Impacts of Competitive Inhibition, Parent Compound Formation and

Environ. Sci. Technol. 2010, 44, 734–742

Impacts of Competitive Inhibition, Parent Compound Formation and Partitioning Behavior on the Removal of Antibiotics in Municipal Wastewater Treatment ´ S Z , * ,† BENEDEK GY. PLO HENRIETTE LEKNES,‡ AND KEVIN V. THOMAS† Norwegian Institute for Water Research (NIVA), Gaustadalle´en 21, NO-0349, Oslo, Norway, Norwegian Institute for Air Research (NILU), 2027 Kjeller, Norway

Received July 27, 2009. Revised manuscript received November 9, 2009. Accepted November 10, 2009.

We present a process model that predicts the removal of theantibioticmicropollutants,sulfamethoxazole(SMX),tetracycline (TCY), and ciprofloxacin (CIP), in an activated sludge treatment system. A novel method was developed to solve the inverse problem of inferring process rate, sorption, and correction factor parameter values from batch experimental results obtained under aerobic and anoxic conditions. Instead of spiking the batch reactors with reference substances, measurements were made using the xenobiotic organic micropollutant content of preclarified municipal sewage. Parent compound formation and removal were observed, and the model developed using the simulation software West showed limited efficiency to describe the selected micropollutants profiles, when growth substrate removal occurs. The model structure was optimized by accounting for competitive inhibition by readily biodegradable substrates on the cometabolic micropollutant biotransformation processes. Our results suggest that, under anoxic conditions, hydrophobicityindependent mechanisms can significantly impact solid-liquid partitioning that our model takes into account by using the sorption coefficient as a lumped parameter. Forward dynamic simulations were carried out to evaluate the developed model and to confirm it for SMX using data obtained in a full-scale treatment plant. Evaluation of measured and simulation results suggest that, robust model prediction can be achieved by approximating the influent load of chemicals biodegrading via a given parent compound, e.g., human conjugates, as an antibiotic mass that is proportional to the parent compound load.

Introduction Reduction of xenobiotic organic micropollutants, e.g., pharmaceutical compounds, in municipal sewage, through conventional biological treatment systems is a significant but insufficient means in mitigating their potential environmental impact (1, 2). Pharmaceutical compounds in discharges derived from hospitals and pharmaceutical * Corresponding author. Phone: +4722185100; Fax: +4722185200; E-mail: [email protected], [email protected]. † Norwegian Institute for Water Research (NIVA). ‡ Norwegian Institute for Air Research (NILU). 734

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manufacturing sites can further increase the risk placed on the environment by municipal wastewater treatment plant (WWTP) effluents (3, 4). Pharmaceuticals such as antibiotics are excreted as the parent compound, in conjugated form, or as oxidation or hydrolysis products. Despite the wide ranging of half-lives of active pharmaceutical ingredients in the human body, the occurrence of pharmaceutical compounds in preclarified municipal sewage can effectively be correlated with drug administration patterns and show characteristic diurnal variations (5). Contaminants present in wastewater can partition between the liquid and solids phase, with sorption onto dissolved and colloidal organic matter (DCOM). This can subsequently influence the availability of contaminants for biodegradation (6, 7). Mechanisms other than hydrophobic partitioning, such as cation exchange, cation bridging, surface complexation, and hydrogenbonding can have a significant impact on sorption (6). Widely used anionic surfactants can also influence partitioning behavior (8). Hydrolysis of particulate and colloidal organic matter along with biodegradation of the soluble mostly readily biodegradable substrates (SS) can therefore influence the biodegradation of compounds such as antibiotics in WWTPs. In activated sludge, it is also possible that certain metabolites of pharmaceuticals can be transformed back to the parent compound as a result of enzyme-catalyzed reactions. For example, cleavage of glucoronide conjugates by glucoronidase (5). It is possible for certain pharmaceuticals that the conjugated form may be present in concentrations greater than the parent (9, 10). Furthermore, numerous xenobiotics can be transformed through the process of cometabolism, i.e. transformation of a nongrowth substrate by growing cells in the presence or absence of a growth substrate (11, 12). Competitive inhibition between the growth and the cometabolic substrate to the nonspecific enzyme active site is shown as a factor that can effectively hinder the biotransformation of cometabolic contaminants (12, 13). In order to optimize xenobiotic organic contaminant removal in WWTPs, enhanced tertiary treatment processes (ETTPs), e.g., controlled ozonation, can be used (14, 15). For ETTPs, reactor design and chemical dosing regimes should be optimized using process models, predicting the biologically treated effluent mass load of selected micropollutants. An assessment of Norwegian priority pharmaceuticals suggests potential environmental risk posed by ciprofloxacin (CIP), sulfamethoxazole (SMX), tetracycline (TCY), diclofenac and ethinylestradiol (2). The effective fate modeling of antibiotics in activated sludge is still a pressing research question that this paper aims to address. Integrated modeling, including sewer networks and central WWTP, can be implemented to assess variations in micropollutant mass-loads in WWTPs, thereby optimizing the identification and the removal of micropollutants using expert systems (16). The objectives of the present work are (i) to investigate the removal of selected antibiotics contained in preclarified municipal wastewater in targeted batch experiments; (ii) to characterize the contaminant fraction in municipal sewage that can degrade via the selected antibiotic parent compounds; (iii) to develop a process model and a calibration method for micropollutant fate in activated sludge using batch experimental data; and (iv) to evaluate a simulation model, describing a full-scale activated sludge secondary treatment step.

Materials and Methods Batch Experiments. Batch experiments were performed in two stirred 25-L reactors. A decanted activated sludge grab 10.1021/es902264w

 2010 American Chemical Society

Published on Web 12/11/2009

sample was collected from the last aerobic reactor upstream to the secondary clarifier of a preanoxic-aerobic (MLEprocess) activated sludge treatment plant (Bekkelaget WWTP, Oslo, Norway) (Figure S1 of the Supporting Information). The sludge samples were used on the same day setting the sludge concentration in the batch reactors to an average of 3.1 gXSS L-1. A preclarified composite sample (1-day) was collected, and the volumetric ratio to the decanted sludge sample used in the experiments was 1:1. Temperature was kept at approximately 19 °C in both reactors. The dissolved oxygen concentration (DO) was monitored using a CellOxtype oxygen-probe (WTW, Germany). Small bubble aeration was used to regulate the DO concentration between 2 and 4 mg L-1 O2. In the anoxic reactor, built as described by Plo´sz et al. (17), we used an initial nitrogen gas sparging by approximately 3 L h-1 for 30 min. Nitrate was added in the form of dissolved KNO3 with an initial nitrate concentration of 80 mg L-1 N. The pH was manually maintained between 7.3-7.6 and 7.2-7.7 by the addition of HCl (1M) and NaOH (1M) into the anoxic and the aerobic reactor, respectively. Full-scale experiments and sampling are described in ref (5). Sampling and Analysis. Prior to start-up, the samples were collected from the preclarified sewage and the decanted activated sludge used in the batch tests. Liquid concentration values detected in the two samples were used to calculate the contaminant concentration value at t ) 0, CLI,0. During the batch experiments, samples were collected using a valve at the bottom of the vessels at the frequency shown in Figure 1a. Samples were analyzed for ammonium, nitrate, nitrite and COD according to (18) using HACH-Lange test kits and a DR2800 spectrophotometer (HACH, Germany) (data not shown). In the batch and full-scale experiments, values of SS data were obtained using soluble COD (0.1 µm membrane filtered) and total influent COD concentration data, respectively, according to (19). For targeted antibiotic analysis, carried out in NILU’s laboratory, samples (2 L) were collected in 2.5 L silanized glass bottles. Following collection, all samples were stored in cool boxes and received on the day of sampling and acidified using a small aliquot of hydrochloric acid (1 M) and added Na2-EDTA as a complexing agent. Aqueous samples were stored at 4 °C and sludge samples at -20 °C until analysis. The aqueous phase was filtered and internal standards added before passing through polymeric solid phase extraction (SPE) columns. Following elution with methanol:acetone (1:2), an aliquot of the sample was concentrated under a stream of nitrogen and analyzed for antibiotics. Chromatographic analysis was performed on a Waters Acquity Ultra Performance Liquid Chromatograph (UPLC, Waters, Milford USA). The analytical detector was a Waters LCT Premier XE time-of-flight (TOF) mass spectrometer (MS) with electrospray ionization (ESI) operated in positive mode. More information on the analytical methods is shown in the SI. Model Simulations. The software, WEST (MostForWater NV, Belgium; Vanhooren et al. (20)) was used to carry out model simulations, and to obtain numerical results on antibiotic fate (Table 1). We used the Activated Sludge Model Nr. 1, ASM1 (21) to simulate biomass growth under aerobic and anoxic conditions. For the micropollutant model and the ASM1, we used the parameter values, shown in Table 2 and in ref (22), respectively. The input data and calibration of ASM1 used to simulate the full-scale plant is not shown in here. The secondary settling tank was simulated using the one-dimensional convection-dispersion model calibrated using values presented in ref (23). Settling-velocity parameters were estimated using values of the diluted sludge volume index measured on the three consecutive days (58, 64, 62 mL g-1) with the correlation equations by ref (23). The configuration of the secondary treatment step modeled (Figure S1

of the Supporting Information) includes preanoxic and aerobic zones, a secondary clarifier, nitrate- and sludgerecirculation streams, and excess sludge removal from the sludge recirculation line. In the aerobic unit, DO concentration was set to values measured in the full-scale system using a control-loop via the mass-transfer coefficient, KLa. During the three measurement days, the average DO concentration in the three aerobic tanks was 1.91 ( 0.39 mg L-1. For the two-stage anoxic zone, to account for the oxygen entry through the liquid surface, we use a constant KLa value, 0.27 h-1 (17). For dynamic simulations of the stiff system, we use the CVODE numerical integration method with variable time step size. Parameter estimation was carried out using trajectory optimization experiments with the Simplex method (24). Predicted Pharmaceutical Mass Consumed in the Area, PMC. Values of PMC were calculated considering 4 681 134 inhabitants, living in Norway in 2007, out of which 281 000 inhabitants are situated in the area examined. To calculate the value of the theoretical antibiotic mass consumed in the area specific to the sampling frequency (8 h) implemented in the full-scale measuring campaign, we use annual human drug consumption data shown in Table 2. Conceptual Approach. As inverse problems are often illposed, to optimize the mathematical description of micropollutant fate, we implement inverse solutions in iterative steps using three independent cases, i.e., SMX, TCY, and CIP. In the initial model structure, for the sorption of the selected antibiotics onto suspended solids, XSS [gXSS L-1], an equilibrium state can be assumed between the concentrations of the dissolved parent compound, CLI [g L-1], and that in the solids phase, CSL [g L-1] that gives: KD )

CSL XSS · CLI

(1)

where the sorption coefficient, KD [L gXSS-1] is independent of the aqueous parent compound concentration, i.e., the Freundlich linearity parameter, n ) 1, e.g., ref (1). In our model, shown in Table 1, solid-liquid partitioning is described by two independent differential equations for desorption and sorption processes. For the desorption rate coefficient, kDes [L d-1], an arbitrary high value, 100, is used for all substances under aerobic and anoxic conditions. For more information on micropollutant partitioning, the reader is referred to ref (1). Separate measurement of the freely dissolved contaminants and the fraction partitioned onto DCOM is very difficult to obtain, and CLI includes both fractions. The numerical description of biodegradation of the selected micropollutants can be written as a simplified version of the well-known Monod-model. When the substrate concentration is significantly lower than the half-saturation coefficient, and thus the biomass transformation capacity increases linearly with the soluble substrate concentration (12, 25), the pseudo first-order kinetic expression, ∂CT ) -kBio · XSS · CLI ∂t

(2)

can be written, where kBio [L gXSS-1 d-1] is the reaction rate coefficient. The total compound concentration, CT [g L-1], includes the soluble and sorbed fractions, and, in case of equilibrium conditions (eq 1): CT ) CLI(1 + XSS · KD)

(3)

For modeling purpose, kBio is a generalized rate constant, accounting for biotic and abiotic micropollutant degradation. The latter process is not explicitly assessed in this study. Diffusive mass transfer inside the floc and across the laminar VOL. 44, NO. 2, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Illustration of the model calibration procedure (A)svalues of the normalized SMX concentration measured in the liquid phase of samples taken from the aerobic and anoxic batch reactors. Values of SMX CLI concentration measured (symbols) and simulated (solid line) and CCJ concentration simulated (dashed line) in the aerobic and anoxic batch tests (B) and CLI concentration measured (symbol) and simulated (solid line) in the preanoxic and aerobic effluent streams of the full-scale WWTP (C). The measured WWTP influent CLI concentration is shown with a square symbol (C), and the concentration values used in the dynamic input time-series are shown with a thin black line. Values of the total effluent SMX concentration specific to the CLI,eff concentration and plotted as a function of the total WWTP influent SMX mass loading normalized to the theoretical antibiotic consumption data, PMCSMX (D). Linear regression is shown with a solid line. boundary layer can additionally influence the observed degradation rates (26). Parent compound formation was observed in the batch experiments, and is described analogously to eq 2: ∂CCJ ) -kDec · XSS · CCJ ∂t

(4)

where kDec [L gXSS-1 d-1] is the rate constant and CCJ [g L-1] is the concentration of substances, biotransformed via the parent compound. We note that CCJ accounts for a broad range of compounds that comprises (i) human conjugates, e.g., glucoronide, acetyl, sulfate; (ii) other pharmaceuticals biotransformed via the parent compound; (iii) and particulate matter filtered out during the preclarified sample preparation for analysis. Our model does not account for sorption of CCJ onto solids, an unknown parameter that can impact the 736

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WWTP effluent total dissolved micropollutant concentration. Human conjugates are typically more water-soluble than parent substances to improve their excretion from the human body (1). In our model, the impacts of the presence and absence of dissolved oxygen on the reaction rates is taken into account using a switching term, analogously to the ASM1 (21), as shown in (Table 1).

Results and Discussion Results, obtained under aerobic and anoxic conditions, are first normalized by the initial concentration. Values of ln(CLI,t/ CLI,0) presented as a function of time elapsed (Figure 1a) are used to estimate model parameter values in the method developed. Parameter Estimation under Aerobic Conditions. Linear extrapolation of the data, constituting to phase 1 in Figure

TABLE 1. Stoichiometric Matrix Representation of the Developed Fate Model for Xenobiotic Organic Micro-Pollutants component f i j process V

1 CLI

desorption

2 CCJ

1

3 CSL

process rate

-1

kDes · CSL

aerobic processes -1

sorption

parent compound formation

1

kDes · KD,Ox · CLI

1

-1

kDec,Ox · CCJ

-1

biodegradation

kBio,Ox · CLI

SO X KO + SO SS

KS · ηDec SO X KS · ηDec + SS KO + SO SS

KS · ηBio SO X KS · ηBio + SS KO + SO SS

anoxic processes -1

sorption

parent compound formation

1

-1

kDec,Ax · CCJ

-1

biodegradation

kBio,Ax · CLI

1a, characterized with increasing CLI values, yields an y-intercept value that is used in the model input as initial liquid concentration (CLI,Th,0). Additionally, we use CLI,Th,0 to approximate the equilibrium liquid concentration as a result of sorption onto solids (see also Supporting Information). Under aerobic conditions, values of the solids-liquid partitioning coefficient, KD,Ox can then be approximated as follows: KD,Ox )

CLI,0 - CLI,Th,0 1 · CLI,Th,0 XSS

(5)

For model simulations, the initial equilibrium solids concentration used as model input, CSL,Th,0, is described as follows: CSL,Th,0 ) KD,Ox · XSS · CLI,Th,0

(6)

As solids-liquid partitioning is assumed to occur several magnitudes faster than biodegradation (27), for t g tPhase_2, to approximate the solution of ∂CCL/∂t, eq 3 can be substituted into eq 2 (1):

( ) ∂CLI ∂t

tgtPhase_2

kDes · KD,Ax · CLI

1

) -kBio,Ox

XSS ·C 1 + KD,Ox · XSS LI

(7)

For model calibration, the kBio,Ox value can then be approximated based on eq 7 using the tangent value of the linear regression line fitted to data measured in phase 2 (Figure 1a), KD,Ox, and a constant value, 3.1 g L-1, for XSS. For tPhase_1 e t e tPhase_2, let us first describe the simultaneous formation and biodegradation of CLI as follows:

( ) ∂CLI ∂t

tPhase_1etetPhase_2

KO X KO + SO SS

KS · ηDec KO X KS · ηDec + SS KO + SO SS

KS · ηBio KO X KS · ηBio + SS KO + SO SS

) kDec

XSS

·C 1 + KD,Ox,CJ · XSS CJ XSS kBio ·C (8) 1 + KD,Ox · XSS LI

At this stage of the parameter estimation, CCJ is unknown and its partitioning coefficient, KD,Ox,CJ, would be very difficult to measure. An approximate solution can be obtained to the value of kDec,Ox using the slope of the linear regression line fitted to the measured concentration data in phase 1 (Figure 1a), the value of kBio,Ox, KD,Ox and the constant XSS, based on:

( ) ∂CLI ∂t

tPhase_1etetPhase_2

) (kDec - kBio)

XSS ·C 1 + KD,Ox · XSS LI

(9)

The transformation of eq 8 to eq 9 is discussed in the Supporting Information. The CCJ,0 value can then be assessed by approximating the CLI values measured in the batch experiments using the model calibrated with kDec,Ox, kBio,Ox, KD,Ox, and XSS values. For compiling the dynamic model input time-series, values of the CLI,0:CCJ,0 ratio are then calculated using the average CCJ,0 values and the CLI,0 measured, and are shown in Table 2. Parameter Estimation under Anoxic Conditions. For anoxic sorption, based on the measured batch data, it is not feasible to estimate KD,Ax using eq 5. The KD,Ax values obtained are incomparably higher than values obtained under aerobic conditions, thereby also deteriorating the assessment of kBio,Ax, kDec,Ax and CCJ,0. A parameter estimation method is thus developed that rests on the assumption that CCJ,0 values VOL. 44, NO. 2, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Information on Sulfamethoxazole, Tetracycline and Ciprofloxacin, As Well As the Model Parameter Values compound symbol

definition acronym used CAS registry no. annual human drug consumption in Norwaya average daily influent CLI load measured average daily influent CCJ load calculated

kDes ηDec ηBio KS KO KD,Ox kDec,Ox kBio,Ox KD,Ax kDec,Ax kBio,Ax CLI,Inf/CCJ,Inf

10:00-18:00 h 18:00-02:00 h 02:00-10:00 h CLI,0,ss

CCJ,0,ss

unit

sulfamethoxazole

tetracycline

ciprofloxacin

SMX 723-46-6

TCY 60-54-8

CIP 85721-33-1

(kg y-1)

218

1068

880

(mg d-1 1000 PE-1)

78 ( 19

737 ( 230

621 ( 51

(mg d-1 1000 PE-1)

174 ( 42

1083 ( 338

6398 ( 544

100

100

5c(1c,e)

20c

2c 10f 0.2f

2c 10f 0.2f

1.1c

0.42c

2.02c

5c

0.44c

0.55c

1.6c(15.4d)

1.1c

2.19c

1.2c

0.04c

0.13c

0.68c

0.12c

0.68c

0.06d

0.68c

0.12c

1108

1422

1632

11852

Kinetic Model Parameters desorption rate (L d-1) 100 coefficient for CSL correction factor for (-) 2c SS inhibition on CLI formation correction factor for SS inhibition on CLI biodegradation (-) 2c half-saturation coefficient for SS (mg L-1) 10f half-saturation coefficient (mg L-1) 0.2f for dissolved oxygen Aerobic Process Parameters aerobic solids-liquid (L gXSS-1) 0.31c sorption coefficient aerobic biotransformation (L gXSS-1 d-1) 6.80c rate coefficient for CCJ aerobic biotransformation (L gXSS-1 d-1) 0.41c rate coefficient for CLI Anoxic Process Parameters anoxic solids-liquid (L gXSS-1) 0.55c sorption coefficient anoxic biotransformation (L gXSS-1 d-1) 7.85c rate coefficient for CCJ anoxic biotransformation (L gXSS-1 d-1) 0.41c rate coefficient for CLI Parameters for the Dynamic Input Time-Series ratio of the preclarified influent CLI and CCJ concentration values for the three daily inflow regimesb parameter value for (-) 0.45d the morning increased inflow parameter value (-) 0.45d for the daily peak inflow parameter value for (-) 0.45d the midnight low inflow steady-state boundary (ng L-1) 210 concentration value used in the dynamic WWTP simulations as initial condition steady-state boundary (ng L-1) 467 concentration value used in the dynamic WWTP simulations as initial condition

a Drug consumption data is presented by (2). b More information on the flow boundary conditions are shown by (5). Parameter values assessed using batch data observed. d Parameter values evaluated by approximating the concentration values measured in the full-scale experiments with dynamic simulation results. e Value of the correction factor obtained under anoxic conditions for TCY (for SMX and CIP, correction factor values obtained are identical under anoxic and aerobic conditions). f ASM1 parameter values according to (22). c

obtained in the anoxic and aerobic batch experiments should be the same or closely mirror each other. To assess the CCJ,0 value obtained under aerobic conditions, the anoxic micropollutant profile is approximated by decreasing the initial KD,Ax values, obtained using eq 6, in an iterative way. Values of kBio,Ax and kDec,Ax parameters are assessed for each iterative step using the linear tangent values, derived from the experimental data, in eqs 7 and 9. The estimated KD,Ax and the derived kBio,Ax and kDec,Ax parameter values are shown in Table 2. For model simulations of the batch data obtained under anoxic conditions, the initial equilibrium liquid and solids concentration used as model input are assessed 738

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analogously to the aerobic input data. Further research is required to identify possible chemical reactions between the selected contaminants and some of the sewage pollutants that can result in excess contaminant removal from the aqueous phase in addition to equilibrium partitioning. Additionally, the impact of redox conditions on solids partitioning behavior of xenobiotic organic contaminants will be investigated in the future. For parameter estimation, as a result of the suboptimal number of experimental data, the model can be considered ill-conditioned to some extent. For future reference, for model parameter estimation from batch data, we recommend using

more frequent sampling than what is shown hereby. To assess the model overfitting, we use full-scale measurements in forward simulations. Co-Metabolism and Competitive Inhibition. For the batch experimental observations, our model significantly overestimates the concentration values measured during the initial 0.12 and 0.3-day periods in the aerobic and anoxic batch experiments (Figure S2 of the Supporting Information). An evaluation of the measured and simulated results shows significant discrepancy, when the initial uptake of readily biodegradable growth substrates (SS) occurs in phase 1. Through cometabolism, the uptake of SS can affect the transformation of cometabolic substrates, causing competitive inhibition on micropollutant biotransformation processes (25) that is described in our model as follows: ∂CLI ) -kBio · ∂t

CLI · XSS SS 1+ KS·ηBio

(10)

and ∂CCJ ) -kDec · ∂t

CCJ · XSS SS 1+ KS · ηDec

(11)

The hydrolysis and biodegradation of DCOM can additionally influence the bioavailability of the aqueous contaminant fraction partitioned onto DCOM. The presence of humic acids can also impact the biodegradation of CLI (28). We simulate the growth substrate uptake (initial SS concentration: 130 mg L-1 COD) in the batch experiments using ASM1, and estimate parameter values for the growth substrate inhibition half-saturation coefficient on the parent compound biodegradation and formation, KS · ηBio and KS · ηDec, respectively. The best fitting ηBio and ηDec values obtained in the model parameter assessment are shown in Table 2. For all substances examined, the inhibitory impact on biodegradation is ηBio ) 2, i.e., the reaction rate can be deteriorated by 50% or more at or beyond KS · ηBio ) 20 mg L-1 COD as SS. For parent compound formation, hindrance by 50% or more through competitive inhibition by SS is obtained above 20-200 mg L-1 COD. Although, not giving direct experimental evidence, Joss et al. (26) suggest that growth substrates present in the influent wastewater can competitively inhibit biodegradation of the human estrogen, estronesresults that are in good agreement with the findings of ref (29). Results Obtained for the Antibiotics Studied. SMX. Figure 1b shows that the model developed can approximate well the SMX concentrations measured under aerobic and anoxic conditions in the batch experiments. The value of the aerobic sorption coefficient obtained is 0.31 L gXSS-1 that is in accordance with the values (0.26 ( 0.17 L gXSS-1) shown by (10). For anoxic sorption, the parameter value of 0.55 L gSS-1 was evaluated. For the SMX biodegradation rate coefficient, Joss et al. (30) report a value of e0.1 that is lower than 0.41 L gXSS-1 d-1 obtained in this study, under both anoxic and aerobic conditions. For the kDec coefficients, values of 6.8 and 7.9 L gXSS-1 d-1 were obtained under aerobic and anoxic conditions, respectively. These are in agreement with values of 5.9-6.7 L gXSS-1 d-1 reported by (30) for the degradation constant of N4-Acetyl-SMX, a human metabolite of SMX possibly degrading to SMX (10). For the CCJ,0 concentration, average values evaluated from the anoxic and aerobic batch results (Figure 1b) were 800 ng L-1. Considering a dilution factor of 2 for the preclarified sewage sample used in the batch experiments, CCJ,0 in the original wastewater spike is approximately 1600 ng L-1. Go¨bel et al. (31) report on the concentration values obtained for N4-Acetyl-SMX in raw WWTP influent of 850-1600 ng L-1, besides SMX parent

compound concentrations of 230-570 ng L-1. These results are in good agreement with the CCJ,0 value obtained and with influent CLI values shown in Figure 1c. For the Go¨bel’s data, the ratio of the influent parent compound to N4-AcetylSMX concentration values calculated is 0.33 that closely relates to the CLI:CCJ ) 0.45 value obtained in this study (Table 2). In Figure 1c, dynamic simulation results can effectively approximate CLI values measured (Figure 1c), thereby confirming the process model developed for SMX. Values of daily SMX loads specific to 1000 inhabitants, shown in Table 2, agrees well with data presented in ref (31). In Figure 1d, values of the total effluent SMX concentration divided by the effluent parent compound concentration are plotted as a function of the influent total antibiotic mass load specific to the antibiotic consumption data, PMCSMX. Values of the influent total SMX mass load vary between 50 and 290% of the PMCSMX. Effluent data show CCJ up to approximately 8-10% of the total secondary effluent SMX mass load. In Figure S3 of the Supporting Information, we illustrate the impact of omitting the CCJ sewage fraction on contaminant mass-balance calculations. TCY. In the batch experiments (Figure 2a), the aerobic sorption coefficient obtained is 1.1 L gXSS-1 that equals the value (1.1 L gXSS-1) presented by (6) using soil organic matter (pH 6.14). For anoxic sorption, a parameter value of 1.6 L gXSS-1 can approximate well the batch data. Evaluation of the dynamic simulation results and measured data show that concentration values detected in the fullscale preanoxic effluent stream are significantly lower than those predicted by the model (data not shown). This shortcoming can be effectively overcome using a partitioning coefficient 14× higher than that compared to the value assessed using the batch data (Figure 2b). One possible way to explain this behavior is that, under the pH and redox conditions prevailing in the anoxic zone, TCY can form relatively stable complexes with iron-(III)-cations. These are dosed into the sludge recirculation line in the form of Fe2+ for phosphorus removal that can also provide cation-bridging, thereby decreasing the freely dissolved TCY fraction. Furthermore, in the batch experiments, the sludge samples derived from the effluent of the aerobic zone and not from the sludge recirculation stream, where the iron-salt is dosed. Our WWTP model approximates the sorption capacity of the sludge wasted using the KD,Ax parameter value. Depending on the aqueous pH and the presence of metal cations, a number of tetracycline degradation products can be formed (32). In the batch experiments, for the kBio,Ox and kBio,Ax, we obtained 0.44 and 0.04 L gXSS-1 d-1, respectively. Values of kDec obtained are 2 and 2.2 L gXSS-1 d-1, under aerobic and anoxic conditions, respectively. In the preclarified sewage sample used in the batch experiments, the initial CLI value measured is 3464 ng L-1, and the initial CCJ value evaluated using model approximations of the batch CLI profile (Figure 2a) is 5100 ng L-1. On the basis of the batch data, the CLI:CCJ ratio calculated is 0.68 (Table 2) that is used to calculate CCJ in the dynamic model input file using the measured preclarified WWTP influent CLI values (Figure 2b). Influent CLI values measured are in good agreement with the 1.9 µg L-1 average influent TCY concentration reported by (33). In Figure 2c, influent TCY mass loading values specific to PMCTCY show a variation between 105-440%. A considerable fraction of the influent CCJ,TCY can derive from TCY sorbed onto solids filtered out during the preclarified sample preparation for analysis. Additionally, we hypothesize that TCY, an antibiotic not metabolized by the human body, can be formed in activated sludge from other commercially available tetracyclines antibiotics, a factor that should be investigated in the future. A striking feature of Figure 2c is that, depending on the sorption VOL. 44, NO. 2, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Values of TCY CLI concentration measured and simulated as well as CCJ concentration simulated in the aerobic and anoxic batch tests (A) and CLI concentration measured and simulated in the influent, preanoxic and aerobic effluent streams of the full-scale WWTP (B). Demonstration of the measured and simulation results obtained for TCY is analogous to Figure 1b and Figure 1c. Values of the total effluent TCY concentration specific to the effluent CLI concentration and plotted as a function of the total WWTP influent TCY mass loading normalized to the theoretical antibiotic consumption data, PMCTCY (C). Linear regression is shown with a solid line.

FIGURE 3. Values of CIP CLI concentration measured and simulated as well as CCJ concentration simulated in the aerobic and anoxic batch tests (A) and CLI concentration measured and simulated in the influent, preanoxic and aerobic effluent streams of the full-scale WWTP (B). Demonstration of the measured and simulation results obtained for CIP is analogous to Figure 1, parts b and c. Values of the total effluent CIP concentration specific to the effluent CLI concentration and plotted as a function of the total WWTP influent CIP mass loading normalized to the theoretical antibiotic consumption data, PMCCIP (C). Linear regression is shown with a solid line.

behavior of CCJ,TCY, this fraction can occur up to approximately 180-200% that of CLI,TCY in the biologically treated effluent, which can cause a severe underestimation of the load on tertiary treatment and subsequently the environmental risk posed by TCY.

CIP. For aerobic sorption, a partitioning coefficient of 0.42 L gXSS-1 is estimated using batch experimental data, shown in Figure 3a, that is in good agreement with the 0.43 L gXSS-1 value reported by (6). For the anoxic sorption coefficient, the value of 1.1 L gXSS-1 is obtained. For the

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kBio parameter, we obtained 0.55 and 0.13 L gXSS-1 d-1 values under aerobic and anoxic conditions, respectively. Values assessed for kDec,Ox and kDec,Ax are 5 and 1.2 L gXSS-1 d-1, respectively. In the preclarified sewage sample used in the batch experiments, the CLI,0 value measured is 1805 ng L-1, and the CCJ,0 value evaluated using model approximations of the batch CLI profile (Figure 3a) is 15000 ng L-1. The resulting CLI,0:CCJ,0 ratio is 0.12 (Table 2) that is used to calculate CCJ value in the dynamic model input file using the CLI measured in the preclarified influent. For the second 8-h period, under hydraulic peak loading, to account for the less efficient parent compound formation in the sewer system, the influent CCJ values are approximated using half the batch CLI:CCJ value, i.e., 0.06 (Table 2). The model can effectively predict the secondary effluent CIP concentration within the first and second eight-hour periods in the three measurement days (Figure 3b). Meanwhile, a considerable discrepancy between the measured and simulation results can be observed for all the third 8-h sampling periods, i.e., during night hours, under low hydraulic loading conditions. The prediction of the anoxic CIP fate shows similar deterioration on the second and at the beginning of the third sampling days. A possible explanation of these observations is that the iron-(II)-salt dosing into the sludge recirculation line, a factor that can influence ionic strength and cation-bridging in the sewage, is set by flow-proportionality. Plant data (not shown) suggest that, in the third 8-h sampling periods, salt-dosing is 5-10% lower than the degree of decrease in the amount of influent sewage. This impact can influence ionic strength that can partly explain the decreased sorption capacity of activated sludge. In the preclarified influent and secondary effluent sewage, pH was on average 7.6 and 6, respectively. The pKa of CIP is 8.0, and such a pH shift can alter the ratio of charged/uncharged CIP, thereby significantly deteriorating CIP adsorption. This impact can effectively increase the freely dissolved CIP concentration as suggested by data obtained with activated carbon (see more in the Supporting Information). In the third 8-h period, to estimate hindered sorption on the activated sludge, the aerobic effluent CLI values are approximated well by using a ten-times lower KD,Ox value, 0.042 L gXSS-1 (see solid line with cross symbol in Figure 2b) that closely mirrors the extent of deterioration of CIP sorption onto activated carbon (see the Supporting Information). Such intermittent deterioration of sorption processes can significantly increase the secondary effluent CIP load, and thus the tertiary advanced oxidation requirement or, in its absence, the environmental impact, that requires further research. In Figure 3c, influent CIP mass load values specific to PMCCIP show a variation between 600 and 1840%. The calculation of PMCCIP does not account for the impact of effluents derived from small hospitals and for the formation of CIP from other human and veterinary fluoroquinolone antibacterials with amine substituents, e.g., enrofloxacin (34). Data plotted in Figure 3c suggest an underestimation of the secondary effluent CIP mass load by up to 30%, if CCJ is omitted in mass-balance calculations. We note that, in the cases of TCY and CIP, model confirmation was not possible, and, to improve the model approximation, additional information on the modified contaminant sorption behavior was introduced. In-Sewer Inhibition on Micropolluntat Biotransformation. Degradation processes and nonspecific enzyme sites responsible for the xenobiotic transformation by microorganisms, prevailing within the drainage system, can be considered similar or the same to that in the activated sludge system. Inhibitory factors on biodegradation (φSewer,Bio) and parent compound formation (φSewer,Dec) processes in sewer

FIGURE 4. Values of average in-sewer biotransformation inhibition factors, OSewer,Bio and OSewer,Dec, obtained for the selected antibiotics using values of the preclarified influent SS concentration in the three consecutive days: 131, 198, 126 mg L-1 COD in eqs 10 and 11, respectively. For SMX and CIP, correction factor values obtained are identical under anoxic and aerobic conditions. For TCY, different correction factor value is obtained for parent compound formation under anoxic and aerobic conditions. networks are assessed using SS values obtained in the WWTP preclarified influent as follows: φSewer,Bio )

ηBio · KS ηBio · KS + SS,inf

(12)

φSewer,Dec )

ηDec · KS ηDec · KS + SS,inf

(13)

and

. In Figure 4, values obtained for SMX and TCY are relatively low, suggesting high in-sewer inhibitory effects on formation and biodegradation processes. This is in good agreement with the conservative WWTP influent quality, i.e. one value for the CLI:CCJ ratio is sufficient to calculate CCJ in the input time-series for the three daily 8-h sampling periods. For CIP, the relatively high φSewer,Dec value, 0.58, suggest low in-sewer inhibitory effects. This correlates well with the relatively high CLI:CCJ ratio (0.12) used in the first and third 8-h sampling periods compared to the 0.06 value, characterizing the second 8-h periods with peak inflow rates, i.e., shorter hydraulic retention time in the sewer system.

Acknowledgments Funding for the study was provided by the Norwegian Research Council (SIP-ES243159) and by NIVA (O29103). Authors express their special thanks to Juliane Hollender, Hansruedi Siegrist and Kai Lehnberg for valuable discussions on experimental and modelling and to WWTP operators, in particular Helle Frodahl.

Supporting Information Available Additional information on the analytical methods used; discussion of the approximation of the equilibrium liquid concentration as a result of sorption; discussion of the transformation of eq 8 to eq 9; additional information on CIP sorption onto activated carbon; layout of the first treatment train in the Bekkelaget WWTP (Figure S1); illustration of simulation inefficiencies using the initial model structure VOL. 44, NO. 2, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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and the need for model development (Figure S2); illustration of the impact of the omitting the CCJ sewage fraction on contaminant mass-balance calculations (Figure S3). This material is available free of charge via the Internet at http:// pubs.acs.org.

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