Recent Advances in Disinfection By-Products : Bromination and

Chapter 4

Bromination and Chlorination of NOM: New Modeling Approaches and Mechanistic Insights Downloaded by GEORGETOWN UNIV on June 25, 2016 | http://pubs.acs.org Publication Date (Web): August 24, 2015 | doi: 10.1021/bk-2015-1190.ch004

Paolo Roccaro,*,1 Federico G. A. Vagliasindi,1 and Gregory V. Korshin2 1Department

of Civil Engineering and Architecture, University of Catania, Viale A. Doria 6, 95125 Catania, Italy 2Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195-2700 *E-mail: [email protected].

This study investigated formation of trihalomethanes (THMs), haloacetic acids (HAAs), haloacetonitriles (HANs) generated in two chlorinated surface waters. DBPs formation reactions and concurrent NOM transformations were examined based on the kinetic analysis of DBP concentrations and also via differential spectroscopy that quantifies the extent of NOM halogenation. The evolution of differential absorbance (ΔA) was correlated with both DBPs yields and speciation. The modeling of DBP formation using ΔA data showed the existence of compoundspecific proportionality coefficients that change predictably as function of Br- concentration but do not depend on chlorine concentration or reaction time. The presented approach was employed to develop a DBP formation model that is based on the kinetics of ΔA changes combined with the bromide-depended DBP yield coefficients.

Background Disinfection of drinking water by chlorine has dramatically reduced the transmission of potentially fatal waterborne diseases. The main issues that affect current chlorination practices include the limited efficiency of chlorine against some pathogens and formation of toxic disinfection by-products (DBPs) (1) formed as a result of reactions of chlorine and other halogen species with natural organic matter (NOM) to produce chlorinated, brominated and, in much © 2015 American Chemical Society Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.

Downloaded by GEORGETOWN UNIV on June 25, 2016 | http://pubs.acs.org Publication Date (Web): August 24, 2015 | doi: 10.1021/bk-2015-1190.ch004

smaller levels, iodinated DBPs (2–4). Even though trihalomethanes (THMs) and haloacetic acids (HAAs) occur at the highest concentrations (up several hundred mg/L) in chlorinated water (5) and are the most regulated DBPs worldwide (6), more than 500 other individual DBPs have been identified (7). DBP formation and speciation are affected by concentration and reactivity of NOM, levels of bromide, iodine and ammonia, chlorine dose, pH, temperature and reaction time (2, 4, 8–10). The formation of brominated DBPs is of particular concern because these compounds are much more toxic than their chlorinated analogues (5, 11, 12). The concentration of bromide in natural waters is usually considerably lower than 1 mg/L (10) but in some case Br- concentrations up to 4 mg/L have been detected (13, 14). Numerous alternative DBP formation models that use either statistical and mechanistic approaches have been presented in prior research (e.g., (15–17)). Due to the complexities of both NOM chemistry and halogenations processes per se, most efforts in DBP modelling have focused on the development of empirical/statistical models that predict DBP concentrations using numerous alternative expressions and a set of fitting parameters applied to represent a specific set of DBP data (18). While multi-parameter statistical fitting employed to develop these models can be very useful for a specific set of DBP generation conditions, they need to be recalibrated to be applicable to any water quality dissimilar from the dataset used to develop such models. Statistical fitting per se also does not provide any insights on DBP formation mechanisms whose inclusion in DBP modelling allows in principle for more concise and clear approaches. Fundamentally, mechanistic approach to DBPs formation modelling incorporates clear assumptions that reflect the nature of NOM halogenation (e.g., formation of various intermediates, branching kinetic pathways), equilibria describing major aspects of the aquatic chemistry of halogen species, and a system of differential equations that represent the consumption of halogen species and reactive sites in NOM, generation and breakdown of halogenated intermediates and cleavage of individual DBP species from them. A number of such models employ the assumption that chlorine and bromine atoms are incorporated in the NOM substrate through several sequential steps of bromine and chlorine reactions with the reactive NOM sites (9, 19–21). In these models, relative yields of chlorinated and brominated products originating from the intermediates formed at each node of halogen incorporation are defined by the dimensionless ratios of the intrinsic kinetic rates of reaction of the corresponding intermediate with chlorine and bromine species (22, 23). This approach can be combined with and enhanced by unambiguous measurements of the extent of engagement of the reactive sites in NOM by halogen species. This can be done using the principle of differential absorbance that quantifies the consumption of NOM reactive sites in halogenation reactions. Applications of this principle has allowed generating very strong relationships between concentrations of a wide range of individual or classes of DBPs and, on the other hands, intensities of differential absorbance measured at any desired wavelength (which frequently has been at 272 nm; the differential absorbance at this wavelength is accordingly denoted as ΔA272) (24). These correlations have 64 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.

been shown to be independent vs. chlorine doses, reaction times and temperature (25) but affected by pH or bromide concentration (26). In this study we pursued the development of a combined approach that incorporates a kinetic model to represent changes of ΔA272 values as a function of time and bromide concentration while concentrations of individual DBPs are obtained in this approach by incorporating DBP vs. ΔA272 relationships. This approach was applied to the formation of three highly important classes of DBPs (THMs, HAAs and haloacetonitriles, HANs) generated for a highly varying reaction times and bromide levels.


Experimental Chlorination experiments were conducted using Lake Washington (LW) water (Seattle, USA) and Ancipa water (Sicily, Italy). Main water quality parameters of LW and Ancipa waters are shown in Table 1. All chemicals were ACS reagent grade or better. Solvents used in extractions were high-purity grade. Reagent water was obtained from a Millipore Super-Q Plus water system. Chlorine stock solution was prepared by dilution of a reagent grade sodium hypochlorite solution (5% available chlorine) with Milli-Q water.

Table 1. Main Water Quality Parameters of Lake Washington Water and Raw, Treated, and Fractionated Ancipa Water Water

DOC (mg/L)

SUVA254 (L mg-1 m-1)

Br- (mg/L)

LW

3.0

2.20

0.02

Ancipa

2.9

2.89

0.05

Chlorination was carried out with free chlorine at pH 7.0 in the presence of 0.03 mol/L phosphate buffer in headspace-free 1.6 L PTFE sampling bags, which were used to prevent the loss of volatile DPBs when samples were taken at different reaction times (10 minutes to 7 days). Selected experiments were carried out at varying bromide concentrations (from its background levels to 2 mg/L) at chlorine dose of 1.5 mg Cl2 per mg DOC. Chlorinated samples were analyzed for DBPs only if chlorine residual was found. Requisite amount of Na2SO3 or NH4Cl were used to quench residual chlorine. Chlorine concentrations were determined using the DPD colorimetric method. Absorbance spectra were obtained with a Perkin-Elmer Lambda 18 spectrophotometer using 5 cm quartz cells. All reported spectra were normalized to the cell length of 1 cm. TOC was analyzed using an O.I. Analytical 1010 or a Shimadzu VCSH organic carbon analyzer. Concentrations of THMs, HANs and HAAs were determined using standard analytical procedures (EPA methods 551.1 and 552.2) and a Perkin-Elmer AutoSystem gas chromatograph equipped with an electron capture detector. Other aspects of these analyses are described in our previous publications (27, 28). 65 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.

Results and Discussion Kinetics of DBP Formation in Chlorinated Water


In agreement with the data of prior research (e.g., (2, 22, 25, 27)), concentrations of THMs, HAAs and dihaloacetonitriles (DHANs) increased gradually with time at given chlorine dose. Higher chlorine doses or temperatures caused higher levels of these DBPs species (25). For instance, Figure 1 shows the formation kinetics of CHCl3, CHCl2Br and CHClBr2 for chlorinated Ancipa water. Similar behaviour was observed for the other investigated individual DBPs formed in chlorinated Ancipa and LW water.

Figure 1. Kinetics of chloroform (TCM), dichlorobromomethane (DCM) and chlorodibromomethane formation for chlorinated Ancipa water.

Modelling the Kinetics of Changes of Differential Absorbance Using Dimensionless Ratios of Bromination/Chlorination Reaction Rates In accord with the results of prior studies, bromide concentrations strongly affected the yields and speciation of THMs, HAAs and DHANs (9, 19, 20, 22, 23) favouring the formation of bromine-containing DBP and faster rates of their release at increased bromine levels. While the presence of bromide did not appreciably affect the shape of the differential spectra, changes of the differential absorbance vs. time were faster at increasing bromide concentrations, especially for reaction times below four hours, as shown in Figure 2 for the kinetic profiles measured at a background bromide concentration (0.04 mg/L) and in the case of addition of 1 mg/L bromide. At higher reactions time, ΔA272 values tended to plateau and reach a level common for all examined conditions. 66 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.


The formal description of the development of the differential absorbance vs. time and effects of bromine on it was pursued based on the three-site model similar to that used to model concentrations at varying bromine levels (22). Formally, the changes of differential absorbance at 272 nm vs. time were described by the following function:

Figure 2. Kinetic profiles of the differential absorbance of chlorinated LK water in the presence of 0.04 and 1.0 mg/L bromide. 67 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.

A more detailed analysis of this function was presented in our preceding publication (22). Suffice it to mention here that values correspond to the changes of absorbance associated with the consumption of the operationally defined very fast, fast and slow NOM reactive sites while

correspond to


the reaction rates of each site with chlorine species. Dimensionless values quantify the relative preference of bromination over chlorination for each site. Fitting the experimental ΔA272 vs. time profiles using the above model showed that the very fast, fast and slow sites contribute to ca. 21%, 34% and 45%, respectively, of the overall change of absorbance observed at highest reaction times and bromide concentrations. The relative preferences of the sites to the bromination pathway were similar, with the values of 5.1, 5.1 and 6.0 for the very fast, fast and slow sites, respectively. These values are comparable with those reported in prior research in formal descriptions of the speciation of THMs and HAAs (9, 19, 20, 22, 23). The use of the above approach allowed achieving precise modelling of the changes of differential absorbance from the entire range of reactions (from 10 minutes to 3 days) and bromine concentrations (from 0.04 to 2.04 mg/L). This allowed using this model for predicting concentrations of individual DBPs species once ΔA272 values have been measured experimentally or calculated using the model. This aspect of our approach is discussed in the sections that follow.

Modelling DBPs Formation and Speciation via Differential Absorbance As was mentioned above, prior studies have established the existence of very strong albeit non-linear correlations between differential absorbance (ΔA272) and DBPs formation (24, 27). For individual DBP species, these correlations are independent of chlorine dose, reaction times, and temperature and they can be well represented by a power function (25). On the other hand, parameters of the fitting power function depend on the pH and bromide concentration. Following these observation, the concentrations of DBPs were modelled in this study using ΔA272 as master parameter and the following expression:

In the above expression, DBPi stands for the concentration of any target individual DBP compound, ki is the proportionality coefficient specific to this species while x is the parameter of the fitting power function. As will be explained below, the parameters x was determined to have the same value for all examined DBPs. Eq.2 can be useful for modelling concentrations of any target DBP (DBPi) because it allows simplifying the number of water quality parameters that need to be taken into account. For a constant pH, Eq.2 can be simplified as: 68 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.


Fitting the experimental DBPs and ΔA272 values with Eq.3 at varying bromide concentrations showed that a single value of x=1.7 can be used to fit the data for all the investigated DBPs and waters. This is demonstrated in Figure 3-6 for data sets showing these correlations for trichloroacetic acid (TCAA) and tribromoacetic acid (TBAA) formed in Lk. Washington water at bromide concentrations 0.1 and 1.0 mg/L. This result allows reducing Eq. 3 to the following simple form:

Similarly to the results for Lk. Washington water, DBP concentrations found in chlorinated Ancipa water could be well fitted by using Eq. 4, as shown in Figure 1 for the formation kinetics of CHCl3, CHCl2Br and CHClBr2. When represented vs. the concentration of bromide, values of the ki proportionality coefficients obtained via the fitting of the experimental DBP and ΔA272 data exhibited a type of functional dependence similar to that of the dimensionless speciation coefficients for THMs, HAAs and HANs reported in prior studies (9, 19, 22, 23). This is demonstrated in Figure 7 and 8 that present the ki coefficients for the trihalogenated (THAA) and dihalogenated HAAs (DHAA), respectively, formed in chlorinated LW water. As a result, the proportionality coefficients, ki, are speciation coefficients which can be determined by the correlations between DBPs and differential absorbance, alternatively to the formal modeling, which employs dimensionless ratios of bromination/chlorination reaction rates. The ki coefficients have also been found to be strongly correlated with the logarithms of bromide concentrations (Figure 9-11). The fitting shown in these figures was achieved using second-order polynomial curves. In the case of DBPs containing only chlorine or bromine, the fitting predicted a gradual decrease or increases, respectively, of the ki coefficients while for mixed compounds the behavior of these coefficients was non-monotonic (Figure 9-11). The use of logarithms of bromide concentration and correlating them with the ki coefficients appears to be a reasonably straightforward procedure that is expected to be applicable to any water quality. Further studies will demonstrate the extent of effects of NOM site-specificity on these correlations of NOM as well as on the absolute values of ki coefficients.

69 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.


Figure 3. Correlations between concentration of trichloroacetic acid (TCAA) and differential absorbance at 272 nm for chlorinated LW water. Br=0.1 mg/L.

Figure 4. Correlations between concentrations of tribromoacetic acid (TBAA) and differential absorbance at 272 nm for chlorinated LW water. Br=0.1 mg/L. 70 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.


Figure 5. Correlations between trichloroacetic acid (TCAA) and differential absorbance at 272 nm for chlorinated LW water. Br=1.0 mg/L.

Figure 6. Correlations between concentrations of tribromoacetic acid (TBAA) and differential absorbance at 272 nm for chlorinated LW water. Br=1.0 mg/L. 71 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.


Figure 7. Effects of bromide concentration on the proportionality coefficients (ki) for trichloroacetic (TCAA), bromodichloacetic (BDCAA), dibromochloroacetic (DBCAA) and (TBAA) tribromoacetic acids formed in LW water.

Figure 8. Effects of bromide concentration on the proportionality coefficients (ki) for dichloroacetic acid (DCAA), bromochloacetic acid (BCAA) and dibromoacetic acid (DBAA) formed in chlorinated LW water. 72 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.


Figure 9. Correlations between proportionality coefficients (ki) for dichloroacetic (DCAA) or trichloroacetic (TCAA) acids and logarithms of bromide concentrations. Data for chlorinated LW water.

Figure 10. Correlations between proportionality coefficients (ki) for dibromoacetic (DBAA) or tribromoacetic (TBAA) acids and logarithms of bromide concentration. Chlorinated LW water. 73 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.


Figure 11. Correlations between proportionality coefficients (ki) for bromodichloacetic (BDCAA), dibromochloroacetic (DBCAA) and bromochloacetic acids (BCAA) and logarithms of bromide concentrations. Chlorinated LW water.

Conclusions This study examined the formation of THM, HAA and HAN species and concurrent NOM transformations reactions based on the kinetic analysis of DBPs data and also via differential spectroscopy. The approach treated the differential absorbance (ΔAλ) as master parameter that can be used to predicts concentrations of any selected individual DBP compounds. The kinetics of ΔAλ changes was determined to be sensitive to the presence of bromide. It also reflected the fact of a faster bromination of NOM reactive sites, compared with the rates of chlorination. Comparison of ΔAλ values and concurrently measured DBP concentrations showed that DBPs levels were correlated with ΔAλ by a uniformly applicable power function having a 1.7 power coefficient but this function also contains a proportionality coefficient which is compound specific and varies with the bromide level. The proportionality coefficients were found to behave similarly to the formal speciation coefficients reported in prior research and are strongly correlated with logarithms of the bromide concentrations. The joint interpretation of the kinetic DBP and differential absorbance data confirmed the applicability of the DBPs speciation model. The results indicate that both the absolute levels and speciation of several classes or bromine-containing DBPs can be interpreted and modeled based on the presented approach. Further research is needed to expand the modeling of DBPs formation to a wider range of surface waters as well as to examine its performance for chloraminated waters containing bromide and/or iodide. 74 Karanfil et al.; Recent Advances in Disinfection By-Products ACS Symposium Series; American Chemical Society: Washington, DC, 2015.

Acknowledgments This study was partially supported by the Water Research Foundation (Project #2597), the United States EPA/Cadmus (grant 069-UW-1), and the Italian Ministry of Instruction, University, and Research (MIUR), through the Research Projects of National Interest - PRIN 2009 (grant 20092MES7A_002). Paolo Roccaro and Gregory Korshin acknowledge the U.S.-Italy Fulbright Commission for supporting their research in the U.S. and in Italy, respectively. Views expressed in this paper do not necessarily reflect those of the funding agencies.


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Recent Advances in Disinfection By-Products : Bromination and

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