Journal of Water Supply: Research and Technology – AQUA sample issue by IWA Publishing

63.1

2014

Quantifying bacterial biomass ﬁxed onto biological activated carbon (PAC and GAC) used in drinking water treatment Céline Stoquart, Benoit Barbeau, Pierre Servais and Gabriela A. Vázquez-Rodríguez

ABSTRACT Hybrid processes coupling the use of powder activated carbon (PAC) with membrane filtration for drinking water production are emerging as promising alternatives to conventional technologies due to their enhanced control of dissolved contaminants. The quantification of biomass colonizing PAC is crucial for modeling, designing control strategies and improving the overall performance of these processes. The aim of this study was to examine the applicability of several common methods developed for colonized granular activated carbon (GAC) to PAC. Six analytical methods (based on the measurement of proteins, polysaccharides, heterotrophic plate counts, potential glucose respiration, bacterial ATP and a potential acetate uptake rate, which is proposed herein) were compared. The results showed that the rates of glucose respiration and of acetate consumption could be used interchangeably. Proteins were also an interesting alternative for on-site measurements. It was concluded that biological PAC-based processes sustained a level of heterotrophic activity similar to or greater than that observed in GAC biofilters. Key words

| bioﬁlters, drinking water, extracellular polymeric substances, microbial activity,

Céline Stoquart Benoit Barbeau Gabriela A. Vázquez-Rodríguez (corresponding author) NSERC Industrial Chair on Drinking Water, Department of Civil, Mining and Geological Engineering, École Polytechnique de Montréal, NSERC-Industrial Chair in Drinking Water, C.P. 6079, Succursale Centre-Ville, Montréal, QC, Canada H3C 3A7 E-mail: g.a.vazquezr@gmail.com, gvazquez@uaeh.edu.mx Gabriela A. Vázquez-Rodríguez Permanent address: Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Carr. Pachuca-Tulancingo km. 4.5, 42184 Mineral de la Reforma, Hgo., Mexico Pierre Servais Écologie des Systèmes Aquatiques, Université Libre de Bruxelles, Campus de la Plaine, CP 221, Boulevard du Triomphe, B-1050 Brussels, Belgium

substrate uptake rates

INTRODUCTION Biological ﬁlters have been used extensively for the pro-

non-adsorptive supports (e.g. sand or anthracite) (Wang

duction of drinking water in North America and Europe.

et al. ). Moreover, both the irregular surface and the

Some of their advantages include the biological stabilization

porous structure of GAC protect the biomass against shear

of treated water via the removal of dissolved organic matter

forces and toxic loads (Chaudhary et al. ; Herzberg

and ammonia, a decrease in chlorine demand and the for-

et al. ).

mation of fewer disinfection by-products. Currently, these

Recent developments in membrane technologies for

processes are becoming increasingly important due to the

drinking water production include the integration of

rise of green technologies and the emergence of new con-

powder activated carbon (PAC) in processes referred to as

taminants that may be amenable to biodegradation, such

hybrid membrane processes (HMPs) (Stoquart et al. ).

as several pharmaceuticals, taste and odor compounds and

PAC targets dissolved compounds that are not retained by

cyanotoxins (Brown ). Granular activated carbon

low-pressure membranes. It offers faster adsorption rates

(GAC) is the preferred medium for biological ﬁlters because

than GAC (Yener et al. ) and can be injected and recov-

it offers more sites for microbial attachment than

ered continuously from a slurry-type suspension. This

doi: 10.2166/aqua.2013.101

C. Stoquart et al.

Quantifying bacterial biomass ﬁxed onto biological activated carbon

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

conﬁguration offers high ﬂexibility to address sudden source

challenges. One promising alternative substrate is acetate,

water quality variations by simply adjusting the age of the

a ubiquitous compound that can be used as a substrate by

PAC slurry to control adsorption performance (Stoquart

almost all eubacterial and fungal cells (Wang et al. ).

et al. ).

For instance, the abundance of acetate-utilizing bacteria

When using activated carbon (AC), biodegradable com-

has been estimated microautoradiographically as 47–93%

pounds can be removed by adsorption, biodegradation or a

of the total bacteria in wastewater-activated sludge (Nielsen

combination thereof. Within the HMP, the relative impor-

& Nielsen ). In addition, acetate is a common ozonation

tance of both mechanisms is intimately related to the age

by-product

of the PAC slurry. Assessing the relative importance of

biodegraders are likely to be present in drinking water bioﬁl-

both mechanisms is paramount for modeling the process

ters. Moreover, acetate is poorly adsorbable onto GAC

and thus optimizing its performance. Therefore, reliable

(Moteleb et al. ) and PAC (Stoquart et al. ), which

techniques for evaluating microbial colonization onto PAC

minimizes potential bias in the evaluation of the acetate

are required.

uptake rate. Thus, measuring the potential acetate uptake

Several methods have been developed to evaluate bacterial biomass ﬁxed on GAC. As proposed by Lazarova

(Urfer

Huck

).

Hence,

acetate-

(PAU) rate could be an interesting alternative for evaluating active bacterial biomass ﬁxed on solid media.

et al. (), they can be classiﬁed as either non-destructive

To the best of our knowledge, no published studies have

or destructive (i.e. techniques that disrupt the bioﬁlm). Non-

aimed to quantify the biomass developed on the PAC sur-

destructive methods include direct microscopy and tech-

face from drinking water treatment processes. This work

niques based on the measurement of speciﬁc activities, such

was thus conducted to evaluate the applicability of several

as the potential glucose respiration (PGR) (Servais et al.

existing analytical methods (i.e. the assessment of PGR,

), the overall oxygen consumption (Urfer & Huck )

HPC, ATP and EPS) and to propose a new method based

or the potential nitrifying activity developed to estimate the

on measuring PAU rates for characterizing heterotrophic

autotrophic nitrifying biomass (Kihn et al. ). On the

biomass on biological PAC obtained from two hybrid pilot

other hand, destructive methods, which require detaching

membrane processes. The results obtained for PAC were

the biomass from the support media, include total cell count-

also compared with those measured on a biological GAC

ing

epiﬂuorescence

(Nishijima

),

the

(BAC) sample collected from a full-scale bioﬁlter. Finally,

enumeration of heterotrophic plate counts (HPC) (Camper

the results obtained from the six tested analytical methods

et al. a, b) and the measurement of compounds related

were compared.

al.

to the viable biomass, such as phospholipids (Wang et al. ) and ATP (Velten et al. ), and the bioﬁlm density, such as the concentrations of polysaccharides (Quintelas

MATERIALS AND METHODS

et al. ) and proteins (Drogui et al. ; Papineau et al. ) in extracted extracellular polymeric substances (EPS).

Description of samples

All the aforementioned techniques have only been applied to GAC, not biological PAC. Non-destructive

AC characteristics

methods are the most interesting because the detachment of the biomass is undesirable due to issues such as incom-

Three types of wood-derived AC commercialized by Pica™

plete recovery and the impact of the procedure on cellular

(Picahydro LP 39, Picahydro L30-260 and Picabiol 2) were

integrity (Lazarova et al. ). Amongst the non-destructive

tested; the non-colonized media are referred to as NP25,

methods, PGR allows the biomass potentially responsible

NP200 and NGAC, respectively. NP25 and NP200 are

for the removal of biodegradable compounds to be evalu-

PAC with median particle sizes of 15–35 and 210–260 μm,

ated. However, the PGR method requires the use of a

respectively. NGAC has a median particle size of 500–

radiolabeled substrate (i.e.

C-glucose), which is expensive,

not always readily available and may pose logistical

700 μm. Their iodine numbers vary between 900 and 1,000 mg/g.

C. Stoquart et al.

Quantifying bacterial biomass ﬁxed onto biological activated carbon

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

(with NGAC-type characteristics; H ¼ 180 cm). The empty

AC colonization

bed contact time in the ﬁlter ranged from 20 to 30 min The colonization of PAC was carried out in four parallel

depending on the water demand. A summary of the characteristics of the ﬁve colonized

HMP pilots operating under two conﬁgurations. The pilots were fed with coagulated-settled river water (Des Mille-Iles

AC samples is presented in Table 1.

River, Laval, Canada). The average main inﬂuent characteristics

were

2.9 mg

DOC/L,

0.23 mg

BDOC/L

and

Biomass measurements

154 μg N-NH3/L. In the first configuration (PAC/UF), the PAC treatment was integrated to a submerged ultrafiltration

The biomass measurements were performed on the same day

(UF) unit described in detail by Léveillé et al. (). Two

for all analytical techniques and sample types tested (i.e. the

PAC/UF reactors were ﬁlled with NP25. In the ﬁrst AC/

ﬁve colonized samples and the three virgin samples). The

UF reactor (P7-5), a PAC concentration of 5 g dry weight

virgin samples (NP25, NP200 and NGAC) were used as con-

(dw)/L, a solids retention time (SRT) of 7 days and a hydrau-

trols. Before any experiment, 10 g dw of each of the virgin AC

lic residence time (HRT) of 69 min were maintained. In the

samples was humidiﬁed overnight at room temperature in 1 L

second PAC/UF pilot (P60-10), the PAC concentration, the

of Milli-Q water. The pH of the slurry (approximately 3) was

SRT and the HRT were 10 g dw/L, 60 days and 77 min,

raised to 7.0 by adding 1 M NaOH. The colonized PAC slur-

respectively.

ries (P7-5, P60-10, P67-1 and P67-5) were withdrawn at the

In the second conﬁguration (PAC þ UF), water was fed

same time from the four reactors. All the PAC samples (colo-

into two parallel stirred-tank reactors ﬁlled with NP200,

nized and virgin) were recovered by ﬁltration on a Grade 41

which was maintained inside the reactors using a 55–

paper ﬁlter (Whatman). Finally, the BAC sample was col-

85 μm sieve. The efﬂuent water was then fed into the UF

lected from the surface of the bioﬁlter.

modules. The PAC concentrations within the reactors were 1 g dw/L (P67-1) and 5 g dw/L (P67-5). Unlike the

PAU rate

PAC/UF conﬁguration, both reactors were operated continuously without any PAC replacement (i.e. with variable

According to Monod kinetics, the rate of substrate depletion

SRT). At the sampling time, an SRT of 67 days had been

(dS/dt) and the speciﬁc microbial growth rate (μ) can be

reached in both PAC þ UF reactors.

expressed by Equations (1) and (2), respectively:

The BAC samples were obtained from the surface of a full-scale dual media ﬁlter (80 m2 surface area) composed of layers of sand (D10 ¼ 0.45 mm, H ¼ 15 cm) and GAC

Table 1

dS μX ¼ dt Y

(1)

Biological AC sample characteristics

Concentration in the reactor Sample IDa

Type of carbon

Median diameter (μm)

Pilot unit

Carbon used for starting up the pilot

SRT (days)

(g dw/L)

P7-5

PAC

15–35

PAC/UF

NP25

P60-10

PAC

15–35

PAC/UFb

NP25

P67-1

PAC

210–260

PAC þ UFc

NP200

P67-5

PAC

210–260

PAC þ UFc

NP200

BAC

GAC

500–700

Biological GAC

NGAC

N.A.

The ﬁrst number refers to the age (d) of the carbon and the second number refers to the PAC concentration (g dw/L) within the contactor. Hybrid membrane process with integrated AC treatment.

b c

Hybrid membrane process with AC pretreatment.

N.A.: not available.

5 d

350

C. Stoquart et al.

μ ¼ μmax

Quantifying bacterial biomass ﬁxed onto biological activated carbon

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

The acetate concentration was determined in duplicate

S S þ KS

(2)

samples by ion chromatography (Dionex Corporation, Sunnyvale, USA). The apparatus was equipped with an

where X is the biomass concentration, Y is the growth yield,

IonPac AS18 column and suppressed conductivity detec-

μmax is the maximum speciﬁc growth rate and KS is the half-

tion. An EluGen® EGC-KOH cartridge was used as the

saturation constant. Equation (3) is obtained by substituting

eluent. The limit of detection, the limit of quantiﬁcation,

Equation (2) into Equation (1) as follows:

the reproducibility and the accuracy of this method are 0.0159 mg/L, 0.0530 mg/L, 3 and 4.5%, respectively. The

dS μ X S ¼ max dt Y S þ KS

PAU rates were calculated from the initial slope of the acet(3)

ate content obtained by linear regression, normalized per gram of AC dw in the assay. These rates are expressed as

If S > >Ks, Equation (3) can be simpliﬁed as:

speciﬁc acetate uptake rates in mmol Na-acetate per g AC dw per hour. Regressions made to calculate PAU rates

dS μ X ¼ max dt Y

(4)

qS, the speciﬁc substrate removal rate, is deﬁned by

were always signiﬁcant (p < 0.05) with R 2 > 0.96, except for P7-5 (R 2 ¼ 0.61) and P60-10 (R 2 ¼ 0.72). PGR rate

Equation (5): The production of dS qS ¼ X dt

(5)

CO2 due to the mineralization of

C-

glucose under saturating conditions was measured according to the method of Servais et al. (). This method uses

Thus, Equation (4) can be rewritten to give Equation (6): qS ¼

μmax Y

(6)

a similar fundamental approach to the PAU method, although radioactive glucose replaces acetate as a substrate and mineralization (CO2 production) is measured rather than substrate consumption. A mixture containing

C-

glucose and non-radiolabeled glucose was prepared to Equation (6) shows that under substrate saturating con-

obtain a ﬁnal glucose concentration of 1 mM and a radioac-

ditions (i.e. S > >Ks), the substrate is removed following

tivity of 0.1–1 μCi/mL. One milliliter of this mixture was

zero-order kinetics during the ﬁrst few hours of incubation.

added to 1 g ww of AC in a penicillin ﬂask closed with a

Thus, qS (hereafter referred to as the PAU rate) can be con-

rubber septum. After 3 h of incubation at 20 C, the

sidered an estimate of the microbial biomass ﬁxed on the

sample was acidiﬁed by adding 2 mL of 10% H2SO4 through

media because the uptake rate and biomass are proportional

the septum. Samples were bubbled for 10 min to extract the

(Equation (6)).

CO2, which was trapped in a mixture of Carbo-SorbE

The PAU rates were estimated in 1 L beakers containing

(Perkin Elmer) and PermaﬂuorEþ (Perkin Elmer, 1:4 v/v).

500 mL of test medium. The latter contained a saturating

The radioactivity was determined by liquid scintillation

initial concentration of sodium acetate (15 mg Na-acetate/L,

with a Packard Tri-Carb (TR-1600) scintillation counter.

equivalent to 4.4 mg C/L) and was supplemented with

The amount of glucose mineralized was measured in tripli-

NH4Cl (0.65 mg N/L) and phosphate buffer (0.06 mg P/L).

cate and expressed as nmol glucose per g AC dw per hour.

After adding 10 g wet weight (ww) of AC, the slurries were W

maintained at 20 C under agitation at approximately

Heterotrophic plate counts

150 rpm using a magnetic stirrer. Samples were withdrawn periodically by ﬁltering 40 mL of slurry through a 0.45 μm

For each sample, 1 g ww of the AC sample was homogen-

polyethersulfone membrane previously rinsed with 1 L of

ized in a blender at 16,000 rpm and 4 C for 3 min with a

Milli-Q water.

mixture

Zwittergent

(10–6 M),

EGTA

(ethylene

C. Stoquart et al.

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Journal of Water Supply: Research and Technology—AQUA

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2014

glycol-bis-(β-aminoethyl ether)-N,N,N0 ,N0 -tetraacetic acid,

supernatants were recovered and ﬁltered through sterile

10–3 M), Tris buffer (0.01 M, pH 7.0) and 0.1% peptone

0.45 μm pore size membranes.

(Camper et al. a). The culturable cells were analyzed

The protein content of the extracts was determined

in duplicate on decimal dilutions using the membrane ﬁl-

using a bicinchoninic acid method-based commercial kit

tration technique (9215 D Standard Methods, (APHA

(Pierce© BCA Protein Assay Kit, Thermo Scientiﬁc, USA)

)). The membrane ﬁlters were incubated on R2A agar

with a bovine serum albumin (BSA) standard. The results

at 20 C in the dark. After 7 days, the HPCs were determined

were expressed in mg of proteins (equivalent BSA) per g

and expressed in CFU per g AC dw.

of AC dw or in mg C per g of AC dw, knowing that carbon represents 45% of the mass of BSA. The polysaccharides were analyzed by the phenol-sulfuric acid method using

Bacterial ATP

glucose as a standard (Dubois et al. ). The results were ®

An ATP detection kit was used (Proﬁle -1 Reagent Kit, New

expressed in mg polysaccharides (equivalent glucose) per g

Horizons Diagnostics, USA). The kit consisted of a microlu-

of AC dw or mg C per g of AC dw, knowing that carbon rep-

minometer, ﬁltration devices (0.4 μm pore diameter), a

resents 40% of the molecular weight of glucose. The total

lysing reagent for non-bacterial ATP (somatic releasing

amount of EPS was obtained by summing the concen-

agent, SRA) and a second reagent ensuring both bacterial

trations of proteins and polysaccharides (both expressed as

lysis and leaching of bacterial ATP (bacterial releasing

mg C per g of AC dw).

agent, BRA). Prior to the analysis, the samples were homogenized, just as for the measurement of HPC, and then

Dry weight

10-fold diluted in phosphate buffer to minimize ATP adsorption on AC. After the ﬁltration of 225 μL of the suspension in

The virgin and biological AC were dried for 24 h at 105 C

the ﬁltration device, 60 μL of SRA was added and evacuated

according to the standard method 2540 B (APHA ) to

twice to discard the non-bacterial ATP. In a second step,

obtain the dry weights of samples. The results of all microbio-

60 μL of BRA was added to the sample in the Filtravette

logical measurements were then expressed per g of AC dw.

device, ﬁltered and recovered. Finally, 25 μL of the ﬁltrate was mixed with 225 μL of reconstituted luciferin-luciferase reagent (Molecular Probes®, USA) in a new Filtravette

RESULTS AND DISCUSSION

device and the relative luminescence units were recorded. Positive controls were obtained by adding two standards

The analytical methods described previously were applied to

with high concentrations of ATP (ranging from 0.8 to

quantify the biomass ﬁxed onto AC samples. Concerning

2.7 μg ATP/L) to the samples. The results were compared

biological PAC samples, different levels of biomass density

to a standard curve (0.01–3 ng ATP/L) and expressed as

were expected because the colonization was carried out

ng ATP per g AC dw. No replicates were made.

under variable PAC ages and concentrations within the reactors. First, P7-5 and P67-5 were two PAC samples colonized at the same concentration in the pilot reactor (i.e.

EPS: polysaccharides and proteins

5 g dw/L). Although these two PAC had distinctive sizes, EPS were extracted in duplicate using approximately 2 g ww

no signiﬁcant differences with respect to biomass density

of sample according to the method proposed by Liu & Fang

were expected due to this factor (Markarian et al. ).

(). The samples were resuspended in 10 mL of phos-

However, higher ages (67 vs. 7 days) were expected to

phate buffer supplemented with 60 μL of formaldehyde

lead to higher biomass densities for AC colonized under

(36.5% v/v) and then incubated at 4 C for 1 hour under agi-

equivalent conditions. Second, two reactors were operated

tation. After the addition of 4 mL of 1 M NaOH, the samples

with the same PAC age (67 days) but variable PAC concen-

were incubated for 3 h at 4 C. Next, the slurries were centriW

fuged twice at 12,000 g and 4 C for 15 min. Finally, the

trations inside the reactor (P67-1 and P67-5). In that case, equivalent colonization (expressed per g of AC dw) is not

C. Stoquart et al.

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Journal of Water Supply: Research and Technology—AQUA

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2014

expected; Markarian et al. () noted that the develop-

after 24 h of contact (data not shown). This ﬁnding conﬁrms

ment of biomass on biological PAC is limited by substrate

that any acetate removal is due to biological activity.

availability rather than the available colonization surface.

The initial acetate concentration applied in the assays

Consequently, it was hypothesized that PAC colonized at a

was approximately 3-fold higher than Ks (the value

lower concentration in the pilot reactor would support a

embedded in Biowin™ software is 5 mg/L). This concen-

higher biomass density on its surface. The results gathered

tration led to the expected zero-order kinetics for acetate

from each analytical method are presented and discussed

removal when the biomass growth is negligible compared

separately as follows.

to the biomass initially present on the AC. Hence, all colonized samples removed acetate at a constant rate during the ﬁrst 4–6 h (Figure 1).

PAU rates

Figure 2(a) depicts the results of the PAU tests. The No decrease in the acetate concentration was observed in

lowest PAU rate was found for the P7-5 sample, which

the experiments conducted with virgin materials, even

was expected to have the lowest level of bacterial colonization due to the short SRT of the reactor (7 days), whilst the maximum PAU rate was measured for the P67-1 sample (colonized at the highest SRT). Actually, the PAU rate measured for the P67-1 sample was not significantly different from that measured for the biological GAC filter (i.e. the BAC sample). Sample P67-5 presented a statistically significantly higher PAU rate than sample P7-5 (p < 0.05) confirming that higher SRT led to a higher active biomass on the PAC surface. In addition, the PAC samples colonized at a lower PAC concentration (P67-1) had a statistically significantly higher PAU rate than its counterpart colonized at 5 g/L (P67-5) (p < 0.05). In this way, the positive effect of a high substrate availability on the activity of the biomass fixed onto PAC appears to be confirmed too. Based on these preliminary results, the measurement of PAU rates is a promising method. This finding should be

Figure 1

Acetate uptake by virgin and colonized samples of AC. The symbols represent the experimental results, and the lines represent the data from the linear regression.

Figure 2

conﬁrmed by validating this method with a wider set of samples, including more BAC samples. In addition,

(a) PAU rates of biological AC samples. Error bars represent the standard errors of the regression slopes used for calculating PAU rates. (b) PGR rates of biological AC samples. Error bars represent standard deviations. Solid lines (

) represent the background values measured on the corresponding virgin samples.

C. Stoquart et al.

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Journal of Water Supply: Research and Technology—AQUA

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monitoring the depletion of acetate in the ﬁrst hours of the

samples ranged from 17.7 to 69.4 μg C/cm3, whereas a bio-

assay in short time intervals is necessary for properly moni-

mass of 38.6 μg C/cm³ was measured at the surface of the

toring the zero-order kinetics.

biological ﬁlter (sample BAC). This value is higher than the PGR measured previously by Niquette et al. () at the top of the same full-scale GAC bioﬁlter (20 μg C/cm3).

PGR rates

This difference could be partly explained by the temperature Figure 2(b) presents the PGR rates measured on colonized

in the ﬁlter (16 C in our case vs. 11 C measured pre-

AC. The background values obtained for the virgin PAC

viously). In an another study reporting the colonization of

were between 0.07 and 0.44 nmol/g AC dw · h. These

a bioﬁlter at 16 C, the PGRs were comprised between 2.5

values were not signiﬁcantly different from one another

and 10.9 μg C/cm3 (Servais et al. ), while an average

(p > 0.05) and were considerably lower (120- to 1,900-fold)

value of 4.7 μg C/cm3 was proposed as representative of

than the values obtained for the colonized materials. There-

the ﬁxed biomass in a GAC bioﬁlter at steady state (Servais

fore, the PGR values shown in Figure 2(b) and elsewhere in

et al. ). The higher biomass densities observed on our

this paper were not corrected for the background

PAC samples are most likely explained by the amount of

measurement.

support area available for the colonization and the nutrients

The PGR of the sample P7-5 (43.1 nmol/g AC dw · h)

limitation. Indeed, the PAC samples were colonized at low

was signiﬁcantly different from the virgin samples (p <

concentrations (i.e. 1 to 10 g/L), whereas a larger mass of

0.05). The PGR rates obtained for the samples with higher

GAC typically provides support for the colonization at the

SRT (i.e. at least 60 days) led to the same conclusions as

surface of a BAC ﬁlter. In addition, the biomass densities

for the PAU method, with P60-10 being more colonized

measured on BAC come from second-stage ﬁlters, while

than P7-5 and P67-1 being more colonized than P67-5. All

our PAC was colonized as a ﬁrst-stage process. Niquette

the samples with a high SRT presented a PGR not signiﬁ-

et al. () observed a similar trend (i.e. higher colonization

cantly different from the BAC sample.

in ﬁrst- as opposed to second-stage BAC ﬁlters). As

To compare our results with the values of PGR found in 3

the literature (usually reported as μg of C-biomass/cm ), the

described earlier, this result is explained by the higher nutrient loading offered in ﬁrst-stage processes.

PGR rates were converted by considering a mean density value of 1.33 g of wet AC per cm3 and a correspondence

Heterotrophic plate counts

factor of 1.1 μg C of bacterial biomass per nmol of glucose respired per hour. This last value was originally obtained

The results of the analysis of HPC are presented in Figure 3(a).

by calibrating the radiochemical method with bacteria

The densities of HPC on biological PAC ranged from

washed off from GAC ﬁlter samples and enumerated by epi-

4.2 × 107 to 3.0 × 108 CFU/g PAC dw, whereas the value

ﬂuorescence microscopy (Servais et al. ). Following this

for the BAC sample was 3.3 × 107 CFU/g GAC dw. The

conversion, our PGR values obtained for the colonized PAC

results obtained are of the same order of magnitude as the

Figure 3

(a) HPC in biological AC samples. Error bars represent standard deviations. (b) Bacterial ATP contents in biological AC samples. B.D.: Below detection limit.

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literature values for BAC. However, no further comparison

capacity of PAC is an important concern when using ATP

can be drawn. The culture conditions (i.e. culture medium

and future work should aim for an improved separation of

and incubation temperature) applied were indeed highly

the bioﬁlm from the PAC particles before conducting the

variable, leading to potentially large variations in the HPC

ATP analysis.

results. LeChevallier et al. () measured 10 CFU/g

For colonized GAC, the measured value (58.9 ng ATP/g

GAC on a TLY medium, Camper et al. (b) measured

BAC dw or 14.07 ng ATP/g BAC ww) was lower than the

5 × 107 CFU/g GAC after incubating at 35 C, and Niemi

usual ATP contents. Niemi et al. () measured 0.5–

0.7 nmol ATP/g GAC dw (equivalent to 254–355 ng/g

et al. () measured 6 × 10 to 10 CFU/g GAC dw after W

GAC dw), while Velten et al. () measured 1,170 ng

incubating at 25 C. As mentioned before, the detachment of bacterial cells

ATP/g GAC ww at the surface of a fully colonized bioﬁlter.

from the support medium is critical when using the plate

In the former case (Niemi et al. ), a prior extraction of

count procedure to assess biomass density. In this study,

the ATP had been carried out, thereby indicating that the

the higher values of culturable cells obtained for biological

method used herein could most likely be improved by

PAC may be the result of the homogenization step being

including an ATP extraction step before the addition of the

more efﬁcient for smaller particles. Indeed, the shear

luciferin-luciferase complex.

stress is inversely proportional to the cross-sectional area of the material with area parallel to the applied force vector.

EPS: polysaccharides and proteins

Bacterial ATP

The results of the analysis of extracellular proteins and polysaccharides are presented in Figure 4(a). The proteins

The results obtained for the bacterial ATP are presented in

ranged from 6.7 to 14.0 mg/g PAC dw and 8.1 mg/g BAC

Figure 3(b). The ATP concentration measured on sample

dw (or 1.9 mg/g BAC ww). The results obtained for the

P7-5 was below the detection limit, whereas sample P60-

BAC were lower than those obtained in laboratory-scale

10 presented an ATP concentration of only 5 ng ATP/g

GAC columns removing microcystin-LR from drinking

PAC dw. Samples P67-5 and P67-1 presented ATP concen-

water (17.2–37.2 mg/g GAC ww) (Drogui et al. ). Poly-

trations of 39 and 80 ng ATP/g PAC dw, respectively.

saccharides were present at lower concentrations than

These results may have been biased by the impact of residual

proteins: 1.0–3.6 mg/g PAC dw and 5.1 mg/g BAC dw.

adsorption. Positive controls conﬁrmed that ATP readily

Figure 4(b) presents the total EPS measurements as well

adsorbed onto PAC particles. Luminescence losses ranging

as the polysaccharide-to-protein ratio. The calculated poly-

from 70 to 99% were detected after the addition of an

saccharide-to-protein ratios were similar in the various

ATP standard to the PAC. Thus, the residual adsorption

samples of biological PAC with SRT of at least 67 days

Figure 4

(a) Measured ( EPS, (

) proteins, (

) background proteins, (

) background total EPS and (

) polysaccharides and (

) background polysaccharides in biological AC samples. (b) Measured (

) polysaccharide-to-protein ratio in biological AC samples. Error bars represent standard deviations.

) total

C. Stoquart et al.

Quantifying bacterial biomass ﬁxed onto biological activated carbon

(0.22 ± 0.04). The youngest PAC (i.e. P7-5) had a lower

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

Comparison of methods

ratio (0.13 ± 0.002), whereas the GAC sample cumulated the greatest amount of polysaccharides (0.56 ± 0.1). As a

A comparison was performed by considering all the tested

reference, higher polysaccharide-to-protein ratios have

methods except for the ATP method, which remains to be

been associated with higher SRT in activated sludge

optimized. Figure 5 compares the results obtained with

(Massé et al. ; Satyawali & Balakrishnan ),

our reference method (i.e. PGR) and the results of the

which is consistent with our results. In fact, the relative

PAU rates, log HPC and the contents of proteins, polysac-

abundance of polysaccharides over proteins has been

charides and total EPS. The corresponding correlation

explained by the slower hydrolysis rate of polysaccharides

matrix is presented in Table 2. This matrix conﬁrms that

(Massé et al. ). The increase in the polysaccharide-to-

the results of the PAU tests were signiﬁcantly correlated

protein ratios at high SRT could also arise from a higher

with the PGR values (r ¼ 0.82), suggesting the possibility of

polysaccharide production, which would increase the bac-

using either technique interchangeably. In addition, there

terial adhesion to AC. Indeed, polysaccharides are widely

were strong correlations between methods based on poten-

recognized as key in mediating both cell-to-cell interactions

tial activity and the EPS analysis (r > 0.75). The total EPS

and cell attachment to supports (Tay et al. ). As poly-

and proteins were better correlated with the PGR method

saccharides tend to accumulate (cf. low hydrolysis rate),

(r ¼ 0.92 and 0.89, respectively), whereas the polysacchar-

this technique may lead to an improper assessment of AC

ides results were better correlated with the PAU data

microbial colonization for high SRT. In particular, compar-

(r ¼ 0.96).

ing fully colonized samples with different SRT is not

All of the methods investigated differentiated between

possible because old AC should lead to higher polysacchar-

the colonized AC and the corresponding virgin material

ides contents for the same amount of active biomass.

(p < 0.05) for all of the samples tested. As conﬁrmed by

However, by considering the total amount of EPS (i.e. the

the background measurements, the EPS methods were the

sum of proteins and polysaccharides) as presented in

least sensitive (see background measurements on Figures 4(a)

Figure 4(b), similar trends as the one observed for the

and (b)). In contrast, the PGR method was the most sensitive

potential activity-based methods (i.e. PAU and PGR) were

method. Indeed, by raising the ratio of the radioactive

obtained.

speciﬁc activity of the glucose solution added to the AC

Figure 5

PAU rate, log HPC, proteins, polysaccharides and total EPS as a function of the PGR rate measured on biological and virgin AC samples.

C. Stoquart et al.

Table 2

Quantifying bacterial biomass ﬁxed onto biological activated carbon

Correlation matrix (I-values, p < 0.05) between the results obtained with the PAU, PGR, proteins, polysaccharides, total EPS and log HPC methods

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

while the ATP and the EPS measurements allowed the evaluation of not only the heterotrophic bacteria, but the entire bacterial and non-bacterial biomass. These differences

PAU

PGR

Proteins

Polysac.

Total EPS

Log HPC

PAU

0.82

0.80

0.96

0.88

0.72

should be kept in mind and the investigation of the

PGR

0.82

0.89

0.84

0.92

0.87

microbial communities established at the surface of the

Proteins

0.80

0.89

0.75

0.98

0.81

AC under variable SRT would provide additional insight

Polysac.

0.96

0.84

0.75

0.85

0.66

on the results of the different methods applied in this study.

Total EPS

0.88

0.92

0.98

0.85

0.89

Log HPC

0.72

0.87

0.81

0.66

0.89

CONCLUSIONS

*Polysac.: Polysaccharides.

The results from PAU, PGR, EPS and HPC methods were (by increasing the proportion of radioactive glucose in this

strongly correlated. The PAU rate is a promising method

solution), the sensitivity can be easily improved.

to assess the bacterial activity on PAC and GAC. However,

Among biological PAC samples, P67-1 carbon exhibited

it should be validated for a wider set of colonized AC

the maximum results of all the six analytical methods, which

samples. Because protein content is well correlated with

were higher than those presented by the P67-5 sample. The

the PAU and PGR results, it would be a good surrogate

enhanced microbial colonization of P67-1 carbon might be

for on-site biomass measurements. A high SRT increased

due to the higher substrate availability that prevailed in

the activity of heterotrophic biomass and the amount of

this reactor (with the lowest PAC concentration). No statisti-

EPS associated to the bioﬁlms. Finally, it was concluded

cally signiﬁcant difference was found between the P67-1 and

that heterotrophic activities comparable to BAC ﬁlters can

BAC samples using the PGR method (p > 0.05), although

be obtained in biological PAC-based drinking water systems.

this ﬁnding may be attributed to the considerable standard deviation of the BAC results (i.e. 41%). The total EPS method indicated that the P67-1 sample was more colonized

ACKNOWLEDGEMENTS

than the BAC sample, whereas the opposite was true for the PAU method. These results support the idea that the bio-

The authors would like to thank the outstanding technical

mass of a PAC-based process is as active as the biomass at

support of Jacinthe Mailly, Jörg Winter, Julie Philibert,

the surface of a conventional biological GAC ﬁlter for drink-

Marcellin Fotsing, Mélanie Rivard, Mireille Blais and Yves

ing water treatment.

Fontaine. This work was completed at the Industrial-

Both potential activity-based methods (PGR and PAU)

NSERC Chair in Drinking Water of École Polytechnique

could be used to improve the modeling of DOC and

de Montréal with the ﬁnancial support of its partners,

BDOC removal as well as the control strategies and the

namely the City of Montreal, John Meunier, Inc., and the

overall performance of biological processes used in drinking

City of Laval.

water production. The protein measurements were highly correlated

with

the

potential

activity

methods

(see

Table 2). From an operational viewpoint, the protein analysis is the least cumbersome and time-consuming of all techniques tested during this project. It could be used as an easily measurable surrogate for monitoring the microbial activity on biological AC in water treatment plants. It is worth highlighting that, amongst the methods tested, the PAU and PGR rates and HPC methods were designed to provide data on the heterotrophic biomass

REFERENCES APHA  Standard Methods for the Examination of Water and Wastewater, 22nd edn. American Public Health Association, American Waterworks Association, Water Environment Federation, Washington, DC. Brown, J. C.  Biological treatments of drinking water. Bridge 37, 30–36. Camper, A. K., LeChevallier, M. W., Broadaway, S. C. & McFeters, G. A. a Evaluation of procedures to desorb

C. Stoquart et al.

Quantifying bacterial biomass ﬁxed onto biological activated carbon

bacteria from granular activated carbon. J. Microbiol. Methods 3, 187–198. Camper, A. K., LeChevallier, M. W., Broadaway, S. C. & McFeters, G. A. b Growth and persistence of pathogens on granular activated carbon filters. Appl. Environ. Microbiol. 50, 1378–1382. Chaudhary, D. S., Vigneswaran, S., Jegatheesan, V., Ngo, H. H., Moon, H., Shim, W. G. & Kim, S. H.  Granular activated carbon (GAC) adsorption in tertiary wastewater treatment: experiments and models. Water Sci. Technol. 47 (1), 113–120. Drogui, P., Daghrir, R., Simard, M. C., Sauvageau, C. & Blais, J. F.  Removal of microcystin-LR from spiked water using either activated carbon or anthracite as filter material. Environ. Technol. 33, 381–391. Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A. & Smith, F.  Colorimetric method for determination of sugars and related substances. Analyt. Chem. 28, 350–356. Herzberg, M., Dosoretz, C. G., Tarre, S. & Green, M.  Patchy biofilm coverage can explain the potential advantage of BGAC reactors. Environ. Sci. Technol. 37, 4274–4280. Kihn, A., Laurent, P. & Servais, P.  Measurement of potential activity of fixed nitrifying bacteria in biological filters used in drinking water production. J. Ind. Microbiol. Biotechnol. 24, 161–166. Lazarova, V., Pierzo, V., Fontvielle, D. & Manem, J.  Integrated approach for biofilm characterization and biomass activity control. Water Sci. Technol. 29 (7), 345–354. LeChevallier, M. W., Hassenauer, T. S., Camper, A. K. & McFeters, G. A.  Disinfection of bacteria attached to granular activated carbon. Appl. Environ. Microbiol. 48, 918–923. Léveillé, S., Carrière, A., Charest, S. & Barbeau, B.  PAC membrane bioreactor as an alternative to biological activated carbon filters for drinking water treatment. J. Water Suppl. Res. Technol. AQUA 62, 23–34. Liu, H. & Fang, H. H. P.  Extraction of extracellular polymeric substances (EPS) of sludge’s. J. Biotechnol. 95, 249–256. Markarian, A., Carrière, A., Dallaire, P.-O., Servais, P. & Barbeau, B.  Hybrid membrane process: performance evaluation of biological PAC. J. Water Suppl. Res. Technol. AQUA 59, 209–220. Massé, A., Spérandio, M. & Cabassud, C.  Comparison of sludge characteristics and performance of a submerged membrane bioreactor and an activated sludge process at high solids retention time. Water Res. 40, 2405–2415. Moteleb, M. A., Suidan, M. T., Kim, J. & Maloney, S. W.  Pertubated loading of a formaldehyde waste in an anaerobic granular activated carbon fluidized bed reactor. Water Res. 36, 3775–3785. Nielsen, J. L. & Nielsen, P. H.  Enumeration of acetateconsuming bacteria by microautoradiography under oxygen

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

and nitrate respiring conditions in activated sludge. Water Res. 36, 421–428. Niemi, R. M., Heiskanen, I., Heine, R. & Rapala, J.  Previously uncultured β-Proteobacteria dominate in biologically active granular activated carbon (BAC) filters. Water Res. 43, 5075–5086. Niquette, P., Prévost, M., MacLean, R. G., Thibault, D., Coallier, J., Desjardins, R. & Lafrance, P.  Backwashing first-stage sand-BAC filters. Does it increase removal of biodegradable organic matter and ammonia? J. Am. Water Works Assoc. 90, 86–97. Nishijima, W., Shoto, E. & Okada, M.  Improvement of biodegradation of organic substance by addition of phosphorus in biological activated carbon. Water Sci. Technol. 36 (12), 251–257. Papineau, I., Tufenkji, N. & Barbeau, B.  Impact of media aging on the removal of Cryptosporidium in granular media filters. J. Environ. Eng. 139, 603–611. Quintelas, C., Fernandes, B., Castro, J., Figueiredo, H. & Tavares, T.  Biosorption of Cr(VI) by a Bacillus coagulans biofilm supported on granular activated carbon (GAC). Chem. Eng. J. 136, 195–203. Satyawali, Y. & Balakrishnan, M.  Effect of PAC addition on sludge properties in an MBR treating high strength wastewater. Water Res. 43, 1577–1588. Servais, P., Billen, G., Ventresque, C. & Bablon, G. P.  Microbial activity in GAC filters at the Choisy-le-Roi treatment plant. J. Am. Water Works Assoc. 83, 62–68. Stoquart, C., Servais, P., Bérubé, P. & Barbeau, B.  Hybrid membrane processes using activated carbon treatment for drinking water production: a review. J. Membr. Sci. 411–412, 1–12. Stoquart, C., Vázquez-Rodríguez, G. A., Servais, P. & Barbeau, B.  Gamma irradiation: a method to produce an abiotic control for biological activated carbon. Environ. Technol. doi:10.1080/09593330.2013.803132. Tay, J.-H., Liu, Q.-S. & Liu, Y.  The role of cellular polysaccharides in the formation and stability of aerobic granules. Lett. Appl. Microbiol. 33, 222–226. Urfer, D. & Huck, P. M.  Measurement of biomass activity in drinking water biofilters using a respirometric method. Water Res. 35, 1469–1477. Velten, S., Boller, M., Köster, O., Helbing, J., Weilenmann, H. U. & Hammes, F.  Development of biomass in a drinking water granular active carbon (GAC) filter. Water Res. 45, 6347–6354. Wang, J. Z., Summers, R. S. & Miltner, R. J.  Biofiltration performance: part 1, relationship to biomass. J. Am. Water Works Assoc. 87, 55–63. Yener, J., Kopac, T., Dogu, G. & Dogu, T.  Dynamic analysis of sorption of methylene blue dye on granular and powdered activated carbon. Chem. Eng. J. 144, 400–406.

First received 12 January 2013; accepted in revised form 1 August 2013. Available online 23 September 2013

63.1

2014

Genotoxicity of disinfection by-products (DBPs) upon chlorination of nine different freshwater algal species at variable reaction time Y. L. Zhang, B. P. Han, B. Yan, Q. M. Zhou and Y. Liang

ABSTRACT Nine local dominant freshwater algal species, including green algae (Chlorella sp., Chlamydomonas sp. and Scenedesmus quadricauda), diatom (Navicula pelliculosa, Nitzschia palea Grunow and Synedra sp.), blue-green algae (Microcystis sp., Chroococcus sp. and Gloeocapsa sp.) were isolated from source water reservoirs and cultivated in the laboratory. The algal biomass was chlorinated (20 C, pH 7, residual chlorine 2 mg L 1). Yields of chloroform, dichloroacetic acid, trichloroacetic W

acid, dichloroacetonitrile and trichloroacetonitrile and genotoxicity of the chlorinated solutions at eight chlorination intervals (0.5, 1, 2, 5, 10, 30, 60 and 120 min) were determined via SOSChromoTest and Comet assay. The results showed that green algae and diatom were more effective disinfection by-products (DBPs) precursors than blue-green algae. Genotoxicity was shown to be chlorination time-dependent, in agreement with our previous ﬁndings, suggesting that intermediate DBPs rather than trihalomethanes or haloacetic acids were major contributors to the genotoxicity of chlorinated solutions. Key words

| algal species, Caco-2 cells, chlorination time, Comet assay, genotoxicity, SOSChromoTest

Y. L. Zhang B. Yan Q. M. Zhou Y. Liang (corresponding author) Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China E-mail: yliang@hkbu.edu.hk Y. L. Zhang Y. Liang Department of Environmental Pollution and Process Control, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, P.O. Box 9825, Beijing 100029, China; Center for Food Safety and Environmental Technology, Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou 511458, China; and Department of Biology, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China B. P. Han Institute of Hydrobiology Jinan University, Guangzhou 510632, China Q. M. Zhou Department of Geography, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China

INTRODUCTION Chlorination is widely used in water disinfection due to its

the identiﬁed chlorination DBPs seems to be a plausible

low cost and effectiveness in eliminating pathogenic micro-

bladder carcinogen (Hrudey ). Emerging DBPs such

organisms. Disinfection by-products (DBPs) are generated

when free chlorine reacts with natural organic matter in

derivatives are likely to be of health concern, according to

the source water. There is considerable epidemiological evi-

a recent analysis based on quantitative structure toxicity

dence for urinary bladder cancer being causally associated

relationship (Bull et al. ). These DBPs appear to be inter-

with chlorination DBPs, and that evidence for colon and

mediate DBPs during disinfection and can be further

rectal cancer is suggestive (IARC ). Currently nearly

oxidized (e.g. with prolonged chlorination), while intermedi-

700 DBPs have been reported in the literature, but none of

ate DBPs are likely to be of health concern since the

doi: 10.2166/aqua.2013.107

haloquinones,

halo-cyclopentene

and

cyclohexene

Y. L. Zhang et al.

Genotoxicity of chlorinated algal organic materials

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

mutagenicity of the chlorinated algal fractions (cells and

In this study, nine dominant local freshwater algal species,

proteins) reached a peak level shortly after chlorination

including green algae (chlorophyta), blue-green algae (cyano-

and then declined afterwards, different from that of chloro-

bacteria) and diatoms (bacillariophyta), were isolated in the

form (CHCl3) generation (Lui et al. , ).

source water (Hu et al. ; Liang et al. ). It was reported

In contrast with many other places, where as much as

that the DOC of surface water is about 1.69–2.18 mg L 1 in this

50% of dissolved organic carbon (DOC) is composed

region (Liang et al. ). We use a DOC concentration of

mainly of humic substances in most natural waters (Nikolaou

50 mg L 1 to quantify algal cells to standardize organic precur-

et al. ), in the Pearl River Delta region (China), the DBP

sor concentration among nine algal species, so that chlorine

precursors in some drinking water reservoirs are primarily

consumption and DBPs production could be easily compared.

from algae (Hong et al. a). During the algal blooming sea-

Changes in genotoxicity at different chlorination times were

sons, the increase in algal biomass and algal-derived organic

examined using Escherichia coli SOS-ChromoTest and Comet

matter may contribute to total organic carbon (TOC) and

assay using human Caco-2 cells. The major objectives were:

DBP formation. Algae are the major sources of dissolved

(1) to identify algal species as effective DBPs precursors in the

organic nitrogen (DON) in natural waters (Westerhoff &

source water; and (2) to identify a pattern of genotoxicity as a

Mash ). Algae cells contain a wide range of organic nitro-

function of chlorination time. We hope information from this

gen compounds, such as polysaccharides, proteins, peptides,

study could contribute to controlling DBPs precursors in the

etc. These organic compounds may contribute to DBPs for-

source water and formation of genotoxic DBPs in chlorination.

mation and particularly to prominent DBP species such as trihalomethanes (THMs) and haloacetic acids (HAAs) (Scully et al. ; Hureiki et al. ; Westerhoff & Mash

MATERIALS AND METHODS

). DON can be transformed to nitrogen-containing DBPs (N-DBPs) during chlorination or chloramination and

Algal culture

non-regulated N-DBPs are considered more toxic than the regulated THMs and HAAs (Richardson et al. ). The role of algae in DBP formation has been investigated

Nine species of algae (chlorophyta: Chlorella sp., Chlamydomonas

sp.

and

Scenedesmus

quadricauda;

cyanophyta:

in several studies (Wardlaw et al. ; Graham et al. ;

Microcystis sp., Chroococcus sp. and Gloeocapsa sp.; diatom:

Glezer et al. ; Plummer & Edzwald ; Nguyen et al.

Navicula pelliculosa, Nitzschia palea Grunow and Synedra

; Hong et al. a; Lui et al. , ). Both the

sp.) algae were obtained from the Limnology Institute of Jinan

nature of precursors and treatment conditions (e.g. pH, reac-

University, previously isolated in reservoirs at the Pearl River

tion time, temperature and relative amount of chlorine)

Delta region. Blue-green and the green alga were cultured in

determine concentration and composition of DBPs (Amy

BG11 medium (Stainer et al. ) and diatoms in diatom

et al. ), which in turn result in different toxicities in chlori-

medium (DM) in 5 L glass jars, at 25 ± 2 C under 1000 lux of

nated drinking water (Marabini et al. ; Pereira et al. ;

illumination with a 14:10 h light:dark cycle. Daily counting

Shi et al. ). Composition of algal cells plays an important

was undertaken using a light microscope and optical density

role in DBPs formation during chlorination. Generally, blue-

(OD 680 nm) was used to quantify the algal biomass. When

green algae have a higher protein content than diatoms, while

the algal growth reached log phase, the algal biomass was har-

diatoms accumulate more lipids than blue-green algae and

vested by centrifugation (3000 rpm, 10 min) and rinsed using

green algae (Hong et al. a). For algal cells, extracellular

Milli-Q water. The pellet was regarded as the algal cell fraction

organic matter and algal fractions (cells and proteins) of

and stored at 80 C, avoiding light.

different algae species were also reported to produce DBPs in different compositions (Wardlaw et al. ; Huang et al.

Chlorination

; Lui et al. ). The majority of the DBP precursors (70%) was attributable to the cellular material (Plummer &

The algal cell pellets were crushed using a liquid nitrogen

Edzwald ).

grinding method and solutions containing algal-derived

Y. L. Zhang et al.

Genotoxicity of chlorinated algal organic materials

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

organic materials (with starting DOC of 50 mg C L 1) were

PQ37 strain supplied by EBPI (Brampton, Ontario,

used for chlorination. Chlorination was conducted with eight

Canada), which was then modiﬁed according to Quillardet

different chlorine contact times (0.5, 1, 2, 5, 10, 30, 60 and

& Hofnung () and Mersch-Sundermann et al. ().

120 min). All samples were dissolved in phosphate buffer

Brieﬂy, the bacteria were ﬁrst incubated in growth

(pH 7, 0.2 M NaH2PO4 and 0.2 M NaOH) to maintain the

medium for 8–12 hours at 37 C. The bacterial suspension

pH. DOC was detected using a TOC analyzer (Shimadzu

was diluted using 10% DMSO in sterile 0.85% saline to

TOC 5000). The stock chlorine solution (NaOCl, 49.7 mg Cl2

OD 0.05–0.06 at 600 nm. Six concentration levels of posi-

mL 1) was standardized by the N,N-diethyl-p-phenylenedia-

tive controls (4 Nitro Quinoline Oxide, 4NQO) and

mine titrimetric method (APHA ). Excess chlorine was

chlorinated algal solutions were prepared respectively by

applied based on a chlorine demand test conducted before-

two-fold dilution using 10% DMSO saline. After adding

hand to ensure a substantial residual of chlorine (about 2 mg

10 μL samples into microwells, 100 μL of bacteria suspen-

L ) after the chlorination period. All chlorination exper-

sion were transferred into each well. The microplate was

iments were conducted at 20 C in 80 mL glass tubes with

incubated at 37 C for 2 h. Activities of β-galactosidase

glass septa in the dark. At the end of the chlorination period,

and alkaline phosphatase were then determined using 5-

samples for THM analyses were collected, head-space free, in

bromo-4-chloro-3-indolyl-β-D-galactoside (X-gal, MYM Bio-

20 mL glass vials containing Na2S2O3 quenching agent,

logical Technology Co. Ltd) and p-nitrophenyl phosphate

while samples for HAA analyses were collected in vials with

(PNPP, MYM Biological Technology Co. Ltd) disodium

NH4Cl quenching agent. Residue chlorine of samples for the

substrates, respectively. The ratio of the activities, β-galac-

toxicity test was quenched with a moderate amount of Na2SO3.

tosidase/alkaline phosphatase, divided by the values of

control samples (10% DMSO) was calculated as the inducDBPs analysis

tion factor. The induction factor increased with sample dose, and SOS inducing potency (SOSIP) was calculated

DBPs including CHCl3, dichloroacetic acid (DCAA), trichlor-

as the slope of the linear region.

oacetic acid (TCAA), dichloroacetonitrile (DCAN) and trichloroacetonitrile (TCAN) were analyzed. Brieﬂy, CHCl3

Comet assay using Caco-2 cells

was extracted by pentane and determined according to the Standard Method (APHA ). DCAA and TCAA were extracted

Human Caco-2 cell line was obtained from the American

by methyl tert-butyl ether (MTBE) and methylated by acidic

Type Culture Collection (ATCC, Rockville, MD). The cells

methanol, and determined following USEPA (). DCAN

were cultured in Dulbecco Modiﬁed Eagle’s Medium

and TCAN were extracted by MTBE, and analyzed according

(DMEM) supplemented with 20% fetal bovine serum (FBS),

to the USEPA (). 1,2-Dibromopropane was used as the

in a humidiﬁed atmosphere of 95% air and 5% CO2 at

internal standard. The DBPs were determined by a GC-ECD

37 C. The medium was changed every 3 days and the cells

system (with a 30 m × 0.25 mm × 0.25 μm HP-5 capillary

were sub-cultured every 7 days when 95% cells conﬂuenced.

column) using nitrogen as the carrier gas. The recovery rates

Oxiselect Comet Assay Kit (Cell Biolabs, Inc.) was used

for CHCl3, DCAA, TCAA, DCAN, TCAN, 1,1-DCP and 1,1,1-

for Comet assays. Procedures mainly followed the product

TCP were 97.5 ± 6.34, 101 ± 8.4, 97.3 ± 5.3, 90.7 ± 4.7,

manual with some modiﬁcations according to Fairbairn

87.7 ± 10.4, 102.7 ± 10.4 and 105.6 ± 8.2, respectively. The

et al. () and Klaude et al. (). Brieﬂy, Caco-2 cells

detection limits for CHCl3, DCAA and TCAA were 0.00938,

were incubated in a 12-well plate at a density of 1 × 105

0.0105 and 0.00894 μmol L , respectively.

cells per well in 1 mL DMEM with 20% FBS. After 24 h, cells settled and formed a monolayer. Cell viability

SOS-ChromoTest

was determined via observing the color change of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium

bro-

SOS-ChromoTest was initially performed without meta-

mide (MTT). Only cells with viability of more than 80%

bolic activation using an SOS-ChromoTest Kit and E. coli

were used for toxicity tests. The cells were then washed

Y. L. Zhang et al.

Genotoxicity of chlorinated algal organic materials

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

with phosphate-buffered saline (PBS, without Mg2þ and

centrifuged, the supernatant discarded and suspended in

Ca2þ) and treated with different samples, and cells treated

100 μL ice-cold PBS. The cells then were mixed with low

simply with DMEM medium (Milli-Q water prepared) were

melting point agarose (37 C) 1:10 brieﬂy and spread on a

used as the control. The MEM powder was dissolved directly

slide using 75 μL mixture per well, at 4 C, avoiding light,

into the chlorinated solutions, followed by ﬁltration through

until solidiﬁcation. The cells were lysed in lysis buffer at

0.22 μm acetate cellulose membrane before incubation with

4 C for 1 h, and then in alkaline buffer for 30 min (protected

the cells. After treatment for 1 h (at 37 C, with 5% CO2), the

from

cells were collected and washed once with ice-cold PBS,

Tris-borate-EDTA (TBE) buffer twice. Electrophoresis was

Table 1

light),

followed

washing

with

ice-cold

CHCl3, DCAN and TCAN formation (mean ± SD, μmol L 1) among nine species during different chlorination time

Chlorophyta

Diatom

Cyanophyta

Time (min) CHLO

CHLA

SCEN

NAVI

NITZ

SYNE

MICR

CHRO

GLOE

CHCl3 0.5

N.D.

17.9 ± 0.106a

16.6 ± 2.01ab

N.D.

9.49 ± 2.54c

N.D.

9.74 ± 2.34c

N.D. e

31.0 ± 0.991

36.1 ± 0.053

36.8 ± 0.0277 6.25 ± 0. 103 c

8.56 ± 0.111

25.3 ± 0.293abc 61.8 ± 0.247ab 63.7 ± 0.100a

54.3 ± 15.2

120 136 ± 4.14

99.2 ± 1.12

136 ± 1.18

29.5 ± 0.240 108 ± 0.111

168 ± 0.578

10.9 ± 1.42

32.7 ± 2.62

43.3 ± 0.386

11.4 ± 3.96

22.8 ± 0.158abc 15.3 ± 6.37

cde

16.7 ± 0.336

N.D. b

9.26 ± 0.182de

18.4 ± 0.214

N.D. 10.1 ± 0.278

16.1 ± 0.101d

38.7 ± 1.74

44.9 ± 8.45abc 27.8 ± 0.207abc 9.99 ± 0.100c 41.2 ± 29.2bc ab

38.8 ± 1.48

106 ± 5.12

54.9 ± 11.4

N.D.

88.3 ± 6.48 f

13.8 ± 0.656

17.5 ± 0.378

29.2 ± 0.272

23.5 ± 7.82abc

30.1 ± 1.64c

45.8 ± 0.00

77.8 ± 0.0404 31.3 ± 3.18g

DCAN 0.5

49,197 ± 244a c

24.8 ± 6.34bc c

270 ± 72.9bc

N.D.

472 ± 51.0a

N.D.

14.0 ± 0.962c N.D.

N.D.

199 ± 27.2b

10.3 ± 6.23c

N.D.

9.64 ± 2.92

N.D.

11.4 ± 0.489b

N.D.

15.5 ± 0.883c

N.D.

53.2 ± 0.788

4,330 ± 205

28.0 ± 6.87

52.7 ± 26.4

13.4 ± 0.236

10.6 ± 2.27

48.0 ± 5.56

111 ± 94.0

86.2 ± 3.14ab

188 ± 161ab

130 ± 46.3ab

34.4 ± 10.0ab

N.D.

9.24 ± 0.552b

272 ± 33.0a

78.6 ± 0.126b

155 ± 7.63a

23.7 ± 6.43c

49.0 ± 7.72bc

N.D.

78.4 ± 19.5b

29.2 ± 3.90

95.8 ± 4.45

15.5 ± 2.76

1,730 ± 71.4

120 17.7 ± 1.21c

21.0 ± 3.39c

30 60

247 ± 12.6

63.4 ± 9.65

729 ± 469

14.8 ± 11.7

9.25 ± 0.788

19.9 ± 1.56

60.2 ± 1.33a

N.D.

21.4 ± 2.21

24.8 ± 0.835

40.3 ± 4.18b

43.0 ± 16.3 c

18.4 ± 5.28

N.D.

19.0 ± 3.07d

13.2 ± 1.58

31.4 ± 14.3

12.8 ± 5.12

15.3 ± 3.94c

N.D.

35.1 ± 1.50b

N.D.

TCAN 0.5

2,690 ± 152a

28.9 ± 0.693b 68.1 ± 19.7b

32.1 ± 4.51

51.5 ± 1.24

84.0 ± 12.0

N.D.

94.2 ± 19.7

N.D.

963 ± 233a

N.D.

2,380 ± 113b

140 ± 6.96d

3,260 ± 29.7a

N.D.

797 ± 40.1c

75.3 ± 21.0d

17.6 ± 4.31d

N.D.

47.4 ± 1.73e

300 ± 0.910b

137 ± 2.84c

N.D.

465 ± 0.780a

11.5 ± 0.633f

128 ± 9.34cd

N.D.

43.2 ± 0.0693

215 ± 2.64

35.4 ± 1.26

N.D.

223 ± 37.7

50.1 ± 1.18cd

173 ± 37.0b

17.2 ± 0.568d

9.72 ± 2.81d

1,440 ± 23.9a 36.2 ± 0.711cd

36.8 ± 8.54cd 230 ± 8.10b

92.5 ± 27.6c

N.D.

88.4 ± 4.36bc

323 ± 63.8a

12.7 ± 4.89c

168 ± 19.0b

20.0 ± 0.793c

45.7 ± 14.7c

176 ± 22.2b

10.6 ± 1.43c

N.D.

22.5 ± 4.42

77.8 ± 2.46

N.D.

46.0 ± 5.42

24.1 ± 12.2

N.D.

120 9.75 ± 0.667

55.5 ± 2.11

N.D.

50.0 ± 2.31

109 ± 13.4d a

542 ± 29.9

N.D.

Means containing the same letter were not signiﬁcantly different (p > 0.05), according to multiple-comparison of means by one-way ANOVA. Detection limits of CHCl3, DCAN and TCAN were 9.38, 9.00 and 9.42 pmol L 1 respectively. N.D.: not detectable; CHLO: Chlorella sp.; CHLA: Chlamydomonas sp.; SCEN: Scenedesmus quadricauda; NAVI: Navicula pelliculosa; NITZ: Nitzschia palea Grunow; SYNE: Synedra sp.; MICR: Microcystis sp.; CHRO: Chroococcus sp.; GLOE: Gloeocapsa sp.

Y. L. Zhang et al.

Genotoxicity of chlorinated algal organic materials

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

performed at 4 C, 1 V cm 1, for 15 min in TBE. Afterwards,

SPSS (Statistic Package for Social Science) software pro-

the slides were neutralized using pre-chilled DI water,

gram for Microsoft Windows (Version 16.0) (SPSS Inc.).

dehydrated with 70% ethanol, and stained using vista green DNA dye for 15 min. The cells were measured for their comet tail moment (TM) using a ﬂuorescence microscope with a CCD camera, to indicate DNA damage in the treated cells. About 50 cells per treatment were randomly measured, and the same level of treatment was conducted in three different generations of cells. Values of average TM were calculated from 50 cells exposed to the same level of treatment.

RESULTS AND DISCUSSION DBPs yields among different algal species In general, algal organic matter upon chlorination was more effective in generating CHCl3 than the other measured DBPs (Tables 1 and 2). In particular, among different species, green algae were shown to form more CHCl3

Data analysis

upon chlorination than the other algal species. At 120 min chlorination time, the highest yield of CHCl3 was

One-way analysis of variance (ANOVA) and independent t-

observed to be formed from Scenedesmus quadricauda

test were used to compare the means analyzed using the

(168 μmol L 1), 5.75 times of that from Microcystis sp.

Table 2

DCAA and TCAA formation (mean ± SD, μmol L 1) among nine species during different chlorination time Chlorophyta

Time (min)

Diatom

CHLO

CHLA

0.5

N.D.

2 5

SCEN

Cyanophyta

NAVI

NITZ

SYNE

MICR

CHRO

GLOE

N.D. N.D.

N.D.

N.D. N.D.

N.D.

60.2 ± 16.0

N.D.

13.6 ± 3.61a

N.D. N.D.

13.8 ± 5.12a

19.5 ± 0.413a

15.3 ± 1.36a

N.D.

11.6 ± 0.867a

12.5 ± 1.18c

N.D. N.D.

46.2 ± 4.07b

N.D.

87.0 ± 13.2a

21.6 ± 3.41c

28.5 ± 0.335bc

DCAA

113 ± 1.40

18.5 ± 0.447b N.D. 33.6 ± 1.45b a

N.D. 17.2 ± 2.39

31.7 ± 7.55 N.D.

32.0 ± 4.39

N.D.

105 ± 9.57a

22.1 ± 2.27b

133 ± 11.4

N.D. N.D.

35.3 ± 0.0576b

N.D. a

84.2 ± 1.24b

19.0 ± 0.369

31.1 ± 11.1

26.1 ± 1.47a

18.3 ± 3.41bc N.D.

41.9 ± 3.82b

182 ± 12.5a

N.D.

14.3 ± 1.20

35.6 ± 0.956bc

19.4 ± 0.440

N.D. N.D.

16.4 ± 0.972

16.4 ± 3.58

120

26.2 ± 2.06bc

N.D. 12.5 ± 3.29c

18.9 ± 5.53bc

0.5

N.D.

N.D. N.D.

10.8 ± 0.246

N.D. N.D.

N.D.

N.D. N.D.

N.D.

12.4 ± 3.80

361 ± 50.1a

N.D. N.D.

13.9 ± 2.93b

N.D.

10.4 ± 1.22b

46.2 ± 8.55b

N.D.

N.D. 448 ± 84.2a

N.D.

9.06 ± 0.538b 16.7 ± 0.661b

N.D.

TCAA

N.D.

N.D. 19.3 ± 4.41

N.D.

12.9 ± 2.75

N.D.

N.D. N.D.

11.4 ± 4.04a

N.D.

11.1 ± 1.46a

120

N.D.

N.D. 18.8 ± 0.369 N.D.

N.D.

Means containing the same letter were not signiﬁcantly different (p > 0.05), according to multiple-comparison of means by one-way ANOVA. Detection limits of DCAA and TCAA were 10.5 and 8.94 pmol L 1 respectively. N.D.: not detectable; CHLO: Chlorella sp.; CHLA: Chlamydomonas sp.; SCEN: Scenedesmus quadricauda; NAVI: Navicula pelliculosa; NITZ: Nitzschia palea Grunow; SYNE: Synedra sp.; MICR: Microcystis sp.; CHRO: Chroococcus sp.; GLOE: Gloeocapsa sp.

Figure 1

Y. L. Zhang et al.

Genotoxicity of chlorinated algal organic materials

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

Time-dependent changes in genotoxicity of chlorinated algae using SOS-test: (a) SOSIP change among green algae; (b) SOSIP among diatom; (c) SOSIP among blue-green algae. CHLO: Chlorella sp.; CHLA: Chlamydomonas sp.; SCEN: Scenedesmus quadricauda; NAVI: Navicula pelliculosa; NITZ: Nitzschia palea Grunow; SYNE: Synedra sp.; MICR: Microcystis sp.; CHRO: Chroococcus sp.; GLOE: Gloeocapsa sp.

(29.2 μmol L 1). Meanwhile, it is also observed that different

et al. ). Particularly, DCAN formation was the highest

algal species in the same group had signiﬁcant differences in

(0.492 μmol L 1) at 0.5 min chlorination of Chlorella sp.,

CHCl3 formation. For example, at 120 min, Nitzschia palea

and the highest TCAN (3.26 μmol L 1) was observed at

CHCl3

2 min chlorination of Scenedesmus quadricauda. The data

(106 μmol L 1) than the other two diatoms (Navicula pelli-

were consistent with a previous study by Nguyen et al.

Grunow

(diatom)

produced 1

culosa 43.3 μmol L

relatively

and Synedra sp. 54.9 μmol L );

() that green algae were more productive in generating

Chroococcus sp. (blue-green algae) produced 77.8 μmol L 1

CHCl3 than blue-green algae and diatoms (Nguyen et al.

CHCl3, twice higher than the other two blue-green algal

). However, in our previous report of CHCl3 formation

species (Microcystis sp. 29.2 μmol L 1 and Gloeocapsa

after 4 d chlorination, the diatom was shown to be the most

sp. 31.3 μmol L 1). The comparison among different algal

effective precursor (Hong et al. b). Obviously chlori-

species was similar in DCAN and TCAN formation

nation time is another key factor in determining CHCl3

(Table 1), as these two DBPs are generally regarded as inter-

generation. On the other hand, Chroococcus sp. (blue-

mediate chlorination products in the process of CHCl3

green algae) formed the highest concentration of DCAA

formation (Peters et al. ; Nikolaou et al. ; Reckhow

(peak level for 0.182 μmol L 1 at 120 min chlorination)

Y. L. Zhang et al.

Genotoxicity of chlorinated algal organic materials

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

among all the algal species, while minor amounts of TCAA

genotoxicity and any of the DBP level was not signiﬁcant

were generated (Table 2). Whether this is due to generally

and therefore the speciﬁc responsible DBPs were not ident-

higher protein in blue-green algae (Hong et al. b)

iﬁed. Chlorination time is determined by raw water quality

requires investigation in the future.

in the water treatment, particularly microbiological quality, while short chlorination time does not always mean less DBPs formation and the associated genotoxicity. Therefore,

Genotoxicity

a sound control of chlorination time means a good A preliminary study was conducted using unchlorinated algal cells at 50 mg L

TOC, and no genotoxicity was

reduction in both pathogens and genotoxic DBPs in water treatment.

detected. All chlorinated solutions showed genotoxicity (Figure 1), and chlorination time appeared to be a critical contributor. Most solutions showed that peak levels of genotoxicity

were

reached

before

30 min

(except

CONCLUSIONS

for

Gloeocapsa sp. at 60 min) and then reduced when chlori-

DBPs formation was different among nine different species

nation time was prolonged. A similar pattern was observed

of algal cells during chlorination. CHCl3 formation

previously (Lui et al. , ), suggesting that intermediate

increased as chlorination time increased while the other

by-products generated within a very short reaction time were

DBPs (DCAA, TCAA, DCAN and TCAN) showed a peak

responsible for the ﬂuctuation. A general pattern is observed

level before 120 min. Green algae generally formed a

that chlorinated green algal solutions, containing higher

higher level of CHCl3. DCAN and TCAN also reached

levels of CHCl3, DCAN and TCAN (Table 1 and Figures 1

higher levels within 120 min. Blue-green algae produced

and 2), were more genotoxic. Both chlorinated Synedra sp.

relatively lower DBPs and also showed lower genotoxicity

(diatom) and Gloeocapsa sp. (blue-green algae) formed rela-

than green algae and diatom. DBPs formation and toxicity

tively less DBPs (especially for DCAN and TCAN) during

differs among different algal groups, and also different

chlorination (Table 1) and also showed relatively lower gen-

algal species in the same group.

otoxicity (Figure 1). However, the link between the

ACKNOWLEDGEMENTS This research was funded by the International Science & Technology

Cooperation

Program

China

(2010DFA92720-24). This study was also supported by the Faculty Research Grant (FRG2/11-12/037, Faculty of Science of Hong Kong Baptist University) and ‘One Hundred Talents’ (Chinese Academy of Science, China) awarded to Yan Liang.

REFERENCES

Figure 2

Average tail moment of Caco-2 cells by the Comet assay, comparing among different algal species. Means containing the same letter were not signiﬁcantly different (p > 0.05), according to multiple-comparison of means by one-way ANOVA. CHLO: Chlorella sp.; CHLA: Chlamydomonas sp.; SCEN: Scenedesmus quadricauda; NAVI: Navicula pelliculosa; NITZ: Nitzschia palea Grunow; SYNE: Synedra sp.; MICR: Microcystis sp.; CHRO: Chroococcus sp.; GLOE: Gloeocapsa sp.

Amy, G., Siddiqui, M., Ozekin, K., Zhu, H. W. & Wang, C.  Empirically Based Models for Predicting Chlorination and Ozonation By-products: Trihalomethanes, Haloacetic Acids, Chloral Hydrate, and Bromate. Colorado University at Boulder, EPA, Washington, DC, USA. Bull, R. J., Reckhow, D. A., Li, X. F., Humpage, A. R., Joll, C. & Hrudey, S. E.  Potential carcinogenic hazards of

Y. L. Zhang et al.

Genotoxicity of chlorinated algal organic materials

non-regulated disinfection by-products: haloquinones, halocyclopentene and cyclohexene derivatives, N-halamines, halonitriles, and heterocyclic amines. Toxicology 286 (1–3), 1–19. Fairbairn, D. W., Olive, P. L. & O’Neill, K. L.  The comet assay: a comprehensive review. Mutat Res. 339 (1), 37–59. Glezer, V., Harris, B., Tal, N., Iosefzon, B. & Lev, O.  Hydrolysis of haloacetonitriles: linear free energy relationship, kinetics and products. Water Res. 33 (8), 1938–1948. Graham, N. J. D., Wardlaw, V. E., Perry, R. & Jiang, J. Q.  The signiﬁcance of algae as trihalomethane precursors. Water Sci. Technol. 37 (2), 39–83. Hong, H. C., Wong, M. H., Mazudmer, A. & Liang, Y. a Trophic state, natural organic matter and disinfection byproduct formation potential of drinking water reservoirs around Pearl River Delta Region (China). J. Hydrol. 359 (1–2), 164–173. Hong, H. C., Mazumder, A., Wong, M. H. & Liang, Y. b Yield of trihalomethanes and haloacetic acids upon chlorinating algal cells, and its prediction via algal cellular biochemical composition. Water Res. 42 (20), 4941–4948. Hrudey, S. E.  Chlorination disinfection by-products, public health risk tradeoffs and me. Water Res. 43 (8), 2057–2092. Hu, R., Lin, Q., Wang, Z. & Zhang, Q.  Phytoplankton and eutrophication of reservoirs in Guangdong Province. In: Evaluation and Remediation of Reservoir Eutrophication in Guangdong Province, South China (B. Han, T. Li & X. Lin, eds). Science Publication, China, pp. 55–61 (in Chinese). Huang, J., Graham, N., Templeton, M. R., Zhang, Y., Collins, C. & Nieuwenhuijsen, M.  A comparison of the role of two blue-green algae in THM and HAA formation. Water Res. 43 (12), 3009–3018. Hureiki, L., Croue, J. P. & Legube, B.  Chlorination studies of free and combined amino-acids. Water Res. 28 (12), 2521–2531. IARC (International Agency for Research on Cancer)  Some drinking-water disinfectants and contaminants, including arsenic. In: IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Vol. 84. IARC, Lyon. Klaude, M., Ericson, S., Nygren, J. & Ahnström, G.  The comet assay: mechanisms and technical considerations. Mutat Res. 363 (2), 89–96. Liang, Y., Hong, H. C., Dong, L. H., Poon, B., Lan, C. Y., Han, B. P. & Wong, M. H.  Sources and properties of natural organic matter (NOM) in water along the Dongjiang River (source of Hong Kong’s drinking water) and toxicological assay of its chlorination by-products. Arch. Environ. Contam. Toxicol. 54 (4), 597–605. Lui, Y. S., Hong, H. C., Zheng Gene, J. S. & Liang, Y.  Fractionated algal organic materials as precursors of disinfection by-products and mutagens upon chlorination. J. Hazard Mater. 209 (10), 278–284. Lui, Y. S., Qiu, J. W., Zhang, Y. L., Wong, M. H. & Liang, Y.  Algal-derived organic matter as precursors of disinfection by-

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

products and mutagens upon chlorination. Water Res. 45 (3), 1454–1462. Marabini, L., Frigerio, S., Chiesara, E. & Radice, S.  Toxicity evaluation of surface water treated with different disinfectants in HepG2 cells. Water Res. 40 (2), 267–272. Mersch-Sundermann, V., Kevekordes, S. & Mochayedi, S.  Sources of variability of the Escherichia coli PQ37 genotoxicity assay (SOS chromotest). Mutat. Res. 252 (1), 51–60. Nikolaou, A. D., Golfinopoulos, S. K., Kostopoulou, M. N. & Lekkas, T. D.  Decomposition of dihaloacetonitriles in water solutions and fortified drinking water samples. Chemosphere 41 (8), 1149–1154. Nikolaou, A. D., Lekkas, T. D., Kostopoulou, M. N. & Golfinopoulos, S. K.  Investigation of the behaviour of haloketones in water samples. Chemosphere 44 (5), 907–912. Nguyen, M. L., Westerhoff, P., Baker, L., Hu, Q., Esparza-Soto, M. & Sommerfeld, M.  Characteristics and reactivity of algae-produced dissolved organic carbon. J. Environ. Eng. 131 (11), 1574–1582. Pereira, T. S., Rocha, J. A. V., Duccatti, A., Silveira, G. A., Pastoriza, T. F., Bringuenti, L. & Vargas, V. M. F.  Evaluation of mutagenic activity in supply water at three sites in the state of Rio Grande do Sul, Brazil. Mutat. Res. 629 (2), 71–80. Peters, R. J. B., De Leer, E. W. B. & De Galan, L.  Dihaloacetonitriles in Dutch drinking waters. Water Res. 24 (6), 797–800. Plummer, J. D. & Edzwald, J. K.  Effect of ozone on algae as precursors for trihalomethane and haloacetic acid production. Environ. Sci. Technol. 35 (18), 3661–3668. Quillardet, P. & Hofnung, M.  The SOS Chromotest, a colorimetric bacterial assay for genotoxins: procedures. Mutat. Res. 147 (3), 65–78. Reckhow, D. A., MacNeill, A. L., Platt, T. L. & McClellan, J. N.  Formation and degradation of dichloroacetonitrile in drinking waters. J. Water Supply Res. Technol-Aqua. 50 (1), 1–13. Richardson, S. D., Plewa, M. J., Wagner, E. D., Schoeny, R. & DeMarini, D. M.  Occurrence, genotoxicity, and carcinogenicity of emerging disinfection by-products in drinking water: a review and roadmap for research, Mutat. Res. Rev. 636 (1–3), 178–242. Scully, F. E., Howell, G. D., Kravltz, R., Jewell, J. T., Hahn, V. & Speed, M.  Proteins in natural waters and their relation to the formation of chlorinated organics during water disinfection. Environ. Sci. Technol. 22 (5), 537–542. Shi, Y., Cao, X. W., Tang, F., Du, H. R., Wang, Y. Z., Qiu, X. Q., Yu, H. P. & Lu, B.  In vitro toxicity of surface water disinfected by different sequential treatments. Water Res. 43 (1), 218–228. Stainer, R. Y., Kunisawa, R., Mandel, M. & Cohen-Bazire, G.  Purification and properties of unicellular blue-green algae (order Chroococcales). Bacteriol. Rev. 35 (2), 171–205. Standard Methods for the Examination of Water and Wastewater  20th edn. American Public Health Association, Washington, DC.

Y. L. Zhang et al.

Genotoxicity of chlorinated algal organic materials

US Environmental Protection Agency (USEPA)  Method 552.3: Determination of Haloacetic Acids and Dalapon in Drinking Water by Liquideliquid Microextraction, Derivatization, and Gas Chromatography with Electron Capture Detection. EPA 815-B-03-002. Revision 1.0. US Environmental Protection Agency, Washington, DC. US Environmental Protection Agency (USEPA)  Method 551.1: Determination of Chlorination Disinfection Byproducts, Chlorinated Solvents, and Halogenated

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Pesticides/Herbicides in Drinking Water by Liquid–Liquid Extraction and Gas Chromatography with Electron-Capture Detection. Revision 1.0. US Environmental Protection Agency, Washington, DC. Wardlaw, V. E., Perry, R. & Graham, N. J. D.  The role of algae as trihalomethane precursors – a review. J. Water Supply Res. Technol-Aqua. 40 (6), 335–345. Westerhoff, P. & Mash, H.  Dissolved organic nitrogen in drinking water supplies: A review. J. Water Supply Res. Technol-Aqua. 51 (8), 415–448.

First received 3 June 2013; accepted in revised form 22 August 2013. Available online 15 October 2013

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Relationships between trihalomethanes, haloacetic acids, and haloacetonitriles formed by the chlorination of raw, treated, and fractionated surface waters Paolo Roccaro, Federico G. A. Vagliasindi and Gregory V. Korshin

ABSTRACT This study examined effects of coagulation and fractionation of natural organic matter on the distribution of trihalomethanes (THM), haloacetic acids (HAA), and haloacetonitriles (HAN) formed in chlorinated water. The precursors of HAA and THM were determined to be associated primarily with the hydrophobic fraction although the hydrophilic fraction is important as well. On the other hand, the precursors of HAN are overwhelmingly hydrophilic with only a negligible contribution of the hydrophobic and transphilic fractions. Higher percentages of HAN were found in the treated waters compared with raw waters, collected from three different full scale water treatment plants. Very strong correlations were found between THM, HAA, and HAN concentrations. Two main correlations between THM and HAN were observed. One was applicable to all raw waters regardless of their fractionation and provenance, while the other was formed by the treated waters, again regardless of

Paolo Roccaro (corresponding author) Federico G. A. Vagliasindi Department of Civil and Environmental Engineering, University of Catania, Viale A. Doria, 6, 95125, Catania, Italy E-mail: proccaro@dica.unict.it Gregory V. Korshin Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA

their source and fractionation. This result emphasizes that a signiﬁcant removal of THM and HAA precursors can be achieved via coagulation but it results in a negligible removal of HAN precursors. As a consequence, some changes in treatment trains or operating conditions may need to be considered to minimize the formation of HAN. Key words

| coagulation, disinfection by-products, drinking water, fractionation, hydrophilic natural organic matter

INTRODUCTION Chlorination of drinking water in public facilities was one of

; Krasner et al. ), but other water quality parameters

the most important public health measures during the twen-

(e.g., pH, temperature, bromide, and iodide) can impact

tieth century because it reduced the transmission of deadly

the concentration and speciation of DBP (Obolensky &

waterborne diseases such as typhoid and cholera. Apart

Singer ). Trihalomethanes (THM) and haloacetic acids

from the limited disinfection efﬁciency against some chlor-

(HAA) are generally present at the highest levels (from a

ine-resistant pathogens, the main drawback of chlorination

few to hundred ppb) in chlorinated drinking water. Among

is the formation of harmful disinfection by-products (DBP)

known but unregulated DBP, several species have been

that result from reactions between chlorine and both natural

found in drinking waters at much lower concentrations

organic matter (NOM) present in all source water or efﬂuent

than those of THM and HAA but their potential toxicity is

organic matter (EfOM) occurring in wastewater impacted

higher (Roccaro et al. ; Richardson et al. ). Recent

water bodies (Krasner et al. ; Sedlak & von Gunten

studies (Muellner et al. ; Richardson et al. ;

).

Plewa et al. ) have determined that the nitrogenous

The content and reactivity of NOM and EfOM plays a

DBP (N-DBP) are much more toxic than carbonaceous

major role in the formation of DBP (Leenheer & Croue

DBP (C-DBP). Haloacetonitriles (HAN) are a class of

doi: 10.2166/aqua.2013.043

P. Roccaro et al.

Relationships between DBP formed in chlorinated waters

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nitrogen-containing DBP which are ubiquitous in chlori-

mg 1 m 1 for MS, MF, DS, and DF samples, respectively.

nated waters (Richardson et al. ) and due to their high

The bromide level in Ancipa and Potomac water was

cytotoxicity and genotoxicity require more consideration

about 0.05 mg/L.

(Muellner et al. ).

All samples were ﬁltered through a 0.45 mm nylon

Although several studies have been carried out to exam-

membrane ﬁlter (Polycap AS Capsule Filter) and stored at

ine the relationships and relative distributions between DBP

4 C. All chemicals were ACS reagent grade or better. Sol-

species (Liang & Singer ; Serodes et al. ; Malliarou

vents used in extractions were high-purity grade. Reagent

et al. ), these relationships have not been fully investi-

water was obtained from a Millipore Super-Q Plus water

gated for nitrogenous DBP such as HAN. Accordingly, the

system. Chlorine stock solution was prepared by dilution

objective of this study was to examine the yields and specia-

of a reagent-grade sodium hypochlorite solution (5% avail-

tion of regulated DBP (THM and HAA) and emerging DBP

able chlorine) with Milli-Q water. Amberlite XAD-8 (now

(HAN) in chlorinated raw, treated, and fractionated surface

called Supelite DAX-8, Supelco #20278) and Amberlite

waters at varying reaction conditions.

XAD-4 (Supelco #20276) resins were used for all fractionation procedures.

MATERIALS AND METHODS

Experimental methods

Water sources and materials

NOM fractionation was carried out using the simpliﬁed NOM fractionation scheme that utilized small (8 mL) resin

Experiments were conducted using water samples from the

volumes packed into a 30 cm × 1.0 cm (24 mL) glass

inlet and outlet of a water treatment plant (WTP) that treats

column. XAD-8 and XAD-4 resins were used for all fraction-

the water from Ancipa reservoir (Sicily, Italy) and water

ation

samples from the Potomac River (Washington, DC) and from

hydrophobic (HPO), transphilic (THP), and hydrophilic

two WTPs (McMillan and Dalecarlia) that utilize its water.

(HPI) fractions, following published methods (Leenheer &

The dissolved organic carbon (DOC) concentration of 1

in order to

separate NOM

into

Croue ; Roccaro & Vagliasindi ). Chlorination experiments were conducted in the dark at

the Ancipa Inlet (inﬂuent of the WTP) sample was 2.9 mg/ 1

procedures,

(SUVA: speciﬁc ultraviolet

20 C at chlorine doses between 1.5 and 2.0 mg of chlorine

absorption). On the other hand, the DOC in Ancipa Outlet

per mg of DOC and varying reaction time (10 min to 3

(efﬂuent of the WTP) sample was 2.0 mg/L, SUVA254 was

days). The water samples were chlorinated with sodium

L, SUVA254 was 2.8 L mg

m . Both Ancipa Inlet and Outlet were also

hypochlorite at pH 7.0 in the presence of 0.03 mol L 1 of

fractionated and the efﬂuents from the XAD-8 (namely

phosphate buffer. In each case the chlorinated samples col-

‘Ancipa Inlet XAD-8 efﬂuent’ and ‘Ancipa Outlet XAD-8

lected were analyzed only if chlorine residual was found.

efﬂuent’) and XAD-4 (namely ‘Ancipa Inlet XAD-4 efﬂuent’

Sodium sulﬁte or ammonium chloride was used to quench

and ‘Ancipa Outlet XAD-4 efﬂuent’) bench scale columns

the residual chlorine. Chlorinated samples were refrigerated

were used for chlorination as well as the unfractionated

at 4 C for no more than 10 days before being analyzed for

samples. The DOC of the Potomac water sample (Potomac

DBP concentrations.

1.8 L mg

Inlet) was 2.7 mg/L and SUVA254 was 2.4 L mg 1 m 1. At the Potomac WTPs, samples of efﬂuents from the sedimen-

Analytical methods

tation tanks (denoted as ‘Potomac MS’ and ‘Potomac DS’ for McMillan and Dalecarlia, respectively) and from ﬁlters

DOC was analyzed using an O.I. Analytical 1010 or a

(denoted as ‘Potomac MF’ and ‘Potomac DF’ for McMillan

Shimadzu VCSH organic carbon analyzer. UV absorbance

and Dalecarlia plants) were taken. DOC values were 1.6,

was analyzed using a 5 cm quartz cell on a Perkin-Elmer

1.7, 2.0, and 2.0 mg/L for MS, MF, DS, and DF samples,

Lamda 18 UV/Vis Spectrophotometer at λ ¼ 200–600 nm.

respectively. SUVA254 values were 1.7, 1.3, 1.8, and 1.5 L

All spectra were normalized to a 1 cm cell length.

P. Roccaro et al.

Relationships between DBP formed in chlorinated waters

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Concentrations of THM, other volatile and semi-volatile

was 57, 65, 45 and 52% at Potomac MS, MF, DS, and DF,

DBP (including HAN) and HAA were determined using

respectively.

standard analytical procedures (EPA methods 551.1 and

Coagulation also resulted in consistent changes of rela-

552.2) and a Perkin-Elmer AutoSystem gas chromatograph

tive contributions of the three different classes of DBP

equipped with an electron capture detector. Other aspects

investigated, namely total HAA (THAA, sum of the nine

of these analyses are described in previous publications

chloro- and bromo-substituted HAAs), total THM (TTHM,

(Korshin et al. ; Roccaro et al. ).

sum of the four chloro- and bromo-substituted THM), and total HAN (THAN, sum of dichloroacetonitrile, bromochloroacetonitrile, dibromoacetonitrile and trichlor-

RESULTS AND DISCUSSION

oacetonitrile)

shown

Figure

Indeed,

the

contribution of THAA decreased after the treatment of PotoImpact of coagulation on the removal of organic

mac water, while TTHM increased (Figure 1). The changes

precursors in the formation THM, HAA, and HAN

in the contributions of THAA and TTHM after the treatment at Ancipa WTP were almost negligible. On the other hand, a

Coagulation followed by clariﬁcation and/or ﬁltration is the

signiﬁcant increase in the contribution of THAN was

standard process used to remove turbidity and NOM from

observed for all the coagulated waters, as shown in Figure 1.

surface waters. However, the removal of NOM by coagu-

These results agree with the data of previous studies

lation is usually limited to about 30–50%, except for water

which showed that the aromatic and high molecular

with a high content of humic material which can result in

weight fraction NOM removed by coagulation contains the

removal higher than 70% (Edzwald ). Coagulation is

main precursors for HAA and some precursors for THM

more efﬁcient for the removal of the most hydrophobic

(Liang & Singer ). However, other research suggested

and high molecular weight fraction of NOM (Chow et al.

that low-MW, non-humic NOM contributes more promi-

, ; Kim & Yu ; Matilainen et al. ). In this

nently in HAA formation (Siddiqui et al. ; Kim & Yu

study, the removal of NOM at the Ancipa WTP was 30%

). The higher relative contribution of THAN in the coa-

as DOC and 56% as A254. The DOC removal for Potomac

gulated waters observed in this study is likely to be

MS and MF was 39 and 35% as DOC, respectively, and

associated with the higher hydrophilic content in coagulated

24% for both Potomac DS and DF. The removal of A254

NOM. This ﬁnding corroborates some recent studies which

Figure 1

Speciation of THAA, TTHM, and THAN in chlorinated raw and treated Ancipa and Potomac waters at reaction time of 24 hours. Chlorine doses from 1.5 to 2.0 mg Cl2 per mg DOC.

P. Roccaro et al.

Relationships between DBP formed in chlorinated waters

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reported moderate removal of nitrogenous DBP (N-DBP)

NOM by coagulation plays a major role in the speciation

precursors by coagulation and higher N-DBP formation

of the investigated DBP.

in chlorinated hydrophilic fractions which are rich in nitrogen (Dotson et al. ; Bond et al. ; Shah &

Contribution of hydrophobic, transphilic, and

Mitch ).

hydrophilic fractions of NOM to the formation of THM,

It is noteworthy that the treated water contains a higher

HAA, and HAN by chlorination

bromide to DOC ratio because of the negligible removal of bromide by coagulation. Therefore, the higher contribution

In order to assess the relative contributions of NOM frac-

of a speciﬁc class of DBP in the treated water (Figure 1)

tions with different polarities in DBP formation and

may be ascribed to the higher bromide to DOC ratio. For

speciation, four rounds of NOM fractionation of Ancipa

instance, Table 1 shows the percentage distribution of

Inlet and Outlet water were carried out. These contributions

THM species for the chlorinated Ancipa and Potomac

were calculated by differences of values obtained by the

water at 24 hours of reaction time. The speciation of

chlorination of unfractionated and fractionated (XAD-8

THM, HAA, and HAN is also affected by the reaction

efﬂuent or XAD-4 efﬂuent) samples. In accord with prior

time, but as reported in prior research carried out on the

studies (Liang & Singer ; Hua & Reckhow ), both

Ancipa water (Roccaro et al. , ), there is a small

hydrophobic and hydrophilic fractions of Ancipa NOM

decrease of the bromine substitution in THM, HAA, and

were found to play an important role in the production of

HAN with the extent of the reaction. Overall, considering

DBP. The contributions of NOM fractions to TTHM,

the moderate level of bromide in Ancipa and Potomac

THAA, and THAN yields in Ancipa Inlet and Outlet

water, the chlorine dose and the resulting change in the spe-

waters varied with the reaction time, as shown in Figures 2

ciation of THM (Table 1), the impact of bromide in total

and 3 for experiments carried out with 1.5 mg of chlorine

THM, HAA and HAN formation is limited according to

per mg of DOC. As expected, higher reaction times were

prior research (Cowman & Singer ; Chang et al. )

associated with higher DBP yields, except for the formation

and the removal of the more aromatic and hydrophobic

of THAN by the chlorination of the THP fraction of Ancipa Inlet water (Figure 2) and for THAN formed by the chlorination of HPO and TPI fractions of Ancipa Outlet water

Table 1

Speciation of THM in chlorinated raw and treated Ancipa and Potomac waters at

(Figure 3). The latter result is probably due to a low

reaction time of 24 hours. Chlorine doses from 1.5 to 2.0 mg Cl2 per mg DOC

reactivity of THP and HPO fractions in HAN formation.

CHClBr2, %

CHBr3, %

The contributions of the HPI fraction to TTHM yields for

Water

CHCl3, %

CHCl2Br, %

Ancipa Inlet

Ancipa Inlet XAD-8 efﬂuent

Ancipa Inlet XAD-4 efﬂuent

Ancipa Outlet

Singer ), generating a large amount of TTHM and of

Ancipa Outlet XAD-8 efﬂuent

THAA, as shown in Figure 2. A negligible contribution of

Ancipa Outlet XAD-4 efﬂuent

Potomac Inlet

Potomac MS

Potomac MF

Potomac DS

Potomac DF

chlorinated Ancipa Inlet waters was somewhat higher than those of the HPO and THP fractions, as shown in Figure 2. In particular, the contribution of the HPI fraction increases with increasing reaction time possibly because the HPO and THP fractions react faster with chlorine (Liang &

the HPO fraction to TTHM yields was observed for chlorinated Ancipa Outlet water (Figure 3). This was apparently caused by a significant removal of the HPO fraction at Ancipa WTP. This point is confirmed by the fractionation data showing that the removal of the HPO, THP, and HPI fractions (quantified by their A254 values) at Ancipa WTP was 63, 48 and 49%, respectively. At reaction times higher than 4 hours, the NOM fraction contribution to TTHM

Figure 2

P. Roccaro et al.

Relationships between DBP formed in chlorinated waters

Journal of Water Supply: Research and Technologyâ&#x20AC;&#x201D;AQUA

Yields of THAA, TTHM, and THAN in chlorinated fractions of Ancipa raw water at varying reaction times. Chlorine dose of 1.5 mg Cl2 per mg DOC.

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Figure 3

P. Roccaro et al.

Relationships between DBP formed in chlorinated waters

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Yields of THAA, TTHM, and THAN in chlorinated fractions of Ancipa treated water at varying reaction times and chlorine dose of 1.5 mg Cl2 per mg DOC.

63.1

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P. Roccaro et al.

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yields was in the order HPI > >THP ¼ HPO for Ancipa Inlet

results in the preferential removal of HAA and some THM

water and HPI > THP > >HPO for Ancipa Outlet water.

precursors (Figure 1), in accord with Liang & Singer

On the other hand, all the NOM fractions contributed

(). In contrast with this result, the hydrophilic HAN pre-

extensively to THAA yields (Figure 2), with especially pro-

cursors escape the coagulation process resulting in higher

minent contributions of both THP and HPI fractions in

relative yields (in μg per mg DOC) of THAN in coagulated

Ancipa Outlet water. These results further support the

waters, as shown in Figure 1. Furthermore, as shown in

point that the HPO fraction plays an important role in

Figure 4, the more hydrophilic NOM fractions of both

TTHM and THAA formation but the HPI fraction also con-

Ancipa Inlet and Outlet waters produced relatively lower

tains important precursors for TTHM (Liang & Singer ;

percentages of THAA and higher percentages of TTHM

Zhao et al. ) and for THAA formation (Kim & Yu ).

and THAN.

Contributions of the examined NOM fractions in THAN yields were different, with the HPI fraction being the domi-

Relationships between trihalomethanes, HAA,

nant part of NOM contributing to THAN (Figures 2 and 3).

and haloacetonitriles

In fact, relative THAN yields increased after coagulation conﬁrming that the HPI fraction contains the major precur-

Very strong correlations were found to exist between

sors for THAN and that coagulation does not remove this

TTHM, THAA, and THAN (Figures 5 and 6). However,

fraction.

while correlations between TTHM and THAA were not sig-

Similarly to the effect of coagulation on the bromide to

niﬁcantly affected by the alterations of NOM properties

DOC ratio, also the fractionation of NOM used in this study

caused by the treatment and/or fractionation, different pat-

increases the bromide to DOC ratio of the resulting sol-

terns were observed in the relationship between TTHM

utions and therefore the speciation of THM, HAA, and

and THAN for raw and treated samples. Two main types

THAN, as shown in Table 1 for THM, but the impact of bro-

of the observed correlations could be distinguished. One of

mide in total THM, HAA, and HAN formation is limited

them was applicable to dataset generated for the raw

according to prior research (Cowman & Singer ;

waters regardless of their fractionation and provenance

Chang et al. ).

while the other corresponded to the combined treated

Overall, the data show that coagulation removes mostly

water data, irrespective of the type of water source and frac-

the more aromatic and hydrophobic fractions of NOM. This

tionation (Figure 6). The negligible impact of fractionation

Figure 4

Speciation of THAA, TTHM, and THAN in chlorinated raw, treated, and fractionated Ancipa water at reaction time of 24 hours and chlorine doses from 1.5 to 2.0 mg Cl2 per mg DOC.

Figure 5

P. Roccaro et al.

Relationships between DBP formed in chlorinated waters

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Correlations between concentrations of THAA and TTHM in Ancipa (raw, treated, and fractionated) and Potomac (raw and treated) waters at varying chlorination conditions (reaction time from 10 min to 3 days and chlorine doses from 0.75 to 2.0 mg Cl2 per mg DOC).

Figure 6

Correlations between concentrations of THAN and TTHM for Ancipa (raw, treated, and fractionated) and Potomac (raw and treated) waters at varying chlorination conditions (reaction time from 10 min to 3 days and chlorine doses from 0.75 to 2.0 mg Cl2 per mg DOC).

on these correlations emphasizes that some of the THAA

It should be recognized that the observed correlations

and TTHM precursors are hydrophilic while almost all

may not be uniformly applicable to all distribution

HAN precursors are hydrophilic and therefore their

systems because some DBP, for instance selected HAA

removal is limited.

species, may undergo decay in them (Tung & Xie ;

P. Roccaro et al.

Relationships between DBP formed in chlorinated waters

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Zhang et al. ), but this issue was not examined in this

paper do not necessarily reﬂect those of the funding

study.

agencies.

REFERENCES

CONCLUSIONS Results obtained in this study indicate that TTHM measurements could be used as a DBP surrogate to estimate the concentrations of the unregulated THAN whose presence in drinking water may prove to be of public health concern. The main precursors for HAA and THM were found to be associated with the hydrophobic

and

transphilic

NOM

fractions

with

the

hydrophilic fraction playing a smaller but important role. In contrast, HAN precursors were determined to be strongly associated with the hydrophilic NOM fraction, with negligible contribution of the HPO and THP fractions. The data show that because conventional full scale coagulation processes tend to preferentially remove THM and HAA precursors but not those of HAN, this results in higher relative contributions of HAN in the sum of THM, HAA, and HAN species formed in treated water. The toxicity of nitrogenous DBP, including HANs, and the relative persistence of their precursors in conventionally treated water may require that alternative advanced water treatment processes and/or operating conditions be considered to control the levels of these N-DBP in drinking water.

ACKNOWLEDGMENTS This study was partially supported by the United States EPA/Cadmus (grant 069-UW-1) and the Italian Ministry of Instruction, University, and Research (MIUR), through the program Research Projects of National Interest ‘Control and monitoring of drinking water quality’ and ‘Reuse of Wastewater in Agriculture: Emerging Pollutants and Operational Problems’. Paolo Roccaro acknowledges the US-Italy Fulbright Commission for supporting his research in the US through the ‘Fulbright Scholar Program Advanced Research and University Lecturing Awards in the United States’. Views expressed in this

Bond, T., Huang, J., Templeton, M. R. & Graham, N.  Occurrence and control of nitrogenous disinfection byproducts in drinking water a review. Water Res. 45 (15), 4341–4354. Chang, E. E., Lin, Y. P. & Chiang, P. C.  Effects of bromide on the formation of THMs and HAAs. Chemosphere 43 (8), 1029–1034. Chow, C. W. K., Fabris, R. & Drikas, M. A.  A rapid fractionation technique to characterize natural organic matter for the optimization of water treatment processes. J. Water Supply Res. Technol.-AQUA 53 (2), 85–92. Chow, C. W. K., Fabris, R., Drikas, M. & Holmes, M.  A case study of treatment performance and organic character. J. Water Supply Res. Technol.-AQUA 54 (6), 385–395. Cowman, G. A. & Singer, P. C.  Effect of bromide ion on haloacetic acid speciation resulting from chlorination and chloramination of aquatic humic substances. Environ. Sci. Technol. 30 (1), 16–24. Dotson, A., Westerhoff, P. & Krasner, S. W.  Nitrogen enriched dissolved organic matter (DOM) isolates and their affinity to form emerging disinfection by-products. Water Sci. Technol. 60 (1), 135–143. Edzwald, J. K.  Coagulation in drinking water treatment: particles, organics and coagulants. Water Sci. Technol. 27 (11), 21–25. Hua, G. & Reckhow, D. A.  Characterization of disinfection byproduct precursors based on hydrophobicity and molecular size. Environ. Sci. Technol. 41 (9), 3309–3315. Kim, H.-C. & Yu, M.-J.  Characterization of natural organic matter in conventional water treatment processes for selection of treatment processes focused on DBP control. Water Res. 39 (19), 4779–4789. Korshin, G. V., Wu, W. W., Benjamin, M. M. & Hemingway, O.  Correlations between differential absorbance and the formation of individual DBP species. Water Res. 36 (13), 3273–3282. Krasner, S. W., Westerhoff, P., Chen, B. Y., Rittmann, B. E., Nam, S. N. & Amy, G.  Impact of wastewater treatment processes on organic carbon, organic nitrogen, and DBP precursors in effluent organic matter. Environ. Sci. Technol. 43 (8), 2911–2918. Leenheer, J. A. & Croue, J.-P.  Characterizing dissolved aquatic organic matter, comprehensive approach to preparative isolation and fractionation of dissolved organic carbon from natural waters and waste waters. Environ. Sci. Technol. 37 (1), 18A–26A. Liang, L. & Singer, P. C.  Factors influencing the formation and relative distribution of haloacetic acids and

P. Roccaro et al.

Relationships between DBP formed in chlorinated waters

trihalomethanes in drinking water. Environ. Sci. Technol. 37 (13), 2920–2928. Malliarou, E., Collins, C., Graham, N. & Nieuwenhuijsen, M. J.  Haloacetic acids in drinking water in the United Kingdom. Water Res. 39 (12), 2722–2730. Matilainen, A., Lindqvist, N. & Tuhkanen, T.  Comparison of the efﬁciency of aluminium and ferric sulphate in the removal of natural organic matter during drinking water treatment process. Environ. Technol. 26 (8), 867–875. Muellner, M. G., Wagner, E. D., Mccalla, K., Richardson, S. D., Woo, Y.-T. & Plewa, M. J.  Haloacetonitriles vs. regulated haloacetic acids: are nitrogen-containing DBP more toxic? Environ. Sci. Technol. 41 (2), 645–651. Obolensky, A. & Singer, P. C.  Development and interpretation of disinfection byproduct formation models using the information collection rule database. Environ. Sci. Technol. 42 (15), 5654–5660. Plewa, M. J., Wagner, E. D., Muellnerm, M. G., Hsu, K. & Richardson, S. D.  Comparative mammalian cell toxicity of N-DBP and C-DBP. ACS Symposium Series 995. In: Occurrence, Formation, Health Effects and Control of Disinfection By-products in Drinking Water (T. Karanﬁl, S. W. Krasner, P. Westerhoff & Y. Xie, eds). American Chemical Society, Washington, DC, pp. 36–50. Richardson, S. D., Plewa, M. J., Wagner, E. D., Schoeny, R. & DeMarini, D. M.  Occurrence, genotoxicity, and carcinogenicity of regulated and emerging disinfection by-products in drinking water: a review and roadmap for research. Mutat. Res. 636 (1–3), 178–242. Roccaro, P. & Vagliasindi, F. G. A.  Differential versus absolute UV absorbance approaches in studying NOM reactivity in DBP formation: comparison and applicability. Water Res. 43 (3), 744–750. Roccaro, P., Mancini, G. & Vagliasindi, F. G. A.  Water intended for human consumptions – Part I: compliance with European water quality standards. Desalination 176 (1–3), 1–11. Roccaro, P., Chang, H.-S., Vagliasindi, F. G. A. & Korshin, G. V.  Differential absorbance study of effects of temperature

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

on chlorine consumption and formation of disinfection by-products in chlorinated water. Water Res. 42 (8–9), 1879–1888. Roccaro, P., Vagliasindi, F. G. A. & Korshin, G. V.  Changes in NOM ﬂuorescence caused by chlorination and their associations with disinfection by-products formation. Environ Sci. Technol. 43 (3), 724–729. Roccaro, P., Vagliasindi, F. G. A. & Korshin, G. V.  Quantifying the formation of nitrogen-containing disinfection by-products in chlorinated water using absorbance and ﬂuorescence indexes. Water Sci. Technol. 63 (1), 40–44. Sedlak, D. L. & von Gunten, U.  The chlorine dilemma. Science 331 (6013), 42–43. Serodes, J.-B., Rodriguez, M. J., Li, H. & Bouchard, C.  Occurrence of THMs and HAAs in experimental chlorinated waters of the Quebec City area (Canada). Chemosphere 51 (4), 253–263. Shah, A. D. & Mitch, W. A.  Halonitroalkanes, halonitriles, haloamides, and N-nitrosamines: a critical review of nitrogenous disinfection byproduct formation pathways. Environ. Sci. Technol. 46 (1), 119–131. Siddiqui, M., Amy, G., Ryan, J. & Odem, W.  Membranes for the control of natural organic matter from surface waters. Water Res. 34 (13), 3355–3370. Tung, H. H. & Xie, Y. F.  Association between haloacetic acid degradation and heterotrophic bacteria in water distribution systems. Water Res. 43 (4), 971–978. Zhang, P., LaPara, T. M., Goslan, E. H., Xie, Y. F., Parsons, S. A. & Hozalski, R. M.  Biodegradation of haloacetic acids by bacterial isolates and enrichment cultures from drinking water systems. Environ. Sci. Technol. 43 (9), 3169–3175. Zhao, Z.-Y., Gu, J.-D., Li, H.-B., Li, X.-Y. & Leung, K. M.-Y.  Disinfection characteristics of the dissolved organic fractions at several stages of a conventional drinking water treatment plant in Southern China. J. Hazard. Mater. 172 (2–3), 1093–1099.

First received 26 February 2013; accepted in revised form 27 August 2013. Available online 30 September 2013

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A long-term prediction of domestic water demand using preprocessing in artiﬁcial neural network Sadegh Behboudian, Massoud Tabesh, Maliheh Falahnezhad and Farrokh Alavian Ghavanini

ABSTRACT Both planning and the design of water supply systems require accurate and reliable prediction of water demand. In this study, artiﬁcial neural network (ANN) was used to predict the long-term water demand to determine the relationship between dependent and independent parameters. Using the stationary chain to solve the interpolating characteristic of ANNs, the study presents a reliable approach for long-term forecasting of water demand. The purpose of this study is to provide a convenient and reliable method for long-term forecasting of urban water demand while reducing the prediction uncertainty. In order to evaluate the accuracy of the prediction, multilayer perceptron (MLP) outputs were compared with results from the linear regression model. Findings indicate that MLP is an appropriate solution for monthly long-term water demand forecasting. Furthermore, it can reduce uncertainties and signiﬁcantly increase the accuracy of the long-term forecasting. Key words

| artiﬁcial neural network, long-term forecasting, preprocessing, water demand

Sadegh Behboudian (corresponding author) Farrokh Alavian Ghavanini College of Engineering, School of Civil Engineering, University of Tehran, PO Box 11155-4563, Tehran, Iran E-mail: s.behboudian@gmail.com Massoud Tabesh Center of Excellence for Engineering and Management Civil Infrastructures, College of Engineering, School of Civil Engineering, University of Tehran, PO Box 11155-4563, Tehran, Iran Maliheh Falahnezhad Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Ghods Street, Tehran, Iran

INTRODUCTION Prediction is a signiﬁcant topic in the water industry. One

water demand and understanding its inﬂuencing factors

of the main applications of forecasting is the estimation of

are among the important steps in water crisis control and

municipal water demand. Yet, achieving the anticipated pre-

management. The effective solution not only lies in supply-

diction accuracy with regard to the forecasting trends is

ing water, but also in adopting necessary policies and

quite challenging.

schemes based on consumption patterns, and considering

Drought, population growth, and increases in per capita

factors on the demand side. For this purpose, different

consumption will give rise to a nearly global water crisis. On

econometric techniques and variables are assessed and ana-

the other hand, water is a non-renewable commodity and

lyzed. Several different data sets have been utilized, ranging

makes the problem more complicated. In this situation,

from individual household data to aggregate data, for

efforts should be made to optimize water consumption

example, cross-sectional data (Chicione & Ramammurthy

and prevent possible conﬂicts and quarrels to dominate

; Chen & Yang ), time-series data (Billings ;

water resources in the future. Furthermore, uneven distri-

Babel et al. ), and the most common cross-sectional

bution, either in time or resources, high evaporation rate,

time series data (Renwick & Archibald ; Mylopoulos

contamination of water resources, shortage of accurate

et al. ). The widely used variables in these studies

information, price discordance, and social problems are

include household income, size of household, density of

yet other reasons that necessitate an accurate plan in

households, gross domestic product, average or marginal

order to optimize the efﬁciency of water resources’ usage.

price, temperature, and precipitation (Kostas & Chrysosto-

In this way, an estimation of future water demand can

mos ; Mazzini & Montini ; Chen & Yang ).

help decision-makers to take necessary measures according

Most demand models are regression-based. They typically

to the possible crises and limitations. Forecasting domestic

use the form of Q ¼ f(P, Z), where P is the price variable

doi: 10.2166/aqua.2013.085

S. Behboudian et al.

Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

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2014

and Z is a factor such as income, household characteristics,

predictions. To obtain the relationship between dependent

and weather (Arbues et al. ; Babel et al. ; Schleich

and independent parameters (estimating demand function)

& Hillenbrand ). In recent years, several studies have

and to forecast domestic water demand, an artiﬁcial

been made with focus on the use of artiﬁcial neural net-

neural network was used in such a way that one can forecast

works (ANNs) for short-term (hourly or daily) or medium-

long-term water demand.

term prediction (Topalli et al. ; Azadeh et al. ; Msiza et al. ; Firat et al. ). Since neural networks are trained based on a range of primary data (inputs and out-

METHODS

puts), they fail to properly predict the data outside the mentioned range (interpolation problem).

Although ANN has been mainly used for short-term forecast-

In most previous studies that have been made on neural

ing, it can be shown that it is an accurate method for long-

network forecasting purposes, a dependent variable (water

term forecasting of monthly water consumption. In this

consumption) is taken into account as an independent vari-

regard, two points should be considered: ﬁrst, increasing

able (considering the time intervals). Using this procedure,

time scope in forecasting (long-term forecasting) and

the consumption value (predicted in previous stages) is

second, increasing reliability and accuracy of forecasting.

used for determining the amount of future consumption.

In this study, a regression model and the ANN model

Therefore, the error is increased at each stage and conse-

were used for modeling water demand. Predicted results

quently these models are unable to predict data more than

with both models were compared with each other. For this

a few time steps. In multivariate models, in which water con-

purpose, after estimating demand function, variables affecting

sumption is presented as a time delay, delaying parameters

per capita water demand were predicted for the desired fore-

have the greatest impact on prediction of water consumption

cast range and were given to the demand function to obtain

due to the high correlation between dependent variable and

the predicted values for per capita water demand.

intervals of the same variable which are used as an independent variable in the model. Therefore, effects of other

Regression model

variables in predicting future consumption of water cannot be assessed.

There are several variables affecting water demand, such as

Problems in long-term forecasting of water consumption

climatic, socioeconomic, and cultural variables which

using neural networks in previous studies can be summar-

should be incorporated in modeling water demand. The

ized as follows:

domestic water demand function can be estimated by differ-

• •

Interpolating nature of neural networks.

ent models (linear and nonlinear). In this approach, for

Increasing error in forecasting results due to using delay-

selecting the best model, the following criteria should be

ing values of dependent variable as independent variables.

considered:

Neglecting the effects of other variables affecting water

•

The model should be consistent with reality and in

•

The model should be used for policy-making and control-

•

consumption due to the high correlation between future water demand and consumption values in previous intervals. This study mainly intends to provide a convenient and

reliable method for predicting long-term water demand of urban areas while increasing prediction range and decreasing uncertainty of results. This paper demonstrates that the ANN approach is a good course of action for predicting

• • • • •

accordance with theoretical expectations. ling purposes. The model should be used for prediction and providence. Statistical data should be collected correctly. All assumptions are based on classic theories. The estimation method should be correct. Model coefﬁcients should be statistically signiﬁcant.

long-term monthly water demand. It also tries to solve the

The model should meet the theoretical expectations; R 2

interpolation problem of neural networks in long-term

should be high, R 2 and adjusted-R 2 should be close to each

S. Behboudian et al.

Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

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2014

other. The F-statistic and the Durbin–Watson statistic verify-

and consumer services, E is the total literate individuals,

ing the entire regression must be signiﬁcant.

and MT denotes the average maximum temperature.

Evaluation indices Neural network A model evaluation method is to assess its estimation perThe feed forward back propagated MLP (multilayer percep-

formance. To this effect, various criteria to assess and

tron) network was selected as the default network type for

evaluate the performance of a model in terms of data esti-

most MLPs. It has multiple neuron layers with nonlinear

mation are available. Among these, conventional criteria

transfer functions that allow the network to learn nonlinear

like correlation coefﬁcient, the square root mean square

and linear relationships between input and output vectors.

error (RMSE), mean absolute error (MAE), and mean absol-

Learning or training in MLPs is supervised by comparing

ute percentage error (MAPE) can be mentioned:

the desired output to a particular input. Learning involves vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP u n u (X Xo )2 tm¼1 p

presenting the input vectors, one at a time, at the input layer and passing them through the hidden and output layers, where the ﬁnal network output is generated. At this

RMSE ¼

(2)

point, the network output is compared with the desired output and the difference, i.e., the error, is then calculated. If the absolute error is larger than an acceptable threshold, the error is back propagated through the network; this pro-

MAPE ¼

n 1X (Xp Xo ) n t¼1 Xo

(3)

cess adjusts weights between input and hidden layers and those between hidden and output layers using an appropriate learning method to minimize the error in repeated processing of inputs by the network. The error threshold, or acceptable limit for error, deﬁnes the accuracy of the model and depends on practical considerations. In this paper, to show the effect of variables affecting the water demand, the water demand function is estimated using

n P Xp Xo

MAE ¼ m¼1

(4)

in which μ is data mean, σ is the standard deviation of data, and n is the number of total data; p and o indices represent simulated and observed data, respectively.

the neural network. To obtain the relationship between dependent and independent variables and in order to predict

Preprocessing of data

the water demand, the feed forward back propagated MLP was selected as the network type (Rumelhart et al. ).

One of the most signiﬁcant issues in long-term prediction

Therefore, delaying variables which decrease the accu-

with ANNs is their interpolating characteristic. To unravel

racy of forecasting were excluded from the estimation

this problem, data should be processed. After training the

model, because they increase error in long-term forecasting.

neural network with input and output data for the period

In this model, the same variables as those of the

1997–2008, it is necessary to predict input values (independent variables) for the upcoming 20 years (predicted values

regression model were used:

are presented in Table 1) and simulate the neural network Perc ¼ f(I, MP, PO, E, MT )

(1)

model with them to obtain future water demand values. Bearing in mind that the independent variables change

where Perc is per capita potable water in cubic meter, I is the

signiﬁcantly over time (with respect to observed data) and

real per capita income of consumers in Rials (30,000 Rials ¼

the model is trained in the range of observed data, the

1 USD), MP is the real mean price for potable water, CPI

model would be unable to interpret these values and then pre-

(consumer price index) is the price index for commodities

dict outputs corresponding to these independent variables.

S. Behboudian et al.

Table 1

Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

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2014

method which makes variables more static and also can

Summary of statistics

Min

Max

Mean

Standard deviation

4.89

3.82

0.50

change the scale of the data. It also eliminates collinearity

Number

Demand per capita

142

Real price

142

1.62

4.80

0.69

2.74

Rials

prediction is over, it is impossible to convert output

Real income per house

142

6.044

12,808

9.247

1,827

Rials

values from the neural network to the same unit as the

Price index

142

195.80

91.23

43.20

–

2.84

Unit

between variables. These removed terms must be added to

Variable

the forecasted water demand. Differencing is also another method for making time series static. However, once the

water demand, so this method was not used (in this case 35.60

the output values are from differencing ones). This preprocessing must be done before the training step in order to

To address this problem, input and output data from the training block are taken to the same scale as input

prevent saturating the neurons and also to increase the accuracy of the network.

data from the prediction period (2011–2031). In doing so, the concept of stationary chain in time series was used (Greene ). Time series are stationarized when par-

RESULTS

ameters of mean, variance, and correlation coefﬁcients remain constant over time. The time series is made of

Study area

trend, seasonal, and irregular components. The most signiﬁcant step in statistical modeling is the irregular part of

Neyshabour with a population of 205,972 is located in the

time series. Predictions based on the irregular component

center of Khorasan Razavi Province in northeastern Iran.

increase accuracy of the modeling and also produce more

The annual average temperature in Neyshabour is 14.2 C.

uniform data scale. Therefore, removing certain terms

Average annual precipitation is about 233.47 mm. A large pro-

from time series and making the data more stationary are

portion of water demand in the Neyshabour plain is supplied

essential for preparing the data series for modeling with

from wells and ground water resources. A nearly 90-cm annual

ANNs. In order to achieve a stationary condition in time

loss in the ground water level has been observed, which leads

series, ﬁrst, the trends existing in data should be removed.

to increasing the water crisis in Neyshabour. The position of

Second, taking logarithm of the variables is another

Neyshabour City is shown in Figure 1.

Figure 1

Location of Khorasan Province and Neyshabour.

S. Behboudian et al.

Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

Data

Model assumptions are:

The utility bill data set used for the estimation was drawn from

θ0 > 0, 0 < θ1 < 1, θ2 < 0, θ3 > 0, θ4 > 0

63.1

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the water supply company in Neyshabour during 12 years from 1997 to 2008 and contained 144 monthly observations. Infor-

Results of the demand function are shown in Table 2.

mation about residential water consumption and its respective

As the t-statistic indicates, all coefﬁcients are signiﬁcant at

price were obtained from the operators of water utilities. Real

the conﬁdence level of 95% and estimated coefﬁcients are per-

water price is the price determined by water utilities, while

fectly consistent with theory. The Durbin–Watson statistic

actual water price is converted from the real water price when

shows that there is no correlation between the error terms.

the price inﬂation is considered. The average price of water

R 2 and F-statistic values are within the desirable level and

was calculated by dividing the total revenue from the Water

the small difference between adjusted-R2 and R 2 values

and Wastewater Company in Neyshabour by the amount of

indicates the absence of additional variables in the model.

water consumption in the same month. Monthly consumer

Initial estimates of the demand function were subjected

price index, household expenditure, and the number of literate

to auto-correlation of disrupting component. AR (1) (autore-

individuals were obtained from the Central Bank of Iran and

gressive (1)) and MA (1) (moving average (1)) were used to

household expenditure is a proxy for household income. The

address this issue. The Dickey–Fuller unit-root test was per-

12-year (1997–2008) monthly temperature data were obtained

formed on all variables and, considering the fact that the

from a meteorological station inside Neyshabour. A summary

degree of all co-integrated variables is zero, there was no

of statistics for all variables is provided in Table 1.

spurious regression. In order to forecast water demand by the estimated model, independent variables should be predicted. Then,

Regression model

using these predicted values and the estimated model,

Since the Stone–Geary utility function – used for wellknown essential goods – is the most adaptable utility func-

future values for water demand can be predicted. According to Table 3, and under HADCM3 (A1B) scenario, the

tion in this regard and has a high compatibility with facts and hypotheses of this research, it was used for extracting the water demand function.

Table 2

Results of demand function

Based on the Stone–Geary utility function, the long-term

Coefﬁcients

Standard error

t-statistic

p-value

water demand function was estimated for 1997 to 2008

Constant

0.866

0.7444

1.16

0.246

using time series data and economic evaluation of the ordin-

PERI

0.000222

0.00003

6.02

0.0217

ary least square method.

PINDEX

0.00898

0.0038

2.32

0.0051

0.00001

0.00

2.84

0.0030

0.00931

0.00307

3.02

0.00

0.917

Durbin–Watson

1.7

0.913

F-statistic

248

The ﬁnal model, considering maximum mean temperature and total literates, was estimated as follows: perct ¼ θ0 þ θ1

It MPt

þ θ2

POt MPt

Adjusted R

þ θ3 Et þ θ4 MTt þ Ut

t ¼ 1, . . . , 120

(5)

It POt By substituting PERIt ¼ Pindex ¼ in MPt MPt Equation (5), for the following equation we have: perct ¼ θ0 þ θ1 Perit þ θ2 Pindext þ θ3 Et þ θ4 MTt þ Ut t ¼ 1, . . . , 120

(6)

Table 3

Forecast assumption variables

Number of literate

Maximum mean

Inﬂation

Income (economic

Water

individuals

temperature

index

growth)

price

Pars Consult Co.

A1B

15%

12%

S. Behboudian et al.

Long-term prediction of domestic water demand

maximum temperature values of the LARS-WG downscal-

Journal of Water Supply: Research and Technology—AQUA

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2014

Results obtained from data preprocessing

ing model are predicted for the next 20 years. To this effect, the predicted values of population and total literates

The feed forward back propagated MLP is the default net-

were used. The predicted values for per capita domestic

work type for most MLPs. It has multiple layers of

income, CPI, and the average water price for 2011 to 2031

neurons with nonlinear transfer functions that allow the net-

were 12, 5 and 15%, respectively.

work to learn nonlinear and linear relationships between input and output vectors. For training the neural network,

Model accuracy

several training algorithms were used with different neurons (4–10 for each layer) and different number of layers (1–3

In order to assess the accuracy of the developed model, a

layers), and the Levenberg–Marquardt algorithm was

comparison between observed and simulated data for

selected as it produced the best result for the available

the observation period (1997–2008) was conducted.

data. The MSE performance function was selected since it

Results depict that the accuracy of the estimated model

is the default performance function for most networks.

is noticeable. Figure 2 depicts the comparison between

Three layers were implemented for the network by trial

actual water demand data and the calculated water

and error with respect to the best result (Exhaustive

demand by the estimated model. Additionally, two stat-

Search). The ﬁrst layer contained nine neurons while the

istics for examining the forecasting accuracy, namely

second one had ten neurons both with TANSIG transfer

RMSE and MSE, were reported. The statistics suggest

function. The third one had one neuron (since there was

(the RMSE is 0.22 and the MSE is 0.13) that the model

only one input) with the PURELIN transfer function.

is fairly suitable for forecasting water demand in the

The network weights were initialized with values equal to inputs. Input data were separated into three blocks:

future. Obviously, for predicting per capita water demand in the

70% were used for training, 15% for validation, and 15%

future, using a neural network and a structural model, a

for testing. If the validation error was increased six times

series of dependent and independent variables were used.

sequentially, the training would be stopped. Training was

Figure 2

Observed and calculated data.

S. Behboudian et al.

Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

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restarted using network weights which had been obtained

According to the diagram, the model could forecast

from the previous run until we would reach acceptable

water demand rather well. Moreover, to assess the accuracy

results on the testing block.

of the model, MAPE, RMSE, and MSE tests and the value of correlation coefﬁcient were used. The values of these statistics are illustrated in Table 4.

First case

These statistics indicate the good accuracy of the modelIn this case, the trend component was removed from all

ing. Therefore, the predicted values of independent variables

data in observation and prediction stages. The trend com-

were put in the simulated neural network as input data to

ponent in the observation data for water demand was:

obtain per capita water demand for the future. Once the output values were predicted using the neural

Y ¼ 0:00703t þ 3:32

(7)

network, the data should be denormalized, and the previously removed trend of water demand values should be

where t is time and Y is per capita demand. Removing the

added to the values predicted by the neural network

trend component improved the Dickey–Fuller test results.

model. Comparing ANN predicted results with predicted

According to the selection criteria, the best neural net-

values for per capita water demand obtained from the

work model in this section was found. The gradient

Stone–Geary function are shown in Figure 4. Based on pre-

descent weight/bias learning and the resilient back propa-

processing which was implemented on the input data, the

gation functions were used in the selected neural network

model could follow the general trend in water demand up

with two middle layers of nine neurons each. According to

to the 170th month. It could also simulate the seasonal

Figure 3, in order to evaluate the model, output data from

changes. As, after the 170th month, predicted input data

the test block were compared precisely with actual values.

were not in the range of data with which the network was

Figure 3

The comparison between observed and predicted per capita water demand for the test period (22 months).

Table 4

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Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

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more static and increases the modeling accuracy (Greene

The values for statistical parameters of variables

RMSE

MSE

MAPE

0.044

0.038

0.91

). After removal of the trend from the time series, they were normalized (standardized) for modeling. The Dickey–Fuller test results showed that taking logarithm improves test values.

trained, it fails in properly modeling the changes; therefore,

The best neural network model in this section was found

the model error increased with increasing values of indepen-

based on the selection criteria. The gradient descent with

dent variables which were increased with time as well. The

momentum weight/bias learning and one step secant back

difference between the water demand values predicted by

propagation functions were used in the selected neural net-

each model in the ﬁrst 170 months was caused by errors

work with the structure of two middle layers with seven

which were generated by modeling.

neurons in the ﬁrst layer and eight neurons in the second layer. Figure 5 shows the accuracy comparison of the

Second case

model in the test area with actual values.

In order that the scale of input data values in prediction

of neurons in the ﬁrst and second layers. In Figure 7, the 3D

phase to be closer to data used in the training phase, logar-

surface of the correlation coefﬁcient value and the number

ithm was taken from data, and the existing trend in

of neurons in the ﬁrst and second layers clearly show that

variables was removed. Taking logarithm makes the data

the neural network in the third case (with conﬁguration of

series smoother. Likewise, it helps a time series to be

5:7:8:1) has a greater value of R and a lower error.

Figure 6 shows the error surface in 3D and the number

Figure 4

The comparison between predicted per capita water demand by Stone–Geary model and ANN for the period 2011–2031.

S. Behboudian et al.

Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

Figure 5

The comparison between observed and predicted per capita water demand for the test period (22 months).

Figure 6

The error value in 3D graph according to total of neurons on ﬁrst and second layer.

63.1

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Furthermore, to assess the accuracy of the model,

These statistical values indicate that the accuracy of

MAPE, RMSE, and MSE tests were used along with the cor-

modeling is acceptable. Therefore, this simulated ANN

relation coefﬁcient value. These statistical values are shown

can be used for predicting water demand. After obtaining

in Table 5.

the output values by the neural network, the data must be

S. Behboudian et al.

Figure 7

Table 5

Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

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The correlation value in 3D graph according to total of neurons on ﬁrst and second layer.

Therefore, the neural network with the 5:7:8:1 structure,

The values for statistical parameters of variables in the ANN model

logarithmic preprocessing and trend removal was recogRMSE

MSE

MAPE

nized as the best network. According to the predicted

0.037

0.03

0.035

0.92

values of independent variables and Figure 8, the per capita consumption for the year 2011 was estimated as 35.53 cubic meters, and for the year 2031 with a 6.48%

denormalized and the removed values of water demand

increase in per capita water demand, the per capita con-

trend should be added to the predicted values calculated

sumption was estimated as 31.79.

by the neural network. Logarithm taking should also be reversed. The comparison of ANN-predicted results with the predicted per capita water demand values derived from the

CONCLUSION

Stone–Geary function is shown in Figure 8. Based on preprocessing which was done on input data, the model

Knowing the amount of water demand is one of the impor-

could nicely predict the overall trend of the future water

tant and effective factors in water resources management.

demand according to the existing trend in descriptive vari-

To date, many reviews have been carried out in the ﬁeld of

ables. Results showed that taking logarithm increases the

water demand forecast but most of these studies have focused

accuracy of prediction. According to Figure 8, in monthly

on the short-term forecast and have used self-correlation

water demand values predicted by the MLP, the effects of

models in forecasting. In this paper, to obtain the relationship

seasonal changes are visible which are created by the

between dependent and independent parameters (estimating

effect of descriptive seasonal changes, and the model also

the demand function) and long-term prediction of urban

succeeded in dealing with these changes. Results indicate

water demand, MLP was used to predict the long-term

that modeling a time series with an MLP using uncertain

water demand. For this purpose, to predict the water

components would produce better results. This is thanks

demand, effective variables for the period 1997–2008 were

to the better scaling of data to allow the ANN to address a

collected from the monthly data of Neyshabour City.

wider range. In the second case, taking logarithms improved

Maximum temperature values were predicted from

the results of the model in which the trend was removed

downscaling

from the values.

HADCM3 (A1B) scenario for the next 20 years. For this

the

model

LARS-WG

and

under

Figure 8

S. Behboudian et al.

Long-term prediction of domestic water demand

Journal of Water Supply: Research and Technology—AQUA

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2014

Comparison between predicted per capita water demand by Stone–Geary model and ANN for the period 2011–2031.

purpose, predicted population values and the number of lit-

accurate the neural network model can follow trends and

erate individuals calculated by Pars Consult Co. were used.

ﬂuctuations.

The predicted values for per capita domestic income, CPI, and the average water price for 2011–2031 were 12, 5, and 15%, respectively. For the long-term prediction using a

REFERENCES

neural network, a preprocessing should be applied to the data to address the interpolation issue of models. For this purpose, the uncertain component of time series was used in the forecasting process. In the ﬁrst case, the data trend was omitted, and in the second case, ﬁrst, logarithm was taken from data and then the trend was removed. With regard to the processed data for both cases, different structures

neural

network

were

examined

and,

considering the performance index, the best model was selected in each case, and output parameters were obtained through the selected model; all preprocessing was also reversed in output parameters. To scrutinize the results of modeling with the neural network, the MLP-applied forecasting results for the next 20 years were compared with the results obtained from the regression model, and it was found that the more stationary the data are, the more

Arbues, F., Garcia-Valinas, M. & Martinez-Espineira, R.  Estimation of residential water demand: a state of the art review. J. Socio-Econ. 32, 81–102. Azadeh, A., Ghaderi, S. F. & Sohrabkhani, S.  Forecasting electrical consumption by integration of neural network, time series and ANOVA. Appl. Math. Comput. 186, 1753–1761. Babel, M. S., Das Gupta, A. & Pradhan, P.  A multivariate econometric approach for domestic water demand modeling: an application to Kathmandu, Nepal. Water Resour. Manage. 21, 573–589. Billings, R. B.  Speciﬁcation of block rate price variables in demand models. Land Econ. 58 (3), 386–394. Chen, H. & Yang, Z. F.  Residential water demand model under block rate pricing: a case study of Beijing, China. Commun. Nonlinear Sci. 14, 2462–2468. Chicione, D. L. & Ramammurthy, G.  Evidence on the speciﬁcation of price in the study of domestic water demand. Land Econ. 62 (1), 26–32.

S. Behboudian et al.

Long-term prediction of domestic water demand

Firat, M., ErkanTuran, M. & Yurdusev, M. A.  Comparative analysis of neural network techniques for predicting water consumption time series. J. Hydrol. 384, 46–51. Greene, W. H.  Econometric Analysis, 6th edn. Prentice-Hall, Upper Saddle River, NJ. Kostas, B. & Chrysostomos, S.  Estimating urban residential water demand determinants and forecasting water demand for Athens metropolitan area, 2000–2010. South-Eastern Eur. J. Econ. 1, 47–59. Mazzini, M. & Montini, A.  The determination of residential water demand: empirical evidence for a panel of Italian municipalities. Appl. Econ. Lett. 13, 107–111. Msiza, I. S., Nelwamondo, F. V. & Marwala, T.  Water demand forecasting using multi-layer perceptron and radial basis functions. In: Proceedings of the IEEE International Conference on Neural Networks, Orlando, FL, 12–17 August, pp. 13–18.

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

Mylopoulos, N., Vagiona, D. & Fafoutis, C.  Urban water pricing in sustainable water resources management: a socioeconomic study. World Acad. Sci. Eng. Technol. 54 (1), 547–553. Renwick, M. E. & Archibald, S. O.  Demand side management policies for residential water use: who bears the conservation burden. Land Econ. 74 (3), 343–360. Rumelhart, D. E., Hinton, G. E. & Williams, R. J.  Learning internal representations by error propagation. In: Parallel Data Processing (D. Rumelhart & J. McClelland, eds). Vol. 1, Chapter 8, MIT Press, Cambridge, MA, pp. 318–362. Schleich, J. & Hillenbrand, T.  Determinants of residential water demand in Germany. Ecol. Econ. 68 (6), 1756–1769. Topalli, A. K., Erkmen, I. & Topalli, I.  Intelligent short-term load forecasting in Turkey. Int. J. Electr. Power 28 (7), 437–447.

First received 23 April 2013; accepted in revised form 30 August 2013. Available online 17 October 2013

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Inﬂuence of porous media on intrusion rate into water distribution pipes Yan Yang, Tuqiao Zhang and David Z. Zhu

ABSTRACT Untreated water can intrude into water distribution systems through pipe leaks and cracks when negative pressure events occur inside drinking water pipelines. The orifice equation is typically used for calculation of intrusion flow rate, which ignores the impact of the soil surrounding the pipeline. This paper presents the results of an experimental study on the effect of porous media surrounding pipelines and the flow Reynolds number on intrusion flow rate for a circular orifice. The porous media, orifice size, and flow regime affect the discharge coefficient of the orifice equation. A new expression was suggested for predicting the intrusion flow rate. A discontinuity in the discharge coefficient was also found at a large Reynolds number. The effect of the pipe curvature on the discharge coefficient was found to be insignificant. Key words

| contaminant intrusion, discharge coefﬁcient, oriﬁce equation, porous media, water distribution system

Yan Yang College of Civil Engineering and Architecture, Zhejiang University, A810 Anzhong Building, Hangzhou 310058, China Tuqiao Zhang College of Civil Engineering and Architecture, Zhejiang University, A511 Anzhong Building, Hangzhou, 310058, China David Z. Zhu (corresponding author) College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China and Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada T6G 2W2 E-mail: david.zhu@ualberta.ca

NOMENCLATURE constants in Equation (2)

cause surrounding groundwater being intruded into the dis-

cross-section area of oriﬁce

tribution systems through various types of pipe cracks and

ﬁtted parameter in Equation (3)

defects (Karim et al. ). Water pipes are usually located

Cd discharge coefﬁcient

below the ground with a certain thickness of soil cover

oriﬁce diameter

above the pipeline. Contaminated soil and groundwater sur-

gravitational acceleration

rounding the pipes may contain mixtures of microbial or

ΔH difference between the external and internal pressure head

chemical contaminants (Karim et al. ; He et al. ;

hydraulic gradient in porous media

Besner et al. ; Payne et al. ). Even if leaking pipes

non-Darcy exponential

are located above the ground water table, the leak will pro-

intrusion ﬂow rate

vide a constant feed of water into the surrounding soil, while

Re Reynolds number

the contaminated water can intrude into the pipeline during

macroscopic velocity in porous media

negative pressure events. Intrusion of contaminants from

average ﬂow velocity at the oriﬁce

the soil-groundwater environment surrounding the pipe

kinematic viscosity of water

into water distribution system has been widely recognized as a signiﬁcant health threat (Karim et al. ; Besner et al. ).

INTRODUCTION

Intrusion is usually seen as a reverse leakage. Pipe leaks can occur over various kinds of defects (e.g. oriﬁces, longi-

Low or negative pressure events may occur in drinking

tudinal cracks, circumferential cracks) (Greyvenstein &

water distribution systems (Gullick et al. ), which can

van Zyl ; Guo et al. ). In order to compute intrusion

doi: 10.2166/aqua.2013.213

Y. Yang et al.

Inﬂuence of porous media on intrusion rate into water distribution pipes

Journal of Water Supply: Research and Technology—AQUA

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volumes, commercial surge models typically simplify the

Collins & Boxall () presents a new analytical expression

pipe defects to circular oriﬁces, and the diameter of these

to describe the contaminants intrusion which considers the

oriﬁces is computed based on the leakage rate of the

viscous and inertial resistance of porous media, and com-

system (Kirmeyer et al. ; Ebacher et al. ). So the

pares with the results of computational ﬂuid dynamics

understanding of ﬂow through circular oriﬁces is important.

(CFD) models and experiments. The new expression indi-

The oriﬁce equation is commonly used for the calculation of

cates a linear relationship between the ﬂow rate and

the intrusion ﬂow rate (Besner et al. ; Mora-Rodriguez

pressure difference when the pressure difference is small

et al. ):

or the viscous resistance of the soil signiﬁcantly exceeds

pffiffiffiffiffiffiffiffiffiffiffiffi Q ¼ Cd A 2gΔH

the inertial resistance. It also reduces to the oriﬁce equation (1)

when no resistance presents in the external porous media. It is shown that the existence of a surrounding porous media

where Q is the intrusion ﬂow rate, Cd is the discharge coefﬁ-

decreases the intrusion ﬂow rate from that predicted by

cient, A is the cross-section area of the oriﬁce, g is the

the standard oriﬁce equation. The analytical expression

gravitational acceleration, ΔH is the difference between the

tends to under-estimate the intrusion rate compared with

external pressure head and internal pressure head. Many

the experimental results. Mansour-Rezaei & Naser ()

factors affect the discharge coefﬁcient, such as the ratio of

used a combination of dimensional analysis and a two-

the oriﬁce diameter to the pipe wall thickness, the sharpness

dimensional ﬁnite element model to ﬁnd the relationship

of the edge of the oriﬁce, the roughness of the inner surface,

among the effective factors. The results show that the expo-

the type of oriﬁce plate (plane or curved), and water

nent of pressure difference was about 2/3, and the effect of

viscosity (Brater & King ).

oriﬁce area was lower than that in the oriﬁce equation.

The oriﬁce equation is commonly used to compute

The present study has three objectives. The effect of sur-

intrusion ﬂow rate without the consideration for the sur-

face type on intrusion ﬂow rate is studied in Experiment

rounding soil. Many researchers investigated the role of

(Exp.) 1. Pipe defects are generally located in a curved sur-

soil hydraulics on the pressure-leakage (under positive

face, while oriﬁces are generally located in plane surface

pressure conditions in pipes) relationship. A simplistic appli-

in the traditional theory. Exp. 2 studies the effect of

cation of Darcy’s law suggests the ﬂow rate should be

porous media surrounding pipelines, as the characteristics

linearly proportional to the pressure head in the pipe. Con-

of porous media affects the intrusion ﬂow. The effect of

sidering the interactions of the external soil properties, the

ﬂow at a large Reynolds number is studied in Exp. 3, as

water ﬂow and the pipe leaks are complex, the relationship

the intrusion ﬂow is unlikely to be laminar.

between pressure and ﬂow rate is unlikely to be linear (Van Zyl & Clayton ). Walski et al. () compared the head loss caused by oriﬁce losses and porous media

METHODS

losses when modeling the ﬂow from leaks. They pointed out that oriﬁce head loss dominates in most water distri-

Experimental apparatus

bution systems, and the oriﬁce equation is suitable for modeling leaks for a ﬁxed-size circular hole. Coetzer et al.

This study investigated the ﬂow discharged through porous

() conducted an experimental study to determine the

media into an oriﬁce on the pipe wall. The low or negative

effect of the media surrounding the pipe leakage on its

pressure inside the pipe is not directly modeled. Instead,

hydraulic behavior, and found that the ﬂow rate for a given

the atmosphere pressure was used inside the pipe in this

pressure could be higher when the media surrounding the

study. This difference is not believed to be important as

leak is under some special conditions. This result is some-

the intrusion ﬂow is driven by the relative pressure head

what counterintuitive, indicating the interesting interaction

difference between the inside and outside of the pipe.

of porous media and the oriﬁce. Collins et al. () and

Figure 1(a) shows the schematics of the experimental

Y. Yang et al.

Inﬂuence of porous media on intrusion rate into water distribution pipes

Journal of Water Supply: Research and Technology—AQUA

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relation between the pressure gradient and the flow rate, which is suitable for low Reynolds number. Under high pressure gradients, there is a nonlinear relationship between the flow rate and the pressure gradient in most surrounding soil during intrusion, thus the Darcy’s law is not valid. Different laws have been proposed like Forchheimer’s or Izbash’s law (Bordiera & Zimmer ). This study employs the Izbash equation (Izbash ) to simulate flow in porous media as it is an extension of the Darcy’s law: i ¼ avm Figure 1

(2)

(a) Schematics of the experimental setup (all dimensions are in millimeters); (b)

where i is the hydraulic gradient; v is the macroscopic vel-

experimental setup with porous media in Exp. 2.

ocity, a and m are constants related to the properties of setup. It consisted of a plexiglass cylindrical container ﬁlled

the porous media and the ﬂuid, m is non-Darcy exponential.

with porous media and had an oriﬁce at the center of the

The equation becomes Darcy’s law when m ¼ 1, and the

bottom. The container was connected to a water tank

ﬂow is fully turbulent when m ¼ 2 (Venkataraman & Rao

which supplied the water with required pressure head. A

).

pressure sensor (Model XSE/A-H1|T2B1V0N; Westzh Tech-

Three kinds of surrounding media were used in the

nologies) with an accuracy of 0.05 kPa was used to measure

experiments: homogeneous glass beads, homogeneous

the pressure at the entrance of the oriﬁce. A camera was

coarse sand and well-graded ﬁne sand. The particle size of

used to record the experimental process. The outﬂow was

homogeneous glass beads is from 4 to 6 mm, with an average

discharged freely through the oriﬁce. All the experiments

size of 5 mm. The particle size of homogeneous coarse sand

were carried out at a room temperature of 23 C.

is from 1.18 to 2.36 mm, with an average of 1.77 mm.

The height of the container was 400 mm, the inner diam-

Figure 2 shows the cumulative grain size distribution of

eter was 200 mm, and the thickness of the container bottom

the ﬁne sand. The permeability of the three kinds of

was 5 mm. The top of the container could be opened for

porous media is tested using a similar apparatus as that of

sand loading. A curved bottom and a plane bottom were

Zhang et al. (). The non-Darcy exponential of three

used in the experiments. Half round pipe was used to form

kinds of porous media m is respectively 2 for glass beads,

a curved surface (Figure 1(a)), which had a diameter of

1.31 for the coarse sand, and 1.04 for the ﬁne sand.

100 mm and a length of 140 mm. Two oriﬁce sizes were used: 2.2 and 4.2 mm. The oriﬁces were sharp-edged. The

Experimental procedures

pressure sensor was installed on the bottom of the container. The difference in the elevations of the pressure sensor and

Preparatory work included adjusting the container bottom

the oriﬁce entrance was subtracted from the pressure

to be horizontal and packing porous media in the container.

sensor readings. Atmospheric pressure corresponds to a

The oriﬁce was ﬁrst plugged with a rubber stopper. In order

gauge pressure value of 0, thus the reading of pressure

to ensure no relative movement of particles, the maximum

sensor is equal to the difference between external pressure

hydraulic pressure in the experiments was applied to the

and internal pressure of the oriﬁce.

porous body for more than 12 hours to make the particles compacted. Experiments were started by unplugging the

Permeability of the porous media

rubber stopper, and adjusting the water level in the water tank to the desired value. After several minutes of waiting

Flow rate through a porous media depends on permeability

to ensure that the head was steady-state, the ﬂow rate was

and pressure gradient. The Darcy equation assumes a linear

measured by the time/weight method by collecting the

Y. Yang et al.

Inﬂuence of porous media on intrusion rate into water distribution pipes

Journal of Water Supply: Research and Technology—AQUA

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sand or fine sand. The thickness of the glass beads layer was 85 mm and the thickness of coarse/fine sand layer was 170 mm. This arrangement could prevent small sand particles leaking into the orifice. The smooth glass beads were used as porous media in Exp. 3 for its large permeability. In China, sand is generally used as backfill material around pipes. The coarse and fine sand used in our experiments have similar properties as the sand used in China. Thus this research did not consider the effect of the soil/ clay around pipes. The physical characteristics of the porous media related to the intrusion flow is the permeability of the sand layers, not the glass beads. We have carried out experiments to test the effect of glass beads, and the results indicated that the presence of glass beads Figure 2

has no effect on porous media permeability.

Cumulative grain size distribution of ﬁne sand.

outﬂow through the oriﬁce over a period of time. This process and the related pressure sensor readings were

RESULTS AND DISCUSSION

recorded in the same video images. The videos recorded at 40 frames per second, so the time could be accurate to 0.025 seconds. The weight of the water was measured by

Effect of surface type

an electronic balance (with an accuracy of 0.1 g). The

Figure 3 shows the ΔH Q curves with different diameters

pressure readings were also averaged. The pressure was

and surface types for Exp. 1 without media. The curves are

increased in a stepwise fashion then decreased after reaching the maximum value in the experiments. Table 1 shows the details of the experimental conditions in this study. Exp. 1 has no porous media in the container to

fitted with Equation (1), and it can be seen that the square root relationship is suitable for both the orifice on the curved and plane surfaces. The discharge coefficient Cd are 0.69 and 0.71 – for the 2.2 mm orifice on curved and

exclude the inﬂuence of porous media. The porous media in Exp. 2 consisted of two layers as shown in Figure 1(b). The lower layer was glass beads, and the top layer was coarse Table 1

Exp. 1

List of experiments

Porous media

Surface type

Oriﬁce size (mm)

No media

Curved

2.2 4.2 2.2 4.2

Plane Exp. 2

Glass beads and coarse sand

Curved

2.2 4.2 2.2 4.2

Curved

4.2

Glass beads and ﬁne sand Exp. 3

Glass beads

Figure 3

| ΔH Q curves for different oriﬁce diameters and surface types in Exp. 1 without media.

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Inﬂuence of porous media on intrusion rate into water distribution pipes

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plane surface. An orifice with a length (pipe thickness) to diameter ratio between 2 and 3 is called a tube, so the 2.2 mm orifice is a tube. According to Brater & King (), values of discharge coefficient for short tubes were found to vary from approximately 0.72 to 0.83. The values of Cd provided confidence that experiments results are consistent with these. Cd is 0.75 and 0.78 for the 4.2 mm orifice on curved and plane surface, respectively. They are a little larger than a typical value for an orifice, which is around 0.6. This is probably because the 4.2 mm orifice is slightly convergent, which could increase the discharge coefficient. The orifice discharge coefficient on curved surface is slightly different from that on plane surface. Brater & King () pointed out that if the plate were slightly warped inward, the coefficient of contraction would decrease. It can be seen that Cd on inward-projecting cylindrical surface is slightly smaller than that on the plane surface in our experiments, but the difference is small. Effect of porous media Figure 4(a) shows the ΔH Q curves for different orifice sizes and porous media, and Figure 4(b) shows the dependence of discharge coefficient on the Reynolds number at the orifice. The discharge coefficient Cd is calculated using Equation (1). The Reynolds number is defined by: Re ¼ Vd/ν, where V is the average flow velocity at the orifice, d is the orifice size, and ν is the kinematic viscosity of water. The range

Figure 4

Intrusion ﬂow rate with different oriﬁce diameters and porous media: (a) ΔH-Q curves; (b) Cd-Re curves.

of Reynolds number is 350 to 10,000. It can be seen from Figure 4(a) that the presence of

smaller impact on the ﬂow rate. Cd with ﬁne sand media

porous media changed the ﬂow rate compared with the

is larger than that with coarse sand media. It seems coun-

no media situation, and it has a more pronounced effect

terintuitive. Particles, especially those located near the

for larger diameter oriﬁces. In Figure 4(b), Cd is no

oriﬁce, likely obstruct part of the oriﬁce and modify intru-

longer a constant as in a standard oriﬁce case. Each

sion jet behavior. The porous media has a resistance on the

curve could be divided into three regimes when Re <

ﬂow into the oriﬁce and also could inhibit the contraction

2000, between 2000 and 4000, and Re > 4000. These

of the ﬂow into the oriﬁce. This phenomenon fully illus-

values are believed to be related to the interaction of the

trates that the coupled porous media and oriﬁce is a

porous media seepage and the oriﬁce ﬂow. Cd increases

complex system, and the traditional oriﬁce equation is

with Re when Re < 2000. When Re > 4000, Cd becomes

not enough to describe the ﬂow.

constant and is independent of Re. For the 4.2 mm oriﬁce,

Liu et al. () demonstrated that there are similarities

the presence of porous media decreases the ﬂow rate sig-

between ﬂow through porous media and ﬂow through ori-

niﬁcantly, and Cd with coarse sand media is larger than

ﬁces. The pressure drop for ﬂow through the coupled

that with ﬁne sand media, but the difference is small. For

porous media and oriﬁce can be described by developing

the 2.2 mm oriﬁce, the presence of porous media had

ﬂow in short ducts and has similar predictable pressure

Y. Yang et al.

Inﬂuence of porous media on intrusion rate into water distribution pipes

drop expression as that for ﬂow through porous media. The

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2014

Effect of the Reynolds number

oriﬁce becomes an extension of the ﬂuid path through the pores. It can be seen from Equation (2) that the pressure

Experiments were repeated under higher water head to

drop is proportional to the mth power of ﬂow rate

study the effect of much larger Reynolds number. A discon-

in porous media. A relationship similar to the Izbash

tinuity can be seen from the Cd Re curve (Figure 5(a)). The

equation can be used to relate the pressure difference and

Cd suddenly changes from 0.57 to 0.46 when Re is about

ﬂow rate:

16,000–17,000. The pattern is similar when Re is reduced from large to small. This result is in conformity with the

Q ¼ CΔH1=m

(3)

ﬁndings reported by Coetzer et al. () in their study of the effect of pressure on leakage in distribution systems.

where C is a ﬁtted parameter, which will be discussed later.

They suggested that this discontinuity is possibly due to

When Re < 2000, the pressure drop is caused by the fric-

the separation of the ﬂuid stream from the wall. The surrounding porous media is composed of curved

tional losses through porous media and along the wall of oriﬁces which are mainly viscous force. This regime will

and meshed passages for ﬂow, and the cross-sectional area

be called seepage regime here. The relationship of ﬂow

of the passages may change. So the ﬂow behavior is different

rate and pressure drop is mainly determined by the nature

from that without media situation. As Re increases, the

of the media, and the value of m is 1.04 for ﬁne sand and

bending at the surface of microscopic solid causes the

1.31 for coarse sand in our experiments. When Re > 4000,

stream line to move gradually. The ﬂuid stream may separate

ﬂow in this regime is fully turbulent, and m ¼ 2. It is similar

from the passage wall at a certain Reynolds number. For

to the oriﬁce equation, and could be named oriﬁce regime.

further research on the ﬂuid stream separation, we made

The range of Re from 2000 to 4000 is transitional regime,

an arbitrary oriﬁce by hands on the bottom of the container

m value is uncertain in this regime. The ΔH Q curves are

to simulate the curved passage with changing cross-sectional

ﬁtted in the seepage regime and the oriﬁce regime, which

area, and no media is loaded in the container. The passage

is segmented according to the range of Re obtained from

had a cross-section of non-axisymmetric, and one side of

the Cd Re curves.

the inclination angle was about 10 . The surface of the pas-

It can be seen from Table 2 that the experimental results

sage was rough, and could be polished with sandpaper. We

ﬁt well with Equation (3) in the seepage regime and oriﬁce

repeated the above experiments before and after polishing.

regime. The coefficient C is related to orifice size, flow

Figure 6 shows the ﬂow before and after the ﬂuid stream

regime, permeability of porous media, and the local geome-

separation extracted from the experiment video with gradu-

try of the particle around the oriﬁce. The values of C are

ally increasing Re. The jet was much more divergent

larger for a large diameter oriﬁce. The inﬂuence of media

(Figure 6(a)) until a part of ﬂuid stream gradually deviated

permeability to C is different in different regimes. The

from the original direction (Figure 6(b)). Finally, the jet

values of C are inversely proportional to the media per-

showed a constrictive appearance, and a vena contracta

meability

are

appeared downstream of the oriﬁce (Figure 6(c)). This

approximately equal for different media in the oriﬁce

shape was maintained with further increase of the Reynolds

regime.

number. Figure 5(b) shows the Cd Re curve of the

Table 2

the

seepage

regime,

and

they

The ﬁtting equations of ΔH-Q curves with different porous media

Media

Oriﬁce size (mm)

Seepage regime

Oriﬁce regime

Course sand

2.2 4.2

Q ¼ 19:67ΔH1=1:31 Q ¼ 68:34ΔH1=1:31

0.995 0.996

Q ¼ 10:78ΔH1=2 Q ¼ 29:39ΔH1=2

0.999 0.997

Fine sand

2.2 4.2

Q ¼ 47:39ΔH1=1:04 Q ¼ 125:39ΔH1=1:04

0.966 0.984

Q ¼ 12:15ΔH1=2 Q ¼ 29:13ΔH1=2

0.999 0.998

Y. Yang et al.

Inﬂuence of porous media on intrusion rate into water distribution pipes

Journal of Water Supply: Research and Technology—AQUA

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2014

experiment results before and after polishing. After the fluid stream separation, the discharge coefficient decreased as the flow contracted. As Re decreased with the decrease in the flow rate, Cd still remained the value after the separation. Thus there was a range of Re corresponding to two values of Cd. The surface of the passage became smoother after being polished, the friction head loss reduced. As a result, Cd increased slightly compared to the value before polishing. The Reynolds number corresponding to the discontinuity was smaller after being polished, which indicates that the fluid stream separation is related to the roughness of the passage.

CONCLUSIONS This study investigated the influence of porous media on the intrusion flow rate through orifices. The intrusion is assumed to occur under steady-state conditions, flow through orifices discharging free liquid jets into air. The porous media in the surrounding environment of the orifice is fully-saturated, homogeneous and isotropic. Experimental results show that the traditional orifice equation is suitable for both the orifice on curved surface and plane surface when there is no porous media surrounding the orifice. Figure 5

Cd-Re curves: (a) with glass beads as porous media for a 4.2 mm oriﬁce; (b) before and after polishing for an arbitrary oriﬁce with no porous media.

The orifice discharge coefficient on the inward curved surface is smaller than that on the plane surface, but the difference is relatively small. The presence of porous media could change the discharge coefficient and added dependencies on the orifice size, flow regime, and permeability of porous media. A function similar to the Izbash equation is suggested for describing the relationship of the pressure head difference and the flow rate, which fits well with the experimental data both in seepage regime and orifice regime. This expression describes well the complex nature of the flow where the flow is impacted by both the porous media and the orifice. It should be pointed out, however, that further research is needed to develop a general equation for practical use. There is a discontinuity in the discharge coefficient at large Reynolds number (Re > 10,000) when the porous

Figure 6

Flow forms before and after fluid stream separation for an arbitrary orifice with no porous media (the arrows indicate the location of the orifice).

media is composed of curved and meshed passages for ﬂow. The value of discharge coefﬁcient may change due to

Y. Yang et al.

Inﬂuence of porous media on intrusion rate into water distribution pipes

the movement of the stream line, leading to changes in the ﬂow rate. This is different from the continuum hypothesis. Previous research simpliﬁed the porous media as a continuous media is inappropriate.

ACKNOWLEDGEMENTS Financial support was received from the Nation High-Tech R&D Program (863) of China (2012AA062608), the Important National Science and Technology Speciﬁc Projects (2011ZX07301-004), and the Program for Zhejiang Leading Team of S&T Innovation (2010R50037).

REFERENCES Besner, M. C., Broseus, R., Lavoie, J., Di Giovanni, G., Payment, P. & Prevost, M.  Pressure monitoring and characterization of external sources of contamination at the site of the payment drinking water epidemiological studies. Environ. Sci. Technol. 44 (1), 269–277. Besner, M. C., Prévost, M. & Regli, S.  Assessing the public health risk of microbial intrusion events in distribution systems: Conceptual model, available data, and challenges. Water Res. 45 (3), 961–979. Bordiera, C. & Zimmer, D.  Drainage equations and nonDarcian modelling in coarse porous media or geosynthetic materials. J. Hydrol. 228 (3–4), 174–187. Brater, E. F. & King, H. W.  Handbook of Hydraulics. McGraw-Hill, New York. Coetzer, A. J., Van Zyl, J. E. & Clayton, C. R. I.  An experimental investigation into the turbulent-flow hydraulics of small circular holes in plastic pipes. In: 8th Annual Water Distribution Systems Analysis Symposium. Cincinnati, Ohio, USA, pp. 1–9. Collins, R. & Boxall, J.  The influence of ground conditions on intrusion flows through apertures in distribution pipes. J. Hydraul. Eng. 139, 1052–1061. Collins, R., Besner, M. C., Beck, S., Karney, B. & Boxall, J.  Intrusion Modelling and Effect of Groundwater Conditions. Water Distribution System Analysis, Tucson Arizona, USA, pp. 585–594. Ebacher, G., Besner, M. C., Clément, B. & Prévost, M. . Sensitivity analysis of some critical factors affecting simulated intrusion volumes during a low pressure transient event in a full-scale water distribution system. Water Res. 46, 4017–4030.

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

Greyvenstein, B. & Van Zyl, J. E.  An experimental investigation into the pressure–leakage relationship of some failed water pipes. J. Water Supply Res. T. 56 (2), 117–124. Gullick, R. M., LeChevallier, M. W., Case, J., Wood, D. J., Funk, J. E. & Friedman, M. J.  Application of pressure monitoring and modelling to detect and minimize low pressure events in distribution systems. J. Water Supply Res. T. 54 (2), 65–81. Guo, S., Zhang, T., Zhang, Y. & Zhu, D. Z.  An approximate solution for two-dimensional groundwater infiltration in sewer systems. Water Sci. Technol. 67 (2), 347–352. He, X. Q., Cheng, L., Zhang, D. Y., Li, W., Xie, X. M., Ma, M. & Wang, Z. J.  First molecular detection of group A rotaviruses in drinking water sources in Beijing, China. Bull. Environ. Contam. Toxicol. 83, 120–124. Izbash, S.  O Filtracii V Kropnozernstom Materiale. Leningrad, USSR. Karim, M. R., Abbaszadegan, M. & Lechevallier, M.  Potential for pathogen intrusion during pressure transients. J. Am. Water Works Assoc. 95 (5), 134–146. Kirmeyer, G. J., Friedman, M., Martel, K., Howie, D., LeChevallier, M., Abbaszadegan, M., Karim, M., Funk, J. & Harbour, J.  Pathogen Intrusion into the Distribution System. American Water Works Association Research Foundation, Denver, CO. Liu, S., Afacan, A. & Masliyah, J. H.  A new pressure drop model for flow-through orifice plates. Can. J. Chem. Eng. 79, 100–106. Mansour-Rezaei, S. & Naser, G. H.  Contaminant intrusion in water distribution systems: an ingress model. In: World Environmental and Water Resources Congress 2012. Albuquerque, New Mexico, USA, pp. 3002–3010. Mora-Rodriguez, J., Lopez-Jimenez, P. A. & Ramos, H. M.  Intrusion and leakage in drinking systems induced by pressure variation. J. Water Supply Res. T. 61 (7), 387–402. Payne, S. J., Besner, M. C., Lavoie, J., Krentz, C., Hansen, L. T., Friedman, M., LeChevallier, M. W., Prevost, M. & Gagnon, G. A.  Molecular techniques and data integration: investigating distribution system coliform events. J. Water Supply Res. T. 59 (5), 298–311. Van Zyl, J. E. & Clayton, C. R. I.  The effect of pressure on leakage in water distribution systems. P. I. Civil Eng.-Water M. 160 (2), 109–114. Venkataraman, P. & Rao, P. R. M.  Darcian, transitional, and turbulent flow through porous media. J. Hydraul. Eng. 124 (8), 840–846. Walski, T., Bezts, W., Posluszny, E. T., Weir, M. & Whitman, B. E.  Modeling leakage reduction through pressure control. J. Am. Water Works Ass. 94 (4), 147–155. Zhang, Y., Liu, W., Shao, W. & Yang, Y.  Experimental study on water permittivity of woven polypropylene geotextile under tension. Geotext. Geomembranes 37, 10–15.

First received 12 June 2013; accepted in revised form 3 September 2013. Available online 8 October 2013

63.1

2014

Optimal sensor placement for event detection and source identiﬁcation in water distribution networks Shuming Liu and Pierre Auckenthaler

ABSTRACT This study focuses on the optimization of sensor placement with respect to the source identiﬁcation and event detection. A multi-objective algorithm is used to solve the optimization problem. The numbers of possible source nodes for contamination events associated with the solutions on the Pareto fronts from the proposed method and benchmark method are calculated under the same conﬁguration and compared. The comparison showed that the proposed method performs better than the benchmark method in detecting a contamination event and identifying its possible source. Key words

| multi-objective genetic algorithm, nodal demand uncertainties, sensor placement, source identiﬁcation

Shuming Liu (corresponding author) Pierre Auckenthaler School of Environment, Tsinghua University, Beijing 100084, China E-mail: shumingliu@tsinghua.edu.cn Shuming Liu Tsinghua University-VEOLIA Environment Joint Research Center for Advanced Environmental Technology, Tsinghua University, Beijing 100084, China

INTRODUCTION Accidental contamination or malicious attack on a water

). It also attracted much interest and several methods

distribution network can have severe effects on the health

have been developed to solve it, especially in recent

of the population relying on this water source. These

BWSNs. For example, Cristo & Leopardi () formulated

events can potentially be detected by a network of online

it as an optimization problem, linearized using the water

sensors. In order to reduce the exposure to contaminated

fraction matrix concept. Liu et al. () solved this problem

water, it is important to design a methodology to efﬁciently

using an adaptive dynamic optimization technique. Wang &

place the sensors (Ostfeld et al. ). Once a contamination

Harrison (, ) applied Bayesian analysis to solve the

event is detected, the next important step is to identify the

source identiﬁcation problem, especially in the condition

source of this event.

of stochastic variation of water demand. Shen & McBean

The sensor placement problem is concerned with locating an array of sensors in a network capable of detecting the

() investigated the false negative/positive issues in contaminant source identiﬁcation.

presence of a contaminant if an event occurs. Since the ﬁrst

Generally, these two problems were solved separately.

article by Lee & Deininger (), the sensor placement pro-

Only a few researchers have attempted to investigate them

blem sparked signiﬁcant research, most notably, leading to

jointly. In 2006, Preis & Ostfeld observed this dependency

the Battle of the Water Sensor Networks (BWSNs) (Ostfeld

and proposed a method to optimize the sensor layout with

et al. ). Due to its complexity, it is often treated as a

respect to source identiﬁcation (Preis & Ostfeld ).

multi-objective optimization problem. Optimization criteria

More recently, Tryby et al. () represented the water dis-

may include population exposed, time to detection,

tribution system as a linear system and optimized the

volume of contaminated water consumed, number of

positions of the sensors in order to improve the conditioning

failed detections, and length of pipe contaminated (Berry

of the matrix. This paper presents a new method to focus on

et al. ). These objectives are mostly related to the sen-

the optimization of sensor placement with respect to source

sor’s ability in event detection.

identiﬁcation and event detection. The proposed method uti-

The source identiﬁcation problem is concerned with identifying the contaminant source location (Tryby et al. doi: 10.2166/aqua.2013.106

lizes different objectives compared with conventional methods.

S. Liu & P. Auckenthaler

Optimal sensor placement

Journal of Water Supply: Research and Technology—AQUA

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2014

alarm threshold. The values of Δ adopted are 10% and

METHODOLOGY

þ10%. Ideally, all concentration values from all nodes at every

Nodal demand uncertainty

time step, for all simulations, should be recorded. HowIn the operation of a network, uncertainties might come

ever, such records would lead to a large amount of data,

from nodal demand, sudden loss of electricity, etc. For sim-

much too large to be used efﬁciently, even for small net-

pliﬁcation, only the nodal demand uncertainty is considered

works (Krause et al. ). Instead, it can record the

in this research, which is realized by multiplying the base

time of ﬁrst detection of the simulated event by each

demand with a random demand multiplier. A truncated

node to reduce computing load (Krause et al. ),

Gaussian probability function (PDF) is employed to gener-

which is the principle used in this research. The time at

ate the random demand multiplier (Babayan et al. ).

which a sensor s (at node s) detects the event e under

This is expressed as:

the nodal demand conﬁguration i is called the detection time t(e, s, i). t(e, s, i). is calculated to be the shortest

2 1 2 pdf ¼ pffiffiffiffiffiffi exp ((x E) =2σ ) x 1 σ 2π

(1)

travel time of water parcels from the contamination event node to the sensor node, which is used as an indicator to identify the contamination source of event e for sensor at

where E and σ are given mean and standard deviation. In

node s. In addition, the interval of detection time is

this research, the values of σ are 0, 0.1 and 0.2, and E ¼ 0.

denoted [Tmin (e, s); Tmax (e, s)], in which Tmin (e, s) ¼

The random nodal demand is calculated as:

min t(e, s, i) Tmax (e, s) ¼ max t(e, s, i). In the case where the contaminant concentration is lower than the sensor’s

w(k, j) ! wb (k, j) M(k, j)

(2)

alarm threshold, it is considered ‘non-detection’. If the event goes undetected during the whole simulation, it is

where w(k, j) is the demand at network junction k at time

deﬁned as t(e, s, i) ¼ 1.

step j, wb(k, j) is the base demand at network junction k at time step j, M(k, j) is the demand multiplier at network junc-

Optimization

tion k at time step j. The sensor placement problem is conﬁgured to be a multiSimulation of contamination data

objective optimization problem. The objectives are to (1) maximize the probability of detection and (2) minimize

Features of contamination events, including contaminant

the overlay of interval of detection time. To solve the optim-

type, duration, contaminant magnitude and starting time,

ization problem, a NSGA-II method is employed. NSGA-II

may have impacts on source identiﬁcation. For simpliﬁca-

is an algorithm proposed by Deb et al. (), which has

tion, it is assumed that the contamination is a single

been broadly used in water distribution area (Preis & Ost-

conservative injection event with speciﬁed unit duration

feld ; Liu et al. ).

and could happen at any point of the discretized timeline in the ﬁrst 12 hours. Such an event is noted e and the

First objective: maximize the probability of detection

set of all possible events is noted E. The type of sensors considered is with an alarm threshold d. If the indicator

The ﬁrst objective is to maximize the probability of detec-

concentration (for example, dissolvedoxygen) is below d,

tion, which is deﬁned as the ratio of detected events

no alarm will be triggered. If the concentration goes

relative to all simulated events:

above d, the sensor immediately reports an abnormal situation.

The

impact

alarm

threshold

model

performance is examined by adding a shift Δ to the original

fitness1 ¼

number of detected events total number of events

(3)

S. Liu & P. Auckenthaler

Optimal sensor placement

Journal of Water Supply: Research and Technology—AQUA

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2014

A greater value of ﬁtness1 suggests an event e can be

Second objective: minimize the overlay of interval of detection time

detected with high probability, while a greater value of ﬁtness means a higher overlay of detection time and lower accuracy of

A single event will give a deterministic value of the detection

source identiﬁcation. In the case of non-detection, a large

time for a sensor. In this case, it is possible to distinguish two

number, for example 1010 in this research, is set to ﬁtness2 as

events by comparing the detection times. In the case where

a penalty.

the detection times are identical, the two events can be distinguished by further comparing the sensor measurements. In the situation of detection, times and sensor measurements are both identical, the two events are the same from the sensor’s point of view and will not be distinguished. This simple dichotomy disappears when nodal demand uncertainties are considered, because the detection time of an event by a sensor becomes a set of possible times. The set of possible detection times of two events can be identical, completely different or partially overlapped. The partial overlap means that under some nodal demand conﬁgurations, the two events can be distinguished by the sensors. While under other nodal demand conﬁgurations, they cannot be distinguished. Therefore, the second objective is to minimize the overlap of internal of detection time. A value v(s, e1, e2) is

Comparison benchmark For comparison, another set of solutions is yielded through optimization with respect to probability of detection (the first objective defined above) and time to detection (objective used in conventional methods). Time to detection is defined as the value of the difference between the contamination time (Tevent) and the median value of the detection interval for the first sensor to detect the event. Small fitness values suggest small detection times: fitness3 (S) ¼ min

(Tmin þ 0:5 (Tmax Tmin ) Tevent )

(7)

ﬁrst calculated, corresponding to the ability for a single Therefore, the benchmark optimal sensor placement

sensor s to distinguish between two events e1 and e2: v(s, e1 , e2 ) ¼

1 overlap overlap þ 2 l1 l2

l1 ¼ Tmax (e1 , s) Tmin (e1 , s) l2 ¼ Tmax (e2 , s) Tmin (e2 , s)

problem has two objectives: maximization of detection probability and minimization of detection time. (4)

These two multi-optimization problems share one same objective. However, the second objective is different. To facilitate the comparison, after the optimization problem is solved, the numbers of possible source nodes for con-

where l1 and l2 are the length of the interval of detection time for the ﬁrst and second event, l1 ≠ 0, l2 ≠ 0, Tmax > 0, Tmin > 0,

tamination events associated with the solutions on the Pareto fronts from both methods are calculated under the

overlap is the length of the overlap between the interval of

same conﬁguration as the one used in the optimization

detection time.

processes.

The value attributed to a single sensor can be extended to a set of sensors by taking the minimum value from each sensor: Source identiﬁcation v(S, e1 , e2 ) ¼ min v(s, e1 , e2 ) s∈S

(5) At time t, if sensor s1 is triggered off and sensor s2 is not,

By summary, the optimization problem for optimal sensor placement is formulated as follows:

and 0 < Tmin(ex, s2) Tmax(ex, s2) Tmin(ex, s1), node x is excluded from the potential contamination source. Otherwise, x is a possible contamination source. At time t þ t0 ,

number of detected events total number of events XX Minimize fitness2 (S) ¼ v(S, e1 , e2 )

if sensor s2 is triggered off and 0 < Tmin(ex, s2) Tmax(ex,

Maximize fitness1 ¼

(e1 ≠ e2 ;

e1 , e2 ∈ E)

s2) t0 , node x is excluded from the potential contamination source. Otherwise, x is a possible contamination (6)

source.

S. Liu & P. Auckenthaler

Optimal sensor placement

Journal of Water Supply: Research and Technology—AQUA

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2014

CASE STUDY

RESULTS

The methodology discussed above was implemented using

Figure 1 presents the Pareto fronts obtained using the pro-

Matlab. The hydraulic and water quality simulations were

posed method. It reveals that nodal demand uncertainty

performed using the EPANET Program’s toolkit (Rossman

does not affect detection probability signiﬁcantly. However,

). Example network Net 3 from EPANET was utilized

for a given number of sensors, with increasing of uncer-

to demonstrate the proposed method. For simpliﬁcation, a

tainty, for the same detection probability, the identiﬁcation

base demand pattern is applied to all nodes.

ﬁtness increases, which suggests a decrease of accuracy of

Simulations were carried out with different levels of

source identiﬁcation. Meanwhile, as shown in Figure 2,

uncertainties of the nodal demand (σ: 0, 0.1 and 0.2) and

when the alarm threshold decreases, the detection prob-

alarm threshold (Δ: 10%, 0%, þ10%). For each level of

ability and the overlay of interval of detection time

uncertainties, the optimization process was conducted for

increase, which means a contamination event can be

three, six and 12 sensors, respectively. The injection dur-

easier to be detected, but with lower identiﬁcation accuracy.

ation is set to 1 minute. The nodal demand w is

In contrast, for a greater alarm threshold value, a contami-

calculated using Equation (2). Based on the initial calcu-

nation event can be more difﬁcult to be detected, but with

lation of the ﬂow velocity, water quality simulation length

higher identiﬁcation accuracy. These two ﬁndings suggest

was set to 24 hours to ensure all contaminant is spread

the proposed method is sensitive to nodal demand and

over the whole network.

alarm threshold.

Figure 1

Pareto front from the proposed method (nodal demand).

Figure 2

Pareto front from the proposed method (alarm threshold).

S. Liu & P. Auckenthaler

Optimal sensor placement

Journal of Water Supply: Research and Technology—AQUA

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2014

The possible source nodes for contamination events

Figure 4 presents the Pareto front obtained with six

associated with the solutions on the Pareto fronts from the

sensors, as well as the explicit placement of the sensors

proposed and benchmark methods are calculated and pre-

for four different sensors sets. The solutions present a gra-

sented in Figure 3. Generally, for the same number of

dual evolution going from the most compact sensor

possible contamination sources, the results of the proposed

placement (Figure 4(a)), giving a better identiﬁcation accu-

method show higher detection probability than the one from

racy with low detection probability, to the most scattered

the benchmark method. Meanwhile, for a given contami-

one, giving a worse identiﬁcation accuracy with high

nation event, the proposed method reports a lower

detection probability (Figure 4(d)). A compact sensor pla-

number of possible contamination sources than the bench-

cement can identify more accurately the source of a

mark method with the same detection probability. This

contamination event but will miss many events. On the

suggests that the proposed method performs better than

contrary, a well spread out sensor network can detect an

the benchmark method in event detection and source

event with higher probability, while accuracy of source

identiﬁcation.

identiﬁcation is low.

Figure 3

Possible contamination sources from proposed method and benchmark method.

S. Liu & P. Auckenthaler

Optimal sensor placement

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

well spread out sensor network can detect an event with higher probability, while accuracy of source identiﬁcation is low. 4. Many factors may have impacts on the model’s performance. This research only simply examined nodal demand and alarm threshold. The impact of the proﬁle of contamination event on method performance, including injection duration and contaminant magnitude, etc., and the use of detection time and sensor measurement to differentiate events should be investigated in future research.

ACKNOWLEDGEMENTS The authors express their thanks to the China Major Water Project (2012ZX07408-002) and Tsinghua Independent Research Grant for their ﬁnancial support. The authors would like to thank the two anonymous reviewers for their valuable comments on this manuscript.

REFERENCES

Figure 4

Four sample solutions of the Pareto front obtained for six sensors with σ ¼ 0.1.

CONCLUSIONS 1. The proposed method employs detection probability and overlap of detection time as objectives, which differentiates it from the conventional methods. By taking the overlap of detection time into consideration, the proposed method performs well, both in detecting a contamination event and identifying its possible sources. 2. The proposed method is sensitive to nodal demand and sensor’s alarm threshold. Increase of nodal demand uncertainty and decrease of alarm threshold both could blur the accuracy of source identiﬁcation. 3. A compact sensor placement will make use of the redundancy between the sensors to identify more accurately the source of a contamination event. On the contrary, a

Babayan, A., Kapelan, Z., Savic, D. & Walters, G.  Least-cost design of water distribution networks under demand uncertainty. J. Water Resour. Plann. Manage. 131 (5), 375–382. Berry, J. W., Fleischer, L., Hart, W. E., Phillips, C. A. & Watson, J. P.  Sensor placement in municipal water networks. J. Water Resour. Plann. Manage. 131 (3), 237–243. Cristo, C. D. & Leopardi, A.  Pollution source identification of accidental contamination in water distribution networks. J. Water Resour. Plann. Manage. 134 (2), 197–202. Deb, K., Pratao, A., Agarwal, S. & Meyarivan, T.  A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 (2), 182–197. Krause, A., Leskovec, J., Guestrin, C., VanBriesen, J. & Faloutsos, C.  Efficient sensor placement optimization for securing large water distribution networks. J. Water Resour. Plann. Manage. 134 (6), 516–526. Lee, B. H. & Deininger, R. A.  Optimal locations of monitoring stations in water distribution system. J. Environ. Eng. 118 (1), 4–16. Liu, L., Ranjithan, R. S. & Mahinthakumar, G.  Contamination source identification in water distribution systems using an adaptive dynamic optimization procedure. J. Water Resour. Plann. Manage. 137 (2), 183–192.

S. Liu & P. Auckenthaler

Optimal sensor placement

Liu, S., Liu, W., Chen, J. & Wang, Q.  Optimal locations of monitoring stations in water distribution systems under multiple demand patterns: a flaw of demand coverage method and modification. Front. Environ. Sci. Eng. 6 (2), 204–212. Ostfeld, A., Uber, J. G., Salomons, E., Berry, J. W., Hart, W. E., Phillips, C. A., Watson, P.-J., Dorini, G., Jonkergouw, P., Kapelan, Z., Pierro, F., Khu, S.-T., Savic, D., Eliades, D., Polycarpou, M., Ghimire, S. R., Barkdoll, B. D., Gueli, R., Huang, J. J., McBean, E. A., James, W., Krause, A., Leskkovec, J., Isovitsch, S., Xu, J., Guestrin, C., VanBriesen, J., Small, M., Fischbeck, P., Preis, A., Propato, M., Piller, O., Trachtman, G. B., Wu, Z. Y. & Walski, T.  The battle of the water sensor networks (BWSN): a design challenge for engineers and algorithms. J. Water Resour. Plann. Manage. 134 (6), 556–568. Preis, A. & Ostfeld, A.  Optimal sensors layout for contamination source identification in water distribution systems. In: Proc. 8th Annual Water Distribution Systems Analysis Symp. ASCE, Reston, VA.

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Preis, A. & Ostfeld, A.  A genetic algorithm for contaminant source characterization using imperfect sensors. Civil Eng. Environ. Syst. 25 (1), 29–39. Rossman, L. A.  The EPANET programmer’s toolkit for analysis of water distribution systems. In: Proc. Annual Water Resources Planning and Management Conf. ASCE, Reston, VA. Shen, H. & McBean, E.  False negative/positive issues in contaminant source identification for water-distribution systems. J. Water Resour. Plann. Manage. 138 (3), 230–236. Tryby, M. E., Propato, M. & Ranjithan, S. R.  Monitoring design for source identification in water distribution systems. J. Water Resour. Plann. Manage. 136 (6), 637–646. Wang, H. & Harrison, K.  Bayesian update method for contaminant source characterization in water distribution systems. J. Water Resour. Plann. Manage. 139 (1), 13–22. Wang, H. & Harrison, K.  Improving efficiency of the Bayesian approach for water distribution contaminant source characterization with support vector regression. J. Water Resour. Plann. Manage. 140 (1), 3–11. doi:10.1061/(ASCE) WR.1943-5452.0000323.

First received 5 June 2013; accepted in revised form 19 September 2013. Available online 31 October 2013

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Effects of the interaction between Microcystis aeruginosa and nitrobenzene on coagulation-sedimentation performance Zhiquan Liu, Zhenling Hou, Hua Ma, Zhenqiang Fan, Zhiwei Zhao, Dongmei Liu and Fuyi Cui

ABSTRACT It is rarely reported that the interaction between xenobiotics and algae may cause potential water quality risks and affect the drinking water treatment process. In the present study, a bench-scale jar test was performed to investigate the effects of the interaction between nitrobenzene (NB) and Microcystis aeruginosa on the performance of coagulation-sedimentation by comparing differences in turbidity, optical density at 680 nm (OD680), trihalomethane formation potential (THMFP) and microcystin-LR (MC-LR) in an M. aeruginosa solution with or without NB. The results indicated that the quality of M. aeruginosa-containing water can be improved signiﬁcantly by coagulationsedimentation, and all of the removal rates were more than 80%. The presence of NB did not affect the performance of coagulation-sedimentation directly. However, the interaction between NB and M. aeruginosa did change the characteristics of the raw water and affect the performance of coagulation-sedimentation. At a non-lethal concentration, NB did not inhibit the growth of M. aeruginosa but weakened the zeta potential of M. aeruginosa, which enhanced the removal of turbidity. The products of NB degradation by M. aeruginosa cause an increased THMFP in extracellular organic matters, which cannot be removed by coagulation-sedimentation efﬁciently, increasing the risk of disinfection by-products formation in the following disinfection unit. Key words

Zhiquan Liu Zhenling Hou Hua Ma Zhenqiang Fan Zhiwei Zhao Dongmei Liu Fuyi Cui (corresponding author) State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (SKLUWRE, HIT), PO Box 2650, Harbin 150090, China E-mail: hit_cuifuyi@hotmail.com Zhiquan Liu Department of Environmental Engineering and Global Water Quality Research Center, National Cheng Kung University, Tainan 70101, Taiwan Zhenling Hou Changchun Institute of Urban Planning & Designing, Changchun 130021, China

| algae, coagulation, sedimentation, THMFP, xenobiotics

INTRODUCTION Eutrophication and xenobiotic pollution are two of the

water treatment process and worsen the efﬂuent water

most common problems in the aquatic environment.

quality. The characteristics and effects of individual algal

Sometimes,

or organic problems on water treatment plants have

these

two

problems

may occur

simul-

taneously. An ofﬁcial Chinese investigation (Ministry of

been well studied (Takaara et al. ; Tian et al. ;

Environmental Protection of the People’s Republic of

Shen et al. ; Zamyadi et al. ). However, the effects

China ) indicates that of the 26 total monitored

of the interaction between xenobiotics and algae are

large lakes and reservoirs, 14 (53.8%) face a eutrophica-

rarely reported.

tion problem and 16 (61.5%) face a xenobiotic pollution

Xenobiotics can inhibit the growth and metabolism of

problem. The study also indicates that Taihu Lake,

algae (Tukaj ; Andreozzi et al. ), while algae and

Chaohu Lake and Dianchi Lake are contaminated by a

cyanobacteria have the ability to adsorb or degrade xeno-

combination of xenobiotics and algae (including cyano-

biotics (Semple et al. ). Both interactions depend on

bacteria) in the summer. When these lakes are used as

the physical and chemical properties of the xenobiotics

drinking water sources, the pollutants can affect the

and the species of algae.

doi: 10.2166/aqua.2013.081

Z. Liu et al.

Effects of the interaction between M. aeruginosa and NB on coagulation

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Due to the interaction between xenobiotics and algae, the

lowest temperature) for M. aeruginosa blooms. Alkalinity

characteristics of the raw water may change, hence affecting

and pH can affect the hydrolysis of coagulants and the per-

the performance of water treatment plants. Our previous

formance of coagulation-sedimentation (Gottfried et al.

study (Liu et al. ) indicated that the presence of nitrobenzene

). Because the low alkalinity in M. aeruginosa solutions

(NB) can enhance the protein productivity of Microcystis aeru-

limited the coagulation efﬁciency (less than 70% for the

ginosa and result in an increased yield of trihalomethane

removal of OD680), 106 mg/L sodium carbonate (equivalent

formation potential (THMFP) by M. aeruginosa. However, it

to 100 mg/L alkalinity as CaCO3) was added to all the

is still not clear whether increased THMFP productivity will

samples before the coagulation-sedimentation experiment.

cause an increased risk of disinfection by-products formation

Aluminum sulfate, ferric chloride and polyaluminum chlor-

in the efﬂuent of water treatment plants or not.

ide (PAC) were used as the coagulants. Following the

In the present study, NB, a typical industrial material

addition of coagulant, the waters were subjected to rapid

widely observed in the aquatic environment (Gao et al.

mixing for 1 min at 250 rpm, ﬂocculation for 14 min (4 min

), and M. aeruginosa, a common dominant species of

at 100 rpm and 10 min at 40 rpm), and settling for 30 min.

cyanobacteria in eutrophic lakes and reservoirs that can

A pre-experiment (Figure S1, supplementary data, avail-

release microcystins (MCs), were selected as the representa-

able online at http://www.iwaponline.com/jws/063/081.

tive pollutants. A bench-scale jar test was conducted to

pdf) showed that at the coagulant dosage optimized for tur-

evaluate the effects of the interaction between NB and M. aer-

bidity, other water quality parameters of the settled water

uginosa on the performance of coagulation-sedimentation.

also approximated optimal values. Thus, in this study, all of the presented data were obtained at the coagulant dosage optimized for turbidity (the residual turbidity with

MATERIALS AND METHODS

different coagulant dosages can be found in Figure S2, available online at http://www.iwaponline.com/jws/063/081.

M. aeruginosa culturing

pdf). All of the jar tests were conducted in triplicate, and the arithmetic mean (±SD) was used as the ﬁnal value.

Axenic M. aeruginosa (FACHB-930) was purchased from the Institute of Hydrobiology at the Chinese Academy of

Analytical methods

Sciences but was still treated by Semple & Cain’s () method to exclude microbial contamination. The stock cul-

The algal density was monitored by spectrophotometry at a

ture and the experimental culture were incubated under the

wavelength of 680 nm (OD680, UV759S, INESA instrument

designed conditions (BG11 medium, 25 C) as described in

Co. Ltd, China), and cell number counting was performed

our previous publication (Liu et al. ), and the com-

with a microscope (Olympus BX41, Olympus Optical Co.,

ponents of BG11 medium have been listed in supporting

Ltd, Japan) according to the method adopted by Ma et al.

information (Tables S1 and S2, available online at http://

(). There was a signiﬁcant linear regression between

www.iwaponline.com/jws/063/081.pdf).

OD680 and cell density (Figure S3, available online at http://www.iwaponline.com/jws/063/081.pdf).

Jar test

used

OD680 to study the removal rate of cell density, and checked the initial cell density by microscope analysis before the

The bench-scale coagulation-sedimentation experiments

coagulation-sedimentation experiment to ensure consistent

were conducted using a standard jar-test unit (ZR2-6, Fuhua

initial conditions in different batch experiments.

Co. Ltd, China) consisting of six 2-L glass jars. The glass

THMFP was measured by gas chromatography with an

jars were used to minimize the adsorption of organic com-

electron capture detector (Agilent 7890N) according to

pounds by the containers. Due to the low room temperature

Liu’s method (). An excess chlorine dosage (1,600 mg/L)

(14–18 C), a water bath was used to maintain the water temp-

was applied to ensure that all the THMFP could be oxidized

erature at 25 ± 1 C, which is a necessary condition (the

to form THMs, independent of the actual chlorine dosage

Z. Liu et al.

Effects of the interaction between M. aeruginosa and NB on coagulation

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used during disinfection; the results do not reﬂect the real pro-

NM5 samples (i.e. NB concentration of 83.5 μg/L and cell

duction of THMs under actual conditions.

density of 2.1 × 108 cells/L), was also subjected to the jar

The intracellular microcystin-LR (MC-LR) and extracellular MC-LR were extracted and measured according to

test. For NMf, it was assumed that there was no interaction between NB and M. aeruginosa in the mixture.

Meriluoto & Spoof’s (a, b) method, but the extracellular MC-LR was undetectable (below 1 μg/L) in most instances because of the low death rate of M. aeruginosa in the exponential growth phase and the low release rate of

RESULTS AND DISCUSSION

MC-LR by the dead cells. Briefly, for the intracellular MC-LR, the algal cells were harvested by filtration (0.45-μm glass fiber filters, Taoyuan Co. Ltd, China),

Effect of NB on the performance of coagulationsedimentation

broken by freeze-thawing, and extracted by 75% methanol, while the extracellular MC-LR in the filtrate was extracted by solid phase extraction. Both the intracellular MC-LR and extracellular MC-LR were analyzed by high performance liquid chromatography. Turbidity was measured using a HACH 2100P turbidimeter. The NB concentration was measured by high performance liquid chromatography (Waters e2695) according to Liu’s () method. The UV absorbance at 254 nm (UV254) was measured using a spectrophotometer (UV759S, INESA Instrument Co. Ltd, China) after the samples were filtered through 0.45-μm filters. Dissolved organic carbon (DOC) was measured using a Shimadzu TOC 5000 analyzer (Shimadzu, Kyoto, Japan) after filtration by 0.45-μm filters. Zeta potential (ZP) was measured using a Zetasizer Nano-Z (Malvern Instruments, Worcestershire, UK). Total alkalinity was measured by the Gran titration method.

The removal rates of turbidity, OD680, total THMFP, THMFP in the extracellular organic matter (EOM) and intracellular MC-LR in the M. aeruginosa solution and in NMf were investigated, and the results are presented in Figure 1. The quality of the water containing M. aeruginosa was signiﬁcantly improved by coagulation-sedimentation at an optimized dosage of coagulant. The removal rates of all ﬁve parameters (Figure 1) were higher than 80%. In this experiment, there were no other impurities in the M. aeruginosa solution except the cyanobacteria cells and their secretions, which caused the turbidity in the water (Divakaran & Pillai ). With the good sedimentation of M. aeruginosa cells, intracellular MC-LR and turbidity were also removed. Extracellular MC-LR in the settled water was undetectable, which indicated that coagulationsedimentation would not destroy the M. aeruginosa cells

EXPERIMENT A mixture of NB and M. aeruginosa (NM5), which was incubated for 5 days with an initial NB concentration of 160 μg/L and an initial cell density of 1.0 × 108 cells/L, was subjected to the jar test. The ﬁnal density of M. aeruginosa in NM5 was 2.1 ± 0.1 × 108 cells/L, and the ﬁnial NB concentration was 83.5 ± 2.2 μg/L. An M. aeruginosa solution with the same initial cell density (1.0 × 108 cells/L) and an NB solution (without M. aeruginosa) with the same initial NB concentration (160 μg/L) were incubated for 5 days and then used as control samples. A fresh mixture of NB and M. aeruginosa without incubation (NMf ), which contained the same NB concentration and cell density as the

Figure 1

Results of coagulation-sedimentation of M. aeruginosa solution and the fresh mixture of NB and M. aeruginosa without incubation (NMf ). Coagulant: Al2(SO4)3 at the optimized dosage of 20 mg Al2O3/L; 25 ± 1 C. W

Z. Liu et al.

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and result in a release of intracellular MC-LR into solution

of colloidal materials and the charge complexation/

as reported by Chow et al. (). Previous studies (Plum-

precipitation of soluble compounds (Randtke ; Ye

mer & Edzwald ; Huang et al. ) show that the

et al. ). Thus, the presence of NB in the M. aeruginosa

majority of THMFP comes from algal cells. Thus, a high

solution did not affect the coagulation-sedimentation per-

removal rate of total THMFP (91.8 ± 0.58%) was obtained.

formance directly.

Surprisingly, a high removal rate of THMFP in EOM (approximately 80%) was also obtained, which is much

Effect of the interaction of NB and M. aeruginosa on the

higher than the removal rate of DOC (31.6%). This phenom-

performance of coagulation-sedimentation

enon can be explained by the different removal efﬁciencies of dissolved organics by coagulation. It is reported that

The performance of coagulation-sedimentation in treating

coagulation can remove hydrophobic organics more efﬁ-

NM5 and M. aeruginosa solution with three types of coagu-

ciently than hydrophilic organics, and the former contains

lant was studied, and the results at the coagulant dosages

considerably more THMFP than the latter (Edzwald ).

optimized for turbidity removal are shown in Figure 2. The

The speciﬁc ultraviolet absorbance (SUVA, the ratio of

speciﬁc coagulant type did not affect the coagulation-sedi-

UV254 to DOC) is regarded as an indicator of organic com-

mentation performance, and the removal rates of turbidity,

position, and a high value indicates that the water mainly

OD680, THMFP and MC-LR were similar for different

contains hydrophobic, aromatic and high molecular

coagulants at their optimized dosages (20 mg Al2O3/L for

weight compounds, which form THMs more easily during

aluminum sulfate, 44.5 mg Fe2O3/L for ferric chloride and

disinfection and can more easily be removed by coagulation

39 mg Al2O3/L for PAC). The removal rate of THMFP in

(Edzwald ). This value also decreased signiﬁcantly after

EOM and residual turbidity in the NM5 settled water were

coagulation, from 2.69 to 1.73 L/mg/m. This change indi-

lower than in M. aeruginosa solution, while the removal

cated that the organic composition was greatly changed by

rates of OD680, total THMFP and intracellular MC-LR

coagulation with the more efﬁcient removal of hydrophobic

were identical. The results of one-way analysis of variance

organics. Previous studies also indicated that proteins

(ANOVA) comparing the removal rates of THMFP in

account for 33% of the total DOC in the algal solution,

EOM and residual turbidity in the settled water for NM5

and can be removed completely by coagulation (Henderson

and M. aeruginosa solution supported this viewpoint

et al. ; Tsai et al. ). Proteins are also THMFPs,

(p ¼ 0.045 for turbidity and p ¼ 3.84 × 10–9 for THMFP).

according to the study by Scully et al. (). Thus, the

After sedimentation, more than 80% of the total THMFP

removal of proteins by coagulation may contribute to the

came from the EOM in the settled water of NM5, which

removal of THMFP in EOM. In the settled water, 70.3 ±

was higher than that observed in the M. aeruginosa solution

9.9% of the total THMFP came from the EOM, which was

(without NB). Obviously, the interaction between NB and

much higher than that before coagulation-sedimentation

M. aeruginosa changed the characteristics of the raw water

(less than 30%).

and affected the coagulation-sedimentation results.

Neither the performance of coagulation-sedimentation

The removal of NB by coagulation-sedimentation is

nor the coagulant dosage was affected by the addition of

shown in Figure 3. In general, coagulation-sedimentation

NB. The removal rates of all ﬁve parameters in NMf by

cannot remove NB efﬁciently, and the removal rates were

coagulation-sedimentation were approximately the same as

always less than 4%. The interaction between NB and M.

those in the M. aeruginosa solution.

aeruginosa did not affect the removal of NB by coagu-

It is predictable that NB does not affect the coagulation-

lation-sedimentation. However, there was a slightly higher

sedimentation of M. aeruginosa. NB is a weak hydrophobic

removal rate of NB in NMf than in NM5, although the differ-

material with a solubility of 2.1 g/L at 25 C (IPCS ),

ence was not statistically signiﬁcant (one-way ANOVA,

and it cannot ionize in the water. These characteristics

p ¼ 0.065). The higher removal rate in NMf might be attrib-

imply that NB would not disturb coagulant hydrolysis or

uted to the adsorption of NB by M. aeruginosa during the

the coagulation process, including the charge neutralization

coagulation process.

Figure 2

Z. Liu et al.

Effects of the interaction between M. aeruginosa and NB on coagulation

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Results of coagulation-sedimentation of M. aeruginosa solution and a mixture of NB and M. aeruginosa after a 5-day incubation (NM5). Coagulant: (a) Al2(SO4)3 20 mg Al2O3/L; (b) FeCl3 44.5 mg Fe2O3/L; (c) PAC 39 mg Al2O3/L.

Proposed mechanisms for the effects of the interaction on coagulation-sedimentation The present study showed that NB did not affect the performance of coagulation-sedimentation directly, and the interaction between NB and M. aeruginosa reduced the removal rate of THMFP in EOM and the residual turbidity in the settled water. These variations were independent of coagulant type. In fact, these differences were also observed in the pre-oxidation and coagulation-sedimentation experiments (Figure S4, supplementary data; available online at http://www.iwaponline.com/jws/ 063/081.pdf). The results indicated that the characteristics of Figure 3

Removal of NB by coagulation-sedimentation with three types of coagulants.

the raw water were changed by the interaction between NB

Coagulant dosage: Al2(SO4)3 20 mg Al2O3/L; FeCl3 44.5 mg Fe2O3/L; PAC 39 mg Al2O3/L. NM5 – a mixture of NB and M. aeruginosa after a 5-day incubation.

and M. aeruginosa, which affected the performance of coagu-

NMf – a fresh mixture of NB and M. aeruginosa without incubation.

lation-sedimentation.

ZP (mV)

initial NB concentration (160 μg/L), the growth of M. aeru-

lation-sedimentation (turbidity removal rate increased from less than 75% to more than 99%, and OD680 removal rate increased from less than 70% to more than 99%). After adjustment, the pH was similar in NM5 and the M. aeruginosa solution (9.34–9.76 for NM5 and 9.46–9.91 for M. aeruginosa). Thus, the initial difference in pH and alkalinity in the present study are negligible. Interaction with NB changed the surface charges of M. aeruginosa, which is responsible for the lower residual turbidity in the settled water. Zeta potential represents the potential stability of the colloidal system, and a weaker zeta potential promotes coagulation (Nachbaur et al. ; Sharp et al. ). The negative zeta potential in NM5

pH Extracellular

0.2729 ± 0.0152 37.20 ± 8.32

27.80 ± 6.51

0.0820 ± 0.0075

water, which greatly improved the performance of coagu-

0.0805 ± 0.0003

adjusted by Na2CO3 according to actual values in surface

37.96 ± 2.01

efﬁciency. However, in the present study, alkalinity was

37.35 ± 3.23

can affect the optimized coagulant dosage and the coagulant

In NM5

play signiﬁcant roles in coagulation-sedimentation, which

In M. aeruginosa solution

out growth inhibition. Previous studies (Ye et al. ; Gottfried et al. ) have indicated that pH and alkalinity

Intracellular

resulting in a lower pH and higher alkalinity in NM5 with-

EOM

Kahara & Vermaat ). NB inhibited photosynthesis,

In the

alkalinity and cause an increase of pH (Talling ;

The total

cantly. Due to photosynthesis, phytoplankton can deplete

OD680 (cm 1)

affect the performance of coagulation-sedimentation signiﬁ-

(NTU)

NB after a 5-day incubation, but these variations did not

Turbidity

The pH and alkalinity were changed by the presence of

MC-LR (μg/L)

parameters.

THMFP (μg/L)

M. aeruginosa were evident from differences in water quality

after a 5-day incubation. However, other effects of NB on

Table 1

were the same in NM5 and the M. aeruginosa solution

Comparison of water quality parameters in the M. aeruginosa solution and the mixture of NB and M. aeruginosa after a 5-day incubation (NM5)

ginosa was not inhibited by NB, and turbidity and OD680

40.36 ± 3.65

The water quality parameters of the M. aeruginosa solution and NM5 are shown in Table 1. With a non-lethal

51.16 ± 2.40

oxidized to form THMs during chlorination (Liu et al. ).

7.53–8.64

dissolved compounds, such as aniline, which can be further

8.76–9.33

dation, and the biodegradation process may produce

–

Alkalinity (mg

enhance the elimination of NB by adsorption and biodegra-

–

rate, among others (Liu et al. ). M. aeruginosa can

51.7 ± 6.4

UV254 (cm 1)

reducing the MC-LR productivity and the photosynthetic

CaCO3/L)

cell activities by enhancing the protein productivity and

68.4 ± 11.2

At a high concentration, NB can inhibit the growth of M. aeruginosa. Even at a low concentration, NB can affect

0.1381 ± 0.0132

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796 ± 45

Effects of the interaction between M. aeruginosa and NB on coagulation

724 ± 72

2764 ± 88

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2515 ± 112

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increased and therefore enhanced the removal of turbidity

The results indicated that coagulation-sedimentation can

by coagulation-sedimentation. Takaara et al. () report

signiﬁcantly improve the quality of water containing

that M. aeruginosa cells can secrete negatively charged

M. aeruginosa. Although NB did not affect the coagu-

hydrophilic substances, which can decrease zeta potential

lation-sedimentation process directly, the presence of NB

and inhibit their coagulation. The presence of NB may inhi-

did change the performance of coagulation-sedimentation

bit the secretion of these compounds by M. aeruginosa,

after a 5-day incubation with M. aeruginosa, including a

which would result in increased zeta potential.

reduced removal rate of THMFP in EOM and a lower

NB-enhanced production of THMFP in M. aeruginosa

residual turbidity in the settled water. These effects resulted

EOM may have led to the low removal rate of THMFP in

from the interaction between NB and M. aeruginosa. The

NM5. Our previous study (Liu et al. ) indicates that

weak zeta potentials caused by the interaction and the

THMFP productivity in EOM can increase up to 50% in

degradation products of NB were responsible for these

the presence of NB, although NB cannot be chlorinated to

variations.

form THMs directly. These increased THMFP values come

The present results imply that the interaction between

from the proteins secreted by M. aeruginosa and the degra-

xenobiotics and algae on water treatment processes should

dation products of NB. Figure 1 shows a high removal rate

be considered when combined polluted water is used as

of THMFP in EOM by coagulation, which implies that

the drinking water source. However, due to the complexity

most of the THMFP secreted by M. aeruginosa can be

of xenobiotics and the diversity of algae and cyanobacteria,

removed by coagulation-sedimentation. Thus, the increased

the present study cannot represent the whole scenario, and

protein content would not affect the removal of THMFP sig-

further study is necessary.

niﬁcantly. However, the soluble degradation products of NB cannot be removed efﬁciently by coagulation-sedimentation, which caused a lower THMFP removal rate than without

ACKNOWLEDGEMENTS

NB and resulted in a higher proportion of THMFP in the EOM of the settled water (more than 80%, compared with

This work was supported by the Funds for Creative

70.3% without NB). These products increased the initial

Research Groups of China (Grant No. 51121062) and the

value of UV254 and resulted in a higher residual THMFP

National Natural Science Foundation of China (Number

concentration in the EOM after coagulation (228 ± 13 μg/

50778048).

L), compared with the M. aeruginosa solution without NB (142 ± 12 μg/L). The interaction between NB and M. aeruginosa increased the disinfection risk signiﬁcantly over the

REFERENCES

non-interactive case, and this risk increased further after coagulation-sedimentation. Although NB did not inhibit the growth of M. aeruginosa,

did

negatively

affect

the

performance

coagulation-sedimentation. This effect should be considered when water containing NB and M. aeruginosa is used as drinking water resource.

CONCLUSIONS In the present study, the impacts of the interaction between NB and M. aeruginosa on the performance of coagulationsedimentation were studied by using a bench-scale jar test.

Andreozzi, R., Caprio, V., Ciniglia, C., Champdoré, M., Giudice, R. L., Marotta, R. & Zuccato, E.  Antibiotics in the environment: occurrence in Italian STPs, fate, and preliminary assessment on algal toxicity of amoxicillin. Environ. Sci. Technol. 38, 6832–6838. Chow, C. W. K., Drikas, M., House, J., Burch, M. D. & Velzeboer, R. M. A.  The impact of conventional water treatment processes on cells of the cyanobacterium Microcystis aeruginosa. Water Res. 15, 3253–3262. Divakaran, R. & Pillai, V. N. S.  Flocculation of algae using chitosan. J. Appl. Phycol. 14 (5), 419–422. Edzwald, J. K.  Coagulation in drinking water treatment: particles, organics and coagulants. Water Sci. Technol. 27 (11), 21–35. Gao, J., Liu, L., Liu, X., Zhou, H., Wang, Z. & Huang, S.  Concentration level and geographical distribution of

Z. Liu et al.

Effects of the interaction between M. aeruginosa and NB on coagulation

nitrobenzene in Chinese surface waters. J. Environ. Sci. 20 (7), 803–805. Gottfried, A., Shepard, A. D., Hardiman, K. & Walsh, M. E.  Impact of recycling filter backwash water on organic removal in coagulation–sedimentation processes. Water Res. 42, 4683–4691. Henderson, R. K., Baker, A., Parsons, S. A. & Jefferson, B.  Characterisation of algogenic organic matter extracted from cyanobacteria, green algae and diatoms. Water Res. 42, 3435–3445. Huang, J., Graham, N., Templeton, M. R., Zhang, Y., Collins, C. & Nieuwenhuijsen, M.  A comparison of the role of two blue–green algae in THM and HAA formation. Water Res. 43 (12), 3009–3018. International Programme on Chemical Safety (IPCS)  Nitrobenzene environmental health criteria 230. Geneva: WHO. Kahara, S. N. & Vermaat, J. E.  The effect of alkalinity on photosynthesis-light curves and inorganic carbon extraction capacity of freshwater macrophytes. Aquat. Bot. 75 (3), 217–227. Liu, Z., Cui, F., Ma, H., Fan, Z. & Zhao, Z.  The role of nitrobenzene on the yield of trihalomethane formation potential in aqueous solutions with Microcystis aeruginosa. Water Res. 45 (19), 6489–6495. Liu, Z., Cui, F., Ma, H., Fan, Z., Zhao, Z., Hou, Z. & Liu, D.  Bio-reaction of nitrobenzene with Microcystis aeruginosa: characteristics, kinetics and application. Water Res. 46 (7), 2290–2298. Liu, Z., Cui, F., Ma, H., Fan, Z., Zhao, Z., Hou, Z. & Liu, D.  The interaction between nitrobenzene and Microcystis aeruginosa and its potential to impact water quality. Chemosphere 92 (9), 1201–1206. Ma, H., Cui, F., Liu, Z. & Fan, Z.  Pilot study on control of phytoplankton by zooplankton coupling with filter-feeding fish in surface water. Water Sci. Technol. 60 (3), 737–743. Meriluoto, J. & Spoof, L. a SOP: extraction of microcystins in biomass filtered on glass-fibre filters or in freeze-dried cyanobacterial biomass. In: TOXIC: Cyanobacterial Monitoring and Cyanotoxin Analysis (J. Meriluoto & G. A. Codd, eds). Åbo Akadmi University Press, Turku, Finland, pp. 69–71. Meriluoto, J. & Spoof, L. b SOP: solid phase extraction of microcystins in water samples. In: TOXIC: Cyanobacterial Monitoring and Cyanotoxin Analysis (J. Meriluoto & G. A. Codd, eds). Åbo Akadmi University Press, Turku, Finland, p. 73. Ministry of Environmental Protection of the People’s Republic of China  Report on the state of environment in China, Beijing. Available from: http://jcs.mep.gov.cn/hjzl/zkgb/ 2010zkgb/201106/t20110602_211577.htm (in Chinese). Nachbaur, L., Nkinamubanzi, P.-C., Nonat, A. & Mutin, J.-C.  Electrokinetic properties which control the coagulation of

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

silicate cement suspensions during early age hydration. J. Colloid Interface Sci. 202 (2), 261–268. Plummer, J. D. & Edzwald, J. K.  Effect of ozone on algae as precursors for trihalomethane and haloacetic acid production. Environ. Sci. Technol. 35 (18), 3661–3668. Randtke, S. J.  Organic contaminant removal by coagulation and related process combinations. J. Am. Water Works Assoc. 80 (5), 40–56. Scully Jr., F. E., Howell, G. D., Kravitz, R. & Jewell, J. T.  Proteins in natural waters and their relation to the formation of chlorinated organics during water disinfection. Environ. Sci. Technol. 22 (5), 537–542. Semple, K. T. & Cain, R. B.  Biodegradation of phenols by the alga Ochromonas danica. Appl. Environ. Microbiol. 62 (4), 1265–1273. Semple, K. T., Cain, R. B. & Schmidt, S.  Biodegradation of aromatic compounds by microalgae. FEMS Microbiol. Lett. 170, 291–300. Sharp, E. L., Jarvis, P., Parsons, S. A. & Jefferson, B.  The impact of zeta potential on the physical properties of ferricNOM ﬂocs. Environ. Sci. Technol. 40 (12), 3934–3940. Shen, Q., Zhu, J., Cheng, L., Zhang, J., Zhang, Z. & Xu, X.  Enhanced algae removal by drinking water treatment of chlorination coupled with coagulation. Desalination 271 (1–3), 236–240. Takaara, T., Sano, D., Masago, Y. & Omura, T.  Surface-retained organic matter of Microcystis aeruginosa inhibiting coagulation with polyaluminum chloride in drinking water treatment. Water Res. 44 (13), 3781–3786. Talling, J. F.  The depletion of carbon dioxide from lake water by phytoplankton. J. Ecol. 64 (1), 79–121. Tian, J., Chen, Z., Nan, J., Liang, H. & Li, G.  Integrative membrane coagulation adsorption bioreactor (MCABR) for enhanced organic matter removal in drinking water treatment. J. Membr. Sci. 352 (1–2), 205–212. Tsai, B.-N., Chang, C.-H. & Lee, D.-J.  Fraction of soluble microbial products (SMP) and soluble extracellular polymeric substances (eps) from wastewater sludge. Environ. Technol. 29 (10), 1127–1138. Tukaj, Z.  The effect of fuel oil on the ultrastructure of the chlorococcal alga Scenedesmus armatus. Protoplasma 151 (1), 47–56. Ye, C., Wang, D., Shi, B., Yu, J., Qu, J., Edwards, M. & Tang, H.  Alkalinity effect of coagulation with polyaluminum chlorides: role of electrostatic patch. Colloids Surf. A Physicochem. Eng. Asp. 294 (1–3), 163–173. Zamyadi, A., MacLeod, S. L., Fan, Y., McQuaid, N., Dorner, S., Sauvé, S. & Prévost, M.  Toxic cyanobacterial breakthrough and accumulation in a drinking water plant: a monitoring and treatment challenge. Water Res. 46 (5), 1511–1523.

First received 26 April 2013; accepted in revised form 20 September 2013. Available online 31 October 2013

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Biosorption studies for the removal of ferrous ion from aqueous solution by Aspergillus terreus and Trichoderma viride: kinetic, thermodynamic and isothermal parameters Sarafadeen Olateju Kareem, Abideen Idowu Adeogun and S. O. Omeike

ABSTRACT Environmental implications and the tendency of heavy metals to accumulate in selected tissues of plants and animals, as well as their overall potential to be toxic even at relatively minor levels of exposure, are a source of concern. The use of cheap and effective biological means for heavy metal remediation has been advocated. This study examines the biosorption and bioaccumulation of ferrous ion by Aspergillus terreus (A. terreus) and Trichoderma viride (T. viride) in a batch system. The effects of some important parameters, such as initial metal concentration, temperature and inoculum concentration on biosorption capacity were examined. Langmuir, Freudlinch, Temkin and Dubinin–Radushkevich models were applied to explain the biosorption isotherms of Fe(II) onto the

Sarafadeen Olateju Kareem S. O. Omeike Department of Microbiology, Federal University of Agriculture, Abeokuta, Nigeria Abideen Idowu Adeogun (corresponding author) Department of Chemistry, Federal University of Agriculture, Abeokuta, Nigeria E-mail: abuaisha2k3@yahoo.com

biosorbents. The process ﬁtted well into pseudo ﬁrst order kinetic model and was best explained by the Langmuir isotherm with maximum absorption capacity of 6.33 and 7.50 mg/g for A. terreus and T. viride, respectively. The calculated thermodynamic parameters (ΔG , ΔH and ΔS ) showed that the W

biosorption of the ferrous ion to the organisms is feasible, spontaneous and exothermic in nature at low temperature. Key words

| Aspergillus terreus, biosorption, kinetics and isotherms, Trichoderma viride

INTRODUCTION The presence of heavy metal ions in the environment is a

in Fenton reactions leading to the production of high levels

major concern due to their toxicity to many life forms.

of toxic hydroxyl radicals (·OH) and other reactive oxygen

Industrial processes, such as mining operations, sludge dis-

species (H2O2, O2 ) (Bode et al. ; Thongbai & Good-

posal, metal as plating and the manufacture of electrical

man ). These compounds react with polyunsaturated

equipment lead to the release of metal ions into the environ-

fatty acids, protein and nucleic acids, thus giving rise to

ment and, invariably, they become pollutants in the

the

environment. The toxic nature of these pollutants has

(Alscher et al. ). Serious toxicity is likely with ingestion

caused increased concern regarding their removal from

of more than 60 mg/kg. Toxic effects of iron may occur at

industrial efﬂuents (Senthilkumar et al. ). Heavy

doses of 10–20 mg/kg of elemental iron (Rao & Georgieff

metals are not biodegradable and tend to accumulate in

). When high concentrations (over 200 ppm) of iron

living organisms, causing various diseases and disorders

are absorbed, for example by haemochromatosis patients,

(Manaham ; Yu ).

iron is stored in the pancreas, the liver, the spleen and

destruction

numerous

endocellular

structures

Although iron is an essential element for life, its excess

the heart and may damage these vital organs. Thus, the

in plant and animal tissues may be responsible for a wide

need for the removal of ferrous ion from wastewater

range of metabolic disorders, due mainly to its involvement

is justiﬁed.

doi: 10.2166/aqua.2013.109

S. O. Kareem et al.

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

Iron removal from water is mostly carried out in drinking water preparation, because mineral water contains high

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

mass (Hemambika et al. ). One mL of deionized water was added to the dried spores as necessary.

amounts of iron ions. These inﬂuence water color, odor and turbidity. In wastewater treatment, iron removal may be

Preparation of aqueous solution of metal ions

achieved by cumbersome and high-cost processes which justify alternative technology. Biosorption of heavy metals by

The aqueous solutions of ferrous ion were prepared freshly

microbial cells has been recognized as a potential alterna-

as necessary from analytical grades of Fe2SO4.H2O

tive to existing technologies for recovery of heavy metals

(Sigma, USA). One thousand mg/L aqueous solution

from industrial waste streams. Most studies of biosorption

(stock solution) of this salt was prepared with deionized

for metal removal have involved the use of either labora-

water in 1% HNO3 solution and the stock solution was

tory-grown microorganisms or biomass generated by the

diluted with deionized water to obtain the working standard

pharmacology and food processing industries or wastewater

solutions. The pH adjustments of the solutions were made

treatment units (Tsezos & Velosky ; Townsley et al.

with aliquots of 1.0 mol/L of HCl and NaOH. The pH of

; de Rome & Gadd ; Macaskie ; Costa &

the solution was checked on an electronic pH meter

Leite ; Rao et al. ; Dias et al. ). Many aquatic

(Thermo Russell, USA), which had earlier been standar-

microorganisms, such as fungi, bacteria, yeast and algae

dized with standard buffer solution.

can take up dissolved metals from their surroundings onto their bodies and can be used for removing heavy metal

Equilibrium studies

ions successfully (Aksu et al. ). In this study, Aspergillus terreus (A. terreus) and Trichoderma viride (T. viride) were

Equilibrium adsorption isotherms were performed in a

used as effective and low-cost sorbents for the removal of

batch process as previously described (Bello et al. ;

ferrous ion from aqueous solution. The effects of initial

Adeogun et al. , ). The amount of metal ion adsorbed

metal concentration, pH, temperature and biosorbent dose

by the biosorbent at equilibrium, Qe (mg/g), was calculated

were also studied. The equilibrium data were analyzed

using Equation (1):

with Langmuir, Freundlich, Redlich–Peterson and Dubinin– Radushkevich (D–R) isotherms. The thermodynamic parameters

were

also

elucidated

from

the

temperature

Qe ¼

(Co Ce )V , W

(1)

dependent of the biosorption process. where Co and Ce (mg/L) are the liquid-phase concentrations of the metal ion at initial and equilibrium, respectively. V is the volume of the solution (L), and W is the mass of dry

MATERIALS AND METHODS

adsorbent used (g).

Biosorbent preparation

Bioaccumulation studies

Metal-degrading fungal strains of A. terreus and T. viride iso-

The procedure was similar to that of equilibrium studies,

lated from contaminated soil were collected from the

except that samples were prepared and withdrawn at an

Microbiology Department of the Federal University of Agri-

interval of 24 hours for the determination of residual iron

culture Abeokuta, southwestern Nigeria. The biomass was

in the solution. The amount of iron absorbed at intervals

suspended in 10 mL deionized water and centrifuged at

was expressed as a percentage:

4,000 rpm for 10 min to separate vegetative materials from spores. The supernatant was decanted and the precipitated spores were dried in a hot air-oven and weighed to constant

Q% ¼

(Co Ce )V × 100 W

S. O. Kareem et al.

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

). Optimum iron sorption at pH 6 and 7 was observed

Batch kinetic studies

for A. terreus and T. viride, respectively (Figure 1). This indiThe procedures for kinetic experiments were basically iden-

cates the presence of weakly positive to negative ions on the

tical to those of equilibrium tests. The aqueous samples were

surface of the biomass to interact with ferrous ion present in

taken at pre-set time intervals, and the concentrations of the

the solution and form bonds, binding the metals to the bio-

metal ions were similarly determined. The amount of metal

mass surface (Faria et al. ). At high pH, there is an

ion adsorbed at time t, Qt (mg/g), was calculated using

abundance of negatively charged ions on the surface of the

Equation (2):

biomass and this will attract more positively charged metal ion to the surface for binding.

Qt ¼

(Co Ce )V W

(2)

where Co and Ct (mg/L) are the liquid-phase concentrations

Effect of biosorbent dosage The effect of biosorbent dosages on the removal of Fe(II) ion

of the metal ion at initial and at time t, respectively. V is the

from aqueous solution is presented in Figure 2. The highest

volume of the solution (L), and W is the mass of dry adsor-

metal concentration was obtained at the dosage of 1.6 mg of

bent used (g).

the biosorbents. Although at lower dosage T. viride remove more of the metal ion than A. terreus, as the dosage increases the capacity for the removal of the metal ion increases to the maximum of about 89% at 1.6 mg dosage

RESULTS AND DISCUSSION

for the two biosorbents. As the biosorbent dosage increases above optimum dosage values, a decrease in the percentage

Effect of hydrogen ion concentration

of metallic ions removal was observed. This may be attributed to the concentration gradient between adsorbate and

pH is described as the most important factor for ion uptake

biosorbent with increasing biomass concentration causing

(Geddie & Sutherland ). It strongly inﬂuences the spe-

a decrease in the amount of metallic ion adsorbed per

ciation and biosorption availability of the metal ions

gram of biomass (Vaghetti et al. ).

(Esposito et al. ), alters activity of binding sites and also affects the types of ion in the solution (Adeogun et al.

Figure 1

Effect of pH on sorption capacities of A. terreus and T. viride.

Figure 2

Effect of biosorbent dosage on sorption capacities of A. terreus and T. viride.

S. O. Kareem et al.

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

Journal of Water Supply: Research and Technology—AQUA

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2014

Effect of temperature Optimization parameters for iron removal showed an optiW

mum uptake at a temperature of 30 C (Figure 3), with reduced uptake above and below this temperature indicating that sorption occurs best at the growth temperature of sorbents. This conforms with several studies which state that temperature has less inﬂuence on sorption performances in W

the range of 20–35 C, and its overall effect is minimal because sorption takes place over a narrow temperature range of 10– 70 2035 C (Aksu et al. ; Vegliò & Beolchini ). W

Effect of contact time and initial metal concentration Figures 4 and 5 illustrate the effect of contact time on Fe(II) ion biosorption by T. viride and A. terreus, respectively.

Figure 4

The effect of contact time on Fe(II) ion biosorption by T. viride.

Figure 5

The effect of contact time on Fe(II) ion biosorption by A. terreus.

From the figures, it can be seen that the equilibrium time for metal ion biosorption T. viride is concentration dependent while that of A. terreus does not depend on initial metal ion concentration. This is as a result of the high affinity of A. terreus for metal ion (Dias et al. ). Biosorption kinetics Pseudo first order kinetics The study of adsorption kinetics is important in the treatment of aqueous effluents as it provides valuable information on

the reaction pathways and the mechanism of the adsorption process. Many kinetic models have been developed in order to ﬁnd intrinsic kinetic adsorption constants; traditionally, the kinetics of metal ions adsorption is described following the expressions originally given by Largegren (). A simple kinetic analysis of adsorption under the pseudo ﬁrst order assumption is given by Equation (3) below:

Figure 3

Effect of temperature on sorption capacities of A. terreus and T. viride.

dQt ¼ k1 (Qe Qt ) dt

(3)

S. O. Kareem et al.

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

Journal of Water Supply: Research and Technology—AQUA

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2014

where Qe and Qt are the amounts of the metal ion biosorbed

where k2 (g mg 1 min 1) is the rate constant of second order

(mg g 1) at equilibrium and at time t (min), respectively, k1 is

adsorption. The parameters for pseudo second order theor-

the rate constant of biosorption (min ) and t is the contact

etical ﬁtting of time-dependent biosorption of the metal

time (min). The integration of Equation (3) with initial con-

ion onto the biosorbent (Qt) are presented in Table 1. The

ditions, Qt ¼ 0 at t ¼ 0 and Qt ¼ Qt at t ¼ t, yields Equation (4):

correlation coefﬁcients for the second order kinetic model ranged between 0.986 and 0.989 indicating the suitability

Qt ¼ Qe (1 e k1 t )

(4)

of the pseudo ﬁrst order kinetics over that of the second order kinetic equation model.

The kinetic data were analyzed with non-linear regression analysis method using a program written on MicroMath Scientist software (Salt Lake City, USA). The parameters

Statistical test for the kinetic data

used for ﬁtting the pseudo ﬁrst order kinetics are shown in

Although the R 2, i.e., the correlation coefﬁcients used to

Table 1. The correlation coefﬁcient values of 0.996 and the

compare the data, the pseudo ﬁrst and second order kinetic

experimental Qe values are not far from the calculated ones.

function which measures the differences (% SSE) in the

Pseudo second order kinetics

amount of the metallic metal ion uptaken by the adsorbent

A second order kinetic model which is based on equilibrium adsorption (Malik ) was used to compare the Qe values with that of pseudo ﬁrst order rate model. It is expressed as shown by Equation (5): dQt ¼ k2 (Qe Qt )2 dt

(5)

Upon integration and rearrangement, the second order rate equation becomes Equation (6): Qt ¼

predicted by the models, (Qcal), and the actual, i.e., Qexp measured experimentally (Vaghetti et al. ; Adeogun et al. ). The validity of each model was determined by the sum of error squares (SSE, %) given by Equation (7): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (q( exp ) q(cal) )2 %SSE ¼ N

where N is the number of data points. The higher the value of R 2 and the lower the value of SSE, the better will be the

(6)

the ﬁrst order model conﬁrm the acceptability of the model over the second order model.

Kinetic parameters for the biosorption of Fe(II) by A. terreus and T. viride Pseudo ﬁrst order Co (mg L 1)

A. terreus

T. viride

(7)

goodness of ﬁt. From Table 1, the lower values of % SSE for

k2 Q2e t

1 þ k2 Qe t

Table 1

models were also evaluated further by the percentage error

Ce (mg L 1)

2.37

Pseudo second order

Qe (exp) (mg g 1)

Qe(cal) (mg g 1)

k1 (h 1)

% SSE

k2 (g mg h 1)

% SSE

2.977

3.112

0.113

0.996

0.015

3.867

0.028

0.989

0.099

(calc)

100

9.84

5.635

5.891

0.113

0.996

0.028

7.319

0.015

0.989

0.187

125

22.89

6.382

6.672

0.113

0.996

0.032

8.289

0.013

0.989

0.212

150

61.74

5.516

5.767

0.113

0.996

0.028

7.165

0.015

0.989

0.183

200

103.11

6.056

6.331

0.113

0.996

0.031

7.865

0.014

0.989

0.201

1.33

3.042

3.180

0.113

0.996

0.015

3.951

0.027

0.986

0.101

100

6.03

5.873

6.140

0.113

0.996

0.030

7.628

0.014

0.986

0.195

125

14.88

6.883

7.195

0.113

0.996

0.035

8.940

0.012

0.989

0.229

150

37.55

7.028

7.347

0.113

0.996

0.035

9.129

0.012

0.986

0.233

200

83.79

7.263

7.593

0.113

0.996

0.037

9.434

0.011

0.989

0.241

10.63 0.978 4.42 6.91 0.999 1.033 0.423 3.607 0.986 6.413 3.945 0.009 0.996 0.549

0.970 9.18

7.505

aRP KRP RL

KF ((mg g 1) (L g 1))1/n

T. viride

Table 2

Biosorption isotherms for Fe(II) biosorption on T. viride.

b (L mg 1)

Qo (mg g 1)

Figure 7

Freundlich

Biosorption isotherms for Fe(II) biosorption on A. terreus.

Langmuir

Figure 6

Isotherm parameters for the biosorption of Fe(II) by A. terreus and T. viride

Redlich–Peterson

Figures 6 and 7 are shown in Table 2.

5.99

USA). The parameters obtained for the isotherm models in

0.997

Dubinin

written on MicroMath Scientist software (Salt Lake City,

1.138

the non-linear regression analysis method using a program

0.163

to describe the equilibrium data. Data were analyzed with

1.830

β × 107 mol2 J 2

Freundlich, D–R and Redlich–Peterson models were used

Qm (mg g 1)

equilibrium relationships. In the present study, Langmuir,

0.978

phase and that on the adsorbent’s surface at a given condition. Several isotherms have been developed to describe

8.183

ship between the adsorbate concentration in the liquid

3.636

An adsorption isotherm represents the equilibrium relation-

0.011

Ea (kJ mol 1)

Adsorption isotherms

7.38

Journal of Water Supply: Research and Technology—AQUA

0.927

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

0.469

6.325

S. O. Kareem et al.

A. terreus

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S. O. Kareem et al.

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

Journal of Water Supply: Research and Technology—AQUA

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where KF is the Freundlich constant related to sorption

Langmuir isotherm model

capacity ((mg g 1) (L g 1))1/n and n is related to the adsorpLangmuir, in 1918, observed sorption phenomena and

tion intensity of the adsorbent. The value of KF from this

suggested that uptake occurs on a homogeneous surface

study showed that biosorbents have similar afﬁnity for the

metal ion. The value of 1/n showed that the surfaces are het-

monolayer

sorption

without

interaction

between

adsorbed molecules. He proposed the sorption isotherm

erogeneous (Krobba et al. ).

based on the assumptions that: (i) adsorbates are chemically adsorbed at a ﬁxed number of well-deﬁned sites; (ii) each

Redlich–Peterson isotherm

site can only hold one adsorbate species; (iii) all sites are energetically equivalent; and (iv) that there are no inter-

A three parameter Redlich–Peterson equation has been pro-

actions between the adsorbate species.

posed to improve the ﬁt by the Langmuir or Freundlich

The Langmuir isotherm equation is written as Equation (8): qe ¼

Qmax bCe 1 þ bCe

(8)

where Ce is the supernatant concentration after the equilibrium of the system (mg L 1), b the Langmuir afﬁnity constant (L mg 1) and Qmax is the maximum adsorption capacity of the material (mg g 1) assuming a monolayer of adsorbate uptaken

equation and is given by Equation (11): Qeq ¼

KRP Ce 1 þ aRP Ceβ

(11)

where KR, bR and β are the Redlich–Peterson parameters, β lies between 0 and 1 (Vaghetti et al. ). For β ¼ 1, Equation (11) converts to the Langmuir form. The adsorption model constants are presented in Table 2 and the isotherms are shown in Figures 5 and 6. The value of β

by the adsorbent. The values of R 2 in Table 2 for the Langmuir isotherm showed better ﬁttings (Figures 6 and 7) for the metal ions

obtained in this study shows that the model operates closely to the Langmuir isotherm.

by the two biosorbents. Separation factor (RL) is deﬁned by the relationship in Equation (9) (Anirudhan & Radhak-

D–R isotherm

rishnan ): RL ¼ 1=(1 þ bCi )

(9)

The D–R model (Dubinin et al. ) was chosen to estimate the heterogeneity of the surface energies and also to deter-

where Ci is the initial concentration (mg L ). The value of

mine the nature of biosorption processes as physical or

the separation parameter provides important information

chemical. The D–R sorption isotherm is more general than

about the nature of adsorption. If RL value of zero implies

the Langmuir isotherm as its derivation is not based on

irreversible adsorption, it is a favorable process when 0 < RL < 1, linear when RL is 1 or unfavorable when RL is greater than 1 (Chen et al. ). The values of RL in this study and presented in Table 2 implied that the process is favorable.

Freundlich isotherm model The Freundlich isotherm is based on the assumption of nonideal adsorption on heterogeneous surfaces and the linear form of the isotherm can be represented as in Equation (10) (Freundlich ): Qt ¼ KF Ce1=n

(10)

Figure 8

Thermodynamic plot for Fe(II) biosorption on A. terreus and T. viride.

S. O. Kareem et al.

Table 3

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

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Thermodynamic parameters for the biosorption of Fe(II) by A. terreus and T. viride A. terreus

T. viride

Temperature (K)

ΔG (kJ mol 1)

ΔS (kJ mol 1 K 1)

ΔH (kJ mol 1)

ΔG (kJ mol 1)

ΔS (kJ mol 1 K 1)

ΔH (kJ mol 1)

308

4.00

0.138

60.43

0.99

4.23

0.279

90.52

0.95

318

2.21

2.18

323

1.24

0.15

ideal assumptions such as, equipotent of the sorption sites,

used to estimate the thermodynamic parameters owing to

absence of hindrance between sorbed and incoming par-

its

ticles and surface homogeneity on microscopic level

(Nkiko et al. ). A plot of ΔG against the temperature

(Weber & Morris ; Malik ). The D–R isotherm is

(Figure 8) gives a straight line graph with intercept as ΔH

represented by Equation (12):

and slope as ΔS . The thermodynamic parameters are pre-

temperature

dependence

previously

explained

sented in Table 3. The negative values of ΔG free energy W

Qt ¼ Qm e

βε2

(12)

where Qm is the theoretical saturation capacity (mol g 1), β

change of the process implies a thermodynamically feasible and spontaneous biosorption process.

is a constant related to the mean free energy of adsorption per mole of the adsorbate (mol2 J 2) and ε is the polanyi potential given by the relation ε ¼ RT ln(1 þ 1/Ce). Ce is the equilibrium concentration of adsorbate in solution (mol L 1), R (J mol 1 K 1) is the gas constant and T (K) is the absolute temperature. The constant β gives an idea about the mean free energy E (kJ/mol) of adsorption per molecule of the adsorbate when it is transferred to the surface of the solid from inﬁnity in the solution and can be

CONCLUSION Results of this study showed that iron could be effectively removed from solution by low amounts of A. terreus and T. viride biomass in solution. The biosorption capacity values of 6.33 and 7.51 mg/g for A. terreus and T. viride, respectively, in this study compared favorably with those

calculated from the relationship (Kundu & Gupta ):

reported in the literature. Sag et al. () reported a value

pffiffiffiffiffiffiffiffiffi E ¼ 1= 2β

() reported a value of 6.04 mg/g using dried sludge for

(13)

If the magnitude of E is between 8 and 16 kJ mol 1, the sorption process is supposed to proceed via chemisorption, while for values of E < 8 kJ mol 1, the sorption process is

of 5.1 mg/g using Rhizopus arrhizus while Shokoohi et al. iron removal. High uptake of iron even at a concentration of 250 mg/L shows that suspension or trapping of the spore mass on suitable matrices will further enhance their sorption capabilities and its scaling-up for use in efﬂuent clean-up, while the Langmuir and Freundlich models further

physical in nature. Since the values obtained in this study

conﬁrm the high sorption property of these biomasses. A.

are within the ranges speciﬁed by the model, the process

terreus and T. viride biomass can be effectively used as

therefore, is purely chemisorption.

material for biological removal of iron from aqueous solution over a wide range of concentration and pH.

Biosorption thermodynamics

REFERENCES Distribution of the metal ion between the solution and the biosorbent is an equilibrium process associated with the equilibrium constant, KD. The equilibrium constant was

Adeogun, A. I., Bello, O. S. & Adeboye, M. D.  Biosorption of lead ions on biosorbent prepared from plumb shells

S. O. Kareem et al.

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

(Spondias mombin): kinetics and equilibrium studies. Pakistan J. Sci. Ind. Res. 53, 246–251. Adeogun, A. I., Ofudje, A. E., Idowu, M. A. & Kareem, S. O.  Equilibrium, kinetic, and thermodynamic studies of the biosorption of Mn(II) ions from aqueous solution by raw and acid-treated corncob biomass. BioResources 6, 4117–4134. Adeogun, A. I., Kareem, S. O., Durosanya, J. B. & Balogun, S. E.  Kinetics and equilibrium parameters of biosorption and bioaccumulation of lead ions from aqueous solutions by Trichoderma longibrachiatum. J. Microbiol. Biotechnol. Food Sci. 1, 1221–1234. Aksu, Z., Sag, Y. & Kutsal, T.  The biosorption of copper (II) by C. vulgaris and Z. ramigera. Environ. Technol. 13, 579–586. Alscher, R. G., Donahue, J. L. & Cramer, C. L.  Reactive oxygen species and antioxidants: relationships in green cells. Physiol. Plantarum 100, 224–233. Anirudhan, T. S. & Radhakrishnan, P. G.  Thermodynamics and kinetics of adsorption of Cu(II) from aqueous solutions onto a new cation exchanger derived from tamarind fruit shell. J. Chem. Thermodynam. 40, 702–709. Bello, O. S., Adeogun, A. I., Ajaelu, J. C. & Fehintola, E. O.  Adsorption of methylene blue onto activated carbon derived from periwinkle shells: kinetics and equilibrium studies. Chem. Ecol. 24, 285–295. Bode, K., Döring, O., Luthje, S., Neue, H. U. & Böttger, M.  The role of active oxygen in iron tolerance of rice (Oryza sativa L.). Protoplasma 184, 249–255. Chen, Z., Ma, M. & Han, M.  Biosorption of nickel and copper onto treated alga (Undaria pinnatiﬁda): Application of isotherm and kinetic models. J. Hazard. Mater. 155, 327–333. Costa, A. C. A. & Leite, S. G. C.  Metal biosorption by sodium alginate immobilized Chlorella homosphaera cells. Biotechnol. Lett. 13, 559–562. de Rome, L. & Gadd, G. M.  Copper adsorption by Rhizopus arrhizus, Cladosporium resinae and Penicillium italicum. Appl. Microbiol. Biotech. 26, 84–90. Dias, M. A., Lacerda, I. C. A., Pimentel, P. F., de Castro, H. F. & Rosa, C. A.  Removal of heavy metals by an Aspergillus terreus strain immobilized in a polyurethane matrix. Lett. Appl. Microbiol. 34, 46–50. Dubinin, M. M., Zaverina, E. D. & Radushkevich, L. V.  Sorption and structure of active carbons. I. Adsorption of organic vapors. Zhurnal Fizicheskoi Khimii 21, 1351–1362. Esposito, A., Pagnanelli, F. & Vegliò, F.  pH-related equilibria models for biosorption in single metal systems. J. Chem. Eng. Sci. 57, 307–313. Faria, P. C. C., Orfao, J. J. M. & Pereira, M. F. R.  Adsorption of anionic and cationic dyes on activated carbons with different surface chemistries. Water Res. 38 (8), 2043–2052. Freundlich, H. M. F.  Over the adsorption in solution. J. Phys. Chem. 57, 385–471. Geddie, J. L. & Sutherland, I. W.  Uptake of metals by bacterial polysaccharides. J. Appl. Microbiol. 74, 467–472. Hemambika, B., Johncy Rani, M., Hemapriya, J. & Rajesh Kannan, V.  Comparative assessment of heavy metal removal by

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

immobilized and dead bacterial cells: a biosorption approach. Afr. J. Environ. Sci. Technol. 4 (2), 077–083. Krobba, A., Nibou, D., Amokrane, S. & Mekatel, H.  Adsorption of copper (II) onto molecular sieves NaY. Desal. Water Treat. 37, 31–37. Kundu, S. & Gupta, A. K.  Arsenic adsorption onto iron oxide-coated cement (IOCC): regression analysis of equilibrium data with several isotherm models and their optimization. Chem. Eng. J. 122 (1–2), 93–106. Largegren, S.  About the theory of so-called adsorption of soluble substances. Kungliga Suensk Vetenskapsakademiens Handlingar 241, 1–39. Macaskie, L. E.  An immobilized cell bioprocess for the removal of heavy metals from aqueous flows. J. Chem. Tech. Biotechnol. 49, 357–379. Malik, P. K.  Dye removal from wastewater using activated carbon developed from sawdust: adsorption equilibrium and kinetics. J. Hazard. Mater. B113, 81–88. Manaham, S. E.  Environmental Chemistry, 7th edn. CRC Press, Boca Raton, FL. Nkiko, M. O., Adeogun, A. I., Babarinde, N. A. A. & Sharaibi, O. J.  Isothermal, kinetics and thermodynamics studies of the biosorption of Pb(II) ion from aqueous solution using the scale of croaker fish (Genyonemus lineatus). J. Water Reuse Desal. 3, 239–248. Rao, C. R. N., Iyengar, L. & Venkobachar, C.  Sorption of copper (II) from aqueous phase by waste biomass. J. Environ. Eng. Div. ASCE 119, 369–377. Rao, R. & Georgieff, M. K.  Iron therapy for preterm infants. Clin. Perinatol. 36 (1), 27–42. Sag, Y., Yalçuk, A. & Kutsal, T.  Full competitive biosorption of chromium (VI) and iron (III) ions from binary metal mixtures by Rhizopus arrhizus: Use of competitive Langmuir model. Process Biochem. 31, 573–585. Shokoohi, R., Saghi, M. H., Ghafari, H. R. & Hadi, M.  Biosorption of iron from aqueous solution by dried biomass of activated sludge. Iran J. Environ. Health Sci. Eng. 6 (2), 107–114. Senthilkumar, S., Bharathi, S. & Nithyanandhi, S.  Biosorption of toxic heavy metals from aqueous solution. Bioresour. Technol. 75, 163–165. Thongbai, P. & Goodman, B. A.  Free radical generation and postanoxic injury in rice grown in an iron-toxic soil. J. Plant Nutr. 23, 1887–1900. Townsley, C. C., Ross, I. S. & Atkins, A. S.  Biorecovery of metallic residues from various industrial effluents using filamentous fungi. In: Fundamental and Applied Biohydrometallurgy (R. W. Lawrence, R. M. R. Branion & H. G. Ebner, eds). Elsevier, Amsterdam, The Netherlands, pp. 279–289. Tsezos, M. & Velosky, B.  The mechanism of uranium biosorption by Rhizopus arrhizus. Biotechnol. Bioeng. 29, 385–401. Vaghetti, J. C. P., Lima, E. C., Royer, B., da Cunha, B. M., Cardoso, N. F., Brasil, J. L. & Dias, S. L. P.  Pecan nutshell as

S. O. Kareem et al.

Removal of Fe(II) from aqueous solution by A. terreus and T. viride

biosorbent to remove Cu(II), Mn(II) and Pb(II) from aqueous solution. J. Hazard. Mater. 162, 270–280. Veglio, F. & Beolchini, F.  Removal of metals by biosorption: a review. Hydrometallurgy 44 (3), 301–316.

Journal of Water Supply: Research and Technology—AQUA

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Weber, W. J. & Morris, J. C.  Kinetics of adsorption on carbon from solution. J. Sanitation Eng. Div. ASCE 89, 31–60. Yu, M. H.  Environmental Toxicology – Biological and Health Effects of Pollutants, 2nd edn. CRC Press, Boca Raton, FL.

First received 4 June 2013; accepted in revised form 26 September 2013. Available online 15 November 2013

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Assessment of water qualities and evidence of seawater intrusion in a deep conﬁned aquifer: case of the coastal Djeffara aquifer (Southern Tunisia) Adel Kharroubi, Soumaya Farhat, Belgacem Agoubi and Zouhir Lakhbir

ABSTRACT Geochemical analyses of groundwater samples from the Djeffara confined aquifer (Southern Tunisia) were performed. The distribution of saline waters was investigated to identify the origin of the groundwater contamination. The aquifer was shown to be affected by an abnormal increase in groundwater salinity. Near the recharge zone, the groundwater salinity does not exceed 2.5 g l 1 but reaches 7 g l 1 in the northeast of the study area. Due to over pumping, groundwater level decline is so important that it disturbs the equilibrium between fresh and saline waters. The salinity distribution coupled with the structure and geology of the Djeffara aquifer suggest current seawater intrusion is possible through the deep fault systems affecting the zone. The groundwater level was shown to be highly correlated with the sea level fluctuation in the area near the fault systems, suggesting a communication between the sea and the confined aquifer. Groundwater salinization is probably

Adel Kharroubi Soumaya Farhat Belgacem Agoubi (corresponding author) Higher Institute of Water Sciences and Techniques, University of Gabes, Tunisia, Cité Erryadh 6072, Gabes, Tunisia E-mail: Belgacem.Agoubi@isstegb.rnu.tn Zouhir Lakhbir Société Nationale d’Exploitation et de Distribution d’Eau (SONEDE), Direction Production, Jerba, Tunisia

related to inﬁltration of seawater through the faults. However, an intrusion on the side of the discharge area of the aquifer may also be possible. The bromide and chlorine analyses coupled with the SO4/Cl ratio conﬁrmed that the mixing between fresh and saline waters is the main origin of groundwater salinization. Key words

| aquifer, fault systems, groundwater, seawater intrusion

INTRODUCTION In arid and semi-arid regions, groundwater is the main

Kouzana et al. ; Martos et al. ). The mixing between

source of fresh water. It is required for drinking purposes,

groundwater and seawater can be diagnosed using many

tourism and agricultural activities. The increasing demands

techniques such as geochemical analyses, minor elements

for fresh water lead to groundwater pumping intensiﬁcation.

and isotopic tracers. In many cases, tracers like Sr and Br

High pumping rates cause groundwater levels to decline and

can be used to identify the origin of groundwater saliniza-

therefore change the ﬂow direction, which comes from the

tion when a single process occurs (Boluda-Botella et al.

sea towards the aquifer. When demanding groundwater

; Kim et al. ). Cl /Br and I /Cl ratios and spatial

withdrawals from aquifers exceed the recharge rate, sea-

distribution of chlorine are generally used to determine the

water intrusion occurs. With excessive pumping, the

source of salinity, especially when the seawater intrusion

natural hydraulic gradient towards the sea may be reversed

is the main source of groundwater contamination (Rao

and the intrusion can then extend to the pumping borehole

et al. ; Kim et al. ). When groundwater salinization

which becomes saline.

results from several processes, the isotopic analyses of δ2H,

Numerous studies deal with groundwater salinization

δ18O and 87Sr/86Sr are more efﬁcient in identifying the poss-

(Kim et al. ; Ghabayen et al. ; Rosenthal et al.

ible origins of saline groundwater ( Jorgensen et al. ).

; Gattacceca et al. ; De Montely et al. ;

Seawater intrusion processes may be considered as a

doi: 10.2166/aqua.2013.105

A. Kharroubi et al.

Seawater intrusion in Djeffara conﬁned aquifer

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multivariate problem, which involves several parameters

via their own private wells to satisfy their water requirements.

such as hydrochemical properties, cationic exchanges and

Extremely low recharge rates of the aquifer and the ground-

transport parameters (Boluda-Botella et al. ).

water overexploitation resulted in fresh water/salt water

The objective of this study is to recognize and identify

mixing and therefore groundwater salinization.

the groundwater salinization origins of the Djeffara conﬁned aquifer using hydrochemical and hydrodynamic analyses. Expected results shall be of paramount importance in a region characterized by an arid climate and fresh water scarcity.

Geological, structural and hydro-geological contexts The coastal Djeffara aquifer is logged in Mio-Pliocene sands. Trapped between marls and clays, the aquifer thickness varies from 25 to 80 m, with a maximum in the coastal area (Trabelsi et al. ). The depth of water-bearing hor-

STUDY AREA

izons does not exceed 300 m. The recharge of the Djeffara

The Djeffara aquifer located in Southern Tunisia (Figure 1) constitutes

the

main

water

reserve

this

region.

aquifer mainly comes from the ‘Continental Intercalaire (C.I)’ Aquifer through the Elhamma and Medenine faults (Figure 2). It is assured by lateral ﬂow into upper cretaceous

Southeastern Tunisia is characterized by weak rainfall, with

and pontien layers. Recent recharge is limited and constitu-

an average annual precipitation of less than 250 mm. Rainfall

tes a small fraction of the aquifer resources (Mamou et al.

runoff is torrential causing more streaming and very low inﬁl-

). The groundwater piezometric maps showed that

tration. Unlike temperate zones, the studied area is characterized by high temperature, and a high rate of evaporation. Intensive groundwater abstraction during the last decades has caused aquifer piezometric level decline and groundwater quality degradation. Since the year 2000, two desalinization units (Jerba, Zarzis) have been pumping together about 50,000 m3 day 1 from the Djeffara aquifer. Moreover, numerous hotels in the region pump groundwater

groundwater ﬂows mainly in the South-West North-East direction. The study region is affected by numerous faults orientated NW–SE (Figure 2) (Floridia ; Busson ; Perthuisot ; in Jedoui ). Their extension in depth remains to be identiﬁed. These faults seem to affect the continuity and the geometry of the aquifer. Two faults concern the sea area between Jerba Island and Jorf. A communication between the aquifer and the sea through these faults is a strong possibility (Figure 3).

Figure 2 Figure 1

Location map of the study area (southeast Tunisia).

Structural map of Tunisian Djeffara showing the fault system with predominant orientation; northwest–southeast.

Figure 3

A. Kharroubi et al.

Seawater intrusion in Djeffara conﬁned aquifer

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2014

Geological cross-section of studied area highlighting the fault systems as well as their depth extension (Mekrazi (1974), modiﬁed).

Sampling and analysis

present state. Comparing a few isolated values of the present groundwater level to the 1950 piezometric map, ground-

Field investigations were carried out from January to March

water level decline is shown to be about 27.5 m between

2010. In total, 24 groundwater samples were taken from

1950 and 2010. In areas where the water table is declining,

wells located all over the study area. A particular interest

the hydraulic head drops may promote seawater intrusion.

was attached to areas suspected to suffer seawater intrusion.

Fresh water salinization is explained by mixing between

Groundwater temperature and electrical conductivity (EC)

groundwater and seawater: (a) at the aquifer outlet, assumed

were measured in the laboratory using a thermometer and

to be northeast of Jerba Island, (b) through the numerous

conductivity electrodes, respectively. Water salinity was cal-

faults located in the marine area between Jerba Island and

culated using EC. Major elements and Br concentrations

the continent.

were analyzed using ionic liquid chromatography. Groundwater level ﬂuctuation was continually measured by an automatic piezometer installed in the well.

Seawater intrusion mechanism Analytical solutions to approximate the seawater intrusion

RESULTS AND DISCUSSION

behavior have been derived since 1901. Under hydrostatic conditions, assuming an homogenous aquifer and steady-

Decline of the aquifer piezometric level

state freshwater ﬂow, the depth of freshwater/saltwater interface can be shown by a model called the Ghyben–

Groundwater piezometric data of the Djeffara aquifer have

Herzberg relation. Assuming that seawater intrusion

been measured with respect to mean sea level since 1950.

comes mainly through faults and taking into account the

Data used to establish piezometric maps were provided by

groundwater level, the depth of the aquifer, fresh water den-

the ‘Regional Agricultural Department of Medenine’.

sity and seawater density (Figure 5), the groundwater

Groundwater piezometric level maps for the years 1950

piezometric level at which a new equilibrium of the sharp

and 1970 (Figures 4(a) and 4(b), respectively) and the pre-

interface is re-established can be determined. The equili-

sent groundwater level observed in several wells allowed

brium piezometric level (H*) can be expressed, using the

us to establish the groundwater level decline of the aquifer.

Ghyben–Herzberg relation, by the following equation:

In fact, because of permanent pumping in Jerba Island, mainly to supply water drinking demands, it is near impossible to establish a groundwater level map describing its

H ¼

ρ1 1 H1 ρ2

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Seawater intrusion in Djeffara conﬁned aquifer

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Figure 5

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2014

Seawater intrusion mechanism scheme showing the communication process between the aquifer and the lagoon.

the water table to the interface is balanced by a saltwater column extending from the sea level to the same depth. The transition zone between sea water and the aquifer fresh water is not a distinct boundary but a transition zone characterized by a mixing of fresh and saline waters. The Ghyben–Herzberg model is used to understand the salinization process of the Djeffara confined aquifer. It can be deduced that when the piezometric level (H ) in the aquifer is greater than H*, the flow is from the aquifer to the sea. Otherwise, the flow is from the sea to the aquifer and seawater intrusion occurs. In the case of the Djeffara aquifer, H1 ¼ 300 m, ρ1 ¼ 1,032 kg m 3, and ρ2 ¼ 1,003.5 kg m 3. Thus, H* ¼ 9.44 m. It can be concluded, therefore, that seawater intrusion occurs through the faults system when the piezometric level is less than 9.44 m. The actual groundwater level in the study area is less than 8 m. The weight of the fresh water cannot balance the weight of saltwater and the flow is from the sea to the aquifer. Figure 4

(a) Groundwater piezometric map for the year 1950 established using piezometric data collected in wells of the study area. (b) Groundwater piezometric map for the year1970 established using piezometric data collected in wells of the study area.

Communication between the aquifer and the sea evidence

where H1 is the distance between sea level and the aquifer

The seawater intrusion in the aquifer can be conﬁrmed

(m), ρ1 is the seawater density (kg m 3) and ρ2 is the fresh

based on the groundwater level ﬂuctuation observed in

water density (kg m 3).

some wells near the faults (Figure 6). Two wells, located at

The Ghyben–Herzberg relation is an analytical model

approximately 800 m from the faults, showed that ground-

which describes the mixing between fresh water and sea

water level ﬂuctuations are closely linked to sea level

water. It assumes that under hydrostatic conditions, the

variations. Simultaneous measurements of the sea wave

pressure of a unit freshwater column extending from

and the groundwater level in well No. G12, show that

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Seawater intrusion in Djeffara conﬁned aquifer

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because of the excessive extraction rates from the Djeffara aquifer. Origin of salinization Geochemical analysis is considered to investigate the origin of groundwater salinization. Chlorine can be used to distinguish between several mixing mechanisms of fresh Figure 6

Piezometric level ﬂuctuation in a well near the sea. Fluctuation in the groundwater level decreases going to the backwaters.

water and saline water (Kim et al. ). The concentration of some minor elements such as Br is considered to be a good marker of seawater intrusion. It can be used to separ-

these two phenomena are perfectly correlated (Figure 7).

ate salinity of marine and non-marine origins (Andreasen &

The ﬂuctuation of the groundwater level which is only

Flek ). The Br/Cl ratio remains constant when saliniza-

observed for the nearest two wells, disappears away from

tion of fresh water is related to seawater and groundwater

the fault system. This behavior can be explained by the

mixing. When the Br/Cl ratio exceeds the normal seawater

fact that the amplitude of the ﬂuctuation is gradually

range (0.0036), potential sources of Br other than seawater

damped with increased distance from the source of the

may be responsible for water salinity. Groundwater samples

signal. Thus, it seems that the assumption of seawater intru-

from selected wells were collected and analyzed to deter-

sion in the Djeffara conﬁned aquifer through faults is quite

mine principally Br and Cl content. The analysis (Table 1)

credible and warrants more detailed investigations to be

showed that most of the treated samples present a signiﬁcant

deﬁnitively demonstrated.

Br concentration.

The ﬂuctuation of the groundwater level depending on

The principal origin of Br in hydrogeological systems is

the tide cannot be the result of a pumping effect. Indeed,

seawater because rock bromide concentrations do not

the additional stress due to the increase of the water level

exceed 6 ppm. High Br concentration in the groundwater

in the lagoon is too small to justify such a ﬂuctuation of

indicates the evidence of mixing between fresh and saline

the piezometric level in the aquifer.

waters. The Br/Cl diagram (Figure 9) was established to determine the origin of groundwater salinity. As Br is a good marker of seawater intrusion, high Br concentration

Salinity

in the Djeffara aquifer coupled with the abnormal salinity values indicate a seawater intrusion to a large extent. A

The groundwater salinity map established using EC values

Br/Cl ratio close to 0.0036 characterizes a fresh aquifer

(Figure 8), shows the distribution of water salinity of the

water contamination. Figure 8 shows that the majority of

aquifer. High values are observed in the northern and

points are near the mixing line, implying thereby that Cl

the eastern sides of the study area. Groundwater near the

and Br have the same origin. Areas affected by marine intru-

coast is associated with the highest salinity, with a maximum

sion are distinguished by Br/Cl ratios similar to those of

of 7 g l . Abnormally high salinity values are observed in

seawater. Therefore, bromide analyses showed that ground-

areas of low hydraulic heads and near the presumed aquifer

water salinity is mainly explained by freshwater/seawater

outlet. Relatively fresh waters (2.2 g l ) are found mainly in

mixing. Groundwater containing a signiﬁcant amount of

the southern part near the recharge zone. Water salinity

Br is located beyond the fault system (Figure 10). This ﬁnd-

remains almost constant within a radius of about 70 km

ing highlights the major role of faults in the transport

from the recharge zone. Beyond this distance, a sudden

process of salt waters from the sea to the aquifer.

increase in salinity is found predominantly in the northern

SO2 4 /Cl ratio has been recognized to be a good natu-

part of Jerba Island and Zarzis Region. High salt content

ral indicator of seawater intrusion in coastal aquifers

in the groundwater is probably due to seawater intrusion

(Pulido-leboeuf et al. ; Kouzana et al. ; Zouhri

Figure 7

A. Kharroubi et al.

Seawater intrusion in Djeffara conďŹ ned aquifer

Journal of Water Supply: Research and Technologyâ&#x20AC;&#x201D;AQUA

Piezometric level and sea level measured simultaneously to show the correlation between the tide and the piezometric level of the aquifer.

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2014

A. Kharroubi et al.

Seawater intrusion in Djeffara conﬁned aquifer

Journal of Water Supply: Research and Technology—AQUA

Figure 9

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2014

Bromide versus chlorine concentrations established for the aquifer waters collected from wells located in the study area.

Figure 8

Groundwater salinity map showing a marked increase in salinity values ranging from the recharge area to the suspected outlet.

Table 1

Sample

Br, Cl, SO4, Na and Ca concentrations in the Djeffara aquifer Chloride

Bromide

Sulfate

Sodium

Calcium

(ppm)

GB1

612

1,723

528

182

MR1

629

1,570

542

187

KT1

590

1,640

512

168

JF1

1,696

4.98

2,550

1,240

212

BG1

1,502

5.21

2,589

980

227

1,794

5.23

2,406

1,318

281

1,437

5.09

1,906

1,347

265

1,964

6.82

2,248

1,546

369

1,769

5.36

1,872

1,358

208

2,195

7.44

2,191

1,583

117

2,282

6.1

2,234

1,600

281

2,492

2,151

1,700

300

G10

1,690

5.03

2,406

1,107

369

G11

1,641

5.1

2,191

1,108

393

G12

1,618

5.02

2,204

1,093

371

BR1

2,405

7.75

2,366

1,900

381

TM1

3,201

9.96

2,061

2,059

361

ZR2

2,235

6.15

2,247

1,420

320

ZR3

2,279

7.58

2,442

1,470

288

ZR4

2,082

5.77

2,579

1,360

264

ZR5

2,158

2,701

1,415

248

ZR6

2,333

6.51

2,481

1,524

269

ZR7

2,423

2,865

1,620

296

ZR8

2,352

6.16

2,236

1,560

212

Figure 10

Figure 11

Localization of seawater intrusion area in the Djeffara conﬁned aquifer.

SO4/Cl ratio versus Cl concentration in aquifer water. This highlights the contributions of the rock and sea water to underground water mineralization process.

A. Kharroubi et al.

Seawater intrusion in Djeffara conﬁned aquifer

Journal of Water Supply: Research and Technology—AQUA

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intrusion was conﬁrmed by the obtained Br/Cl and SO4/Cl ratios, which are close to those of seawater. Areas of the aquifer downstream of the fault system were shown to be highly contaminated, thereby preventing any groundwater use. Therefore, seawater intrusion and groundwater quality shall be monitored to establish integrated water management strategies and preserve the limited water resources of the region.

REFERENCES Figure 12

Chemical facies of underground waters sampled in the Djeffara deep aquifer.

et al. ). This ratio decreases when the proportion of sea water in the aquifer increases. Low SO2 ratios 4 /Cl

(Figure 11) indicate evidence of saltwater/freshwater mixing in some parts of the aquifer. However, a plot of þ 2þ Cl /SO2 leads to identiﬁcation 4 as a function of Na /Ca

of Na-SO4 and Na-Cl chemical facies of underground water (Figure 12). The sodium chloride pole demonstrates the evidence of seawater/freshwater mixing. Although the current contamination level seems to be not very signiﬁcant, the high pumping rates in the aquifer may magnify the seawater intrusion phenomenon in the coming years.

CONCLUSION The Djeffara aquifer abnormal salinization is mainly related to seawater intrusion and freshwater/seawater mixing. High pumping rates disturb the natural equilibrium between fresh and saline waters, resulting in the subsidence of the groundwater table below sea level, which causes seawater intrusion into the aquifer. Although seawater intrusion at the aquifer outlet is not excluded, the fault system is believed to be predominant in the process of groundwater salinization. Seawater intrusion in the aquifer through the fault system was conﬁrmed based on the groundwater level ﬂuctuation observed in wells near the faults. Hydrochemical analyses, based on Cl and Br ions, showed that changes of groundwater quality and high degrees of groundwater salinization are especially controlled by the mixing process. Seawater

Andreasen, D. C. & Flek, W. B.  Use of bromide:chloride ratios to differentiate sources of chloride in a shallow unconfined aquifer affected by brackish-water intrusion. Hydrol. Geol. 5, 17–26. Boluda-Botella, N., Gomis-Yagües, V. & Ruiz-Bevia, F.  Influence of transport parameters and chemical properties of the sediment in experiments to measure reactive transport in seawater intrusion. J. Hydrol. 357, 29–41. Busson, G.  Le Mésozoique saharien (première partie): L’extrème sud tunisien. C.N.R.S., Série Géologique 8, 195. De Montely, V., Radakovitch, O., Vallet-Coulomb, C., Blavoux, B., Hermitte, D. & Valles, V.  Origin of groundwater salinity and hydrogeochemical processes in a confined coastal aquifer: Case of the Rhône delta (Southern France). Appl. Geochem. 23, 2337–2349. Floridia, S.  Sondage de reconnaissance S.Z1 de la sebkha de Zarzis. Internal Report. O.N.M., Tunis. Gattacceca, J. C., Vallet-Coulomb, C., Mayer, A., Claude, C., Radakovitch, O., Conchetto, E. & Hamelin, B.  Isotopic and geochemical characterization of salinization in the shallow aquifers of a reclaimed subsiding zone: the southern Venice Lagoon coastland. J. Hydrol. 378, 46–61. Ghabayen, S. M. S., McKee, M. & Kemblowski, M.  Ionic and isotopic ratios for identification of salinity sources and missing data in the Gaza aquifer. J. Hydrol. 318, 360–373. Jedoui, Y.  Sédimentologie et géochimie des dépôts littoraux quaternaires: reconstitution des variations des climats et du niveau marin dans le sud est tunisien. Doctoral Thesis, Université de Tunis II, Tunis, Tunisia. Jorgensen, N. O., Andersen, M. S. & Engesgaard, P.  Investigation of a dynamic seawater event using strontium isotopes (87Sr/86Sr). J. Hydrol. 348, 257–269. Kim, Y., Lee, K. S., Koh, D. C., Lee, D. H., Lee, S. G., Park, W. B., Koh, G. W. & Woo, N. C.  Hydrogeochemical and isotopic evidence of groundwater salinization in a coastal aquifer: a case study in Jeju volcanic island, Korea. J. Hydrol. 270, 282–294. Kouzana, L., Ben Mamou, A. & Gaaloul, N.  Intrusion marine et salinisation des eaux d’une nappe phréatique cotière (Korba, Cap-Bon, Tunisie). Geo. Eco. Trop. 31, 57–70.

A. Kharroubi et al.

Seawater intrusion in Djeffara conﬁned aquifer

Kouzana, L., Ben Mamou, A. & Sfar Lelfoul, M.  Seawater intrusion and associated processes: case of Korba aquifer (Cap-Bon, Tunisia). Comptes Rendus Geosciences 1, 21–35. Mamou, A., Ben Baccar, B. & Khalili, B.  Carte des ressources en eau souterraines de la Tunisie. Direction Générale des Ressources en Eau, Tunisia. Martos, F. S., Bosch, A. P., Sanchez, L. M. & Izquierdo, A. V.  Identiﬁcation of the origin of salinization in groundwater using minor ions (lower Andarax, Southeast Spain). Sci. Total Environ. 297, 43–58. Mekrazi, A. O.  Etude sommaire de la nappe Mio-Pliocène de la presqu’ile de Jorf (DRE). Rapport DGRE Gabès. Perthuisot, J. P.  Le lambeau de Tlet et la structure néotectonique de l’île de Djerba (Tunisie). C. R. Acad. Sci. 285, 1091–1093. Pulido-leboeuf, P., Pulodo-Bosch, A., Calvache, M. L., Vallejos, A. 2þ 2þ & Andreu, J. M.  Strontium, SO2 4 /Cl and Mg /Ca ratios as tracers for the evolution of seawater into coastal

Journal of Water Supply: Research and Technology—AQUA

63.1

2014

aquifers: the example of Castell de Ferro aquifer (SE Spain). C. R. Geosci. 335, 1039–1048. Rao, U., Hollocher, K., Sherman, J., Eisele, I., Frunzi, M. N., Swatkoski, S. J. & Hammons, A. L.  The use of 36Cl and chloride/bromide ratios in discerning salinity sources and ﬂuid mixing patterns: a case study at Saratoga Springs. Chem. Geol. 222, 94–111. Rosenthal, E., Zilberbrand, M. & Livshitz, Y.  The hydrochemical evolution of brackish groundwater in central and northern Sinai (Egypt) and in the western Negev (Israel). J. Hydrol. 337, 294–314. Trabelsi, R., Kacem, A., Zouari, K. & Rozanski, K.  Quantifying regional groundwater ﬂow between Continental Intercalaire and Djeffara aquifers in southern Tunisia using isotope methods. Environ. Geol. 58, 171–183. Zouhri, L., Toto, E., Carlier, E. & Debieche, T. H.  Salinité des resources en eau: intrusion marine et interaction eaux-roches (Maroc occidental). Hydrol. Sci. J. 55, 1337–1347.

First received 20 September 2012; accepted in revised form 8 August 2013. Available online 7 October 2013