Predicting trace organic compound attenuation by ozone oxidation: Development of indicator and surrogate models

Water Research 119 (2017) 21e32

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Predicting trace organic compound attenuation by ozone oxidation: Development of indicator and surrogate models Minkyu Park a, Tarun Anumol a, b, Kevin D. Daniels a, Shimin Wu a, Austin D. Ziska a, Shane A. Snyder a, * a b

Department of Chemical & Environmental Engineering, University of Arizona, 1133 E James E Rogers Way, Harshbarger 108, Tucson, AZ 85721-0011, USA Agilent Technologies Inc., 2850 Centerville Road, Wilmington, DE 19808, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 January 2017 Received in revised form 3 April 2017 Accepted 8 April 2017 Available online 10 April 2017

Ozone oxidation has been demonstrated to be an effective treatment process for the attenuation of trace organic compounds (TOrCs); however, predicting TOrC attenuation by ozone processes is challenging in wastewaters. Since ozone is rapidly consumed, determining the exposure times of ozone and hydroxyl radical proves to be difficult. As direct potable reuse schemes continue to gain traction, there is an increasing need for the development of real-time monitoring strategies for TOrC abatement in ozone oxidation processes. Hence, this study is primarily aimed at developing indicator and surrogate models for the prediction of TOrC attenuation by ozone oxidation. To this end, the second-order kinetic equations with a second-phase Rct value (ratio of hydroxyl radical exposure to molecular ozone exposure) were used to calculate comparative kinetics of TOrC attenuation and the reduction of indicator and spectroscopic surrogate parameters, including UV absorbance at 254 nm (UVA254) and total fluorescence (TF). The developed indicator model using meprobamate as an indicator compound and the surrogate models with UVA254 and TF exhibited good predictive power for the attenuation of 13 kinetically distinct TOrCs in five filtered and unfiltered wastewater effluents (R2 values > 0.8). This study is intended to help provide a guideline for the implementation of indicator/surrogate models for real-time monitoring of TOrC abatement with ozone processes and integrate them into a regulatory framework in water reuse. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Ozone Kinetics Micropollutant Surrogate Indicator

1. Introduction Climatological droughts are evident and have the ability to alter the global hydrological water cycle (Huntington, 2006). With burgeoning population, this dynamic alteration of the water cycle has caused increased pressure around the world to secure reliable and dependable drinking water resources (Park et al., 2013; Shannon et al., 2008). Recently, potable water reuse has gained attention throughout the world as a drought-proof source (Gerrity et al., 2014). In particular, semi-arid or arid areas with intense water scarcity such as Australia, Singapore and Southern USA (including Arizona, California, Florida and Texas) currently practice potable water reuse applications (Angelakis and Gikas, 2014). The ubiquitous availability of source water (i.e., wastewater effluent) and lower implementation cost compared to other alternatives, such as desalination or importing additional water (Harris-Lovett et al.,

* Corresponding author. E-mail address: [email protected] (S.A. Snyder). http://dx.doi.org/10.1016/j.watres.2017.04.024 0043-1354/© 2017 Elsevier Ltd. All rights reserved.

2015), has increased the demand for potable water reuse. In potable water reuse, trace organic compounds (TOrCs) are of tremendous concerns since some of these compounds can cause adverse impacts on human health and ecosystems at trace concentrations (Gerrity and Snyder, 2011). In addition, TOrCs are recalcitrant by conventional wastewater treatment processes, thereby present ubiquitously in wastewater effluent (Rosario-Ortiz et al., 2010). Ozone is a chief treatment strategy in water reuse for TOrC attenuation. Recently, ozone-based treatment schemes such as ozone followed by biological activated carbon have gained increasing attention in water reuse due to cost benefits over reverse osmosis-based schemes (Gerrity et al., 2013). Although ozone has been widely studied and practically applied, significant challenges are prompted when monitoring TOrCs. First, even if current analytical techniques such as gas- and liquidchromatography mass spectrometry are sensitive enough to detect sub-nanogram per liter levels of any target compound, analysis times are too long for real time monitoring. Secondly, monitoring of every compound is vastly infeasible. Although advances in analytical techniques enables simultaneous detection of

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M. Park et al. / Water Research 119 (2017) 21e32

several dozens of contaminants (Anumol et al., 2013), more than 80,000 individual chemicals are estimated to be present in municipal wastewater effluents (Drewes et al., 2012). In addition, real-time monitoring of TOrCs requires highly-skilled instrument operators, extensive pretreatment and relatively overlong data analysis time for a real-time monitoring application (Park et al., 2015). In order to address such limitations, suites of representative chemical indicators are analyzed in a systematic approach to ensure the reliability of treatment processes (Snyder, 2014). Indicator compounds are individual compounds that represent attenuation characteristics of a family of TOrCs in a treatment process (Drewes et al., 2012). To be specific, attenuation of a compound selected from a group of key structural moieties for oxidation attack is indicative of treatment efficacy (Dickenson et al., 2009). In a similar vein, surrogates can be another monitoring strategy to secure treatment efficacy for TOrC attenuation. Surrogates can be a quantifiable water quality parameter that represents removal characteristics of TOrCs of concern (Dickenson et al., 2009). Water quality parameters that can be easily measured by on-line or hand-held instruments are suitable for surrogates. For instance, spectroscopic parameters such as UV absorbance and fluorescence can be good candidates due to their capability of high sampling frequency (in a second level) and has been shown to have a correlation with TOrC attenuation (Liu et al., 2012; Pisarenko et al., 2012). In addition to UV absorbance and fluorescence, Texas Water Development Board also suggested monitoring of total organic carbon (TOC) and dissolved organic carbon (DOC) as surrogates (TWDB, 2015). Currently, the State of California requires continuous monitoring of at least one surrogate to ensure oxidation treatment efficacy for recycled water (CDPH, 2015). Ultimately, indicator and surrogate approaches can be tailored to monitor treatment efficacy, alarm treatment failure and trigger corrective actions (Yu et al., 2015). Recently, indicator and surrogate models based on UV absorbance at 254 nm (UVA254) and total fluorescence (integrated fluorescence intensity) have been widely studied and exhibited good correlations between TOrC attenuation and surrogate reduction in various physical and chemical water treatment processes (Anumol et al., 2015a; Gerrity et al., 2012; Nanaboina and Korshin, 2010; Wert et al., 2009; Ziska et al., 2016). Although indicator and surrogate approaches shed light on monitoring of treatment efficacy for TOrC attenuation, several challenges remain. When applying an indicator approach, treatment efficacy assessment is based upon key structural moieties for oxidant attack. However, many compounds have multiple sites amenable to oxidation attack, hence may be equivocal to determine a predominant site (Dickenson et al., 2009). In addition, the majority of surrogate models is based on regression models that do not explain physical and chemical phenomena of oxidation (Chon et al., 2015; Gerrity et al., 2012). This may cause failure of prediction when water quality (interchangeably, characteristics of ozone decomposition and hydroxyl radical formation yield) abruptly changes with respect to time. The primary aim of this study is to deterministically elucidate

the correlation of indicator and spectroscopic surrogates such as UVA254 and fluorescence with TOrC attenuation. To this end, indicator and surrogate models were derived using a second-order oxidation kinetic equation with a steady-state Rct value (i.e., R R ½$OHdt= ½O3 dt) that is relatively easy to measure. The derived model was validated by comparing the model prediction with experimentally determined attenuation of 12 TOrCs in filtered and unfiltered secondary wastewater effluents. With the developed model, meeting the regulatory and performance requirements of ozone process for TOrC attenuation was further discussed. 2. Material and methods 2.1. Ozonation of wastewater effluents Secondary wastewater effluent was collected in 20 L polypropylene carboys from two local wastewater treatment plants (WWTP1 and WWTP2) in Tucson, AZ, USA. Samples were collected on four different dates at WWTP1, while a single sample from WWTP2 was collected. In order to investigate the effects of filtration on oxidation exposure and TOrC attenuation, filtered and unfiltered wastewaters were tested. Glass-fiber filters (Whatman GF/F, 0.7 mm nominal pore size) were used for the filtration. For the unfiltered sample, wastewater supernatant was collected from the carboy and used for ozonation. The water quality of the collected samples was summarized in Table 1. Ozonation was performed with a concentrated ozone stock (~1.1 mM O3) by bubbling gaseous ozone with a diffuser into ultrapure water in a specialized liquid-jacketed vessel that was precooled to 1  C using a recirculating chiller. An aliquot of ozone stock solution was then placed into the ozone reaction vessel containing samples to achieve the desired ozone concentration (i.e., 0.5, 1, 2, 3, 4, 6 and 8 mg O3/L). The ozone reaction continued until the residual ozone in the samples were completely consumed. The ozone concentration of stock solution and residual ozone concentration of samples were determined using indigotrisulfonate (ITS) method and the detailed procedure is described in the literature (Rakness et al., 2010). For the determination of ozone decay kinetics, 5 mL of sample was taken at 10 s intervals and immediately added to 10 mL of prepared ITS solution. In order to assess hydroxyl radical exposure, p-chlorobenzoic acid (pCBA, ACROS Organics™) was added to samples for final concentration of 100 mg/L before ozonation. The quenched samples were aliquoted to 2 mL vials for the measurement of pCBA described in the following section. The calculation procedure for hydroxyl radical exposure using pCBA can be found elsewhere (Elovitz and von Gunten, 1999). 2.2. Analytical methods A Shimadzu TOC-L CSH Total Organic Carbon Analyzer was used to determine the TOC and DOC of the samples. For DOC, samples

Table 1 Summary of water quality of tested wastewater effluents.

TOC (mg/L) DOC (mg/L) SUVA (L mg1m1) pH Alkalinity (mg/L as CaCO3) NHþ 4 (mg-N/L) NO 2 (mg-N/L)  NO3 (mg-N/L)

WWTP1-1

WWTP1-2

WWTP1-3

WWTP1-4

WWTP2

7.4 7.1 1.84 7.4 155 <0.39 0.30 1.17

7.0 6.5 1.96 7.2 151 2.22 0.45 5.53

6.9 6.8 1.80 7.3 166 <0.39 0.33 1.22

6.5 6.4 1.88 7.2 147 0.98 0.52 7.84

9.0 8.7 1.71 7.6 148 <0.39 <0.01 2.33

M. Park et al. / Water Research 119 (2017) 21e32

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were first filtered by a 0.45 mm PVDF syringe filter (EMD Millipore) before acidification. Approximately 15 mL of the samples were transferred into 20 mL glass vials for TOC and DOC analysis. They were then acidified to pH 3 or lower using hydrochloric acid (35%, Fisher Scientific). To ensure the precision of the measurements, every sample including calibration standards and lab blanks were injected five times, and the average of the three closest measurements was reported as the final results. UV and fluorescence spectra were simultaneously obtained using Horiba Aqualog fluorometer (Horiba Scientific). UV spectra as well as UVA254 were obtained by scanning UV absorbance between 200 nm and 580 nm. Excitation-emission matrix (EEM) was obtained by scanning fluorescence from excitation wavelength of 225 nme450 nm and from emission wavelength of 250 nm and 580 nm. Since UV absorbance of all the wastewater effluents is greater than 0.05 cm1, inner filter effects were corrected based on the method described in the literature (Lakowicz, 2013). Light scattering, including Rayleigh and Tyndall, were removed using three-dimensional interpolation after the subtraction of fluorescence spectra of Milli-Q water from those of samples (Zepp et al., 2004), then the unit conversion from arbitrary unit to Raman unit was made based on the integrated area of Raman peak of Milli-Q water (Lawaetz and Stedmon, 2009). For total fluorescence (TF) calculation, operationally-defined five fluorescence regions were selected and their values at each region were individually integrated (Chen et al., 2003). The summation of the integrated fluorescence values then yielded TF. All the EEM data process and visualization were conducted using MATLAB R2015b (Mathworks). Liquid chromatography (LC) with a diode array detector (Agilent 1260, Agilent Technologies) at 234 nm wavelength was used to determine the pCBA concentrations for the calculation of hydroxyl radical exposure. Isocratic gradient with 0.5 mL/min was used for chromatographic separation with Agilent Poroshell 120 EC C-18 (2.1  50 mm, 2.7 mm). The mobile phase was composed of 55% acetonitrile (Fisher Scientific) and 45% 10 mM H3PO4 buffer. Sample injection volume was 10 mL. Twenty TOrCs (Table 2) were analyzed using direct large volume injection (80 mL) onto an ultra-high performance LC system coupled to a tandem mass spectrometer (MS/MS). Isotope-dilution with a known mass of stable isotopically-labeled versions of each TOrC were implemented (Vanderford and Snyder, 2006). An Agilent 1260 binary LC pump equipped with a Pursuit XRs C-8 column (100 mm  2.0 mm, 3 mm) was used for chromatographic separation. Mass spectrometry was performed with an Agilent 6490 MS system and an electrospray ionization source equipped with Agilent jet stream and iFunnel technology. The detailed procedure and information including method optimization parameters, compound transitions and relevant quality assurance/quality control (QA/QC) are available in previous work (Anumol et al., 2015b). All data analysis was performed using the Agilent MassHunter software (Ver. 6.00).

wastewater effluents was dependent on the reaction rates of each TOrC and their exposures to oxidants (Fig. SI-1 in SI). TOrCs were grouped with regard to the rate constants with molecular ozone and hydroxyl radical (Table 2) in the same way with the previous researches (Gerrity et al., 2012; Lee et al., 2013). TOrCs in Group I exhibited good removal and nearly 80% attenuation was achieved at an O3 dose of 4 mg/L (specific ozone dose, O3 dose normalized by DOC, ranging from 0.46 to 0.63 g O3/g DOC). Almost 100% attenuation was achieved at an O3 dose of 8 mg/L (0.92e1.25 g O3/g DOC). Group II compounds possess lower rate constants to molecular ozone than Group I, and exhibited slightly lower attenuation of TOrCs at the corresponding ozone dose although their difference was not significant. The compounds in Group III and IV have low reactivity with molecular ozone. TOrCs in Group III showed slightly higher abatement at high ozone doses (i.e., 6 and 8 mg/L) than Group IV compounds due to higher rate constant values with hydroxyl radical. Even if the grouping approach, based on reaction rate constants, provides insightful classification for TOrC attenuation, it may not be sufficient to be used as a predictive approach. In addition, attenuation of some compounds were not clearly distinguished among groups. For instance, the attenuation of compounds in Group III and Group IV were not clearly differentiated at low ozone doses (i.e., 0.5e2 mg/L). Development of indicator and surrogate parameters to predict TOrC attenuation by ozone is the ultimate goal for real-time online monitoring of ozone treatment efficacy. A majority of surrogate/ indicator studies focus on ozonation of water with dissolved organic matter (i.e., filtered with 0.45 mm filters), where this study looked at both filtered and unfiltered water equivalent to what water treatment plants normally received. When filtered, the tested wastewater effluents organic carbon content was slightly decreased for all the tested waters (<8% change, Table 1), indicating that dissolved organic fractions are predominant. Filtration exhibited little difference of TOrC attenuation. In general, bigger particles generally consume more ozone based upon a rule of additivity (Westerhoff et al., 1999), thereby removing particles can enhance mitigation of TOrCs (Zucker et al., 2015). No noticeable difference of TOrC attenuation among tested water qualities were observed. This is possibly because of similar effluent organic matter (EfOM) composition and/or similar organic carbon content among the tested waters (Table 1).

2.3. Sensitivity analysis

  d½C ¼  kO3 ½O3  þ k$OH ½$OH ½C dt

A global sensitivity analysis using Latin-Hypercube One-factorAt-a-Time (LH-OAT) method was conducted to explain the effects of model parameters on the prediction of TOrC attenuation (Park et al., 2011). The detailed implementation procedure was described in Supplementary Information (SI). 3. Results and discussion 3.1. Attenuation of TOrCs The attenuation of the 20 selected TOrCs by ozone in the tested

3.2. Indicator model for TOrCs with low reactivity to molecular ozone (group III and IV) The preceding section showed that the extent of TOrC attenuation varies with respect to rate constants. This section focuses on the development of an indicator model for Group III and IV compounds that exhibited low reactivity with molecular ozone. To this end, the attenuation of those compounds was derived using a kinetic equation:

(1)

where [C] is the molar concentration of a TOrC; t is the oxidant contact time; kO3 and kOH are the rate constants of the TOrC with molecular ozone and hydroxyl radical, respectively; and [O3] and [OH] are the molar concentrations of molecular ozone and hydroxyl radical, respectively. Similarly, the kinetics of indicator mitigation can be expressed by the following equation:

  d½I ¼  kO3 ;I ½O3  þ k$OH;I ½$OH ½I dt

(2)

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M. Park et al. / Water Research 119 (2017) 21e32

Table 2 Structure and second-order reaction rate constants with ozone and hydroxyl radical for the TOrCs in this study. Compound (use) [CDPH classificationa]

kO3 kO3, pH7 (M1s1) (M1s1)

kOH Reference (  109 M1s1)

3.0  105 3.0  105

8.8

(Dodd et al., 2006)

Diclofenac (nonsteroidal anti-inflammatory drug) [Group D]

1.0  106 1.0  106

7.5

(Huber et al., 2003)

Diltiazem (calcium channel blocker) [Group D]

N.A.

8.3

(Aruoma et al., 1991)

Propranolol (beta blocker) [Group E]

1.0  105 1.0  105

10

(Benner et al., 2008)

Sulfamethoxazole (antibiotics) [Group B]

5.7  105 5.7  105

5.5

(Huber et al., 2003)

Triclosan (antimicrobials) [Group A]

5.1  108 3.8  107

9.6

(Lee et al., 2013)

Trimethoprim (antibiotics) [Group D]

5.2  105 2.7  105

6.9

(Dodd et al., 2006)

Group II: 10  kO3,pH7 < 1  105 and kOH  5  109 Atenolol (beta blocker) [Group D]

6.3  105 1.7  103

8.0

(Benner et al., 2008)

Benzotriazole (corrosion inhibitor) [Group C]

22

22

6.2

(Vel Leitner and Roshani, 2010)

Diphenhydramine (antihistamine) [Group D]

N.A.

N.A.

5.42

(Wols and HofmanCaris, 2012)

Group I: kO3,pH7  1  105 Carbamazepine (anticonvulsant) [Group C]

Structure

N.A.

M. Park et al. / Water Research 119 (2017) 21e32

25

Table 2 (continued ) Compound (use) [CDPH classificationa]

Structure

kO3 kO3, pH7 (M1s1) (M1s1)

kOH Reference (  109 M1s1)

Gemfibrozil (lipid regulator) [Group F]

5.0  104 5.0  104

10

(Lee et al., 2013)

Hydrochlorothiazide (diuretic) [Group D]

N.A.

N.A.

5.7

(Wols and HofmanCaris, 2012)

Triclocarban (antimicrobials) [Group D]

5  103

5  103

N.A.

(Tizaoui et al., 2011)

Group III: kO3,pH7 < 10 and kOH  5  109 N,N-Diethyl-meta-toluamide (DEET) (insect repellent) [Group G]

<10

<10 (10)b

5.0

(Song et al., 2009)

Fluoxetine (selective serotonin reuptake inhibitor, also called Prozac) [Group F]

N.A.

N.A.

8.4

(Lam et al., 2005)

Primidone (anticonvulsant) [Group G]

<10

<10 (10)b

6.7

(Real et al., 2009)

Group IV: 1  109  kOH < 5  109 Acesulfame (artificial sweetner) [Group D]

N.A.

88

3.8

(Kaiser et al., 2013; Toth et al., 2012)

Iohexol (X-ray contrast media) [Group H]

N.A.

N.A.

3.81

(Wols and HofmanCaris, 2012)

Iopamidol (X-ray contrast media) [Group H]

N.A.

N.A.

3.42

(Huber et al., 2003)

(continued on next page)

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M. Park et al. / Water Research 119 (2017) 21e32

Table 2 (continued ) Compound (use) [CDPH classificationa]

Structure

kO3 kO3, pH7 (M1s1) (M1s1) <1 (1)b

<1

Meprobamate (antianxiety drug) [Group H]

kOH Reference (  109 M1s1) 3.7

(Lee et al., 2013)

N.A.: not available. a CDPH classification: AeHydroxy aromatic, BeAmino/acylamino aromatic, CeNonaromatic with carbon double bonds, DeDeprotonated amine, EeAlkoxy polyaromatic, FeAlkoxy aromatic, GeAlkyl aromatic, HeSaturated aliphatic, IeNitro aromatic (CDPH, 2015). b The apparent rate constant in bracket was used for the model development.

where [I] is the concentration of an indicator compound, and kO3,I and kOH,I are the rate constants of the indicator compound with molecular ozone and hydroxyl radical, respectively. Eqs. (1) and (2) can be individually integrated over contact time and combined to yield the following equation:

  Z Z     ½O3 dt þ k$OH ½$OHdt kO3 ½C ½I  ln Z Z ¼ ln ½C0 ½I0 kO3 ;I ½O3 dt þ k$OH;I ½$OHdt

(3)

3.3. Generalization of indicator model

It is beneficial to select a TOrC with low reactivity to molecular ozone as an indicator compound (e.g., Group III and IV) because those compounds are more likely quantifiable after large ozone exposure. In addition, the TOrCs in Group III and IV can be assumed to have negligible contribution of molecular ozone to the degraR dation of TOrC and indicator compound (kO3 ½O3 dt and R R kO3 ;I ½O3 dt) compared to those of hydroxyl radical (k$OH ½$OHdt R and k$OH;I ½$OHdt), respectively. In such cases, Eq. (3) can be then simplified to:

    k· OH ½C ½I ¼ ln ln ½C0 ½I0 k· OH;I

slight parabolic trend. Since exponential transformation of Eq. (4) yields an analogous form with a power function, the trend line becomes linear if the rate constants of TOrC and indicator compound are the same or very close each other. In the case that the rate constant of TOrCs for hydroxyl radical is greater than indicator compound, the trend becomes a right handed parabola, for example primidone and DEET; otherwise, it becomes left handed parabola.

The developed indicator model in the preceding section is applicable only for the compounds with low reactivity for molecular ozone (Group III and IV). In order to develop a generalized indicator model, it is necessary to consider the exposure of molecular ozone to TOrCs. To this end, Eq. (3) can be re-written in the following equation:

 ln

½C ½C0

Z

 ¼

kO3

(4)

The slope of a natural log plot of [C]/[C]0 versus [I]/[I]0 is linear and dependent only upon the hydroxyl radical rate constant of the TOrC and indicator. In other words, the prediction of TOrC attenuation for Group III and IV compounds does not require the measurement of oxidant exposures that differs with different EfOM constituents and inorganic compositions such as bicarbonate (Buffle et al., 2006a). This substantially facilitates the prediction of TOrC attenuation since only measured parameters are the initial and final indicator concentrations ([I] and [I]0, respectively). Fig. 1A, B and C shows the natural logarithmic plots of the dimensionless concentration (C/C0) of three selected TOrCs with low molecular ozone reactivity versus that of the indicator compound. The other compounds in the same groups are shown in Fig. SI-2. Meprobamate (Group IV) was chosen as an indicator compound due to its low reactivity with molecular ozone (kO3 ¼ ~1) and consistent detection over the sampling campaigns. It should be noted that any compound in Group III and IV can be selected as an indicator. Using meprobamate as an indicator model showed good predictive power for the selected compounds (R2 values for primidone, DEET and acesulfame were 0.85, 0.87 and 0.86, respectively). In addition, there was no obvious difference found between WWTPs and filtration, which bolsters the independency of Eq. (4) on the oxidant exposure. It is often more intuitive to express the extent of attenuation in percentage (Fig. 1D). The percent attenuation of acesulfame is linear along with the percent attenuation of the indicator compound; whereas, primidone and DEET displayed

¼

k$OH k$OH;I

Z ½O3 dt þ k$OH ½$OHdt   ½I Z ln ½I0 k$OH;I ½$OHdt 

(5)

   kO3 ½I 1þ ln ½I0 k$OH Rct

R R where Rct is ½$OHdt= ½O3 dt. The Rct concept was developed to quantify the relative contribution of molecular ozone and hydroxyl radical to TOrC attenuation, which is dependent on water quality (Elovitz and von Gunten, 1999). Rct values rapidly decreased during the first phase of ozone oxidation (<30 s) and reached steady state during the second phase (Fig. SI-3) (Buffle et al., 2006b). Initially, we attempted to use the steady state Rct value in Eq. (5), but this resulted in a gross overestimation of the slope for the highly reactive TOrCs with molecular ozone. For instance, kO3 =ðk$OH Rct Þ of trimethoprim from filtered WWTP1-1 samples is 254, hence its calculated slope became 476, which is much greater than the actual slope of 7.1. This overestimation can be attributed to the disregard of the first phase of Rct. Although consideration of the first-phase Rct may result in a good prediction, the use of the steady-state Rct is preferred since it not only facilitates derivation of the model, but also does not require a specifically designed apparatus for the timedependent Rct measurement, such as a continuous quench-flow system (Buffle et al., 2006b). In order to correct the overestimation, Eq. (5) was empirically corrected as follows:

    ½C k ½I ¼ $OH ð1 þ aÞln ln ½C0 ½I0 k$OH;I

(6)

where a ¼ b lnð1 þ kO3 =ðk$OH Rct ÞÞ and b is a fitting parameter. This

M. Park et al. / Water Research 119 (2017) 21e32

27

Fig. 1. Prediction of indicator model for (A) primidone, (B) DEET and (C) acesulfame in natural log-log plots. Percent attenuation of the three TOrCs is shown in (D).

semi-empirical equation was formulated based on the observation that log transformation of the term 1 þ kO3 =ðk$OH Rct Þ in Eq. (5) is linearly proportional to the slopes of TOrCs (Fig. SI-4). b was introduced during the semi-empirical formulation and it compensates disregard of the transient Rct. The special form of this equation is Eq. (4), when the hydroxyl radical is the sole effective oxidant for TOrC attenuation in the given Rct value (i.e., a ¼ 0) (Group III and IV). In other words, a demonstrates the relative contribution of ozone to TOrC attenuation compared to that of hydroxyl radicals. For instance, unity of a means that molecular ozone and hydroxyl radicals equally contribute to TOrC abatement. The developed indicator model was then applied to 12 TOrCs, as shown in Fig. 2. The TOrCs whose rate constants for both molecular ozone and hydroxyl radical that were not available were excluded in the model validation. In addition, filtered versus unfiltered samples at the different WWTPs were not differentiated for the validation purposes. As shown in Fig. 2, the semi-empirical model successfully predicted the TOrC attenuation with good R2 values ranging from 0.81 to 0.92. For this study, b was arbitrarily estimated to be 0.25 and used for validation in all water qualities. It is not clear whether b is a function of Rct with the given experimental data; therefore, additional studies are required to scrutinize how b differs with regard to water quality. Meanwhile, the results show that a single value of b can simultaneously predict attenuation of many kinetically distinct TOrCs regardless of water quality. Once b is determined, the developed indicator model works as a deterministic model and can predict attenuation for the abatement of any organic compound whose rate constants with molecular ozone and hydroxyl radical are known. The predictive indicator model (Eq. (6)) can shed light on use of

Fig. 2. Measured versus modeled attenuation of 12 TOrCs for the indicator model. All the tested waters were included without differentiation. Sy,x refers to the standard P errors of estimate and calculated as ð ðyi  yi Þ=df Þ1=2 where yi and yi indicate the observed data and their mean value, respectively; and df is the degree of freedom of the fit. The shaded area represents 95% confidence interval. The R2 values for individual compounds were calculated by comparing the observed data with the predicted values where the overall R2 value was calculated by the data points shown in the graph with the one-to-one correspondence line.

indicators to meet regulatory criteria, for example Title 22 of California Code of Regulations. In advanced oxidation processes for water reclamation in the State of California, a total of at least nine indicator compounds with at least one from each of the designated functional groups described in Table 2 are required to be monitored (CDPH, 2015). Based on the regulation, 0.5-log removal of Group A to G and 0.3-log removal of Group H and I must be demonstrated, respectively. A major disadvantage of functional group-based classification is that some compounds have multiple sites to be

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M. Park et al. / Water Research 119 (2017) 21e32

attacked by ozone; therefore, are equivocal to classify. For instance, diclofenac possesses amine and aromatic ring structures that are amenable to the reaction with ozone. The reaction rate of ozone with the amine of diclofenac is ~106 M1s1, where the ring without chlorine is ~103 M1s1; therefore, the amine is the major site to be predominantly attacked by molecular ozone (Sein et al.,

2008). In addition, a wide range of reaction rates for some functional groups challenges the selection of appropriate indicators. For example, the rate constants of olefins (Group C) for molecular ozone varies eight orders of magnitude with respect to the nature of substituents for CeC bonds (Sonntag and Von Gunten, 2012). In contrast to the grouping approach, the proposed indicator model can unambiguously provide treatment efficacy for TOrC attenuation. Fig. 3 demonstrates the calculated log removals of 12 TOrCs at 0.3-log removal of meprobamate as an indicator in the filtered WWTP1-1. All the compounds except for acesulfame met the CDPH regulatory requirement when 0.3-log removal of the indicator was achieved. Acesulfame (Group D) did not meet the regulatory criteria (i.e., 0.5-log removal) based on the CDPH classification; whereas, the classification based on rate constants indicates that acesulfame (Group IV) exhibits similar oxidative degradation with meprobamate (Group IV). Interestingly, the regulatory compliance removal for the majority of TOrCs, except for acesulfame and sulfamethoxazole, could be achieved by hydroxyl radical exposure. This infers the use of Eq. (4) to monitor ozone treatment efficacy for TOrC attenuation as a conservative approach, which can predict the attenuation without the measurement of oxidant exposure. 3.4. Development of spectroscopic surrogate models

Fig. 3. Calculated log removal of TOrCs using the indicator model (Eq. (6)). The removal of TOrC by ozone was calculated to use a instead of (1þ a) whereas that by hydroxyl radical was computed by setting a zero.

In this study, UVA254 and TF were chosen as surrogate parameters. The ozonation of wastewater effluent drastically decreased

Fig. 4. Normalized reduction in (A) UVA254 and (B) TF. Excitation-emission matrix (EEM) of WWTP1-2 with respect to ozone dose was shown in (C). Five operationally-defined regions such as (I) tyrosine-like aromatic protein, (II) tryptophan-like aromatic protein, (III) fulvic acid-like substance, (IV) soluble microbial byproduct and (V) humic acid-like substance were shown on the EEM at zero ozone dose.

M. Park et al. / Water Research 119 (2017) 21e32

UVA254 (Fig. 4A) (Chon et al., 2015). Apart from 254 nm, other wavelengths between 220 nm and 350 nm can also be considered as surrogates since UV absorbance monotonically decreased as ozone dose increased (Fig. SI-5). Reaction kinetics of molecular ozone is highly reliant on the structure of the reactants, which is specific for absorption wavelength (Dodd et al., 2006). Non wavelength-specific reduction in the absorbance spectra at 220e350 nm indicates that the ozonation would produce electron donor-acceptor complexes, which exhibit new broad absorption bands that are not shown with individual electron-donating and accepting molecules (Del Vecchio and Blough, 2004). Similarly, ozonation significantly bleaches out fluorescence over the entire range of wavelengths scanned (Fig. 4C). All major operationallydefined fluorescent constituents of EfOM such as aromatic proteins, soluble microbial byproducts-like, fulvic acid-like and humic acid-like substances, decreased with increasing ozone dose; thereby, TF decreased (Fig. 4B). When comparing the kinetics of surrogates, TF exhibited greater reduction than UVA254 at the corresponding specific ozone dose. The faster TF reduction infers that ozonation would efficiently alters intersystem crossing of EfOM, thereby significantly increasing fluorescence quenching (Cory and McKnight, 2005). Among WWTPs, the unfiltered WWTP2 exhibited the greatest extent of surrogate reduction at the corresponding specific ozone dose whereas the filtered WWTP1-1 showed the lowest reduction for both UVA254 and TF. For instance, their differences for UVA254 and TF at ~0.9 g O3/g DOC were estimated ~15% and ~18%, respectively. EfOM consists of innumerable compounds and each oxidizable organic matter of EfOM has different kinetics by ozone and hydroxyl radical; therefore, it is inappropriate and nearly impossible to consider individual fluorescent organic compounds for surrogate model development. As a more practical approach, the chromophoric dissolved organic matter represented by UV absorbance and fluorescence can be assumed to behave as hypothetical compounds in ozone oxidation processes so that the kinetic equation can be applied to the model development. The monotonic decrease of surrogates with respect to specific ozone dose bolsters this assumption (Fig. 4A and B). Based upon the aforementioned assumptions, the kinetics of spectroscopic surrogate (S) can be expressed as follows:

  d½SO  d½S  SI  d½S ¼ ¼ ¼  kO3 ;SO ½O3  þ k$OH;SO ½$OH ½SO  dt dt dt   ¼  kO3 ;SO ½O3  þ k$OH;SO ½$OH ð½S  ½SI Þ    ¼  kO3 ;SO ½O3  þ k$OH;SO ½$OH ½S  q½S0

(7)

where [S] indicates the spectroscopic surrogate “concentration” quantified as UVA254 or TF; SO and SI indicate the oxidizable and inert fractions of S, respectively; and kO3,S and kOH,S are the rate constants of the surrogate with molecular ozone and hydroxyl radical, respectively. Here, we assumed that S is composed of two main fractions: oxidizable and inert fractions. In addition, their optical properties are assumed to be electronically independent each other and linearly proportional to their concentrations (i.e., superposition model). Even though optical characteristics of EfOM cannot be fully explained by the superposition model while intramolecular interactions such as charge transfers between electron donors and acceptors play important roles on optical properties (Sharpless and Blough, 2014), the superposition assumption allows the use of kinetic equations for surrogate model development and further discussion on the use of superposition model will be made later. The inert fraction (SI) assumes not to change over the course of oxidation, hence its value remains constant and can be expressed as a portion (q) of initial S value ([S]0), which was obtained by

29

calculating [S]/[S]0 at 2.0 g O3/g DOC. Therefore, a surrogate model can be derived as follows:

Z Z     ½O3 dt þ k$OH ½$OHdt kO3 ½C ½S  q½S0 Z Z ln ¼ ln ½C0 ½S0  q½S0 kO3 ;S ½O3 dt þ k$OH;S ½$OHdt     k$OH 1 þ a ½S ln  q  lnð1  qÞ ½S0 k$OH;S 1 þ aS     k ð1 þ aÞ ½S ¼ $OH ln  q  lnð1  qÞ KS ½S0

¼

(8)

aS ¼ b lnð1 þ kO3 ;S =ðk$OH;S Rct ÞÞ; and KS is defined as the overall apparent rate constant of surrogate (k$OH;S ð1 þ aS Þ). The same b value (i.e., 0.25) was used as the one employed for the indicator model since b is assumed constant for a given water quality. Fig. 5 depicts the comparison of measured attenuation versus modeled attenuation of TOrCs by the UVA254 and TF surrogate models. Both surrogate models exhibited excellent predictive power (R2 ranging 0.81e0.92 and 0.82e0.96 for UVA254 and TF, respectively). The estimated overall rate constant value (KS) of TF ranges from 9.87  109 to 12.4  109 M1s1; whereas, UVA254 exhibited comparatively lower KS values (ranging 5.94  109e10.1  109 M1s1) (Fig. SI-6). KS values were water quality-specific, particularly for structural composition of EfOM. KS of TF varies the least with respect to the tested waters compared to UVA254 (smaller coefficient of variation, COV in Fig. SI-6), which infers that UVA254 kinetics are more water quality-specific. Fig. 6 depicts the surrogate correlations for the four selected TOrCs in the filtered WWTP1-1. The elliptic leaf-shape distribution of data was observed for the both UVA254 and TF. As the surrogate attenuation approaches (1-q), which means complete reduction of the oxidizable surrogate, the selected TOrC attenuation approaches 100%. The primary difference between UVA254 and TF was the portion of inert fraction (q) of each surrogate. UVA254 exhibited much greater inert fraction (q ¼ 0.45) compared to TF (q ¼ 0.08). This difference can be caused by their different quenching mechanisms. Oxidized EfOM constituents would have significantly improved efficiency of intersystem crossing, thereby losing fluorescing capability nearly completely (Cory and McKnight, 2005). In contrast, although oxidized molecules lose UV-absorbing capability, they still electronically interact with the other EfOM constituents and influence overall UV absorbance (Sharpless and Blough, 2014), which caused relatively high UVA254 at high ozone doses (>1 g O3/g DOC, Fig. 4A). Due to this charge interaction phenomenon, increasing ozone dose is unlikely to decrease UVA254 at high ozone dose ranges, therefore considered as inert fractions. This allows the use of superposition model for the surrogate modeling even if charge transfer plays important roles in the spectroscopic properties of EfOM. The surrogate model can also be interpreted to meet CDPH regulatory efficacy of ozone processes. In the preceding section, 0.3-log removal of the indicator (i.e., meprobamate) met the regulatory requirements except for acesulfame. The same treatment efficacy can be demonstrated when 0.70e0.87 log TF reduction (80%e87%) and 0.22e0.33 log UVA254 reduction (40%e54%) were achieved. 3.5. Sensitivity of parameters and their simplification for surrogate models The sensitivity of the estimated surrogate model parameters including b, KS and Rct for four selected compounds was computed

30

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Fig. 5. Measured versus modeled attenuation of 13 TOrCs for (left) UVA254 and (right) TF. All the tested waters were included without differentiation. The shaded area represents 95% confidence interval. The R2 values for individual compounds were calculated by comparing the observed data with the predicted values where the overall R2 value was calculated by the data points shown in the graph with the one-to-one correspondence line.

Fig. 6. Attenuation of four selected TOrCs versus indicator/surrogate attenuation for the filtered WWTP1-1. Symbols and lines indicate experimental and modeled data, respectively.

using LH-OAT method (Fig. SI-7 and Fig. SI-8). This procedure provides insights into more important parameters on model prediction. The higher sensitivity value indicates the greater influence on model prediction. KS consistently showed high sensitivity for all the tested TOrCs. Their values are similar to each other across TOrCs since KS is the oxidation characteristics of EfOM only and is not related to compound oxidation characteristics. Conversely, b and Rct are the parameters related to the exposure of oxidants, therefore affecting the extent of TOrC ozonation even though they are water quality-specific parameters too. Interestingly, the greater sensitivity index for b was observed for TOrC with greater ozone reactivity. Particularly, low reactive TOrCs (i.e., acesulfame and primidone) shows sensitivity index values. As explained earlier, b functions to correct overestimation of TOrC attenuation with high reactivity for ozone; therefore its sensitivity index was proportional to the reactivity. Rct also varied with respect to TOrC, but their sensitivity index values were low. Ultimately, the surrogate model development was intended for real-time monitoring of TOrC abatement. However, Rct measurement requires time-consuming analytical method, which hinders real-time application. In addition, the estimation of KS for each time of prediction is not viable for real-time monitoring. Based on the sensitivity analysis, it is reasonable to assume that use of constant Rct of wastewater from a single WWTP minimally affects model prediction. In addition, KS for UVA254 and TF are in the order of 1010 M1s1; the extent of their oxidation is assumed to be controlled by diffusion and does not noticeably vary over certain

time period, which allows the use of constant KS values for TF and UVA254 for all the tested waters. Therefore, constant values of Rct and KS were employed for the surrogate model to predict TOrC attenuation (Fig. SI-9). The overall R2 for the UVA254 and TF surrogate models were 0.90 and 0.92, respectively. Therefore, the use of constant Rct and KS values facilitate surrogate implementation while maintaining good predictive power. 3.6. Implication of indicator and surrogate models In this study, the developed indicator model with a single indicator compound can accurately predict the attenuation of a large number of compounds regardless of structural similarities. Even though current analytical techniques including mass spectrometry are challenging to apply to real-time on-line detection of TOrCs, it is noteworthy that rapid advances in analytical instrumentation and methods including automated sample handling, preparation, and direct aqueous injection enables at-line monitoring of TOrCs (Anumol and Snyder, 2015; Anumol et al., 2015b), which can help the implementation of the developed indicator model for ensuring treatment efficacy in water reuse. Spectroscopic surrogates can also be readily measured without any specialized pretreatment or sample preparation. In general, typical sample preparation for the spectroscopic analysis is filtration using a 0.45 mm filter to separate the dissolved fraction from suspended matter. The developed surrogate model was proven to be applicable for unfiltrated water too, which facilitates application of real-time on-line surrogate

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measurement. Current operation of ozone in water reuse applications is based upon specific ozone dose (g O3/g DOC) since similar specific ozone dose yielded similar ozone and hydroxyl radical exposures (Lee et al., 2013). However, this operating scheme cannot fully appreciate TOrC abatement of ozone processes with various wastewater effluents with distinct ozone decomposition and hydroxyl radical formation characteristics while the developed models in this study take into account different oxidation kinetics. Therefore, the applications of developed indicator and surrogate models can help establish the critical control points or critical operating points for optimal TOrC control in water reuse. Furthermore, they will also help build confidence in verifying that a potable reuse facility is meeting the regulatory and performance requirements. 4. Conclusions Monitoring water quality in potable water reuse is a critical component to its viability. With this in mind, TOrC attenuation prediction is challenging due to an enormous number of TOrCs present in wastewater effluent and current analytical limitations to directly measure for real-time monitoring. Ozone oxidation is a key process in several water reuse treatment trains and chiefly responsible for TOrC attenuation. As such, this study was primarily aimed at developing indicator and surrogate models to predict efficacy of ozone oxidation for TOrC attenuation in wastewater effluents. To this end, second-order kinetic equations were used to calculate comparative kinetics of TOrC attenuation based on the reduction of meprobamate as an indicator compound. Further, spectroscopic surrogate models with UVA254 and TF were also developed. Both indicator and surrogate models exhibited excellent predictive power (R2 > 0.80) for twelve compounds with wide ranges of rate constants with molecular ozone and hydroxyl radical. Overall, the developed models exhibited good prediction of TOrC attenuation by ozone oxidation although there are still remaining tasks to elucidate the empirical parameters (b and KS) with various water qualities. Additional studies would be necessary to further refine the indicator and surrogate models with many water qualities that exhibit distinct oxidation characteristics; thereby, ultimately shedding light on the use of indicator and surrogate in potable water reuse. Acknowledgements The authors would like to thank the operators at the wastewater treatment plants for their assistance in sample collection. We especially wish to acknowledge Jens Scheideler from Xylem Wedeco for providing the ozone generator and related technical support. We also appreciate Agilent Technologies for help with acquisition and maintenance of the instrumentation used in this study. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.watres.2017.04.024. References Angelakis, A., Gikas, P., 2014. Water Reuse: Overview of Current Practices and Trends in the World with Emphasis on EU States. Anumol, T., Merel, S., Clarke, B.O., Snyder, S.A., 2013. Ultra high performance liquid chromatography tandem mass spectrometry for rapid analysis of trace organic contaminants in water. Chem. Cent. J. 7, 104. Anumol, T., Sgroi, M., Park, M., Roccaro, P., Snyder, S.A., 2015a. Predicting trace organic compound breakthrough in granular activated carbon using

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