsequent decontamination treatment. 2. After the preliminary screening of coarse and fine screens, it is necessary to leave the sewage in the primary settling tank for some time. 3. The activated sludge method (Park et al., 2019) is used to decompose the organic matter in the sewage using the microbial community in the activated sludge. With this process, air is constantly injected into the tank. 4. After degradation by the aeration tank, the sewage enters the sedimentation tank. The main function of the sedimentation tank is to precipitate the sludge, discharge the sludge into the sludge storage tank, and return it to the aeration tank to participate in the degradation of organic matter. 5. After standing in the sedimentation tank, the sewage enters the ultraviolet disinfection tank for disinfection (Ksenofontov et al., 2019). Finally, the treated sewage is discharged and is usually used for flushing toilets and watering green belts in cities. The above steps describe conventional sewage treatment routes, which can effectively decontaminate general urban sewage, and the heavy metal elements in the sewage can also be effectively treated. The screen and primary settling tank initially remove the insoluble heavy metals; the aeration tank removes the heavy metals through oxidation by air; the sludge containing microbial flocculants in the sedimentation tank plays a role in absorption, and the microorganisms remove some heavy metals (Vialkova et al., 2020).
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In the improved process, heavy metals are removed from the treated wastewater by the reverse osmosis method (Ali et al., 2019). A semi-permeable membrane is a kind of membrane with selective permeability. Its working principle can be simply understood as a sieve with a very small pore size, which only allows water molecules or smaller substances to pass through. In heavy metal treatment for the treated sewage, the sewage is pressurized to pass through the reverse osmosis membrane (semi-permeable membrane) to intercept the heavy metals in the sewage. The improved process route is shown in Figure 2. The detailed process route is described below: 1. Steps 1 through 4 are the same as those described for the traditional sewage treatment flow. 2. After sedimentation in the sedimentation tank, the sewage does not enter the ultraviolet disinfection tank, as in the traditional route, but first undergoes reverse osmosis treatment. The sewage is pressurized and passes through the semi-permeable membrane. 3. Then, the wastewater enters the ultraviolet disinfection tank for disinfection, and the concentrated heavy metal wastewater not passing through the semi-permeable membrane flows back to the aeration tank for re-aeration and is discharged after disinfection in the ultraviolet disinfection tank. In the above treatment process, the sludge precipitation in the sedimentation tank will flow into the sludge storage tank and partially return to the aeration tank for re-aeration. The main purpose for this step is to force the microorganisms in the activated sludge to fully participate in the treatment of sewage pollutants; the heavy metal concentrate after reverse osmosis treatment will also return to the aeration tank for continuous treatment with activated sludge. CASE ANALYSIS Overview of a Sewage Treatment Plant Sewage treatment plant X is located in Hohhot, Inner Mongolia. It has been running for 5 years since its initial construction. During the operation, it mainly receives sewage from urban sources. Because of expansion, the content of heavy metals in the sewage to be treated by the sewage treatment plant greatly increased, and the traditional sewage treatment and control process became less effective in reducing the content of heavy metals. After the trial operation of the expanded sewage treatment plant, the traditional process route was improved. The improved process
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Treatment and Control of Urban Sewage
Figure 2. The traditional and improved flows of the urban sewage treatment control process.
route is the process route combined with reverse osmosis technology mentioned above. Experimental Materials and Methods The case analysis in this article entailed verifying the effectiveness of reverse osmosis technology in treating heavy metal pollution and the protection it offers for the ecological environment through comparing the heavy metal treatment effect before and after the improvement of the sewage treatment process.
samples were taken, and the mass of every soil sample was 500 g. The reason for sampling the sewage at the inlet and outlet is to directly test the heavy metal content in the sewage, so as to understand the effect of the sewage treatment process on heavy metals. The reason for sampling the soil near the outlet is that the treated sewage would be discharged not only into the watershed water but also into the surrounding soil, and after being discharged into the watershed water, the watershed water would also gradually infiltrate into the surrounding soil, thereby transferring heavy metals into the soil and causing pollution of the soil environment.
Sample Collection To achieve the above goal, this study collected the samples related to the detection experiment in three periods: before expansion, in the trial operation after expansion, and during the period in which the improved process route after the expansion was applied. The samples comprised the sewage before treatment, the sewage after treatment, and the soil around the treatment plant. The treatment effect of the treatment plant on heavy metals was tested by detecting heavy metals in the sewage samples before and after treatment, and the impact of the sewage treated by the treatment plant on the surrounding ecological environment was tested by detecting heavy metals the soil. The sewage before treatment was sampled using a clean and dry sampling bottle at a depth of 0.2 m at the water inlet, and the sampling volume was 1 L. The reason for selecting such a depth is that the sewage was relatively stable at this depth. The sewage after treatment was sampled using a clean and dry sampling bottle at the same depth at the water outlet, and the sampling volume was 1 L. The soil was sampled at multiple points within the range of 20 m near the water outlet of the treatment plant; a total of 20 soil
Experimental Materials Main reagents included a standard solution containing single elements, such as cadmium, copper, lead, and chromium (concentration: 1,000 μm/mL), nitric acid (analytically pure), hydrogen fluoride (analytically pure), HCl (analytically pure), HClO4 (analytically pure), H2 O2 (30% content), and deionized water. Specific Experimental Steps The specific experimental treatment steps are as follows: 1. Sample pre-treatment: The samples collected in this study included sewage samples and soil samples. The pre-treatment of the sewage sample is as follows. After 0.5 hours of standing, 50 mL of water sample was taken and transferred to a closed digestion apparatus. Then, 2.0 mL of H2 O2 and 10 mL of concentrated nitric acid were added. After digestion, suction filtration was conducted using a fiber filter. The pH value of the filter liquor was adjusted to 2.5 by diluted hydrochloric acid. Finally, the filter liquor was preserved at 0° to ∼4°C for
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Figure 3. The content of heavy metals in the sewage before and after treatment in different periods.
standby. The pre-treatment of the soil sample is as follows. The soil was ground in a mortar after ventilation and drying and was then sieved through a 200-mesh sieve. After sieving, the soil was digested by a closed digestion apparatus, combined with 4 mL of HClO4 , and heated at 150°C. Finally, suction filtration was conducted using a fiber filter, and the volume of the filter liquor was adjusted to 25 mL. 2. Quantitative detection of heavy metals in samples: The standard solution of corresponding heavy metal elements was prepared, and the standard curve was drawn with a spectrophotometer (Multiskan Sky) (Castro, 2019). Then, the content of heavy metals in the samples to be tested was calculated according to the standard curve.
Experimental Results As can be seen in Figure 3, the content of heavy metals in the sewage after treatment was reduced using all three methods. The content of heavy metals in the sewage at the inlet after the expansion of the treatment plant was higher than that before the expansion, regardless of whether it was measured during the trial operation or after the improved treatment process was adopted, because the business from the chemical industry zone was received after the expansion and the heavy metal content rose. In the trial operation period, when the traditional treatment process was still in use, the content of heavy metals in the treated sewage was lower than that before treatment but still higher than that before expansion, showing poor performance in heavy metal treatment. After using the improved treatment process, the content of heavy metals in the treated
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sewage was not only lower than that before treatment but also lower than that after expansion of the pretreatment plant. It was concluded that the improved reverse osmosis technology could effectively treat the heavy metals in the sewage, significantly reduce the content of heavy metals in the treated sewage, and avoid the excessive heavy metals in the sewage discharge. After the treatment by the sewage treatment plant, the sewage will be discharged to the natural environment. After the sewage is absorbed by the soil, the heavy metals will gradually accumulate because of the heavy metal residues, eventually damaging the ecological environment. Therefore, it is necessary to pay attention not only to the reduction of heavy metal content before and after sewage treatment but also to the heavy metal pollution of soil in the ecological environment after sewage treatment. If the degree of heavy metal pollution of soil is high, even if the treatment plant reduces the heavy metal content of sewage, it is also unqualified. In this study, the degree of heavy metal pollution in the soil was measured by the Nemerow index, and its equation is as follows: ⎧ ⎨
Pi = cci,0i 2 2 Pi,ave +Pi,max ⎩ PI = 2
(1)
where Pi is the Nemerow index of heavy metal i; PI is the comprehensive pollution index; ci and ci,0 are the actual content and the background content of the soil, respectively; and Pi,ave and Pi,max are the average and maximum Nemerow indexes of all heavy metals, respectively.
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Treatment and Control of Urban Sewage Table 1. Heavy metal pollution degree of the soil ecological environment around the treatment plant in different periods. Content of Heavy Metal (mg/kg)
Nemerow Index
Comprehensive Pollution Index
0.145 19.7 25.1 31.1
— — — —
0.22 41.37 12.55 24.88
1.5 2.1 0.5 0.8
1.20 114.26 22.59 59.09
8.3 5.8 0.9 1.9
0.07 13.79 5.02 18.66
0.5 0.7 0.2 0.6
— — — — 1.72 — — — — 6.59 — — — — 0.61 — — — —
Soil background value Cadmium Copper Lead Chromium Before the expansion of the treatment plant Cadmium Copper Lead Chromium Trial operation after expansion Cadmium Copper Lead Chromium After adopting the improved process Cadmium Copper Lead Chromium
The final results are shown in Table 1. For the Nemerow index, PI ࣘ 0.7 is grade 1 (clean), 0.7 < PI ࣘ 1.0 is grade 2, 1.0 < PI ࣘ 2.0 is grade 3, 2.0 < PI ࣘ 3.0 is grade 4, and 3.0 < PI is grade 5. It was seen from Table 1 that the pollution level of copper in the soil environment around the treatment plant was the highest before the expansion of the treatment plant, when it was moderate. In the trial operation period after the expansion, the pollution level of cadmium was the highest, followed by copper, and the increase of pollution was significant and the pollution was severe. After the improvement of the process, the pollution level of copper was still the highest, but it was hovering around the warning line. Before the expansion, the comprehensive pollution index of heavy metals in the soil was 1.72, which was considered mild. In the trial operation period after the expansion, the comprehensive pollution index rose to 6.59, which was considered serious; after the improvement of the process, the comprehensive pollution index decreased to 0.61, which was deemed “clean.” DISCUSSION In this article, the traditional wastewater treatment process was improved by reverse osmosis technology, so as to enhance the ability to remove heavy metal pollution in the sewage treatment process. This article presents an example analysis of the sewage treatment process before and after the expansion of a sewage treatment plant. The treatment process was di-
vided into a trial period and a formal operation period after expansion. In the trail operation period, the sewage was treated according to the original treatment process. In the formal operation period, the sewage was treated according to the treatment process, which was improved by the addition of reverse osmosis technology. The final results have been described above. In this article, the evaluation of the treatment effect of heavy metal pollution in the sewage treatment process was based not only on the heavy metal content before and after treatment but also on the the Nemerow index. Before expanding the treatment plant, the traditional treatment process had been adopted, and its main goal was to treat the sewage from urban areas and living areas. Major parts of heavy metals could be removed, which eventually led to the slight pollution of the soil environment (i.e., 1.72, or grade 3 pollution). After the expansion of the scale and industrialization, the traditional treatment process was still used during the trial operation. Before the expansion, the factory only needed to process the sewage from urban areas and living areas, but after adding the sewage treatment tasks from the chemical zone with high heavy metal content, it was more difficult to remove the heavy metals, which eventually led to a sharp increase in the degree of soil contamination (i.e., 6.59, or grade 5 pollution). After adopting the improved treatment process, the reverse osmosis treatment removed the residual heavy metals left in the previous steps to the greatest extent, making the pollution of the soil
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decrease to 0.61 (grade 1 pollution; i.e., the treatment result was better than before plant expansion). CONCLUSIONS This article briefly introduced the conventional treatment method for wastewater with excessive heavy metals and the traditional treatment process for urban sewage, improved the traditional treatment process by including reverse osmosis technology, and conducted an analysis of the sewage treatment plant X located in Hohhot, Inner Mongolia. The results are as follows. 1. Before the expansion, during the trial operation period after expansion, and after applying the improved treatment process, the content of heavy metals in the treated sewage decreased. 2. In the trial operation after expansion, as the plant added the responsibility for treating the sewage from the chemical industrial zone, the content of heavy metals in the sewage before treatment sharply increased and the content of heavy metals was high after treatment by the traditional treatment process, but the content of heavy metals in the sewage reduced greatly after utilization of the improved treatment process. 3. Before the expansion of the treatment plant, the soil was slightly polluted; during the trial operation period after the expansion, the soil pollution became severe; and after the improvement of the treatment process, the soil pollution was reduced to “clean.” REFERENCES Ali, F.; Rehman, S. U.; Tareen, N. M.; Ullah, K.; and Laghari, S., 2019, Effect of waste water treatment on the growth of selected leafy vegetable plants: Applied aliEcology Environmental Research, Vol. 17, No. 2, pp. 1585–1597. Castro, L. F. C., 2019, Linking chemical exposure to lipid homeostasis: A municipal waste water treatment plant influent is obesogenic for zebrafish larvae: Ecotoxicology Environmental Safety, Vol. 182, p. 109406. Chen, L.; Han, Q. Q.; Li, W. X.; Zhou, Z. Y.; Fang, Z.; Xu, Z. W.; Wang, Z. X.; and Qian, X. M., 2018, Three-dimensional
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graphene-based adsorbents in sewage disposal: A review: Environmental Science Pollution Research, Vol. 25, p. 25840–25861. Culhane, F. E.; Briers, R. A.; Tett, P.; and Fernandes, T. F., 2019, Response of a marine benthic invertebrate community and biotic indices to organic enrichment from sewage disposal: Journal Marine Biological Association UK, Vol. 99, No. 8, pp. 1–14. Hegazy, E. S. A.; Abd El-Rehim, H. A.; Khalifa, N. A.; Atwa, S. M.; and Shawky, H., 2015, Anionic/cationic membranes obtained by a radiation grafting method for use in waste water treatment: Polymer International, Vol. 43, No. 4, pp. 321–332. Iurchenko, V.; Lebedeva, E.; and Brigada, E., 2016, Environmental safety of the sewage disposal by the sewerage pipelines: Procedia Engineering, Vol. 134, pp. 181–186. Kaczor, G.; Bergel, T.; Bugajski, P.; and Pijanowski, J., 2015, Aspects of sewage disposal from tourist facilities in national parks and other protected areas: Polish Journal Environmental Studies, Vol. 24, No. 1, pp. 107–114. Ksenofontov, B. S.; Kozodaev, A. S.; Taranov, R. A.; and Vinogradov, M. S., 2019, Experimental validation of waste water treatment processes aimed at metals removal: Tsvetnye Metally, pp. 96–100. Mintenig, S.; Int-Veen, I.; Löder, M. G. J.; Primpke, S.; and Gerdts G., 2017, Identification of microplastic in effluents of waste water treatment plants using focal plane array-based micro-Fourier-transform infrared imaging: Water Research, Vol. 108, pp. 365–372. Park, J. E.; Lee, G. B.; Hong, B. U.; and Hwang, S. Y., 2019, Regeneration of activated carbons spent by waste water treatment using KOH chemical activation: Applied Sciences, Vol. 9, No. 23, p. 5132. Rahayu, S. S.; Budiarti, V. S. A.; Sumiyarso, B.; Amrul, A.; and Triyono, E., 2019, Application of waste water treatment technology from exhaust electroplating and anodizing process using electro-coagulation method: Journal Physics: Conference Series, Vol. 1217, p. 012002. Razak, M. R.; Yusof, N. A.; Haron, M. J.; Ibrahim, N.; Mohammad, F.; Kamaruzaman, S.; and Al-Lohedan, H. A., 2018, Iminodiacetic acid modified kenaf fiber for waste water treatment: International Journal Biological Macromolecules, Vol. 112, pp. 754–760. Sharma, S.; Meenu, P. S.; Asha Latha, R.; Shashank, B. S.; and Singh, D., 2016, Characterization of sediments from the sewage disposal lagoons for sustainable development: Journal Testing Evaluation (JOTE), Vol. 5, No. 1, p. 20150010. Vialkova, E.; Sidorenko, O.; and Glushenko, E., 2020, Influence of probiotic products on the quality of waste water treatment in dairy industries: Urban Construction Architecture, Vol. 10, No. 1, pp. 47–55.
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Technical Note Landscape and Ecological Foundations for the Organization of Regional Systems of Special Protected Areas JIANMEI WANG* BAO YU College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, 066004, 360 West Section of Hebei Street, Haigang District, Qinhuangdao, Hebei Province, China LINA NIU College of Information, Shanxi Agricultural University, 030800, 8 Xueyuan Road, Taigu District, Jinzhong, Shanxi Province, China
Key Terms: Natural Areas, Region, Ecology, Biodiversity, Nature Management INTRODUCTION The most important area of modern regional studies is the substantiation of systems of special protected areas (SPAs) and landscape-ecological foundations of their organization, which together will contain all the landscape diversity. Landscape planning of the SPA system should be based on an analysis of the results of field research and remote sensing data processed using various automated and automatic methods. Such methods allow assessing the landscape diversity of the territory, identifying landscape boundaries, and classifying the studied area by geophysical surface type. The results of such studies are an important qualitative source of relevant spatial data and can be disseminated both by traditional methods and through the functionality of modern geoportal systems. The development of SPAs should contribute to the preservation of natural ecosystems that create ecological corridors and trails in the region. It should be understood that each protected object is limited in area and that its existence is impossible without landscape and ecological arrangement of adjacent areas. Therefore, the organization of a system of SPAs must be carried out taking into account their relationship with the ecological carcass of the territory. Often, areas designated for economic activities occupy a border position with protected areas, which does not contribute to the safety of natural ecosystems. Protected areas are of exceptional importance for the preservation of biological and landscape diversity *Corresponding author email: jian-wang6453@murdoch.in
as the basis of the biosphere. Given the growing threat of natural disasters and changes in the natural environment as a result of economic activities, the main purposes of specially protected natural areas are the following:
r Supporting the ecological stability of the territories essentially changed by economic activities
r Reproducing valuable renewable natural resources in natural conditions
r Maintaining a healthy living environment and creating conditions for the development of regulated tourism and recreation r Implementing environmental education programs r Conducting basic and applied research in the field of natural sciences The lands of SPAs include protected areas occupied by state nature reserves and natural parks, natural monuments, arboretum parks, botanical gardens, and health resorts. In addition to natural territories, the category of land includes land occupied by objects of physical culture and sports, recreation and tourism, and historical and cultural monuments. These categories of land have been declared natural areas under special protection. In order to ensure their preservation, they are withdrawn from economic turnover in whole or in part. The legal regime of land plots classified in this category depends on the legal regime of the territories in which they are located or the objects located on them. Biodiversity is undoubtedly one of the most important but not the main value of SPAs. It is clear that the diversity of abiotic components and subsystems also determines the stability of biocenoses. Therefore, it seems more correct to speak about natural, or structural, diversity, in the understanding of Martynov et al. (1995), as one of the most important values of
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protected areas. In addition to Martynov et al. in world practice, the issue of specially protected natural areas was studied by Gaston et al. (2008), Pellizzaro et al. (2015), Blanco et al. (2019), Yakusheva (2019), and others. It should be borne in mind that the dominance of biodiversity conservation over other environmental tasks has emerged in recent years to some extent due to the Convention on the Protection of Biological Resources (1992). However, looking at the objectives of this convention, “The conservation of biological diversity, the sustainable use of its components and the shared and equitable benefits associated with the use of genetic resources, including through the provision of necessary access to genetic resources and the proper transfer of relevant resources. technologies, taking into account all the rights to such resources and technologies, as well as through adequate funding.” It is clear that its creators were concerned not so much with the preservation of ecosystems and life-sustaining biosphere systems as with specific material benefits from the use of genetic resources. Since the general value of wildlife can be considered its ability to generate and sustain life in all its manifestations, perhaps the main value of SPAs (both the system as a whole and its individual components) can be considered self-sustaining biosphere systems with a natural “capacity” to regulate the environment. Many of the natural mechanisms of ecosystem self-sufficiency remain incomprehensible; they are perceived by humans as harmony, beauty, mystery, or the divine principle present in nature. Apparently, it can be assumed that these are two sides of the same phenomenon. Combining these values is important primarily for the presence of information and spiritual value in the untouched areas of the nature reserve fund. Then the chances of preserving the protection and preservation of natural landscapes will increase. Today, it is widely believed that the greater the biodiversity of a given area, the higher the resilience of these ecosystems (Efremov et al., 2018). Although in most situations this is true, it is very important for the SPAs to set the right priorities in each case and, accordingly, the correct organization of landscape and environmental planning and protection of natural areas. If the preservation of maximum biodiversity is unconditionally accepted as the main goal of protection, nature reserves will inevitably turn into botanical gardens and zoos. And this is not the desired result. Thus, it is worth investigating and generalizing the mechanisms that form the landscape and ecological foundations for the organization of regional systems of SPAs, which is the purpose of this study.
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LITERATURE REVIEW An important stage of the study is the analysis of the best practices in the organization of the protection of natural areas and the conservation of biodiversity. Jalkanen et al. (2020) addressed the issue of SPAs in Finland, the Commission on Protected Areas of the IUCN French Committee (2013) detailed the issues of SPAs in France, and Underwood et al. (2014) detailed the issues of protected areas in the Czech Republic, the Netherlands, Estonia, Germany, Croatia, and Spain. SPAs are the main tools for nature conservation. In the European Union (EU), the Birds and Habitats Directives are the most important policy directions for conservation strategies, legally preserving the characteristic, rare, endemic, and endangered biota of Europe (Hoffmann et al., 2017). The team of authors used data on the birth of species listed in the annexes to the directives to assess the uniqueness of the main SPAs in the EU (national parks and biosphere reserves). This is important for the conservation of key biota species in Europe. A new, multifunctional approach has been developed for calculating various indicators of conservation value, which represent various components of species diversity within protected areas, including diversity, deviations from the relationship between species and range, species rarity, and diversity of differentiation. Ideas of conservation and development in and around SPAs create confrontation and uncertainty that harm biodiversity and ecosystem services that support human well-being. The organization of SPAs is one of the most important activities for the conservation of natural resources, biodiversity, and gene pools, in particular, the promotion of healthy lifestyles in the world. The concept of conservation of landscape and ecological diversity and sustainable development contributes to the improvement of socioeconomic indicators and at the same time ensures the protection of the environment and ecosystems. The aim of an article by Ismayilov and Jabrayilov (2019) is to investigate the relationship between the landscape and ecological diversity of SPAs in Azerbaijan and the factors that contribute to this relationship with sustainable development. The article identifies landscape and ecological differences, including the principles and development of protected areas, such as national parks, nature reserves, and habitat management zones, in Azerbaijan. Specially protected natural areas are traditional and effective measures for the preservation of natural habitat, biological and landscape diversity, and the natural state of the biosphere in Armenia (Ministry of Nature Protection, 2014). Elaev (2018) examines the issue of protected areas in the example of the Baikal region, which represents a large part of Asia, with its characteristic natural
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objects that require special protection measures. Forest protection policy is an important strategy to improve the quantitative and qualitative characteristics of forest plantations and their related products (Sasanifar et al., 2019). In addition, global climate change and the depletion of natural resources have forced scientists to focus on sustainable forest use. Unfortunately, increased land use has led to urbanization, industrialization, and environmental risks. Protected areas are a vital part of ensuring the prosperity and quality of life of people in China (Miao and An, 2020), and the legal framework for the management of preserved areas is enshrined in various laws and regulations. To better manage protected areas in China, the Chinese government has issued and revised some laws, regulations, and policies for the preservation and management of protected areas. However, the management of protected areas still faces some challenges. The legal literature does not provide sufficient answers to this question, and the team of authors is trying to tackle this issue. The most important laws, regulations, and policies for the management of protected areas are briefly presented. Some possible suggestions for how best to solve the recent problem in the management of SPAs in China are proposed. These proposals include improving governance systems, improving relevant legislation, promoting public participation, and creating systems to guarantee funding diversification. An article by Pustovalova and Veselkin (2020) is devoted to topical issues of functioning of SPAs of state, regional, and local significance and is a critical analysis of legislative documents governing the organization and development of protected areas. The main characteristics of different categories of protected areas are considered. The most general problems of the effective functioning of protected areas are revealed. Particular attention is paid to the management of land resources within protected areas and the search for possible ways to attract additional sources of funding through the development of educational ecotourism. The necessity of developing a strategic document on the development of protected areas as part of the strategy of the socioeconomic development of the region and the state of the Russian Federation as a whole is substantiated. An article by Fedorov et al. (2020) is devoted to the theoretical and methodological substantiation of regional systems of protected areas. The method of analysis of geographical representativeness of the system of protected objects on the basis of landscape mapping is considered. The use of typological classification of landscapes for the formation of the hierarchical structure of the system of SPAs is proposed. The developed technique is tested with the help of evaluative research using a medium-scale landscape map. Thus, the
analysis of literature shows a constant scientific search for mechanisms to solve the problem of biodiversity conservation and protection of special natural areas. Therefore, the generalization of mechanisms and criteria for the formation of regional protected areas is a topical issue. MATERIALS AND METHODS The main method of research was landscape indication (automatic and automated algorithms for distinguishing landscape boundaries and determining landscape heterogeneity); in addition, process and system analysis was applied. Comparative geographical, cartographic, statistical, and geoinformation research methods were used as auxiliary methods. The comparative geographical method consists of identifying the features of the similarities and differences between the objects under study. It is used to compare socioeconomic systems in time and space to analyze the results of economic activity, population development, services, and so on. The geographic information method is a modern computer technology that allows combining a model image of the territory (electronic display of maps, diagrams, space, and aerial images of Earth’s surface) with tabular information (various statistics, lists, economic indicators, and so on). In the cartographic method, the map acts as a model of the object under study and an intermediate link between the object and the researcher. Measurements on maps, mathematical processing of these measurements, and so on are carried out. As research materials, the study used the existing literature on the processes of organizing regional protected areas with their important landscape and ecological characteristics of spatial distribution. Analysis of historical and geographical studies to assess the distribution of rare and endangered species of flora and fauna (as well as the state of SPAs in general) was carried out. The structural and logical study flow diagram is presented in Figure 1. RESULTS AND DISCUSSION Any natural or social formations can be considered as systems. SPAs are no exception; on the contrary, all types of protected areas are well suited to the definition of the system as a set of interacting structures and perform a common function, unconscious of the functions of its components. The system structurally consists of subsystems. It is implied that each subsystem is defined in the form of relationships representing its elements. The relationships of the elements of subsystems are described (formalized in the model) by mathematical expressions in tabular or graphical form. Protected areas belong to the type of closed systems.
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Figure 1. The structural and logical study flow diagram.
Information about changes in the indicators of protected areas is received by the administrative apparatus (control device) and is taken into account later in subsequent acts of management. External management and self-regulation are carried out through influence. The choice of influence is made on the basis of information about their properties. The object of management is a biogeosystem, where the forest ecosystem and the system of protected species of flora and fauna are consistently allocated. As subsystems, the control device includes infrastructure subsystems (scientific, production and technical base, supply, and so on). The desire to create a territorially connected system of SPAs is hindered by the lack of methodological developments in design principles. It is possible to use self-organization, which is implemented in the presence of common goals and will ultimately lead to the construction of a structure in which SPAs will play the role of applications in problem areas of the landscape. Currently, this principle underlies the formation of the entire existing global network of protected areas. However, it has its drawbacks associated with uncertainty of choice, possible redundancy, and the need for only complete knowledge of the entire territory against the background of urgent action, with many accompanying conditions. Currently, there is a transition to the ecosystem (landscape-ecological) level of the organization of SPAs associated with the dissemination of environmental knowledge and the desire to implement it. The organization of networks of SPAs requires comprehensive knowledge of the region and should be formalized in a certain scientific and methodological approach with the development of a system of criteria. The development of ecological classification continues to be relevant, especially with a systematic approach to the organization of SPAs (Tirendi, 2020). With the apparent need to take into account the criteria for allocation in landscape and ecological planning of protected areas in practice, there is subjectivity in the approaches to their organization. The following will help
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to avoid subjectivity. First, theoretically, the ecological network should be focused on maintaining primarily functional connections and energy flows in landscapes and between them and so on. Second, in the conditions of the privatization of the use of forest resources and the development of the market (private property), protection and use of objects of economic activity should be carried out by the owner. The state controls the mode of operation only with the help of environmental services and prevents the degradation of a particular component of the landscape, having developed criteria for assessing their productivity. The ecological network as a state-protected area system must preserve the entire landscape. Therefore, the main motivation for the selection of SPAs should not be exploited and economically used objects or facilities. Third, the development of geoinformation systems allows analyzing the structure of Earth’s surface: to decipher the individual components, create information layers on various topics, and simulate the consequences and make a retrospective analysis. GIS technology reduces subjectivity and creates the preconditions for a systematic analysis of the functioning of the geosystem. This provides for the possibility of using an integrated approach in the assessment of sites in the selection of SPAs by creating thematic layers followed by overlapping and the coordination of contours. At the same time, the protected area network is related primarily to the protection of industrial and rare animals or their reacclimatization. In this case, only one component of the ecosystem is taken (animals), and then its industrial part is taken. There is no information regarding the other components, so there is no comprehensive cadastral valuation of SPAs, which leads to the irrational use of areas withdrawn from economic turnover. The analysis and review of the current principles of creating a network of SPAs allowed the authors to develop and propose an approach to identifying areas that require special environmental attention. According to the basic ecological principle of the functional connections of all components of the
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ecosystem, the optimality of the position of protected areas and its rank should be determined by the characteristics of relief, climate, soils, vegetation, and animal population. At each of these thematic layers, current and prospective protected areas are identified by common criteria, such as reference (representativeness) for a certain type of ecosystem, the uniqueness of ecological properties, the ability of elements of nature to preserve, scientific and economic significance. The imposition of thematic layers with identified contours in accordance with the proposed criteria determines the rank of protected areas. It became obvious that in order to maintain the ecological balance and the organization of rational nature management, it is necessary to scientifically substantiate the organization of a comprehensive system of specially protected natural areas. This system should be multifunctional and regional in nature, which can be further scaled to a higher level: the state, the territory of several states, a geographical belt, or the continent. It is no coincidence that the flora and fauna of many states and their regions, rich in endemic and rare species, specific forms, subspecies, and ecosystems of different climatic zones and subzones, are characterized by great diversity and uniqueness (Kuzyakina and Gura, 2020). A comprehensive protected area system of a particular region should be aimed at ensuring conditions for sustainable development and improvement of the region’s environment. In the course of its implementation, the gradual formation of region-specific and relevant landscape and ecological features of SPAs and the introduction of special modes of nature management includes the following purposes:
r Conserving biological and landscape diversity r Maintaining ecological balance and the most important natural processes
r Saving unique natural objects located on the territory of the region
r Protecting traditional nature management areas in the current conditions
r Developing recreational areas (Jantassova, 2019) The problems of the organization of protected areas are complex because complexity is inherent in nature itself. Indeed, from an ecological standpoint, natural systems perform numerous environmental functions that are closely related to natural resources. Therefore, the creation of a comprehensive system of protected areas, including nature reserves, is the highest form of organization of ecosystem nature management, which has a deep ecological, socioeconomic significance and content (Borodin, 2005). To preserve landscapes and to protect the habitats of rare species of plants and animals that need special protection and that are included
in the endangered species list of a country or continent, the system should provide comprehensive natural protected areas of regional importance. The method of planning a protected area network using top-down approach was proposed by Puzachenko (2000) based on the processing of space photography materials. The essence of the concept of the organization of the regional ecological network offered by the researcher is reduced to the following: 1. The ecological centers (cores) or nodes are organized. These are areas with the best-preserved, close-to-natural landscapes. They are connected by corridors. Both corridors and environmental centers are fenced off with buffer zones where necessary. 2. The ecological network is based on ecological cores: places that are protected natural landscapes and ecosystems or habitats of species whose importance has a regional status. Ideally, ecological cores should be as follows: • Characteristic of the region’s natural and terrestrial, aquatic habitats, representing the whole range of natural conditions at different successional stages • Sustainable (viable) aggregates and groups of species of regional significance • Natural processes in the environment on which the state of the landscape, habitat, and/or group of species depends 3. Corridors should create opportunities for the free migration of species between ecological cores, that is, create favorable landscape and ecological conditions for the existing representatives of the biota in a certain natural area, which is subject to special protection. The value of corridors depends on their structure and individual characteristics of species, including their mobility and ability to migrate to certain landscapes. Despite the fact that each species has its own needs and special mobility, it is still possible to create corridors, the structure of which would meet the needs of most species and, moreover, would be consistent with economic activities. Although the highest priority of the regional landscape and ecological network of a specially protected natural area is the preservation of the existing diversity of natural and disturbed habitats and landscapes, in some cases it is also necessary to restore habitats in both cores and corridors and buffer zones. Restoration action is a top priority where habitats are severely disturbed, fragmented, or isolated. The experience in restoration has already been gained in many countries,
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and these actions are particularly effective for rivers, wetlands, and forests. Landscape and ecological corridors of a certain SPA should be long zones of concentration of ways of movement and settlement of different species of animals. Ways of migration and resettlement of animals in the final version should be attached to the following elements of landscapes and take into account their ecological features:
r Riverbeds and river valleys containing watersheds with the lowest elevations
r Chains of lakes and swamps r Chains of forests and strip forests within the foreststeppe and steppe
r Borders of contrasting landscape zones, such as the border of steppe or forest-steppe with foothills or mountain systems covered with vegetation r Features of flora and fauna of river deltas and marine estuaries r Features of flora and fauna of coastal lakes CONCLUSIONS It is important to realize that the system of specially protected natural areas will be effective when it is a single network integrated into the life of the local community, providing not only the exclusion of areas from economic turnover but also the organization of rational, scientifically sound economic activities to form a cultural landscape. Thus, it is necessary to investigate and generalize the mechanisms that form the landscape and ecological foundation for the organization of regional systems of protected areas for their further effective use in the organization of specially protected natural areas, which became the purpose of this study. This article provides recommendations for the organization of regional systems of SPAs and specific conditions of their landscape and ecological operation for the conservation of biodiversity. In further study it is necessary to pay more attention to the organization of SPAs depending on the natural zone and climate because this is a significant category of indicators that require a separate systematization. REFERENCES Blanco, J.; Sourdril, A.; Deconchat, M.; Ladet, S.; and Andrieu, E., 2019, Social drivers of rural forest dynamics: A multi-scale approach combining ethnography, geomatic and mental model analysis: Landscape Urban Planning, Vol. 188, pp. 132–142. Borodin, A., 2005, Operational functions of ecologic and economic management: Terra Economicus, Vol. 3, pp. 104–113. Commission on Protected Areas of the IUCN French Committee, 2013, Protected Areas in France: Electronic docu-
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ment, available at https://uicn.fr/wp-content/uploads/2016/ 08/Espaces_naturels_proteges-EN-ok.pdf Convention on the Protection of Biological Resources, 1992, United Nations: Electronic document, available at https://www.un.org/ru/documents/decl_conv/conventions/ biodiv.shtml Efremov, Y. V.; Zimnitsky, A. V.; Shulyakov, D. Y.; and Lipilin, D. A., 2018, Snow patches of the Lagonaky highlands (Western Caucasus): Led i Sneg—Ice and Snow, Vol. 58, pp. 359–372. Elaev, J. N., 2018, Landscape and ecological zoning of specially protected natural areas of Baikal Siberia: Nature Inner Asia, Vol. 3, pp. 84–91. Fedorov, N. I.; Muldashev, A. A.; Martynenko, V. B.; Baisheva, E. Z.; Shirokikh, P. S.; Elizaryeva, O. A.; and Kutueva, A. G., 2020, Identifying highly diverse areas of rare plant species as a basis for assessing representativeness and improving the network of protected areas: Contemporary Problems Ecology, Vol. 13, pp. 418–428. Gaston, K. J.; Jackson, S. F.; Nagy, A.; Cantú-Salazar, L.; and Johnson, M., 2008, Protected areas in Europe: Annals New York Academy Sciences, Vol. 1134, pp. 97–119. Hoffmann, S.; Beierkuhnlein, C.; Field, R.; Provenzale, A.; and Chiarucci, A., 2017, Uniqueness of protected areas for conservation strategies in the European Union: Scientific Reports, Vol. 8, 6445. Ismayilov, M. and Jabrayilov, E., 2019, Protected areas in Azerbaijan: Landscape-ecological diversity and sustainability: Ankara Üniversitesi Çevrebilimleri Dergisi, Vol. 7, pp. 31–42. Jalkanen, J.; Toivonen, T.; and Moilanen, A., 2020, Identification of ecological networks for land-use planning with spatial conservation prioritization: Landscape Ecology, Vol. 35, pp. 353–371. Jantassova, A., 2019, Legal bases of protection of specially protected territories: Journal Advanced Research Law Economics, Vol. 10, pp. 195–207. Kuzyakina, M. V. and Gura, D. A., 2020, Assessment of bioclimatic comfort of the Krasnodar Territory, Russia through the application of GIS-technologies: South Russia-Ecology Development, Vol. 15, pp. 66–76. Martynov, A. S.; Flint, V. E.; and Artjuhov, V. V., 1995, Analysis of Socio-Economic Factors Affecting Biological Diversity: PAIMS, Moscow, Russian Federation, 410 p. Miao, H. and An, C., 2020, Challenges for protected areas management in China: Sustainability, Vol. 12, pp. 1–29. Ministry of Nature Protection, 2014, Strategy and State Program of Conservation and Use of Specially Protected Nature Areas of the Republic of Armenia: Electronic document, available at http://www.mnp.am/uploads/1/1551885719hatuk_ pahpan_eng.pdf Pellizzaro, P. C.; Hardt, L. P. A.; Hardt, C.; Hardt, M.; and Sehli, D. A., 2015, Stewardship and management of protected natural areas: The international context: Ambiente Sociedade, Vol. 18, pp. 19–36. Pustovalova, L. A. and Veselkin, D. V., 2020, Rapid changes in plant communities of natural parks due to recreational use: Russian Journal Ecology, Vol. 51, pp. 399–407. Puzachenko, J. U., 2000, Planning of Regional Ecological Networks Based on the Analysis of Satellite Images: Nauka, Moscow, Russian Federation, 102 p. Sasanifar, S.; Alijanpour, A.; Shafiei, A. B.; Rad, J. E.; Molaei, M.; and Azadi, H., 2019, Forest protection policy: Lesson learned from Arasbaran biosphere reserve in Northwest Iran: Land Use Policy, Vol. 87, 104057.
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Landscape and Ecological Foundations for the Organisation of Regional Systems of Specially Protected Areas Tirendi, D., 2020, Environmental economics and evaluation of the benefits deriving from the regeneration of natural ecosystems: The case of the diecimare nature oasis: Green Energy Technology, Vol. 2, pp. 303–322. Underwood, E.; Kettunen, M.; McConville, A.; and Ashcroft, R., 2014, Protected Area Approaches in the EU:
Electronic document, available at https://www.researchgate. net/publication/304011267_Protected_area_approaches_in_ the_EU Yakusheva, N., 2019, Managing protected areas in Central Eastern Europe: Between path dependence and Europeanisation: Land Use Policy, Vol. 87, pp. 1–13.
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Book Review Applied Multidimensional Geological Modeling: Informing Sustainable Human Interactions with the Shallow Subsurface
(A. K. Turner, H. Kessler, and M. J. van der Meulen, Editors) Review by: Jeffrey R. Keaton Wood Environment and Infrastructure Solutions, Inc., 4600 East Washington Street, Suite 600, Phoenix, AZ 85034
Applied Multidimensional Geological Modeling (2021) is a reference monograph written by 132 contributors to be “a valuable resource for those who want to begin geological modeling, to further develop geological models, or to apply geological models to resolving societal issues” (p. xxviii). It was published in June 2021 by Wiley in hardcover and e-book format. The book is organized into five parts: Chapters 1 through 4 comprise Part I (92 pages), which introduces modeling terminology and concepts, discusses the role of geological survey organizations, explores the shift from two-dimensional maps and cross sections to three-dimensional (3-D) and four-dimensional models, identifies limits on building height imposed by regulations and existing uses of the ground surface as major factors resulting in increased demand for utilization of subsurface space, and describes the economic value of accurate subsurface ground conditions and geological models. Part II, “Building and Managing Models,” consists of Chapters 5 through 15 (289 pages, 45 percent of the monograph) and begins with an overview and history of 3-D modeling approaches. Part II concludes with uncertainty in 3-D geological models. Modeling workflows, data sources and management, model creation using three different methods (stacked surfaces, digitized borehole records, and 3-D cellular voxel arrays), and three different geological-modelingbased considerations (explicit and implicit rules, discrete and stochastic approaches, and linkages to timevarying processes) are addressed. The introduction to the overview and history of 3-D modeling (Chapter 5) includes guidance on modeling approaches for organizations embarking on geological modeling, and notes that “geologists primarily experienced in making cross-sections or hand-drawn structure contour maps may have a very difficult time moving to a modeling approach that does not rely on these formats. … modeling approaches must be consolidated and formalized, along with the supporting data management, and staff must be chosen and trained accordingly. This is a prerequisite for the required consistency in space and over
time when modeling is no longer a project, but an ongoing process” (p. 95). Section 14.5, “Building Hydrogeological Models Based on Geological Models,” describes a multi-step process consisting of progressive refinement of geological context and hydrogeological context with the following principal stages:
r r r r r r r
data acquisition data management geological model development hydrogeological model development numerical simulation model assessment and validation model application to predict scenarios and inform decision-making.
Chapter 15, “Uncertainty in 3-D Geological Models,” notes that quantification of overall model uncertainty is an essential step and acknowledges that establishing it is a challenge, but that “an even greater challenge is conveying that uncertainty to the end-user in a useful and understandable manner so the models can support real-world decision-making” (p. 357). Chapter 15 defines and discusses the main sources of uncertainty within the framework of 3-D geological models:
r quality of geological data r complexity of the geology being modeled and the scale at which it is conveyed
r experience of the modeling geologist r geological modeling methodology r application of the model output. Part III, “Using and Disseminating Models,” contains two chapters in 40 pages: Chapter 16, “Emerging User Needs in Urban Planning,” and Chapter 17, “Providing Model Results to Diverse User Communities.” The main audience of Applied Multidimensional Geological Modeling is knowledgeable about geology but not urban planning; therefore, Chapter 16 contains basic concepts used in spatial and urban planning and information needs in this domain. Chapter 17 lists
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visualization principles and provides examples of static visual products, digital geological models and data, and currently available geological model viewers. Section 17.10, “Conclusions,” includes a revealing statement about the status of model results: “Even during the time of compiling this chapter advances have been made …” (p. 422). Part IV, “Case Studies,” consists of Chapters 18 through 25 (187 pages, 29 percent of the monograph). Each of the eight chapters addresses a different application theme with two to five case study examples:
r Chapter 18, “Urban Planning” ◦ Case study 18.1, urban underground, Darmstadt, Hesse, Germany ◦ Case study 18.2, subsurface information for urban renewal, Glasgow, Scotland ◦ Case study 18.3, mega-city urban planning, Dhaka, Bangladesh
r Chapter 19, “Groundwater Evaluations” ◦ Case study 19.1, Esker model for municipal water supply, Uppsala, Sweden ◦ Case study 19.2, groundwater resource protection, Orangeville-Fergus area, Ontario, Canada ◦ Case study 19.3, modeling aquifers, Kent and Sussex Counties, Delaware, U.S. ◦ Case study 19.4, REGIS II 3-D hydrogeological model, The Netherlands
r Chapter 20, “Geothermal Heating and Cooling” ◦ Case study 20.1, geothermal resource assessment, Zaragoza, northeast Spain ◦ Case study 20.2, cross-border geothermal resources, Germany-Poland ◦ Case study 20.3, deep geothermal potential, Hesse, Germany
r Chapter 21, “Regulatory Support” ◦ Case study 21.1, confined aquifer management, Hertfordshire and North London, England ◦ Case study 21.2, resource administration, Bremen, Germany
r Chapter 22, “Geohazards and Environmental Risk Applications” ◦ Case study 22.1, post-earthquake rebuilding, Christchurch, New Zealand
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Case study 22.2, cliff instability, Barton-onSea, Hampshire, England ◦ Case study 22.3, coastal change, Trimingham, Norfolk, England ◦ Case study 22.4, geochemical contaminated excavation, Nantes, France ◦ Case study 22.5, contamination flow and transport, Ljubljana, Slovenia
r Chapter 23, “Urban Infrastructure” ◦
Case study 23.1, design and construction, new crossrail station, London, England ◦ Case study 23.2, design evaluation for railway infrastructure renewal ◦ Case study 23.3, reference design, Silvertown Tunnel, East London, England
r Chapter 24, “Building Construction” ◦
Case study 24.1, volume change potential, London Clay, U.K. ◦ Case study 24.2, Dutch experience in aggregate resource modeling ◦ Case study 24.3, sand and gravel distribution and quality, Thames Basin, U.K.
r Chapter 25, “Historical Preservation and Anthropogenic Deposits” ◦
Case study 25.1, Bryggen World Heritage site, Bergen, Norway ◦ Case study 25.2, near-surface geology, Newcastle-upon-Tyne, England ◦ Case study 25.3, techniques and issues, artificially modified ground Part V, “Future Possibilities and Challenges,” consists of Chapter 26, “Anticipated Technological Advances” (13 pages). This chapter includes a list of 20 general technological trends and notes that characterization of a location currently requires data from a variety of sensors, suggesting that improved workflow will necessarily depend on interoperability, which also should facilitate development of new tools. The authors recognize that most projects do not warrant a software development team and that custom tools typically reside in the developer’s organization. Progress could be realized by changing the approach to data ownership, balancing data presentation and simulation in project planning and delivery, leveraging recognition that most mobile-device users have powerful data-capture and transmission devices in their pockets, and improving utilization of advanced visualization technologies (virtual reality,
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augmented reality, group-based visualization in appropriately equipped meeting rooms). They recognize that some of these tools have been available for several years and wonder if the lack of widespread use is a tool issue, an adoption issue, or a not-fit-forpurpose issue. They describe game engines for rockfall simulation, 3-D visualization for stakeholder communication, and 3-D geological models for enhanced decision-making. The authors offer future operational considerations including artificial intelligence to help make sophisticated techniques easier to use so that increasingly complex geological models can and will be used on more routine projects that provide decisions satisfying user needs and expectations. Among the challenges the authors describe are the need for models and visualizations to clearly incorporate and display uncertainties in data or interpretations. They ask how users identify reality, and does model
visualization indicate accuracy, or does it attempt to mislead? Applied Multidimensional Geological Modeling is a well-illustrated, thoroughly referenced book that fulfills the intent of the editors to be a valuable resource for geological modeling beginners, geological model developers, and societal-issue resolution. The 16-pagelong table of contents lists headings and page numbers to the sub-subsection level, which further enhances the utility of this comprehensive reference book. REFERENCE Turner, A. K.; Kessler, H.; and van der Meulen, M. J. (Editors), 2021, Applied Multidimensional Geological Modeling: Informing Sustainable Human Interactions with the Shallow Subsurface: John Wiley & Sons Ltd., Chichester, West Sussex, U.K. 644 p. (List price $210 [€85] hardcover, $168 [€77] electronic.)
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Book Review Geology of National Parks, Seventh Edition
(D. Hacker and D. Foster) Review by: Greg M. Stock National Park Service, Yosemite National Park, 5083 Forests Road Box 700, El Portal, CA 95318
Encompassing 423 national park sites, the U.S. National Park System preserves some of Earth’s most spectacular geologic features and landforms. From glaciers to hot springs, volcanoes to coral reefs, or canyons to caves, national parks and preserves curate our geologic heritage while sparking the imaginations of tens of millions of park visitors each year. The fascinating and compelling geologic stories of many of these parks are told in the newest edition of the book Geology of National Parks by David Hacker and David Foster (2019), with contributions from Esther Tuttle and Sherwood D. Tuttle and senior editing by Ann G. Harris. Intended primarily for students, the book reveals fundamental elements of physical geology through detailed exploration of the many diverse parks in the National Park System. As can be expected, the book addresses the most iconic national parks in the system, such as Grand Canyon, Yellowstone, and Yosemite, but I was happy to see many lesser known but no less spectacular parks and preserves also highlighted, including Kobuk Valley, Biscayne, North Cascades, Lake Clark, and Pinnacles. This edition features a total of 59 parks, each of which is used to highlight a specific topic in geology that collectively provides a comprehensive picture of Earth’s complex and dynamic history. The book begins with a short preface on art and the National Park System, followed by a longer introductory chapter covering essential background information such as geologic time, the dynamic earth system, earth processes and cycles, and plate tectonics. The remainder of the book is divided into five parts focused on different themes, with each part highlighting a dozen or so parks that exemplify various aspects of these themes. Part I, “Scenery Developed by Weathering and Erosion on Flat-Lying Rocks,” features parks that are primarily located in the Colorado Plateau physiographic province, including Grand Canyon, Zion, Arches, and Canyonlands national parks. Part II, “Caves and Reefs,” highlights the formation and dissolution of carbonate rock, with sections on Mammoth Cave, Wind Cave, Guadalupe Mountains, Everglades, and Virgin Islands national
parks. Part III, “Landscapes Shaped by Continental and Alpine Glaciation,” focuses on glacial erosion, with examples from Acadia, Rocky Mountain, Glacier Bay, and Denali national parks and preserves. Part IV, “Volcanic Features and Volcanic Activity,” covers the diverse volcanic features of parks such as Mount Rainier, Crater Lake, Katmai, and Haleakala. The journey concludes in Part V, “Landscapes and Structures in Areas of Complex Mountains,” which showcases mountainous parks boasting complicated histories, including Grand Teton, Great Basin, Joshua Tree, Death Valley, and Shenandoah national parks. Each section includes the pertinent geologic information for the highlighted park, as well as brief human and natural histories, all accompanied by informative tables, detailed maps and figures, and many striking photos. The book has a thorough glossary and a comprehensive index. Although it is difficult to critically evaluate all of the information provided in this lengthy book, I feel qualified to do a “deep dive” on the section on Yosemite National Park, having worked as Yosemite’s geologist for the past 17 years. I found the information about Yosemite to be generally accurate, reasonably up to date, and well presented overall. There are some minor factual errors (e.g., El Capitan was never a nunatak) and a few questionable statements (e.g., by most definitions El Capitan is not the largest granite monolith in the world, eclipsed by larger walls on Baffin Island and in the Karakorum range). A photo purported to be Lembert Dome (misspelled as “Lambert Dome”) is actually of nearby Mariuolumne Dome. More concerning to me is the fact that mass wasting—Yosemite’s most active geologic process, which frequently generates newspaper headlines as well as research papers— receives three scant sentences. Nevertheless, in the end these are mostly quibbles with what is otherwise a solid summary of Yosemite’s geology. The same is true for Hawaii Volcanoes and Sequoia and Kings Canyon national parks, where I have also worked; if these sections are indicative of the book as a whole then the contents can generally be regarded as well researched and accurate.
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Those who appreciate having a bound volume on their bookshelf will be disappointed, as this book is offered only as a digital e-book or as printed loose-leaf pages. Perhaps this is due to the sheer size of the tome (1,049 pages) or to the decreasing demand for printed media, but I was surprised to learn that there is not an actual bound book format available. The available products list at the relatively steep price of $68.25 for the e-book or $136.50 for the loose-leaf printed format. Overall, the seventh edition of Geology of National Parks is an impressive collection of detailed facts on
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our nation’s most iconic geologic sites, coupled with the geologic stories that make these sites so captivating. The book is packed with great information and the aesthetic design makes it very approachable. It is a valuable resource for students learning fundamental geologic concepts, teachers planning field trips, and geologists planning their next geology-focused vacation. Hacker, D. and Foster, D., 2019, Geology of National Parks, 7th ed.: Kendall Hunt Publishing Company, Dubuque, IA. 1049 pages.
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Book Review Roadside Geology of Northern and Central California, Second Edition
(D. Alt and D. W. Hyndman) Review by: Robert Anderson 2552 Humphrey Road, Loomis, CA 9565
Reading Roadside Geology of Northern and Central California, Second Edition (Alt and Hyndman, 2016), was like getting reacquainted with an old friend. A few things were different but nothing of consequence. For those looking for a short overview of local and regional geology along the alignments described, it is an easy and quick read supported by color images, easy-to-read cross sections, and simplified local geologic maps.
At $26.00, the book is small enough to carry in a day pack or suitcase and travels well. The book is quite a bargain and would make a great gift for anyone interested in geology or as a handy basic book for someone learning about the geology of the region. Alt, D. and Hyndman, D. W., 2016, Roadside Geology of Northern and Central California, 2nd ed.: Mountain Press Publishing Company, Missoula, MT, 371 p.
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Book Review Mass Extinctions, Volcanism, and Impacts: New Developments: Geological Society of America Special Paper 544
(T. Adatte, D. P. G. Bond, and G. Keller) Reviewed by: Robert Anderson 2552 Humphrey Road, Loomis, CA 95650
Mass Extinctions, Volcanism, and Impacts: New Developments: Geological Society of America Special Paper 544 (Adatte et al., 2020), the second of two special papers on mass extinctions, volcanism, and impacts, contains nine chapters. (Note: This review was written without the benefit of having read the first special paper.) The majority of this collection is focused on interpreting models regarding the acidification of water and the impact on organisms that depended on carbonates to grow. The first chapter, Volcanism as a Prime Cause of Mass Extinctions: Retrospectives and Perspective, by Grzegorz Racki, provides the reader with a general starting point discussing changes in gasses and ash during a large igneous province placement episode. This chapter is an interesting basic discussion on the western view of volcanism over the last 2,000 years. It discusses the role of volcanism and tectonics and the role of select gasses and their concentration changes in the atmosphere. Also included are pH changes in seawater during large igneous province development. This chapter should be kept readily available for easy referral. Chapters 2 through 8 look at the interaction of mostly large igneous, but also large silicic, provinces with seawater and the atmosphere and their impact on select sea life. The stressors include sulfur dioxide, carbon dioxide, or sulfates, each affecting the lowering or raising of temperature on a global scale. In addition,
a lowering in pH of seawaters may have led to the decrease of available carbonate, which may have led to the extinction of organisms that used carbonate in the building of their shells. The last chapter discusses factors influenced by the Chicxulub impact that occurred during the Deccan Traps extrusions. This chapter points out that life was under duress by changes in temperature, a lowering of pH of seawater, and available light, as well as degassed compounds from the Deccan Traps. This indicated that certain marine organisms were already going extinct by the time of the impact. Additional stressing of the biosphere by changes in ambient light and temperature change, as well as a spike in the amount of gasses, ash, and soot, helped push additional species into extinction; however, the chapter does not cover the impact on terrestrial organisms. Overall, the book is decidedly interesting and gives some insight into effects of large-scale changes to the atmosphere that we see on a smaller scale from single large eruptions. It may also help us assess potential hazards from atmospheric pollution from large-scale releases of gasses from industry. The book lists at $42.00 for GSA members; nonmember price is $60.00. Adatte, T.; Bond, D. P. G.; and Keller, G. (Editors), 2020, Mass Extinctions, Volcanism, and Impacts: New Developments: Special Paper 544, Geological Society of America, Boulder, CO, 245 p.
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ASSOCIATE EDITORS Ackerman, Frances Ramboll Americas Engineering Solutions, Inc. Bastola, Hridaya Lehigh University Beglund, James Montana Bureau of Mines and Geology Bruckno, Brian Virginia Department of Transportation Clague, John Simon Fraser University, Canada Dee, Seth University of Nevada, Reno Fryar, Alan University of Kentucky Gardner, George Massachusetts Department of Environmental Protection
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Cover photo Head scarp of an active landslide in colluvium near the Tanana River in Interior Alaska. Photo by Margaret Darrow. See article on page 255.
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