3D QSPR models for the removal of trace organic contaminants by ozone and free chlorine

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Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

3D QSPR models for the removal of trace organic contaminants by ozone and free chlorine Hongxia Lei, Shane A. Snyder Southern Nevada Water Authority, 1350 Richard Bunker Avenue, Henderson, NV 89015, USA

art i cle info

ab st rac t

Article history:

Endocrine-disrupting compounds (EDCs) and pharmaceuticals and personal care products

Received 11 October 2006

(PPCPs) have been detected at low levels in water resources around the world and one

Received in revised form

impact of their detection is the continuous concern on their fate and removal by various

1 March 2007

water treatment processes. In this research, a 3D quantitative structure–property relation-

Accepted 9 May 2007

ship (QSPR) model characterized by the utilization of 3D molecular structures is explored as

Available online 16 May 2007

a potential tool to prescreen these compounds and help focus research on more persistent

Keywords:

compounds during typical water treatment processes. Monte Carlo (MC) statistical

Endocrine-disrupting compounds

mechanics simulations were utilized to generate 3D molecular descriptors and physico-

Pharmaceuticals

chemical properties for the development of multiple linear regression analysis. The

Ozone

relevance of each parameter to removals of target compounds by ozone (O3) and free

Chlorine

chlorine was determined based on data matrices generated in bench- and pilot-scale

Modeling

experiments. Calculated removals were correlated with experimental data with linear

QSPR

regression coefficients of 0.84 for ozonation and 0.71 for chlorination. The increased predictability of ozone removal reflects the fundamental simplicity of ozone reaction mechanisms, which is dominated by oxidation reactions. Interestingly, the weakly polar surface area, in addition to the p surface area of these molecules, seems critical to ozone removal. The removal of these compounds by free chlorine is related to their ozone removal, ionization potential and three other parameters. The developed QSPR models help disclose the removal mechanism during ozonation and chlorination. Published by Elsevier Ltd.

1.

Introduction

With the advent of new advances in analytical instrumentations, more contaminants have been detected at low ng/L levels in natural waters (Vanderford et al., 2003; Petrovic´ et al., 2005; Stuart et al., 2005). Samples taken from raw and finished drinking and wastewater treatment facilities also contain detectable levels of microcontaminants (Bruchet et al., 2005; Nakata et al., 2005; Pedersen et al., 2005; Santos et al., 2005; Loraine and Pettigrove, 2006; Trenholm et al., 2006; Vanderford and Snyder, 2006). One group of trace organic contaminants is endocrine-disrupting compounds (EDCs) and pharmaceuticals and personal care products (PPCPs). These compounds create unique challenges to water treatment Corresponding author. Tel.: +1 714 535 8010x307; fax: +1 714 535 8664.

E-mail address: [email protected] (H. Lei). 0043-1354/$ - see front matter Published by Elsevier Ltd. doi:10.1016/j.watres.2007.05.010

since the number of compounds detected is large and keeps increasing, and their physicochemical properties are highly diversified. The oxidation of some EDCs and PPCPs has been reported, with most following apparent second-order reaction kinetics (Huber et al., 2003; Dodd et al., 2006). For instance, the antiepileptic drug carbamazepine can be oxidized by ozone and hydroxyl radical with second-order rate constants falling in the magnitude of 105 and 109 M/s, respectively (Huber et al., 2003; McDowell et al., 2005). Ferrate shows only mild oxidizing ability toward phenols and phenolic endocrine disruptors (Lee et al., 2005). Free chlorine mainly results in substitution and addition reactions. Three antibacterial agents, ciprofloxacin, enrofluxacin and flumequine, all with organic amine groups,

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show different reactivity toward free chlorine (Dodd et al., 2005). Reaction rate constants provide valuable tools to understand the fate of these microcontaminants during water treatment processes (Huber et al., 2005). However, rate constants are not widely available for most compounds. The aim of this research is to utilize 3D molecular structure information to develop quantitative structure–property relationship (QSPR) models to better understand the removal of general EDCs and PPCPs during ozonation and chlorination processes. These two processes capture different molecular features to attain eventual removal. Ozone is a strong oxidant dominated by oxidation reactions, while free chlorine is a less powerful oxidant and shows a mixture of reaction mechanisms. Various molecular descriptors have been used previously in oxidation QSPR models. Electron-donating groups favor ozone oxidation reactions while electron-withdrawing groups tend to decrease the reaction rate of organic contaminants (Hoigne and Bader, 1983a, b). Their effect was incorporated in the predictive model as the energy of the highest occupied molecular orbital (HOMO). The correlation between HOMO and ozone kinetics rate constants suggests the nucleophilic mechanism during oxidation of these compounds (Hu et al., 2000). In one case, the reactivity of substituted phenols to ozone was related to Hammett’s constant (Hoigne and Bader, 1983a). Other molecular descriptors pertaining to hydroxyl radical reactions, such as polarity, ionization potential and hydrophobicity, have also been commonly used for oxidation QSPR models (Cooper et al., 1992; Percival et al., 1995; Tang, 2004). Important descriptors pertaining to the reactions of chlorine with organic compounds still remain to be investigated. A Hammett-type linear free energy relationship was developed for the reactivity of chlorine, yet only limited to triclosan and other chlorophenols (Rule et al., 2005). The majority of previous QSPR research focuses on compounds of similar classes or structures, where predictions were based on very limited information on properties of target compounds experimentally obtained or predicted from empirical one- or two-parameter relationships. The applicability of some empirical QSPR research was further limited due to the fact that only data obtained under strictly controlled laboratory conditions without the presence of natural organic matter (NOM) were utilized. The 3D QSPR described here is a molecular modeling approach that relies on 3D molecular structures to generate 3D molecular descriptors and physicochemical properties for model development with simultaneous utilization of MC simulations for statistical analysis. The removal data used here were generated with surface water and wastewater effluent with complex matrices that included background contaminations.

2.

Methods

2.1.

EDC/PPCP compounds and analytical techniques

To investigate the removal potential and mechanisms of EDCs and PPCPs by ozonation and chlorination, 62 compounds were selected in bench-scale studies that covered a wide range of physicochemical properties and molecular characteristics (Table 1). In addition to the 62 compounds, 21 more

were selected for pilot-scale ozone experiments. These compounds covered herbicides, polycyclic aromatic hydrocarbons (PAHs), one fire retardant, human hormone steroids, pharmaceuticals and several types of personal care products, for instance, fragrances, sunscreen and insect repellents. Their log Kow ranges from less than 0 to as high as 6.91 and represents compounds with very different hydrophobicity (Table 1). All compounds were purchased as reagent grade or the highest purity commercially available from several major suppliers in the US. Spiking solutions were prepared at relatively high concentrations (10–250 mg/L) in methanol instead of water due to solubility limitations. This resulted in a dilution factor of at least 1.2  105 during spiking to achieve the initial concentration of several hundred ng/L. The effect of methanol solvent on removal data is expected to be minimal due to the high stock concentration that only increases the DOC by approximately 0.7 mg/L. The selected target compounds were analyzed by either LC–MS/MS or GC–MS/MS, depending on the polarity and volatility of each compound according to methods published previously (Vanderford et al., 2003; Westerhoff et al., 2005; Trenholm et al., 2006; Vanderford and Snyder, 2006). The spiked recoveries were generally above 70% for LC–MS/MS and above 60% for GC–MS/MS analysis, with relative standard deviation (RSD) consistently less than 20%. Only 30–50% recoveries were observed for mirex, galaxolide, aldrin and acetaminophen. These compounds were included in the data matrix as their RSD was less than 15%.

2.2.

Removal by ozone and free chlorine

The bench-scale removal data for the selected EDCs and PPCPs by ozone and free chlorine were published previously (Westerhoff et al., 2005). In summary, bench-scale experiments were performed on three different surface waters: Colorado River water (CRW) from Lake Mead in Nevada, Ohio River water (ORW) near Louisville in Kentucky, and Passaic River water (PRW) near Totowa, New Jersey. Water quality data are provided in Table 2. The water was filtered to remove particulate matter and then spiked with target compounds at a concentration range of 10–250 ng/L. Pilot-scale experiments were performed with tertiary municipal wastewater effluent at ambient pH by dosing the water with three different ozone concentrations (4.9, 7.3 and 8.7 mg/L) with 18 min contact time. The schematic of the pilot setup is shown in Fig. 1. It was modeled after a full-scale ozone system, using a 12-contactor design with a total contact time of 24 min. Test water was fed to the first column in counter-current mode, with water introduced from the top and the ozone bubble diffuser located at the bottom. Each column provided a contact time of 2 min. The effluent from column 9, where ozone residual was non-detectable, was sampled for instrumental analysis. Wastewater used in these experiments was not spiked with any chemicals: instead, compounds were monitored before and after ozone addition. These included the same 62 compounds studied in benchscale experiments, plus 10 more compounds analyzed by LC–MS/MS and an additional 11 compounds analyzed by GC–MS/MS. Out of these additional 21 compounds, those

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Table 1 – Predicted and experimentally obtained descriptors for target compoundsa Compound name

mol MW

FISA

PISA

WPSA

#rtvFG

#metab

IP(eV)

EA(eV)

log S (exp)

log Kow (exp)

Acenaphthene Acenaphthylene Acetaminophen Aldrin Androstenedione Anthracene Atrazine Benzo[a]anthracene Benzo[a]pyrene Benzo[b]fluoranthene Benzo[k]fluoranthene Caffeine Carbamazepine Chrysene DDD DDE DDT DEET Diazepam Diclofenac Dieldrin Dilantin Endrin Erythromycin Estradiol Estriol Estrone Ethinyl estradiol Fluoranthene Fluorene Fluoxetine Galaxolide Gemfibrozil Heptachlor Heptachlor epoxide Hydrocodone Ibuprofen Iopromide Meprobamate Methoxychlor Metolachlor Mirex Musk ketone Naphthalene Naproxen Octylphenol-4t Oxybenzone Pentoxifylline Phenanthrene Progesterone Pyrene Sulfamethoxazole TCEP Testosterone Triclosan Trimethoprim a-BHC a-Chlordane b-BHC d-BHC g-BHC g-Chlordane

154.2 152.2 151.2 364.9 286.4 178.2 215.7 228.3 252.3 252.3 252.3 194.2 236.3 228.3 320.0 318.0 354.5 191.3 284.7 296.2 380.9 252.3 380.9 733.9 272.4 288.4 270.4 296.4 202.3 166.2 309.3 258.4 250.3 373.3 389.3 299.4 206.3 791.1 218.3 345.7 283.8 545.5 294.3 128.2 230.3 206.3 228.2 278.3 178.2 314.5 202.3 253.3 285.5 288.4 289.5 290.3 290.8 409.8 290.8 290.8 290.8 409.8

0 0 111.8 0 99.3 0 59.7 0 0 0 0 102.8 91.9 0 0 0 0 33.8 59.7 81.3 0 111.1 0 100.6 94.9 141.1 97.1 89.8 0 0 24.0 0 86.0 0 0 49.8 92.5 261.0 237.0 0 26.4 0 156.2 0 92.9 54.5 79.4 136.4 0 88.0 0 183.8 38.2 96.9 48.2 154.3 0 0 0 0 0 0

264.0 357.6 161.9 59.0 27.9 404.3 28.0 471.0 480.0 485.6 490.2 56.3 324.7 459.6 246.5 260.1 230.3 126.7 266.6 267.2 0 339.8 0.0 0 111.1 111.1 111.0 148.7 421.1 338.4 298.3 27.4 105.5 62.4 0.2 68.1 106.8 15.0 0 211.8 103.5 0.2 12.3 334.8 197.5 129.6 293.9 57.2 398.4 29.3 414.5 206.0 0 27.9 223.9 80.5 0 0.2 0 0 0 0.2

0 0 0 287.9 0 0 75.6 0 0 0 0 0 0 0 258.9 259.3 290.9 0 71.4 100.5 301.9 0 300.7 0 0 0 0 0 0 0 118.7 0 0 335.4 352.4 0 0 150.0 0 148.0 62.2 506.2 0 0 0 0 0 0 0 0 0 0.6 224.3 0 206.6 0 322.4 399.9 326.0 322.1 313.3 407.4

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 4 0 0 0 0 0 0 0 0 0 1 2 1 0 0 0 0 0 3 0 0 0 0 0 1 0 1 0 0 3 0 0 0 6 2 6 6 6 2

2 0 1 2 3 1 0 0 1 0 1 0 2 0 1 0 1 1 1 4 0 2 0 9 4 5 4 4 0 1 4 3 3 2 0 5 2 6 0 3 5 0 2 0 2 1 2 1 0 3 1 2 0 3 1 5 0 0 0 0 0 0

8.37 8.85 8.53 9.55 10.27 7.95 8.99 8.05 7.70 8.38 7.96 8.90 8.54 8.21 9.48 9.50 9.50 9.34 9.11 8.31 9.56 9.87 9.68 9.45 8.96 8.92 9.01 8.91 8.42 8.71 9.39 9.13 8.91 9.67 9.71 8.81 9.51 8.97 10.02 9.11 9.45 9.21 10.94 8.60 8.51 9.02 9.25 8.93 8.50 10.18 7.95 8.87 10.62 10.17 9.12 8.51 10.78 9.70 10.86 10.81 10.63 9.74

0.56 1.23 0.09 0.33 0.22 1.25 0.11 1.19 1.55 1.48 1.43 0.43 0.59 1.05 0.38 0.35 0.51 0.07 0.78 0.30 0.39 0.21 0.52 0.81 0.24 0.28 0.19 0.29 1.45 0.47 0.36 0.26 0.29 0.43 0.49 0.09 0.09 1.90 0.85 0.25 0.07 2.91 1.69 0.63 0.74 0.30 0.35 0.46 0.76 0.13 1.29 0.49 0.08 0.12 0.43 0.08 0.20 0.49 0.13 0.10 0.31 0.53

4.60 3.98 1.03 7.33 3.70 6.61 3.79 7.39 8.19 8.23 8.50 0.95 2.58 8.06 6.55 6.90 7.81 2.82 3.76 5.10 6.29 3.90 6.18 2.65 4.88 3.64 3.95 4.42 5.89 4.99 4.66 5.17 4.12 6.32 6.29 1.41 3.99 3.65 1.67 6.54 2.73 6.75 5.81 3.62 4.16 3.83 3.15 0.56 5.19 4.55 6.18 2.62 1.61 4.09 4.46 2.86 5.16 6.86 6.08 4.46 4.60 6.69

3.92 3.94 0.46 6.50 2.75 4.45 2.61 5.76 6.13 5.78 6.11 0.07 2.45 5.81 6.02 6.51 6.91 2.18 2.82 4.51 5.40 2.47 5.20 3.06 4.01 2.45 3.13 3.67 5.16 4.18 4.05 5.90 4.77 6.10 4.98 1.78 3.97 2.05 0.70 5.08 3.13 6.89 4.31 3.30 3.18 3.92 3.79 0.29 4.46 3.87 4.88 0.89 1.44 3.32 4.76 0.91 3.80 6.16 3.78 4.14 3.72 6.30

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Table 1 (continued )

Compound name

mol MW

FISA

PISA

WPSA

#rtvFG

#metab

IP(eV)

EA(eV)

log S (exp)

log Kow (exp)

Benzophenone Bisphenol A Hydrocortisone Indolebutyric acid Isobutylparaben Propylparaben Triclocarban

182.2 228.3 362.5 203.2 194.2 180.2 315.6

40.6 109.0 190.7 136.0 101.9 103.7 61.9

378.0 252.1 27.8 215.9 150.2 155.5 274.9

0 0 0 0 0 0 201.9

0 0 0 0 1 1 0

0 2 5 2 1 1 0

9.94 8.98 10.14 8.26 9.51 9.52 8.79

0.54 0.27 0.08 0.02 0.34 0.34 0.41

3.12 3.28 3.05 2.91 2.93 2.56 4.62

3.18 3.32 1.61 2.30 2.34 3.04 3.73

a

˚ 2, and S in mg/L. FISA, PISA, WPSA in units of A

Table 2 – Water quality in O3/Cl2 bench- and pilot-scale experiments Parameter

CRW

ORW

PRW

Secondary effluent

DOC (mg/L) UV at 254 nm (cm1) SUVA (L mg/m) Ambient pH Alkalinity (ppm as CaCO3) Hardness (ppm as CaCO3) Chlorine dosage (mg/L as Cl2) Ozone dosage (mg/ L)

3.0 0.048

3.5 0.08

3.5 0.09

6.86a (TOC) 0.123

1.6 8.2 140

2.3 7.9 128

2.6 6.8 38

1.8a 6.95 127

307

103

80

3.5

2.8

3.8

2.5

3.5

3

a

as the possible candidates for QSPR model construction (Table 3). The performance of QikProp was evaluated by comparing the experimentally obtained log Kow and solubility against predicted values. Results presented in Fig. 2 indicated the general good reliability of the predicted values from QikProp. The experimentally obtained values for log Kow and solubility are widely available. Therefore, these values were used in model development rather than predicted values. For those compounds that did not have experimental values, predictions were used and these numbers are highlighted in bold in Table 1. It was assumed that removal of the selected compounds is a function of QikProp-generated descriptors following the general format of

% removal

TOC was measured and SUVA was calculated based on TOC.

detected are presented in the bottom portion of Table 1 and water quality data are presented in Table 2. Dibutyl phthalate and vanillin were eliminated due to severe background contamination.

2.3.

QSPR model development

2D molecular structures were obtained from National Library of Medicine (NLM) online resources (http://chem.sis.nlm.nih. gov/chemidplus/). LigPrep (2005), QikProp (2005) and Strike (2005) from Schro¨dinger, a commercial computer software, were used as the main tools for model development. LigPrep took 2D molecular structures as inputs and generated 3D molecular structures with minimized molecular energy and various possibilities for molecular chirality. Information obtained from NLM was used to select the unique structure with the correct chiral carbons. A total number of 32 descriptors, including molecular descriptors and physicochemical properties, were generated by QikProp in fast mode utilizing Monte Carlo simulations for each of the target compounds based on the accurate 3D molecular files generated by LigPrep (Duffy and Jorgensen, 2000; Jorgensen and Duffy, 2000). Seven medicinal chemistry properties were eliminated from further consideration, leaving 25 properties

1 geometrical properties ðvolume; SASA; PISA; FOSA; etc:Þ C B C B physicochemical properties ðK ; S; H ; polarizability; etc:Þ ow c C B ¼ fB C. C B @ structural characteristics ðfunctional groups; hydrogen bonding; etc:Þ A previously reported properties 0

4.9

where Kow is the octanol-water partition coefficient, Hc is the Henry’s Law constant and the remaining symbols are defined in Table 3. The multiple linear regression from Strike (2005) and previous studies was used to help identify the most relevant parameters.

3.

Results and discussions

3.1. Impact of functional groups on removal of EDCs and PPCPs by ozonation The removal of 62 EDCs and PPCPs in bench-scale experiments obtained at ambient pH did not appear to vary significantly as a result of different water qualities presented in Table 2 (Westerhoff et al., 2005). Therefore, only average removals were considered in order to obtain a generalized mechanism. Only 10 compounds of the 62 showed less than 20% removal by ozonation, indicating strong reactivity of ozone toward most compounds. Of the 11 PAHs, benzo(a)pyrene was removed by 75%, and the remaining 10 PAHs were removed by over 80%. The unique structures of PAHs confer a relatively high degree of unsaturation and high electron density due to condensed benzene rings, and this results in higher removals since ozone is a strong electrophile. The six steroids exhibited

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on/of 1

2

3

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OFF GAS FROM COLUMN 6–12

4

OFF GAS COLLECTION MANIFOLD I MASS FLOW FL CONTROLLER LLER

GAS VENTILATION

GAS IN O3 BYPASS

O3 O2 TANK

ANALYZER

MnO2 O3 DESTRUCTION

1.02

FEED GAS OZONE ANALYZER FLOW METER CHECK VALVE 2

Ozone generator

STATIC MIXER CHECK VALVE 1

TO COLUMN 6–12

FEED WATER

FLOW CONTROLLER

CHEMICAL INJECTION

NG PORTS SAMPLING

FLOW CONTROLLER

Fig. 1 – Schematic of the pilot-scale experiment setup.

Table 3 – Descriptors and properties generated by QikProp used for QSPR model development Descriptor/property Molecular descriptor MW, volume SASA, FOSA, FISA, PISA, WPSA #amine, #amidine, #acid, #amide #rotor #rctvFG, #mebabol DonorHB, accptHB Dipole QPpolrz IP, EA

Explanation

Molecular weight and volume Molecular surface area, hydrophobic, hydrophilic, p and weakly polar components of surface area Number of non-conjugated functional groups for each type Number of non-trivial (not CX3), non-hindered (not alkene, amide, small ring) rotatable bonds Number of reactive functional groups, number of metabolites Number of hydrogen bonding donated or accepted by the solute from water Dipole moment Polarizability Ionization potential and electron affinity

Physicochemical properties S QplogPC16 QPlogPoct QPlogPw QplogPo/w

more than 90% removal in all waters. All steroids have benzene ring structures or double/triple bonds, known to be highly reactive with ozone (Hoigne and Bader, 1983a; Barron et al., 2006; Snyder et al., 2006). Additionally, some steroids contain phenolic or aliphatic hydroxyl groups, which can be easily oxidized by ozone to the corresponding ketones. These observations suggest that the p surface area and descriptors related to functional groups that are easily attacked by ozone are likely important parameters that should be included in ozone removal models. One such functional group is sulfide.

Solubility Hexadecane/gas partition coefficient Octanol/gas partition coefficient Water/gas partition coefficient Octanol/water partition coefficient

Previous studies demonstrated that ozone could electrophilically attack the sulfide group in amoxicillin to result in the formation of sulfoxide derivatives (Spry, 1972; Andreozzi et al., 2005). Ozone reacts selectively with moieties such as aniline, neutral tertiary amine, trimethoxytolyl and several other electron-rich moieties contained in 14 antibacterial compounds (Dodd et al., 2006). In pilot-scale experiments, 16 out of the 62 compounds included in bench-scale studies were detected and their percentage removals at low ozone dose were consistent with

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8

0

6

-2

4

LogS (exp)

LogKow (exp)

WAT E R R E S E A R C H

2 0

-4

-6

-8

-2 -2

0

2

4

6

8

-10 -10

-8

QPlogPow (QikProp)

-6

-4

-2

0

QPlogS (QikProp)

Fig. 2 – Comparison between the experimentally obtained log Kow and solubility and predicted values.

Table 4 – EDCs and PPCPs detected in pilot-scale experiments and comparison with bench-scale average removals

O3 dosage (mg/L) Caffeine Carbamazepine DEET Diclofenac Dilantin Erythromycin Fluoxetine Galaxolide Hydrocodone Ibuprofen Iopromide Meprobamate Musk ketone Sulfamethoxazole TCEP Trimethoprim Benzophenone Bisphenol A Hydrocortisone 3-Indolebutyric acid Isobutylparaben Propylparaben Triclocarban

493 498 78 493 483 496 490 85 498 78 58 58 33 490 1.7 498

Pilot-scale 4.9

7.3

8.7

480 100 79 498 89 100 494 96 100 94 72 58 37 100 0 497

480 100 95 498 98 100 494 100 100 495 91 81 46 100 5.6 497

480 100 98 498 100 100 494 100 100 495 496 87 68 100 10.5 497

50 72 493 83 74 87 99

460 83 493 85 91 494 100

460 86 493 83 81 87 99

bench-scale data (Table 4). This enabled the inclusion of wastewater results, which covered more compounds. Among the additional 21 compounds, only seven were detected in the wastewater before ozonation. These data are summarized in Table 4. Once again, similar impacts from functional groups were observed in wastewater. Those bearing hydroxyl groups, such as the two parabens (isobutylparaben and propylparaben), showed greater than 70% removal by ozone. Those carrying electron-withdrawing groups or atoms, such as

R2 of linear regression

Bench-scale

1.0 0.8 0.6 0.4 0.2 0.0 0

2

4

6

8

10

12

14

16

# of Dependent variables Fig. 3 – Progress in R2 as the number of dependent variables increases during the QSPR model development for ozonation.

benzophenone, on the other hand, showed much less removal.

3.2.

QSPR model for ozonation process

Of the 62 compounds studied in bench-scale tests, 55 were randomly selected by embedded function from Schro¨dinger to construct the QSPR model. The remaining seven, plus the additional results from pilot-scale experiments, were used for model validation. Multiple linear regression statistical analysis was applied by initially including all 25 relevant properties. As expected, the R2, representing the linearity of the regression analysis as well as the extent of consistency between the model predictions and experimental data, improved dramatically as more dependent parameters were included in the QSAR model (Fig. 3). However, beyond four descriptors, the QSPR model did not improve much by the inclusion of more descriptors. Table 5 described the elimination of relevant parameters during the statistical analysis and their relative significance represented by T values. When five

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Table 5 – Evolvement of significant dependent variables in QSPR modeling for the ozone removal of EDCs and PPCPsa No. of descriptors 5 4 3 2 1

Model calculated ozone removal (%)

a

WPSA

#metab

PISA

#rtvFG

EA

| | | | |

| | |  

| | |  

| |  | 

|    

0.858 0.837 0.808 0.757 0.702 T value decreasing

Note: | indicates a parameter found significant in the model and  indicates a parameter excluded from the model.

100

Training data Testing data

80 60 40 20 0 0

20

40

60

80

100

Experimental ozone removal (%) Fig. 4 – Comparison between the experimentally obtained ozone removal of selected EDCs and PPCPs and model calculated results. The long dashed line represents the 95% confidence interval of the linear regression and the dashdot-dot line represents the 95% prediction interval.

Model calculated chlorine removal (%)

R2

100 80 60 40 20 0 0

20

40

60

80

100

Average chlorine removal (%) Fig. 5 – Comparison between the experimentally obtained chlorine removal of selected EDCs and PPCPs and model calculated results. The long dashed line represents the 95% confidence interval of the linear regression and the dashdot-dot line represents the 95% prediction interval.

parameters were included in the model, the removal of PPCPs and EDCs seemed to depend on weakly polar surface area, number of metabolites, p surface area, number of reactive function groups and electron affinity (related to the energy of the lowest unoccupied molecular orbital). The T value associated with each descriptor decreased along the list, indicating the decreasing significance of the corresponding parameter. Since electron affinity was least relevant in the five-descriptor model, it was eliminated during the next run when only four parameters were allowed in the model. Apparently, the most important parameter in the QSPR model was the weakly polar surface area, which remained throughout all statistical analysis no matter what number of parameters was allowed. The results summarized in Table 5 indicate that a four-parameter QSPR model is most appropriate to describe the ozonation process for selected groups of contaminants considering the compromise between number of dependent variables and accuracy of the model: % ozone removal ¼ 67:3 þ 0:0506  ðPISAÞ þ 5:20  ð#metabÞ  4:34  ð#rtvFGÞ  0:114  ðWPSAÞ: All the descriptors in the above equation are defined in Table 3. PISA and WPSA were calculated assuming a probe ˚ radius, with the former from the unsatumolecule of 1.4 A rated covalent bonds and the latter from the weakly polar component of surface area, such as sulfur, phosphorus and halogens. #Metab corresponds to bonds amenable to electrophilic attack, and #rtvFG is indicative of bonds unstable to nucleophilic attack and will partly correct for over-counted PISA. For instance, acceptor carbonyl double bond, contributing to PISA, is actually not reacting toward ozone and will be subtracted out via #rtvFG. Details on these two descriptors are provided in Schro¨dinger references (QikProp, 2005). Among all four descriptors, WPSA appears most critical in determining ozone removal, and it alone results in the R2 of 0.7 in a single dependent variable QSPR model. The results are presented in Fig. 4 and the R2 for the linear relationship between calculated and experimental removals was 0.837. The good consistency between model calculations and experimental data indicates that the inclusion of PISA, #metab, #rtvFG and WPSA in the QSPR model is appropriate. The 55 compounds included in the training set can generally be well described by the QSPR model, with musk ketone as the worst outliner, possibly due to unique properties from charges on its two nitro groups. The triangles in the figure are

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predictions calculated from the QSPR model and are in reasonably good agreement with experimental data. Analytical challenges or inefficiency of the model are possible reasons for the observed deviations from the model (Fig. 5).

3.3. Impact of functional groups on the removal of EDCs and PPCPs by chlorination The pH value of all water samples was adjusted to 5.5 for chlorination to eliminate any impact from dissociation of acidic or basic functional groups and impact of pH on reaction kinetics. The average removals were used again for model development (Westerhoff et al., 2005). As expected, lower removals in general were noticed during chlorination as opposed to ozonation. By grouping the selected compounds according to their functional groups, some general patterns in the removal of these compounds are discovered. Compounds with phenolic groups tend to have good removal. These compounds include acetaminophen, estradiol, estriol, estrone, ethinyl estradiol, oxybenzone and triclosan, which have removal above 95%. The reaction mechanisms of phenolic compounds with free chlorine mainly proceed via an electrophilic substitution pathway, with a mixture of substituted products at the ortho and para positions (Burttschell et al., 1959; Lee and Morris, 1962). Eventually cleavage of the ring structure may occur. Compounds with amine or amide groups show various removals by chlorine, from zero removal to over 95% removal. By examining these compounds, it was discovered that compounds with primary amine attached to a conjugated ring structure, such as trimethoprim and sulfamethoxazole, have excellent removal (490%). Conversely, a decreased reactivity with chlorine was observed for compounds with amide structures. For instance, less than 30% of iopromide and meprobamate can be removed by chlorination. Unsubstituted aromatic rings such as benzene and biphenyl show little reactivity for chlorination. Biphenyl itself does not usually show high reactivity toward chlorine. However, highly alkylated benzenes may become very reactive with chlorine due to the increased electron density from the substituents. Good examples of this case are gemfibrozil and hydrocodone, both of which can undergo electrophilic reaction with free chlorine that eventually leads to nearly complete removal. PAHs with electron-donating substituents or compounds with highly conjugated systems tend to exhibit high reactivity with chlorine for the same reason, which explains the relatively high removal of carbamazepine, benzo(a)pyrene, diclofenac and naproxen. In the above cases, chlorine mostly reacts via electrophilic substitution and addition. As a result, functional groups are critical in determining a compound’s reactivity with chlorine. Under some conditions, chlorine can also act as an oxidant. The oxidation of acetone to lactic acid and phenyl ketones in the a position to the corresponding acidic products was reported, with the latter presumably due to the benzilic acid rearrangements (Guthrie and Cossar, 1986; Guthrie et al., 1991). For the compounds selected in this research, ring cleavage is actually an oxidation process, even though at much slower kinetics.

3.4.

QSPR model for chlorination process

Using the same logics as stated for ozonation QSPR model development, chlorine removal was found to be related to five descriptors: % chlorine removal ¼ 106:8 þ 0:791  % ðozone removalÞ þ 7:89  ð#rtvFGÞ þ 4:80  ðQP log PowÞ þ 0:175  ðFISAÞ  15:0  ðIPÞ; where all terms are defined in Table 3. The lower R2 of 0.705 compared with ozone removal is expected because free chlorine reactions involve many possible mechanisms. As an oxidant, free chlorine can oxidize primary alcohol to carboxylic acid and subsequently form esters with unreacted alcohols (Nwaukwa and Keehn, 1982). Other species, such as secondary alcohols and ethers, can be oxidized by free chlorine to form various products (Nwaukwa and Keehn, 1982). Additionally, free chlorine can also have substitution and addition reactions with various species, for instance, phenols, phenolic acids, aromatic hydrocarbons and alkenes (Soper and Smith, 1926; Norwood et al., 1980; Takagi et al., 2002). The formation of known DBPs, such as THMs, is via chlorine reacting with NOM or compounds of specific structural features (Larson and Weber, 1994). Among the five dependent variables, % (ozone removal) and IP are statistically most important judged by the T values, which suggests that oxidation during chlorination is significant and the likelihood of both ozone and chlorine attacking the same functional groups/moieties is relatively high. For instance, the conjugated double bond in microcystin analogs was found susceptible to chlorine and ozone oxidation (Ho et al., 2006).

3.5.

Practical applications and limitations of QSPR models

Based on this study, it should be feasible to estimate the removal of organic contaminants by ozone and free chlorine under conditions evaluated during model development. Despite the fact that the models were based on 3D molecular structures, their applications require only the capability to obtain molecular descriptors as well as some common physicochemical properties, all easily obtained using molecular modeling software. The models suggest that removal of organic contaminants by these two common disinfectants is related to functional groups, as indicated by the inclusion of hydrophilic, weakly polar and p surface areas. Overall molecular structures are important too, as reflected by the inclusion of ionization potential, log Kow and electrophilic reactivity. Under no circumstances should the models be extrapolated to other conditions as disinfection reactions are complicated. Many factors affect the destruction of EDCs and PPCPs by chlorination and ozonation, among which pH, water matrices and contact time are important. In addition, it should be recognized that since our target compounds were spiked at ambient levels, which was at the low ng/L range, the analysis was challenging. Ozonation data were obtained at ambient pH, with the variation of pH among the three test waters as high as 1.4. These factors introduced some variability to the removal of these contaminants. The pH of water samples was adjusted to 5.5 for chlorination, which is not likely to be

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encountered in natural environment. Lower chlorination removal is expected at an environmentally relevant pH. It is foreseen that these limiting factors can be incorporated into the model with more research efforts by the inclusion of contact time or the CT (concentration  time) concept, pH and more representative compounds. The models developed in this research, however, serve as a prescreening tool with generalized removal prediction. They also provide insightful information on the removal mechanisms during these common processes and demonstrated a good tool for the prediction of physicochemical properties.

4.

Conclusions

EDCs and PPCPs can be efficiently removed by ozonation and to a lesser extent by chlorination. The two QSPR models developed by statistical analysis have indicated that the removal of these microcontaminants by the two treatment processes are related. Ozone removal is largely determined by WPSA, followed by PISA and the number of functional groups that can be oxidized, while IP is found to be most critical in determining chlorine removal in addition to its dependence on ozone removal. The dependence of both QSPR models on multiple parameters suggests the presence of more than one type of reaction site during ozonation and chlorination processes. The two QSPR models for ozonation and chlorination treatments provide a useful prescreen tool to evaluate removal of organic contaminants, and future work should be conducted by incorporating operational parameters, such as contact time and pH, to make the models more applicable to a wide range of treatment conditions (Table S1).

Acknowledgments This research was supported by grants from the US Water Environment Research Foundation (Project #03-CTS-21UR) and the American Water Works Association Research Foundation (Project #2758). Eric Wert and Yeomin Yoon provided assistance for bench- and pilot-scale studies. The authors would like to thank Dr. Jorg Drewes, Dr. Eric Dickenson from the Colorado School of Mines and Paul Westerhoff from Arizona State University for suggestive discussions, and Brett J. Vanderford, Rebecca A. Trenholm and Janie Holady for analytical support. The financial support from Ron Zegers and administrative help from Dave Rexing were greatly appreciated.

Appendix A.

Supplementary materials

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.watres.2007.05.010

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