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NDMA formation kinetics from three pharmaceuticals in four water matrices
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NDMA formation kinetics from three pharmaceuticals in four water matrices Ruqiao Shen*, Susan A. Andrews Department of Civil Engineering, University of Toronto, 35 St. George St., Toronto, Ontario, Canada M5S 1A4
article info
abstract
Article history:
N, N-nitrosodimethylamine (NDMA) is an emerging disinfection by-product (DBP) that has
Received 17 May 2011
been widely detected in many drinking water systems and commonly associated with the
Received in revised form
chloramine disinfection process. Some amine-based pharmaceuticals have been demon-
11 August 2011
strated to form NDMA during chloramination, but studies regarding the reaction kinetics
Accepted 20 August 2011
are largely lacking. This study investigates the NDMA formation kinetics from ranitidine,
Available online 27 August 2011
chlorphenamine, and doxylamine under practical chloramine disinfection conditions. The formation profile was monitored in both lab-grade water and real water matrices, and
Keywords:
a statistical model is proposed to describe and predict the NDMA formation from selected
NDMA
pharmaceuticals in various water matrices. The results indicate the significant impact of
Ranitidine
water matrix components and reaction time on the NDMA formation from selected
Chlorphenamine
pharmaceuticals, and provide fresh insights on the estimation of ultimate NDMA forma-
Doxylamine
tion potential from pharmaceutical precursors.
Chloramination
ª 2011 Elsevier Ltd. All rights reserved.
Kinetics
1.
Introduction
N, N-nitrosodimethylamine (NDMA) is a member of N-nitrosamines found in food, beer, cured meats, rubber products, tobacco smoke and more recently, drinking water. There is growing concern regarding the health effects associated with exposure to nitrosamines because of their potential carcinogenicity (EPA IRIS, 1993). The occurrence of NDMA in finished drinking water has been commonly associated with the application of chloramine as a final disinfectant. Recent surveys in Canada and the U.S. have revealed occurrence of NDMA in many chloraminated drinking water systems with concentration up to 630 ng/L (Blute et al., 2010; Charrois et al., 2007). The widespread detections of NDMA in source water and treated drinking water have spurred local governments and agencies to take actions. The Ontario Ministry of the Environment (MOE) has established a maximum acceptable
level of 9 ng/L for NDMA (MOE, 2003), and the California Department of Health Services has implemented an NDMA notification level of 10 ng/L (OEHHA, 2006). The USEPA has placed NDMA together with other four nitrosamines on the latest drinking water contaminant candidate list 3 (CCL3) (USEPA, 2009). More recently, Health Canada has proposed a maximum acceptable concentration for NDMA of 40 ng/L in drinking water (Health Canada, 2010). A number of research efforts have been invested in identifying potential NDMA precursors relevant to drinking water. Theoretically, any amine compounds containing dimethylamine (DMA) groups may react with chloramine to form NDMA. Typical precursors found in source water include some tertiary and quaternary amines (Kemper et al., 2010; Mitch et al., 2003; Mitch and Schreiber, 2008), and fractions of natural organic matter (NOM) (Chen and Valentine, 2007; Dotson et al., 2007; Gerecke and Sedlak, 2003; Mitch and
* Corresponding author. Tel.: þ1 4169783141; fax: þ1 4169783674. E-mail addresses: [email protected] (R. Shen), [email protected] (S.A. Andrews). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.08.034
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Sedlak, 2004). Some chemicals used in water treatment processes may also contribute to NDMA formation, such as certain amine-based polymers and anion exchange resins (Kohut and Andrews, 2003; Mitch and Sedlak, 2004; Najm and Trussell, 2001; Wilczak et al., 2003). More recently, dimethylsulfamide, a degradation product of the fungicide tolyfluanide, was newly identified as an NDMA precursor during ozonation (Schmidt and Brauch, 2008). Pharmaceuticals first came to attention as potential NDMA precursors when ranitidine was demonstrated to convert into NDMA at high conversion rate during chloramination (Sacher et al., 2008; Schmidt et al., 2006). Krasner (2009) has suggested that amine-based pharmaceuticals and their breakdown products might be part of the NDMA precursor pool. A recent study by the authors has demonstrated the formation of nitrosamines from twenty amine-based pharmaceuticals and personal care products (PPCPs) upon chloramine disinfection (Shen and Andrews, 2011). Up to now, studies on NDMA formation via pharmaceuticals have been mostly conducted using lab-grade water. Specifically, data regarding the reaction kinetics in real water matrices are largely lacking. Krasner et al. (2010) have investigated the NDMA formation over time from ranitidine under different pH and temperature, but only conducted the experiments in deionized water. Due to the lack of knowledge about the reactivity and chemistry, it is difficult to predict NDMA formation from pharmaceuticals using traditional kinetic models. In the literature, some kinetic models have been developed for the prediction of NDMA formation from DMA (Choi and Valentine, 2002; Kim and Clevenger, 2007) and from NOM (Chen and Valentine, 2006); however, these models use comparable concentrations of precursors and chloramines, and thus might not apply to pharmaceuticals which are usually present at trace levels in the source water and are at much lower concentrations relative to chloramine concentrations in real samples. This study demonstrates the NDMA formation kinetics from three amine-based pharmaceuticals in four different water matrices, and proposes a statistical model to describe and predict the NDMA molar conversion from selected pharmaceuticals during chloramination.
2.
Materials and methods
Three pharmaceuticals were selected to determine their NDMA formation potential (NDMA-FP) over time, including chlorphenamine, doxylamine, and ranitidine (Fig. 1). Stock solutions of pharmaceuticals were prepared in methanol and stored at 4 C until use. NDMA (reagent grade) and deuterated NDMA (d6-NDMA, 98 atom %D) were used as standard and internal standard, respectively. All chemicals were purchased from SigmaeAldrich Canada (Oakville, Ontario). Experiments were carried out under the Simulated Distribution System (SDS) conditions (pH ¼ 7.0 0.1; 21 C; Cl2: N mass ratio ¼ 4.2:1; chloramine dosage ¼ 2.5 0.2 mg/L after satisfying 24 hr chloramine demand). Further details concerning the experimental procedure and NDMA analysis have been described in Shen and Andrews (2011). NDMA formation from each pharmaceutical was monitored for up to 144 hr
Fig. 1 e Structures of selected pharmaceuticals.
based on preliminary results, depending on the compound and water matrix. At each time point samples were prepared in duplicate, together with one blank control to account for the potential background interference. Error bars in all the kinetic graphs represent the maximum and minimum values in the formation potential tests under the same reaction conditions (n ¼ 2). Experiments were conducted in four water matrices; the water sources and basic water quality parameters are summarized in Table 1. Lake and river water samples were taken from the influent of two drinking water treatment plants in June and October, 2010, respectively. The pH was determined using a pH meter (Model 8015, VWR Scientific Inc., Mississauga, Ontario). Alkalinity was measured based on an end-point titration, according to Standard Method 2320B (APHA, 2005). The total organic carbon (TOC) was analyzed with an Aurora 1030 TOC analyzer (O.I. Analytical, College Station, Texas). The ultraviolet absorbance at 254 nm (UV254) was determined by a CE3055 Reflectance Spectrophotometer (Cecil Instruments Ltd., Cambridge, England). The specific UV absorbance (SUVA) is calculated by normalizing the UV254 to the TOC.
3.
Results and discussion
3.1.
Formation kinetics in MQ water
Kinetic experiments in MQ (Milli-Q, Ultra Pure Water System, MilliPore, Etobicoke, Ontario) water were conducted for ranitidine, chlorphenamine, and doxylamine at two concentration levels (5 and 25 nM), as shown in Fig. 2. The markers in the figure are the measured NDMA molar conversion values, and the lines are model-estimated results. Details about the model development and estimation will be discussed in Section 3.3. NDMA formation via the three pharmaceuticals followed similar pattern over time. Generally, an initial lag period was observed, followed by a fast increase in NDMA concentration; the molar conversion then gradually leveled off and eventually reached a plateau (maximum molar conversion). Moreover, the formation kinetic behavior was observed to be relatively independent of the initial pharmaceutical concentration, except that NDMA formation from doxylamine in MQ water showed a more significant difference after 24 hr than did the other pharmaceuticals. Given the large excess of chloramine relative to the pharmaceuticals (mg/L vs. lower mg/ L), availability of chloramine was not a limiting factor at the concentration range of pharmaceuticals tested. These results also support observations made in an earlier study where the NDMA molar conversion at 24 hr for 20 selected
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Table 1 e Water matrix source and quality. Matrix Milli-Q (MQ) Tap water (TAP) Lake water (LW) River water (RW)
Source
pH
Alkalinity (mg/L)
Ultra Pure Water System (MilliPore, Etobicoke, Ontario) Toronto, Ontario Lake Ontario, Ajax, Ontario Otonabee River, Peterborough, Ontario
7.5 0.1
1.8 0.3
7.1 0.1 8.0 0.1 7.8 0.1
88.5 2.8 94.6 1.5 86.5 1.2
pharmaceuticals was found to be independent of their initial concentrations (Shen and Andrews, 2011).
3.2.
Formation kinetics in different water matrices
Kinetic experiments were also performed using real water samples dosed with selected pharmaceuticals. The same shape of NDMA formation curve was observed in real water matrices as in MQ water except that the initial lag phase was longer, especially for tests performed using lake or river water (Fig. 3). Similarly to Fig. 2, the markers are the measured NDMA molar conversion values, and the lines are the modelestimated results that will be discussed in detail in 3.3. Although the NDMA formation kinetics was shown to be unique to each water matrix tested, the kinetic behavior was relatively independent of the initial pharmaceutical concentration within a given water matrix, further confirming that observation in MQ water. The different NDMA formation profiles were likely influenced by the water matrix components, rather than by added reagents, since the pH of the water samples was controlled with a phosphate buffer and the same chloramine dosage was applied to all samples. Both bromide and NOM have been shown to influence NDMA formation, with bromide being reported to either catalyze NDMA formation (Mitch et al., 2003; Valentine et al., 2005) or have an inhibitory effect (Chen et al., 2010). However, bromide levels in the water sources that were tested are typically much lower than those for studies that have reported these effects, so the differences in the observed formation profiles were thought to be due to some aspect of the NOM. Since bromide is in higher concentration and so may be more of a concern in coastal waters due to saltwater intrusion, the potential impact from bromide was considered
TOC (mg/L)
UV254 (cm1)
SUVA (L,m/mg)
0.0
0.000
0.000
2.1 0.1 2.3 0.2 6.2 0.5
0.021 0.001 0.024 0.002 0.143 0.002
1.01 0.05 1.08 0.12 2.32 0.17
to be outside the scope of the present tests but would be of interest for future study. NOM may affect the NDMA formation in two ways. The influence of NOM’s competition for chloramine was considered to be minimal due to the small observed chloramine decay (data not shown) and the large excess of chloramine relative to the pharmaceuticals (mg/L vs. lower mg/L) at the end of the kinetic experiment. On the other hand, NOM may interact with the pharmaceuticals and then inhibit the reaction to form NDMA, and it is these interactions that were thought to better explain the observed results. NOM components can be at least partially described by the samples’ TOC and SUVA values. It was observed that water with higher TOC and SUVA levels tended to have a longer initial lag phase; all three pharmaceuticals exhibited their longest initial lag period in river water samples. However, while tap and lake water samples had similar TOC and SUVA values, ranitidine showed a longer initial lag phase in lake water samples. This suggests that some specific NOM fractions or moieties might be more relevant than would be indicated by simple bulk measurements of water quality, such as TOC and SUVA. Previous studies have demonstrated that aromatic amines undergo reversible covalent binding with carbonyls and quinones in soil humic substances in the environment (Parris, 1980; Thorn et al., 1996; Weber et al., 1996). Therefore, it is possible that certain fractions or functional groups in NOM may interact with these amine-based pharmaceuticals and thus hinder their initial contact with chloramine species. As the binding is reversible and chloramine is in large excess, eventually the NDMA conversion from pharmaceuticals can still reach the maximum level given enough reaction time. Currently, although no direct spectroscopic evidence exists for the NOM-pharmaceutical binding in aqueous phase, this
Fig. 2 e NDMA molar conversion over time for ranitidine, chlorphenamine, and doxylamine in MQ water (SDS conditions).
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Fig. 3 e NDMA molar conversion over time for (a) ranitidine; (b) chlorphenamine; and (c) doxylamine in different water matrices (SDS conditions).
theory is indirectly supported by some literature investigating the removal of pharmaceuticals during coagulation/flocculation process, where the removal of pharmaceuticals was likely due to the sorption onto particulate organic matter and coremoved through the settling process (Ballard and Mackay, 2005; Stackelberg et al., 2007; Vieno et al., 2006; Westerhoff et al., 2005). Stackelberg et al. (2007) also detected the target pharmaceuticals in the dried solids of settled sludge. In addition, De Ridder et al. (2011) observed enhanced removal of some positively charged pharmaceuticals using granular activated carbon preloaded with NOM. They attributed the enhancement to the electrostatic attraction since the surface of NOM is usually negatively charged due to abundant carboxyl groups. In the current study, the selected amine
pharmaceuticals are positively charged at neutral pH, therefore the possible electrostatic attraction may also lead to the formation of NOM-pharmaceutical complexes. Future studies are needed to further investigate the role of NOM components in the conversion of pharmaceuticals into NDMA, and alternative methods for the characterization of NOM components will be helpful, such as the application of size-exclusion chromatography with organic carbon detection (Huber et al., 2011).
3.3.
Kinetic model
The NDMA formation curves for the pharmaceuticals in this study have a sigmoidal shape that resembles the typical shape
Table 2 e Model parameter estimation and model verification. Compound
Concentration
Matrix
Parameter Estimation a
q Ranitidine
5 nM
25 nM
Chlorphenamine
5 nM
25 nM
Doxylamine
5 nM
25 nM
MQ TAP LW RW MQ TAP LW RW MQ TAP RW MQ TAP RW MQ TAP RW MQ TAP RW
0.912 0.902 0.729 0.822 0.906 0.847 0.769 0.841 0.027 0.023 0.037 0.033 0.030 0.043 0.062 0.068 0.059 0.106 0.092 0.060
(0.045) (0.045) (0.032) (0.056) (0.045) (0.039) (0.018) (0.044) (0.003) (0.003) (0.002) (0.002) (0.002) (0.002) (0.005) (0.006) (0.003) (0.007) (0.006) (0.002)
a
Lag (hr)
6.3 (0.5) 6.7 (0.8) 13.3 (0.7) 20.8 (1.8) 4.6 (0.5) 6.5 (0.7) 13.1 (0.4) 21.9 (1.4) 7.0 (1.3) 10.8 (3.0) 38.4 (3.4) 8.7 (0.6) 19.4 (1.8) 39.9 (1.9) 16.6 (2.2) 48.8 (5.3) 67.0 (4.3) 22.0 (2.0) 46.1 (4.1) 51.1 (1.8)
Model Verification 1 a
k (hr ) 0.225 0.169 0.251 0.086 0.313 0.177 0.252 0.083 0.143 0.065 0.039 0.234 0.070 0.056 0.068 0.027 0.026 0.069 0.030 0.051
(0.043) (0.045) (0.057) (0.026) (0.084) (0.043) (0.028) (0.021) (0.050) (0.024) (0.007) (0.079) (0.015) (0.009) (0.017) (0.005) (0.006) (0.018) (0.005) (0.011)
R
2
0.992 0.986 0.997 0.984 0.991 0.988 0.999 0.990 0.974 0.953 0.990 0.994 0.988 0.996 0.975 0.993 0.990 0.985 0.992 0.996
a Numbers in the bracket represent the 95% confidence interval of each model parameter. b Numbers in the bracket represent standard deviation from multiple tests (n ¼ 3).
Model-predicted conversion @ 24 h 91.2% 90.1% 72.7% 53.6% 90.6% 84.6% 76.8% 50.4% 2.7% 2.0% 0.8% 3.3% 2.0% 0.5% 4.7% 1.2% 0.4% 6.1% 1.6% 0.2%
Measured conversion @ 24 hb 85.2% 83.4% 64.1% 51.4% 82.7% 88.4% 70.1% 43.2% 2.9% 2.0% 1.0% 1.8% 1.5% 0.5% 3.8% 2.5% 1.1% 4.2% 3.2% 0.5%
(0.8%) (8.1%) (3.6%) (4.9%) (2.4%) (5.9%) (4.8%) (7.1%) (0.2%) (0.8%) (0.1%) (0.1%) (0.1%) (0.02%) (0.1%) (0.2%) (0.03%) (0.1%) (0.3%) (0.04%)
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of doseeresponse curves. A standard doseeresponse curve can be defined by a four-parameter logistic function, ab y¼þ 1 þ 10c$ðdxÞ where y is the response caused by certain dose of pharmaceuticals (x); a and b are the maximum and baseline response, respectively; c is the slope of the curve; and d is the dose which provokes a response halfway between the baseline and maximum (Motulsky and Christopoulos, 2003). Accordingly, the following model was proposed to describe the reaction kinetics for NDMA formation from selected pharmaceuticals, q Y¼ 1 þ 10k$ðLagtÞ where Y is the NDMA molar conversion at given reaction time (t); q is the ultimate NDMA molar conversion, i.e., the maximum molar conversion obtained at the plateau during kinetic testing; k is the pseudo-first order reaction rate constant; Lag is the time required to achieve 50% of the ultimate molar conversion, and thus is associated with the length of initial lag phase observed. Comparing this model with the four-parameter logistic function, the parameter b was set to zero because any possible NDMA in the background and any potential NDMA formed from
the matrix components were accounted for by the blank control samples. It is noted that the proposed model does not pass through the point of (0, 0), although, once background NDMA has been subtracted, there should be zero molar conversion at the beginning. Since the doseeresponse model is based on log (drug dose), it is always positive on the x-axis, and thus does not go through the point of (0, 0). Therefore, the proposed model was arbitrarily set to be: Y¼
8 < :
0 q 1 þ 10k$ðLagtÞ
ðt ¼ 0Þ ðt > 0Þ
The formation curve was fitted using GraphPad Prism 5 software, and the estimated model parameters for each compound in different matrices are summarized in the Parameter Estimation section of Table 2. The proposed model fit the experimental data very well, with correlation coefficients (R2) higher than 0.95 in all cases, and predicted accurately all three phases of the NDMA formation curve, as shown previously in Figs. 2 and 3. It is worth noting that the model requires data capturing all three phases of the NDMA formation curve in order to acquire reliable model parameters. For datasets lacking the plateau data, the model will arbitrarily assume the last point as the
Fig. 4 e Linear correlation between (a) Lag and TOC; (b) Lag and SUVA; (c) k and TOC; (d) k and SUVA for three pharmaceuticals (SDS conditions; [Pharmaceutical] [ 5 and 25 nM; error bars represent the 95% confidence interval for estimated model parameters).
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plateau. In this study, the NDMA formation curve for ranitidine in LW samples did not achieve the plateau within the 24hr of the experiment (Fig. 3a). Therefore, the calculated q may underestimate the ultimate NDMA molar conversion for ranitidine in LW samples. The estimated model parameters each well reflected the different aspects of the NDMA formation profiles that were observed in the different water matrices. Generally, the matrix had a minor impact on the ultimate NDMA molar conversion for ranitidine (q ¼ 84.1 6.7%), chlorphenamine (q ¼ 3.2 0.7%), and doxylamine (q ¼ 7.5 2.0%). Instead, the matrix components had a more profound impact on the initial lag phase (Lag) and the pseudo-first order rate constant (k). As summarized in Fig. 4, the Lag value is positively correlated with both TOC and SUVA values for all three pharmaceuticals; k value is negatively correlated with TOC and SUVA values for ranitidine and chlorphenamine, but not well related for doxylamine. The estimated model parameters and these correlations support the theory that water matrix components can affect NDMA formation from selected pharmaceuticals by inhibiting the initial reaction with chloramine and/or slowing down subsequent reactions. Chen and Westerhoff (2010) recently found NDMA-FP very difficult to predict based upon bulk water quality measurements such as DOC or UVA254. Results from the current study have
indicated that knowledge of the reaction kinetics is essential in the prediction of NDMA formation. While typical water quality measurements like TOC and SUVA can have significant impact on the reaction kinetics, they are not directly associated with the ultimate NDMA molar conversion, and thus are not appropriate for directly predicting NDMA-FP using empirical models. Moreover, knowledge of the reaction time employed is specifically crucial for compounds that react slowly with chloramine, such as doxylamine. Recently, more and more utilities have shown interest in conducting NDMA-FP tests. Because there is no standard protocol at the moment, many are considering adopting typical disinfection by-products formation potential tests (Summers et al., 1996) and have applied a 24 hr incubation time from a practical viewpoint; however, the NDMA formed after 24 hr from some compounds may only represent a small portion of their ultimate formation potential, especially in real water matrices where the initial reaction could be significantly inhibited. For example, the NDMA molar conversion at 24 hr for doxylamine in TAP and RW samples only accounted for less than 20% of its ultimate NDMA molar conversion. The results have suggested that typical bench-scale NDMA-FP tests may underestimate the ultimate NDMA-FP for some precursors. For water systems with higher water age, prolonged NDMA formation in the outreaches of the distribution system might be a potential risk and should be taken into consideration.
Fig. 5 e Linear correlation between the model-predicted and the independently measured NDMA molar conversion at 24 h for (a) ranitidine; (b) chlorphenamine; (c) doxylamine; and (d) three compounds together (SDS conditions; data from four matrices (MQ, TAP, LW, and RW) and two concentration levels ([Pharmaceutical] [ 5 and 25 nM) were included; the slope was reported as “the best fit value ± the standard error” at 95% confidence level).
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The model was verified by comparing the 24hr NDMA-FP predicted using the estimated model with that measured from independent 24 hr formation potential tests, as summarized in the last two columns of Table 2 (Model Verification). Linear regression was applied between the measured and predicted NDMA molar conversion for each compound individually and for three compounds all together (Fig. 5); the Student’s t-test was then conducted to determine whether the slope of each regression line differed significantly from 1.0. This goodness of fit test has suggested that there is significant correlation between the measured and model-predicted molar conversion (F-test, 95% confidence level), except for chlorphenamine, although even this correlation was determined to be significant at 90% confidence level. The t-test has indicated that the slope of the regression line for ranitidine and chlorphenamine did not differ from 1.0 ( p-value of 99.9% and 15.9%, respectively; 95% confidence level); yet the slope was determined to be smaller than 1.0 for doxylamine and three compounds altogether ( p-value of 3.3% and 0.1%, respectively; 95% confidence level). In general, however, the model-predicted molar conversion was within the 95% confidence interval of the measured value.
4.
Conclusions
NDMA formation kinetics from ranitidine, chlorphenamine, and doxylamine during chloramination was determined in four water matrices. The NDMA conversion over time followed a general three-phase formation curve: an initial lag phase was observed, followed by a fast increase in NDMA formation, and eventually a plateau was reached that represented the ultimate NDMA molar conversion. The NDMA formation profile was relatively independent of the initial pharmaceutical concentration in the same matrix. Water matrix components affected the NDMA conversion rates, most likely by inhibiting their initial contact with chloramine and slowing down the reaction, while they had less impact on the ultimate NDMA molar conversion. A three-parameter kinetic model was proposed to describe the NDMA formation over time during chloramination. The model accurately reflected all the three significant characteristics of the NDMA formation curve, and was able to predict the NDMA molar conversion from the selected pharmaceuticals to within the 95% confidence interval of the measured values. The model needs to be further verified using different potential precursors, water matrices, and reaction conditions. Bulk water quality measurements such as TOC and SUVA were found to correlate better with model parameters Lag and k than with the ultimate NDMA molar conversion (q), indicating interactions between the pharmaceuticals and NOM that might impact NDMA formation are not limited to those based on the general organic character or aromatic nature of either substance. Alternative methods are needed to better characterize the matrix components in order to further investigate their impact on NDMA formation from pharmaceuticals. Knowledge about the formation kinetics is essential in the prediction of NDMA formation from pharmaceuticals. Shortterm NDMA-FP tests (24 h), although practical, may underestimate the contribution of certain slow-reacting precursors.
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Thus, prolonged tests (perhaps >4 days for substances examined in this study) are required to determine the ultimate NDMAFP, especially in distribution systems with long water age.
Acknowledgment This research was supported by the Canadian Water Network, the Natural Sciences and Engineering Research Council of Canada, and the Ontario Research Fund. Special thanks are dedicated to Richard Jones and John Armour in the water treatment plants for their assistance in water sampling.
references
American Public Health Association (APHA), American Water Works Association (AWWA), Water Environment Federation (WEF), 2005. In: Eaton, A.D., Clesceri, L.S., Rice, E.W., Greenberg, A.E. (Eds.), Standard Methods for the Examination of Water and Wastewater, 21st ed. Washington, DC. Ballard, B.D., Mackay, A.A., 2005. Estimating the removal of anthropogenic organic chemicals from raw drinking water by coagulation flocculation. Journal of Environmental Engineering 131, 108e118. Blute, N., Russell, C., Chowdhury, Z., Wu, X., Via, S., 2010. Nitrosamine occurrence in the U.S. e analysis and interpretation of UCMR2 data. In: Proceedings of the AWWA Water Quality Technology Conference Savannah, GA, November 14-18, 2010. Charrois, J.W.A., Boyd, J.M., Froese, K.L., Hrudey, S.E., 2007. Occurrence of N-nitrosamines in Alberta public drinkingwater distribution systems. Journal of Environmental Engineering and Science 6, 103e114. Chen, B., Westerhoff, P., 2010. Predicting disinfection by-product formation potential in water. Water Research 44, 3755e3762. Chen, Z., Valentine, R.L., 2006. Modeling the formation of N-Nitrosodimethylamine (NDMA) from the reaction of natural organic matter (NOM) with monochloramine. Environmental Science & Technology 40, 7290e7297. Chen, Z., Valentine, R.L., 2007. Formation of N-Nitrosodimethylamine (NDMA) from humic substances in natural water. Environmental Science & Technology 41, 6059e6065. Chen, Z., Yang, L., Zhai, X., Zhao, S., Li, A., Shen, J., 2010. Nnitrosamine formation during chlorination/chloramination of bromide-containing water. Water Science and Technology Water Supply 10, 462e471. Choi, J., Valentine, R.L., 2002. A kinetic model of Nnitrosodimethylamine (NDMA) formation during water chlorination/chloramination. Water Science and Technology 46 (3), 65e71. De Ridder, D.J., Verliefde, A.R.D., Heijman, S.G.J., Verberk, Q.J.C., Rietveld, L.C., van der Aa, L.T.J., Amy, G.L., van Dijk, J.C., 2011. Influence of natural organic matter on equilibrium adsorption of neutral and charged pharmaceuticals onto activated carbon. Water Science and Technology 63, 416e423. Dotson, A., Westerhoff, P., Krasner, S.W., 2007. Nitrosamine formation from natural organic matter isolates and sunlight photolysis of nitrosamines. In: Proceedings of AWWA Annual Conference and Exposition Toronto, ON, Canada, June 24e28, 2007. EPA Integrated Risk Information System (IRIS), 1993. N-Nitrosodimethylamine; CASRN 62-75-9. www.epa.gov/iris/ subst/0045.htm.
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Gerecke, A.C., Sedlak, D.L., 2003. Precursors of N-nitrosodimethylamine (NDMA) in natural waters. Environmental Science & Technology 37, 1331e1336. Health Canada, 2010. Guideline Technical Document on N-Nitrosodimethylamine (NDMA) in Drinking Water for Public Comment. http://www.hc-sc.gc.ca/ewh-semt/consult/_2010/ ndma/draft-ebauche-eng.php#a3. Huber, S.A., Balz, A., Abert, M., Pronk, W., 2011. Characterisation of aquatic humic and non-humic matter with size-exclusion chromatography e organic carbon detection e organic nitrogen detection (LC-OCD-OND). Water Research 45, 879e885. Kemper, J.M., Walse, S.S., Mitch, W.A., 2010. Quaternary amines as nitrosamine precursors: a role for consumer products? Environmental Science & Technology 44, 1224e1231. Kim, J., Clevenger, T.E., 2007. Prediction of Nnitrosodimethylamine (NDMA) formation as a disinfection byproduct. Journal of Hazardous Materials 145, 270e276. Kohut, K.D., Andrews, S.A., 2003. Polyelectrolyte age and Nnitrosodimethylamine formation in drinking water treatment. Water Quality Research Journal of Canada 38 (4), 719e735. Krasner, S.W., 2009. The formation and control of emerging disinfection by-products of health concern. Philosophical Transactions of the Royal Society A 367, 4077e4095. Krasner, S.W., Dale, M.S., Lee, C.F.T., Garcia, E.A., Wong, T.M., Mitch, W., Von Gunten, U., 2010. Difference in reactivity and chemistry of NDMA precursors from treated wastewater and from polyamine polymers. In: Proceedings of the AWWA Water Quality Technology Conference Savannah, GA, November 14e18, 2010. Mitch, W.A., Schreiber, I.M., 2008. Degradation of tertiary alkylamines during chlorination/chloramination: implications for formation of aldehydes, nitriles, halonitroalkanes, and nitrosamines. Environmental Science & Technology 42, 4811e4817. Mitch, W.A., Sedlak, D.L., 2004. Characterization and fate of N-nitrosodimethylamine precursors in municipal wastewater treatment plants. Environmental Science & Technology 38, 1445e1454. Mitch, W.A., Sharp, J.O., Trussell, R.R., Valentine, R.L., AlvarezCohen, L., Sedlak, D.L., 2003. N-nitrosodimethylamine (NDMA) as a drinking water contaminant: a review. Environmental Engineering Science 20 (5), 389e404. MOE, 2003. Ontario Regulation 268/03 made Under the Safe Drinking Water Act, 2002. http://www.e-laws.gov.on.ca/html/ source/regs/english/2003/elaws_src_regs_r03268_e.htm June 25, 2003. Motulsky, H.J., Christopoulos, A., 2003. Fitting Models to Biological Data using Linear and Nonlinear Regression. A Practical Guide to Curve Fitting. GraphPad Software Inc., San Diego, CA. www. graphpad.com. Najm, I., Trussell, R.R., 2001. NDMA formation in water and wastewater. Journal of the American Water Works Association 93 (2), 92e99. Office of Environmental Health Hazard Assessment (OEHHA), 2006. Public Health Goal for N-nitrosodimethylamine and Cadmium in Drinking Water. http://www.oehha.org/water/ phg/cadndma122206.html.
Parris, G.E., 1980. Covalent binding of aromatic amines to humates. 1. reactions with carbonyls and quinones. Environmental Science & Technology 14, 1099e1106. Sacher, F., Schmidt, C.K., Lee, C., von Gunten, U., 2008. Strategies for Minimizing Nitrosamine Formation during Disinfection. Water Research Foundation, Denver, C.O. AwwaRF Report 91209, August 15, 2008. Schmidt, C.K., Brauch, H.J., 2008. N, N-dimethylsulfamide as precursor for N-Nitrosodimethylamine (NDMA) formation upon ozonation and its fate during drinking water treatment. Environmental Science & Technology 42, 6340e6346. Schmidt, C.K., Sacher, F., Brauch, H.J., 2006. Strategies for minimizing formation of NDMA and other nitrosamines during disinfection of drinking water. In: Proceedings of the AWWA Water Quality Technology Conference Denvor, CO, November 5e9, 2006. Shen, R., Andrews, S.A., 2011. Demonstration of 20 pharmaceuticals and personal care products (PPCPs) as nitrosamine precursors during chloramine disinfection. Water Research 45, 944e952. Stackelberg, P.E., Gibs, J., Furlong, E.T., Meyer, M.T., Zaugg, S.D., Lippincott, R.L., 2007. Efficiency of conventional drinkingwater-treatment processes in removal of pharmaceuticals and other organic compounds. Science of the Total Environment 377, 255e272. Summers, R.S., Hooper, S.M., Shukairy, H.M., Solarik, G., Owen, D. , 1996. Assessing DBP yield: uniform formation conditions. Journal of the American Water Works Association 88 (6), 80e93. Thorn, K.A., Pettigrew, P.J., Goldenberg, W.S., Weber, E.J., 1996. Covalent binding of aniline to humic substances. 2. 15N NMR studies of nucleophilic addition reactions. Environmental Science & Technology 30, 2764e2775. USEPA, 2009. Contaminant Candidate List 3 (CCL3). http://water. epa.gov/scitech/drinkingwater/dws/ccl/ccl3.cfm. Valentine, R.L., Choi, J., Chen, Z., Barrett, S.E., Hwang, C., Guo, Y.C. , Wehner, M., Fitzsimmons, S., Andrews, S.A., Werker, A.G., Brubacher, C., Kohut, K., 2005. Factors affecting the Formation of NDMA in Water and Occurrence, Denver, CO, pp. 81e90. Vieno, N., Tuhkanen, T., Kronberg, L., 2006. Removal of pharmaceuticals in drinking water treatment: effect of chemical coagulation. Environmental Technology 27, 183e192. Weber, E.J., Spidle, D.L., Thorn, K.A., 1996. Covalent binding of aniline to humic substances. 1. kinetic studies. Environmental Science & Technology 30, 2755e2763. Westerhoff, P., Yoon, Y., Snyder, S., Wert, E., 2005. Fate of endocrine-disruptor, pharmaceutical, and personal care product chemicals during simulated drinking water treatment processes. Environmental Science & Technology 39, 6649e6663. Wilczak, A., Assadi-Rad, A., Lai, H.H., Hoover, L.L., Smith, J.F., Berger, R., Rodigari, F., Beland, J.W., Lazzelle, L.J., Kincannon, E.G., Baker, H., Heaney, C.T., 2003. Formation of NDMA in chloraminated water coagulated with DADMAC cationic polymer. Journal of the American Water Works Association 95 (9), 94e106.
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NDMA formation kinetics from three pharmaceuticals in four water matrices Ruqiao Shen*, Susan A. Andrews Department of Civil Engineering, University of Toronto, 35 St. George St., Toronto, Ontario, Canada M5S 1A4
article info
abstract
Article history:
N, N-nitrosodimethylamine (NDMA) is an emerging disinfection by-product (DBP) that has
Received 17 May 2011
been widely detected in many drinking water systems and commonly associated with the
Received in revised form
chloramine disinfection process. Some amine-based pharmaceuticals have been demon-
11 August 2011
strated to form NDMA during chloramination, but studies regarding the reaction kinetics
Accepted 20 August 2011
are largely lacking. This study investigates the NDMA formation kinetics from ranitidine,
Available online 27 August 2011
chlorphenamine, and doxylamine under practical chloramine disinfection conditions. The formation profile was monitored in both lab-grade water and real water matrices, and
Keywords:
a statistical model is proposed to describe and predict the NDMA formation from selected
NDMA
pharmaceuticals in various water matrices. The results indicate the significant impact of
Ranitidine
water matrix components and reaction time on the NDMA formation from selected
Chlorphenamine
pharmaceuticals, and provide fresh insights on the estimation of ultimate NDMA forma-
Doxylamine
tion potential from pharmaceutical precursors.
Chloramination
ª 2011 Elsevier Ltd. All rights reserved.
Kinetics
1.
Introduction
N, N-nitrosodimethylamine (NDMA) is a member of N-nitrosamines found in food, beer, cured meats, rubber products, tobacco smoke and more recently, drinking water. There is growing concern regarding the health effects associated with exposure to nitrosamines because of their potential carcinogenicity (EPA IRIS, 1993). The occurrence of NDMA in finished drinking water has been commonly associated with the application of chloramine as a final disinfectant. Recent surveys in Canada and the U.S. have revealed occurrence of NDMA in many chloraminated drinking water systems with concentration up to 630 ng/L (Blute et al., 2010; Charrois et al., 2007). The widespread detections of NDMA in source water and treated drinking water have spurred local governments and agencies to take actions. The Ontario Ministry of the Environment (MOE) has established a maximum acceptable
level of 9 ng/L for NDMA (MOE, 2003), and the California Department of Health Services has implemented an NDMA notification level of 10 ng/L (OEHHA, 2006). The USEPA has placed NDMA together with other four nitrosamines on the latest drinking water contaminant candidate list 3 (CCL3) (USEPA, 2009). More recently, Health Canada has proposed a maximum acceptable concentration for NDMA of 40 ng/L in drinking water (Health Canada, 2010). A number of research efforts have been invested in identifying potential NDMA precursors relevant to drinking water. Theoretically, any amine compounds containing dimethylamine (DMA) groups may react with chloramine to form NDMA. Typical precursors found in source water include some tertiary and quaternary amines (Kemper et al., 2010; Mitch et al., 2003; Mitch and Schreiber, 2008), and fractions of natural organic matter (NOM) (Chen and Valentine, 2007; Dotson et al., 2007; Gerecke and Sedlak, 2003; Mitch and
* Corresponding author. Tel.: þ1 4169783141; fax: þ1 4169783674. E-mail addresses: [email protected] (R. Shen), [email protected] (S.A. Andrews). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.08.034
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Sedlak, 2004). Some chemicals used in water treatment processes may also contribute to NDMA formation, such as certain amine-based polymers and anion exchange resins (Kohut and Andrews, 2003; Mitch and Sedlak, 2004; Najm and Trussell, 2001; Wilczak et al., 2003). More recently, dimethylsulfamide, a degradation product of the fungicide tolyfluanide, was newly identified as an NDMA precursor during ozonation (Schmidt and Brauch, 2008). Pharmaceuticals first came to attention as potential NDMA precursors when ranitidine was demonstrated to convert into NDMA at high conversion rate during chloramination (Sacher et al., 2008; Schmidt et al., 2006). Krasner (2009) has suggested that amine-based pharmaceuticals and their breakdown products might be part of the NDMA precursor pool. A recent study by the authors has demonstrated the formation of nitrosamines from twenty amine-based pharmaceuticals and personal care products (PPCPs) upon chloramine disinfection (Shen and Andrews, 2011). Up to now, studies on NDMA formation via pharmaceuticals have been mostly conducted using lab-grade water. Specifically, data regarding the reaction kinetics in real water matrices are largely lacking. Krasner et al. (2010) have investigated the NDMA formation over time from ranitidine under different pH and temperature, but only conducted the experiments in deionized water. Due to the lack of knowledge about the reactivity and chemistry, it is difficult to predict NDMA formation from pharmaceuticals using traditional kinetic models. In the literature, some kinetic models have been developed for the prediction of NDMA formation from DMA (Choi and Valentine, 2002; Kim and Clevenger, 2007) and from NOM (Chen and Valentine, 2006); however, these models use comparable concentrations of precursors and chloramines, and thus might not apply to pharmaceuticals which are usually present at trace levels in the source water and are at much lower concentrations relative to chloramine concentrations in real samples. This study demonstrates the NDMA formation kinetics from three amine-based pharmaceuticals in four different water matrices, and proposes a statistical model to describe and predict the NDMA molar conversion from selected pharmaceuticals during chloramination.
2.
Materials and methods
Three pharmaceuticals were selected to determine their NDMA formation potential (NDMA-FP) over time, including chlorphenamine, doxylamine, and ranitidine (Fig. 1). Stock solutions of pharmaceuticals were prepared in methanol and stored at 4 C until use. NDMA (reagent grade) and deuterated NDMA (d6-NDMA, 98 atom %D) were used as standard and internal standard, respectively. All chemicals were purchased from SigmaeAldrich Canada (Oakville, Ontario). Experiments were carried out under the Simulated Distribution System (SDS) conditions (pH ¼ 7.0 0.1; 21 C; Cl2: N mass ratio ¼ 4.2:1; chloramine dosage ¼ 2.5 0.2 mg/L after satisfying 24 hr chloramine demand). Further details concerning the experimental procedure and NDMA analysis have been described in Shen and Andrews (2011). NDMA formation from each pharmaceutical was monitored for up to 144 hr
Fig. 1 e Structures of selected pharmaceuticals.
based on preliminary results, depending on the compound and water matrix. At each time point samples were prepared in duplicate, together with one blank control to account for the potential background interference. Error bars in all the kinetic graphs represent the maximum and minimum values in the formation potential tests under the same reaction conditions (n ¼ 2). Experiments were conducted in four water matrices; the water sources and basic water quality parameters are summarized in Table 1. Lake and river water samples were taken from the influent of two drinking water treatment plants in June and October, 2010, respectively. The pH was determined using a pH meter (Model 8015, VWR Scientific Inc., Mississauga, Ontario). Alkalinity was measured based on an end-point titration, according to Standard Method 2320B (APHA, 2005). The total organic carbon (TOC) was analyzed with an Aurora 1030 TOC analyzer (O.I. Analytical, College Station, Texas). The ultraviolet absorbance at 254 nm (UV254) was determined by a CE3055 Reflectance Spectrophotometer (Cecil Instruments Ltd., Cambridge, England). The specific UV absorbance (SUVA) is calculated by normalizing the UV254 to the TOC.
3.
Results and discussion
3.1.
Formation kinetics in MQ water
Kinetic experiments in MQ (Milli-Q, Ultra Pure Water System, MilliPore, Etobicoke, Ontario) water were conducted for ranitidine, chlorphenamine, and doxylamine at two concentration levels (5 and 25 nM), as shown in Fig. 2. The markers in the figure are the measured NDMA molar conversion values, and the lines are model-estimated results. Details about the model development and estimation will be discussed in Section 3.3. NDMA formation via the three pharmaceuticals followed similar pattern over time. Generally, an initial lag period was observed, followed by a fast increase in NDMA concentration; the molar conversion then gradually leveled off and eventually reached a plateau (maximum molar conversion). Moreover, the formation kinetic behavior was observed to be relatively independent of the initial pharmaceutical concentration, except that NDMA formation from doxylamine in MQ water showed a more significant difference after 24 hr than did the other pharmaceuticals. Given the large excess of chloramine relative to the pharmaceuticals (mg/L vs. lower mg/ L), availability of chloramine was not a limiting factor at the concentration range of pharmaceuticals tested. These results also support observations made in an earlier study where the NDMA molar conversion at 24 hr for 20 selected
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Table 1 e Water matrix source and quality. Matrix Milli-Q (MQ) Tap water (TAP) Lake water (LW) River water (RW)
Source
pH
Alkalinity (mg/L)
Ultra Pure Water System (MilliPore, Etobicoke, Ontario) Toronto, Ontario Lake Ontario, Ajax, Ontario Otonabee River, Peterborough, Ontario
7.5 0.1
1.8 0.3
7.1 0.1 8.0 0.1 7.8 0.1
88.5 2.8 94.6 1.5 86.5 1.2
pharmaceuticals was found to be independent of their initial concentrations (Shen and Andrews, 2011).
3.2.
Formation kinetics in different water matrices
Kinetic experiments were also performed using real water samples dosed with selected pharmaceuticals. The same shape of NDMA formation curve was observed in real water matrices as in MQ water except that the initial lag phase was longer, especially for tests performed using lake or river water (Fig. 3). Similarly to Fig. 2, the markers are the measured NDMA molar conversion values, and the lines are the modelestimated results that will be discussed in detail in 3.3. Although the NDMA formation kinetics was shown to be unique to each water matrix tested, the kinetic behavior was relatively independent of the initial pharmaceutical concentration within a given water matrix, further confirming that observation in MQ water. The different NDMA formation profiles were likely influenced by the water matrix components, rather than by added reagents, since the pH of the water samples was controlled with a phosphate buffer and the same chloramine dosage was applied to all samples. Both bromide and NOM have been shown to influence NDMA formation, with bromide being reported to either catalyze NDMA formation (Mitch et al., 2003; Valentine et al., 2005) or have an inhibitory effect (Chen et al., 2010). However, bromide levels in the water sources that were tested are typically much lower than those for studies that have reported these effects, so the differences in the observed formation profiles were thought to be due to some aspect of the NOM. Since bromide is in higher concentration and so may be more of a concern in coastal waters due to saltwater intrusion, the potential impact from bromide was considered
TOC (mg/L)
UV254 (cm1)
SUVA (L,m/mg)
0.0
0.000
0.000
2.1 0.1 2.3 0.2 6.2 0.5
0.021 0.001 0.024 0.002 0.143 0.002
1.01 0.05 1.08 0.12 2.32 0.17
to be outside the scope of the present tests but would be of interest for future study. NOM may affect the NDMA formation in two ways. The influence of NOM’s competition for chloramine was considered to be minimal due to the small observed chloramine decay (data not shown) and the large excess of chloramine relative to the pharmaceuticals (mg/L vs. lower mg/L) at the end of the kinetic experiment. On the other hand, NOM may interact with the pharmaceuticals and then inhibit the reaction to form NDMA, and it is these interactions that were thought to better explain the observed results. NOM components can be at least partially described by the samples’ TOC and SUVA values. It was observed that water with higher TOC and SUVA levels tended to have a longer initial lag phase; all three pharmaceuticals exhibited their longest initial lag period in river water samples. However, while tap and lake water samples had similar TOC and SUVA values, ranitidine showed a longer initial lag phase in lake water samples. This suggests that some specific NOM fractions or moieties might be more relevant than would be indicated by simple bulk measurements of water quality, such as TOC and SUVA. Previous studies have demonstrated that aromatic amines undergo reversible covalent binding with carbonyls and quinones in soil humic substances in the environment (Parris, 1980; Thorn et al., 1996; Weber et al., 1996). Therefore, it is possible that certain fractions or functional groups in NOM may interact with these amine-based pharmaceuticals and thus hinder their initial contact with chloramine species. As the binding is reversible and chloramine is in large excess, eventually the NDMA conversion from pharmaceuticals can still reach the maximum level given enough reaction time. Currently, although no direct spectroscopic evidence exists for the NOM-pharmaceutical binding in aqueous phase, this
Fig. 2 e NDMA molar conversion over time for ranitidine, chlorphenamine, and doxylamine in MQ water (SDS conditions).
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Fig. 3 e NDMA molar conversion over time for (a) ranitidine; (b) chlorphenamine; and (c) doxylamine in different water matrices (SDS conditions).
theory is indirectly supported by some literature investigating the removal of pharmaceuticals during coagulation/flocculation process, where the removal of pharmaceuticals was likely due to the sorption onto particulate organic matter and coremoved through the settling process (Ballard and Mackay, 2005; Stackelberg et al., 2007; Vieno et al., 2006; Westerhoff et al., 2005). Stackelberg et al. (2007) also detected the target pharmaceuticals in the dried solids of settled sludge. In addition, De Ridder et al. (2011) observed enhanced removal of some positively charged pharmaceuticals using granular activated carbon preloaded with NOM. They attributed the enhancement to the electrostatic attraction since the surface of NOM is usually negatively charged due to abundant carboxyl groups. In the current study, the selected amine
pharmaceuticals are positively charged at neutral pH, therefore the possible electrostatic attraction may also lead to the formation of NOM-pharmaceutical complexes. Future studies are needed to further investigate the role of NOM components in the conversion of pharmaceuticals into NDMA, and alternative methods for the characterization of NOM components will be helpful, such as the application of size-exclusion chromatography with organic carbon detection (Huber et al., 2011).
3.3.
Kinetic model
The NDMA formation curves for the pharmaceuticals in this study have a sigmoidal shape that resembles the typical shape
Table 2 e Model parameter estimation and model verification. Compound
Concentration
Matrix
Parameter Estimation a
q Ranitidine
5 nM
25 nM
Chlorphenamine
5 nM
25 nM
Doxylamine
5 nM
25 nM
MQ TAP LW RW MQ TAP LW RW MQ TAP RW MQ TAP RW MQ TAP RW MQ TAP RW
0.912 0.902 0.729 0.822 0.906 0.847 0.769 0.841 0.027 0.023 0.037 0.033 0.030 0.043 0.062 0.068 0.059 0.106 0.092 0.060
(0.045) (0.045) (0.032) (0.056) (0.045) (0.039) (0.018) (0.044) (0.003) (0.003) (0.002) (0.002) (0.002) (0.002) (0.005) (0.006) (0.003) (0.007) (0.006) (0.002)
a
Lag (hr)
6.3 (0.5) 6.7 (0.8) 13.3 (0.7) 20.8 (1.8) 4.6 (0.5) 6.5 (0.7) 13.1 (0.4) 21.9 (1.4) 7.0 (1.3) 10.8 (3.0) 38.4 (3.4) 8.7 (0.6) 19.4 (1.8) 39.9 (1.9) 16.6 (2.2) 48.8 (5.3) 67.0 (4.3) 22.0 (2.0) 46.1 (4.1) 51.1 (1.8)
Model Verification 1 a
k (hr ) 0.225 0.169 0.251 0.086 0.313 0.177 0.252 0.083 0.143 0.065 0.039 0.234 0.070 0.056 0.068 0.027 0.026 0.069 0.030 0.051
(0.043) (0.045) (0.057) (0.026) (0.084) (0.043) (0.028) (0.021) (0.050) (0.024) (0.007) (0.079) (0.015) (0.009) (0.017) (0.005) (0.006) (0.018) (0.005) (0.011)
R
2
0.992 0.986 0.997 0.984 0.991 0.988 0.999 0.990 0.974 0.953 0.990 0.994 0.988 0.996 0.975 0.993 0.990 0.985 0.992 0.996
a Numbers in the bracket represent the 95% confidence interval of each model parameter. b Numbers in the bracket represent standard deviation from multiple tests (n ¼ 3).
Model-predicted conversion @ 24 h 91.2% 90.1% 72.7% 53.6% 90.6% 84.6% 76.8% 50.4% 2.7% 2.0% 0.8% 3.3% 2.0% 0.5% 4.7% 1.2% 0.4% 6.1% 1.6% 0.2%
Measured conversion @ 24 hb 85.2% 83.4% 64.1% 51.4% 82.7% 88.4% 70.1% 43.2% 2.9% 2.0% 1.0% 1.8% 1.5% 0.5% 3.8% 2.5% 1.1% 4.2% 3.2% 0.5%
(0.8%) (8.1%) (3.6%) (4.9%) (2.4%) (5.9%) (4.8%) (7.1%) (0.2%) (0.8%) (0.1%) (0.1%) (0.1%) (0.02%) (0.1%) (0.2%) (0.03%) (0.1%) (0.3%) (0.04%)
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of doseeresponse curves. A standard doseeresponse curve can be defined by a four-parameter logistic function, ab y¼þ 1 þ 10c$ðdxÞ where y is the response caused by certain dose of pharmaceuticals (x); a and b are the maximum and baseline response, respectively; c is the slope of the curve; and d is the dose which provokes a response halfway between the baseline and maximum (Motulsky and Christopoulos, 2003). Accordingly, the following model was proposed to describe the reaction kinetics for NDMA formation from selected pharmaceuticals, q Y¼ 1 þ 10k$ðLagtÞ where Y is the NDMA molar conversion at given reaction time (t); q is the ultimate NDMA molar conversion, i.e., the maximum molar conversion obtained at the plateau during kinetic testing; k is the pseudo-first order reaction rate constant; Lag is the time required to achieve 50% of the ultimate molar conversion, and thus is associated with the length of initial lag phase observed. Comparing this model with the four-parameter logistic function, the parameter b was set to zero because any possible NDMA in the background and any potential NDMA formed from
the matrix components were accounted for by the blank control samples. It is noted that the proposed model does not pass through the point of (0, 0), although, once background NDMA has been subtracted, there should be zero molar conversion at the beginning. Since the doseeresponse model is based on log (drug dose), it is always positive on the x-axis, and thus does not go through the point of (0, 0). Therefore, the proposed model was arbitrarily set to be: Y¼
8 < :
0 q 1 þ 10k$ðLagtÞ
ðt ¼ 0Þ ðt > 0Þ
The formation curve was fitted using GraphPad Prism 5 software, and the estimated model parameters for each compound in different matrices are summarized in the Parameter Estimation section of Table 2. The proposed model fit the experimental data very well, with correlation coefficients (R2) higher than 0.95 in all cases, and predicted accurately all three phases of the NDMA formation curve, as shown previously in Figs. 2 and 3. It is worth noting that the model requires data capturing all three phases of the NDMA formation curve in order to acquire reliable model parameters. For datasets lacking the plateau data, the model will arbitrarily assume the last point as the
Fig. 4 e Linear correlation between (a) Lag and TOC; (b) Lag and SUVA; (c) k and TOC; (d) k and SUVA for three pharmaceuticals (SDS conditions; [Pharmaceutical] [ 5 and 25 nM; error bars represent the 95% confidence interval for estimated model parameters).
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plateau. In this study, the NDMA formation curve for ranitidine in LW samples did not achieve the plateau within the 24hr of the experiment (Fig. 3a). Therefore, the calculated q may underestimate the ultimate NDMA molar conversion for ranitidine in LW samples. The estimated model parameters each well reflected the different aspects of the NDMA formation profiles that were observed in the different water matrices. Generally, the matrix had a minor impact on the ultimate NDMA molar conversion for ranitidine (q ¼ 84.1 6.7%), chlorphenamine (q ¼ 3.2 0.7%), and doxylamine (q ¼ 7.5 2.0%). Instead, the matrix components had a more profound impact on the initial lag phase (Lag) and the pseudo-first order rate constant (k). As summarized in Fig. 4, the Lag value is positively correlated with both TOC and SUVA values for all three pharmaceuticals; k value is negatively correlated with TOC and SUVA values for ranitidine and chlorphenamine, but not well related for doxylamine. The estimated model parameters and these correlations support the theory that water matrix components can affect NDMA formation from selected pharmaceuticals by inhibiting the initial reaction with chloramine and/or slowing down subsequent reactions. Chen and Westerhoff (2010) recently found NDMA-FP very difficult to predict based upon bulk water quality measurements such as DOC or UVA254. Results from the current study have
indicated that knowledge of the reaction kinetics is essential in the prediction of NDMA formation. While typical water quality measurements like TOC and SUVA can have significant impact on the reaction kinetics, they are not directly associated with the ultimate NDMA molar conversion, and thus are not appropriate for directly predicting NDMA-FP using empirical models. Moreover, knowledge of the reaction time employed is specifically crucial for compounds that react slowly with chloramine, such as doxylamine. Recently, more and more utilities have shown interest in conducting NDMA-FP tests. Because there is no standard protocol at the moment, many are considering adopting typical disinfection by-products formation potential tests (Summers et al., 1996) and have applied a 24 hr incubation time from a practical viewpoint; however, the NDMA formed after 24 hr from some compounds may only represent a small portion of their ultimate formation potential, especially in real water matrices where the initial reaction could be significantly inhibited. For example, the NDMA molar conversion at 24 hr for doxylamine in TAP and RW samples only accounted for less than 20% of its ultimate NDMA molar conversion. The results have suggested that typical bench-scale NDMA-FP tests may underestimate the ultimate NDMA-FP for some precursors. For water systems with higher water age, prolonged NDMA formation in the outreaches of the distribution system might be a potential risk and should be taken into consideration.
Fig. 5 e Linear correlation between the model-predicted and the independently measured NDMA molar conversion at 24 h for (a) ranitidine; (b) chlorphenamine; (c) doxylamine; and (d) three compounds together (SDS conditions; data from four matrices (MQ, TAP, LW, and RW) and two concentration levels ([Pharmaceutical] [ 5 and 25 nM) were included; the slope was reported as “the best fit value ± the standard error” at 95% confidence level).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 8 7 e5 6 9 4
The model was verified by comparing the 24hr NDMA-FP predicted using the estimated model with that measured from independent 24 hr formation potential tests, as summarized in the last two columns of Table 2 (Model Verification). Linear regression was applied between the measured and predicted NDMA molar conversion for each compound individually and for three compounds all together (Fig. 5); the Student’s t-test was then conducted to determine whether the slope of each regression line differed significantly from 1.0. This goodness of fit test has suggested that there is significant correlation between the measured and model-predicted molar conversion (F-test, 95% confidence level), except for chlorphenamine, although even this correlation was determined to be significant at 90% confidence level. The t-test has indicated that the slope of the regression line for ranitidine and chlorphenamine did not differ from 1.0 ( p-value of 99.9% and 15.9%, respectively; 95% confidence level); yet the slope was determined to be smaller than 1.0 for doxylamine and three compounds altogether ( p-value of 3.3% and 0.1%, respectively; 95% confidence level). In general, however, the model-predicted molar conversion was within the 95% confidence interval of the measured value.
4.
Conclusions
NDMA formation kinetics from ranitidine, chlorphenamine, and doxylamine during chloramination was determined in four water matrices. The NDMA conversion over time followed a general three-phase formation curve: an initial lag phase was observed, followed by a fast increase in NDMA formation, and eventually a plateau was reached that represented the ultimate NDMA molar conversion. The NDMA formation profile was relatively independent of the initial pharmaceutical concentration in the same matrix. Water matrix components affected the NDMA conversion rates, most likely by inhibiting their initial contact with chloramine and slowing down the reaction, while they had less impact on the ultimate NDMA molar conversion. A three-parameter kinetic model was proposed to describe the NDMA formation over time during chloramination. The model accurately reflected all the three significant characteristics of the NDMA formation curve, and was able to predict the NDMA molar conversion from the selected pharmaceuticals to within the 95% confidence interval of the measured values. The model needs to be further verified using different potential precursors, water matrices, and reaction conditions. Bulk water quality measurements such as TOC and SUVA were found to correlate better with model parameters Lag and k than with the ultimate NDMA molar conversion (q), indicating interactions between the pharmaceuticals and NOM that might impact NDMA formation are not limited to those based on the general organic character or aromatic nature of either substance. Alternative methods are needed to better characterize the matrix components in order to further investigate their impact on NDMA formation from pharmaceuticals. Knowledge about the formation kinetics is essential in the prediction of NDMA formation from pharmaceuticals. Shortterm NDMA-FP tests (24 h), although practical, may underestimate the contribution of certain slow-reacting precursors.
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Thus, prolonged tests (perhaps >4 days for substances examined in this study) are required to determine the ultimate NDMAFP, especially in distribution systems with long water age.
Acknowledgment This research was supported by the Canadian Water Network, the Natural Sciences and Engineering Research Council of Canada, and the Ontario Research Fund. Special thanks are dedicated to Richard Jones and John Armour in the water treatment plants for their assistance in water sampling.
references
American Public Health Association (APHA), American Water Works Association (AWWA), Water Environment Federation (WEF), 2005. In: Eaton, A.D., Clesceri, L.S., Rice, E.W., Greenberg, A.E. (Eds.), Standard Methods for the Examination of Water and Wastewater, 21st ed. Washington, DC. Ballard, B.D., Mackay, A.A., 2005. Estimating the removal of anthropogenic organic chemicals from raw drinking water by coagulation flocculation. Journal of Environmental Engineering 131, 108e118. Blute, N., Russell, C., Chowdhury, Z., Wu, X., Via, S., 2010. Nitrosamine occurrence in the U.S. e analysis and interpretation of UCMR2 data. In: Proceedings of the AWWA Water Quality Technology Conference Savannah, GA, November 14-18, 2010. Charrois, J.W.A., Boyd, J.M., Froese, K.L., Hrudey, S.E., 2007. Occurrence of N-nitrosamines in Alberta public drinkingwater distribution systems. Journal of Environmental Engineering and Science 6, 103e114. Chen, B., Westerhoff, P., 2010. Predicting disinfection by-product formation potential in water. Water Research 44, 3755e3762. Chen, Z., Valentine, R.L., 2006. Modeling the formation of N-Nitrosodimethylamine (NDMA) from the reaction of natural organic matter (NOM) with monochloramine. Environmental Science & Technology 40, 7290e7297. Chen, Z., Valentine, R.L., 2007. Formation of N-Nitrosodimethylamine (NDMA) from humic substances in natural water. Environmental Science & Technology 41, 6059e6065. Chen, Z., Yang, L., Zhai, X., Zhao, S., Li, A., Shen, J., 2010. Nnitrosamine formation during chlorination/chloramination of bromide-containing water. Water Science and Technology Water Supply 10, 462e471. Choi, J., Valentine, R.L., 2002. A kinetic model of Nnitrosodimethylamine (NDMA) formation during water chlorination/chloramination. Water Science and Technology 46 (3), 65e71. De Ridder, D.J., Verliefde, A.R.D., Heijman, S.G.J., Verberk, Q.J.C., Rietveld, L.C., van der Aa, L.T.J., Amy, G.L., van Dijk, J.C., 2011. Influence of natural organic matter on equilibrium adsorption of neutral and charged pharmaceuticals onto activated carbon. Water Science and Technology 63, 416e423. Dotson, A., Westerhoff, P., Krasner, S.W., 2007. Nitrosamine formation from natural organic matter isolates and sunlight photolysis of nitrosamines. In: Proceedings of AWWA Annual Conference and Exposition Toronto, ON, Canada, June 24e28, 2007. EPA Integrated Risk Information System (IRIS), 1993. N-Nitrosodimethylamine; CASRN 62-75-9. www.epa.gov/iris/ subst/0045.htm.
5694
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 8 7 e5 6 9 4
Gerecke, A.C., Sedlak, D.L., 2003. Precursors of N-nitrosodimethylamine (NDMA) in natural waters. Environmental Science & Technology 37, 1331e1336. Health Canada, 2010. Guideline Technical Document on N-Nitrosodimethylamine (NDMA) in Drinking Water for Public Comment. http://www.hc-sc.gc.ca/ewh-semt/consult/_2010/ ndma/draft-ebauche-eng.php#a3. Huber, S.A., Balz, A., Abert, M., Pronk, W., 2011. Characterisation of aquatic humic and non-humic matter with size-exclusion chromatography e organic carbon detection e organic nitrogen detection (LC-OCD-OND). Water Research 45, 879e885. Kemper, J.M., Walse, S.S., Mitch, W.A., 2010. Quaternary amines as nitrosamine precursors: a role for consumer products? Environmental Science & Technology 44, 1224e1231. Kim, J., Clevenger, T.E., 2007. Prediction of Nnitrosodimethylamine (NDMA) formation as a disinfection byproduct. Journal of Hazardous Materials 145, 270e276. Kohut, K.D., Andrews, S.A., 2003. Polyelectrolyte age and Nnitrosodimethylamine formation in drinking water treatment. Water Quality Research Journal of Canada 38 (4), 719e735. Krasner, S.W., 2009. The formation and control of emerging disinfection by-products of health concern. Philosophical Transactions of the Royal Society A 367, 4077e4095. Krasner, S.W., Dale, M.S., Lee, C.F.T., Garcia, E.A., Wong, T.M., Mitch, W., Von Gunten, U., 2010. Difference in reactivity and chemistry of NDMA precursors from treated wastewater and from polyamine polymers. In: Proceedings of the AWWA Water Quality Technology Conference Savannah, GA, November 14e18, 2010. Mitch, W.A., Schreiber, I.M., 2008. Degradation of tertiary alkylamines during chlorination/chloramination: implications for formation of aldehydes, nitriles, halonitroalkanes, and nitrosamines. Environmental Science & Technology 42, 4811e4817. Mitch, W.A., Sedlak, D.L., 2004. Characterization and fate of N-nitrosodimethylamine precursors in municipal wastewater treatment plants. Environmental Science & Technology 38, 1445e1454. Mitch, W.A., Sharp, J.O., Trussell, R.R., Valentine, R.L., AlvarezCohen, L., Sedlak, D.L., 2003. N-nitrosodimethylamine (NDMA) as a drinking water contaminant: a review. Environmental Engineering Science 20 (5), 389e404. MOE, 2003. Ontario Regulation 268/03 made Under the Safe Drinking Water Act, 2002. http://www.e-laws.gov.on.ca/html/ source/regs/english/2003/elaws_src_regs_r03268_e.htm June 25, 2003. Motulsky, H.J., Christopoulos, A., 2003. Fitting Models to Biological Data using Linear and Nonlinear Regression. A Practical Guide to Curve Fitting. GraphPad Software Inc., San Diego, CA. www. graphpad.com. Najm, I., Trussell, R.R., 2001. NDMA formation in water and wastewater. Journal of the American Water Works Association 93 (2), 92e99. Office of Environmental Health Hazard Assessment (OEHHA), 2006. Public Health Goal for N-nitrosodimethylamine and Cadmium in Drinking Water. http://www.oehha.org/water/ phg/cadndma122206.html.
Parris, G.E., 1980. Covalent binding of aromatic amines to humates. 1. reactions with carbonyls and quinones. Environmental Science & Technology 14, 1099e1106. Sacher, F., Schmidt, C.K., Lee, C., von Gunten, U., 2008. Strategies for Minimizing Nitrosamine Formation during Disinfection. Water Research Foundation, Denver, C.O. AwwaRF Report 91209, August 15, 2008. Schmidt, C.K., Brauch, H.J., 2008. N, N-dimethylsulfamide as precursor for N-Nitrosodimethylamine (NDMA) formation upon ozonation and its fate during drinking water treatment. Environmental Science & Technology 42, 6340e6346. Schmidt, C.K., Sacher, F., Brauch, H.J., 2006. Strategies for minimizing formation of NDMA and other nitrosamines during disinfection of drinking water. In: Proceedings of the AWWA Water Quality Technology Conference Denvor, CO, November 5e9, 2006. Shen, R., Andrews, S.A., 2011. Demonstration of 20 pharmaceuticals and personal care products (PPCPs) as nitrosamine precursors during chloramine disinfection. Water Research 45, 944e952. Stackelberg, P.E., Gibs, J., Furlong, E.T., Meyer, M.T., Zaugg, S.D., Lippincott, R.L., 2007. Efficiency of conventional drinkingwater-treatment processes in removal of pharmaceuticals and other organic compounds. Science of the Total Environment 377, 255e272. Summers, R.S., Hooper, S.M., Shukairy, H.M., Solarik, G., Owen, D. , 1996. Assessing DBP yield: uniform formation conditions. Journal of the American Water Works Association 88 (6), 80e93. Thorn, K.A., Pettigrew, P.J., Goldenberg, W.S., Weber, E.J., 1996. Covalent binding of aniline to humic substances. 2. 15N NMR studies of nucleophilic addition reactions. Environmental Science & Technology 30, 2764e2775. USEPA, 2009. Contaminant Candidate List 3 (CCL3). http://water. epa.gov/scitech/drinkingwater/dws/ccl/ccl3.cfm. Valentine, R.L., Choi, J., Chen, Z., Barrett, S.E., Hwang, C., Guo, Y.C. , Wehner, M., Fitzsimmons, S., Andrews, S.A., Werker, A.G., Brubacher, C., Kohut, K., 2005. Factors affecting the Formation of NDMA in Water and Occurrence, Denver, CO, pp. 81e90. Vieno, N., Tuhkanen, T., Kronberg, L., 2006. Removal of pharmaceuticals in drinking water treatment: effect of chemical coagulation. Environmental Technology 27, 183e192. Weber, E.J., Spidle, D.L., Thorn, K.A., 1996. Covalent binding of aniline to humic substances. 1. kinetic studies. Environmental Science & Technology 30, 2755e2763. Westerhoff, P., Yoon, Y., Snyder, S., Wert, E., 2005. Fate of endocrine-disruptor, pharmaceutical, and personal care product chemicals during simulated drinking water treatment processes. Environmental Science & Technology 39, 6649e6663. Wilczak, A., Assadi-Rad, A., Lai, H.H., Hoover, L.L., Smith, J.F., Berger, R., Rodigari, F., Beland, J.W., Lazzelle, L.J., Kincannon, E.G., Baker, H., Heaney, C.T., 2003. Formation of NDMA in chloraminated water coagulated with DADMAC cationic polymer. Journal of the American Water Works Association 95 (9), 94e106.