Science of the Total Environment 408 (2010) 4202–4210

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Disinfection by-product formation following chlorination of drinking water: Artificialneural network models and changes in speciation with treatment

Pranav Kulkarni a,1, Shankararaman Chellam a,b,⁎,2

a Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77204-4003, United Statesb Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX 77204-4004, United States

⁎ Corresponding author. Department of Civil and Envirsity of Houston, Houston, TX 77204-4003, United Stafax: +1 713 743 4260.

E-mail address: chellam@uh.edu (S. Chellam).1 Present address: Trinity Consultants, Houston, TX, U2 Originally submitted to Science of the Total Enviro

Revised version submitted on May 23, 2010.

0048-9697/$ – see front matter © 2010 Elsevier B.V. Adoi:10.1016/j.scitotenv.2010.05.040

a b s t r a c t

a r t i c l e i n f o

Article history:Received 17 February 2010Received in revised form 21 May 2010Accepted 29 May 2010Available online 26 June 2010

Keywords:Artificial neural networksDisinfection by-productsDrinking waterTrihalomethanesHaloacetic acidsTotal organic halideChlorine disinfectionNanofiltrationGranular activated carbon

Artificial neural network (ANN) models were developed to predict disinfection by-product (DBP) formationduring municipal drinking water treatment using the Information Collection Rule Treatment Studiesdatabase complied by the United States Environmental Protection Agency. The formation of trihalomethanes(THMs), haloacetic acids (HAAs), and total organic halide (TOX) upon chlorination of untreated water, andafter conventional treatment, granular activated carbon treatment, and nanofiltration were quantified usingANNs. Highly accurate predictions of DBP concentrations were possible using physically meaningful waterquality parameters as ANN inputs including dissolved organic carbon (DOC) concentration, ultravioletabsorbance at 254 nm and one cm path length (UV254), bromide ion concentration (Br−), chlorine dose,chlorination pH, contact time, and reaction temperature. This highlights the ability of ANNs to closely capturethe highly complex and non-linear relationships underlying DBP formation. Accurate simulations suggest thepotential use of ANNs for process control and optimization, comparison of treatment alternatives for DBPcontrol prior to piloting, and even to reduce the number of experiments to evaluate water quality variationswhen operating conditions are changed. Changes in THM and HAA speciation and bromine substitutionpatterns following treatment are also discussed.

onmental Engineering, Univer-tes. Tel.: +1 713 743 4265;

nited States.nment on February 17, 2010.

ll rights reserved.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

In addition to inactivating microorganisms, chemical disinfectantssuch as chlorine also react with natural organic matter (NOM) andbromide ion in water to form numerous disinfection by-products(DBPs), which have been implicated as human mutagens, carcino-gens, and teratogens (Hamidin et al., 2008). Trihalomethanes (THMs)and haloacetic acids (HAAs) constitute the major halogenated DBPscurrently regulated in drinking water, accounting for approximatelyhalf the total organic halide (TOX) concentration. Since THMs andHAAs are not typically present in the sourcewater but are by-productsformed during chlorination as an unintended consequence, they aremost often controlled by reducing the concentrations of theirprecursors (particularly NOM) prior to adding chlorine.

In this manuscript, we consider three important water treatmentprocesses employed for NOM (and DBP precursor) removal from

drinking water sources; (i) conventional treatment (coagulation–flocculation–sedimentation–media filtration), (ii) granular activatedcarbon (GAC) adsorption, and (iii) nanofiltration (NF) (Chen et al.,2007). We are particularly interested in the formation of THMs, HAAs,and TOX following these processes using free chlorine as thedisinfectant. It should be emphasized that even though currentregulations do not include TOX, good treatment practices necessitateits control as well.

It is difficult to derive purely mechanistic models of DBPformation in natural waters due to the inherent heterogeneity ofNOM, the complex background chemistry of municipal watersupplies, and large variations in water quality of surface watersupplies with season and location in terms of NOM concentrations,origin, and characteristics. Additionally, since the removal of specificNOM components depends on the treatment processes employed(e.g. coagulation and GAC preferentially remove hydrophobicportions and NF preferentially removes higher molecular weightportions) the DBP yield is changed upon treatment furthercomplicating the chemistry and prediction of DBP formation.Hence, DBP mass concentrations [DBP] are typically modeledempirically by linearly regressing each of the water qualityparameters influencing DBP formation including dissolved organiccarbon concentration (DOC), ultraviolet absorbance at 254 nm andone cm path length (UV254), bromide ion concentration (Br−),chlorine dose (Cl2), chlorination pH (pH), contact time (Time), and

4203P. Kulkarni, S. Chellam / Science of the Total Environment 408 (2010) 4202–4210

reaction temperature (Temp) specific to each water supply andtreatment process:

DBP½ � = k × DOCa × UVb254 × Br−c × Cld2 × pHe × Timef × Tempg ð1Þ

where k, a, b, c, d, e, f, and g are empirical constants. Log-linear powerfunctions similar to Eq. (1) are extensively employed to model THMand HAA formation e.g. (Chowdhury et al., 2009; Hong et al., 2007;Sadiq and Rodriguez, 2004a; Sohn et al., 2004; Uyak et al., 2007;Westerhoff et al., 2000) even though they are best suited only topredict central tendencies of databases used to develop them.

In contrast, artificial neural networks (ANNs) have the capabilityto approximate any function (and its derivatives) to any degree ofaccuracy. Also, the superior ability of ANNs to handle noisy, distortedmultivariate data makes them a more powerful modeling toolcompared with regression models such as Eq. (1). Paradoxically,even though ANNs have the potential to better predict DBP formationcompared to multivariate regression models, only a very limitednumber of studies have considered ANNs for DBP formation uponchlorination. Rodriguez and co-workers (Milot et al., 2002; Rodriguezet al., 2003) focused exclusively on THMs (HAAs and TOX wereexcluded in these studies) and also evaluated DBPs' health risks usinga fuzzy logic models (Sadiq and Rodriguez, 2004b). Additionalresearch is necessary to specifically evaluate the capability of ANNsto predict DBP formation following many water treatment processesimplemented for DBP control.

The principal objective of this research is to derive accurate ANNmodels for THM, HAA, and TOX formation following chlorination ofraw and treated (conventional treatment, GAC, and NF) waters. Wealso provide additional experimental data on changes in THM andHAA speciation focusing on variations in bromine substitution withtreatment. ANNs were implemented and validated using a largedataset that includes an extensive set of bench-scale, pilot-scale, andfull-scale experiments from numerous water treatment plants locatedin the United States (Allgeier et al., 1998). Network connectionweights are also interpreted quantitatively to develop more insightsinto the relative importance of individual physicochemical factorsknown to influence DBP formation and speciation.

2. Neural networks

A commercially available software program (Matlab neuralnetwork toolbox 6.1, The Math Works, Inc., Natick, MA) was used toimplement ANNs on a personal computer. In this study, feed forward,back propagation ANNs consisting of an input layer, one or twohidden layers, and an output layer were developed. To minimizenetwork complexity and improve its performance, the least number ofphysically meaningful input parameters was employed; viz. DOCconcentration, UV254, Br− concentration and chlorination conditionsincluding Cl2 dose, contact time, pH, and temperature, which are thesame parameters in Eq. (1). The output consisted of a single neuronrepresenting TOX, THM or HAA concentrations.

As commonly practiced, the number of hidden layers and thenumber of hidden neuronswere determined iteratively using trial anderror. In this study, one or two hidden layers consisting of 4–8 neuronswere found to be satisfactory for all simulations. To improve theefficiency of batch training, the Levenberg–Marquardt algorithm withoptimum learning rate between 0.01 and 0.0001 was chosen throughexperimentation to avoid instability and excessive convergence time.All synaptic weights were initialized randomly in the range (−0.5,+0.5) and accordingly readjusted (via back propagation) to reducethe difference between actual and desired output in terms of sum ofsquared error (SSE);

SSE = ∑ntrain

i=1DBP½ �obs− DBP½ �pred

� �2 ð2Þ

where [DBP]obs is the experimental or observed DBP concentrationand [DBP]pred is the corresponding ANN output or prediction and ntrain

is the number of observations employed for ANN training. For eachsimulation, the networkwas trained iteratively until SSE b10−5 or themaximum gradient was reached.

As is usually necessary, datawere normalized by the correspondingmaximum value to put them in the range 0–1. Training data werecarefully chosen so that they had a greater descriptive ability whilesimultaneously making an effort to use a minimum number ofobservations. Because ANNs are better suited to interpolate ratherthan extrapolate, the maximum and minimum values of each inputparameter were always chosen to train the network leavingintermediate measurements for validation. The optimum networkarchitecture for each type of treatment and untreated water wasobtained by multiple runs. Network validation was performed byproviding only those input values that were not included in theoriginal training set. The quality of DBP predictions in comparison tothe desired outputs in the validation dataset was evaluated both interms of the regression coefficient and its N25 value, which representspercentage of predictions that have less than 25% absolute relativeerror calculated as:

AbsoluteRelativeError =absð½DBP�pred− ½DBP�obsÞ

½DBP�obs: ð3Þ

The contributions of each input variable to predict DBP concentra-tions were determined by the Garson weight partitioning methodusing the absolute values of the neuronal connectionweights (Garson,1991; Goh, 1994):

Relativeimportanceof input variablev =

∑nH

j=1

ivj

∑nv

k=1ikj

Oj

24

35

∑nv

i=1∑nH

j=1

ivj

∑nv

k=1ikj

Oj

0@

1A

24

35

ð4Þ

where, nv and nH are the number of input and hidden neuronsrespectively, ij and Oj denote the absolute value of connection weightsbetween input to hidden layer and hidden to output layer,respectively. Eq. (4) is best suited to interpret the trends in relativeimportance of input variables rather than the calculated absolutevalues.

2.1. Experimental dataset

All data used in this manuscript are available in the ICR TreatmentStudy Database developed by the United States EnvironmentalProtection Agency using bench-, pilot-, and full-scale DBP precursorremoval data provided by public water systems meeting certaincriteria (Allgeier et al., 1998). GAC and NF studies were performedencompassing seasonal variations in ground- and surface watersrepresenting numerous full-scale water treatment plants across theUnited States (Allgeier and Summers, 1995; Bond and DiGiano, 2004).It should be emphasized that under the ICR Treatment Studyrequirements, DBP precursor removal was evaluated using simulateddistribution system (SDS) testing (Koch et al., 1991).

Limited available data suggests a greater complexity in predictingDBPs from actual real-world distribution systems compared withstudies employing SDS testing e.g. (Platikanov et al., 2007; Shimazu etal., 2005). This is because distribution systems have several non-idealities (e.g. dead-zones, potential presence of biofilms, differentchlorine decay kinetics caused by pipe wall roughness, a distributionof residence times, non-uniform disinfectant concentrations, etc.) thatcannot be accurately simulated in a simple SDS test run in batchmode.

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In any case, all DBP data used in this research were generatedthrough SDS testing under the ICR Treatment Study requirement(rather than actual measurements from various locations within anactual distribution system). Qualifying municipalities evaluated GACand NF to provide technical and economic data to assess the feasibilityof these technologies for precursor control in anticipation of morestringent DBP regulations. SDS testing allowed municipalities toevaluate various design parameters for these advanced technologiesunder site specific conditions without sending the treated water totheir customers through the distribution system. In other words, thepurpose of ICR Treatment Studies was not to measure DBPconcentrations in full-scale distribution systems following existingtreatment techniques. Rather, its purposewas to determine the extentof DBP control achieved by GAC and NF for a geographically diverse setof water supplies so that they can be potentially installed for large-scale water treatment in the future.

A separate 18-month DBP monitoring requirement was alsorequired separately under the ICR, wherein water samples werecollected at numerous locations within full-scale water treatmentplants including the distribution system (McGuire et al., 2002;Obolensky and Singer, 2005). Multiple linear regression modelsincorporating important water chemistry parameters have beenrecently developed for DBP formation using these data (Obolenskyand Singer, 2008). A potential research opportunity is to use thismonitoring (as opposed to the Treatment Study) database to comparethe relative accuracy of regression and ANNs to predict DBPconcentrations in real-world distribution systems.

Detailed information on pretreatment, unit processes, waterquality, etc. is available in the ICR database (EPA, 2000). In additionto the extensive data obtained, the ICR imposed stringent qualitycontrol requirements for the conduct of treatment studies as well asanalytical methods for water quality and DBP measurements, makingit a very high quality database ideally suited to develop DBP formationmodels.

A summary of the database with the range of each of the sevenANN input parameters (DOC, UV254, Br−, chlorination conditionsincluding Cl2 dose, contact time, pH, and temperature) is given inTable 1. The Environmental Protection Agency established MinimumReporting Levels (MRLs) separate from method detection limits foreach water quality parameter corresponding to laboratories' ability tomeasure analytes with a predetermined accuracy and precision. TheMRLs specified under the ICR have been reported elsewhere (Chellamand Taylor, 2001; EPA, 1996). If any of the seven ANN input values wasbelow the minimum reporting level (BMRL), that entire set ofreadings was excluded whereas output values (DBP concentrations)with BMRL values were assigned a value of half the MRL.

Datasets wherein ammonia was detected were excluded in thisresearch because only DBP formation with free chlorine is consideredin this manuscript. Based on the water treatment unit processes usedand available data, the entire database was divided in 4 subsets, viz.untreated water (5 datasets), conventional treatment (30 datasets),water treated by GAC (57 datasets), and NF (26 datasets). Each bench-scale study includes four seasonal THMs, HAAs and TOX measure-ments at different operating conditions alongwith related operationaland water quality parameters such as sampling time, operation time,pH, turbidity, DOC, UV254, Br−, and simulated distribution systemconditions using free chlorine. Bench-scale experiments were per-

Table 1Summary of ANN input parameters for the treatment studies employed in this manuscript.

Treatment TOC (mg/L) UV254 (cm−1) Br− (μg/L)

Untreated water 1.72–14.40 0.047–0.673 37–510Conventional treatment 0.90–5.86 0.021–0.161 20–1850GAC treatment 0.05–8.25 0.001–0.225 20–1810Nanofiltration 0.08–4.80 0.001–0.124 6–670

formed either with a flat sheet of a commercially availablenanofiltration membrane (Allgeier and Summers, 1995) or using therapid small scale column test protocol to evaluate GAC treatment(Bond and DiGiano, 2004). Simulated distribution system tests (Kochet al., 1991) were also performed in existing full-scale municipalwater treatment plants that employed NF or GAC. (Note that even full-scale treatment facilities were not required to sample from theirexisting distribution system but to report SDS testing results.) Insummary, a large number of measurements spanning a wide range ofinfluent water quality parameters from surface- and ground-waters,and from unit processes with varying design variables, pretreatments,and operating conditions have been used to model the complexrelationships that exist between DBP concentrations and variousfactors responsible for their formation.

3. Results and discussion

3.1. Need to derive separate ANNs for each treatment technique

Principal Component Analysis (PCA)was employed to determine ifa single neural network was capable of predicting DBP concentrationsformed in the four waters (raw, conventional treatment, GAC, and NF)or whether four separate ANNs were necessary, one for each watertype (Massart et al., 1997). Specifically, PCA was carried out to assessthe associations between the contributions of rawwater and the threeDBP precursor removal techniques (conventional treatment, GAC, andNF) in THM, HAA, and TOX formation. PCA also reduced the number ofindependent variables by generating a new coordinate system ofuncorrelated variables called principal components. One uniquefeature of PCA is that variables with similar properties group togetherseparating out the variables with dissimilar properties.

A data subset in which all input variables except DOC varied onlyin a narrow range for untreated water and the other 3 unit processeswas initially identified. All DBP concentrations in this subset were firstnormalized by the corresponding DOC concentrations to calculate theDBP yield. Next, PCA was performed using singular value decompo-sition. Fig. 1 depicts the PCA plot in which each symbol representsthese normalized concentrations of TOX, THM4 (sum of the four THMspecies, viz. CHCl3, CHClBr2, CHCl2Br, and CHBr3), and HAA6 (sum ofsix HAA species, viz. CH2ClCOOH, CHCl2COOH, CCl3COOH, CH2-

BrCOOH, CHBr2COOH, and CHClBrCOOH) from the subset. (Note thatduring the development of the ICR database stable analyticalstandards for three other HAAs containing chlorine and bromine viz.CCl2BrCOOH, CClBr2COOH, and CBr3COOH were not widely availablecommercially.) Fig. 1 shows the distinct separation of each of the foursymbols, even appearing in different quadrants, implying largevariations in the extent of DBP formation in raw water and afterconventional treatment, NF, and GAC under similar chlorinationconditions. In mechanistic terms, PCA demonstrates that DBP yieldsand formation mechanisms depend on the type of precursor removalmethod employed. Hence, individual ANN models for each of thetreatment process and raw water were necessary to capture thediffering underlying aqueous chemistries and NOM characteristicsthat resulted in varying DBP yields upon chlorination.

These results are consistent with the practice of deriving separateregression models for different unit processes (Chowdhury et al.,2009; Legube et al., 2004; Sadiq and Rodriguez, 2004a; Sohn et al.,

Cl2 dose (mg/L) Temp. (°C) pH Contact time (h)

3.70–20.0 20.0–28.8 7.7–9.1 6.3–830.76–14.5 4.0–30.2 5.9–10.1 1.9–1200.67–9.5 1.0–33.0 5.9–10.4 1.8–120.01.5–19.1 5.4–28.0 5.5–9.5 6.0–72.0

Fig. 1. Principal Component Analysis to determine the need for separate ANNs for individual water types. Raw, Conv., GAC, and NF denote raw water, conventionally treated water,GAC effluent, and NF permeate respectively.

4205P. Kulkarni, S. Chellam / Science of the Total Environment 408 (2010) 4202–4210

2004) since the nature of DBP precursors (and consequently the yieldand kinetics) are different in raw water and after various treatments.For example, coagulation is known to preferentially remove thefraction of NOM that is more hydrophobic, of higher molecularweight, and that has more binding sites (Singer, 1999). NF removesthe higher molecular weight fractions, changes the specific ultravioletabsorbance (SUVA) and the Br−/DOC ratio between the feed andpermeate waters, (Chellam and Krasner, 2001). Similarly, precursorremoval by GAC is a function of pore size distribution, NOMmolecularweight distribution and heterogeneity (Singer, 1999). In other words,separate ANNs were necessary since NOM reactivity towards chlorineand DBP yield is changed based on the treatment process employed.

Therefore, separate neural networks were developed to predictDBP formation in raw water, as well as after conventional treatment,GAC adsorption, and NF. The network configuration and parameterssuch as learning rate, number of hidden layers, number of neurons ineach layer, initial weights, etc. were varied for each simulation prior topredictions to obtain the most reliable ANN model in each case. Theoptimal neural network architecture that gave best N25 values for eachwater type was determined using this procedure.

3.2. ANN predictions of DBP concentrations

Figs. 2, 3, 4, and 5 depict comparisons of ANN predictions of TOX,THM4, and HAA6 concentrations with experimental observations inuntreated water, and waters purified by conventional treatment, GACadsorption, and NF respectively. Measurements from bench-, pilot-,and full-scale treatment processes are all shown. In each case, thenumber of data points used for training (Ntrain), validation (Ntest),

Fig. 2. Comparisons of ANN predictions with experimen

percentage of predictions within 25% absolute relative error (N25),and the regression coefficient (R2) is also reported. Note that to bemore stringent, only experimental measurements employed forvalidating the neural network model are shown. Training datasetsare not depicted since they were extremely well predicted by ANNmodels and were superposed directly on the line of perfectagreement.

A summary of the number of points used for ANN training andvalidation along with the N25 values and regression coefficients forindividual DBPs are given in Table 2. As observed in Table 2 and Figs. 2,3, 4, and 5 neural networks gave consistently high N25 values (77–98%) and high regression coefficients (0.78–0.98) even when usingonly 7–22% data for training for each water type. Good THM, HAA, andTOX predictions using ANNs agree with earlier reports for THMs(Rodriguez et al., 2003) and bromate (Legube et al., 2004) andunequivocally demonstrate that they are capable of accuratelyincorporating complex relationships that exist between precursorcharacteristics and chlorination conditions in forming DBPs evenwhen using only a small fraction of available data for training.

For raw water and conventionally treated water training with∼20–25% of the available data was sufficient to obtain N25 N80% (seeFigs. 2 and 3). For the GAC treated water (Fig. 4), training with only 8%data was adequate to obtain N25 N80% potentially since this datasetcontained numerous measurements (∼3500) representing ~60drinking water treatment units that allowed the network to excluderepetitive measurements from training data.

Satisfactory predictions of DBP formation in NF permeate watersshown in Fig. 5 required two hidden layers, each containing 4–8 neurons, unlike the other three networks (for untreated water,

tal measurements for DBP formation in raw waters.

Fig. 3. Comparisons of ANN predictions with experimental measurements for DBP formation in conventionally treated waters.

4206 P. Kulkarni, S. Chellam / Science of the Total Environment 408 (2010) 4202–4210

conventionally treatedwater, and GAC treatedwater), where only onehidden layer was sufficient. The scatter observed in Figs. 2–5 can bepartially attributed to geographical diversity of source waters,different treatment schemes, pH, and chemicals employed atindividual locations, seasonal changes in water quality (especiallyNOM characteristics), varying design and operational conditions ateach location, differences in GAC andmembrane type, small variationsin flow rates which are difficult to maintain and monitor preciselyduring large-scale on-site experiments and so on (Bond and DiGiano,2004). It should be emphasized that changes in coagulant dosage andpH, type of coagulant, filtration conditions and filter design inconventional treatment, empty bed contact time (EBCT) andpretreatment in GAC adsorption, type of membrane, flux andrecoveries in NF with season and location were all modeled usingonly one ANN for each unit process. Importantly, in spite of thisvariability, ANNs were able to satisfactorily predict DBP concentra-tions in all cases with meaningful input parameters demonstratingtheir robustness and ability to accurately model THM, HAA, and TOXformation in a variety of water treatment scenarios. Further, processvariables (e.g. nanofilter permeate flux and feed water recovery, GACEBCT, particle size and surface area, etc.) were not explicitly used asinputs. Rather, effluent water quality parameters obtained in a rangeof process operating conditions were input to ANNs. Accurate DBPpredictions even in the absence of operating parameters imply thatANNs inherently captured the role of changing nature, characteristics,and concentrations of precursors with treatment (e.g. changingmolecular weight distribution and functionality, specific ultravioletabsorbance at 254 nm, hydrophobicity, etc.).

Fig. 4. Comparisons of ANN predictions with experiment

3.3. Error distribution

ANNs' predictive ability was evaluated in terms of the overalldistribution of absolute relative error (Bowen et al., 1998) for TOX,THM4, and HAA6 in each of the waters. As observed in Fig. 6, using lessthan 25% of experimental measurements for training was still sufficientto predict the majority of observations within 10% absolute relativeerror (N10 N60%) under a wide range of operational and water qualityconditions. Our results demonstrate that size of training datasets couldbe substantially reduced compared with the more than 50% used in aprevious study employing ANNs for predicting THM concentrations(Rodriguez et al., 2003). Similar resultswere also obtained for individualTHM and HAA species in our study (see Table 2), which demonstratesthat ANNs can satisfactorily estimate DBP concentrations duringmunicipal water treatment even when using only a small fraction ofavailable data for training. Reducing the training burden on ANNs is apractically important issue since simulated distribution system (SDS)testing and DBP analysis is time consuming, requires well trainedlaboratory personnel, and consequently expensive to conduct.

3.4. Relative importance of input variables

Neural networks' ability of partitioning the influence of inputvariables to the output was exploited in a manner similar tointerpreting independent variables' contributions to a dependentvariable in regression equations (Garson, 1991; Goh, 1994). Thesecontributions expressed as percent relative importance (calculatedusing Eq. (4)) were used to interpret input–output relations in terms

al measurements for DBP formation in GAC effluent.

Fig. 5. Comparisons of ANN predictions with experimental measurements for DBP formation in nanofiltered waters.

4207P. Kulkarni, S. Chellam / Science of the Total Environment 408 (2010) 4202–4210

of the chemistry of DBP formation. Table 3 summarizes the relativeimportance (in percentage terms) of each of the 7 inputs employed topredict TOX, THM4, and HAA6 concentrations in this study.

Even though these are purely empirical predictions, some of theweights are consistent with mechanistic interpretations. For example,DOC was by far the most important factor for DBP formation in rawwater accounting for ∼40% weight for THM4, HAA6, and TOX. This isconsistent with the most popular current approach for DBP control,which is to reduce NOM concentrations prior to chlorination.However, DOC was not always the most important factor in treatedwaters, especially for NF and GAC, suggesting different DBP formationmechanisms in untreated- and treated-waters. This result confirmsPCA results summarized in Fig. 1 and the need to derive separate ANNsfor each water type. For each DBP, the relative importance of Br− ionconcentrations was higher for GAC effluent and NF permeatecompared with raw water and conventional treatment, which isattributed to the large increase in Br−/DOC ratio by GAC and NFtechnologies leading to the preferential formation of the highlybrominated DBPs (Chang et al., 2001; Chellam and Krasner, 2001;Singer, 1999; Symons et al., 1993). These results are discussed in moredetail in the next section. Chlorine dose was the most importantsimulated distribution system (SDS) parameter compared withcontact time, temperature, and pH. This is also consistent with currentpractice of reducing disinfectant dosage to reduce DBP concentrations.

3.5. Changes in DBP speciation with treatment

As discussed in the previous sections and Table 2, ANNs were ableto statistically predict not only total THMs and HAA6 but also changes

Table 2Summary of ANN simulations of DBP concentrations in four water types.

Raw water Conventional treatment

DBP N25 (%) R2 Ntrain Ntest N25 (%) R2 Ntrain N

TOX 84 0.96 22 79 82 0.88 90 4CHCl3 83 0.90 21 91 88 0.86 98 4CHBrCl2 89 0.94 20 99 BMRLCHBr2Cl 93 0.90 24 101 90 0.82 110 4CHBr3 BMRL 97 0.98 47 2THM4 83 0.97 22 79 85 0.90 102 4CH2ClCOOH 99 0.98 15 47 88 0.98 46 2CHCl2COOH 85 0.94 14 88 85 0.90 52 2CCl3COOH 88 0.94 18 73 84 0.92 84 4CH2BrCOOH BMRL 90 0.98 42 1CHBr2COOH 88 0.90 16 83 83 0.98 92 3CHClBrCOOH 87 0.78 19 99 86 0.90 108 5HAA6 79 0.92 18 76 82 0.71 92 4

BMRL means that the majority of measurements were below minimum reporting level.

in concentrations of individual THM and HAA species for raw andtreated waters. Additionally, neuronal connection weights weremeaningful from the standpoint of the chemistry underlying DBPformation. Hence, ANNs appear to be able to capture at least certainmechanistic aspects of DBP formation and not just make purelyempirical calculations. This suggests that they are more robust thanpurely statistical regression models.

In this section, the role of water treatment processes in inducingchanges in individual DBP species is considered in more detail.Conventional treatment, GAC, and NF remove NOM to a much greaterextent compared with the bromide ion increasing the Br−/DOC ratio.Therefore, these treatment processes not only reduce total DBPformation but also change THM and HAA speciation (Chellam andKrasner, 2001; Gould et al., 1983; Symons et al., 1996). This isparticularly important since studies indicate increasing carcinogenicityand mutagenicity with bromine substitution e.g. (Myllykangas et al.,2003). Effects of treatment using a bituminous coal based GAC (F-400,Calgon Corp.) with 15-minute empty bed contact time on DBP controland changing THM speciation observed in this study are summarized inFig. 7.

The breakthrough curves for TOC, SDSTOX, and SDSTTHMs are seenin Fig. 7a. As expected, NOM removal by GAC decreased DBP precursorconcentrations consequently decreasing TOX and total THM formationin the effluent over time under SDS conditions. NOM removal alsoincreased Br−/DOC which can be expected to influence DBP speciation.Fig. 7b depicts mole fractions of individual THM species as functions ofBr−/DOC molar ratio. Under the experimental conditions investigated,CHCl3 monotonically decreased and CHBr3 monotonically increasedwith increasing Br−/DOC whereas the mixed bromochloro species

GAC effluent NF permeate

test N25 (%) R2 Ntrain Ntest N25 (%) R2 Ntrain Ntest

80 85 0.88 220 2885 83 0.92 53 29266 82 0.96 577 2726 83 0.98 48 247

82 0.94 511 2996 85 0.98 63 26455 85 0.94 620 2814 87 0.96 70 30417 80 0.96 452 2662 87 0.75 80 27283 83 0.92 250 3300 80 0.92 42 31912 64 0.94 140 436 BMRL84 81 0.92 438 2739 87 0.98 71 25033 79 0.94 289 2088 85 0.98 48 18279 78 0.96 183 873 BMRL65 84 0.96 538 2892 85 0.92 54 28714 79 0.91 726 2975 82 0.96 45 25279 82 0.92 237 3200 81 0.90 41 274

Fig. 6. Summary of relative error distributions for all ANN simulations.

4208 P. Kulkarni, S. Chellam / Science of the Total Environment 408 (2010) 4202–4210

peaked within the range of Br−/DOC ratios encountered. CHCl2Br thatcontains one mole Br per mole THM peaked at 5 μM/mM Br−/DOC.CHClBr2 that contains twomoles Br per mole THM and peaked at twicethe Br−/DOC at approximately 10 μM/mM.

The bromine incorporation factor and bromide utilization in THMswas also quantified to study the degree of bromine substitution(Chellam and Krasner, 2001; Gould et al., 1983; Symons et al., 1993):

Bromineincorporationfactor =∑3

k=0k CHCl3�kBrk½ �

∑3

k=0CHCl3�kBrk½ �

ð5Þ

Bromideutilization =∑3

i=1i × CHCl3−iBri½ �

½Br−� : ð6Þ

Bromide substitution parameters for the same GAC run(corresponding to Fig. 7a and b) are shown in Fig. 7c. Increases in Br−/DOC increased totalBr incorporationwhile simultaneouslydecreasingClincorporation into THMs. Br andCl incorporationwere found to be equal

Table 3Relative importance of water quality parameters and chlorination conditions on DBP forma

Parameter Trihalomethanes (THM4) Haloacetic ac

Raw Conv. GAC NF Raw

DOC 40 24 18 21 39UV254 8 12 10 17 11Bromide 20 22 26 32 9Cl2 dose 12 18 30 13 17Temperature 4 8 8 5 7pH 8 10 4 8 9Contact time 8 6 4 4 8

at Br−/DOC of 8.2 μM/mMcorresponding to Br−/Cl2molar ratio of 0.044confirming that HOBr is more reactive than HOCl in forming THMs. Thedecreasing trend in bromide utilization with Br−/DOC in Fig. 7c can beattributed to reductions in DOC concentrations at a fixed Br−

concentration. Low NOM concentrations in the GAC effluent signifythe availability of only a very few sites for bromine substitution. SinceHOBr is amore powerful halogenating agent thanHOCl, the brominatedDBPs are formed first with bromine consuming the available sites onNOM. In precursor limited waters, bromide utilization is reducedbecause excess Br− cannot react further once all available NOM reactivesites are occupied. In otherwords, at the start of aGAC run (lowDOCandhigh Br−/DOC), only a small fraction of the total Br− is substituted intoNOM due to the paucity of total reactive sites and the majority of Br−

cannot react, resulting in a low bromide utilization. As DOC breaksthrough over time during the course of a GAC run (increasing DOC anddecreasing Br−/DOC), the number of sites available for substitutionconcomitantly increases allowing for greater bromide utilization.

NFwasextremely effective inDBPprecursor control but also inducedsignificant shifts towards the brominated THM species. THM molefractions in the NF feed water was in the order CHCl3NCHCl2-BrNCHClBr2NCHBr3 (Fig. 8). Very high NOM removal combined withpoor bromide ion removal by NF resulted in a large increase in the Br−/

tion.

ids (HAA6) Total organic halide (TOX)

Conv. GAC NF Raw Conv. GAC NF

22 16 18 42 26 24 2211 13 18 17 21 11 2114 20 29 3 8 14 2130 35 21 29 21 36 186 5 3 4 9 3 6

10 6 8 1 11 9 97 5 3 4 4 3 3

Fig. 7. a. BreakthroughofNOM(measured as TOC) andprecursors to TOXand TTHMs. Feedwater TOC=4.5 mg/L, SDSTOX=223.7 mg Cl−/L, and SDSTTHM=85.1 μg/L, Br−=115 μg/L. b. Shift towards more brominated THM species with increasing Br−/DOC ratioin theGACeffluent. SDS conditions: 6-hourhold time,pH9,Cl2 residual0.75 mg/L. c. Effectsof Br−/DOC ratio onhalogen incorporation and bromide utilization in THMs followingGACtreatment.

Fig. 8. General increase of brominated THMs in NF permeate compared with the feedwater.

4209P. Kulkarni, S. Chellam / Science of the Total Environment 408 (2010) 4202–4210

DOC in the permeate water (60 μM/mM) compared to the feed water(1.8 μM/mM). This shifted the THM formation towards the brominatedspecies in the permeate water where concentrations were in the order:CHClBr2NCHCl2BrNCHCl3NCHBr3. Even though NF significantly re-moved total THM precursors, concentrations of the highly brominatedspecies (CHBr3) actually increased in the permeate compared with thefeed water. Similar observations have been made in other membrane–source water combinations as well (Chellam and Krasner, 2001).

Changes in THM speciation summarized Figs. 7 and 8 would alsohave been influenced by changing Br−/Cl2 (Symons et al., 1993). Aconstant Cl2/TOC ratio could not be used in this study since a higherchlorine dose was required for waters with a higher TOC concentra-

tion in order to achieve a target chlorine residual of 0.75 mg/L at theend of SDS testing. Under these experimental conditions, Br−/Cl2increased in a logarithmic manner as 9.2Ln(Br−/DOC)+23.9, inwhich both ratios are expressed in μM/mM. Thus, the HOBr/HOCl ratioalso increased with Br−/DOC, preferentially shifting DBP speciationtowards the more brominated species.

It should be noted that HAA speciation was difficult to interpretquantitatively from the ICR treatment studies since only six of thenine HAA species containing chlorine and bromine were typicallyanalyzed and several HAA species were often below minimumreporting levels. The interested reader can refer to earlier publicationsthat have a detailed interpretation of changes in HAA speciation withtreatment e.g. (Chellam and Krasner, 2001; Liang and Singer, 2003).

4. Implications and conclusions

Robust ANNs requiring low quantities of data for trainingsatisfactorily predicted formation of total trihalomethanes, sum ofsix haloacetic acids, total organic halide, as well as individual THM andHAA species in chlorinated waters covering a geographically diversearea of the United States. Benchmarking predictions to an extensiveset of experimental measurements demonstrated that ANNs canclosely predict DBP concentrations (under SDS conditions) followingconventional and advanced treatment. Hence, complex and non-linear relationships between water quality parameters and chlorina-tion conditions influencing DBP formation and speciation weresuccessfully captured by ANNs suggesting that they are viablealternatives to bench-scale laboratory testing to simulate large-scaleunit processes. In other words, ANNs could be successfully used forprocess optimization and control and even for evaluating changes inDBP formation when operating conditions are changed or whenadvanced technologies are implemented for NOM removal. Hence,ANNs are valuable tools to compare and select NOM removalalternatives and can also reduce the experimental burden associatedwith relatively expensive and time consuming pilot-scale tests. Itshould be emphasized that since DBP control in ICR treatment studieswas evaluated through SDS testing, ANNmodels presented herein arenot strictly applicable to predict DBP concentrations in existing full-scale distribution systems.

Even though ANNs can provide water purveyors with quantitativeestimates of DBP concentrations, they do not provide a comprehen-sive mechanistic understanding of the chemical reactions and kineticsinvolved in DBP formation e.g. (Hua and Reckhow, 2008; Liang andSinger, 2003; Obolensky and Singer, 2005). Importantly, as with allempirical models, care should be taken not to implement ANNsbeyond the range of water quality parameters for which they werederived. Also, the existence of outliers in Figs. 2–5 demonstrates theinherent difficulties in accurately predicting individual DBP concen-trations especially when a single ANN is applied to a very wide range

4210 P. Kulkarni, S. Chellam / Science of the Total Environment 408 (2010) 4202–4210

of complex water chemistries, geographically diverse locations, andtreatment parameters.

In any case, all inputs for ANNs derived herein are simple tomeasure water quality parameters that are routinely monitored indrinking water facilities thereby facilitating their implementation toscreen preliminary process alternatives for DBP control. Furthermore,ANNs have also been reported to closely predict kinetics of Giardiainactivation (Haas, 2004). Hence, ANNs appear to be able to respondto water quality variations and closely capture complex aqueousphase behavior of both protozoa and chemical contaminants. Incontrast, mechanistic models are yet unavailable to predict eithermicroorganism inactivation or DBP formation under conditions ofdrinking water treatment. Hence, ANNs appear to be a potentiallyuseful tool to quantify the seemingly conflicting requirements ofmicrobial and DBP regulations and subsequently make betterdecisions related to design and operation of drinking water facilitiesto simultaneously meet existing primary drinking water standards.

Acknowledgments

This research has been funded by a grant from the National ScienceFoundation CAREER program (BES-0134301). The contents do notnecessarily reflect the views and policies of the sponsors nor does themention of trade names or commercial products constitute endorse-ment or recommendation for use.

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