Emerging investigators series: occurrence and fate of emerging organic contaminants in wastewater treatment plants with an enhanced nitrification step†

Yue Xing ^a, Yaochun Yu ^a and Yujie Men *^ab
aDepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 3209 Newmark Civil Engineering Laboratory, MC-250, 205 North Mathews Ave., Urbana, IL 61801-2352, USA. E-mail: ymen2@illinois.edu; Tel: +217 244 8259
^bInstitute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

First published on 18th July 2018

---

---

Water impact A comprehensive view of the occurrence and fate of emerging organic contaminants in each wastewater treatment step, particularly the enhanced nitrification step, was obtained. Persistent contaminants and the contributions of biological removal in the secondary and enhanced nitrification steps were identified, which provides insights into future monitoring, regulation, and treatment practices of emerging organic contaminants in wastewater treatment plants and receiving environments.

1. Introduction

Emerging organic contaminants (EOCs) include pharmaceuticals, personal care products (PCPs), pesticides and many other types of anthropogenic chemicals. EOCs occur in natural and built environments at low concentrations (a few ng L⁻¹ to tens of μg L⁻¹), thus they are also known as micropollutants.^1–3 Many EOCs are persistent in the environment, and some more hydrophobic ones might be bioaccumulated in organisms. Pharmaceuticals such as norsertraline, and industrial chemicals such as per-/polyfluoroalkyl substances were found in different fish species.^1,4 Although the environmental impacts of EOCs are not fully understood, several studies have indicated their deleterious effects and chronic damage to the ecosystem. For instance, exposure to environmental levels of antidepressant drugs can disturb neuronal activities and cause adverse reproductive responses in freshwater fish and other aquatic organisms.^5,6 Given their confirmed and potential adverse effects, it is important to closely monitor their environmental occurrence and get a better understanding of their transformation routes.

Municipal wastewater treatment plants (WWTPs) are one of the major basins of down-the-drain EOCs. As current WWTPs are not designed for EOC removal, incompletely removed EOCs in WWTP effluents can be released into aquatic environments. This leads to great efforts in understanding the occurrence and fate of EOCs before and after wastewater treatment.^7–13 A number of studies have focused on the secondary biological treatment step, where EOCs undergo various transformations, such as dilution, sorption, volatilization, and abiotic and biological transformations. Among these, biotransformation is considered to contribute most to the EOC removal.¹⁰ Moreover, for the biological treatment using nitrifying activated sludge (NAS), nitrifiers are likely playing important roles in EOC removal.^14–18 It has been observed in batch experiments that the removal of some EOC compounds was enhanced/decreased as the nitrification activity was stimulated/inhibited.^19–21 In addition, pure culture studies have shown the capabilities of ammonia-oxidizing microorganisms to biotransform several EOCs, such as 17α-ethynylestradiol, bisphenol A, mianserin, and ranitidine.^{14,15,22–25} However, the NAS community studies and pure culture studies still have limitations in understanding the contribution of the nitrifiers to EOC removal in WWTPs. In the NAS studies, it is difficult to disentangle the roles of co-existing heterotrophs from those of the nitrifiers. In pure culture studies, the actual conditions in WWTPs such as nitrifier diversity and temperature effects are usually not taken into consideration. To get a better understanding of the contributions of nitrifiers to EOC removal in WWTPs, more environmentally relevant samples are needed.

In some WWTPs located in cold regions, an enhanced nitrification step is implemented after the secondary biological treatment to refine ammonia removal in winter when the ammonia oxidation activity decreases in the aeration tank. This step typically uses a trickling tower, which receives the secondary effluent with a low BOD level (<5 mg L⁻¹), thus autotrophs such as ammonia oxidizers and oligotrophs are favored over typical heterotrophs. This full-scale bioreactor with enriched nitrifiers serves as a more relevant setup to study the actual roles played by nitrifiers/nitrification in EOC biotransformation under real wastewater treatment conditions. However, little has yet been known about EOC removal during such an enhanced nitrification treatment. The hypothesis was that if nitrifiers contributed to EOC removal in WWTPs, a similar biotransformation performance would be observed in the nitrification tower as previously reported in lab-scale nitrifying enrichments and pure ammonia-oxidizing cultures.^14–25

Analytical power is essential for comprehensive EOC monitoring. There are thousands of EOCs occurring at very low levels (ng–μg L⁻¹). Although with low detection limits, the commonly used target screening by LC-MS/MS requires reference compounds and is low-throughput. This becomes tedious and even impractical when researchers intend to monitor unknown EOC species in large quantities. Suspect screening using LC-high resolution mass spectrometry (LC-HRMS) emerges as a high-throughput method. It links features in mass spectral acquisitions to suspect compounds. Several parameters, such as accurate masses and isotopic patterns, are employed to identify suspect hits and confirm their formulas. This method allows us to target an extensive list of EOCs by compiling a comprehensive suspect database containing thousands of compounds of interest. Several studies have exploited suspect screening to detect EOCs in various matrices, such as wastewater,^26,27 surface water,²⁸ lake sediments,²⁹ and processed animal products.³⁰ More recently, a sensitive and comprehensive suspect screening workflow has been developed to characterize the occurrence of EOCs in water samples.³¹ However, quantification of the EOCs identified by the high-throughput suspect screening still remains challenging due to the limited available reference compounds and their corresponding internal standards.³² To have an accurate estimation of the occurrence levels of EOCs without reference compounds, a prediction of the ionization efficiencies on HRMS is needed. In previous studies, the response factor (RF), which is the ratio of the signal intensity and the concentration of a substance in the solution, was used to represent the ionization efficiency.^33–36 Models have been constructed to correlate the RF to the physical and chemical properties of investigated compounds, such as nonpolar and polar surface areas, proton affinity, molecular volume, retention time and octanol–water distribution coefficient (logD).^33–37 These semi-quantification methods have been applied in the semi-quantification of suspected substances in peptides, metabolite samples, and acidic analytes.^{33,34,36,38,39} Semi-quantification is a more practical way when analyzing the occurrence levels of a large number of compounds in environmental samples and the corresponding reference compounds are not available. However, high-throughput suspect screening and semi-quantification have not yet been widely applied to study the occurrence levels and removals of a broad range of EOCs during each treatment step in WWTPs.³²

The goal of this study was to have a better understanding of EOC occurrence and fate in wastewater treatment processes, particularly the EOC removal during the enhanced nitrification step. A comprehensive database of EOC suspects was compiled and a suspect screening workflow was used to determine the seasonal occurrence and removal of EOCs in each treatment step at two local municipal WWTPs with the enhanced nitrification step. Furthermore, a semi-quantification method was established to estimate EOC occurrence levels. The contributions of each treatment step, including the enhanced nitrification step to EOC removals were determined. The seasonal and locational effects on EOC removal were also evaluated. The detected EOCs were prioritized according to their occurrence levels, overall removals, and popularity in previous monitoring records in WWTPs.

2. Materials and methods

2.1 Reference compounds

Standards of EOCs, including amantadine, O-desmethyl venlafaxine, N,N-diethyl-meta-toluamide (DEET), dimethachlor, metaxalone, nalidixic acid, padimate O, paracetamol, trimethoprim, ranitidine, N4-acetylsulfamethoxazole and benzotriazole were purchased from Sigma-Aldrich (Steinheim, Germany). Two other EOCs, fluconazole and flucytosine were obtained from AK Scientific (Union City, USA). The internal standard, trimethoprim-d3 was purchased from CDN Isotopes (Quebec, Canada). Individual stock solutions of the reference compounds and internal standards were prepared in methanol with a concentration of 1 g L⁻¹, and stored at −20 °C until use.

2.2 Sampling campaign

Two local municipal wastewater treatment plants (plant NE and SW) were selected. The treatment process of plant NE includes a primary clarifier, a secondary biological treatment, a secondary clarifier, a nitrification tower and a tertiary treatment (cloth disk filtration). Plant SW consists of the same treatment steps as plant NE, except that the primary clarifier is replaced with an anaerobic compartment for enhanced biological phosphorous removal (EBPR) (Fig. 1). More detailed operational parameters of the two WWTPs are shown in Table S1.† Time-proportional, 24-hour composite samples were collected in July (summer) and November (winter) 2016. The total volume of each sample was 500 mL. Samples were cooled on ice during sampling and transportation for laboratory analyses.

2.3 Solid phase extraction

A previously reported solid phase extraction (SPE) procedure⁴⁰ was applied immediately when the samples arrived at the laboratory. In general, the samples were filtered through GF/F filters (ca. 0.7 μm pore size, Whatman), and the pH was adjusted to 6.5 ± 0.2 with formic acid or ammonia hydroxide. The SPE recovery was accounted for by adding the internal standard to 500 mL of the filtered sample at a concentration of 100 ng L⁻¹. Cartridges were manually assembled, which from top to bottom consist of 200 mg Oasis HLB (Waters, Milford, MA), 350 mg mixture (1:1:1.5) of Strata-X-AW (Phenomenex, Torrance, CA), Strata-X-CW (Phenomenex, Torrance, CA) and IsoluteENV+ (Biotage, Charlotte, NC), and 200 mg ENVI-Carb (Sigma-Aldrich, St. Louis, MO), to recover a broad range of neutral, anionic, and cationic compounds. The cartridges were conditioned with 10 mL of methanol followed by 15 mL of nanopure water. The samples were passed through the SPE cartridges at a maximum rate of 10 mL min⁻¹ using a vacuum manifold. The cartridges were dried by passing nitrogen through for at least 2 h. The analytes were then eluted using a basic solution (6 mL of ethyl acetate/methanol (50/50, v/v) containing 0.120 mL of 25% ammonia hydroxide solution), followed by an acidic solution (3 mL of ethyl acetate/methanol (50/50, v/v) containing 0.051 mL of >98% formic acid solution). The eluent was dried under a gentle flow of nitrogen, then re-dissolved in 500 μL of nanopure water (1000 times concentration). Finally, the re-dissolved samples were subjected to centrifugation at 10000 rpm for 10 min at room temperature, and the supernatant was collected and stored at 4 °C until LC-HRMS analysis (max. storage time was 3 weeks). The recoveries of the SPE process (ranged from 4% to 24% for different samples) were calculated based on the LC-HRMS signal intensities of the internal standards, which were used to correct the signal intensities of the EOC suspect hits.

2.4 Analytical method

The SPE-enriched samples were analyzed on a high-performance liquid chromatograph (HPLC) coupled with a high-resolution mass spectrometer (HRMS) (Q Exactive, Thermo Fisher Scientific). Briefly, a 50 μL sample was loaded into a C₁₈ Atlantis-T3 column (particle size 3 μm, 3.0 × 150 mm) (Waters, Milford, CA), and eluted with nanopure water + 0.1% formic acid (A) and acetonitrile + 0.1% formic acid (B) at a gradient of 5% B (0–1 min), 5–100% B (1–8 min), 100% B (8–20 min) and 5% B (21–26 min) with a flow rate of 0.35 mL min⁻¹. The HRMS was equipped with electrospray ionization, and full-scan mass spectra at an m/z range of 100 to 1500 in the positive and negative modes with a resolution of 70000 at 200 m/z were acquired. Data-dependent tandem mass spectra (MS²) were obtained at the exact masses of all investigated EOCs with a normalized collision energy (NCE) of 27.5.

2.5 EOC suspect database compilation and data processing workflow

A database of the EOC suspects was compiled containing 1225 compounds, which are classified into five categories based on their applications: pharmaceuticals (403), personal care products (77), pesticides (619), industrial chemicals (37) and human/animal metabolites (89) (see details in the ESI† Excel file). Compound names, molecular formulas, and exact masses of [M], [M + H]⁺, and [M − H]⁻ are included in the database for suspect screening analysis. MarvinSketch (NET6.2.0, 2014) was used for drawing, displaying, and characterizing chemical structures (ChemAxon, http://www.chemaxon.com).

We adopted a previously reported suspect screening workflow with modifications to characterize the occurrence of EOCs (Fig. 2).³¹ Briefly, the suspect hits were acquired from the full scan mass spectra using TraceFinder v4.1 EFS software (Thermo Fisher Scientific) against the self-compiled suspect database using exact masses of [M + H]⁺ and [M − H]⁻ in the positive and negative modes, respectively. The suspect hits were identified using the following criteria: i) mass tolerance <5 ppm, ii) isotopic pattern score >70%, iii) signal intensities above 5 × 10⁶ after correction with SPE recoveries, and iv) signal-to-noise ratio >300. For the suspect hits identified in both polarity modes, the one resulting in a higher signal intensity was used for further analysis.

2.6 Structure validation

The structures of the identified suspect hits were validated using MS² spectra obtained from i) commercially available standard compounds of 14 suspect EOCs chosen from different categories with various abundance levels and great environmental concerns and ii) the MassBank database (http://massbank.eu) under similar LC-HRMS/MS conditions (e.g., instrument, column, and collision energy). Based on previously proposed confidence levels of structure elucidation,⁴¹ the structure-validated EOC suspects were assigned to two levels: level 1 includes suspects whose MS² profiles match those of the reference standards, thus they are structure-confirmed; level 2 includes suspects with probable structures, whose MS² profiles match those from the literature or library (MassBank) recorded with similar acquisition parameters (e.g., instrumentation, ionization, resolution, and collision energy).

2.7 Estimation of EOC occurrence levels and removals

The quantification of the 11 structure-confirmed EOCs was conducted with the corresponding reference compounds. Specifically, the standard solutions with serial dilution were used to obtain the calibration curves. The matrix effect was taken into account using the internal standard. The slope of the calibration curve represents the RF of the respective compound, which is defined according to eqn (1):

	(1)

The signal intensities of the EOC suspects to be quantified were corrected with SPE recovery, which was calculated using the recovery of the added internal standard and assuming it to be the same for all EOC suspects. The corrected signal intensity of each EOC suspect was used for quantification and semi-quantification.⁴²

For the semi-quantification, Spearman's rank correlation was used to test the statistical associations between RF and the physical and chemical properties of the reference compounds, as well as their retention times. The physical and chemical properties (i.e., polar surface area, logD, pK_a, and molecular volume; see details in Table S3†) were obtained from PubChem and ChemAxon (https://chemicalize.com/#/calculation). The parameter most significantly correlated with RF was used for estimating the RF of the structure-validated EOC suspects without available reference compounds based on eqn (1).

The overall removal (R_tot) was calculated using eqn (2) to evaluate the persistence of EOCs and EOC residual levels in the WWTP effluent, where SI_1,in represents the signal intensity in the influent of step 1 (i.e., primary clarifier) and SI_6,eff represents the signal intensity in the effluent of step 6 (i.e., tertiary disk filter).

	(2)

The relative removal (R) of each treatment step was determined by eqn (3) to represent the relative contribution of the treatment step i to the overall EOC removal, where R_i is the relative removal of treatment step i, SI_i,in represents the signal intensity in the influent of step i, and SI_i,eff represents the signal intensity in the effluent of step i

	(3)

Single-step removal (R_SS) was used to evaluate the biotransformation performance of each individual treatment step, which is determined by eqn (4).

	(4)

The term “collective removal” is used to represent the overall/relative/single-step removal of all detected EOCs under each of the five categories, which was estimated by bringing the sum of the signal intensities of all EOCs in a certain category into eqn (2)–(4).

3. Results and discussion

3.1 Occurrence of EOCs in WWTPs

After the suspect screening process, among the 1225 EOCs in the suspect list, 292–341 suspect hits with a match of the exact mass of [M + H]⁺ or [M − H]⁻ were retrieved in the influent of the two plants (Fig. 3A). These include 34–41% pharmaceuticals, 27–31% pesticides, 12–14% PCPs, 4–5% industrial chemicals, and 14–15% metabolites. We then validated the structures of these EOC suspect hits by comparing their MS² spectra with those of the corresponding reference compounds or with those available from the MassBank database. The MS² spectra of the reference compounds for 14 EOC suspects were obtained by HPLC-HRMS/MS, and 11 of them were structurally confirmed, which were considered with an identification confidence level 1 (Fig. 3B).⁴¹ The structures of those 11 confirmed suspect hits include pharmaceuticals (i.e., amantadine and paracetamol), pesticides (e.g., DEET), industrial chemicals (i.e., benzotriazole), and metabolites (i.e., O-desmethylvenlafaxine) (Table S2†). Another 72 EOC suspect hits were subjected to structure validation according to the MS² spectra available in MassBank. Forty-five of them had MS² spectra matching those in the library under similar instrumentation conditions. Therefore, the probable structures of these 45 suspect hits were proposed at confidence level 2,⁴¹ which indicates a match with the library spectrum data, but without further confirmation with the reference compounds. For the other 27 tested suspect hits, their MS² spectra did not match those in the library. Thus, only the formula can be confirmed according to the exact mass and the matching score of the isotopic patterns. Their actual structures were not the ones suspected and remained elusive. Collectively, the suspect screening exhibited an overall successful ratio of 65% (56/86) for structure validation, with 91% (32/35), 15% (3/20), 50% (4/8), 50% (4/8), and 87% (13/15) for the tested pharmaceuticals, pesticides, PCPs, industrial chemicals, and metabolites, respectively (Fig. 3B). Validation details can be found in Table S2 of the ESI.† Using the suspect screening and structure validation workflow, we detected some EOCs that were rarely targeted in major review articles with occurrence records in wastewater,^{9,10,62,91–97} including pharmaceuticals, such as cefalexin, citalopram, fexofenadine, flecainide, lignocaine, losartan, metaxalone, and oxcarbazepine; PCPs such as triethanolamine; industrial compounds such as triethylphosphate; and human/animal metabolites such as N4-acetylsulfamethoxazole (Table 1).

---

For the 11 structure-confirmed EOCs with reference compounds, their occurrence levels were quantified by constructing calibration curves with a limit of quantification of 500 ng L⁻¹ with HPLC-HRMS (c.a., 2–12.5 ng L⁻¹ in actual samples given a recovery of 4–24% and a concentration factor of 1000 during SPE). To have a better estimation of the occurrence level of the structure-validated EOC suspect hits without reference compounds available, we built a semi-quantification framework based on the RFs of the reference compounds of the 11 confirmed EOCs by HPLC-HRMS. For the compounds that form adducts in both the positive and negative modes, the signal intensity in the negative mode was much lower than that in the positive mode (Table S4†). As most of the structure-validated compounds were detected in the positive mode, the RFs in the positive mode were used to investigate their correlation to the physical and chemical properties of the compounds. The polar surface area, logD, pK_a, molecular volume, and RT of the 11 reference compounds (Table S3†) were correlated to their respective RFs using Spearman's correlation test. A significant positive correlation between the RF and logD was observed (Spearman's ρ = 0.70; P < 0.05), as well as a significant negative correlation between the RF and polar surface area (Spearman's ρ = 0.64; P < 0.05) (Table S5†). No significant correlation was observed for the other compound properties or RT. Therefore, logD values were used for the semi-quantification. In general, the RF of the reference compound that has a similar logD value with the target compound was used for concentration calculations. For instance, the logD of metformin, an EOC suspect without a reference compound is −1.3, which is closest to the logD of the reference compound flucytosine (−0.9). Therefore, the RF of flucytosine (0.36) was used to calculate the concentration of metformin. We acknowledge that this semi-quantification method may still introduce bias in comparison to the standard quantification method using the calibration curves of the reference compounds. Nevertheless, given the challenge of obtaining reference compounds for a large number of suspects, this method provides an easier and a more rapid estimation of the occurrence levels, more accurate than a typical semi-quantification using a single RF without taking consideration of the difference in the ionization efficiency. Furthermore, the semi-quantification could serve as a pivotal analyzing step to prioritize the compounds with highest estimated concentrations and narrow down the compound list for further quantitative analysis with reference compounds.

The occurrence levels of detected EOCs in the influent samples were then determined using quantification or semi-quantification methods. Pharmaceuticals such as gabapentin, metformin, caffeine, paracetamol, and valsartan were detected with high abundance (at μg L⁻¹ level) (Table 1). Particularly, metformin, a type II diabetes medicine, exhibited the highest level among all the detected pharmaceuticals with an influent concentration of 36–73 μg L⁻¹ and an overall removal of 74–97%. The concentration levels in the German sewage wastewater influent were in the 100 μg L⁻¹ range, which was comparable with our detection records.⁴³ The concentration of metformin is also in line with another recent study, where the occurrence of metformin in stream water was pervasive (max. ∼5 μg L⁻¹), suggesting a potential release from WWTPs.⁴⁴ The concentrations of other high-level pharmaceutical contaminants were comparable to the studies on EOC occurrence in WWTPs.^{10,11,45–51} For example, the high occurrence level of paracetamol (24.9–46.6 μg L⁻¹) in this study was similar to that in the range (1.57–56.9 μg L⁻¹) reported for WWTPs located in other countries.¹⁰ High levels (2.5–6.2 μg L⁻¹) of the pesticide DEET, an insect repellent, were observed in both the summer and winter seasons (Table 1). The detected influent concentration of DEET (6.2 μg L⁻¹) was about 2-fold higher than the maximum influent concentration (3.19 μg L⁻¹), which was the highest value among other WWTPs.^{10,49,52–55} Among the detected PCPs, triethanolamine, which is widely used in makeup products, sunscreens, and fragrances to help form emulsions, showed the highest concentration (3.1–5.7 μg L⁻¹), and benzotriazole, methylbenzotriazole, and triethylphosphate were found to have high concentrations among the detected industrial compounds (Table 1). The detected metabolites were all derived from pharmaceuticals, of which N4-acetylsulfamethoxazole, atenolol acid, and O-desmethylvenlafaxine were found with high influent concentrations. It is noteworthy that their influent concentrations were all higher than the corresponding parent compounds. These pharmaceutical metabolites with high occurring concentrations were likely formed by metabolism in human/animal bodies as the corresponding parent drugs were being consumed,^56–60 and were then discharged into WWTPs. This indicates the importance of monitoring the metabolites of EOCs (particularly pharmaceuticals) that are formed before entering WWTPs to obtain a more comprehensive understanding of EOC occurrence and fate in WWTPs.

Seasonal variations of the EOC influent concentrations were observed (Table 1). As the daily average flow of raw sewage was similar for the two sampling times (Table S1†), the seasonal variations to some extent suggest consumption patterns of certain EOCs. A number of pharmaceuticals exhibited higher influent concentrations in winter, such as carbamazepine, gabapentin, metformin, paracetamol and ranitidine, whereas some such as fexofenadine treating season-related allergy symptoms occurred at higher levels in summer when there is a high incidence of allergy. Some industrial chemicals, including benzotriazole and triethylphosphate, as well as the common pesticide DEET showed higher influent concentrations in summer.

3.2 Overall removals of the EOCs

We first looked at the collective overall removals of all detected EOCs under each of the five categories. The pharmaceuticals, PCPs, and pesticides showed overall removals of >80%, whereas the industrial chemicals and metabolites exhibited relatively lower removals (50–80%) (Fig. 4). The collective overall removals of PCPs, industrial chemicals and metabolites varied over seasons and locations. The non-metric multidimensional scaling analysis indicates that the seasonal effect on EOC removal was more significant than that of location (Fig. S1†). The similar components of municipal wastewater collected from the local area by the two WWTPs and the similar treatment processes (i.e., secondary biological treatment followed by an enhanced nitrification step and a tertiary filtration treatment) probably result in similar occurrence and overall removals of EOCs. In contrast, the change of wastewater temperature between the summer (23 °C) and winter (18 °C) seasons might affect the activities of some microorganisms in the activated sludge, thus affecting the EOC removal via biological processes, as biodegradation can be greatly affected by temperature.^61,62 Sorption, which is another removal mechanism for a few EOCs, was less sensitive to temperature change according to previous studies.^63,64 Volatilization could be another removal mechanism, particularly for volatile EOCs. However, volatilization of the EOC suspects was ignored given that volatilization did not contribute significantly in previous studies of EOC removal by activated sludge,¹⁰ and most of the suspects in this study were small, polar compounds with rather low volatility. For individual EOCs (structurally validated), the following compounds showed higher removals in summer: citalopram, fexofenadine, flecainide, and oxcarbazepine, whereas metaxalone, atenolol, and methylbenzotriazole exhibited higher removals in winter (Table 1). Previous studies also revealed seasonal effects on EOC removals wherein a higher removal was observed in summer than in winter, although with a few exceptions, like diclofenac acid and sulfadiazine, which exhibited lower removals in summer in a municipal WWTP in Xiamen, China.⁶⁵

Although >80% collective overall removals were observed for the detected EOCs in general, one should note that a number of specific EOC compounds underwent <80% overall removal (Table 1). Two pervasively used corrosion inhibitors, benzotriazole and methylbenzotriazole, had low removal efficiencies (33–71%), which is consistent with previous findings and suggests the inability of conventional WWTPs to effectively remove benzotriazoles.^66–69 Most of the less frequently targeted pharmaceuticals had low removal efficiencies, such as citalopram, fexofenadine, flecainide, lignocaine, and losartan. Besides these drugs, carbamazepine, metoprolol, metronidazole, sulfamethoxazole, tramadol, trimethoprim, and venlafaxine were also transformation during the treatment processes. The removals of some of these compounds varied between seasons or locations. For example, the overall removals of citalopram, fexofenadine, flecainide, and oxcarbazepine were substantially higher in summer than in winter, and higher in plant NE than in plant SW (Table 1). In contrast, metformin, valsartan, and metaxalone showed higher removals in winter (Table 1). Some of the less-removed pharmaceuticals were also found to be persistent in other conventional municipal WWTPs, including carbamazepine, trimethoprim, metoprolol, and sulfamethoxazole.^{10,46–50,70–74} Notably, some of the less-removed pharmaceuticals such as fexofenadine, oxcarbazepine, tramadol, ranitidine, metaxalone, citalopram, and venlafaxine were detected in surface water in the U.S. with concentrations over 0.1 μg L⁻¹.³ This strongly indicates that the incomplete removal of these compounds in WWTPs will lead to their release into the receiving water bodies, causing potential environmental risks. It is worth noting that for EOCs detected at levels >10 μg L⁻¹ in the influent, such as gabapentin and metformin, even with high overall removals, it is still possible to have their residuals enter the receiving environments with concentrations at μg L⁻¹ levels in the WWTP effluent. Therefore, more attention is required in future monitoring and regulation practices for the EOCs listed in Table 1 with high occurring levels, low or unstable overall removals, and those that have been overlooked before.

3.3 Removal of EOCs in the wastewater treatment steps

The contribution of each treatment step, i.e., primary treatment, secondary treatment, the enhanced nitrification step, and the tertiary filtration step to the overall removal was evaluated by comparing the relative removal of EOCs in each step. The secondary biological treatment played the most important role in the overall removal of EOCs under different categories (Fig. 4). One major difference in the secondary biological treatment between the two plants is that plant SW employs an anaerobic enhanced biological phosphorus removal (EBPR) process prior to the aeration tank, whereas plant NE only has the aerobic process. Biotransformation of EOCs occurred in both the anaerobic and aerobic compartments of plant SW. Anaerobic biotransformation accounted for more than 50% of the biological removal, particularly in winter (Fig. 4). Industrial chemicals and metabolites exhibited rather low summer removals in the anaerobic compartment compared to the winter removals (Fig. 4). The significant contribution of the anaerobic biological process was also reported in several monitoring studies. For example, some antibiotics, such as tetracycline, and ofloxacin, showed a substantial removal (>50%) in summer,¹³ and other pharmaceuticals including caffeine and trimethoprim were removed by 20–40% in the anaerobic reactors for wastewater treatment.⁵² Many EOCs, including pesticides, endocrine disrupting chemicals, PCPs, and pharmaceuticals were substantially removed (>50%) by activated sludge, under either aerobic or anaerobic conditions.⁷⁵ Anaerobic biotransformation could complement the overall removal of certain EOCs, which might be transformation to aerobic treatment, thus offering better overall removals. Given the difference in redox potential, EOCs highly likely underwent distinct biotransformations under anaerobic and aerobic conditions.

Although biological removal contributed the most to the overall EOC removal, a number of compounds resistant (single-step removal <20%) to biotransformation were identified, including prometon (pesticide) and several pharmaceuticals (i.e., venlafaxine, tramadol, losartan and sulfamethoxazole) (Table S6†). Similarly, low removals of venlafaxine and tramadol by activated sludge were observed in batch reactor studies.^19,76–78 Given the limited monitoring records of prometon and losartan, we fill the knowledge gap regarding their fate in WWTPs by observing their poor removals during the secondary biological treatment. This suggests that more attention should be given to these compounds in future monitoring efforts.

In general, primary and secondary clarifiers, as well as the tertiary filtration step did not contribute to EOC removal. In contrast, negative collective removals were observed, indicating the potential formation of parent EOCs in these treatment steps.

The enhanced nitrification step accounts for up to 27.5% removal of EOCs in the influent suggesting that the enhanced nitrification step made a small contribution to the EOC overall removal under different categories. For the EOCs with high removals during the secondary biological treatment, this is not surprising because the EOCs have already been removed to a large extent prior to entering the nitrification tower. The lowered influent concentrations of EOCs in the enhanced nitrification step resulted in a smaller proportion of the total amount removed in this step. Therefore, single-step EOC removals that directly indicate the removal capability of each treatment step were further analyzed in section 3.4 to evaluate the performance of the enhanced nitrification step on EOC transformation.

3.4 EOC removal during the enhanced nitrification step

In general, plant SW exhibited better removals for all five EOC categories than plant NE, and pesticides and PCPs were more effectively removed than the others (Fig. 5). The collective removal of all detected pesticides and PCPs reached up to 70%. The removals for pharmaceuticals and metabolites were 40–50% in plant SW, and lower in plant NE, particularly in the summer season. The nitrification towers did not show good removals for industrial chemicals, with only ∼20% in plant SW, and no removal in plant NE. Overall, the enhanced nitrification step showed moderate removal of EOCs in both WWTPs.

The influent BOD of the nitrification tower at the two plants were about the same (5 mg L⁻¹) with little removal (<20%) in the effluent, indicating low heterotrophic activities in the nitrification towers. The nitrification tower at plant NE was receiving a higher concentration of ammonium (0.8–6.5 mg L⁻¹) than the one at plant SW (0.1–0.14 mg L⁻¹) in both the summer and winter seasons (Table S1†). It is likely that in the nitrification tower at plant SW, nitrifiers with high affinities to substrates can be more favorably enriched than in the nitrification tower at plant NE. Higher substrate affinities might result in the higher removal of EOCs in the nitrification tower of plant SW, given that a number of EOCs were likely co-metabolized by ammonia monooxygenases, which exhibited diverse substrate affinities between different ammonia oxidizers.^{10,14,15,24,79–81}

The removal of beta blockers, atenolol and metoprolol under the nitrification conditions was inconsistent in previous studies. Park et al. found that the biotransformation of the two structurally similar beta blockers were correlated with ammonia oxidation.⁸⁰ However, Sathyamoorthy et al. observed biodegradation of atenolol by nitrification enrichment culture, but not metoprolol.⁸¹ Similar to Sathyamoorthy et al.'s study, our results showed that the single-step removal of atenolol in the enhanced nitrification step ranged from 32.3% to 76.2%, suggesting the positive role of the enhanced nitrification step in removing atenolol, whereas negative removals of metoprolol were observed (−58.6% in summer and −3.4% in winter, NE) (Table S6†). Sulfamethoxazole was slightly removed in the enhanced nitrification step, ranging from −28.4% to 26.2%. This observation was in agreement with a previous finding that sulfamethoxazole was not removed by ammonia-oxidizing microorganisms.¹⁹ It has been validated that ranitidine can be biotransformed by ammonia oxidizers.²⁴ In this study, ranitidine was removed by 25.7–68.1% in the enhanced nitrification step, indicating that nitrifiers likely contributed to the ranitidine removal (Table S6†). Inconsistency regarding the role of nitrifiers in trimethoprim biotransformation was revealed by different studies. Some demonstrated that the nitrifying cultures were not able to biotransform trimethoprim,^15,82 others claimed that ammonia-oxidizing bacteria are highly involved in the removal of this antibiotic.^17,19 The results in this study favor the latter observation since trimethoprim showed positive removal during the enhanced nitrification step in three out of the four samples (Table S6†).

Notably, for some EOCs (i.e., fexofenadine, losartan, and trimethoprim), higher biotransformation capabilities were observed in the enhanced nitrification step than in the secondary biological treatment step at winter time when the removals of the three compounds in the secondary biological treatment step substantially decreased while those in the nitrification tower were retained or even enhanced (Fig. 6). The decreased biotransformation activities in the secondary biological treatment is likely caused by the decrease in the activities of some microorganisms in the activated sludge, which were sensitive to temperature change. As the attached growth of microorganisms in the nitrification tower makes them less sensitive to temperature change than the suspended growth in the secondary biological compartment,^10,76,83 more stable EOC biotransformation activities were observed in the nitrification tower when the temperature is lower. This suggests that enriched nitrifiers with attached growth in the nitrification tower could complement not only the ammonia removal, but also the biotransformation of some EOCs at low temperatures in winter.

3.5 Reverse transformation from in-plant transformation products (TPs) to parent compounds

It is interesting that some parent compounds were formed (negative removals) during certain treatment steps. The regeneration of parent EOCs after the primary treatment (step 1 in Fig. 7), particularly pharmaceuticals, was likely attributed to the deconjugation of corresponding conjugated forms, which were typically generated during human/animal metabolism and released into raw sewage.^61,84–86 Desorption from biomass or particles could also contribute to the parent compound formation, particularly in the secondary clarifier (step 3 in Fig. 7).^10,87,88 Moreover, our results provide support for the occurrence of the previously reported reverse transformation from non-conjugated TPs to the parent compounds for some pharmaceuticals.^89,90 The non-conjugated TPs in our study were the same as the human/animal metabolites of some pharmaceuticals detected in the WWTP influent. It was found that the removal/formation of parent compounds was in line with the formation/removal of the non-conjugated TPs for venlafaxine, galaxolide, and citalopram as indicated in Fig. 7. For venlafaxine, transformation from one to another among the three metabolites/TPs was also observed. Although its underlying mechanism still remains elusive, this phenomenon provides a possible reason for the low removals of some pharmaceuticals and suggests the need of monitoring both parent compounds and the corresponding metabolites/TPs in WWTPs.

4. Conclusion

This study gives a comprehensive view of EOC occurrence and removal during each wastewater treatment step, particularly during the enhanced nitrification step in two WWTPs by employing a high-throughput suspect screening workflow. A semi-quantification workflow was also established to estimate the concentrations of various EOCs detected by the suspect screening in environmental samples without available authentic standards. The following findings were obtained: first, a number of EOCs with high occurrence levels, low or unstable overall removals, or limited monitoring records were identified; second, biotransformation in the secondary biological treatment step (either aerobic or anaerobic) contributed the most to the overall removal of EOCs. The change in temperature influenced EOC transformation, particularly in the secondary biological treatment, and complementary biotransformation of some EOCs was achieved by the enhanced nitrification step at winter time. Nutrient limitation in the nitrification tower might favor the growth of microbes with higher affinity to EOCs, leading to more efficient EOC removal. Third, the TP-to-parent transformation was likely occurring for some EOCs in the treatment processes. Collectively, the above findings expand our knowledge of EOC occurrence and transformation in WWTPs, which will help prioritize EOCs for future monitoring and regulation purposes. By particularly looking at the enhanced nitrification step, this study also provides insights into the roles played by nitrifiers in EOC removal from a more environmentally relevant perspective.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

We would like to give thanks to Mr. Bruce Rabe from the Urbana and Champaign Sanitary District for his kind help with the sampling campaigns and to Dr. Lucas Li from the Roy J. Carver Biotechnology Center for the HPLC-HRMS/MS measurements.

References

1. P. Arnnok, R. R. Singh, R. Burakham, A. Pérez-Fuentetaja and D. S. Aga, Selective uptake and bioaccumulation of antidepressants in fish from effluent-impacted Niagara River, Environ. Sci. Technol., 2017, 51, 10652–10662 CrossRef PubMed .
2. M. M. Schultz, E. T. Furlong, D. W. Kolpin, S. L. Werner, H. L. Schoenfuss, L. B. Barber, V. S. Blazer, D. O. Norris and A. M. Vajda, Antidepressant pharmaceuticals in two U.S. effluent-impacted streams: Occurrence and fate in water and sediment, and selective uptake in fish neural tissue, Environ. Sci. Technol., 2010, 44, 1918–1925 CrossRef PubMed .
3. P. M. Bradley, C. A. Journey, K. M. Romanok, L. B. Barber, H. T. Buxton, W. T. Foreman, E. T. Furlong, S. T. Glassmeyer, M. L. Hladik, L. R. Iwanowicz, D. K. Jones, D. W. Kolpin, K. M. Kuivila, K. A. Loftin, M. A. Mills, M. T. Meyer, J. L. Orlando, T. J. Reilly, K. L. Smalling and D. L. Villeneuve, Expanded target-chemical analysis reveals extensive mixed-organic-contaminant exposure in U.S. streams, Environ. Sci. Technol., 2017, 51, 4792–4802 CrossRef PubMed .
4. Y. Shi, R. Vestergren, Z. Zhou, X. Song, L. Xu, Y. Liang and Y. Cai, Tissue distribution and whole body burden of the chlorinated polyfluoroalkyl ether sulfonic acid F-53B in crucian carp (Carassius carassius): Evidence for a highly bioaccumulative contaminant of emerging concern, Environ. Sci. Technol., 2015, 49, 14156–14165 CrossRef PubMed .
5. N. Melnyk-Lamont, C. Best, M. Gesto and M. M. Vijayan, The antidepressant venlafaxine disrupts brain monoamine levels and neuroendocrine responses to stress in rainbow trout, Environ. Sci. Technol., 2014, 48, 13434–13442 CrossRef PubMed .
6. B. Campos, C. Rivetti, T. Kress, C. Barata and H. Dircksen, Depressing antidepressant: Fluoxetine affects serotonin neurons causing adverse reproductive responses in Daphnia magna, Environ. Sci. Technol., 2016, 50, 6000–6007 CrossRef PubMed .
7. E. Archer, B. Petrie, B. Kasprzyk-Hordern and G. M. Wolfaardt, The fate of pharmaceuticals and personal care products (PPCPs), endocrine disrupting contaminants (EDCs), metabolites and illicit drugs in a WWTW and environmental waters, Chemosphere, 2017, 174, 437–446 CrossRef PubMed .
8. M. Gros, M. Petrovic and D. Barcelo, Development of a multi-residue analytical methodology based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) for screening and trace level determination of pharmaceuticals in surface and wastewaters, Talanta, 2006, 70, 678–690 CrossRef PubMed .
9. B. Petrie, R. Barden and B. Kasprzyk-Hordern, A review on emerging contaminants in wastewaters and the environment: Current knowledge, understudied areas and recommendations for future monitoring, Water Res., 2015, 72, 3–27 CrossRef PubMed .
10. Y. Luo, W. Guo, H. H. Ngo, L. D. Nghiem, F. I. Hai, J. Zhang, S. Liang and X. C. Wang, A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment, Sci. Total Environ., 2014, 473–474, 619–641 CrossRef PubMed .
11. C. I. Kosma, D. A. Lambropoulou and T. A. Albanis, Investigation of PPCPs in wastewater treatment plants in Greece: Occurrence, removal and environmental risk assessment, Sci. Total Environ., 2014, 466–467, 421–438 CrossRef PubMed .
12. P. Guerra, M. Kim, A. Shah, M. Alaee and S. A. Smyth, Occurrence and fate of antibiotic, analgesic/anti-inflammatory, and antifungal compounds in five wastewater treatment processes, Sci. Total Environ., 2014, 473, 235–243 CrossRef PubMed .
13. L. J. Zhou, G. G. Ying, S. Liu, J. L. Zhao, B. Yang, Z. F. Chen and H. J. Lai, Occurrence and fate of eleven classes of antibiotics in two typical wastewater treatment plants in South China, Sci. Total Environ., 2013, 452–453, 365–376 CrossRef PubMed .
14. J. Shi, S. Fujisawa, S. Nakai and M. Hosomi, Biodegradation of natural and synthetic estrogens by nitrifying activated sludge and ammonia-oxidizing bacterium Nitrosomonas europaea, Water Res., 2004, 38, 2323–2330 CrossRef PubMed .
15. W. O. Khunjar, S. A. Mackintosh, J. Skotnicka-Pitak, S. Baik, D. S. Aga and N. G. Love, Elucidating the relative roles of ammonia oxidizing and heterotrophic bacteria during the biotransformation of 17α-ethinylestradiol and trimethoprim, Environ. Sci. Technol., 2011, 45, 3605–3612 CrossRef PubMed .
16. Q. Sun, Y. Li, P.-H. Chou, P.-Y. Peng and C.-P. Yu, Transformation of bisphenol A and alkylphenols by ammonia-oxidizing bacteria through nitration, Environ. Sci. Technol., 2012, 46, 4442–4448 CrossRef PubMed .
17. A. L. Batt, S. Kim and D. S. Aga, Enhanced biodegradation of iopromide and trimethoprim in nitrifying activated sludge, Environ. Sci. Technol., 2006, 40, 7367–7373 CrossRef PubMed .
18. T. Yi and W. F. Harper, The link between nitrification and biotransformation of 17α-ethinylestradiol, Environ. Sci. Technol., 2007, 41, 4311–4316 CrossRef PubMed .
19. Y. Men, S. Achermann, D. E. Helbling, D. R. Johnson and K. Fenner, Relative contribution of ammonia oxidizing bacteria and other members of nitrifying activated sludge communities to micropollutant biotransformation, Water Res., 2017, 109, 217–226 CrossRef PubMed .
20. D. E. Helbling, D. R. Johnson, M. Honti and K. Fenner, Micropollutant biotransformation kinetics associate with WWTP process parameters and microbial community characteristics, Environ. Sci. Technol., 2012, 46, 10579–10588 CrossRef PubMed .
21. F. Li, B. Jiang, P. Nastold, B. A. Kolvenbach, J. Chen, L. Wang, H. Guo, P. F. Corvini and R. Ji, Enhanced transformation of tetrabromobisphenol a by nitrifiers in nitrifying activated sludge, Environ. Sci. Technol., 2015, 49, 4283–4292 CrossRef PubMed .
22. H. Roh, N. Subramanya, F. Zhao, C.-P. Yu, J. Sandt and K.-H. Chu, Biodegradation potential of wastewater micropollutants by ammonia-oxidizing bacteria, Chemosphere, 2009, 77, 1084–1089 CrossRef PubMed .
23. S. W. Chang, M. R. Hyman and K. J. Williamson, Cooxidation of naphthalene and other polycyclic aromatic hydrocarbons by the nitrifying bacterium, Nitrosomonas europaea, Biodegradation, 2002, 13, 373–381 CrossRef PubMed .
24. Y. Men, P. Han, D. E. Helbling, N. Jehmlich, C. Herbold, R. Gulde, A. Onnis-Hayden, A. Z. Gu, D. R. Johnson, M. Wagner and K. Fenner, Biotransformation of two pharmaceuticals by the ammonia-oxidizing archaeon Nitrososphaera gargensis, Environ. Sci. Technol., 2016, 50, 4682–4692 CrossRef PubMed .
25. N. H. Tran, T. Urase, H. H. Ngo, J. Hu and S. L. Ong, Insight into metabolic and cometabolic activities of autotrophic and heterotrophic microorganisms in the biodegradation of emerging trace organic contaminants, Bioresour. Technol., 2013, 146, 721–731 CrossRef PubMed .
26. J. A. Baz-Lomba, M. J. Reid and K. V. Thomas, Target and suspect screening of psychoactive substances in sewage-based samples by UHPLC-QTOF, Anal. Chim. Acta, 2016, 914, 81–90 CrossRef PubMed .
27. R. M. A. Sjerps, D. Vughs, J. A. van Leerdam, T. L. ter Laak and A. P. van Wezel, Data-driven prioritization of chemicals for various water types using suspect screening LC-HRMS, Water Res., 2016, 93, 254–264 CrossRef PubMed .
28. C. Moschet, I. Wittmer, J. Simovic, M. Junghans, A. Piazzoli, H. Singer, C. Stamm, C. Leu and J. Hollender, How a complete pesticide screening changes the assessment of surface water quality, Environ. Sci. Technol., 2014, 48, 5423–5432 CrossRef PubMed .
29. A. C. Chiaia-Hernandez, M. Krauss and J. Hollender, Screening of lake sediments for emerging contaminants by liquid chromatography atmospheric pressure photoionization and electrospray ionization coupled to high resolution mass spectrometry, Environ. Sci. Technol., 2013, 47, 976–986 CrossRef PubMed .
30. J. Nácher-Mestre, M. Ibáñez, R. Serrano, C. Boix, L. Bijlsma, B. T. Lunestad, R. Hannisdal, M. Alm, F. Hernández and M. H. G. Berntssen, Investigation of pharmaceuticals in processed animal by-products by liquid chromatography coupled to high-resolution mass spectrometry, Chemosphere, 2016, 154, 231–239 CrossRef PubMed .
31. A. L. Pochodylo and D. E. Helbling, Emerging investigators series: Prioritization of suspect hits in a sensitive suspect screening workflow for comprehensive micropollutant characterization in environmental samples, Environ. Sci.: Water Res. Technol., 2017, 3, 54–65 RSC .
32. K. Oetjen, C. G. S. Giddings, M. McLaughlin, M. Nell, J. Blotevogel, D. E. Helbling, D. Mueller and C. P. Higgins, Emerging analytical methods for the characterization and quantification of organic contaminants in flowback and produced water, Trends Environ. Anal. Chem., 2017, 15, 12–23 CrossRef .
33. K. R. Chalcraft, R. Lee, C. Mills and P. Britz-McKibbin, Virtual quantification of metabolites by capillary electrophoresis-electrospray ionization-mass spectrometry: Predicting ionization efficiency without chemical standards, Anal. Chem., 2009, 81, 2506–2515 CrossRef PubMed .
34. M. A. Raji, P. Frycak, C. Temiyasathit, S. B. Kim, G. Mavromaras, J. M. Ahn and K. A. Schug, Using multivariate statistical methods to model the electrospray ionization response of GXG tripeptides based on multiple physicochemical parameters, Rapid Commun. Mass Spectrom., 2009, 23, 2221–2232 CrossRef PubMed .
35. M. Oss, A. Kruve, K. Herodes and I. Leito, Electrospray ionization efficiency scale of organic compounds, Anal. Chem., 2010, 82, 2865–2872 CrossRef PubMed .
36. E. N. Pieke, K. Granby, X. Trier and J. Smedsgaard, A framework to estimate concentrations of potentially unknown substances by semi-quantification in liquid chromatography electrospray ionization mass spectrometry, Anal. Chim. Acta, 2017, 975, 30–41 CrossRef PubMed .
37. L. Tang and P. Kebarle, Dependence of ion intensity in electrospray mass spectrometry on the concentration of the analytes in the electrosprayed solution, Anal. Chem., 1993, 65, 3654–3668 CrossRef .
38. A. Kruve, K. Kaupmees, J. Liigand and I. Leito, Negative electrospray ionization via deprotonation: Predicting the ionization efficiency, Anal. Chem., 2014, 86, 4822–4830 CrossRef PubMed .
39. T. Henriksen, R. K. Juhler, B. Svensmark and N. B. Cech, The relative influences of acidity and polarity on responsiveness of small organic molecules to analysis with negative ion electrospray ionization mass spectrometry (ESI-MS), J. Am. Soc. Mass Spectrom., 2005, 16, 446–455 CrossRef PubMed .
40. J. E. Schollee, E. L. Schymanski, S. E. Avak, M. Loos and J. Hollender, Prioritizing unknown transformation products from biologically-treated wastewater using high-resolution mass spectrometry, multivariate statistics, and metabolic logic, Anal. Chem., 2015, 87, 12121–12129 CrossRef PubMed .
41. E. L. Schymanski, J. Jeon, R. Gulde, K. Fenner, M. Ruff, H. P. Singer and J. Hollender, Identifying small molecules via high resolution mass spectrometry: Communicating confidence, Environ. Sci. Technol., 2014, 48, 2097–2098 CrossRef PubMed .
42. C. Hao, X. Zhao and P. Yang, GC-MS and HPLC-MS analysis of bioactive pharmaceuticals and personal-care products in environmental matrices, TrAC, Trends Anal. Chem., 2007, 26, 569–580 CrossRef .
43. M. Scheurer, F. Sacher and H.-J. Brauch, Occurrence of the antidiabetic drug metformin in sewage and surface waters in Germany, J. Environ. Monit., 2009, 11, 1608–1613 RSC .
44. P. M. Bradley, C. A. Journey, D. T. Button, D. M. Carlisle, J. M. Clark, B. J. Mahler, N. Nakagaki, S. L. Qi, I. R. Waite and P. C. VanMetre, Metformin and other pharmaceuticals widespread in wadeable streams of the southeastern United States, Environ. Sci. Technol. Lett., 2016, 3, 243–249 CrossRef .
45. E. Gracia-Lor, J. V. Sancho, R. Serrano and F. Hernández, Occurrence and removal of pharmaceuticals in wastewater treatment plants at the Spanish Mediterranean area of Valencia, Chemosphere, 2012, 87, 453–462 CrossRef PubMed .
46. S. K. Behera, H. W. Kim, J.-E. Oh and H.-S. Park, Occurrence and removal of antibiotics, hormones and several other pharmaceuticals in wastewater treatment plants of the largest industrial city of Korea, Sci. Total Environ., 2011, 409, 4351–4360 CrossRef PubMed .
47. K. Choi, Y. Kim, J. Park, C. K. Park, M. Kim, H. S. Kim and P. Kim, Seasonal variations of several pharmaceutical residues in surface water and sewage treatment plants of Han River, Korea, Sci. Total Environ., 2008, 405, 120–128 CrossRef PubMed .
48. J. L. Santos, I. Aparicio, M. Callejón and E. Alonso, Occurrence of pharmaceutically active compounds during 1-year period in wastewaters from four wastewater treatment plants in Seville (Spain), J. Hazard. Mater., 2009, 164, 1509–1516 CrossRef PubMed .
49. S. Terzić, I. Senta, M. Ahel, M. Gros, M. Petrović, D. Barcelo, J. Müller, T. Knepper, I. Martí, F. Ventura, P. Jovančić and D. Jabučar, Occurrence and fate of emerging wastewater contaminants in Western Balkan Region, Sci. Total Environ., 2008, 399, 66–77 CrossRef PubMed .
50. N. K. Stamatis and I. K. Konstantinou, Occurrence and removal of emerging pharmaceutical, personal care compounds and caffeine tracer in municipal sewage treatment plant in Western Greece, J. Environ. Sci. Health, Part B, 2013, 48, 800–813 CrossRef PubMed .
51. C. I. Kosma, D. A. Lambropoulou and T. A. Albanis, Occurrence and removal of PPCPs in municipal and hospital wastewaters in Greece, J. Hazard. Mater., 2010, 179, 804–817 CrossRef PubMed .
52. Q. Sui, J. Huang, S. Deng, W. Chen and G. Yu, Seasonal variation in the occurrence and removal of pharmaceuticals and personal care products in different biological wastewater treatment processes, Environ. Sci. Technol., 2011, 45, 3341–3348 CrossRef PubMed .
53. Q. Sui, J. Huang, S. Deng, G. Yu and Q. Fan, Occurrence and removal of pharmaceuticals, caffeine and DEET in wastewater treatment plants of Beijing, China, Water Res., 2010, 44, 417–426 CrossRef PubMed .
54. X. Yang, R. C. Flowers, H. S. Weinberg and P. C. Singer, Occurrence and removal of pharmaceuticals and personal care products (PPCPs) in an advanced wastewater reclamation plant, Water Res., 2011, 45, 5218–5228 CrossRef PubMed .
55. S. L. Bartelt-Hunt, D. D. Snow, T. Damon, J. Shockley and K. Hoagland, The occurrence of illicit and therapeutic pharmaceuticals in wastewater effluent and surface waters in Nebraska, Environ. Pollut., 2009, 157, 786–791 CrossRef PubMed .
56. R. Gurke, M. Rößler, C. Marx, S. Diamond, S. Schubert, R. Oertel and J. Fauler, Occurrence and removal of frequently prescribed pharmaceuticals and corresponding metabolites in wastewater of a sewage treatment plant, Sci. Total Environ., 2015, 532, 762–770 CrossRef PubMed .
57. J. H. Writer, I. Ferrer, L. B. Barber and E. M. Thurman, Widespread occurrence of neuro-active pharmaceuticals and metabolites in 24 Minnesota rivers and wastewaters, Sci. Total Environ., 2013, 461–462, 519–527 CrossRef PubMed .
58. F. Bonvin, J. Omlin, R. Rutler, W. B. Schweizer, P. J. Alaimo, T. J. Strathmann, K. McNeill and T. Kohn, Direct photolysis of human metabolites of the antibiotic sulfamethoxazole: Evidence for abiotic back-transformation, Environ. Sci. Technol., 2013, 47, 6746–6755 CrossRef PubMed .
59. K. O. Borg, E. Carlsson, K.-J. Hoffmann, T.-E. Jönsson, H. Thorin and B. Wallin, Metabolism of metoprolol-(3H) in man, the dog and the rat, Acta Pharmacol. Toxicol., 1975, 36, 125–135 CrossRef .
60. K. F. Ilett, L. P. Hackett, L. J. Dusci, M. J. Roberts, J. H. Kristensen, M. Paech, A. Groves and P. Yapp, Distribution and excretion of venlafaxine and O-desmethylvenlafaxine in human milk, Br. J. Clin. Pharmacol., 1998, 45, 459–462 CrossRef PubMed .
61. N. M. Vieno, T. Tuhkanen and L. Kronberg, Seasonal variation in the occurrence of pharmaceuticals in effluents from a sewage treatment plant and in the recipient water, Environ. Sci. Technol., 2005, 39, 8220–8226 CrossRef PubMed .
62. P. Verlicchi, M. Al Aukidy and E. Zambello, Occurrence of pharmaceutical compounds in urban wastewater: Removal, mass load and environmental risk after a secondary treatment—A review, Sci. Total Environ., 2012, 429, 123–155 CrossRef PubMed .
63. F. I. Hai, K. Tessmer, L. N. Nguyen, J. Kang, W. E. Price and L. D. Nghiem, Removal of micropollutants by membrane bioreactor under temperature variation, J. Membr. Sci., 2011, 383, 144–151 CrossRef .
64. R. Salgado, R. Marques, J. P. Noronha, G. Carvalho, A. Oehmen and M. A. M. Reis, Assessing the removal of pharmaceuticals and personal care products in a full-scale activated sludge plant, Environ. Sci. Pollut. Res., 2012, 19, 1818–1827 CrossRef PubMed .
65. Q. Sun, M. Lv, A. Hu, X. Yang and C.-P. Yu, Seasonal variation in the occurrence and removal of pharmaceuticals and personal care products in a wastewater treatment plant in Xiamen, China, J. Hazard. Mater., 2014, 277, 69–75 CrossRef PubMed .
66. S. Weiss, J. Jakobs and T. Reemtsma, Discharge of three benzotriazole corrosion inhibitors with municipal wastewater and improvements by membrane bioreactor treatment and ozonation, Environ. Sci. Technol., 2006, 40, 7193–7199 CrossRef PubMed .
67. D. Voutsa, P. Hartmann, C. Schaffner and W. Giger, Benzotriazoles, alkylphenols and bisphenol A in municipal wastewaters and in the Glatt River, Switzerland, Environ. Sci. Pollut. Res., 2006, 13, 333–341 CrossRef PubMed .
68. T. Reemtsma, U. Miehe, U. Duennbier and M. Jekel, Polar pollutants in municipal wastewater and the water cycle: Occurrence and removal of benzotriazoles, Water Res., 2010, 44, 596–604 CrossRef PubMed .
69. A. G. Asimakopoulos, A. Ajibola, K. Kannan and N. S. Thomaidis, Occurrence and removal efficiencies of benzotriazoles and benzothiazoles in a wastewater treatment plant in Greece, Sci. Total Environ., 2013, 452–453, 163–171 CrossRef PubMed .
70. B. Kasprzyk-Hordern, R. M. Dinsdale and A. J. Guwy, The removal of pharmaceuticals, personal care products, endocrine disruptors and illicit drugs during wastewater treatment and its impact on the quality of receiving waters, Water Res., 2009, 43, 363–380 CrossRef PubMed .
71. R. Loos, R. Carvalho, D. C. António, S. Comero, G. Locoro, S. Tavazzi, B. Paracchini, M. Ghiani, T. Lettieri, L. Blaha, B. Jarosova, S. Voorspoels, K. Servaes, P. Haglund, J. Fick, R. H. Lindberg, D. Schwesig and B. M. Gawlik, EU-wide monitoring survey on emerging polar organic contaminants in wastewater treatment plant effluents, Water Res., 2013, 47, 6475–6487 CrossRef PubMed .
72. S. Martin Ruel, M. Esperanza, J. M. Choubert, I. Valor, H. Budzinski and M. Coquery, On-site evaluation of the efficiency of conventional and advanced secondary processes for the removal of 60 organic micropollutants, Water Sci. Technol., 2010, 62, 2970 CrossRef PubMed .
73. H. Singer, S. Jaus, I. Hanke, A. Lück, J. Hollender and A. C. Alder, Determination of biocides and pesticides by on-line solid phase extraction coupled with mass spectrometry and their behaviour in wastewater and surface water, Environ. Pollut., 2010, 158, 3054–3064 CrossRef PubMed .
74. J. W. Kwon and J. M. Rodriguez, Occurrence and removal of selected pharmaceuticals and personal care products in three wastewater-treatment plants, Arch. Environ. Contam. Toxicol., 2014, 66, 538–548 CrossRef PubMed .
75. M. B. Ahmed, J. L. Zhou, H. H. Ngo, W. Guo, N. S. Thomaidis and J. Xu, Progress in the biological and chemical treatment technologies for emerging contaminant removal from wastewater: A critical review, J. Hazard. Mater., 2017, 323, 274–298 CrossRef PubMed .
76. P. Falås, P. Longrée, J. la Cour Jansen, H. Siegrist, J. Hollender and A. Joss, Micropollutant removal by attached and suspended growth in a hybrid biofilm-activated sludge process, Water Res., 2013, 47, 4498–4506 CrossRef PubMed .
77. P. Falås, A. Wick, S. Castronovo, J. Habermacher, T. A. Ternes and A. Joss, Tracing the limits of organic micropollutant removal in biological wastewater treatment, Water Res., 2016, 95, 240–249 CrossRef PubMed .
78. A. Wick, G. Fink, A. Joss, H. Siegrist and T. A. Ternes, Fate of beta blockers and psycho-active drugs in conventional wastewater treatment, Water Res., 2009, 43, 1060–1074 CrossRef PubMed .
79. M. Könneke, A. E. Bernhard, J. R. de la Torre, C. B. Walker, J. B. Waterbury and D. A. Stahl, Isolation of an autotrophic ammonia-oxidizing marine archaeon, Nature, 2005, 437, 543 CrossRef PubMed .
80. J. Park, N. Yamashita, G. Wu and H. Tanaka, Removal of pharmaceuticals and personal care products by ammonia oxidizing bacteria acclimated in a membrane bioreactor: Contributions of cometabolism and endogenous respiration, Sci. Total Environ., 2017, 605–606, 18–25 CrossRef PubMed .
81. S. Sathyamoorthy, K. Chandran and C. A. Ramsburg, Biodegradation and cometabolic modeling of selected beta blockers during ammonia oxidation, Environ. Sci. Technol., 2013, 47, 12835–12843 CrossRef PubMed .
82. M. Rattier, J. Reungoat, J. Keller and W. Gernjak, Removal of micropollutants during tertiary wastewater treatment by biofiltration: Role of nitrifiers and removal mechanisms, Water Res., 2014, 54, 89–99 CrossRef PubMed .
83. M. Escolà Casas and K. Bester, Can those organic micro-pollutants that are recalcitrant in activated sludge treatment be removed from wastewater by biofilm reactors (slow sand filters)?, Sci. Total Environ., 2015, 506–507, 315–322 CrossRef PubMed .
84. K. Kimura, H. Hara and Y. Watanabe, Removal of pharmaceutical compounds by submerged membrane bioreactors (MBRs), Desalination, 2005, 178, 135–140 CrossRef .
85. A. Piram, A. Salvador, J.-Y. Gauvrit, P. Lanteri and R. Faure, Development and optimisation of a single extraction procedure for the LC/MS/MS analysis of two pharmaceutical classes residues in sewage treatment plant, Talanta, 2008, 74, 1463–1475 CrossRef PubMed .
86. N. Vieno, T. Tuhkanen and L. Kronberg, Elimination of pharmaceuticals in sewage treatment plants in Finland, Water Res., 2007, 41, 1001–1012 CrossRef PubMed .
87. M. Cirja, P. Ivashechkin, A. Schäffer and P. F. X. Corvini, Factors affecting the removal of organic micropollutants from wastewater in conventional treatment plants (CTP) and membrane bioreactors (MBR), Rev. Environ. Sci. Biotechnol., 2008, 7, 61–78 CrossRef .
88. M. Köck-Schulmeyer, M. Villagrasa, M. López de Alda, R. Céspedes-Sánchez, F. Ventura and D. Barceló, Occurrence and behavior of pesticides in wastewater treatment plants and their environmental impact, Sci. Total Environ., 2013, 458–460, 466–476 CrossRef PubMed .
89. S. Qu, E. P. Kolodziej, S. A. Long, J. B. Gloer, E. V. Patterson, J. Baltrusaitis, G. D. Jones, P. V. Benchetler, E. A. Cole, K. C. Kimbrough, M. D. Tarnoff and D. M. Cwiertny, Product-to-parent reversion of trenbolone: Unrecognized risks for endocrine disruption, Science, 2013, 342, 347 CrossRef PubMed .
90. L. B. Stadler, L. Su, C. J. Moline, A. S. Ernstoff, D. S. Aga and N. G. Love, Effect of redox conditions on pharmaceutical loss during biological wastewater treatment using sequencing batch reactors, J. Hazard. Mater., 2015, 282, 106–115 CrossRef .
91. J. Q. Jiang, Z. Zhou and V. K. Sharma, Occurrence, transportation, monitoring and treatment of emerging micro-pollutants in waste water — A review from global views, Microchem. J., 2013, 110, 292–300 CrossRef .
92. N. Ratola, A. Cincinelli, A. Alves and A. Katsoyiannis, Occurrence of organic microcontaminants in the wastewater treatment process. A mini review, J. Hazard. Mater., 2012, 239–240, 1–18 CrossRef PubMed .
93. A. Jelic, M. Gros, A. Ginebreda, R. Cespedes-Sánchez, F. Ventura, M. Petrovic and D. Barcelo, Occurrence, partition and removal of pharmaceuticals in sewage water and sludge during wastewater treatment, Water Res., 2011, 45, 1165–1176 CrossRef PubMed .
94. A. Pal, K. Y. H. Gin, A. Y. C. Lin and M. Reinhard, Impacts of emerging organic contaminants on freshwater resources: Review of recent occurrences, sources, fate and effects, Sci. Total Environ., 2010, 408, 6062–6069 CrossRef PubMed .
95. J. Wang and S. Wang, Removal of pharmaceuticals and personal care products (PPCPs) from wastewater: A review, J. Environ. Manage., 2016, 182, 620–640 CrossRef PubMed .
96. J. Rivera-Utrilla, M. Sánchez-Polo, M. Á. Ferro-García, G. Prados-Joya and R. Ocampo-Pérez, Pharmaceuticals as emerging contaminants and their removal from water. A review, Chemosphere, 2013, 93, 1268–1287 CrossRef PubMed .
97. J. Margot, L. Rossi, D. A. Barry and C. Holliger, A review of the fate of micropollutants in wastewater treatment plants, Wiley Interdiscip. Rev.: Water, 2015, 2, 457–487 CrossRef .

---

Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c8ew00278a

This journal is © The Royal Society of Chemistry 2018