Developing a Computer Model to Simulate DBP Formation During Water Treatment

Transcription

1 Developing a Computer Model to Simulate DBP Formation During Water Treatment Gregory W. Harrington, Zaid K. Chowdhury, and Douglas M. Owen A computer program was developed to simulate disinfection by-product formation, removal of natural organic matter, inorganic water quality changes, and disinfectant decay in water treatment processes. This article presents equations that simulate the formation of total trihalomethanes (TTHMs), removal of total organic carbon (TOC) and ultraviolet absorbance by alum coagulation, and changes in alkalinity and ph. These equations represent only a small fraction of the entire computer model. Model simulations are compared with limited sets of observed values. The central tendency of the model is to under-predict finished-water ph by 4 percent, finished-water TOC by 7 percent, and simulated distribution system TTHMs by percent. A discussion of model limitations and research needs concludes the article. The regulations resulting from the pas- tentially more advanced, technologies. sage of the 1986 amendments to the Safe Two rules receiving a great degree of Drinking Water Act are challenging util- attention are the Surface Water Treatities to meet stricter water quality re- ment Rule (SWTR) and the forthcoming quirements using conventional, and po- Disinfectant-Disinfection By-Products Figure 1. Conceptual diagram of water treatment plant model (D-DBP) Rule. The former places an increased emphasis on achieving minimum levels of disinfection for surface waters, whereas the latter will limit the concentrations of disinfectant residuals and DBPs. This pair of regulations is expected to require many utilities to implement a treatment approach that balances the benefits of disinfection against undesirable concentrations of D-DBPs. From an engineering perspective, these regulations decrease the region of feasible process operation to an extent that many utilities will need to investigate modified and advanced treatment processes. The utilities are not alone in their dilemma. The US Environmental Protection Agency (USEPA), under a mandate from Congress to develop a D-DBP Rule, is faced with a more complicated rulemaking process than it has faced with previous regulations. This complexity results from the fact that a reduction in the theoretical risks of DBP production could conceivably increase the risks from less microbiological control. The approach used in developing the D-DBP Rule, therefore, is expected to reduce health risks resulting from DBP production while accounting for the potential for an increase in health risks resulting from less microbiological control, the in- creased costs of treatment, and technical feasibility. -i An evaluation of the trade-offs between required levels of disinfection and the presence of D-DBPs favors a modeling approach. If such an approach is to be implemented effectively for the D-DBP rule-making process, then a model that can adequately simulate the formation of 78 RESEARCH AND TECHNOLOGY JOURNAL AWWA

2 DBPs and the decay of disinfectants must be developed. In order to simulate DBP formation and disinfectant decay, the model must also be capable of simulating the removal of natural organic matter (NOM) and changes in inorganic water quality parameters such as ph. This article describes the development and limitations of several portions of a model designed to assist the USEPA in the D-DBP rule-making effort. Primarily because of space limitations, the article is limited to those portions of the model that have been compared with observed data. The portions of the model described include the simulation of total trihalomethane (TTHM) formation, total organic carbon (TOC) removal, removal of those substances that absorb ultraviolet light at 254 nm (UV-254), and ph changes in treatment plants that employ alum coagulation, flocculation, clarification, and filtration processes. Other portions of the model, which simulate the formation of individual THM species, the formation of non-thm DBPs, the decay of chlorine and chloramines, and the removal of NOM by ferric salt coagulation, precipitative softening, granular activated carbon adsorption, and membrane separation are critical to the analysis of regulatory alternatives and are described elsewhere.8-0 Their exclusion from this article is not intended to minimize their importance. The model described in this article was developed to simulate the mean performance of water treatment processes in the United States. In other words, the model was developed to simulate the general case rather than the site-specific case. The output from such a model should not replace sound engineering judgment based on bench-, pilot-, and field-scale treatability studies for specific waters. Data collection To meet the objectives, an effort was made to integrate various models that simulate inorganic water quality changes, DBP formation, disinfectant decay, and removal of NOM into a single, interactive, and user-friendly computer program (Figure 1). Models published in the literature were evaluated to determine their usefulness in this effort and, when published models were not available, data were collected from various sources to develop new models. The primary purpose of this section is to describe the various data sets assembled in the course of model development. A secondary purpose of this section is to elucidate differences between data bases with regard to the measures that were used to quantify NOM. In some studies, TOC analyses were conducted after filtration with 0.45~pm filters. The results of such analyses are referred to TABLE 1 Characteristics of data base Parameter Minimum Percentile Median Percentile Maximum Raw-water quality Total organic carbon--mg/l uv-254~-cm? o.oi PH ci:i ~ Alkalinity--mg/L as &CO:, 5.x 25!I2 Hi 149 Total hardness--m&l as CaC0.j Temprraturc- C Turbidity--ntu Chloride--mg/L Finished-water quality Total organic carbon--mg/l IJV-254-m? ~ Turbidity-&u Treatment practices Coayulation ph Alum doscmg/l x Parameter TABLE 2 Characteristics of data base 3 Minimum,&I, ~ Percentile 7.5 Median ~ Percentile ~ Maximum Raw-water quality Total organic carbon--nzg/l Dissolved.. ljv-254--c ~ 0.7ii PH x.1 x.7 Alkalinity--mg/l. as CaCO:~ Total hardness--mg/l, as CaCO:j rcnlperatlre--c Turbidity--ntu x Chloride--mg/L 15 ~ Finished-water quality Total organic carbon--my/l x i Dissolved ljv-254--ck Turbidity-&u Trratmrnt practices Coagulation ph 6.0 ti ~ 7.7 x.1 Ahm dose--mg/l i as dissolved organic carbon (DOC) throughout this article. The data in other studies that did not perform this filtration step are referred to as TOC. Similarly, some studies measured the absorbance of ultraviolet light at 254 nm after tiltration with 0.45.pm filters, whereas other studies did not use such a filtration step. Data obtained after filtration with pm filters were considered representative of dissolved substances that absorb 254.nm light and are referred to as DUV- 254; data obtained without this filtration step are referred to as UV254. Because turbidity can significantly interfere with the spectrophotometric measurement of UV absorbance, the difference between UV-254 and DUV254 is typically more significant than the difference between TOC and DOC. Data that describe the formation kinetics of TTHMs were obtained from a single study that evaluated the bench-scale chlorination of nine waters. This data base is referred to as data base 1. Data base 1 included more than 1,000 data points obtained from nine sources (Figure 2). Summary statistics of the data base are shown elsewhere. NOM in data base 1 was quantified by measuring DOC concentration and DUV-254. Data for treatment plants using alum coagulation, flocculation, clarification, and filtration were collected from three field-scale studies. -14 NOM was quantified in these studies by measuring TOC concentration and UV-254. Treatment plants were included in the analysis only when the alum dosage and the ph of coagulation, flocculation, clarification, and filtration could be characterized for the day oftoc and UV-254 sampling. For example, if a clarification ph was reported and coagulation and flocculation ph values were not reported, that treatment plant was included only if no ph-adjusting chemicals were added between the point of alum addition and the point of ph measurement. If such information was not available in the authors final re- NOVEMBER 1992 GREGORY W. HARKINGTON ETAL 79

3 ports, the authors were contacted for additional information. The data base that resulted from this analysis is referred to as data base 2. Data base 2 included 45 data points obtained from the 17 locations shown in Figure 2, which shows a wide geographic distribution. Characteristics of the data base are shown in Table 1, which indicates that the data base covers a wide range of raw-water qualities and treatment conditions. Data for treatment plants using alum coagulation, flocculation, clarification, and filtration were also obtained from a separate field-scale study. In this study, NOM was quantified by measuring TOC concentration and DIJV-254. Again, treatment plants were included in the analysis only when the alum dosage and the ph of coagulation, flocculation, clarification, and filtration could be characterized for the day of TOC and DUV-254 sampling. The data base that resulted from this analysis is referred to as data base 3. The geographic distribution of the treatment plants in data base 3 is shown in Figure 2, and summary statistics are shown in Table 2. Finally, data for 19 southern California treatment plants that used either alum or ferric chloride coagulation followed by flocculation, clarification, and filtration were also obtained from a field-scale study that was used for the comparison of several model components with observed data. The quantification of NOM in this study was performed by measuring DOC concentration and DUV-254. Treatment plants were included in the data base only when the coagulant dosage and the ph of coagulation, flocculation, clarification, and filtration could be characterized for the day of DOC and DUV254 sampling. The data base that resulted from this analysis is referred to as data base 4. The geographic distribution of the treatment plants in data base 4 is shown in Figure 2, and summary statistics are shown in Table 3. Model development TTHM formation. Numerous studies have used linear regression techniques to correlate ITHM formation potential ( ITHMFP) withtoc and W Although these results showed good correlations, general use of the regressions is limited because they do not include parameters such as chlorine dosage, ph, temperature, and time. A similar effort resulted in a correlation between distribution system TTHM concentration and the chlorine dosage applied at the treatment plant. Other studies have taken a more thorough approach by modeling the kinetics of THM formation. ~ ~ Of these studies, the one performed by Amy and coworkers was based on the largest number of different waters, the largest number of observations, and the widest - Figure 2. Sources represented in data bases 1 through 4 80 KESEAI(CHANDTECHNOI~OC,Y JOUKNAL AWWA

4 range of parameter types and values. Because of the more extensive testing conditions used in this study, the relationship developed to describe TTHM formation was considered the most representative of the general case and was selected for further evaluation. Amy and co-workers studied the laboratory chlorination of nine natural waters from various locations throughout the United States. The waters were spiked with several different bromide concentrations and were adjusted to several different temperatures and ph values prior to chlorination. These studies produced data base 1 and the following relationship: TTHM = [(DOC) (DLJV - 254)l 44 (CL) (1) x (t ) 2,s (T) uh (ph -2,G)u (Br+ 1) in which TTHM is the total trihalomethane concentration in umol/l, DOC is the dissolved organic carbon concentration in mg/l, DLJV-254 is the absorbance of ultraviolet light at a wavelength of 254 nm (in cm- ), Cl2 is the chlorine dosage in mg/l, t is the reaction time in hours, T is the temperature in C, and Br is the bromide concentration in mg/l. The basis for using the variables (ph - 2.6) and (Br + 1) is described elsewhere. As discussed later, the applicability of this equation to treated waters, to waters with high bromide concentration, and to waters with high ammonia concentration may be limited. Because ITHMs are regulated on a cumulative mass basis, one limitation associated with Eq 1 is its simulation of ITHM formation in umol/l. The molecular weights of the four THM species vary significantly from 119 pg/umol for chloroform to 252 pg/ymol for bromoform. Therefore, conversion from micromoles per litre to micrograms per litre is not possible unless the distribu- tion of the four species is known or an apparent molecular weight (AMW) can be determined. The AMW of the THM species present in a water was expected to increase with an increase in bromide concentration, which is expected to lead to higher concentrations of brominated species. Likewise, an increase in chlorine dosage with no change in bromide concentration could possibly result in a decrease in AMW, because the speciation might be expected to shift to higher concentrations of chlorinated species. Further, changes in the type and quantity of NOM could also conceivably change the nature of the kinetic competition between bromine and chlorine for reaction with NOM. Data base 1 was used to develop a relationship that calculates the AMW of the THM species formed. Using a forward stepwise variable selection procedure, the following equation was developed with multiple linear regression: In (AMW) = [In (Br + l)] TABLE 3 Characteristics of data base [In (DUV - 254)] (3 [adj r = 0.91, F= 2423 (a < O.OOl), n = Parameter Raw-water quality Dissolved organic carbon--mg/l Dissolved UV-254-cm- PH Alkalinitv--mP as CaCOd Total ha;dnes;-mg as C&03/L Temuerature- % Turdidity--ntu Chloride--mg, L Bromide--mg/L Ammonia--mg as N/I. Finished-water quality Dissolved organic carbon--mg/l Dissolved UV-254-w- Treatment practices Coagulation ph Alum Dose-mg/I> Variabie Constant In(Br+l) ln(dw-254) Source of Variation I Median 75 Percentile Maximum O.OY! TABLE 4 Coefficient statistics for Equation 2 Coefficient Standard Error TABLE 5 Analysis of variance for Equation P-Value <O.OOl <O.OOl Degrees Sum of Mean of Squared Squared Freedom EtlKlrs Error Regression Residual I NOVEMBEK 1992 GREGORY W. HARRINGTON ETAL in which AMW is the apparent molecular weight in pg/nmol of the THM species formed. The number of data points (n) available, the ad usted coefficient of determination (adj Y 1 ), and the F-statistic at a significance level of a are also shown with Eq 2. The latter two statistics are indicators of the goodness of fit. The stepwise variable selection procedure examined 13 variables. which were required to meet an M of 0.05 or less in order to be included in the equation. Tables 4 and 5 present additional statistics for Eq 2. The results of Eqs 1 and 2 can be multiplied together to calculate TTHM formation in ug/l. Eq 2 shows that an increase in bromide concentration leads to an increase in the AMW of the THM species formed. In addition, an increase in the concentration of dissolved substances that absorb 254- nm light leads to a decrease in the AMW. The effect of chlorine dosage on AMW was not statistically significant. This observation may have been influenced by the relatively high chlorine dosages used in data base 1. Removal of NOM. Many investigators have evaluated the effects of alum dosage and ph on the removal of NOM at a bench-scale level. - In general, these studies have found that the removal of NOM is optimized by maintaining a ph in the range 5-6 during coagulation, flocculation, and clarification. Despite extensive bench-scale testing, a model that described the effects of ph and coagulant dosage on TOC or UV-254 removal has not been published to date. A forward stepwise variable selection procedure was employed with a multiple linear regression analysis to analyze the

5 influence of raw water TOC (TO&); alum dosage (in mg as Alz(S04)3.14HzO/L); the ph of coagulation, flocculation, clarification, and filtration (ph,); and temperature and chloride on finished-water TOC (TOCf ) in data base 2. Neither temperature nor chloride had a statistically significant effect on TO& The apparent lack of significance of temperature in TOC removal agrees with the results of bench-scale studies performed by Knocke and co-workers. The regression analysis was conducted a second time without temperature and chloride. The resulting equation and its associated statistics are: [In (alumdose)] [In ( TOCc )] [In (alum dose)] (3) [(ph, ) [ln (alum dose)] [adj Y = 0.965, F= 297 (n < O.(Nil), n = 441 The stepwise variable selection procedure examined seven variables, which were required to meet an a of 0.05 or less in order to be included in the equation. Tables 6 and 7 present additional statistics for Eq 3. As shown above, the interaction between the natural logarithm of alum dosage and ph, was found to be statistically significant in describing the removal of TOC by alum coagulation. However, when considered by itself, ph, was not found to be statistically significant. The reasons for the latter observation are not clear. A sensitivity analysis of Eq 3 is shown in Figure 3, which indicates that TOC removal increases with increasing alum dosage. However, the magnitude of the increase becomes smaller as alum dosage increases. TOC removal also increases with decreasing ph in the range. However, an optimal ph was not determined because of the lack of data at ph values ~5.5. The curves shown in Figure 3 were generated from Eq 3, which represents the mean performance of the treatment plants in data base 2 under the treatment conditions employed survey. For example, at the time of the if all 17 treatment plants in data base 2 operated at ph 5.5 and an alum dose of 50 mg/l, the mean TOC removal is expected to be 55 percent. Some of these 17 utilities would observe greater removals under these conditions, whereas others would observe lower removals. Although the regression analysis inherently assumes that deviations from the mean are due to randomness, these deviations may be the result of differences in NOM characteristics 1, m40 or in pretreatment practices such as preoxidation.41s4 A more complete discussion of these limitations is given in a later section of this article. Figure 3. Simulation of TOC removal by alum coagulation, flocculation, clarification, and filtration using Eq 3 (raw-water TOC-2.8 mg/l [median of data base 21; ph values represent the minimum, median, and maximum values in data base 2) The relationship for calculating filtered-water DUV-254 was developed from data bases 3 and 4 by using statisti- cal techniques similar to those used for developing Eq 3. The resulting equation and its associated statistics are: In (DW-25&)= [h(duv-254u)l [ln (alum dose)] (ph, ) (4) [adj rz = 0.702, F= 23.8 (a < O.OOl), n = 301 in which DUV-2540 is the raw-water DUV- 254 in cm-, and DUV-2541 is the tilteredwater DUV254 in cm-. The stepwise vari- able selection procedure examined three variables, which were required to meet an a of 0.05 or less in order to be included in the equation. Tables 8 and 9 present additional statistics for Eq 4. A lower quality of fit was obtained for Eq 4 compared with Eq 3. This was expected because the decrease in DUV- 254 through coagulation, flocculation, clarification, and filtration processes may also occur as a direct result of oxidation carried out in conjunction with these processes. Oxidation is known to significantly decrease DUV- 254, although it has little effect on the concentration of TOC. Alkalinity and ph changes. The simulation of alkalinity and ph changes resulting from chemical addition is driven by changes in the concentrations of charged species. The simulation is based on the following expression: Alkalinity= [HO3 I + 2 [CO:3 - I + [OK I- [Hi I =CR +2 [Ca +]+ [CaOP] + 2 [Mg [MgOH+ I + [NH4 + I -c,4-[ocl-i in which CR and CA are the concentrations (in equivalents per litre) of all posi- (5) tively and negatively charged species, respectively, not shown in Eq 5. As shown elsewhere,8 Eq 5 can be rewritten in terms of a set of equilibrium constants and the concentrations of total dissolved carbonate, total dissolved calcium, total dissolved magnesium, total dissolved ammonia, total dissolved hypochlorite, CB, CA, and hydrogen ion. An iterative procedure is used to calculate a new hydrogen ion concentration whenever chemical addition leads to a change in &I, CA, or the concentration of total dissolved carbonate, calcium, magnesium, ammonia, or hypochlorite. The new hydrogen ion concentration is then used to calculate a new ph. A new alkalinity is calculated from the new hydrogen ion concentration and from appropriate values of the other variables. The equilibrium constants are assumed to be temperature-dependent in accordance with published enthalpies of reaction.4 The model is based only on equilibrium considerations and does not account for the kinetics of processes such as metal hydrolysis, calcium carbonate precipitation, and carbon dioxide dissolution. A detailed description of the alkalinity and ph change simulation model can be found elsewhere.8 Comparisons with observed data The simulation of TOC removal by alum coagulation and the simulation of ph changes were compared with independent data bases that were not used in the development of these submodels. Because verification is a procedure by which a model is compared with observed data that were not included in model formulation, the ph change and TOC removal submodels are considered to be verified. Calculated ITHM concentrations were compared with data base 4, which was not used in the formulation of Eq 1 and 2. However, TTHM simulations were based on DUV-254 values predicted 82 RESEARCH AND TECHNOLOGY JOURNAL AWWA

6 Variable TABLE 6 Coefficient statistics for Equation 3 CO&iCient Standard ElTYX P-Value Constant In(TOCo) <O.OOl In(alum dose) <O.OOl Iln(TOCo)l Un(alum dose) I [phcl [ln(alum dose)] <O.OOl Regression Residual Source of Variation TABLE 7 Analysis of variance for Equation 3 DeglX?es of Freedom 4 39 by Eq 4, which was developed from data base 4. Therefore, the TTHM submodel was not verified with a completely independent data base. However, verification of this submodel against a limited, independent data base of North Carolina utilities was recently performed.44 ph changes. Comparisons between calculated and observed finished-water ph values were conducted by using treatment plants in a nationwide data base.14 Precipitative softening plants were not included in the analysis, but plants that used disinfection chemicals without any additional treatment were included. In this verification effort, known raw water qualities and known chemical dosages were input into the model for each treatment plant evaluated. The finished-water ph calculated by the model for a given plant was compared with the observed finished-water ph at that plant. Figure 4 is a histogram that describes the relative frequency of occurrence of deviations between calculated and observed finishedwater ph levels. In this figure, the percent deviation is defined as: % Deviation = 6) loo t Calculated value - Observed value Obsrrvedvalue The median deviation was to underpredict finished-water ph by 4 percent. In terms of simulating TTHM formation, the underprediction of ph will lead to an underprediction of TTHM concentrations. For example, when using median values from data base 1 as inputs into Eq 1 and 2, underpredicting ph by 4 percent leads to a 4.5 percent underprediction of TTHM concentration. Therefore, for waters having these median inputs, 50 percent of the ITHM concentrations will be underpredicted by more than 4.5 percent as a result of using a calculated ph in Eq 1 rather than an 1 Sum of Squared Errors Mean Squared Error observed ph. The remaining 50 percent of the ITHM concentrations will either be underpredicted by ~4.5 percent or over-predicted when a calculated ph is used in Eq 1 rather than an observed ph. The underprediction of ph will also propagate into an underprediction of filtered-water TOC in Eq 3 and filteredwater DUV254 in Eq 4. For example, when median values from data base 2 are used as inputs into Eq 3, underpredicting ph by 4 percent leads to a 3.9 percent underprediction of finished-water TOC concentration. Therefore, for waters having these median inputs, 50 percent of the finished-water TOC concentrations will be underpredicted by more than 3.9 percent as a result of using a calculated ph in Eq 3 rather than an observed ph. The remaining 50 percent of the tinishedwater TOC concentrations will either be underpredicted by c3.9 percent or overpredicted when a calculated ph is used in Eq 3 rather than an observed ph. TOC. The empirical model for TOC removal by alum coagulation, which was developed from data base 2, was tested against data from data bases 3 and 4. For this verification effort, observed values of raw-water organic carbon concentration (TOC for data base 3, DOC for data base 4), alum dosage, and ph, were obtained for each treatment plant and input into Eq 3. The concentration of organic carbon calculated by Eq 3 for a given treatment plant was compared with the observed finished-water concentration for that plant. Figure 5 is a histogram of the deviations between observed and calculated values. For data base 3, approximately 85 percent of the calculated values were within 20 percent of the observed values. All of the calculated values for data base 4 were within 20 percent of the observed values. This figure also shows that the central tendency was to underpredict fin- ished-water organic carbon concentration (i.e., overpredict organic carbon removal) by O-5 percent in data base 3, by 5-10 percent in data base 4, and by 5-10 percent when both data bases are considered. The deviation was not observed to be a function of alum dosage, coagulation ph, or raw-water TOC. From the standpoint of simulating TTHM formation, the underprediction of TOC will translate to an underprediction of TTHM concentrations. For example, when median values from data base 1 are used as inputs into Eq 1 and 2, underpredicting TOC by 7 percent leads to a 3.1 percent underprediction of TTHM concentration. Therefore, for waters having these median inputs, 50 percent of the TTHM concentrations will be un- derpredicted by more than 3.1 percent as a result of using a calculated TOC concentration in Eq 1 rather than an observed TOC concentration. The remaining 50 percent of the TTHM concentrations will either be underpredicted by ~3.1 percent or overpredieted when a calculated TOC concentration in Eq 1 is used rather than an observed TOC concentration. TRIM. TTHM concentrations were calculated from Eq 1 and 2, which were developed from data base 1, and were compared with observed simulated distribution system ITHM concentrations (SDS-TTHM) in data base 4. Compared with the range of water quality and operating characteristics observed on a national level, data base 4 contains a narrow range of water qualities and operating conditions. Although the TTHM submodel has been verified with another lim- ited data base,44 verification of Eqs 1 and 2 with a nationwide data base is essential. Observed SDS ITHM concentrations were determined by holding finished water samples collected from each plant for 24 hours prior to quenching the disinfectant residual. The calculated SDS- TTHM concentrations were based on calculated DOC concentrations (from Eq 3) calculated values of DUV-254 (from Eq 4), and calculated ph (based on Eq 5) values in Eqs 1 and 2. Observed bromide concentrations, chlorine dosages, and temperatures were also used in the calculations. Contact times used in the calculation were estimated from basin size and baffling characteristics, from plant flow rates, and from the 24-h holding period. Therefore, deviations between calculated and observed values include the propagation of errors resulting from estimates of DOC concentration (which includes the use of an equation based on TOC to estimate DOC), DUV-254, ph, and contact time. Figure 6 is a histogram of deviations between observed and calculated SDS- TTHM concentrations. This figure includes plants that used chlorine as the only disinfectant and other plants that NOVEMBER 1992 GREGORY W. HARRINGTON mal 83

7 used both chlorine and chloramines. No attempt was made to assess the ability of the model to predict TTHM formation as a function of disinfection practice. The figure indicates that 33 percent of the calculated concentrations were within 20 percent of the measured concentrations and 80 percent of the calculated concentrations were within 40 percent of the observed concentrations. The figure also indicates that the central tendency was to underpredict SDS-TTHM concentrations by percent. These results are quantitatively similar to results obtained in an independent verification effort.44 Considering the compounding errors that result from predicting variables such as DOC, DUV254, and ph, this level of accuracy can be expected. Model limitations and research needs There are several ways by which the overall model can be improved. However, because of space constraints, limitations are only discussed for those submodels that were compared with observed data. Amore complete discussion of model limitations is provided elsewhere.8 Removal of NOM. There are several limitations common to the simulation of DOC and DUV-254. First, the alum submodels (Eqs 3 and 4) were based primarily on the performance of treatment plants at their current state of operation. Only two of the plants in data base 2 and none of the plants in data base 3 underwent full-scale dosage-response studies. Therefore, Eqs 3 and 4 may not accurately describe possible changes in performance resulting from changes in alum dosage or ph. Studies are needed to determine whether a more accurate model could be developed by completing a fullscale dose-response survey. Such a survey should include the evaluation of ph effects while a constant alum dosage is maintained. A recent bench-scale study evaluated the effects of alum dosage and ph on TOC and DOC removal in each of 18 nationally distributed waters.4 The results showed that Eq 3 had a central tendency to underpredict filtered-water TOC (i.e., overpredict TOC removal) by approximately percent. However, the authors used &urn filters as surrogates for treatment plant filters without verifying the applicability of such filters for this purpose. If treatment plant filters remove particulate organic carbon better than do 8-pm filters, then the predicted values would be closer to the observed values. The results also showed that Eq 3 had a central tendency to underpredict filtered-water DOC by approximately 5-10 percent when raw water DOC was substituted into Eq 3. Also, the chemistry between aluminum and NOM may be somewhat different in bench-scale jar tests, because jar Variable constant ln(dw-2540) In(Alum dose) PK TABLE 8 Coejicient statisticsfor Equation 4 Coefficient <O.OOl <O.OOl co.001 Figure 4. Occurrence of deviations between calculated and observed ph values of finished water tests are typically conducted with stock solutions containing aluminum species that are already hydrolyzed to some degree.4fi In typical full-scale applications, aluminum hydrolysis must compete with aluminum-nom interactions.47 Therefore, the results of bench-scale work may be inappropriate for verifying full-scale models. Studies are needed to evaluate the effects of stock solution strength on TOC and DOC removal in bench-scale jar tests and to determine the usefulness of bench-scale data in verifying models developed from full-scale data. An evaluation is needed to determine whether Eqs 3 and 4 can be improved by including other NOM characteristics such as molecular weight, hydrophilicity, and molecular charge distribution. 1a -4 If such models are developed, then a nationwide occurrence survey will be needed to assess the distribution of the NOM characteristics on which the model is based. Equations that simulate the removal of DOC and DUV-254 by ferric chloride coagulation have been developed and are available in the model8 The equations are based on 215 observations from batch and continuous-flow studies;4r- 2 observations from full-scale, operating plants were not used. The ability of these equations to simulate DOC and DUV-254 removal for the general case is not expected to be as good as that of the alum coagulation equations because (1) fewer waters were included in the analysis and (2) the results may be skewed by a large number of observations (186, or 87 percent) from two sources. Because coagulation with ferric chloride is gaining more popularity in the water treatment industry, enhancement of the data base and improvement of the equations are desirable. Finally, the effects of preoxidation on NOM removal processes also need to be adequately simulated in a general manner. Preoxidation can influence process performance by decreasing the size of NOM molecules and by increasing the charge on NOM molecules. For instance, preozonation is known to influence the removal of NOM by coagulation.41,42 In addition, ozonation can significantly decrease DUV254 without significantly decreasing DOC. A general characterization of these effects will be useful because many utilities are expected to examine alternative preoxidation schemes as a result of the forthcoming D-DBP Rule. ph simulation. The calculation of ph changes in the model is based only on 84 RESEARCH AND TECHNOLOGY JOURNAL AWWA

8 TABLE 9 Analysis of variance for Equation 4 Degrees of Freedom Sum of Squared Errors MeaIl Squared Error Regression 3 I Residual Figure 5. Occurrence of deviations between calculated and observed organic carbon concentrations in the finished water of alum coagulation plants equilibrium considerations. This is a limitation of the model because the kinetics of some processes, such as metal hydrolysis, calcium carbonate precipitation, and carbon dioxide dissolution, may be important. A more realistic model would account for the kinetics of carbon dioxide transfer from the air to the water by incorporating appropriate mass transfer coefficients. As an alternative to such a kinetic model, two equilibrium models were considered for the water treatment plant model described in this article. One equilibrium model, referred to as the open-system model, assumes that the concentration of carbon dioxide dissolved in the water in open treatment plant basins is in equilibrium with the concentration of carbon dioxide in the atmosphere above the water. The other equilibrium model, referred to as the closed-system model, assumes no exchange of carbon dioxide between the water and the atmosphere. The water treatment plant model described in this article assumes that the closed-system model more closely approximates actual conditions in a water treatment plant than the open-system model. This assumption needs to be verified, and the possibility of including the kinetics of carbon dioxide dissolution in the water treatment plant model needs further consideration. In addition, the simulation of aluminum hydrolysis and solubility should be addressed in future versions of this model. This is important for simulating ph changes when alum is used at ph levels farther from the ph of minimum aluminum solubility (approximately ph ). This simulation is also important because many utilities are using alum coagulation at ph levels that are incompatible with the secondary maximum contaminant level for aluminum. llhm formation. Eqs 1 and 2 are also limited in several respects. First, the equations were based on TTHM formation in untreated waters. Because some studies have shown that coagulation selectively removes TTHM precursors, the applicability of these equations to treated waters is unknown. A significant limitation appears to be the ability of the model to predict TTHM concentrations in waters with high bromide concentration.i6 This limitation appears to be associated with the relatively high chlorine dosages used in the development of data base 1. The formation of chlorinated THMs becomes more favored as the ratio of chlorine dosage to bromide increases, whereas the forma- tion of brominated THMs becomes more favored as the ratio of chlorine dosage to bromide decreases. More than 75 percent of the tests conducted to develop data base 1 used a chlorine-dosage-to- DOC ratio of 3:l or greater. The ratio observed in most treatment plants is likely to be between 1:l and 2:l. This higher chlorine-dosage-to-doc ratio likely indicates a higher chlorine-dosageto-bromide ratio than normally observed, even though some samples in data base 1 were spiked with bromide prior to chlorination. The observation that chlorine dosage did not have a statistically significant effect on AMW (see Eq 2) may have resulted from a too narrow range of tested chlorine-dosage-to-bromide ratios in data base 1. Additional studies are needed to focus on the modeling of bromine incorporation at chlorine dosages considered more typical of water treatment practice. In addition, coagulation removestoc, UV-254, and chlorine demand but does not remove bromide. As a result, the ratio of TOC to bromide and the ratio of chlorine dosage to bromide will be smaller during the chlorination of treated waters than they would be during the chlorination of untreated waters. The decreases in these ratios can be expected to shift THM speciation to the heavier, brominated species, resulting in an increase in AMW. Eq 2, which calculates AMW, would predict an increase in AMW upon coagulation because a decrease in DUV- 254 with no change in bromide concentration would produce a higher value of AMW. However, this equation has not been verified for its ability to predict such an increase accurately. The formation of THMs and other organic DBPs also depends on NOM characteristics such as chemical functionality. Studies are needed to determine whether Eqs 1 and 2 can be improved with other measures of NOM characteristics. If such models are developed, then a nationwide occurrence survey will be needed to assess the distribution of the NOM characteristics on which the model is based. Eq 1 may also be inappropriate for systems that use free chlorine and have high raw-water ammonia concentrations. If it is in sufficient concentration, the ammonia could consume enough chlorine to have a significant effect on ITHM formation. Eq 1 should be verified against such waters, and studies are needed to assess whether the chlorine dosage input into Eq 1 should account for inorganic chlorine demand. Eq 1 and 2 are further limited by their power law nature. The development of differential rate equations would improve the ability to simulate TTHM formation through mid-treatment water quality changes, multiple points of chlorination, and basin baffling characteristics. In ad- NOVEMBER 1992 GREGORY W. HARRINGTON ETAI. 85

9 Figure 6. Occurrence of deviations between calculated and observed SDS-TTHMs in data base 4 dition, such equations can be linked with published rate equations for the interactions between aqueous chlorine, ammonia, and chloramines.56 At this time, the model estimates TTHM formation in the presence of chloramines to be 20 percent of the formation estimated in the presence of free chlorine. A more complete discussion of this issue is provided elsewhere. Summary and conclusion A computer program has been developed to simulate the formation of individual and total trihalomethanes; the formation of several haloacetic acids; the removal of TOC (or DOC) and DUV-254 by alum coagulation, ferric chloride coagulation, precipitative softening, granular activated carbon, and membrane processes; the decay of free and combined chlorine; the effectiveness of disinfection practices by using CT concepts; and changes in ph and alkalinity. This article describes the selection, development, and limitations of submodels that simulate TOC and DUV-254 removal by alum coagulation, ph changes, and TTHM formation. The ability of the model to simulate ph changes and TOC removal by alum coagulation was verified with data bases not used in the formulation of these submodels. The central tendencies observed in this verification effort are to underpredict finished-water ph by 4 percent and to underpredict finished-water TOC by 7 percent. The simulation of TTHM formation was compared, not truly verified, with observed values in a limited data base of southern California utilities. The central tendency of this comparison is to underpredict TTHMs by percent. The results of this comparison are similar to those achieved in an indepen- dent verification study performed in North Carolina.44 At the present time, the model is being used in analysis of the effect of the D- DBP Rule. One function of the model in this analysis is to estimate the national distribution of treatment modifications that would be required to meet alternative regulatory scenarios. Another function of the model is to estimate the national distribution of DBP and pathogenic microorganism concentrations to which the public would be exposed under alternative regulatory scenarios. These distributions are then used to estimate national treatment costs and national public health risks (and their associated costs)for each alternative considered. The deviations between observed and simulated TTHM concentrations demonstrated in this article are significant for the application of the model in this regulatory analysis. By itself, the model may be a limited tool in the regulatory decision-making process. However, when used in conjunction with known deviations from observed data, the output of the model becomes more useful because these known deviations can be accounted for. Although the importance of deviations cannot be minimized, the regulatory decision process cannot account for unknown deviations. Therefore, the verification of all portions of this model against an independent, nationwide data base is essential. A detailed description of the effect and handling of the known deviations in the regulatory analysis is planned for a followup article. Papers describing preliminary applications of the model have already been presented As stated earlier, the model described in this article was developed to simulate the general case rather than the site-specific case. Some water plant operators may be tempted to use this model as a substitute for site-specific studies. However, the output from the model is not intended to, nor should it, replace sound engineering judgment based on bench-, pilot-, and field-scale treatability studies for specific waters. Acknowledgment Funding for the work described in this article was provided by the US Environmental Protection Agency and the Metropolitan Water District of Southern California. The technical assistance provided by members of these two organizations, by Gary Amy of the University of Colorado, by engineers at Malcolm Pirnie Inc., and by engineers at James M. Montgomery Consulting Engineers is greatly appreciated. In addition, Montgomery Engineers, the Metropolitan Water District, Patricia Snyder-Fair of USEPA s Technical Support Division, and Philip Singer of the University of North Carolina were very cooperative in providing necessary, unreported data from their surveys. Singer also reviewed this article. Appreciation is also extended to Kenneth Curry, of Malcolm Pirnie Inc., whose programming expertise was critical to the development of the computer program. This article has not been reviewed by the USEPA or the Metropolitan Water District of Southern California and is not intended to reflect their views. References 1. HARRINC.TON, G.W. Developing a Disinfection By-Product Control Strategy AWWA Ann. Conf., Philadelphia, Pa. 2. CKOMWELL, J.E. & REGLI, S. Analytical Framework for Regulatory Impact Analysis of Disinfection/Disinfection By-Products Rulemaking AWWA WQTC, Orlando, Fla. 3. LETKIEWICZ, F.J. ET AL. Monte Carlo Simulation of Microbiological and Disinfection By-Product Contamination in Public Water Supplies. lando, Fla AWWA WQTC, Or- 4. MACLER, B. M AI.. Simulation of Pathogen Occurrence and Exposure Resulting From Water Treatment Processes AWWA WQTC, Orlando, Fla. 5. GIX~LXI.OOS, A.B. ET AL. Simulation of Compliance Choices to Meet Both Microbial and Disinfection By-Product Treatment Objectives AWWA WQTC, Orlando, Fla. 6. SCHAEFER, J.K. & HAIUUNGTON, G.W. 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RECKHOW, D.A.; SIN(XK, P.C.; & MALCOLM, R.L. Chlorination of Humic Materials: Byproduct Formation and Chemical Interpretations. Enuir. (1990). Sci. & Technol., 24:11:1655 MORRIS, J.C. & ISAAC, R.A. A Critical Review of Kinetic and Thermodynamic Constants for the Aqueous Chlorine-Ammonia System. Water Chlorination: Chemistry, Environmental Impact and Health Effects, Vol. 4, Lewis Publ., Chelsea, Mich. (1985). About the authors: Gregory W. Harrington is a doctoral student and research assistant in the Department of Environmental Sciences and Engineering, CB #7400, Rosenau Hall, University of North Carolina, Chapel Hill, NC The work described in this article was performed while Harrington was a project engineer at Malcolm Pirnie Inc., Newport News, Va. He is a graduate of Stanford University, Stanford, Calg, and of the University of North Carolina, Chapel Hill. Zaid K. Chowdhury is a project engineer for Malcolm Pirnie Inc., 4636 E. University Dr., Suite 150, Phoenix, AZ Douglas M. Owen is a senior associate with Malcolm Pirnie Inc., 703 Palomar Airport Rd., Suite 150, Carlsbad, CA GREGORYW.HARRlNGTONETAL 87