Environ. Sci. Technol. 2000, 34, 1813-1820
Effects of Nanofiltration on Trihalomethane and Haloacetic Acid Precursor Removal and Speciation in Waters Containing Low Concentrations of Bromide Ion SHANKARARAMAN CHELLAM† Cullen College of Engineering, Department of Civil and Environmental Engineering, University of Houston, 4800 Calhoun Road, Houston, Texas 77204-4791
A study was undertaken to determine trihalomethane (THM) and haloacetic acid (HAA) precursor removal by nanofiltration (NF). Cross-flow experiments were conducted with three NF membranes and six natural waters spanning total organic carbon (TOC) concentrations in the range 3.313.1 mg/L and bromide ion concentrations in the range 0.52.3 µM. Permeate and feedwaters were analyzed for all nine HAA species containing bromine and chlorine. The rejection of THM and HAA precursors decreased with increasing feedwater recovery suggesting that the transport of these materials is controlled by molecular diffusion across the polymeric membranes rather than by physical sieving at membrane surfaces. Therefore, the maximum feedwater recovery in NF systems designed for disinfection byproduct control may be limited by deteriorations in permeate water quality in addition to fouling caused by precipitation of sparingly soluble salts. High rejections of TOC coupled to low removals of bromide ion by some NF membranes resulted in a shift toward more brominated THM and HAA species in permeate waters upon chlorination. Concentrations of selected highly brominated species are shown to increase upon NF under certain conditions. Empirical equations relating THM and HAA bromine incorporation factors in feed and permeate waters to pH, Br-/TOC, and Cl2/TOC ratios are also presented.
Introduction Trihalomethanes (THMs) and haloacetic acids (HAAs) are two classes of disinfection byproducts (DBPs) formed during chlorination of waters containing organic matter. A total of four THMs (chloroform (CHCl3), dichlorobromo methane (DCBM), chlorodibromo methane (CDBM), and bromoform (CHBr3)) and nine HAAs (monochloro acetic acid (MCAA), dichloro acetic acid (DCAA), trichloro acetic acid (TCAA), monobromo acetic acid (MBAA), dibromo acetic acid (DBAA), bromochloro acetic acid (BCAA), dibromochloro acetic acid (DBCAA), dichlorobromo acetic acid (DCBAA), and tribromo acetic acid (TBAA)) containing chlorine and bromine can be formed upon chlorination of waters containing natural organic matter (NOM) and bromide (Br-). † Corresponding author phone: (713)743-4265; fax: (713)743-4260; e-mail:
[email protected].
10.1021/es991153t CCC: $19.00 Published on Web 03/30/2000
2000 American Chemical Society
Current evidence points to the potential carcinogenic, developmental, and reproductive toxicity of individual THM and HAA species (1, 2). Existing DBP regulations are based on the sum of the four THMs (TTHMs) and sum of MCAA, DCAA, TCAA, MBAA, and DBAA (HAA5) (1). However, regulatory negotiations are being undertaken currently to possibly further reduce the maximum contaminant levels of TTHMs and HAAs and possibly introduce new ones for individual species. Because of possibly large differences in their human health impacts, it is important to study changes in concentration of individual THM and HAA species with treatment. A summary of research on DBP chemistry and removal by various water treatment processes has been published recently (2). Most of the studies reported therein only considered six individual HAA species (MCAA, DCAA, TCAA, MBAA, DBAA, and BCAA) because until recently commercial analytical standards for DBCAA, DCBAA, and TBAA were not available. Very few peer-reviewed publications are available to date where all nine HAA species have been analyzed. Pourmoghaddas et al. (1993) analyzed all nine HAAs during chlorination of a commercial humic acid and identified a shift toward more brominated species with increasing Brconcentration. Similar results have been reported using natural humic substances extracted from surface and groundwaters. At constant TOC concentration and chlorination conditions, relatively invariant molar concentration of HAA9 was reported even with changes in the Br- concentration (4). In contrast, increasing molar concentrations of HAA9 have also been measured with increases in the Br- concentration for reaction times greater than 24 h (5). Many previous studies on THM and HAA formation chemistry have been conducted by artificially varying concentrations of NOM and Br- in model waters (2). In contrast, nanofiltration (NF) membranes offer inherently different permeabilities to NOM and Br- ion and therefore can be expected to change not only DBP precursor concentrations but also the speciation of THMs and HAAs upon chlorination. NF has been shown to be highly effective for DBP precursor removal (6-8). Early research on DBP precursor removal by NF employed formation potential tests (e.g. refs 6 and 9). However, formation potential testing has been shown to cause a preferential shift toward chlorinated species (10) and therefore may not represent actual concentrations of THM and HAA species formed under distribution system conditions. Summers et al. (11) investigated the effects of dead-end filtration using asymmetric cellulose acetate membranes on THM speciation. In contrast to this study, commercial NF membranes are typically thin film composites and are operated exclusively in the cross-flow mode. Allgeier and Summers (7) evaluated DBP precursor removal using a thin film composite membrane and reported a shift toward more brominated HAA6 species in the permeate compared to the feedwater. Effects of feedwater recovery (Rf) on DBP removal and speciation were not reported. Because NF is one of the two most promising technologies available for DBP precursor control (granular activated carbon is the other), systematic research evaluating the effects of Rf on changes in TTHM and HAA9 precursor rejection and their speciation is necessary prior to standard setting by the Environmental Protection Agency (EPA). Increasing Rf to reduce waste will be possible only if permeate water quality does not deteriorate. Further, in anticipation of more stringent DBP regulations, new NF membranes are being formulated specifically for organics removal. Data on DBP precursor removal by these membranes and associated VOL. 34, NO. 9, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Summary of Membranes Employed parameter designation manufacturer model number compositiona MWCO (Dalton)a hydrophobicitya surface chargea TOC rejection (%)c bromide rejection (%)c conductivity rejection (%)c TTHM precursor molar rejection (%)d HAA9 precursor molar rejection (%)d
membrane characteristics I Hydranautics, San Diego, CA NTR 7450 modified polysulfone 600-800 hydrophobic negative 70.7 3.0 7.9 76.6 75.0
II Koch Fluid Systems, San Diego, CA TFC-SR polyamide 300 NAb slightly negative 93.6 10.0 26.9 96.0 92.0
III Dow FilmTec, Midland, MI NF 200B polypiperazine 200-400 hydrophilic highly negative 91.4 -3.3 25.1 93.7 81.2
a Specified by the manufacturer. Surface charge reported at near neutral pH. b Denotes not available. c Median of all measurements at 70% recovery in this study. d Median of all measurements at 70% Rf and SDS conditions of 24 °C, 24 h, 7.8 < pH < 9.2 and free Cl2 residual ∼ 1 mg/L.
TABLE 2. Summary of Source Water Locations and Average Membrane Feed Water Qualitiesa designation
source water location
TOC concn (mg/L)
UV254 (cm-1)
Br- concn (µM)
Br-/TOC ratio (µg/mg)
membranes employed
A B C D E F
Biscayne Aquifer, FL Biscayne Aquifer, FL Biscayne Aquifer, FL Biscayne Aquifer,FL Lake Meade, VA Caloosahatchee River, FL
12.1 13.1 6.35 4.01 3.3 7.1
0.417 0.579 0.187 0.150 0.053 0.154
1.93 1.79 1.59 0.94 0.53 2.33
12.7 10.9 20.0 18.7 12.8 26.2
I, II I, II, III I, II I, II I I, II, III
a A, B, C, and D are groundwaters. The only NF pretreatment for these waters was laboratory scale depth filtration using a 5 µm cartridge filter. E and F are surface waters. Prior to NF, they were pretreated (coagulation and flocculation at full-scale and sedimentation (∼3 h) and 5 µm cartridge filtration at bench-scale). The water quality parameters represent an average of quarterly measurements made during a 1-year period on the membrane feedwater. For the surface waters, these represent concentrations following pretreatment.
changes in DBP speciation are not yet available in the peerreviewed literature even though they are crucial to the negotiated rule making process for future DBP regulations and for the design and operation of NF facilities designed for NOM removal. For these reasons EPA promulgated the Information Collection Rule (ICR), a component of which specifically targeted advanced water treatment processes including NF (12). Early research on THM and HAA occurrence included several water supplies with high Br- concentrations (13) and served as an important database for initial DBP regulations that have set more stringent HAA standards than THMs (1). However, HAA concentrations in water supplies that do not contain appreciable levels of Br- (such as those in the Eastern United States) have been shown to be comparable (or even greater than) THM concentrations (14). TTHM and HAA9 precursor removal from such waters containing low Brconcentrations need to be established under actual distribution system conditions in order to set more equitable future DBP regulations. This manuscript summarizes results from six ICR treatment studies employing feedwaters with low concentrations of bromide ion (0.5-2.3 µM) and focuses on the effects of NF feedwater recovery and membrane type on the removal of THM and HAA precursors. Changes in speciation and concentration of all four THM and all nine HAA species containing bromine and chlorine upon NF are presented. Empirical equations relating bromine incorporation into THMs and HAAs to water quality parameters are also reported.
Experimental Work Membranes Employed. Three thin film composite NF membranes spanning a wide range of molecular weight cutoffs, 1814
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surface charge (at near neutral pH), and hydrophobicity were evaluated (Table 1). These membranes were selected because they constitute a new generation of TFC membranes particularly designed for NOM removal during water treatment applications. Source Waters and Pretreatment. Four groundwaters and two surface waters containing low concentrations of bromide ion (0.5-2.3 µM) were employed (Table 2). These membrane influent waters span a wide range of TOC concentrations (3.3-13.1 mg/L) and UV absorbances at 254 nm and 1 cm path length (UV254) (0.05-0.58 cm-1). For each set of quarterly experiments, ∼350 L of the source water was collected prior to the addition of any oxidant or disinfectant and shipped to the laboratory. Depth filtration using a cartridge nominally rated at 5 µm (Ryan Herco, Burbank, CA) was the only pretreatment employed for the four groundwaters (designated as A, B, C, and D). Surface waters (designated as E, and F) were pretreated using coagulation and flocculation (using alum at the full-scale water treatment plant), followed by bench-scale sedimentation (∼3 h), and bench-scale filtration using a cartridge nominally rated at 5 µm. NF Apparatus and Experiments. Experiments were conducted in the feed and bleed mode using a cell pressurized with compressed air and equipped with a flat membrane sheet (model SEPA-CF, Osmonics, Minnetonka, MN). The feed and bleed configuration represents operation when a portion of the concentrate is recycled in order to maintain a specific cross-flow velocity (0.1 m/s in the experiments reported herein). The cell was fitted with a 0.07 cm feed spacer and a 0.025 cm permeate spacer in an attempt to simulate the hydrodynamics of a spiral-wound membrane element. Pressure fluctuations were minimized by employing positive displacement gear pumps (Cole Palmer, Vernon Hills, IL) for both feedwater (model 74011-11)
TABLE 3. Analytical Methods and Minimum Reporting Levels Employed in This Study analyte
units
method(s)
minimum reporting level
total organic carbon (TOC) bromide ion (Br-) CHCl3, BDCM, DBCM, CHBr3 MCAA, DCAA, TCAA, MBAA, DBAA, TBAA, BCAA, BDCAA, DBCAA
mg/L µg/L µg/L µg/L
SM 5310C EPA 300.0 EPA 502.2, EPA 524.2, or EPA 551.1 SM 6251B or EPA 552.2
0.20 20 1 for each analyte 2, 1, 1, 1, 1, 4, 1, 1, and 2, respectively
and recycle water (model 07002-23). All tubings and connections were fabricated using inert materials (stainless steel or Teflon). Dual float rotameters were used in conjunction with manual measurements to accurately monitor flow rates. For each source water, quarterly experiments were conducted with two different and new NF membranes. Thus, not all three membranes investigated in this study were employed with each source water. Membranes evaluated with each source water are given in Table 2. Prior to conducting experiments using the test water, deionized water was passed through the experimental apparatus for ∼24 h. The transmembrane pressure was adjusted to obtain an initial permeate flux of ∼25 L/m2/h (15 gfd). Following this membrane-setting period, constant pressure experiments were conducted continuously at four feedwater recoveries in random order (70, 90, 50, and 30%). The experiment at 70% Rf was run for a minimum of 78 h. During this time, two 5 L samples were collected to perform duplicate analyses. Experiments at other recoveries were run for sufficient duration to collect a minimum of 4 L of permeate water to conduct all required analyses. As specified in the ICR treatment study guidelines, chemical cleaning was not undertaken when changing Rf. Composite sampling began only after UV254, and conductivity reached a steady value in the permeate water (defined in these experiments as III > I (Table 1). As seen in Figure 3, for any given membrane HAA9 precursors were rejected to a greater extent than TTHM precursors. Again, TTHM and HAA9 precursor rejections decreased with increasing feedwater recovery, suggesting that molecular diffusion plays an important role in the transport of these materials across the NF membranes. THM and HAA Concentrations and Speciation. Micromolar concentrations of individual THM and HAA9 species in membrane feedwater F and NF permeate waters at 90% Rf are depicted in Figure 4 (parts a and b, respectively). (Data at lower recoveries were difficult to interpret because some HAA concentrations were below method detection limits due to increased TOC removal.) All THM species were detected in membrane feed and permeate waters. However, TBAA was typically not detected in membrane feedwater F. MCAA was not detected in all NF permeates using feedwater F. MBAA and TBAA were also not detected in membrane I permeate. Additionally, DCAA, TCAA, MBAA, and TBAA were not detected in membrane II permeate. For comparison purposes, individual specie concentrations were assumed to be equal to half the detection limit if not detected in a specific sample. Figures 4a and 4b show that THM and HAA speciation in feedwater F was in the order, CHCl3 > DCBM > CDBM > CHBr3 and DCAA > TCAA > BDCAA > BCAA > MCAA > DBCAA > DBAA > MBAA >TBAA, respectively. Very high NOM removal combined with poor bromide ion removal by membranes employed (see Table 1) resulted in large increases in the Br- ion to TOC ratio in the permeate
FIGURE 4. Effect of NF on THM (a) and HAA (b) speciation for source water F. (Rf ) 90%, SDS incubation at 24 °C, pH ∼ 8.0, Cl2 residual ∼1 mg/L at 48 h.) compared to the feedwater. The Br-/TOC ratio (expressed in µg/mg) was in the range 10-29 for the 6 feedwaters employed in this study. However, it ranged from 33 to 257 and 96-515 in membranes I and II permeates, respectively. This alone should result in an increase the formation of the brominated THM and HAA species in permeate waters (2, 11). As expected, THM speciation was shifted in the following order: CDBM ∼ CHBr3 > DCBM > CHCl3 in the membranes II and III permeates (Figure 4a). Similarly, HAA9 speciation was shifted in membrane III permeate at 90% Rf and was observed to be in the following the order: DBAA > BCAA > TBAA >DBCAA > DCAA ∼ BDCAA > MBAA ∼ TCAA > MCAA. In contrast, little change in THM and HAA speciation was caused by membrane I because of smaller changes in the Br-/TOC ratio. Concentrations of TTHM and HAA9 precursors decreased upon NF corresponding to positive rejection values as seen in Figure 3. However as observed in Figure 4, concentrations of the highly brominated species such as bromoform, DBAA, and TBAA increased following NF using membrane III (corresponding to negative rejection). Similar results were observed using membrane II. For example, Figure 5 (parts a and b) depicts concentrations of the highly brominated THM and HAA species in various feedwaters and membrane II permeates, respectively, at 90% Rf under identical SDS conditions. Again, permeate concentrations of CDBM, CHBr3, DBAA, and TBAA were typically higher than in the feedwater. Thus, NF can increase the concentrations of highly brominated THM and HAA species by increasing the Br-/TOC ratio. This may necessitate the use of a less powerful oxidant than chlorine (e.g. chloramines) in order to limit the concentrations of individual THM and HAA specie. The impacts of these changes in concentrations and speciation upon NF need to be discussed during regulatory negotiations prior to setting maximum contaminant levels for individual species and also to better select chemical disinfectant type, dose, and contact time following NF for primary disinfection.
FIGURE 5. Speciation of highly brominated THMs (a) and HAA (b) species in feed and NF permeate waters. (Membrane II, Rf ) 90%, SDS incubation at 24 °C, pH ∼ 8.0, Cl2 residual ∼ 1 mg/L at 24 h.) Bromine Incorporation. One method of expressing the relative molar concentrations of brominated THM species is to calculate the bromine incorporation factor n, which quantifies the degree of bromine substitution (16) 3
∑k × [CHCl n)
3-kBrk]
k)0
(2)
3
∑[CHCl
3-kBrk]
k)0
where each THM specie is expressed in µmol/L. Equation 2 has been extended to define a HAA6 bromine incorporation factor (17). A similar expression can be written for a HAA9 bromine incorporation factor n′ (eq 3).
n′ ) {[MBAA] + [BCAA] + [BDCAA] + 2 × [DBCAA] + 2 × [DBAA] + 3 × [TBAA]}/{[MCAA] + [DCAA] + [TCAA] + [MBAA] + [BCAA] + [BDCAA] + [DBCAA] + [DBAA] + [TBAA]} (3) Both n and n′ lie in the range [0, 3] and increase with the degree of bromine substitution. One NF membrane has been reported to increase n and n′ for HAA6 by a factor of 1.2-5.4 (7). No data are available yet for changes in n′ incorporating all nine HAAs upon NF. The effects of feedwater recovery on n and n′ are depicted in Figure 6. For membranes I and III, n and n′ decreased at 90% Rf. (Membrane II data are difficult to interpret because many individual HAA species were typically not detected in experiments at 30 and 50% Rf.) These results can be explained by comparing Br-/TOC ratios. The Br-/TOC ratio ranged from 10 to 29 µg/mg in the feedwaters. Because NOM rejection was diffusion-controlled and NF membranes employed in this study did not reject substantial amounts VOL. 34, NO. 9, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 6. Effects of feedwater recovery and membrane type on TTHM and HAA9 bromine incorporation factors for feedwater F. Solid lines with hollow symbols represent n and dashed lines with solid symbols denote n′. of Br- ion, the Br-/TOC ratio typically decreased as Rf increased resulting in a shift toward the chlorinated species (lower n and n′ values). Thus, the Br-/TOC ratio ranged from 109 to 500 µg/mg at 70% Rf for membrane II but decreased to a range from 78 to 380 µg/mg at 90% Rf. THM bromine incorporation increased by a factor of 2.1-11.4 and 3.617.4 for membranes I and II permeates, respectively. Similarly, n′ values increased by a factor in the range 1.9-8.1 for membrane I and 3.1-14 for membrane II permeate waters compared to the feedwater. Thus, NF resulted in large shifts toward brominated THM and HAA species. The toxicological impacts of these shifts are currently not well understood and need to be included in future DBP regulations. When the final free chlorine residual is kept constant (∼1 mg/L in the SDS experiments reported herein), NF permeate waters were precursor limited compared to the feedwaters. This precursor limitation was exacerbated with decreasing recovery because NOM removal was diffusiondominated under the experimental conditions of this study. Under these conditions of negligible Br- rejection combined with high TOC removal, the brominated THM and HAA species form first consuming the reactive precursor sites and in the process restricting formation of the chlorinated species (because HOBr is a stronger halogenating agent than HOCl) (18). Thus, n and n′ in NF permeates increased compared to the feedwater. Additionally, n and n′ decrease with increasing recovery due to lower precursor limitation. Empirical Correlations for n and n′. As demonstrated in the previous section, bromine incorporation into THMs and HAAs will depend on TOC and Br- rejection characteristics of the membrane as well as chlorination conditions. Experimental n and n′ values at a pH range of 7.8-9.2 for 24 h incubation at 24 °C for feed and permeate waters were 1818
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FIGURE 7. Power law correlation of TTHM bromine incorporation factor. The error bars represent 95% confidence intervals for predicted n values obtained using nonlinear regression. Parameter estimates and confidence intervals are given in Table 4. modeled using a power law expression of the type shown in eq 4
( )( )
n or n′ ) pHa
BrTOC
b
Cl2 TOC
c
(4)
where Br-/TOC is expressed in µg/mg and Cl2/TOC is in mg/mg and a, b, and c are parameters. Under the conditions employed in this study, the Br-/Cl2 ratio appeared not to influence bromine incorporation. The Levenberg-Marquardt algorithm (19) was employed to minimize the sum of squares of the residuals for all data points (error sum of squares (SSE)) to determine a, b, and c. Confidence intervals for the exponents were determined by propagating errors produced during parameter estimation. Lower and upper bounds for a, b, and c were calculated using the F ratio test (eq 5) in which Θz is the parameter vector that bounds the confidence region, Θo is the optimal solution (corresponding to the minimum error sum of squares), N is the total number of observations, z is the number of parameters ()3 in eq 4), and F is the cumulative Fisher F distribution with z and N-z degrees of freedom at the upper 1-R percentile (R ) 0.05) (20)
[ ( )( )]
z z SSE(Θz) ) SSE(Θo) 1 + F N-z(1-R) N - z
(5)
Typically, a high initial guess compared to the optimal value for the parameter converged to the upper confidence limit, and a low initial guess converged to the lower confidence limit when the other two parameters were held at their optimal values. Separate regressions were obtained for feed and permeate waters because NF is expected to change the
TABLE 4. Summary of Parameter Estimates and Boundary Conditions for n and n′ for Feed and Permeate Waters Using Nonlinear Regressiona boundary conditions water feedwater n n′ permeate n water n′ a
optimal values a b c -2.21 -2.06 -0.86 -0.50
1.13 1.01 0.46 0.22
-0.56 -0.17 -0.13 -0.04
95% confidence intervals a b c [-2.26,-2.16] [-2.11,-2.01] [-0.88,-0.83] [-0.52,-0.47]
[1.10,1.17] [0.97,1.05] [0.45,0.47] [0.21,0.23]
[-0.95,-0.23] [-0.57,0.16] [-0.16,-0.10] [-0.07,-0.01]
R2
SSE(Θo) 0.047 0.060 7.980 3.223
N
pH
Br-/TOC (µg/mg)
Cl2/TOC (mg/mg)
0.81 32 7.8-9.1 9.7-28.8 0.81-1.83 0.74 31 0.65 122 7.8-9.2 19.4-515.2 0.8-18.4 0.42 113
Corresponding SDS conditions: 24 h, 24 °C, Cl2 residual ∼ 1 mg/L.
FIGURE 8. Power law correlation of HAA9 bromine incorporation factor. The error bars represent 95% confidence intervals for predicted n′ values obtained using nonlinear regression. Parameter estimates and confidence intervals are given in Table 4. nature of the precursor material. The best-fit values of the exponents, their 95% confidence intervals, SSE(Θo), and the boundary conditions are summarized in Table 4. These empirical fits for n and n′ along are depicted in Figures 7 and 8, respectively. The horizontal error bars depict the 95% confidence limits for the predicted values calculated using the appropriate lower and upper limits for each of the parameters in Table 4. These equations show that under the conditions employed in this study, n and n′ increased with decreasing pH, Cl2/TOC, and/or increasing Br-/TOC ratio. This behavior is consistent with previous research where n has been shown to decrease in a straight line fashion with pH for low bromide ion concentrations (16). These empirical equations may assist in calculating bromine incorporation into THMs and HAAs upon NF only if site specific parameters are within the boundary conditions shown in Table 4. It should be noted that in addition to decreasing bromine incorporation, increasing pH may increase the total yield of THMs while decreasing HAA formation (21). Design Implications. For any feedwater-membrane combination, the permeate flux, feedwater recovery, and tem-
perature will primarily determine permeate concentrations of diffusion-dominated solutes. This bench-scale study has focused on the impacts of recovery at an approximate flux of 25 L/m2/h at 20 °C. Even though these results still need to be verified with larger scale studies, important implications for design and operation of NF plants designed for NOM removal can be inferred. Because it has been previously thought that NOM removal by NF in water treatment applications is primarily controlled by sieving (6, 9), most studies to date have focused on limiting the feedwater recovery solely to control precipitative fouling. However, as shown in Figures 1-3 depending on the membrane employed contaminant rejections may decrease by as much as 13% for TOC and HAA9 precursors and 20% for TTHM precursors, when Rf is increased from 70 to 90%. Thus, when NF is considered for DBP precursor removal, the maximum feedwater recovery may be also limited by permeate water quality rather than fouling considerations alone. An increase in the number of stages from 2 to 3 will be necessary to increase Rf from 70 to 90%. In a staged NF plant, permeate flux in each successive stage decreases due to a combination of increased osmotic pressure and hydraulic pressure losses. Also, full-scale installation designs provide greater membrane area for the first stage compared to second and third stages (typically in a ∼4:2:1 ratio) further reducing stagewise permeate water production. Therefore, the negative impacts of reduced permeate water quality with stage on the output of a full-scale NF plant will be somewhat compensated by a decrease in flow. Hence, the staged design of NF systems may offer inherent (and fortuitous) safeguards against substantial permeate water quality degradations caused by diffusion-limited transport of materials across polymeric membranes.
Acknowledgments The detailed comments of Douglas Owen and three anonymous reviewers on an earlier draft of this manuscript are greatly appreciated. Gerry Filteau, Stuart McClellan, Terry Smith, Tom Stocker, and Mark Wilf provided membrane samples for testing. Joseph Jacangelo and David Wilkes provided support during the experimental phase of this study. Jennifer Abrajano, Daniel Bush, Eric Landsberg, and Jason Radgowski assisted in the conduct of experiments.
Literature Cited (1) Fed. Regist. 1998, 40 CFR Parts 9, 141, and 142, Part IV 63(241), 69390-69476. (2) Formation and Control of Disinfection By-Products in Drinking Water; Singer, P. C., Ed.; AWWA: Denver, CO, 1999. (3) Pourmoghaddas, H. et al. 1993, 85(1), 82-87. (4) Cowman, G. A.; Singer, P. C. Environ. Sci. Technol. 1996, 30(1), 16-24. (5) Wu, W. W.; Chadik, P. A. J. Environ. Eng. 1998, 124(10), 932938. (6) Blau, T. J. et al. J.sAm. Water Works Assoc. 1992, 84(12), 104116. (7) Allgeier, S. C.; Summers, R. S. J.sAm. Water Works Assoc. 1995, 87(3), 87-99. VOL. 34, NO. 9, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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(8) Chellam, S. et al. J.sAm. Water Works Assoc. 1997, 89(10), 7789. (9) Taylor, J. S. et al. J.sAm. Water Works Assoc. 1989, 81(11), 52-60. (10) Symons, J. M. et al. J.sAm. Water Works Assoc. 1993, 85(1), 51-62. (11) Summers, R. S. et al. J.sAm. Water Works Assoc. 1993, 85(1), 88-95. (12) EPA. ICR Manual for Bench- and Pilot-Scale Treatment Studies; EPA 814-B-96-003; Cincinnati, OH, 1996. (13) Krasner, S. W. et al. J.sAm. Water Works Assoc. 1989, 81(8), 41-53. (14) Singer, P. C. et al. J.sAm. Water Works Assoc. 1995, 87(10), 72-87. (15) Taylor, J. S. et al. J. Water SRT - Aqua 1994, 43(5), 238-245. (16) Gould, J. P. et al. In Water Chlorination: Environmental Impact and Health Effects; Jolley, R. L. et al., Ed.; 1983; Vol. 4, pp 297-310.
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(17) Shuikary, H. M. et al. J.sAm. Water Works Assoc. 1994, 86(6), 72-87. (18) Symons, J. M. et al. In Disinfection by-Products in Water Treatment: The Chemistry of Their Formation and Control; Minear, R. A., Amy, G. L., Eds.; Lewis Publishers: New York, NY, 1996; pp 91-130. (19) Press: W. H. et al. Numerical Recipes in FORTRAN, 2nd ed.; Cambridge University Press: New York, 1992. (20) Bates, D. M.; Watts, D. G. Nonlinear Regression Analysis and Its Applications; John Wiley and Sons: New York, NY, 1988. (21) Singer, P. C. J. Environ. Eng. 1994, 120(4), 727-744.
Received for review October 7, 1999. Revised manuscript received January 31, 2000. Accepted February 23, 2000. ES991153T
