Clustering Chlorine Reactivity of Haloacetic Acid Precursors in

(1-4) Rising levels of dissolved organic matter (DOM) in inland lakes and streams have been ... A total of seventeen model DOM precursors (Figure 1) w...
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Clustering Chlorine Reactivity of Haloacetic Acid Precursors in Inland Lakes Teng Zeng*,§ and William A. Arnold Department of Civil Engineering, University of Minnesota, 500 Pillsbury Drive Southeast, Minneapolis, Minnesota 55455, United States S Supporting Information *

ABSTRACT: Dissolved organic matter (DOM) represents the major pool of organic precursors for harmful disinfection byproducts, such as haloacetic acids (HAAs), formed during drinking water chlorination, but much of it remains molecularly uncharacterized. Knowledge of model precursors is thus a prerequisite for understanding the more complex whole water DOM. The utility of HAA formation potential data from model DOM precursors, however, is limited due to the lack of comparability to water samples. In this study, the formation kinetics of dichloroacetic acid (DCAA) and trichloroacetic acid (TCAA), the two predominant HAA species, were delineated upon chlorination of seventeen model DOM precursors and sixty-eight inland lake water samples collected from the Upper Midwest region of the United States. Of particular interest was the finding that the DCAA and TCAA formation rate constants could be grouped into four statistically distinct clusters reflecting the core structural features of model DOM precursors (i.e., non-β-diketone aliphatics, β-diketone aliphatics, non-β-diketone phenolics, and β-diketone phenolics). A comparative approach built upon hierarchical cluster analysis was developed to gain further insight into the chlorine reactivity patterns of HAA precursors in inland lake waters as defined by the relative proximity to four model precursor clusters. This work highlights the potential for implementing an integrated kineticclustering approach to constrain the chlorine reactivity of DOM in source waters.

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INTRODUCTION Inland waters only cover about 1% of the Earth’s surface but play an integral role in regulating the carbon cycle and climate on a global scale.1−4 Rising levels of dissolved organic matter (DOM) in inland lakes and streams have been observed in North America and Europe.5−9 This widespread phenomenon, although not entirely understood, likely results from the combined effects of multiple forcings, including increased precipitation, elevated atmospheric carbon dioxide, decreased atmospheric sulfur deposition, and land use changes.10 Increasing DOM levels in source waters present a challenge to downstream water treatment utilities in that the chemical disinfectants (e.g., chlorine) applied to inactivate waterborne pathogens promote the production of disinfection byproducts (DBPs) via reactions with DOM moieties.11−15 Elucidating DBP formation mechanisms, however, is hindered by the intrinsic heterogeneity of DOM chemical composition and molecular structure.16,17 Haloacetic acids (HAAs) are produced during disinfection with chlorine, chlorine dioxide, chloramines, and ozone, but their yields are highest with chlorination.18,19 In chlorinated waters, HAAs are typically present at low- to mid-μg/L levels.20−23 Evidence from in vitro bioassays shows that exposure to HAAs may induce cytotoxicity and genotoxicity in bacterial, mammalian, and human cells.24,25 Among nine chloro-bromo HAA species (HAA9), dichloroacetic acid © 2013 American Chemical Society

(DCAA; CHCl2COOH) and trichloroacetic acid (TCAA; CCl3COOH) are of particular concern owing to their predominance over other species.21 Considerable research has explored the HAA formation potential of model DOM precursors as well as DOM isolates fractionated from various source waters.26−30 Chlorination of model precursors revealed that β-dicarbonyl aliphatics preferentially generate DCAA, while activated phenolics predominantly form TCAA.29−32 Chlorination of DOM fractions isolated from whole water samples indicated that DCAA precursors are more hydrophilic and lower in molecular weight than TCAA precursors.27,28,33 A defined picture of the chlorine reactivity of HAA precursors in given source waters is, however, difficult to derive from the presently available literature largely due to the conceptual challenge in quantifiably relating observed formation potential data of model precursors to those of water samples.34 HAA formation kinetics, on the other hand, have received comparatively scant attention in this context, although markedly distinct DCAA and TCAA formation rates upon chlorination of model precursors were implicated in analogous patterns seen in natural waters.35−37 Kinetics of HAA formation Received: Revised: Accepted: Published: 139

August 23, 2013 November 7, 2013 December 3, 2013 December 3, 2013 dx.doi.org/10.1021/es403766n | Environ. Sci. Technol. 2014, 48, 139−148

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Figure 1. Structures of model DOM precursors. Physicochemical properties of precursors are given in Table S1.

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represents a critical component needed to formulate the initial free chlorine dosing strategy prior to the production of chloramines.29,38 A key research question remains whether linking kinetic data sets of DCAA and TCAA formation from model precursors and water samples would permit more chemically explicit characterization of precursor reactivity than relying solely on formation potential tests. The present study aims to extend the usefulness of model precursor chemistry by exploiting the relevance of DCAA and TCAA formation kinetics for the whole water DOM, with a larger goal to delineate the chlorine reactivity patterns of HAA precursors in inland lakes across the Upper Midwest region of the U.S.. This region is of interest because it is one of the most lake-rich landscapes on Earth.39 One possible path forward is to establish a comparative method that can distinguish relationships and trends among model precursors and water samples based on the notion of proximity measure. Statistical clustering techniques, such as hierarchal cluster analysis (HCA), are particularly useful for such purpose in that its discriminatory power relies on statistical distance measuring. To this end, we systematically determined the pseudo-first-order formation rate constants of DCAA and TCAA from a broad spectrum of model DOM precursors and inland lake water samples under standardized chlorination conditions. By implementing HCA on the combined kinetic data sets, we sought to enable a direct clustering of HAA precursor reactivity in inland lakes.

MATERIALS AND METHODS

A complete description of chemical sources and purities and reagent preparation is provided in the Supporting Information (SI). Selection of Model DOM Precursors. A total of seventeen model DOM precursors (Figure 1) were chosen on account of their structural resemblance to functional moieties in humic substances as well as previously reported DCAA and TCAA formation potential.26,29,30,40 The first category of precursors includes aliphatic compounds containing α-diketone (2-ketobutyric acid (KBA) and 2-ketoglutaric acid (KGA)), β-diketone (2-oxosuccinic acid (OSA), 3-oxoglutaric acid (OGA), 3-oxoadipic acid (OAA), and ethyl acetoacetate (EAA)), and γ-diketone (4-ketovaleric acid (KVA) and citric acid (CTA)) moieties. The second category comprises phenolic compounds containing mono- and polyhydroxy (phenol (PHE), resorcinol (RSC), phloroglucinol (PLG), and 4hydroxybenzaldehyde (HBA)), vanillyl (vanillin (VNL)), syringyl (syringaldehyde (SRA)), and coniferyl (ferulic acid (FRA)) moieties. Resveratrol (RVT) and ethyl 3,4,5trimethoxybenzoylacetate (EMB) represent a mixed structural configuration, with the former consisting of mono- and dihydroxy phenolic moieties joined by an unsaturated alkyl chain and the latter a syringyl-type of moiety with paraaldehyde group substituted by a β-diketone group. 140

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Figure 2. Map of inland lakes sampled in the Upper Midwest Region of the U.S. Lake Superior, Lake Michigan, Mississippi River, and Missouri River were also sampled. Further details on the lakes are provided in Figure S1 (a zoomed-in map with individual lakes labeled) and Table S2.

Delineation of DCAA and TCAA Formation Kinetics. Chlorination experiments were performed in at least duplicate to determine the formation rate constants of DCAA and TCAA. For model precursors, reaction solutions were prepared with phosphate buffer (10 mM; pH 7.2) containing individual compounds at a DOC concentration of 1 mgC/L. Chlorination was initiated by spiking in free chlorine under rapid-mix conditions to yield an initial dose of 10 mgCl2/mgDOC. This chlorine dose was higher than practice to achieve a sufficient chlorine excess to ensure pseudo-first-order kinetics.29,30 Samples were incubated in the dark at 21 ± 1 °C unless otherwise stated. Individual samples were quenched by a stoichiometric amount of Na2SO3 at selected time points and were extracted according to the USEPA Method 552.3 with minor modifications (see the SI for additional details). Typical chlorination time courses contained eight to ten data points within the predetermined time scales, which ranged from 30 min to 120 h to accommodate varying DCAA and TCAA formation rates. Blanks containing only phosphate buffer were processed following the same protocol. For all inland lakes, filtered water samples were first diluted with phosphate buffer to the DOC level of 1 mgC/L to allow for quantitative comparison with model precursors and for the different waters. Diluted water samples were then chlorinated and analyzed as described above. Additional chlorination experiments were performed using twelve selected inland lake water samples subjected to photobleaching, freeze−thaw, or dark incubation treatment. For photobleaching, raw waters were photolyzed in an Atlas Suntest CPS+ solar simulator equipped with a xenon arc lamp (light intensity set at 765 W/m2) for a total of 56 h

Sampling of Inland Lakes. A total of sixty-eight inland lakes were sampled in the summer of 2009 and 2010 from four states in the U.S. Upper Midwest (Figure 2). Because it was logistically challenging to acquire intensive temporal replicates, a wide spatial distribution of water samples was collected to capture variations in the quality and quantity of DOM. Samples in Minnesota were collected from the North Central Hardwood Forests (NCHF) and the Northern Lakes and Forests (NLF) ecoregions. Five lakes are near Minneapolis-St. Paul in southeastern Minnesota, and nine lakes lie across northeastern Minnesota near the Superior National Forest and the Voyageurs National Park. A third group of fourteen lakes is situated within north-central Minnesota along the Mississippi River. A fourth group of sixteen lakes is located in western Minnesota bordered by the Northern Glaciated Plains (NGP) ecoregion. Samples in Dakotas were collected from the NGP ecoregion. Six lakes are located within the Glacial Lake District of the Coteau des Prairies in eastern South Dakota, while the rest of the four are prairie pothole lakes located within the Cottonwood Lake Area of the Coteau du Missouri in eastcentral North Dakota. Samples in Wisconsin were collected from the NCHF and the Southeastern Wisconsin Till Plains (SWTP) ecoregions. Seven of the lakes are located within or north of the Yahara River Lake District of Madison in southeastern Wisconsin. A second group of seven lakes is situated within the Northern Highland Lake District in northern Wisconsin. Details of sample collection and analysis (e.g., dissolved organic carbon (DOC), specific ultraviolet absorbance (SUVA), HAA9 formation potential (HAA9FP), and bromine incorporation factor (BIF)) are given in the SI. 141

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Table 1. Characteristics of Inland Lake Waters

parametera pH DOC (mgC/L) UV254 SUVA254 (L/mgC-m) Cl− (mg/L) Br− (μg/L) NH3−N (mg/L) Br/DOC (μg/mgC) chlorine demand (mgCl2/mgC) HAA9FP (μg/L) specific HAA9FP (μg/mgC) BIF

NGP ecoregion [n = 15]b range (mean ± S.D.)

NLF ecoregion [n = 30] range (mean ± S.D.)

NCHF ecoregion [n = 21] range (mean ± S.D.)

SWTP ecoregion [n = 7] range (mean ± S.D.)

7.73−8.69 (8.22 ± 0.34) 19.88−60.56 (30.12 ± 10.37) 0.399−1.098 (0.569 ± 0.162) 1.48−2.60 (1.94 ± 0.29) 3.60−108.16 (23.96 ± 25.24) 26.60−101.84 (57.47 ± 24.37) 0.010−0.227 (0.068 ± 0.073) 1.1−4.1 (2.0 ± 0.9) 1.64−1.79 (1.76 ± 0.03) 301.0−1318.1 (618.3 ± 239.5) 12.8−33.5 (20.8 ± 5.3) 0.05−0.14 (0.09 ± 0.02)

4.67−8.65 (7.59 ± 0.89) 2.02−16.70 (7.20 ± 3.48) 0.058−0.543 (0.232 ± 0.136) 1.39−4.94 (3.11 ± 0.74) 0.27−19.84 (4.51 ± 5.29) 20.45−122.07 (40.72 ± 24.03) 0.010−0.223 (0.051 ± 0.059) 1.6−14.1 (6.6 ± 3.6) 1.40−1.75 (1.64 ± 0.08) 31.8−260.3 (137.7 ± 71.1) 4.3−39.8 (20.4 ± 8.8) 0.09−0.58 (0.21 ± 0.11)

7.48−8.98 (8.09 ± 0.39) 5.17−10.64 (7.29 ± 1.59) 0.124−0.453 (0.243 ± 0.087) 2.00−4.35 (3.27 ± 0.56) 2.39−102.92 (22.41 ± 27.95) 36.64−104.63 (67.92 ± 17.89) 0.010−0.333 (0.128 ± 0.090) 3.7−18.2 (9.9 ± 3.8) 1.49−1.73 (1.66 ± 0.05) 39.5−356.2 (188.5 ± 81.2) 6.2−47.1 (26.3 ± 10.9) 0.09−0.36 (0.24 ± 0.08)

7.98−8.57 (8.29 ± 0.23) 4.95−10.94 (6.82 ± 2.07) 0.144−0.289 (0.208 ± 0.059) 2.63−3.56 (3.07 ± 0.39) 19.62−98.29 (51.76 ± 26.31) 20.34−102.09 (74.48 ± 26.86) 0.073−0.340 (0.172 ± 0.093) 2.8−16.9 (11.7 ± 5.0) 1.60−1.70 (1.65 ± 0.03) 40.5−209.1 (130.8 ± 71.0) 5.3−37.6 (22.2 ± 14.8) 0.25−0.38 (0.35 ± 0.05)

Measured in filtered water samples. Data for individual samples are tabulated in Tables S2 and S3. bSix lakes in South Dakota were sampled once, resulting a total of six samples. Four prairie pothole lakes in North Dakota were sampled on multiple occasions throughout 2010 to track the seasonal dynamics of water chemistry,53 resulting in a total of nine samples. a

ment, land use, and management practices) factors.41 A positive and strong correlation existed between HAA9FP and DOC (Figure S2(a)), indicating that the DOC level is a key driver of the HAA yields. The dominant HAA species formed were DCAA and TCAA,42 accounting for 83 ± 8% of the HAA9 pool by weight. In many, but not all cases, SUVA can be taken as a coarse predictor for HAA9FP.27,43−45 SUVA and specific HAA9FP, however, were poorly correlated (Figure S2(b)), supporting the notion that the effectiveness of SUVA in predicting HAA yields is water-specific.46 Furthermore, SUVA as a bulk optical proxy primarily captures the aromatic character of DOM but not the potentially relevant aliphatic components.28,29 Consistent with the general observation that increasing bromide levels shift the distribution of HAAs to more brominated and mixed-halogenated species,47−51 higher Br/DOC ratios resulted in increased BIF (Figure S2(c)). The extent of bromine incorporation was, however, independent of SUVA across the water samples tested (Figure S2(d)), confirming that SUVA is unable to rationalize the reactive DOM moieties which chlorine or bromine attacks.52 Reactivity Clustering of Model DOM Precursors. Multistep, branching reactions are involved in the DCAA and TCAA formation from the chlorination of model precursors, including electrophilic substitution, oxidation, hydrolysis, and addition to unsaturated bonds.54 The pseudo-first-order formation rate constants of DCAA and TCAA from model precursors spanned a wide range of values, from 1.24 × 10−5 to 8.56 × 10−3 s−1 for DCAA and 1.55 × 10−6 to 4.76 × 10−2 s−1 for TCAA, respectively. Representative kinetic time courses for DCAA and TCAA formation are shown in Figures S3 to S6.

over one week. For freeze−thaw, raw waters were removed from and returned to a −20 °C laboratory freezer for a total of seven cycles over one week. For dark incubation, raw waters were incubated in a foil-wrapped chamber for approximately one year with periodical reaeration. At the end of each treatment, water samples were filtered and diluted for use in chlorination experiments. Data Analysis. The rate constants for DCAA and TCAA production were derived from the concentration versus time data using Prism 6.0 (GraphPad Software Inc.). Crossplots of log-transformed DCAA and TCAA formation rate constants were constructed to increase the resolution of differentiation. HCA was performed to aggregate the model precursor and inland lake data into clusters such that the proximity was as close to each other as possible within clusters and as distant as possible among clusters. The optimal data clustering was achieved using the log-transformed formation rate constants with Euclidean distance and complete linkage and was visualized by dendrograms (geometric spacing scale) using JMP 10.0 (SAS Institute Inc.). Data normality was evaluated using the Kolmogorov−Smirnov test, and statistical significance was determined at α = 0.05.

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RESULTS AND DISCUSSION HAA Formation and Speciation in Inland Lake Waters. The descriptive statistics for water quality of sixty-eight inland lakes are tabulated in Table 1. The considerable across-site variability in water chemistry profiles reflects joint influences from both natural (e.g., bedrock geology, watershed size, and hydrologic regime) and anthropogenic (e.g., shoreline develop142

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Figure 3. Kinetic-clustering analysis of DCAA and TCAA formation from model DOM precursors upon chlorination (pH 7.2, temperature 21 °C, and chlorine dose of 10 mgCl2/mgDOC): (a) Crossplot of DCAA and TCAA formation rate constants; (b) Dendrogram of formation rate constants; (c) Crossplot of DCAA and TCAA formation potential. DCAAFP and TCAAFP were calculated based on the final DCAA and TCAA yields derived from the pseudo-first-order model fit. Note that the clustering notion in panel (a) is used to assign clusters in this panel. Values of formation rate constants and formation potential for individual model precursors are tabulated in Table S4. Literature data on the chlorine demand and DCAA and TCAA formation potential of selected model precursors are summarized in Table S5 for comparison.

nor sufficient evidence of common rate-limiting steps.60 Previous mechanistic work has postulated that the formation of DCAA and TCAA proceeds through a CHCl2−CO-R type of intermediate (where R is an oxidizable group), but TCAA does not readily form from DCAA by further chlorine substitution,26 suggesting that the co-occurrence of DCAA and TCAA likely involves different precursor chemistry yet intertwined pathways. No consistent pattern was observed in the crossplot of DCAA and TCAA formation potential of model precursors (Figures 3(c) and S7), indicating that comparable DCAA or TCAA yields may result from significantly different formation rates, and vice versa. Indeed, past research on the chlorination of model precursors mostly found a lack of correlation between DCAA and TCAA formation potential (Figure S7).26,29−31,61 One possible explanation is that concurrent degradation pathways following chlorine substitution can liberate multiple intermediates that form either DCAA or TCAA.30 Reactivity Clustering of Inland Lake Waters. The formation rate constants of DCAA and TCAA from inland lake water samples spanned a range from 10−6 to 10−2 s−1 (Figure 4(a)), with the HCA dendrogram identifying four unique, statistically different subgroups that track closely with the clustering for model precursors (Figure 4(b)). No evident trend was identified in the crossplot of DCAA and TCAA formation potential (Figure 4(c)). Representative kinetic time courses for DCAA and TCAA formation are shown in Figures S8 to S11. In interpreting these results, it should be noted that model precursors only functioned as baseline reactivity proxies in this comparative approach, meaning that the proximity between water sample and model precursor data points does not necessarily translate into the extent of the presence of specific moieties in the inland lake DOM. Furthermore, it was assumed that the complex DOM pool in a given water sample could be approximated by an average DOM molecule, and, thus, the comparative analysis yields only qualitative information about the bulk chlorine reactivity of this hypothetical mean entity. Over 70% of inland lake water samples tested herein were classified within Clusters C and D, suggesting that the reactivity

Through visual inspection of the crossplot (Figure 3(a)), aliphatic β-diketone precursors such as OSA, OGA, OAA, and EAA formed DCAA and TCAA at the highest rates, while aliphatic non-β-diketone precursors such as KBA, KGA, and KVA exhibited the lowest rates. The fast kinetics of β-diketones have been attributed to the rapid electrophilic substitution reaction at the activated α-carbon due to the enhanced stability of the enol through electron delocalization conferred by the two adjacent carbonyl groups.55 The slow kinetics of non-βdiketones may be explained by the ketone group being associated with the α- or γ-carbon, which hinders chlorination.29 CTA, the non-β-diketone acid having a carboxyl and hydroxyl group associated with the β-carbon, can be transformed into OGA, the β-diketone acid, via an oxidative decarboxylation step.55 This pathway, however, did not seem to prevail given the similar slow kinetics of CTA to other non-βdiketones.29 The phenolic precursors containing hidden or apparent β-diketone moieties such as RSC, PLG, RVT, and EMB demonstrated faster DCAA and TCAA formation rates than other phenolic derivatives such as PHE, HBA, VNL, SRA, and FRA. In analogy to aliphatic β-diketone precursors, the relatively fast kinetics of phenolic β-diketone precursors arise from the facile electrophilic aromatic substitution at the doubly activated α-carbon flanked by hydroxyl groups, which ultimately leads to the formation of polychlorinated intermediates.54−57 The moderate kinetics of PHE itself and substituted derivatives are mainly dictated by the ortho- or para-directing hydroxyl group.54,58,59 The HCA dendrogram provided further statistical support that model precursors could be grouped into distinctive clusters reflective of their structural features (Figure 3(b)). It is surmised that the DCAA and TCAA formation kinetics, which stratify clearly by precursor groups, may imply a causal structure−reactivity relationship. The clustering was directed toward four operationally defined subgroups, namely non-β-diketone aliphatics (Cluster A), βdiketone aliphatics (Cluster B), non-β-diketone phenolics (Cluster C), and β-diketones phenolics (Cluster D). Although the log-transformed DCAA and TCAA formation rate constants seemed to positively correlate across precursors with a slope close to unity (i.e., ∼1.4), it is neither necessary 143

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Figure 4. Kinetic-clustering analysis of DCAA and TCAA formation from inland lake water samples (symbol ×) upon chlorination (pH 7.2, temperature 21 °C, and chlorine dose of 10 mgCl2/mgDOC): (a) Crossplot of DCAA and TCAA formation rate constants. Note that twelve selected inland lake water samples subjected to photobleaching, freeze−thaw, or dark incubation treatments are annotated; (b) Dendrogram of formation rate constants; (c) Crossplot of DCAA and TCAA formation potential. DCAAFP and TCAAFP were calculated based on the final DCAA and TCAA yields derived from the pseudo-first-order model fit. Note that the clustering notion in panel (a) was used to assign clusters in this panel; (d) Clustering patterns of inland lakes by ecoregion. Model precursor data from Figure 3 are replotted for comparison. Values of formation rate constants and formation potential for individual water samples are summarized in Table S6.

samples fell into Cluster D, with the remainder being distributed almost equally to Clusters A, B, and C (∼19− 23%), respectively. Among these, all samples collected from northeastern Minnesota resided within Cluster D. The majority of NCHF samples fell into Cluster C (∼83%), with much less samples grouped within Clusters A and B (∼9% each). Virtually all samples collected near or within the metropolitan area of southeastern Minnesota belonged to Cluster C. Likewise, all

of the inland lake DOM with respect to DCAA and TCAA formation is better captured by that of monohydroxy and/or polyhydroxy phenolic derivatives. The clustering patterns by ecoregions is further illustrated in Figure 4(d). It seems plausible that a west-east gradient existed with regards to the lake clustering, which gradually evolved from Clusters A, B, and D toward Cluster C. Both NLF and NCHF samples spread across multiple clusters. Nearly half (∼40%) of the NLF 144

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and β-diketone types of HAA precursors at the expense of phenolic moieties. Consistently, previous work has demonstrated that high molecular weight aromatic DOM constituents in estuarine waters underwent photodegradation into low molecular weight aliphatic compounds upon simulated sunlight irradiation, causing a net decrease in both DCAA and TCAA yields.64 Dark incubation, on the other hand, shifted all samples originally belonging to Clusters C and D to Cluster A, suggesting the production of aliphatic non-β-diketone type of HAA precursors from phenolic moieties. Indeed, recent studies have supplied fluorescent evidence for the selective degradation of aromatic DOM components in boreal lake waters as a result of the continual microbial remineralization during the longterm incubation in the darkness.68,69 Differences between photobleaching and dark incubation likely stemmed from divergent photochemical fragmentation and microbial extracellular enzymatic hydrolysis pathways.70 Unlike photochemical and microbial transformations, repeated freeze−thaw cycles did not exert discernible influence on the clustering pattern of samples, implying that the character of HAA precursors remained largely unaltered after such physical processing. Likewise, earlier research has observed no simple relationship between the DOM fluorescence and the manner in which the DOM responds to freeze−thaw episodes in riverine waters.71,72 Assessing the impact of aquatic processing provides a step toward understanding the temporal variability in the chlorine reactivity of the inland lake DOM. This is exemplified by the seasonal patterns of reactivity clustering seen with prairie pothole lakes located in the NGP ecoregion (see the SI for additional details). Briefly, winter and summer pothole samples fell into Clusters A and B, respectively, while spring and fall samples belonged to Cluster D, which may be taken as, at least in part, an indirect evidence of the alternating chlorine reactivity of HAA precursors over the year. The spring-summer transition from Cluster D to B supports the likelihood of photobleaching in prairie potholes,73−75 while the fall-winter transition from Cluster D to A may reflect dark decomposition during ice cover.76 Carbon stable isotope measurements are, however, required to further constrain the interplay of different HAA precursor pools in these lakes. Collectively, these observational results suggest that the proposed kineticclustering approach is capable of tracing the impact of aquatic processing on the chlorine reactivity patterns of HAA precursors, which also provides a foundation for the formulation of specific hypotheses related to DOM dynamics that can be addressed in future studies. Environmental Implications. From an applied, water supply point of view, the attribution of DOM quality specific to the biogeochemical pools is essential to improve the informational value of source water monitoring and to implement targeted watershed protection programs. The identification of characteristic reactive HAA precursors also allows for planning of pretreatment options specific to short-term meteorologic and hydrologic events as well as long-term trends driven by land use shifts and climate change. Understanding broader patterns of variability in HAA formation rates may also lead to improved disinfection practice via the optimization of existing operational schemes (e.g., the length of free chlorine contact time during chlorine/chloramine disinfection). These are all of timely importance for utilities facing the need of adapted treatment strategies due to increasing DOM levels in source waters. From a methodological perspective, the comparative analysis of kinetic data sets of model precursors and water samples

SWTP samples, representing those collected near the urban regions of southeastern Wisconsin, related exclusively to Cluster C. In contrast, NGP samples collected from the Dakotas were associated with Clusters B and D, respectively. Given these subregional clustering patterns, one might be tempted to draw a connection to the underlying differences in geomorphology and prevailing land uses among major lake areas. For instance, the lake watersheds in northeastern Minnesota are largely forested and sparsely populated with remnants of presettlement ecosystems, while those in southeastern Minnesota are mostly human-impacted landscapes characterized by agricultural, urban, or suburban development.62,63 Whether such landscape differences render the spatial variability in the chlorine reactivity of the inland lake DOM, however, necessitates long-term, regionally extensive sampling grids in water quality monitoring programs for the assessment of DOM source and fate. Impact of Aquatic Processing on Reactivity Clustering. Many, if not most, chemical, physical, and biological processes involved in aquatic carbon cycling can alter the composition and structure of DOM before chlorination, resulting in different DBP formation and speciation patterns than those expected from the fresh DOM.64−67 With the goal of further differentiating the chlorine reactivity of HAA precursors based not only on DOM source but also on its fate, twelve inland lakes (as annotated in Figure 4(a)) from four ecoregions were chosen for an extended series of chlorination experiments, including two originally classified in Cluster A (Crystal Bog and Itasca), two in Cluster B (Shingobee and Pothole), five in Cluster C (Nokomis, Millie Lacs, Carlos, Kegonsa, and Winnebago), and three in Cluster D (Rainy, Poinsett, and Vermilion). The chlorine reactivity patterns of HAA precursors in this subset of lakes were contrasted before and after photobleaching, freeze−thaw, or dark incubation treatment on the time scale relevant to processing at the watershed level (see the SI for additional details). As illustrated by the clustering patterns in Figure 5, photobleaching shifted samples previously in Cluster C to A and those in Cluster D to B, respectively, pointing toward the enrichment of aliphatic non-β-diketone

Figure 5. Clustering patterns of twelve selected inland lakes by treatment types. Crossplots and dendrograms of DCAA and TCAA formation rate constants are shown in Figure S12. Values of formation rate constants and formation potential for individual water samples are summarized in Table S7. 145

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collected under defined chlorination conditions allows additional qualitative knowledge on the chlorine reactivity of HAA precursors to be gained. This approach resembles, in principle, fingerprinting techniques used in applied ecotoxicology and ecological bioassessment.77,78 We argue that this integrated kinetic-clustering analysis approach holds good promise for displaying similarities, and uncovering differences, among the chlorine reactivity of the whole water DOM. Our findings, however, should not be misconstrued to suggest that formation potential tests do not allow an informed assessment of HAA occurrence. Further characterization of DOM composition on the molecular level using analytical techniques such as Fouriertransform ion cyclotron resonance mass spectrometry (FTICR-MS), nuclear magnetic resonance spectroscopy (NMR), or pyrolysis-gas chromatography mass spectrometry (py-GC/MS) is necessary to more thoroughly evaluate the suitability of the proposed approach for monitoring DOM reactivity.79 The versatility of this approach will also benefit from comparative studies of HAA formation from source waters along wider climate, geomorphology, and land use gradients.

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ASSOCIATED CONTENT

S Supporting Information *

Additional experimental details, tables, and figures as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 612-625-8582. Fax: 612-626-7750. E-mail: zengx067@ umn.edu. Present Address §

Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA 94305, United States. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We gratefully acknowledge Professor Raymond M. Hozalski (Department of Civil Engineering, University of Minnesota (U of MN)) for the use of extraction setup and Carlene J. Dooley (Undergraduate Research Opportunities Program, U of MN) for assistance in sample analysis. We also thank three anonymous reviewers for their constructive comments, which greatly improved the quality of this manuscript. T.Z. was supported by the Joseph T. and Rose S. Ling Graduate Environmental Engineering Fellowship. This research was partially funded by the Joseph T. and Rose S. Ling Professorship in Civil Engineering held by W.A.A.

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