Abstract

This paper presents a comprehensive and critical analysis of the removal of pharmaceuticals (PhCs), the governing physicochemical properties, and removal mechanisms in constructed wetlands (CWs). The average removal efficiency of the most widely studied 34 PhCs ranges from 21% to 93%, with the exception of one PhC that exhibited negative removal. Moreover, CWs are effective in significantly reducing the environmental risk caused by many PhCs. Based on risk assessment, 12 PhCs were classified under high risk category (oxytetracycline > ofloxacin > sulfamethoxazole > erythromycin > sulfadiazine > gemfibrozil > ibuprofen > acetaminophen > salicylic acid > sulfamethazine > naproxen > clarithromycin), which could be considered for regular monitoring, water quality standard formulation and control purposes. Biodegradation (aerobic and anaerobic) is responsible for the removal of the majority of PhCs, often in conjunction with other mechanisms (e.g., adsorption/sorption, plant uptake, and photodegradation). The physicochemical properties of molecules play a pivotal role in the elimination processes, and could serve as important predictors of removal. The correlation and multiple linear regression analysis suggest that organic carbon sorption coefficient (Log Koc), octanol-water distribution coefficient (Log Dow), and molecular weight form a good predictive linear regression model for the removal efficiency of PhCs (R2 = 0.65, P-value <0.05).

INTRODUCTION

Pharmaceuticals (PhCs) are among the emerging organic contaminants (EOCs) that are discharged to water resources and the environment through various sources such as domestic wastewater (excretion), effluent discharge from wastewater treatment plants (WWTPs), hospital and PhCs' industrial waste streams, landfill leachate, and animal excretion (Caliman & Gavrilescu 2009; Michael et al. 2013; Luo et al. 2014; Barbosa et al. 2016). Therefore, PhC pollution can be seen as a worldwide concern for almost every country – no matter how much of the total wastewater produced is treated before discharge into the environment. Although PhCs are found to be in relatively small concentrations (e.g., ng L−1 to μg L−1) in water resources, their presence (as individual compounds, transformation products (TPs), and multitude of compounds) could pose risk for aquatic and terrestrial life. The continuous discharge of PhCs through various sources including WWTPs could make these ‘pseudo-persistent’ organic chemicals a potential source of risk, especially when present in large concentrations, and the combination of a wide range of compounds that may act synergistically (e.g., Gorito et al. 2017). Furthermore, in WWTPs, during biological treatment, the development of antibiotic resistant bacteria (ARB) and/or antibiotic resistance genes (ARGs) due to the sub-therapeutic concentrations of antibiotics is of major concern. Antibiotic resistance is the ability of bacteria and other microorganisms to resist the effects of an antibiotic to which they were once sensitive (Berglund et al. 2014; Liu et al. 2014; Santos et al. 2019). Since these antibiotics, ARB, and ARGs are also not eliminated by conventional WWTPs (Hijosa-Valsero et al. 2011a; Rowan 2011; Huang et al. 2017), the effluent discharge from WWTPs and the use of activated sludge containing antibiotics and ARB/ARGs makes them one of the major sources of antibiotics and ARB/ARGs in the environment (Finley et al. 2013; Rodriguez-Mozaz et al. 2015; Santos et al. 2019). Several studies have indicated negative impacts of PhCs on aquatic and plant life (e.g., Caliman & Gavrilescu 2009; Carvalho et al. 2014).

Various strategies may be employed to reduce the discharge of PhCs into the environment. The efforts on source reduction and use of more degradable compounds with comparable therapeutic effects (green pharmacy) could reduce the quantity of PhCs consumed and discharged into the environment (Ruhoy & Daughton 2008). Other promising strategies focus on improving wastewater treatment technologies and their scale of adoption (Michael et al. 2013; Luo et al. 2014). The development of treatment trains that are more suited for the removal of PhCs by upgrading existing WWTPs or designing new ones are important areas of research and development. Therefore, many experimental investigations have been carried out in recent years to test technologies for their ability to reduce the concentrations of PhCs in the final effluent. For instance, advanced chemical and biological systems have been assessed: ozonation, ozone/ultraviolet irradiation, ozone/hydrogen peroxide (Ternes et al. 2003; Hollender et al. 2009; Benitez et al. 2011), ultrafiltration, reverse osmosis, granular activated carbon contact (Acero et al. 2010; Michael et al. 2013), and membrane biological reactors (Radjenovic et al. 2009; Lipp et al. 2012). Modern WWTPs could be equipped with these technologies for a polishing step, as these technologies are proven to be effective in many cases (Huber et al. 2005; Michael et al. 2013; Papaevangelou et al. 2016). However, their capital and operational costs are very high (Ternes et al. 2003; Reif et al. 2011), which highlights the need for cost-effective, sustainable, and efficient wastewater treatment technologies.

Constructed wetlands (CWs) are low cost and nature-based treatment technologies that have been extensively investigated for the removal of conventional pollution parameters such as organic matter and nutrients (nitrogen and phosphorus) (e.g., Kadlec & Wallace 2009; Vymazal 2011, 2013; Ilyas & Masih 2017a, 2017b, 2018) as well as PhCs from wastewater. Regarding PhCs, to date, more than 50 individual case studies have been published in peer-reviewed journals, with rapidly growing numbers since the last decade. Among the investigated CWs are free water surface CW (FWSCW), horizontal flow CW (HFCW), vertical flow CW (VFCW), and hybrid CW (HCW). For instance, Matamoros et al. (2008b) compared the removal efficiencies of a few EOCs including six PhCs (ibuprofen, naproxen, diclofenac, ketoprofen, clofibric acid, and carbamazepine) between a HFCW and a conventional WWTP. The influent in the CW was secondary wastewater that had trace concentrations of these PhCs. Removal efficiencies were found to be compound-dependent (e.g., ibuprofen and naproxen >70%, and carbamazepine <20%). Seasonal variations in the removal efficiency were observed, with higher efficiencies in the warm season compared with the cold season due to higher biodegradation and photodegradation in summer. Hijosa-Valsero et al. (2010a) investigated the performance of seven CWs (e.g., surface and sub-surface flow, planted and unplanted, with and without gravel bed), and a conventional activated sludge treatment plant. The removal efficiencies of six PhCs (ketoprofen, naproxen, ibuprofen, diclofenac, salicylic acid, and carbamazepine) were investigated over a period of nine months. This study asserted most of the findings of Matamoros et al. (2008b), such as effect of seasonality, compound specific variation in removal efficiency, and similar or better performance of CWs compared with the conventional WWTP. Additionally, superior performance of planted CWs for some PhCs compared with unplanted ones was demonstrated. Vymazal et al. (2017) investigated the presence of 31 PhCs in four HFCWs used to treat rural wastewater in the Czech Republic. Seven out of 31 PhCs were detected in all sampling campaigns in the influent samples (acetaminophen, caffeine, diclofenac, furosemide, hydrochlorothiazide, ibuprofen, and metoprolol) and five were found in at least 75% of the samples (clarithromycin, gabapentin, ketoprofen, tramadol, and warfarin). The removal efficiencies showed a large variation among the studied CWs as well as PhCs' type. The highest removal efficiency was reported for acetaminophen (91%), caffeine (84%), and furosemide (75%), whereas warfarin (31%), ketoprofen (31%), and gabapentin (14%) demonstrated poor removal efficiency. The authors also assessed environmental risk due to the PhCs by estimating risk quotient (RQ) (a ratio between the predicted or measured environmental concentration (PEC or MEC)), and the worst-case predicted no effect concentration (PNEC) (Hernando et al. 2006). Ibuprofen, acetaminophen, and clarithromycin were assessed under high risk category (RQ > 1.0).

An in-depth overview of all the reviewed studies on PhCs' removal by CWs, including the few mentioned above, indicates the following major thematic areas of research: (1) identification of PhCs in influent and effluent wastewater, and the removal efficiencies by different treatment technologies; (2) investigation of mechanisms and processes leading to the removal of PhCs; (3) impact of physicochemical properties of PhCs on treatment process efficiencies; (4) relationship of the design and operational factors with removal efficiencies; (5) temporal variations in the performance due to seasonal and aging effects; and (6) environmental risk assessment and risk reduction due to treatment. A large number of published research studies offer an opportunity to summarize and critically reflect on the available knowledge on these thematic areas. However, only a few review studies, with specific focus on PhCs' removal by CWs, have been conducted in order to summarize the available knowledge on some of the above-mentioned themes (Imfeld et al. 2009; Carvalho et al. 2014; Li et al. 2014; Verlicchi & Zambello 2014; Zhang et al. 2014; Gorito et al. 2017). An excellent scientific description regarding the removal processes in CWs has been provided by Imfeld et al. (2009), which was further advanced by Zhang et al. (2014) supported by (limited) scientific evidence from available studies on the subject. Li et al. (2014) summarized the role of design parameters such as physical configuration, hydraulic mode, and vegetation species in the removal of PhCs. Verlicchi & Zambello (2014) provided a detailed overview of the removal of several PhCs by CWs, with in-depth analysis of performance when CWs are used as primary, secondary, and tertiary treatment purposes. Although Li et al. (2014) and Verlicchi & Zambello (2014) shed light on the influence of physicochemical properties of PhCs on their removal efficiencies, they did not advance the knowledge on the correlation of physicochemical properties and removal mechanisms. A comprehensive review on plant–PhCs interactions was conducted by Carvalho et al. (2014), indicating the potential of CWs for phytoremediation. Gorito et al. (2017) presented the removal of four PhCs (azithromycin, clarithromycin, diclofenac, and erthromycin), which are on the watch list of the European Union (EU) as per EU decision 2015/495, with discussion on associated removal processes and influence of design and operation parameters of CWs. While these studies have significantly advanced the knowledge on various aspects of PhCs' removal by CWs, there is still a need to conduct more research on the studied thematic areas in order to formulate sound and evidence-based general conclusions. Moreover, most of the previous reviews target a limited number of PhCs and are often constrained by a limited number of available studies on certain topics.

This study aims to fill some of the above-mentioned knowledge gaps by building on the previous reviews and a large number of published case studies, including recently published sources (e.g., after 2013, as most of the previous reviews were published before 2014). Therefore, the main objectives of this study are: (1) to conduct a comprehensive assessment of a large number of PhCs in wastewater and their removal by four types of CWs; (2) to critically evaluate and summarize the available evidence on major PhCs' removal mechanisms in CWs; (3) to examine the impact of physicochemical properties of PhCs on their removal mechanisms; and (4) to assess the environmental risk posed by a large number of PhCs, and contribution of CWs in risk reduction.

METHODOLOGY

The research papers, review papers, and books were searched from various sources, such as Scopus, Google Scholar, and individual journal websites, related to the performance of different types of CWs for the removal of different categories of PhCs. The snowball sampling method yielded over 100 journal articles, which were further screened and used for the purpose of this research. The screening was carried out to check the quality of published data. Only peer-reviewed journal papers were selected for this research, which helped to ensure the reliability of given data. The selected studies have used generally accepted and reliable analytical methods such as solid phase extraction-gas chromatography-tandem mass spectrometry (SPE-GC-MS/MS); SPE-(ultra) high performance liquid chromatography-diode array detector (SPE-(U)HPLC-DAD); liquid-liquid phase extraction-gas chromatography-micro electron capture detector (LLPE-GC-μECD); and SPE-rapid resolution liquid chromatography-MS/MS (SPE-RRLC-MS/MS). The samples were analyzed soon after collection, as the storage time was less than one or two days in most cases. The selected studies contained the required information on most of the key parameters such as concentration of PhCs in influent and effluent waters, removal efficiency, chemical oxygen demand (COD), biochemical oxygen demand (BOD), hydraulic loading rate (HLR), and hydraulic retention time (HRT).

In this way, a global database was compiled containing information about 260 CWs that were reported in 66 peer-reviewed journal publications with case studies from 19 countries (Supplementary materials 1: Tables S1–S4). This database contains influent and effluent concentrations, removal efficiencies, and removal rates of 148 PhCs grouped into 33 categories according to their therapeutic classes and 25 TPs (Table 1). The treatment performance of four types of CWs (FWSCW, HFCW, VFCW, and HCW) was evaluated for PhCs' removal. The other parameters, such as treatment scale and type, wastewater type, depth, area, HLR, organic loading rate (OLR), HRT, experiment duration, system age, filter media, temperature, pH, dissolved oxygen (DO), and redox potential were considered for the comparison of four types of CWs. The performance analysis was based on the information compiled from the available studies, as stated above.

Table 1

The studied PhCs categorized according to their therapeutic classes and their TPs

Categories of pharmaceuticals
No. of categoriesTherapeutic classesPhCs
Analgesic/anti-inflammatory drugs Buprenorphine, Diclofenac, Fenoprofen, Ibuprofen, Ketoprofen, Mefenamic acid, Naproxen, Salicylic acid, Phenylbutazone, Propyphenazone 
Analgesic Acetaminophen, Codeine, Dihydrocodeine, Gabapentin, Methadone, Morphine, Nefopam, Orphenadrine, Tramadol 
Anti-inflammatory drugs Indomethacin, Prednisolone 
Antibiotics Amoxicillin, Ampicillin, Azithromycin, Chlortetracycline, Ciprofloxacin, Clarithromycin, Clindamycin, Difloxacin, Doxycycline, Enrofloxacin, Enoxacin, Erythromycin, Lincomycin, Metronidazole, Monensin, Nifuroxazide, Norfloxacin, Novobiocin, Ofloxacin, Oxytetracycline, Roxithromycin, Spiramycin, Sulfadiazine, Sulfadimethoxine, Sulfamerazine, Sulfamethazine, Sulfamethoxazole, Sulfapyridine, Sulfathiazole, Tetracycline, Tilmicosin, Trimethoprim 
Antifungals Clotrimazol, Fluconazole 
Antivirals Acyclovir 
Antiallergic drugs Cetirizine, Desloratidin, Diphenhydramine, Fexofenadine 
Antidysenterics Loperamide 
Antiasthma Theophylline 
10 Antiepileptic Lamotrigine, Levetiracetam, Oxcarbazepine, Topiramate 
11 Antiulcer Esomeprazole, Omeprazole, Sulfasalazine 
12 Antispasmodic Phloroglucinol 
13 Stimulants/psychoactive drugs 3,4-Methylenedioxy-amphetamine, 3,4-Methylenedioxy-methamphetamine, Caffeine, Cocaine, Ephedrine/pseudoephedrine, Naloxone, Nicotine 
14 Antihypertensives Diltiazem, Dipyridamol, Eprosartan, Hydrochlorothiazide, Irbesartan, Lisinopril, Losartan, Telmisartan, Valsartan, Verapamil 
15 Psychiatric drugs Alprazolam, Amitryptiline, Bupropion, Carbamazepine, Citalopram, Fluoxetine, Hydroxyzine, Levomepromazine, Lorazepam, Maprotilin, Mianserin, Mirtazapin, Oxazepam, Paroxetin, Perphenazine, Sertraline, Venlafaxine, Zoldipem 
16 Anti-Alzheimer drugs Memantin 
17 Beta-blockers Atenolol, Bisoprolol, Metoprolol, Propranolol, Sotalol, Timolol 
18 Beta-agonists Clenbuterol, Salbutamol, Terbutaline 
19 Hormone inhibitors Finasteride 
20 Receptor antagonists Alfuzosin, Cimetidine, Famotidine, Loratadine, Ranitidine 
21 Lipid regulators Atorvastatin, Bezafibrate, Clofibric acid, Fenofibrate, Gemfibrozil, Mevastatin, Pravastatin, Rosuvastatin 
22 Antineoplastic Cyclophosphamide 
23 Antiarrhythmic Propafenone 
24 Anti-ischaemic Pentoxifylline 
25 Neuroleptics Haloperidol 
26 Barbiturates Butalbital, Pentobarbital, Phenobarbital 
27 TTT coronary insufficiency Candesartan, Nadolol 
28 Cardiovascular Ramipril 
29 Anticoagulant Warfarin 
30 Antidiabetics Glibenclamide, Gliclazide, Metformin 
31 Diuretics Furosemide 
32 Anesthetic drugs Ketamine 
33 Radiocontrast agents Diatrizoate, Iomeprol, Iopromide 
Categories of pharmaceuticals
No. of categoriesTherapeutic classesPhCs
Analgesic/anti-inflammatory drugs Buprenorphine, Diclofenac, Fenoprofen, Ibuprofen, Ketoprofen, Mefenamic acid, Naproxen, Salicylic acid, Phenylbutazone, Propyphenazone 
Analgesic Acetaminophen, Codeine, Dihydrocodeine, Gabapentin, Methadone, Morphine, Nefopam, Orphenadrine, Tramadol 
Anti-inflammatory drugs Indomethacin, Prednisolone 
Antibiotics Amoxicillin, Ampicillin, Azithromycin, Chlortetracycline, Ciprofloxacin, Clarithromycin, Clindamycin, Difloxacin, Doxycycline, Enrofloxacin, Enoxacin, Erythromycin, Lincomycin, Metronidazole, Monensin, Nifuroxazide, Norfloxacin, Novobiocin, Ofloxacin, Oxytetracycline, Roxithromycin, Spiramycin, Sulfadiazine, Sulfadimethoxine, Sulfamerazine, Sulfamethazine, Sulfamethoxazole, Sulfapyridine, Sulfathiazole, Tetracycline, Tilmicosin, Trimethoprim 
Antifungals Clotrimazol, Fluconazole 
Antivirals Acyclovir 
Antiallergic drugs Cetirizine, Desloratidin, Diphenhydramine, Fexofenadine 
Antidysenterics Loperamide 
Antiasthma Theophylline 
10 Antiepileptic Lamotrigine, Levetiracetam, Oxcarbazepine, Topiramate 
11 Antiulcer Esomeprazole, Omeprazole, Sulfasalazine 
12 Antispasmodic Phloroglucinol 
13 Stimulants/psychoactive drugs 3,4-Methylenedioxy-amphetamine, 3,4-Methylenedioxy-methamphetamine, Caffeine, Cocaine, Ephedrine/pseudoephedrine, Naloxone, Nicotine 
14 Antihypertensives Diltiazem, Dipyridamol, Eprosartan, Hydrochlorothiazide, Irbesartan, Lisinopril, Losartan, Telmisartan, Valsartan, Verapamil 
15 Psychiatric drugs Alprazolam, Amitryptiline, Bupropion, Carbamazepine, Citalopram, Fluoxetine, Hydroxyzine, Levomepromazine, Lorazepam, Maprotilin, Mianserin, Mirtazapin, Oxazepam, Paroxetin, Perphenazine, Sertraline, Venlafaxine, Zoldipem 
16 Anti-Alzheimer drugs Memantin 
17 Beta-blockers Atenolol, Bisoprolol, Metoprolol, Propranolol, Sotalol, Timolol 
18 Beta-agonists Clenbuterol, Salbutamol, Terbutaline 
19 Hormone inhibitors Finasteride 
20 Receptor antagonists Alfuzosin, Cimetidine, Famotidine, Loratadine, Ranitidine 
21 Lipid regulators Atorvastatin, Bezafibrate, Clofibric acid, Fenofibrate, Gemfibrozil, Mevastatin, Pravastatin, Rosuvastatin 
22 Antineoplastic Cyclophosphamide 
23 Antiarrhythmic Propafenone 
24 Anti-ischaemic Pentoxifylline 
25 Neuroleptics Haloperidol 
26 Barbiturates Butalbital, Pentobarbital, Phenobarbital 
27 TTT coronary insufficiency Candesartan, Nadolol 
28 Cardiovascular Ramipril 
29 Anticoagulant Warfarin 
30 Antidiabetics Glibenclamide, Gliclazide, Metformin 
31 Diuretics Furosemide 
32 Anesthetic drugs Ketamine 
33 Radiocontrast agents Diatrizoate, Iomeprol, Iopromide 
Transformation products
No. of PhCsPhCsTPs
Diclofenac 4-Hydroxydiclofenac 
Ibuprofen 1-Hydroxyibuprofen, 2-Hydroxyibuprofen, Carboxyibuprofen 
Ketoprofen 3-Ethylbenzophenone, Dihydroketoprofen 
Naproxen O-desmethylnaproxen 
Tramadol O-desmethyltramadol, N-desmethyltramadol, N,O- didesmethyltramadol 
Methadone 2-Ethylidene − 1,5-dimethyl − 3,3-diphenylpyrrolidine 
Sulfamethoxazole N-acetylsulfamethoxazole 
Carbamazepine 10,11-Dihydro − 10,11-dihydroxycarbamazepine, 10,11-Dihydro − 10-hydroxycarbamazepine, 2 Hydroxycarbamazepine, 3-Hydroxycarbamazepine, Carbamazepine 10,11-epoxide 
Citalopram N-desmethylcitalopram 
10 Venlafaxine O-desmethylvenlafaxine, N-desmethylvenlafaxine, N,O-didesmethylvenlafaxine 
11 Cocaine Benzoylecgonine, Cocaethylene 
12 Nicotine Cotinine 
13 Ketamine Norketamine 
Transformation products
No. of PhCsPhCsTPs
Diclofenac 4-Hydroxydiclofenac 
Ibuprofen 1-Hydroxyibuprofen, 2-Hydroxyibuprofen, Carboxyibuprofen 
Ketoprofen 3-Ethylbenzophenone, Dihydroketoprofen 
Naproxen O-desmethylnaproxen 
Tramadol O-desmethyltramadol, N-desmethyltramadol, N,O- didesmethyltramadol 
Methadone 2-Ethylidene − 1,5-dimethyl − 3,3-diphenylpyrrolidine 
Sulfamethoxazole N-acetylsulfamethoxazole 
Carbamazepine 10,11-Dihydro − 10,11-dihydroxycarbamazepine, 10,11-Dihydro − 10-hydroxycarbamazepine, 2 Hydroxycarbamazepine, 3-Hydroxycarbamazepine, Carbamazepine 10,11-epoxide 
Citalopram N-desmethylcitalopram 
10 Venlafaxine O-desmethylvenlafaxine, N-desmethylvenlafaxine, N,O-didesmethylvenlafaxine 
11 Cocaine Benzoylecgonine, Cocaethylene 
12 Nicotine Cotinine 
13 Ketamine Norketamine 

A detailed analysis of the reported PhCs was conducted from the studied literature including the designed database, which focused on therapeutic classes, types of PhCs, and impact of their physicochemical properties and contribution of removal mechanisms in CWs. The mechanisms were identified for the selected PhCs as presented in the published case studies. The majority of the studies only attributed removal to certain mechanisms (e.g., biodegradation, adsorption/sorption, plant uptake, and photodegradation). The relative contribution of mechanisms in removal was only quantified in a few experimental studies. Therefore, the analysis on removal mechanisms was based on a critical oversight from both qualitative and quantitative information. The information on the physicochemical properties of PhCs was gathered from various sources (e.g., journal papers, reports, and websites) for molecular formula/structure/weight, water solubility, dissociation constant (pKa), organic carbon sorption coefficient (Log Koc), octanol-water partition coefficient (Log Kow), and distribution coefficient (Log Dow). The available evidence on the role of these properties in the removal of PhCs in CWs was comprehensively and critically analyzed. The linkages between physicochemical properties and removal mechanisms were delineated from this analysis. Moreover, a statistical analysis (Pearson correlation and multiple linear regression) was conducted for removal efficiency and physicochemical properties. The causality of the observed relationship was established through a synthesis of available knowledge and the authors' own insights. Additionally, risk assessment was carried out by estimating RQ. Following the recommendations by Hernando et al. (2006) and several applications (Gros et al. 2010; Verlicchi et al. 2012; Kosma et al. 2014; Zhu & Chen 2014; Chen et al. 2016b; Matamoros et al. 2016, 2017; Auvinen et al. 2017b; Vymazal et al. 2017), the risk was categorized into four levels: high risk (RQ > 1.0), medium risk (0.1 ≤ RQ ≤ 1.0), low risk (0.01 ≤ RQ ≤ 0.1), and no risk (RQ < 0.01).

RESULTS AND DISCUSSION

Removal of PhCs and TPs by CWs

Figure 1 presents the average removal efficiencies of the examined PhCs and TPs estimated for 113 out of 173 compounds for which three or more data points were available. The results indicate a very high range of variability in the removal efficiencies. The assessment indicates that CWs are capable of removing a large number of PhCs and their metabolites from wastewater. Positive removal was observed for 96 compounds, with average removal efficiencies in the range of 7.0%–100%. On the other hand, negative removal was estimated for 17 compounds (both PhCs and TPs) in the range of −5.0% to −2,043%. There is no distinct removal pattern among PhCs and TPs – both show large variability in removal. Moreover, high standard deviations were estimated, which showed high variability in the results. The estimated statistics (mean and standard deviation of influent and effluent concentration, removal rate, and removal efficiency) for each of the 113 compounds are presented in Supplementary materials 2: Table S5.

Figure 1

Removal efficiency of the investigated compounds (both PhCs and TPs). Note: The statistics are for 113 compounds with three or more data points. Negative values were capped at −100 to improve the readability of the graph. Actual values (e.g., below −100) can be found in Supplementary materials 2.

Figure 1

Removal efficiency of the investigated compounds (both PhCs and TPs). Note: The statistics are for 113 compounds with three or more data points. Negative values were capped at −100 to improve the readability of the graph. Actual values (e.g., below −100) can be found in Supplementary materials 2.

We choose to illustrate a few more observations from this analysis by an in-depth and critical evaluation of four PhCs that are on the EU watch list. The studies on azithromycin removal by CWs were very limited (only four data points). The observed concentrations were very small (influent range: 0.007–0.01 μg L−1; effluent range: 0.0–0.02 μg L−1). A positive removal was observed in three CWs (removal efficiency range: 14–100%) as reported by Breitholtz et al. (2012), Verlicchi et al. (2013), and Berglund et al. (2014). However, Breitholtz et al. (2012) also reported negative removal (−350%) in one of the studied FWSCWs. It is important to note that the high negative removal could be influenced by the quantity of the PhCs present in the influent and effluent. For example, small changes in concentration for the compounds present in very small concentrations may lead to remarkably high percentage changes. In the case of azithromycin, the effluent concentration was slightly increased (influent: 0.008 vs effluent: 0.04 μg L−1) but a very high value of negative removal was estimated. Based on these findings, it is not possible to draw any general conclusions about azithromycin removal by CWs because there is a very limited number of studies available, and their findings are also contradictory. Unfortunately, this is true for a large number of PhCs and TPs. Therefore, more experimental studies are inevitable in order to draw sound evidence-based conclusions on the removal of several PhCs by CWs. In general, the selection of sampling strategy is important for reliable results (Auvinen et al. 2017a). For instance, some studies reported that sampling can be started once the concentration of the tracer (potassium bromide-KBr) is stabilized at the effluent of the system to achieve reliable estimation of the removal efficiency of PhCs under steady state conditions (Ávila et al. 2010, 2013, 2014b) and sampling should be done on the same day and at the same time (Hijosa-Valsero et al. 2010a, 2010b, 2016; Reyes-Contreras et al. 2012; Ávila et al. 2014a, 2015; Rühmland et al. 2015). Furthermore, primary treatment (homogenization tank-HRT: 1 day) might reduce the huge influent variability (Matamoros et al. 2016), and sampling at different locations should also be done to assess the reduction capacity of different treatment phases in CWs (Conkle et al. 2008; Matamoros et al. 2009; Park et al. 2018; Wang et al. 2019). Moreover, the sample holding time should be lower (sample should not be kept for a long time before analysis) (Matamoros et al. 2008a, 2008b, 2012; Hijosa-Valsero et al. 2010a, 2010b, 2011a; Reyes-Contreras et al. 2012; Zhang et al. 2012a, 2018b; Chen et al. 2016a; Vymazal et al. 2017; Sgroi et al. 2018; Nivala et al. 2019).

Fortunately, a number of PhCs have been extensively studied for which sound evidence-based conclusions could be drawn. Diclofenac, an analgesic/anti-inflammatory drug which is on the EU watch list, is among the most extensively studied PhCs. It was examined by 37 studies with 127 data points (Table 2) under a wide range of design and operating conditions (e.g., lab, pilot, and full-scale applications; treatment of primary, secondary, and tertiary effluent; different CW types such as FWSCW, HFCW, VFCW, HCW, aerated and non-aerated CWs) (Tables S1–S4). There is a large variation across case studies for the estimated statistics. For instance, the mean and standard deviation for removal efficiency stood at 38 ± 35%. The influent and effluent concentrations were calculated as 17 ± 33 and 9 ± 17 μg L−1, respectively. These values indicate low to moderate removal of diclofenac by most of the studied CWs. On the other hand, clarithromycin was investigated by seven studies with 21 CW systems. Negative removal was observed only in one CW studied by Breitholtz et al. (2012); a positive removal was reported for all the other CWs (e.g., Hijosa-Valsero et al. 2011a; Breitholtz et al. 2012; Verlicchi et al. 2013; Vymazal et al. 2017; Petrie et al. 2018). The statistics on removal efficiency for clarithromycin (39 ± 33%) are somewhat similar to those of diclofenac. However, the influent and effluent concentrations of clarithromycin (<0.5 μg L−1) are much lower than for diclofenac (Table 2). The reported data on erythromycin depicted stark differences, which prohibit any generalization. For example, positive removal was documented by Rühmland et al. (2015) and Chen et al. (2016a) but negative removal was observed by Hijosa-Valsero et al. (2011a) and Nuel et al. (2018). Another noteworthy observation from these data is the difference among the influent concentrations in the case study regions. For example, a case study from China by Chen et al. (2016a) reported influent concentration of 12 μg L−1, while a few studies from Europe (e.g., Hijosa-Valsero et al. 2011a; Verlicchi et al. 2013; Nuel et al. 2018) reported much lower values (e.g., <2.0 μg L−1).

Table 2

Statistics (mean and standard deviation) of 34 widely studied PhCs

Therapeutic class/PharmaceuticalNo. of observation based on removal (%)Influent conc. (μg L−1) mean ± stdevEffluent conc. (μg L−1) mean ± stdevRemoval rate (mg m−2 d−1) mean ± stdevRemoval efficiency (%) mean ± stdev
Analgesic/anti-inflammatory drugs 
Diclofenac 127 17 ± 33 9 ± 17 0.9 ± 2.1 38 ± 35 
Ibuprofen 138 30 ± 34 13 ± 20 1.7 ± 2.9 57 ± 30 
Ketoprofen 76 20 ± 36 8 ± 17 1.4 ± 3.0 40 ± 40 
Naproxen 112 21 ± 34 7 ± 16 1.4 ± 2.6 62 ± 29 
Salicylic acid 52 16 ± 10 2.3 ± 1.8 0.7 ± 1.3 79 ± 22 
Analgesic 
Acetaminophen 28 17 ± 29 1.4 ± 4.3 0.2 ± 0.9 64 ± 85 
Codeine 11 0.4 ± 0.3 0.1 ± 0.2 0.4 ± 0.8 68 ± 23 
Tramadol 23 17 ± 47 9 ± 40 0.4 ± 1.2 39 ± 43 
Antibiotics 
Clarithromycin 21 0.3 ± 0.2 0.2 ± 0.1 0.2 ± 0.4 39 ± 33 
Erythromycin 26 5.7 ± 5.9 2.3 ± 3.1 0.6 ± 0.8 26 ± 63 
Lincomycin 20 0.2 ± 0.6 0.05 ± 0.07 0.002 ± 0.006 −441 ± 1,001 
Doxycycline 0.6 ± 1.2 0.07 ± 0.04 0.009 ± 0.002 69 ± 15 
Ofloxacin 17 37 ± 122 8 ± 30 4 ± 12 89 ± 27 
Oxytetracycline 10 501 ± 439 21 ± 33 158 ± 194 87 ± 30 
Sulfadiazine 30 13 ± 35 0.5 ± 1.7 4 ± 10 51 ± 31 
Sulfamethazine 39 42 ± 170 9 ± 43 21 ± 112 43 ± 33 
Sulfamethoxazole 51 34 ± 119 6 ± 32 1.9 ± 5.8 43 ± 42 
Sulfapyridine 25 1.0 ± 0.6 0.2 ± 0.1 0.3 ± 0.2 83 ± 5 
Trimethoprim 45 0.2 ± 0.3 0.4 ± 2.1 −0.2 ± 1.7 48 ± 116 
Monensin 12 0.1 ± 0.0 0.01 ± 0.01 0.03 ± 0.01 93 ± 3 
Antiallergic drugs 
Fexofenadine 0.9 ± 0.9 0.6 ± 0.6 1.1 ± 1.7 21 ± 28 
Stimulants/psychoactive drugs 
Caffeine 115 29 ± 23 6 ± 11 2.1 ± 1.9 80 ± 22 
Antihypertensives 
Diltiazem 0.05 ± 0.02 0.02 ± 0.02 0.06 ± 0.08 67 ± 19 
Psychiatric drugs 
Carbamazepine 113 19 ± 60 13 ± 38 0.6 ± 3.7 22 ± 30 
Mirtazapin 0.04 ± 0.01 0.03 ± 0.02 0.01 ± 0.01 31 ± 29 
Venlafaxine 12 0.4 ± 0.2 0.3 ± 0.2 0.03 ± 0.06 36 ± 27 
Beta-blockers 
Atenolol 22 2.1 ± 2.6 0.5 ± 0.8 0.3 ± 0.4 72 ± 29 
Metoprolol 25 17 ± 37 4 ± 11 0.8 ± 1.7 56 ± 34 
Sotalol 12 0.8 ± 0.6 0.7 ± 0.6 0.04 ± 0.04 25 ± 36 
Receptor antagonists 
Ranitidine 0.6 ± 0.6 0.4 ± 0.4 0.6 ± 0.8 43 ± 41 
Lipid regulators 
Bezafibrate 10 0.5 ± 0.4 0.3 ± 0.2 0.5 ± 0.8 42 ± 27 
Clofibric acid 16 15 ± 13 10 ± 8 0.2 ± 0.2 45 ± 24 
Gemfibrozil 17 67 ± 48 42 ± 35 4.1 ± 3.2 46 ± 28 
Diuretics 
Furosemide 10 12 ± 22 2.3 ± 3.6 0.03 ± 0.01 72 ± 26 
Therapeutic class/PharmaceuticalNo. of observation based on removal (%)Influent conc. (μg L−1) mean ± stdevEffluent conc. (μg L−1) mean ± stdevRemoval rate (mg m−2 d−1) mean ± stdevRemoval efficiency (%) mean ± stdev
Analgesic/anti-inflammatory drugs 
Diclofenac 127 17 ± 33 9 ± 17 0.9 ± 2.1 38 ± 35 
Ibuprofen 138 30 ± 34 13 ± 20 1.7 ± 2.9 57 ± 30 
Ketoprofen 76 20 ± 36 8 ± 17 1.4 ± 3.0 40 ± 40 
Naproxen 112 21 ± 34 7 ± 16 1.4 ± 2.6 62 ± 29 
Salicylic acid 52 16 ± 10 2.3 ± 1.8 0.7 ± 1.3 79 ± 22 
Analgesic 
Acetaminophen 28 17 ± 29 1.4 ± 4.3 0.2 ± 0.9 64 ± 85 
Codeine 11 0.4 ± 0.3 0.1 ± 0.2 0.4 ± 0.8 68 ± 23 
Tramadol 23 17 ± 47 9 ± 40 0.4 ± 1.2 39 ± 43 
Antibiotics 
Clarithromycin 21 0.3 ± 0.2 0.2 ± 0.1 0.2 ± 0.4 39 ± 33 
Erythromycin 26 5.7 ± 5.9 2.3 ± 3.1 0.6 ± 0.8 26 ± 63 
Lincomycin 20 0.2 ± 0.6 0.05 ± 0.07 0.002 ± 0.006 −441 ± 1,001 
Doxycycline 0.6 ± 1.2 0.07 ± 0.04 0.009 ± 0.002 69 ± 15 
Ofloxacin 17 37 ± 122 8 ± 30 4 ± 12 89 ± 27 
Oxytetracycline 10 501 ± 439 21 ± 33 158 ± 194 87 ± 30 
Sulfadiazine 30 13 ± 35 0.5 ± 1.7 4 ± 10 51 ± 31 
Sulfamethazine 39 42 ± 170 9 ± 43 21 ± 112 43 ± 33 
Sulfamethoxazole 51 34 ± 119 6 ± 32 1.9 ± 5.8 43 ± 42 
Sulfapyridine 25 1.0 ± 0.6 0.2 ± 0.1 0.3 ± 0.2 83 ± 5 
Trimethoprim 45 0.2 ± 0.3 0.4 ± 2.1 −0.2 ± 1.7 48 ± 116 
Monensin 12 0.1 ± 0.0 0.01 ± 0.01 0.03 ± 0.01 93 ± 3 
Antiallergic drugs 
Fexofenadine 0.9 ± 0.9 0.6 ± 0.6 1.1 ± 1.7 21 ± 28 
Stimulants/psychoactive drugs 
Caffeine 115 29 ± 23 6 ± 11 2.1 ± 1.9 80 ± 22 
Antihypertensives 
Diltiazem 0.05 ± 0.02 0.02 ± 0.02 0.06 ± 0.08 67 ± 19 
Psychiatric drugs 
Carbamazepine 113 19 ± 60 13 ± 38 0.6 ± 3.7 22 ± 30 
Mirtazapin 0.04 ± 0.01 0.03 ± 0.02 0.01 ± 0.01 31 ± 29 
Venlafaxine 12 0.4 ± 0.2 0.3 ± 0.2 0.03 ± 0.06 36 ± 27 
Beta-blockers 
Atenolol 22 2.1 ± 2.6 0.5 ± 0.8 0.3 ± 0.4 72 ± 29 
Metoprolol 25 17 ± 37 4 ± 11 0.8 ± 1.7 56 ± 34 
Sotalol 12 0.8 ± 0.6 0.7 ± 0.6 0.04 ± 0.04 25 ± 36 
Receptor antagonists 
Ranitidine 0.6 ± 0.6 0.4 ± 0.4 0.6 ± 0.8 43 ± 41 
Lipid regulators 
Bezafibrate 10 0.5 ± 0.4 0.3 ± 0.2 0.5 ± 0.8 42 ± 27 
Clofibric acid 16 15 ± 13 10 ± 8 0.2 ± 0.2 45 ± 24 
Gemfibrozil 17 67 ± 48 42 ± 35 4.1 ± 3.2 46 ± 28 
Diuretics 
Furosemide 10 12 ± 22 2.3 ± 3.6 0.03 ± 0.01 72 ± 26 

The above-mentioned results highlight extremely high compound specific variations. Moreover, several factors play a role in removal efficiencies. The results reveal that the recent studies have examined a higher number of PhCs and TPs, which is a promising trend to comprehensively investigate the occurrence and removal of these compounds. However, the number of data points for several compounds is still limited. Additionally, contradictory results on the removal of numerous PhCs have been reported as well. This prohibits generalization of individual case study results for these PhCs. Therefore, a comprehensive assessment is not possible for every compound. However, we identified 34 PhCs that were studied by several authors for a comprehensive assessment and critical review of the available knowledge. Therefore, the following sections present results and discussion on these selected PhCs.

Removal of widely studied PhCs by CWs

The following insights can be drawn from the analysis presented in Table 2. A very high range of variability in the influent and effluent concentrations, and removal efficiencies exist among the studied PhCs. Despite large variability, CWs prove to be a promising treatment technology for a large number of PhCs. For the 34 selected PhCs, CWs demonstrate a moderate to high potential for successful treatment (e.g., average removal efficiency >50%) in the case of monensin (93%), ofloxacin (89%), oxytetracycline (87%), sulfapyridine (83%), caffeine (80%), salicylic acid (79%), atenolol (72%), furosemide (72%), doxycycline (69%), codeine (68%), diltiazem (67%), acetaminophen (64%), naproxen (62%), ibuprofen (57%), metoprolol (56%), and sulfadiazine (51%). The PhCs with least removal efficiency (average removal efficiency <25%) are carbamazepine (22%) and fexofenadine (21%). In contrast, lincomycin shows a negative removal (on average), although with very high standard deviation. This indicates a high level of uncertainty in making sound conclusions about this and other such cases (e.g., acetaminophen, erythromycin, and trimethoprim). In fact, the two available studies on lincomycin reveal a stark contrast. Chen et al. (2016a) reported a positive removal efficiency in the range of 9.0–81%. On the other hand, Hijosa-Valsero et al. (2011a) reported a negative removal efficiency ranging from −283% to −4,067%. These variations stress the need for better understanding of factors behind the observed variability.

It is pertinent to note that the performance of CWs is influenced by a wide range of design and operational factors. The impact of these factors on the performance of CWs was comprehensively examined by the authors in another publication (Ilyas & van Hullebusch 2019). The studied factors, such as area, depth, OLR, HLR, HRT, DO, pH, and temperature, play an important role in the removal of PhCs by CWs. However, the impacts of these factors are variable for different PhCs, which makes it very difficult to draw specific conclusions. For instance, high HRT facilitates removal of some PhCs but also indicates no effect or negative correlation in the case of some PhCs. The type of CW was considered an important factor for the removal of PhCs, with HCW being the most efficient followed by VFCW, HFCW, and FWSCW (Ilyas & van Hullebusch 2020). Additionally, in this research, the removal efficiency of widely studied PhCs was analyzed when CWs were used for primary, secondary, or tertiary treatment (Supplementary materials 3: Table S6). The results indicate no clear pattern of high or low performance in the case of primary, secondary, or tertiary treatment. For example, in some cases, higher removal efficiencies are achieved when CWs are used as tertiary treatment compared to primary treatment and vice versa. Verlicchi & Zambello (2014) compared the performance of different types of CWs used as primary, secondary, and tertiary treatment. This study also indicated a very high variability in removal efficiency under the studied systems, and it is difficult to establish which level of treatment is better in performance and risk reduction.

Environmental risk assessment for the selected PhCs

Ecological risk was assessed for 27 PhCs for which the PNEC estimates were available from the literature, such as from a comprehensive study by Verlicchi et al. (2012). The PNECs for a certain PhC are reported based on experimental and modeling studies related to several organisms, such as fish, Daphnia, algae, invertebrates, and bacteria (e.g., Verlicchi et al. 2012). We adopted the approach of Verlicchi et al. (2012) and used the lowest estimate of PNEC in our calculations of RQ. For instance, PNEC estimates for erythromycin were available from the study by Sanderson et al. (2003), cited in Verlicchi et al. (2012), for fish (61–900 μg L−1), Daphnia (7.8 μg L−1), algae (0.02–4.3 μg L−1), and invertebrates (15 μg L−1). In this case, the lowest value of 0.02 μg L−1 was used as the PNEC for our assessment.

Then, RQ was calculated using the lowest PNEC value and the MEC of influent and effluent of PhCs. These calculations were performed for the selected PhCs based on all the available data points. The mean RQ were estimated from this analysis and discussed in detail in this section. Since mean could be biased towards high values, median and various other percentiles were also estimated. The RQ was also estimated based on extremes (minimum and maximum values). The resulting statistics are given in Supplementary materials 4: Table S7. The mean RQ estimates are given by Figure 2 and Table 3. Based on effluent RQ assessment, 12 out of 27 PhCs could be grouped under the high risk category. These are in order from high to low risk as: oxytetracycline > ofloxacin > sulfamethoxazole > erythromycin > sulfadiazine > gemfibrozil >ibuprofen > acetaminophen > salicylic acid > sulfamethazine > naproxen > clarithromycin. Most of these PhCs are antibiotics, analgesic and anti-inflammatory drugs. Similar to our findings, Vymazal et al. (2017) reported ibuprofen, acetaminophen, and clarithromycin under the high risk category. Chen et al. (2016b) reported that ibuprofen had a high to medium risk, and diclofenac had a medium risk. The study by Matamoros et al. (2017) indicated that ibuprofen had a medium risk in the effluent. Based on our study with data from several countries, we see the need for including several PhCs (e.g., those that emerged under high risk category in our assessment in regulatory monitoring, water quality standard formulation, and control purposes. For instance, the EU watch list of four PhCs (azithromycin, clarithromycin, erythromycin, and diclofenac) (Barbosa et al. 2016; Gorito et al. 2017) could be enhanced by considering these PhCs.

Table 3

Risk assessment of the 27 selected PhCs based on influent and effluent concentration in CWs

Therapeutic class/PharmaceuticalPNEC (μg L−1)(MEC) influent conc. (μg L−1)(MEC) effluent conc. (μg L−1)Influent RQEffluent RQRisk rank* influent/effluentReferences for PNEC values
Analgesic/anti-inflammatory drugs 
Diclofenac 9.7 17 8.7 1.8 0.9 High/Medium Verlicchi et al. (2012)  
Ibuprofen 1.65 30 13 18 7.9 High/High Verlicchi et al. (2012)  
Ketoprofen 15.6 19 7.6 1.2 0.5 High/Medium Verlicchi et al. (2012)  
Naproxen 2.62 21 7.5 7.9 2.9 High/High Verlicchi et al. (2012)  
Salicylic acid 1.28 16 2.3 12 1.8 High/High Verlicchi et al. (2012)  
Analgesic 
Acetaminophen 1.0 16 2.5 16 2.5 High/High Verlicchi et al. (2012)  
Codeine 16 0.4 0.2 0.02 0.009 Low/No Verlicchi et al. (2012)  
Antibiotics 
Clarithromycin 0.07 0.3 0.2 4.4 2.2 High/High Verlicchi et al. (2012)  
Erythromycin 0.02 5.7 2.2 287 109 High/High Verlicchi et al. (2012)  
Lincomycin 82 0.2 0.05 0.002 0.001 No/No Verlicchi et al. (2012)  
Doxycycline 0.3 0.6 0.07 1.9 0.2 High/Medium Verlicchi et al. (2012)  
Ofloxacin 0.016 37 8.1 2,319 504 High/High Verlicchi et al. (2012)  
Oxytetracycline 0.207 501 21 2,421 101 High/High Verlicchi et al. (2012)  
Sulfadiazine 0.135 14 0.5 100 3.4 High/High Verlicchi et al. (2012)  
Sulfamethazine 4.0 42 8.6 10 2.1 High/High Gros et al. (2010)  
Sulfamethoxazole 0.027 34 5.6 1,247 209 High/High Verlicchi et al. (2012)  
Sulfapyridine 21.61 1.0 0.2 0.05 0.007 Low/No Verlicchi et al. (2012)  
Trimethoprim 2.6 0.2 0.4 0.08 0.2 Low/Medium Verlicchi et al. (2012)  
Stimulants/psychoactive drugs 
Caffeine 46 28 5.9 0.6 0.1 Medium/Medium Kosma et al. (2014)  
Antihypertensives 
Diltiazem 1.9 0.05 0.02 0.03 0.01 Low/Low Verlicchi et al. (2012)  
Psychiatric drugs 
Carbamazepine 13.8 19 12 1.3 0.9 High/Medium Verlicchi et al. (2012)  
Beta-blockers 
Atenolol 30 2.1 0.5 0.07 0.02 Low/Low Verlicchi et al. (2012)  
Metoprolol 8.0 17 3.4 2.1 0.4 High/Medium Verlicchi et al. (2012)  
Receptor antagonists 
Ranitidine 63 0.6 0.4 0.009 0.006 No/No Verlicchi et al. (2012)  
Lipid regulators 
Bezafibrate 5.3 0.5 0.3 0.09 0.05 Low/Low Verlicchi et al. (2012)  
Clofibric acid 40.2 14 9.3 0.4 0.2 Medium/Medium Verlicchi et al. (2012)  
Gemfibrozil 0.9 67 42 74 47 High/High Verlicchi et al. (2012)  
Therapeutic class/PharmaceuticalPNEC (μg L−1)(MEC) influent conc. (μg L−1)(MEC) effluent conc. (μg L−1)Influent RQEffluent RQRisk rank* influent/effluentReferences for PNEC values
Analgesic/anti-inflammatory drugs 
Diclofenac 9.7 17 8.7 1.8 0.9 High/Medium Verlicchi et al. (2012)  
Ibuprofen 1.65 30 13 18 7.9 High/High Verlicchi et al. (2012)  
Ketoprofen 15.6 19 7.6 1.2 0.5 High/Medium Verlicchi et al. (2012)  
Naproxen 2.62 21 7.5 7.9 2.9 High/High Verlicchi et al. (2012)  
Salicylic acid 1.28 16 2.3 12 1.8 High/High Verlicchi et al. (2012)  
Analgesic 
Acetaminophen 1.0 16 2.5 16 2.5 High/High Verlicchi et al. (2012)  
Codeine 16 0.4 0.2 0.02 0.009 Low/No Verlicchi et al. (2012)  
Antibiotics 
Clarithromycin 0.07 0.3 0.2 4.4 2.2 High/High Verlicchi et al. (2012)  
Erythromycin 0.02 5.7 2.2 287 109 High/High Verlicchi et al. (2012)  
Lincomycin 82 0.2 0.05 0.002 0.001 No/No Verlicchi et al. (2012)  
Doxycycline 0.3 0.6 0.07 1.9 0.2 High/Medium Verlicchi et al. (2012)  
Ofloxacin 0.016 37 8.1 2,319 504 High/High Verlicchi et al. (2012)  
Oxytetracycline 0.207 501 21 2,421 101 High/High Verlicchi et al. (2012)  
Sulfadiazine 0.135 14 0.5 100 3.4 High/High Verlicchi et al. (2012)  
Sulfamethazine 4.0 42 8.6 10 2.1 High/High Gros et al. (2010)  
Sulfamethoxazole 0.027 34 5.6 1,247 209 High/High Verlicchi et al. (2012)  
Sulfapyridine 21.61 1.0 0.2 0.05 0.007 Low/No Verlicchi et al. (2012)  
Trimethoprim 2.6 0.2 0.4 0.08 0.2 Low/Medium Verlicchi et al. (2012)  
Stimulants/psychoactive drugs 
Caffeine 46 28 5.9 0.6 0.1 Medium/Medium Kosma et al. (2014)  
Antihypertensives 
Diltiazem 1.9 0.05 0.02 0.03 0.01 Low/Low Verlicchi et al. (2012)  
Psychiatric drugs 
Carbamazepine 13.8 19 12 1.3 0.9 High/Medium Verlicchi et al. (2012)  
Beta-blockers 
Atenolol 30 2.1 0.5 0.07 0.02 Low/Low Verlicchi et al. (2012)  
Metoprolol 8.0 17 3.4 2.1 0.4 High/Medium Verlicchi et al. (2012)  
Receptor antagonists 
Ranitidine 63 0.6 0.4 0.009 0.006 No/No Verlicchi et al. (2012)  
Lipid regulators 
Bezafibrate 5.3 0.5 0.3 0.09 0.05 Low/Low Verlicchi et al. (2012)  
Clofibric acid 40.2 14 9.3 0.4 0.2 Medium/Medium Verlicchi et al. (2012)  
Gemfibrozil 0.9 67 42 74 47 High/High Verlicchi et al. (2012)  

Note: Predicted no effect concentration (PNEC); Measured environmental concentration (MEC); PNEC values are taken from the referred studies; Bold values indicate a high risk category; Risk rank is based on our results (*); Risk is categorized into four levels: high risk (RQ > 1.0), medium risk (0.1 ≤ RQ ≤ 1.0), low risk (0.01 ≤ RQ ≤ 0.1), and no risk (RQ < 0.01).

Figure 2

Risk quotient (RQ) of the 27 selected PhCs based on influent and effluent concentration in CWs. Note: Risk is categorized into four levels: high risk (RQ > 1.0; above red line), medium risk (0.1 ≤ RQ ≤ 1.0; between red and orange line), low risk (0.01 ≤ RQ ≤ 0.1; between orange and green line), and no risk (RQ < 0.01; below green line) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

Figure 2

Risk quotient (RQ) of the 27 selected PhCs based on influent and effluent concentration in CWs. Note: Risk is categorized into four levels: high risk (RQ > 1.0; above red line), medium risk (0.1 ≤ RQ ≤ 1.0; between red and orange line), low risk (0.01 ≤ RQ ≤ 0.1; between orange and green line), and no risk (RQ < 0.01; below green line) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

Our results also reveal that, in general, the estimated RQs based on effluent concentrations are significantly lower than those based on influent values (Figure 2 and Table 3), thus indicating the effective role of CWs in reducing the ecological risk posed by PhCs. These observations are similar to the studies by Zhu & Chen (2014) and Matamoros et al. (2016, 2017). Conversely, CWs may trigger higher ecological risk for certain PhCs and TPs whose concentrations increase in effluent water; however, such cases of risk assessment are not yet reported in the published literature. Moreover, while CWs do, indeed, contribute to reduce the ecological risk of several PhCs, they are not fully eliminated in most of the cases.

A critical evaluation of our estimates and a review of the available studies on risk assessment indicate a careful interpretation and application of the estimated ecological risk. Thus, a thorough understanding of the given context and governing factors is recommended. First, the choice of PNEC estimates plays a central role in resultant RQs and associated classification into risk categories. It is recognized that the toxicity data are significantly affected by many factors such as the lifecycle assessment stage of the organism, characteristics of the surrounding environment, and the experimental conditions (Soares et al. 2008). Additionally, the approach of using the lowest PNEC value is very stringent, although safest from an ecological protection point of view. It implies higher possibilities of categorizing a PhC under a high risk class compared to when other approaches are used (e.g., PNEC based on an indicator species). For instance, Daphnia magna has been used as an indicator species to estimate PNEC (Matamoros et al. 2016). The PNEC values of 74.0, 2.5, and 76.3 μg L−1 for carbamezapine were reported for Desmodesmus subspicatus (algae), Ceriodaphnia dubia, and D. magna (invertebrates), respectively (Auvinen et al. 2017b). Obviously, RQs' assessment based on C. dubia will be much higher than estimated using D. magna in this case. Moreover, different PNECs were reported for a PhC for a certain organism. For instance, in the case of diclofenac, a few experimental studies on D. magna reported PNEC estimates in the range of 22.4–68.0 μg L−1 (Verlicchi et al. 2012). Second, it must be recognized that the overall risk due to the presence of multiple PhCs and TPs may be higher than that of an individual compound (Cleuvers 2003; Yang et al. 2008; Zhu & Chen 2014; Matamoros et al. 2017). Third, a large variability in the RQ estimates demands an in-depth study for a specific environmental context. Fourth, the choice of MEC statistics, for example, using parametric (e.g., mean) and non-parametric (e.g., median or other percentiles/quartiles), may also influence the risk categorization (Table S7). Our analysis on this aspect revealed that using median statistics resulted in seven PhCs under high risk category (gemfibrozil, oxytetracycline, erythromycin, sulfamethoxazole, ibuprofen, clarithromycin, and salicylic acid) instead of 12 rated under high risk when mean RQ values were used for the classification. This indicates that the mean is a more stringent measure than the median, and could be a preferable statistic from a better environmental protection point of view. The choice of mean values aligns with the logic of using the lowest PNEC value in order to aim for better environmental and ecological protection. On the other hand, the risk categorization based on the 25th percentile (P25) shows only a few PhCs under the high risk category, and may result in severe underestimation of risk. However, the risk categorization based on the 75th percentile (P75) is more stringent than the mean, and could also be used as a risk classification threshold, then a more stringent approach can be adopted. The extreme value analysis was not recommended for the risk classification because of significant underestimation in the case of using minimum RQ value or significant overestimation in the case of maximum RQ value. Finally, while aiming at achieving the best ecosystem protection can be recommended in theory, several trade-offs may apply in practice, which require sound scientific evidence to make an informed decision on the target levels of PhCs in a specific aquatic environment.

Role of physicochemical properties of PhCs and removal mechanisms in CWs

Based on the available evidence, synthesis on the role of physicochemical properties and removal mechanisms for 34 PhCs was conducted. For a few PhCs, experimental studies were available to quantify the relative contribution of various mechanisms. This work is summarized in Figure 3 (hydroponic microcosms and media adsorption experiments), Figure 4 (CWs), and Supplementary materials 5: Tables S8 and S9. Correlation and multiple linear regression analyses were performed to examine the linkages between removal efficiency and physicochemical properties, which are discussed in this section (details are given in Table 4 and Supplementary materials 6: Tables S10–S14). The summary of removal mechanisms reported in the literature for the PhCs is presented in Table 5. The physicochemical properties are compiled in Table 6. The main observations and insights drawn from these results are discussed below.

Table 4

Results of the Pearson correlation analysis

ParameterRemoval efficiency (%)Log KocLog DowLog KowMolecular weight (g mol−1)Water solubility (mg L−1)pKa
Removal efficiency (%) 1.000       
Log Koc −0.697 1.000      
Log Dow −0.420 0.126 1.000     
Log Kow −0.342 0.247 0.568 1.000    
Molecular weight (g mol−1−0.384 0.135 0.247 0.218 1.000   
Water solubility (mg L−10.225 −0.125 −0.512 −0.713 −0.162 1.000  
pKa −0.065 −0.116 0.167 −0.311 0.130 −0.023 1.000 
ParameterRemoval efficiency (%)Log KocLog DowLog KowMolecular weight (g mol−1)Water solubility (mg L−1)pKa
Removal efficiency (%) 1.000       
Log Koc −0.697 1.000      
Log Dow −0.420 0.126 1.000     
Log Kow −0.342 0.247 0.568 1.000    
Molecular weight (g mol−1−0.384 0.135 0.247 0.218 1.000   
Water solubility (mg L−10.225 −0.125 −0.512 −0.713 −0.162 1.000  
pKa −0.065 −0.116 0.167 −0.311 0.130 −0.023 1.000 

Note: Organic carbon sorption coefficient (Log Koc); Octanol-water partition coefficient (Log Kow); Distribution coefficient (Log Dow); Dissociation constant (pKa); Bold values indicate a significant correlation at 95% confidence level; The number of data points per pair was 17.

Table 5

Removal mechanisms of 34 selected PhCs in CWs

Therapeutic class/PharmaceuticalPossible removal mechanismReferencesDominant removal mechanism*
Analgesic/anti-inflammatory drugs 
Diclofenac Biodegradation (anaerobic) Ávila et al. (2010, 2014a); Hijosa-Valsero et al. (2010b); Chen et al. (2016b); Kahl et al. (2017); He et al. (2018); Zhang et al. (2018b); Nivala et al. (2019)  Photodegradation; Biodegradation (aerobic)** 
 Biodegradation (aerobic) Hijosa-Valsero et al. (2010a, 2010b, 2011b); Ávila et al. (2013, 2014a); Kahl et al. (2017)   
 Photodegradation Matamoros et al. (2008a); Matamoros & Salvadó (2012); Ávila et al. (2014b, 2015); Rühmland et al. (2015); Chen et al. (2016b); Francini et al. (2018); Zhang et al. (2018b)   
 Plant uptake Hijosa-Valsero et al. (2010a); Zhang et al. (2011, 2012c)   
Ibuprofen Biodegradation (aerobic) Matamoros et al. (2007, 2008b); Hijosa-Valsero et al. (2010a, 2011c); Ávila et al. (2010, 2013, 2014a, 2014b, 2015); Matamoros & Salvadó (2012); Li et al. (2014); Zhu & Chen (2014); Chen et al. (2016b); Vymazal et al. (2017); Březinova et al. (2018); Zhang et al. (2018b); Nivala et al. (2019)  Biodegradation (aerobic) 
 Sorption Dordio et al. (2010); Dordio & Carvalho (2013)   
 Adsorption Auvinen et al. (2017b)   
 Photodegradation Reyes-Contreras et al. (2012); Zhang et al. (2014)   
 Plant uptake Hijosa-Valsero et al. (2010a); Li et al. (2016b)   
Ketoprofen Biodegradation Hijosa-Valsero et al. (2010a); Zhang et al. (2012a); Chen et al. (2016b); Francini et al. (2018); Zhang et al. (2018b)  Photodegradation 
 Photodegradation Matamoros et al. (2008a); Matamoros & Salvadó (2012); Reyes-Contreras et al. (2012); Francini et al. (2018); Zhang et al. (2018b)   
Naproxen Biodegradation (aerobic) Matamoros et al. (2007, 2009); Hijosa-Valsero et al. (2010a); Matamoros & Salvadó (2012); Zhang et al. (2012b); Chen et al. (2016b); He et al. (2018); Zhang et al. (2018b); Nivala et al. (2019)  Biodegradation (aerobic)**; Photodegradation 
 Biodegradation (anaerobic) Matamoros et al. (2009); Ávila et al. (2010); Li et al. (2014); He et al. (2018); Nivala et al. (2019)   
 Photodegradation Matamoros et al. (2008a); Reyes-Contreras et al. (2012); Hijosa-Valsero et al. (2016); Zhang et al. (2018b)   
 Plant uptake Hijosa-Valsero et al. (2010a); Zhang et al. (2013b); He et al. (2018)   
Salicylic acid Biodegradation Hijosa-Valsero et al. (2010a, 2011b); Reyes-Contreras et al. (2012); Zhang et al. (2012a)  Biodegradation (aerobic)** 
 Plant uptake Hijosa-Valsero et al. (2016)   
Analgesic 
Acetaminophen Biodegradation (aerobic) Ávila et al. (2013, 2015); Koottatep et al. (2017); Li et al. (2017); Vystavna et al. (2017)  Biodegradation (aerobic)** 
 Biodegradation (anaerobic) Chen et al. (2016b)   
 Photodegradation Ávila et al. (2015); Li et al. (2017)   
 Adsorption Ávila et al. (2015); Koottatep et al. (2017)   
 Sorption Chen et al. (2016b)   
 Plant uptake Li et al. (2017)   
Codeine Biodegradation (aerobic) Rühmland et al. (2015); Petrie et al. (2018)  Sorption; Biodegradation (aerobic) 
 Sorption Petrie et al. (2018)   
Tramadol Biological transformation Rühmland et al. (2015); Chen et al. (2016b); Petrie et al. (2018)  Biological transformation 
Antibiotics    
Clarithromycin Biodegradation Hijosa-Valsero et al. (2011a); Berglund et al. (2014)  Photodegradation; Sorption 
 Sorption Hijosa-Valsero et al. (2011a); Berglund et al. (2014)   
 Photodegradation Hijosa-Valsero et al. (2011a); Berglund et al. (2014)   
Erythromycin Biodegradation (aerobic) Rühmland et al. (2015); Chen et al. (2016a)  Biodegradation (aerobic); Adsorption 
 Adsorption Chen et al. (2016a)   
 Plant uptake Hijosa-Valsero et al. (2011a)   
Lincomycin Biodegradation Chen et al. (2016a)  Biodegradation (aerobic)** 
 Sorption Chen et al. (2016a)   
Doxycycline Biodegradation Hijosa-Valsero et al. (2011a); Ávila et al. (2014b)  Biodegradation (aerobic)**; Adsorption 
 Adsorption/retention processes Hijosa-Valsero et al. (2011a); Berglund et al. (2014)   
Ofloxacin Adsorption Chen et al. (2016a)  Biodegradation (anaerobic)**; Adsorption 
 Biodegradation Chen et al. (2016a); Yan et al. (2016)   
Oxytetracycline Adsorption Dordio & Carvalho (2013); Berglund et al. (2014); Huang et al. (2017)  Adsorption; Plant uptake 
 Plant uptake Dordio & Carvalho (2013); Huang et al. (2017)   
 Biodegradation (aerobic) Dordio & Carvalho (2013); Huang et al. (2017)   
Sulfadiazine Biodegradation Xian et al. (2010)  Biodegradation (anaerobic)** 
 Fermentation Dan et al. (2013)   
Sulfamethazine Adsorption Liu et al. (2014); Chen et al. (2016a); Choi et al. (2016)  Biodegradation (aerobic)**; Plant uptake 
 Biodegradation Xian et al. (2010); Liu et al. (2014); Chen et al. (2016a); Choi et al. (2016)   
 Fermentation Dan et al. (2013)   
 Plant uptake Xian et al. (2010)   
Sulfamethoxazole Adsorption Choi et al. (2016); Liang et al. (2018)  Biodegradation (aerobic; anaerobic)** 
 Sorption Zhu & Chen (2014)   
 Biodegradation (aerobic) Conkle et al. (2008); Choi et al. (2016); Sgroi et al. (2018); Button et al. (2019)   
 Biodegradation (anaerobic) Hijosa-Valsero et al. (2011a); Dan et al. (2013); Rühmland et al. (2015); Liang et al. (2018); Sgroi et al. (2018)   
 Photodegradation Hijosa-Valsero et al. (2011a)   
 Plant uptake Xian et al. (2010); Hijosa-Valsero et al. (2011a)   
Sulfapyridine Biodegradation (aerobic) Conkle et al. (2008)  Biodegradation (anaerobic)** 
 Biodegradation (anaerobic) Dan et al. (2013)   
Trimethoprim Biodegradation (aerobic) Hijosa-Valsero et al. (2011a); Rühmland et al. (2015)  Biodegradation (anaerobic)** 
 Biodegradation (anaerobic) Dan et al. (2013)   
Monensin Biodegradation Chen et al. (2016a)  Biodegradation (aerobic)** 
Antiallergic drugs 
Fexofenadine NA NA Adsorption/retention processes** 
Stimulants/psychoactive drugs 
Caffeine Biodegradation (aerobic) Matamoros & Bayona (2006); Hijosa-Valsero et al. (2010b); Zhang et al. (2014); Chen et al. (2016b); Li et al. (2017); Vymazal et al. (2017); Vystavna et al. (2017); He et al. (2018)  Biodegradation (aerobic)**; Plant uptake 
 Biodegradation (anaerobic) Hijosa-Valsero et al. (2010a); Carranza-Diaz et al. (2014); He et al. (2018)   
 Adsorption onto carbon-rich surfaces of the gravel bed Matamoros & Bayona (2006); Dettenmaier et al. (2009); Wang et al. (2014); Li et al. (2017)   
 Plant uptake Hijosa-Valsero et al. (2010a); Zhang et al. (2013a); Zhu & Chen (2014); Chen et al. (2016b); Li et al. (2017); Petrie et al. (2018)   
Antihypertensives 
Diltiazem Plant uptake Petrie et al. (2018)  Plant uptake 
 Sorption Petrie et al. (2018)   
Psychiatric drugs 
Carbamazepine Adsorption onto the available organic surfaces Matamoros et al. (2005, 2008b); Hijosa-Valsero et al. (2011b); Carranza-Diaz et al. (2014); Sharif et al. (2014); Vystavna et al. (2017); Park et al. (2018)  Adsorption; Sorption; Plant uptake 
 Sorption Dordio et al. (2010); Dordio & Carvalho (2013); Park et al. (2018)   
 Biodegradation (aerobic) Hijosa-Valsero et al. (2010a)   
 Reductive transformation Kahl et al. (2017); Nivala et al. (2019)   
 Plant uptake Hijosa-Valsero et al. (2010a, 2016); Macci et al. (2015); Yan et al. (2016); Petrie et al. (2018); He et al. (2018)   
Mirtazapin Biodegradation Breitholtz et al. (2012)  Biodegradation (aerobic)**; Plant uptake 
 Plant uptake Petrie et al. (2018)   
Venlafaxine Precipitation Breitholtz et al. (2012); Vystavna et al. (2017)  Plant uptake; Precipitation 
 Biological transformation Rühmland et al. (2015); Petrie et al. (2018)   
 Plant uptake Petrie et al. (2018)   
Beta-blockers 
Atenolol Biodegradation (aerobic) Conkle et al. (2008); Rühmland et al. (2015)  Sorption 
 Biodegradation (anaerobic) Chen et al. (2016b)   
 Adsorption Auvinen et al. (2017b); Park et al. (2018)   
 Sorption Petrie et al. (2018); Park et al. (2018)   
 Photodegradation Salgado et al. (2013)   
 Plant uptake Francini et al. (2018)   
Metoprolol Biodegradation (aerobic) Conkle et al. (2008); Rühmland et al. (2015); Chen et al. (2016b); He et al. (2018)  Biodegradation (aerobic) 
Sotalol NA NA Biodegradation (aerobic)** 
Receptor antagonists 
Ranitidine Biodegradation Breitholtz et al. (2012)  Sorption 
 Sorption Petrie et al. (2018)   
Lipid regulators 
Bezafibrate Biodegradation (aerobic) Rühmland et al. (2015)  Biodegradation (anaerobic)** 
 Biodegradation (anaerobic) Petrie et al. (2018)   
Clofibric acid Plant uptake Dordio et al. (2010)  Plant uptake 
Gemfibrozil Biodegradation (aerobic) Conkle et al. (2008); Yi et al. (2017); Zhang et al. (2018b)  Biodegradation (aerobic) 
Diuretics 
Furosemide Hydrolysis Chen et al. (2016b); Vymazal et al. (2017)  Hydrolysis; Biodegradation (aerobic)** 
 Photolysis Chen et al. (2016b)   
Therapeutic class/PharmaceuticalPossible removal mechanismReferencesDominant removal mechanism*
Analgesic/anti-inflammatory drugs 
Diclofenac Biodegradation (anaerobic) Ávila et al. (2010, 2014a); Hijosa-Valsero et al. (2010b); Chen et al. (2016b); Kahl et al. (2017); He et al. (2018); Zhang et al. (2018b); Nivala et al. (2019)  Photodegradation; Biodegradation (aerobic)** 
 Biodegradation (aerobic) Hijosa-Valsero et al. (2010a, 2010b, 2011b); Ávila et al. (2013, 2014a); Kahl et al. (2017)   
 Photodegradation Matamoros et al. (2008a); Matamoros & Salvadó (2012); Ávila et al. (2014b, 2015); Rühmland et al. (2015); Chen et al. (2016b); Francini et al. (2018); Zhang et al. (2018b)   
 Plant uptake Hijosa-Valsero et al. (2010a); Zhang et al. (2011, 2012c)   
Ibuprofen Biodegradation (aerobic) Matamoros et al. (2007, 2008b); Hijosa-Valsero et al. (2010a, 2011c); Ávila et al. (2010, 2013, 2014a, 2014b, 2015); Matamoros & Salvadó (2012); Li et al. (2014); Zhu & Chen (2014); Chen et al. (2016b); Vymazal et al. (2017); Březinova et al. (2018); Zhang et al. (2018b); Nivala et al. (2019)  Biodegradation (aerobic) 
 Sorption Dordio et al. (2010); Dordio & Carvalho (2013)   
 Adsorption Auvinen et al. (2017b)   
 Photodegradation Reyes-Contreras et al. (2012); Zhang et al. (2014)   
 Plant uptake Hijosa-Valsero et al. (2010a); Li et al. (2016b)   
Ketoprofen Biodegradation Hijosa-Valsero et al. (2010a); Zhang et al. (2012a); Chen et al. (2016b); Francini et al. (2018); Zhang et al. (2018b)  Photodegradation 
 Photodegradation Matamoros et al. (2008a); Matamoros & Salvadó (2012); Reyes-Contreras et al. (2012); Francini et al. (2018); Zhang et al. (2018b)   
Naproxen Biodegradation (aerobic) Matamoros et al. (2007, 2009); Hijosa-Valsero et al. (2010a); Matamoros & Salvadó (2012); Zhang et al. (2012b); Chen et al. (2016b); He et al. (2018); Zhang et al. (2018b); Nivala et al. (2019)  Biodegradation (aerobic)**; Photodegradation 
 Biodegradation (anaerobic) Matamoros et al. (2009); Ávila et al. (2010); Li et al. (2014); He et al. (2018); Nivala et al. (2019)   
 Photodegradation Matamoros et al. (2008a); Reyes-Contreras et al. (2012); Hijosa-Valsero et al. (2016); Zhang et al. (2018b)   
 Plant uptake Hijosa-Valsero et al. (2010a); Zhang et al. (2013b); He et al. (2018)   
Salicylic acid Biodegradation Hijosa-Valsero et al. (2010a, 2011b); Reyes-Contreras et al. (2012); Zhang et al. (2012a)  Biodegradation (aerobic)** 
 Plant uptake Hijosa-Valsero et al. (2016)   
Analgesic 
Acetaminophen Biodegradation (aerobic) Ávila et al. (2013, 2015); Koottatep et al. (2017); Li et al. (2017); Vystavna et al. (2017)  Biodegradation (aerobic)** 
 Biodegradation (anaerobic) Chen et al. (2016b)   
 Photodegradation Ávila et al. (2015); Li et al. (2017)   
 Adsorption Ávila et al. (2015); Koottatep et al. (2017)   
 Sorption Chen et al. (2016b)   
 Plant uptake Li et al. (2017)   
Codeine Biodegradation (aerobic) Rühmland et al. (2015); Petrie et al. (2018)  Sorption; Biodegradation (aerobic) 
 Sorption Petrie et al. (2018)   
Tramadol Biological transformation Rühmland et al. (2015); Chen et al. (2016b); Petrie et al. (2018)  Biological transformation 
Antibiotics    
Clarithromycin Biodegradation Hijosa-Valsero et al. (2011a); Berglund et al. (2014)  Photodegradation; Sorption 
 Sorption Hijosa-Valsero et al. (2011a); Berglund et al. (2014)   
 Photodegradation Hijosa-Valsero et al. (2011a); Berglund et al. (2014)   
Erythromycin Biodegradation (aerobic) Rühmland et al. (2015); Chen et al. (2016a)  Biodegradation (aerobic); Adsorption 
 Adsorption Chen et al. (2016a)   
 Plant uptake Hijosa-Valsero et al. (2011a)   
Lincomycin Biodegradation Chen et al. (2016a)  Biodegradation (aerobic)** 
 Sorption Chen et al. (2016a)   
Doxycycline Biodegradation Hijosa-Valsero et al. (2011a); Ávila et al. (2014b)  Biodegradation (aerobic)**; Adsorption 
 Adsorption/retention processes Hijosa-Valsero et al. (2011a); Berglund et al. (2014)   
Ofloxacin Adsorption Chen et al. (2016a)  Biodegradation (anaerobic)**; Adsorption 
 Biodegradation Chen et al. (2016a); Yan et al. (2016)   
Oxytetracycline Adsorption Dordio & Carvalho (2013); Berglund et al. (2014); Huang et al. (2017)  Adsorption; Plant uptake 
 Plant uptake Dordio & Carvalho (2013); Huang et al. (2017)   
 Biodegradation (aerobic) Dordio & Carvalho (2013); Huang et al. (2017)   
Sulfadiazine Biodegradation Xian et al. (2010)  Biodegradation (anaerobic)** 
 Fermentation Dan et al. (2013)   
Sulfamethazine Adsorption Liu et al. (2014); Chen et al. (2016a); Choi et al. (2016)  Biodegradation (aerobic)**; Plant uptake 
 Biodegradation Xian et al. (2010); Liu et al. (2014); Chen et al. (2016a); Choi et al. (2016)   
 Fermentation Dan et al. (2013)   
 Plant uptake Xian et al. (2010)   
Sulfamethoxazole Adsorption Choi et al. (2016); Liang et al. (2018)  Biodegradation (aerobic; anaerobic)** 
 Sorption Zhu & Chen (2014)   
 Biodegradation (aerobic) Conkle et al. (2008); Choi et al. (2016); Sgroi et al. (2018); Button et al. (2019)   
 Biodegradation (anaerobic) Hijosa-Valsero et al. (2011a); Dan et al. (2013); Rühmland et al. (2015); Liang et al. (2018); Sgroi et al. (2018)   
 Photodegradation Hijosa-Valsero et al. (2011a)   
 Plant uptake Xian et al. (2010); Hijosa-Valsero et al. (2011a)   
Sulfapyridine Biodegradation (aerobic) Conkle et al. (2008)  Biodegradation (anaerobic)** 
 Biodegradation (anaerobic) Dan et al. (2013)   
Trimethoprim Biodegradation (aerobic) Hijosa-Valsero et al. (2011a); Rühmland et al. (2015)  Biodegradation (anaerobic)** 
 Biodegradation (anaerobic) Dan et al. (2013)   
Monensin Biodegradation Chen et al. (2016a)  Biodegradation (aerobic)** 
Antiallergic drugs 
Fexofenadine NA NA Adsorption/retention processes** 
Stimulants/psychoactive drugs 
Caffeine Biodegradation (aerobic) Matamoros & Bayona (2006); Hijosa-Valsero et al. (2010b); Zhang et al. (2014); Chen et al. (2016b); Li et al. (2017); Vymazal et al. (2017); Vystavna et al. (2017); He et al. (2018)  Biodegradation (aerobic)**; Plant uptake 
 Biodegradation (anaerobic) Hijosa-Valsero et al. (2010a); Carranza-Diaz et al. (2014); He et al. (2018)   
 Adsorption onto carbon-rich surfaces of the gravel bed Matamoros & Bayona (2006); Dettenmaier et al. (2009); Wang et al. (2014); Li et al. (2017)   
 Plant uptake Hijosa-Valsero et al. (2010a); Zhang et al. (2013a); Zhu & Chen (2014); Chen et al. (2016b); Li et al. (2017); Petrie et al. (2018)   
Antihypertensives 
Diltiazem Plant uptake Petrie et al. (2018)  Plant uptake 
 Sorption Petrie et al. (2018)   
Psychiatric drugs 
Carbamazepine Adsorption onto the available organic surfaces Matamoros et al. (2005, 2008b); Hijosa-Valsero et al. (2011b); Carranza-Diaz et al. (2014); Sharif et al. (2014); Vystavna et al. (2017); Park et al. (2018)  Adsorption; Sorption; Plant uptake 
 Sorption Dordio et al. (2010); Dordio & Carvalho (2013); Park et al. (2018)   
 Biodegradation (aerobic) Hijosa-Valsero et al. (2010a)   
 Reductive transformation Kahl et al. (2017); Nivala et al. (2019)   
 Plant uptake Hijosa-Valsero et al. (2010a, 2016); Macci et al. (2015); Yan et al. (2016); Petrie et al. (2018); He et al. (2018)   
Mirtazapin Biodegradation Breitholtz et al. (2012)  Biodegradation (aerobic)**; Plant uptake 
 Plant uptake Petrie et al. (2018)   
Venlafaxine Precipitation Breitholtz et al. (2012); Vystavna et al. (2017)  Plant uptake; Precipitation 
 Biological transformation Rühmland et al. (2015); Petrie et al. (2018)   
 Plant uptake Petrie et al. (2018)   
Beta-blockers 
Atenolol Biodegradation (aerobic) Conkle et al. (2008); Rühmland et al. (2015)  Sorption 
 Biodegradation (anaerobic) Chen et al. (2016b)   
 Adsorption Auvinen et al. (2017b); Park et al. (2018)   
 Sorption Petrie et al. (2018); Park et al. (2018)   
 Photodegradation Salgado et al. (2013)   
 Plant uptake Francini et al. (2018)   
Metoprolol Biodegradation (aerobic) Conkle et al. (2008); Rühmland et al. (2015); Chen et al. (2016b); He et al. (2018)  Biodegradation (aerobic) 
Sotalol NA NA Biodegradation (aerobic)** 
Receptor antagonists 
Ranitidine Biodegradation Breitholtz et al. (2012)  Sorption 
 Sorption Petrie et al. (2018)   
Lipid regulators 
Bezafibrate Biodegradation (aerobic) Rühmland et al. (2015)  Biodegradation (anaerobic)** 
 Biodegradation (anaerobic) Petrie et al. (2018)   
Clofibric acid Plant uptake Dordio et al. (2010)  Plant uptake 
Gemfibrozil Biodegradation (aerobic) Conkle et al. (2008); Yi et al. (2017); Zhang et al. (2018b)  Biodegradation (aerobic) 
Diuretics 
Furosemide Hydrolysis Chen et al. (2016b); Vymazal et al. (2017)  Hydrolysis; Biodegradation (aerobic)** 
 Photolysis Chen et al. (2016b)   

Note: Authors' own insight based on physicochemical properties, removal mechanisms, and limited evidence in the literature (*). Authors' own insight based on physicochemical properties and removal mechanisms (**).

Table 6

Physicochemical properties of 34 selected PhCs

Therapeutic classes/PhCs/Molecular weight (g mol−1)Molecular formulaMolecular structureWater solubility at 25 °C (mg L−1)Log KowLog KocLog DowHenry's law constant (atm m3 mol−1)pKa/charge at pH 7Reference
Analgesic/anti-inflammatory drugs 
Diclofenac/296.15 C14H11Cl2NO2  4.52 4.51 2.921 0.96 4.73 × 10−12 4.15/negative Hijosa-Valsero et al. (2010a); Zhang et al. (2012b, 2012c); Verlicchi et al. (2012, 2013); He et al. (2018); Petrie et al. (2018); Zhang et al. (2018b)
Ibuprofen/206.29 C13H18O2  41.05 3.97 2.596 1.25 1.52 × 10−7 4.91/negative Hijosa-Valsero et al. (2010a); Verlicchi et al. (2012, 2013); He et al. (2018); Park et al. (2018); Petrie et al. 2018); Zhang et al. (2018b); Wang et al. (2019)  
Ketoprofen/254.29 C16H14O3  120.4 3.12 2.459 0.45 2.12 × 10−11 4.45/negative Reyes-Contreras et al. (2012); Verlicchi et al. (2012, 2013); Hijosa-Valsero et al. (2016); Petrie et al. (2018); Zhang et al. (2018b)  
Naproxen/230.27 C14H14O3  144.9 3.18 2.543 0.30 3.39 × 10−10 4.15/negative Verlicchi et al. (2012, 2013); Hijosa-Valsero et al. (2016); He et al. (2018); Petrie et al. (2018); Zhang et al. (2018b)  
Salicylic acid/138.12 C7H6O3  3.80 × 103 2.26 1.379 1.79 1.42 × 10−8 2.97/negative Verlicchi et al. (2012, 2013); Hijosa-Valsero et al. (2011b, 2016
Analgesic 
Acetaminophena/151.17 C8H9NO2  3.04 × 104 0.46 – 0.90 6.42 × 10−13 9.38/neutral Verlicchi et al. (2012, 2013); Chen et al. (2016b); Petrie et al. (2018)  
Codeine/299.37 C18H21NO3  1.21 × 104 1.28 2.845 −0.23 7.58 × 10−14 8.21/positive Verlicchi et al. (2012, 2013); Petrie et al. (2018)  
Tramadolb/263.38 C16H25NO2  1.15 × 103 3.01 – 0.72 1.54 × 10−11 9.61/positive Verlicchi et al. (2012); Rühmland et al. (2015); Chen et al. (2016b); Petrie et al. (2018)  
Antibiotics 
Clarithromycin/747.97 C38H69NO13  0.342 3.16 2.174 2.31 1.73 × 10−29 8.99/positive Verlicchi et al. (2012, 2013); Chen et al. (2016b); Petrie et al. (2018)  
Erythromycin/733.93 C37H67NO13  0.517 3.06 2.754 – 5.42 × 10−29 8.9/positive Verlicchi et al. (2012, 2013); Chen et al. (2016b); Yi et al. (2017)  
Lincomycinc/406.54 C18H34N2O6 92.19 0.72 – – – 8.78/positive; neutral *; Verlicchi et al. (2012); Yi et al. (2017)  
Doxycycline/444.44 C22H24N2O8  312.9 −0.02 1.693 – – pK1 = 3.5; pK2 = 7.7; pK3 = 9.5/negative Verlicchi et al. (2012, 2013
Ofloxacin/361.37 C18H20FN3O4  2.83 × 104 −0.39 1.086 – – 5.97/neutral; negative **; Verlicchi et al. (2012, 2013
Oxytetracyclined/460.43 C22H24N2O9  1.4 × 103 1.22 1.867 – – pK1 = 3.3; pK2 = 7.3; pK3 = 9.1/negative Verlicchi et al. (2012, 2013); Choi et al. (2016); Huang et al. (2017)  
Sulfadiazine/250.28 C10H10N4O2 2.81 × 104 −0.09 1.871 −0.23 to −1.5 – pK1 = 6.4; pK2 = 2.1/neutral; negative Verlicchi et al. (2012, 2013); Dan et al. (2013)  
Sulfamethazine/278.33 C12H14N4O2 1.12 × 104 0.89 2.282 0.79–0.16 3.05 × 10−13 pK1 = 7.6; pK2 = 2.3/neutral; negative Verlicchi et al. (2012, 2013); Dan et al. (2013); Chen et al. (2016b)  
Sulfamethoxazole/253.28 C10H11N3O3 3.94 × 103 0.89 2.412 −0.03 9.56 × 10−13 pK1 = 5.7; pK2 = 1.8/neutral; negative Verlicchi et al. (2012, 2013); Chen et al. (2016b); Petrie et al. (2018)  
Sulfapyridinee/249.29 C11H11N3O2 1.20 × 104 0.53 – −0.08 to −0.16 – 8.43/neutral; negative *; Verlicchi et al. (2012); Dan et al. (2013)  
Trimethoprim/290.32 C14H18N4O3  2.33 × 103 0.91 2.857 1.13 2.39 × 10−14 7.12/neutral; positive Verlicchi et al. (2012, 2013); Petrie et al. (2018); Yi et al. (2017); Sgroi et al. (2018)  
Monensinf/670.87 C36H62O11  3.0 × 10−3 5.43 – – – 6.6/negative *; ** 
Antiallergic drugs 
Fexofenadineg/501.67 C32H39NO4  2.4 × 10−2 2.81 – 2.93 1.19 × 10−18 pK1 = 8.8; pK2 = 4.3/neutral **; Breitholtz et al. (2012); Petrie et al. (2018)  
Stimulants/psychoactive drugs 
Caffeineh/194.19 C8H10N4O2  2.16 × 104 −0.07 1.00 −0.55 3.58 × 10−11 10.4/positive **; Hijosa-Valsero et al. (2011b); Chen et al. (2016b); Yi et al. (2017); He et al. (2018); Petrie et al. (2018); Sgroi et al. (2018); Wang et al. (2019)  
Antihypertensives 
Diltiazemi/414.52 C22H26N2O4 12.3 2.79 – 1.97 8.61 × 10−17 8.94/positive Breitholtz et al. (2012); Verlicchi et al. (2012); Petrie et al. (2018)  
Psychiatric drugs 
Carbamazepine/236.28 C15H12N2 17.7 2.45 3.59 2.77 1.08 × 10−10 13.9/neutral Zhang et al. (2012b); Verlicchi et al. (2012, 2013); Carranza-Diaz et al. (2014); Hijosa-Valsero et al. (2016); He et al. (2018); Park et al. (2018); Petrie et al. (2018); Wang et al. (2019)  
Mirtazapinj/265.35 C17H19N3  1,100 3.00 – 3.15 – 8.10/positive **; Breitholtz et al. (2012); Giulia et al. (2016); Petrie et al. (2018)  
Venlafaxineb/277.41 C17H27NO2  266.7 3.28 – 1.32 2.87 × 10−11 9.3/positive Breitholtz et al. (2012); Rühmland et al. (2015); Giulia et al. (2016); Vystavna et al. (2017); Petrie et al. (2018)  
Beta-blockers 
Atenolol/266.34 C14H22N2O3  685.2 0.16 1.825 −1.71 1.37 × 10−18 9.6/positive **; Verlicchi et al. (2012, 2013); Yi et al. (2017); Park et al. (2018); Petrie et al. (2018)  
Metoprolol/267.37 C15H25NO3  4.77 × 103 1.88 2.057 −0.38 1.40 × 10−13 9.6/positive Verlicchi et al. (2012, 2013); Chen et al. (2016b); He et al. (2018); Petrie et al. (2018)  
Sotalol/272.36 C12H20N2O3 5.51 × 103 0.24 1.351 – – pK1 = 8.2; pK2 = 9.8/positive *; Verlicchi et al. (2012, 2013
Receptor antagonists 
Ranitidinek/314.41 C13H22N4O3 2.47 × 104 0.27 3.839 −1.56 3.42 × 10−15 2.4/positive Breitholtz et al. (2012); Verlicchi et al. (2012, 2013); Petrie et al. (2018)  
Lipid regulators 
Bezafibrate/361.83 C19H20ClNO4  1.224 4.25 2.617 0.46 2.12 × 10−15 3.6/negative Breitholtz et al. (2012); Verlicchi et al. (2012, 2013); Chen et al. (2016b); Petrie et al. (2018)  
Clofibric acid/214.65 C10H11ClO3  582.5 2.57 1.64 – – 3.18/negative Hijosa-Valsero et al. (2010a); Verlicchi et al. (2012, 2013); Yi et al. (2017)  
Gemfibrozil/250.33 C15H22O3  4.964 4.77 2.636 – 1.2 × 10−8 4.8/negative **; Verlicchi et al. (2013); Yi et al. (2017); Zhang et al. (2018b); Wang et al. (2019)  
Diuretics 
Furosemide/330.7 C12H11ClN2O5 149.3 2.03 2.043 – 3.94 × 10−16 3.9/negative Hijosa-Valsero et al. (2010a); Chen et al. (2016b); Verlicchi et al. (2012, 2013
Therapeutic classes/PhCs/Molecular weight (g mol−1)Molecular formulaMolecular structureWater solubility at 25 °C (mg L−1)Log KowLog KocLog DowHenry's law constant (atm m3 mol−1)pKa/charge at pH 7Reference
Analgesic/anti-inflammatory drugs 
Diclofenac/296.15 C14H11Cl2NO2  4.52 4.51 2.921 0.96 4.73 × 10−12 4.15/negative Hijosa-Valsero et al. (2010a); Zhang et al. (2012b, 2012c); Verlicchi et al. (2012, 2013); He et al. (2018); Petrie et al. (2018); Zhang et al. (2018b)
Ibuprofen/206.29 C13H18O2  41.05 3.97 2.596 1.25 1.52 × 10−7 4.91/negative Hijosa-Valsero et al. (2010a); Verlicchi et al. (2012, 2013); He et al. (2018); Park et al. (2018); Petrie et al. 2018); Zhang et al. (2018b); Wang et al. (2019)  
Ketoprofen/254.29 C16H14O3  120.4 3.12 2.459 0.45 2.12 × 10−11 4.45/negative Reyes-Contreras et al. (2012); Verlicchi et al. (2012, 2013); Hijosa-Valsero et al. (2016); Petrie et al. (2018); Zhang et al. (2018b)  
Naproxen/230.27 C14H14O3  144.9 3.18 2.543 0.30 3.39 × 10−10 4.15/negative Verlicchi et al. (2012, 2013); Hijosa-Valsero et al. (2016); He et al. (2018); Petrie et al. (2018); Zhang et al. (2018b)  
Salicylic acid/138.12 C7H6O3  3.80 × 103 2.26 1.379 1.79 1.42 × 10−8 2.97/negative Verlicchi et al. (2012, 2013); Hijosa-Valsero et al. (2011b, 2016
Analgesic 
Acetaminophena/151.17 C8H9NO2  3.04 × 104 0.46 – 0.90 6.42 × 10−13 9.38/neutral Verlicchi et al. (2012, 2013); Chen et al. (2016b); Petrie et al. (2018)  
Codeine/299.37 C18H21NO3  1.21 × 104 1.28 2.845 −0.23 7.58 × 10−14 8.21/positive Verlicchi et al. (2012, 2013); Petrie et al. (2018)  
Tramadolb/263.38 C16H25NO2  1.15 × 103 3.01 – 0.72 1.54 × 10−11 9.61/positive Verlicchi et al. (2012); Rühmland et al. (2015); Chen et al. (2016b); Petrie et al. (2018)  
Antibiotics 
Clarithromycin/747.97 C38H69NO13  0.342 3.16 2.174 2.31 1.73 × 10−29 8.99/positive Verlicchi et al. (2012, 2013); Chen et al. (2016b); Petrie et al. (2018)  
Erythromycin/733.93 C37H67NO13  0.517 3.06 2.754 – 5.42 × 10−29 8.9/positive Verlicchi et al. (2012, 2013); Chen et al. (2016b); Yi et al. (2017)  
Lincomycinc/406.54 C18H34N2O6 92.19 0.72 – – – 8.78/positive; neutral *; Verlicchi et al. (2012); Yi et al. (2017)  
Doxycycline/444.44 C22H24N2O8  312.9 −0.02 1.693 – – pK1 = 3.5; pK2 = 7.7; pK3 = 9.5/negative Verlicchi et al. (2012, 2013
Ofloxacin/361.37 C18H20FN3O4  2.83 × 104 −0.39 1.086 – – 5.97/neutral; negative **; Verlicchi et al. (2012, 2013
Oxytetracyclined/460.43 C22H24N2O9  1.4 × 103 1.22 1.867 – – pK1 = 3.3; pK2 = 7.3; pK3 = 9.1/negative Verlicchi et al. (2012, 2013); Choi et al. (2016); Huang et al. (2017)  
Sulfadiazine/250.28 C10H10N4O2 2.81 × 104 −0.09 1.871 −0.23 to −1.5 – pK1 = 6.4; pK2 = 2.1/neutral; negative Verlicchi et al. (2012, 2013); Dan et al. (2013)  
Sulfamethazine/278.33 C12H14N4O2 1.12 × 104 0.89 2.282 0.79–0.16 3.05 × 10−13 pK1 = 7.6; pK2 = 2.3/neutral; negative Verlicchi et al. (2012, 2013); Dan et al. (2013); Chen et al. (2016b)  
Sulfamethoxazole/253.28 C10H11N3O3 3.94 × 103 0.89 2.412 −0.03 9.56 × 10−13 pK1 = 5.7; pK2 = 1.8/neutral; negative Verlicchi et al. (2012, 2013); Chen et al. (2016b); Petrie et al. (2018)  
Sulfapyridinee/249.29 C11H11N3O2 1.20 × 104 0.53 – −0.08 to −0.16 – 8.43/neutral; negative *; Verlicchi et al. (2012); Dan et al. (2013)  
Trimethoprim/290.32 C14H18N4O3  2.33 × 103 0.91 2.857 1.13 2.39 × 10−14 7.12/neutral; positive Verlicchi et al. (2012, 2013); Petrie et al. (2018); Yi et al. (2017); Sgroi et al. (2018)  
Monensinf/670.87 C36H62O11  3.0 × 10−3 5.43 – – – 6.6/negative *; ** 
Antiallergic drugs 
Fexofenadineg/501.67 C32H39NO4  2.4 × 10−2 2.81 – 2.93 1.19 × 10−18 pK1 = 8.8; pK2 = 4.3/neutral **; Breitholtz et al. (2012); Petrie et al. (2018)  
Stimulants/psychoactive drugs 
Caffeineh/194.19 C8H10N4O2  2.16 × 104 −0.07 1.00 −0.55 3.58 × 10−11 10.4/positive **; Hijosa-Valsero et al. (2011b); Chen et al. (2016b); Yi et al. (2017); He et al. (2018); Petrie et al. (2018); Sgroi et al. (2018); Wang et al. (2019)  
Antihypertensives 
Diltiazemi/414.52 C22H26N2O4 12.3 2.79 – 1.97 8.61 × 10−17 8.94/positive Breitholtz et al. (2012); Verlicchi et al. (2012); Petrie et al. (2018)  
Psychiatric drugs 
Carbamazepine/236.28 C15H12N2 17.7 2.45 3.59 2.77 1.08 × 10−10 13.9/neutral Zhang et al. (2012b); Verlicchi et al. (2012, 2013); Carranza-Diaz et al. (2014); Hijosa-Valsero et al. (2016); He et al. (2018); Park et al. (2018); Petrie et al. (2018); Wang et al. (2019)  
Mirtazapinj/265.35 C17H19N3  1,100 3.00 – 3.15 – 8.10/positive **; Breitholtz et al. (2012); Giulia et al. (2016); Petrie et al. (2018)  
Venlafaxineb/277.41 C17H27NO2  266.7 3.28 – 1.32 2.87 × 10−11 9.3/positive Breitholtz et al. (2012); Rühmland et al. (2015); Giulia et al. (2016); Vystavna et al. (2017); Petrie et al. (2018)  
Beta-blockers 
Atenolol/266.34 C14H22N2O3  685.2 0.16 1.825 −1.71 1.37 × 10−18 9.6/positive **; Verlicchi et al. (2012, 2013); Yi et al. (2017); Park et al. (2018); Petrie et al. (2018)  
Metoprolol/267.37 C15H25NO3  4.77 × 103 1.88 2.057 −0.38 1.40 × 10−13 9.6/positive Verlicchi et al. (2012, 2013); Chen et al. (2016b); He et al. (2018); Petrie et al. (2018)  
Sotalol/272.36 C12H20N2O3 5.51 × 103 0.24 1.351 – – pK1 = 8.2; pK2 = 9.8/positive *; Verlicchi et al. (2012, 2013
Receptor antagonists 
Ranitidinek/314.41 C13H22N4O3 2.47 × 104 0.27 3.839 −1.56 3.42 × 10−15 2.4/positive Breitholtz et al. (2012); Verlicchi et al. (2012, 2013); Petrie et al. (2018)  
Lipid regulators 
Bezafibrate/361.83 C19H20ClNO4  1.224 4.25 2.617 0.46 2.12 × 10−15 3.6/negative Breitholtz et al. (2012); Verlicchi et al. (2012, 2013); Chen et al. (2016b); Petrie et al. (2018)  
Clofibric acid/214.65 C10H11ClO3  582.5 2.57 1.64 – – 3.18/negative Hijosa-Valsero et al. (2010a); Verlicchi et al. (2012, 2013); Yi et al. (2017)  
Gemfibrozil/250.33 C15H22O3  4.964 4.77 2.636 – 1.2 × 10−8 4.8/negative **; Verlicchi et al. (2013); Yi et al. (2017); Zhang et al. (2018b); Wang et al. (2019)  
Diuretics 
Furosemide/330.7 C12H11ClN2O5 149.3 2.03 2.043 – 3.94 × 10−16 3.9/negative Hijosa-Valsero et al. (2010a); Chen et al. (2016b); Verlicchi et al. (2012, 2013
Figure 3

Relative contribution of removal mechanisms for PhCs in hydroponic microcosms and media adsorption experiments. Note: The studies examined the contribution of one or more removal mechanisms. When the sum of the reported contribution by different mechanisms exceeded 100%, we standardized the contribution from each mechanism to 100% by adding removal of all the studied mechanisms and dividing it by the total. For example, in the case of ibuprofen, the contribution by biodegradation, adsorption, and plant uptake was 30%, 53%, and 24.6%, respectively. The total removal is 107.6%. However, out of 100%, the contribution of biodegradation, adsorption, and plant uptake was 27.9%, 49.3%, and 22.9%, respectively. Adsorption is the adhesion of dissolved solid molecules (adsorbate) to a surface of the substrate (adsorbent). It is a surface phenomenon. Absorption is a process in which a fluid (absorbate) permeates a solid (absorbent), thus involving the whole volume of the material. In the case of sorption both processes take place. Data are taken from Dordio et al. (2009a, 2009b); Matamoros et al. (2012); Zhang et al. (2012c, 2013a, 2013b, 2013c); Sharif et al. (2014); Choi et al. (2016); Zhang et al. (2016); Li et al. (2017); Zhang et al. (2018a); Petrie et al. (2018); and Park et al. (2018).

Figure 3

Relative contribution of removal mechanisms for PhCs in hydroponic microcosms and media adsorption experiments. Note: The studies examined the contribution of one or more removal mechanisms. When the sum of the reported contribution by different mechanisms exceeded 100%, we standardized the contribution from each mechanism to 100% by adding removal of all the studied mechanisms and dividing it by the total. For example, in the case of ibuprofen, the contribution by biodegradation, adsorption, and plant uptake was 30%, 53%, and 24.6%, respectively. The total removal is 107.6%. However, out of 100%, the contribution of biodegradation, adsorption, and plant uptake was 27.9%, 49.3%, and 22.9%, respectively. Adsorption is the adhesion of dissolved solid molecules (adsorbate) to a surface of the substrate (adsorbent). It is a surface phenomenon. Absorption is a process in which a fluid (absorbate) permeates a solid (absorbent), thus involving the whole volume of the material. In the case of sorption both processes take place. Data are taken from Dordio et al. (2009a, 2009b); Matamoros et al. (2012); Zhang et al. (2012c, 2013a, 2013b, 2013c); Sharif et al. (2014); Choi et al. (2016); Zhang et al. (2016); Li et al. (2017); Zhang et al. (2018a); Petrie et al. (2018); and Park et al. (2018).

The experimental studies reveal various possible removal mechanisms of PhCs: biodegradation (aerobic and anaerobic), adsorption, sorption, plant uptake, biological transformation, reductive transformation, fermentation, precipitation, hydrolysis, and photodegradation. Although all these mechanisms do not come into play for every PhC, there is often more than one mechanism responsible for the removal of a PhC. This indicates the need of compound specific examination for removal mechanisms. Additionally, it is important to note that certain types of CWs (e.g., FWSCW, HFCW, and VFCW) provide certain environmental conditions to facilitate the specific mechanisms to take place. For instance, FWSCW provides a suitable environment for photodegradation, while VFCW and HFCW are known for the presence of microbial communities that can be directly involved in the biodegradation of PhCs.

Moreover, physicochemical properties play a pivotal role in the removal processes which are governed by molecular weight/structure, solubility in water, Log Koc, Log Kow, Log Dow, cationic or anionic nature (pKa/charge), and the presence of certain elements (e.g., chlorine, well known for its recalcitrance against biodegradation). Due to a large array of removal mechanisms and physicochemical properties, it is very difficult to draw general conclusions for all the PhCs on the responsible removal mechanisms, physicochemical properties, and other governing factors. Thus, a compound specific examination is inevitable. Nevertheless, a few generic observations can be made from an overall analysis of the available evidence, which could be used as a preliminary screening of possible removal efficiencies and governing factors. The main insights are noted below.

The Log Kow is the ratio of the concentration of unionized compound between octanol and water, and has been assumed as the standard measure of hydrophobicity of organic compounds (Burken & Schnoor 1998; Reinhold et al. 2010). The hydrophobicity of non-ionic organic compounds is invariable under different pH conditions but the hydrophobicity of ionic organic compounds varies due to ionization of the compound at a certain pH. In that case, Log Dow is appropriate to represent the hydrophobicity of ionic organic compounds (Lee et al. 2011). Log Dow is the distribution ratio of the concentration of ionizable organic compound between octanol and buffer phase. The concentration in octanol is the total concentration of an ionizable organic compound which is assumed to be dominated by the non-dissociated form. The concentration in the buffer phase is considered as the fraction in dissociated and non-dissociated forms, which depends on the pH and pKa of the organic compound (ECETOC Technical report 123 Log Dow). Log Dow is nearly the same as Log Kow but the modification in ionizable functional groups may affect their removal by biological treatment processes (Zhang et al. 2012b). Nevertheless, the Log Kow and Log Dow, which are significantly correlated (Table 4), indicate the hydrophobicity of organic compounds, and thus are important parameters to understand the behavior of PhCs in environmental media.

It is widely considered that organic compounds with moderate hydrophobicity (1.0 < Log Kow < 3.5; Log Dow < 2.5) and low molecular weight (MW < 500 g mol−1) have adequate properties to move through cell membranes, and thus are easily taken up by plant roots and translocated into shoots (Briggs et al. 1983; Dietz & Schnoor 2001; Alvarez et al. 2004; Pilon-Smits 2005; Le-Minh et al. 2010; Yan et al. 2016). Hydrophobic compounds (Log Kow > 3.5) bind so strongly to the surface of roots and soils that they cannot be translocated easily within the plant, and the hydrophilic compounds (Log Kow < 1.0), which are quite water soluble, are not sufficiently sorbed to roots nor actively transported through plant membranes (Dietz & Schnoor 2001). Plant uptake is an important mechanism, although dominant only for a few PhCs such as clofibric acid, diltiazem, and venlafaxine. These PhCs also indicate low to moderate removal possibilities. Furthermore, it is revealed that cationic compounds which can partition into the lipophilic cell structure of negatively charged biomembranes of the plant roots show their uptake by the plant (Petrie et al. 2018). Negatively charged compounds cannot be taken up by the plants because the charge repulsion with the negatively charged biomembrane restricts their uptake by the plant root (Matamoros et al. 2012; Petrie et al. 2018). It has been indicated that neutral compounds which are hydrophilic in nature (Log Kow < 1.5) may still be taken up by rooted vascular plants via hydrogen bonding with water molecules into the transpiration stream (Dietz & Schnoor 2001). It has been suggested that hydrophobic compounds (Log Kow > 3.5) are most likely removed by phytostabilization and/or rhizosphere bioremediation (biodegradation) due to their long residence time in the root zone (Dietz & Schnoor 2001).

Biodegradation is the major removal mechanism in most of the studied PhCs (19 out of 34 selected PhCs) (Table 5). This means that the treatment systems targeting multiple PhCs must ensure environmental conditions conducive for biodegradation. Next to this, it can be recognized that the readily biodegradable compounds often demonstrate the highest removal efficiencies (e.g., acetaminophen, ibuprofen, caffeine, monensin, and salicylic acid) (Tables 2 and 5). However, the biodegradability of a PhC cannot be determined only by its physicochemical properties, since a large number of PhCs have Log Kow < 3.5 and their removal is attributed to biodegradation. Therefore, experimental studies are essential to establish biodegradability of every PhC. Moreover, biodegradation as a dominant process does not guarantee higher removal possibilities in all the cases (e.g., bezafibrate, gemfibrozil, mirtazapin, sotalol, and trimethroprim) (Tables 2 and 5). This reveals the complexity of the biodegradation process itself (how much a compound is biodegradable) but also the role of other processes in CWs and physicochemical properties of PhCs.

Furthermore, for the compounds which are most hydrophilic (Log Kow < 1.0), the most water soluble (WS > 1,000 mg L−1), and have the lowest molecular weight (MW < 100 g mol−1), adsorption cannot be considered as a dominant removal mechanism because more time is required for the sorption/sedimentation of those compounds (Vystavna et al. 2017). It is generally considered that for the organic compounds which are most hydrophobic (Log Kow > 4; Log Dow > 2.5), and have high molecular weight (MW > 500 g mol−1), adsorption processes are more common (Briggs et al. 1983; Alvarez et al. 2004; Le-Minh et al. 2010; Dan et al. 2013; Yan et al. 2016). When adsorption/sorption is a dominant removal mechanism, the removal efficiencies are either moderate (e.g., atenolol and codeine) or low (e.g., fexofenadine, ranitidine, and carbamazepine) even in the CWs that can provide good media for adsorption/sorption (Tables 2 and 5). It has been observed that adsorption/sorption potential of a CW may decrease due to creation of biofilms around the filter media that may prohibit access to adsorption/sorption surfaces (Dordio et al. 2009a, 2010).

The Log Koc is the ratio of the mass of a compound that is adsorbed in the soil per unit mass of organic carbon in the soil. Log Koc values are useful in predicting the mobility of organic soil contaminants; the higher Log Koc values correlate to less mobile organic compounds, while lower Log Koc values correlate to more mobile organic compounds (Piwoni & Keeley 1990). It has been suggested that non-polar organic compounds are sorbed by soils as a function of their hydrophobicity (Log Kow) and the organic carbon content of the soil (Piwoni & Keeley 1990). This can be seen by a positive correlation of Log Koc with Log Kow (although non-significant) (Table 4), which can be further supported by the negative correlation (although non-significant) of Log Koc and water solubility (Table 4). The Log Koc is a good indicator of the sorption potential and mobility of the PhCs, which can influence their fate in CWs. We found a significant negative correlation of Log Koc with removal efficiency (r: −0.697) (Table 4), which could explain about 49% of the variance in the available data (R2: 0.49) (Table S10). This indicates the possibility of using Log Koc as a screening parameter, even though its proportionate contribution in overall removal may be limited. Similarly, Log Dow shows a significant negative correlation with removal efficiency (r: −0.420) (Table 4), although this could only explain about 18% variance in the available data (R2: 0.18) (Table S11). A multiple linear regression of removal efficiency with these two physicochemical properties was found as a significant predictor (R2: 0.60; P-value <0.05) (Table S13).

In particular, the PhCs that have chlorine in their molecular structure (e.g., bezafibrate, clofibric acid, diclofenac, and furosemide) are considered recalcitrant to biodegradation. Most of these PhCs are very difficult to remove by CWs, and thus show low removal efficiencies (<50%) with the exception of furosemide (72%) (Tables 2 and 6). As mentioned above, molecular structure/weight plays an important role in the removal processes, although molecular weight did not show a statistically significant correlation with removal efficiency (r: −0.384 and R2: 0.15) (Tables 4 and S12). Nevertheless, when molecular weight was included in the multiple linear regression with Log Koc and Log Dow, the result was the best possible model for predicting removal efficiency (R2: 0.65; P-value <0.05) (Table S14), compared to when other physicochemical properties (water solubility, Log Kow, and pKa) were included in regression with Log Koc and Log Dow. A statistically significant model was also possible with four physicochemical properties but was not able to explain more variance compared with the best model. Therefore, we preferred a model with three variables, which was derived using the data of all types of examined CWs. The novel relationship developed in this study is given below and details on statistics are presented in Table S14. 
formula
(1)
where: RE is removal efficiency in % and MW is molecular weight in g mol−1; Log Koc and Log Dow are already defined in this paper.

The other processes were found to be dominant only in very few PhCs such as photodegradation in the case of diclofenac, clarithromycin, and ketoprofen, and hydrolysis of furosemide. These processes also demonstrate low to moderate removal efficiencies of these PhCs (Tables 2 and 5).

The above insights clearly indicate the removal mechanisms in CWs and physicochemical properties of PhCs are simultaneously interacting in a complex manner, and resulting impacts on the removal of PhCs are highly variable in nature. The above-mentioned observations are supported in this paper by an in-depth analysis and discussion on 34 PhCs besides adding more insights. In the following sections, synthesis on 12 selected PhCs is presented, which shows the highest environmental risk (as mentioned in the previous section) including two of the four PhCs on the EU watch list (clarithromycin and erythromycin). Additionally, diclofenac (EU watch list) is also added in this section. Azithromycin is not discussed because sufficient data are not available for this PhC. A description of the rest of the 21 PhCs is given in Supplementary materials 7.

Analgesic/anti-inflammatory drugs

Diclofenac

The removal efficiency of diclofenac was moderate in HCW (56 ± 32%) and VFCW (50 ± 17%), and comparatively lower in FWSCW (42 ± 24%) and HFCW (39 ± 24%). It is suggested that the presence of chlorine in its structure (Table 6) makes it highly recalcitrant to biodegradation (Kimura et al. 2005). It is a hydrophobic compound (Log Kow = 4.51) with moderate molecular weight (296.15 g mol−1) and anionic in nature under neutral conditions (pH = 7) (Table 6), which suggests the removal by adsorption onto soil particles following complex formation with metal ions, but its low distribution coefficient (Log Dow = 0.96) (Table 6) might restrict this removal pathway. However, its removal by adsorption has not been tested in adsorption experiments as well as it is not reported in CWs. Nevertheless, its low removal by plant uptake in hydroponic microcosm (4.4 ± 2.7%) explains that it is not a possible removal pathway (Zhang et al. 2012c, 2013b) (Figure 3 and Table S8). This was confirmed by Zhang et al. (2012c), who calculated the bioaccumulation factor (BAF) and reported that its BAF in the shoots was less than half (0.17–0.51) compared with BAF in the roots (0.40–1.36). This can be attributed to both its high hydrophobicity and relatively low water solubility (4.52 mg L−1 at 25 °C) (Table 6). It has been suggested that organic compounds with Log Kow > 3.5 have a high potential for retention in the plant roots (Dietz & Schnoor 2001). Therefore, the difference in the removal efficiency of planted and unplanted HFCW (50 ± 24% and 32 ± 16%, respectively) (Hijosa-Valsero et al. 2010a; Zhang et al. 2011, 2012a, 2018b; Carranza-Diaz et al. 2014; He et al. 2018) (Table S2), and planted and unplanted HCW (38% and 25%, respectively) (Hijosa-Valsero et al. 2010a) (Table S4) might be due to indirect positive effects of plants' presence such as degradation by enzymatic exudates as well as an increase in the amount of oxygen released by the plant roots in the rhizosphere which can support high microbial activity (biodegradation). However, in hydroponic microcosms it has been revealed that the contribution of biodegradation to its removal was low (3.0%) (Zhang et al. 2013b) (Figure 3 and Table S8). Its high removal by photodegradation was achieved in hydroponic microcosms (79 ± 2%) (Zhang et al. 2012c, 2013b) (Figure 3 and Table S8) and it was confirmed in the unplanted HCW system with a free water surface (FWS) on top of the horizontal flow filter (HFF) which provides the most appropriate environment for photodegradation (Reyes-Contreras et al. 2012). Its higher removal efficiency in unplanted HCW (29%) compared with planted HCW (1.7%) during summer was attributed to photodegradation (Reyes-Contreras et al. 2012).

Ibuprofen

The removal efficiency of ibuprofen was much higher in VFCW (79 ± 24%) but moderate in HCW (62 ± 29%), FWSCW (57 ± 28%), and HFCW (53 ± 27%). Its removal by plant uptake is expected to be low since it is hydrophobic (Log Kow = 3.97) and anionic, although slightly water soluble (41.05 mg L−1 at 25 °C) (Calderón-Preciado et al. 2011; Matamoros et al. 2012) (Table 6). This can be explained by its low plant uptake (0.4–5.0%) in the planted CWs (Figure 4 and Table S9) (Zhang et al. 2017a, 2017b). Therefore, the removal efficiency differences between the planted and unplanted hydroponic microcosms (78% and 30%, respectively) (Matamoros et al. 2012) (Figure 3 and Table S8), planted and unplanted HFCW (68 ± 25% and 41 ± 26%, respectively) (Dordio et al. 2010; Hijosa-Valsero et al. 2010a; Zhang et al. 2011, 2012a, 2018b; Reyes-Contreras et al. 2012; Carranza-Diaz et al. 2014; Li et al. 2016a, 2016b; He et al. 2018) (Table S2), and planted and unplanted HCW (64 ± 9% and 51 ± 11%, respectively) (Hijosa-Valsero et al. 2010a, 2011b; Reyes-Contreras et al. 2012) (Table S4) might be due to indirect effects of plants' presence such as enhancement in biodegradation (Matamoros et al. 2012). This can be seen by the high contribution of biodegradation to its removal in hydroponic microcosms (30%) (Matamoros et al. 2012) (Figure 3 and Table S8). Nevertheless, its detection in the plant leaves (Typha angustifolia) with an even distribution in the lamina and sheath tissues reveals the phytoextraction process for its removal as well. It had been taken up by the plant roots, which is translocated from the roots to the leaves and accumulated in leaf tissues. For instance, its root uptake was partially transformed to carboxyibuprofen, 2-hydroxyibuprofen, and 1-hydroxyibuprofen in the sheath (1,375, 236, and 302 μg kg−1, respectively) and in the lamina (1,051, 694, and 179 μg kg−1, respectively). The accumulation of its metabolites in the plant leaves indicates its phytotransformation in the plant tissues (Li et al. 2016b). Although it has Log Kow < 4 and Log Dow < 2.5, its high sorption coefficient (Log Koc = 2.60) (Table 6) and anionic form under neutral pH conditions favors its adsorption onto soil particles following complexation with metal ions such as Ca2+, Mg2+, Fe3+, or Al3+ (Berglund et al. 2014). This explains its removal by adsorption onto light expanded clay aggregates (LECA) (protonated under the same pH conditions) during adsorption experiments in aqueous solutions (93%) (Dordio et al. 2009a) (Figure 3 and Table S8). However, the positive charge of substrate surfaces might alter over time due to the development of negatively charged biofilms on substrate surfaces which might lead to its lower retention (Dordio et al. 2009a, 2010). This could be the reason for its low removal in adsorption experiments using different substrate media (13%) (Zhang et al. 2018a) (Figure 3 and Table S8) as well as in CWs by adsorption (0.5 ± 0.1%) (Zhang et al. 2017a,, 2017b) (Figure 4 and Table S9).

Naproxen

The removal efficiency of naproxen was higher in VFCW (75 ± 17%) but moderate in HCW (64 ± 24%), HFCW (63 ± 26%), and FWSCW (50 ± 22%). Its slight water solubility (144.9 mg L−1 at 25 °C), moderate hydrophobicity (Log Kow = 3.18; Log Dow = 0.30), and anionic form (Table 6) might decrease its removal by plant uptake and its retention in the plant roots as well as adsorption to the substrate media. This can be explained by its low uptake by the plants in hydroponic microcosms (6.5 ± 1.3%) (Zhang et al. 2013b, 2013c) (Figure 3 and Table S8). Therefore, the higher removal efficiency in planted compared with unplanted HFCW (74 ± 26% and 50 ± 19%, respectively) (Hijosa-Valsero et al. 2010a; Zhang et al. 2011, 2012a, 2018b; Reyes-Contreras et al. 2012; Carranza-Diaz et al. 2014; Hijosa-Valsero et al. 2016; He et al. 2018) (Table S2) might be due the enhancement in biodegradation in the presence of plants, which can be seen by its higher removal through biodegradation (58 ± 1%) in hydroponic microcosms (Zhang et al. 2013b, 2013c) (Figure 3 and Table S8). Furthermore, its considerable removal by photodegradation (37 ± 12%) was achieved in hydroponic microcosms (Matamoros et al. 2012; Zhang et al. 2013b, 2013c) and its higher removal efficiency in the unplanted HCW (FWS on top of HFF) compared with planted HCW during summer was attributed to photodegradation (71 ± 4% and 59 ± 10%, respectively) (Reyes-Contreras et al. 2012; Hijosa-Valsero et al. 2016).

Salicylic acid

The removal efficiency of salicylic acid was higher in VFCW (98%) compared with HCW (86 ± 17%), HFCW (79 ± 21%), and FWSCW (76 ± 19%). The high water solubility (3.81 g L−1 at 25 °C), hydrophilic nature (Log Kow = 2.26; Log Dow = 1.79), and low molecular weight (138.12 g mol−1) (Table 6) might decrease its retention in the plant roots as well as to the adsorption media but favors its uptake by the plants. Hijosa-Valsero et al. (2016) reported that in CWs its uptake by the roots was 0.2 μg g−1, but in hydroponic systems, its higher concentration was observed in plants (2.5 μg g−1) due to uptake by the roots compared with adsorption on the roots (<0.1 μg g−1) in CWs as well as hydroponic systems. However, it has been reported that it is a phytohormone involved in plant growth, disease resistance, and response to environmental stresses and can be naturally found at concentrations of about 1.0 μg g−1 fresh weight in vegetal tissues (Hayat et al. 2010). In addition, exogenous salicylic acid and its methylated forms can be rapidly absorbed and translocated by plants (Hayat et al. 2010). Some studies reported that its removal efficiency was not significantly different in planted and unplanted HFCW (81 ± 12% and 77 ± 17%, respectively) (Hijosa-Valsero et al. 2010a, 2016; Zhang et al. 2012a) (Table S2), and planted and unplanted HCW (88 ± 5% and 87 ± 8%, respectively) (Hijosa-Valsero et al. 2010a, 2016; Reyes-Contreras et al. 2012; Zhang et al. 2012a) (Table S4). Since it is anionic under neutral pH condition, the repulsion with the negatively charged biomembranes might restrict its removal by plant uptake. However, Hijosa-Valsero et al. (2011b) reported that it was not removed in unplanted HCW but removed in planted HCW (29–97%) (Table S4). This removal by the planted system could be due to degradation by enzymatic exudates as well as biodegradation.

Analgesic

Acetaminophen

The removal efficiency of acetaminophen was higher in FWSCW (99%) and VFCW (97 ± 1%) compared with HCW (83 ± 25%) and HFCW (70 ± 24%). The high water solubility (30.4 g L−1 at 25 °C), high hydrophilicity (Log Kow = 0.46; Log Dow = 0.90), and low molecular weight (151.17 g mol−1) (Table 6) suggest that adsorption is not its main removal mechanism in CWs (Vystavna et al. 2017). This can be explained by its low removal through media adsorption (laterite soil) (0.1 μg g−1) at HRT of 6.0 days in VFCW (Koottatep et al. 2017) (Figure 4 and Table S9). It is neutral at pH = 7, as well as hydrophilic in nature, thus uptake by the plant might be through hydrogen bonding with water molecules (Dietz & Schnoor 2001). The removal by plant uptake was investigated by Li et al. (2017) and they reported that plants contributed to the removal of acetaminophen in FWSCW. For instance, its removal efficiency in the systems with and without plants was 98% and 84%, respectively. However, Koottatep et al. (2017) reported its negligible removal (0.04%) by plant uptake (Figure 4 and Table S9), which indicates that the removal difference between the planted and unplanted FWSCW might be due to the enhancement in biodegradation (Li et al. 2017), which is evident from the major contribution of biodegradation pathways (46%) to its removal in CWs (Koottatep et al. 2017; Li et al. 2017) (Figure 4 and Table S9). Furthermore, Koottatep et al. (2017) suggested that by-product transformation contributed up to 15% of its total removal (Table S9).

Antibiotics

Clarithromycin

The removal efficiency of clarithromycin was low and almost similar in VFCW (49 ± 57%), HCW (46 ± 9%), HFCW (45 ± 20%), and FWSCW (41 ± 21%). Although it is slightly hydrophobic (Log Kow = 3.16; Log Dow = 2.31) and cationic under neutral pH conditions, its removal by plant uptake cannot be considered due to its very high molecular weight (747.97 g mol−1) and very low water solubility (0.342 mg L−1 at 25 °C) (Table 6). Therefore, no difference was observed in the removal efficiency of planted and unplanted HFCW (32% in both) (Hijosa-Valsero et al. 2011a) (Table S2). Its removal efficiency in unplanted HCW (FWS on top of HFF) was higher (52%) compared with planted HCW (40%) (Hijosa-Valsero et al. 2011a) (Table S4). The removal can be ascribed to photodegradation, since in FWS this is considered the main removal mechanism for PhCs' removal. The adsorption to the substrate and retention in the plant roots can be considered the dominant removal mechanisms considering its low water solubility and high molecular weight. Furthermore, it is present in the cationic form and has moderate sorption capacity (Log Koc = 2.17) (Table 6), which favors its removal by sorption due to electrostatic interactions with the predominantly negatively charged biofilm on the substrate (Zhang et al. 2014).

Erythromycin

The removal efficiency of erythromycin was higher in VFCW (89 ± 4%) and FWSCW (85 ± 16%) compared with HFCW (61 ± 25%). Its very high molecular weight (733.93 g mol−1) and very low water solubility (0.517 mg L−1 at 25 °C) (Table 6) suggest that adsorption to the substrate and retention in the plant roots can be considered the dominant removal mechanisms. This is evident by the high contribution of adsorption (35%) to its total removal efficiency of 82% in HFCW (Chen et al. 2016a) (Figure 4). Since it is present in the cationic form with high sorption capacity (Log Koc = 2.75) (Table 6), its electrostatic interactions with the biofilm on the substrate recommend that sorption is a possible removal mechanism (Zhang et al. 2014). The slight hydrophobicity (Log Kow = 3.06) (Table 6) favors its uptake by the plant, but due to very low water solubility this process was not possible. However, in HFCW its removal (63%) was only achieved in the planted system indicating that its removal is favored by the presence of plants due to the improvement in biodegradation (Hijosa-Valsero et al. 2011a) (Table S2). This is evidenced by the eminent contribution of biodegradation pathways (35%) to its total removal efficiency of 82% in HFCW (Chen et al. 2016a) (Figure 4 and Table S9).

Ofloxacin

The removal efficiency of ofloxacin was higher in HFCW (98 ± 4%) and VFCW (87 ± 10%). However, its removal was not achieved in FWSCW. Its moderate molecular weight (361.37 g mol−1) and anionic form (Table 6) favor its adsorption to the substrate media. This can be seen by its complete removal (100%) in HFCW, 24% of which was removed by adsorption onto zeolite (Chen et al. 2016a) (Figure 4 and Table S9). Microporous structures in zeolite can provide a high surface area for chemical sorption and microbial attachment, while bridging hydroxyls (Si-OH) are catalytically active for various chemical reactions (Chen et al. 2016a). It is highly water soluble (28.3 g L−1 at 25 °C) and anionic or neutral at pH = 7, but due to less lipophilic characteristics (Log Kow = −0.39) (Table 6), the lower ability to partition into lipophilic cell structure hinders its removal by plant uptake in CWs. This can be seen by its low uptake by the plant (Callitriche palustris) (13 μg kg−1) (Nuel et al. 2018) and low concentration in the plant leaves (Cyperus alternifolius) (7.4 ± 0.1 μg kg−1) (Yan et al. 2016). However, its higher removal by planted HFCW and VFCW (Tables S2 and S3) indicates its removal by biodegradation. This is obvious by the major contribution of biodegradation pathways (67%) for its total removal efficiency (100%) in HFCW (Chen et al. 2016a) (Figure 4 and Table S9).

Oxytetracycline

The removal efficiency of oxytetracycline was higher and almost the same in FWSCW (97%) and VFCW (96 ± 4%). Its removal by adsorption to the substrate media can be considered due to its moderate molecular weight (460.43 g mol−1), moderate sorption capacity (Log Koc = 1.87), and anionic form (Table 6), which favor adsorption onto soil particles following complex formation with metal ions (Berglund et al. 2014; Huang et al. 2017). Huang et al. (2017) reported that its removal was affected by substrate type. The brick particles-based media showed stronger removal capacities compared with oyster shell, which can be attributed to the larger porosity and average micropore size, and high percentage of crystalline iron oxide in brick particle (Fe2O3, 32%). Furthermore, its adsorption to LECA (89%) has been observed in unplanted VFCW (Dordio & Carvalho 2013) (Figure 4 and Table S9). Its uptake by the plants can be attributed to its high water solubility (1.4 g L−1 at 25 °C) and moderate hydrophilicity (Log Kow = 1.22) (Table 6), which is obvious by its higher removal efficiency in the planted VFCW (97%) compared with unplanted VFCW (89%) (Dordio & Carvalho 2013) (Figure 4 and Table S9). However, the enhancement in removal in the presence of plants might be due to the improvement in biodegradation (Dordio & Carvalho 2013).

Sulfadiazine

The removal efficiency of sulfadiazine was moderate in FWSCW (61 ± 35%) and VFCW (52 ± 22%) but low in HFCW (46 ± 30%). Adsorption to the substrate cannot be considered its main removal mechanism in CWs due to its high water solubility (28.14 g L−1 at 25 °C) and very low hydrophobicity (Log Kow = −0.09), although its molecular weight is moderate (250.28 g mol−1) (Table 6). This can be seen by the non-significant difference in the removal efficiency between HFCW and FWSCW in winter (19 ± 2% and 19 ± 5%, respectively) which represented full substrate system and half substrate system, respectively (Dan et al. 2013) (Tables S1 and S2). Similarly, although its sorption capacity is moderate (Log Koc = 1.87), due to its neutral or anionic nature under neutral pH conditions (Table 6), its binding to biomass through cation exchange with anionic sites is also likely to be minimal (Dan et al. 2013). However, it is highly water soluble, but its less lipophilic character (very low Log Kow) obstructs its removal by plant uptake in CWs. Nevertheless, Xian et al. (2010) reported that planted FWSCW performed better than the unplanted FWSCW (99% and 91%, respectively) (Table S1), which might be due to enhanced biodegradation. This can be explained by its higher removal efficiency in HFCW (72 ± 7%) compared with FWSCW (55 ± 3%) during summer (high temperature enhanced biodegradation) (Dan et al. 2013) (Tables S1 and S2), since substrates provide a surface area suitable for the growth of microorganisms and the formation of biofilm (Dan et al. 2013).

Sulfamethazine

The removal efficiency of sulfamethazine was moderate in HCW (74%) and FWSCW (53 ± 48%) but lower in HFCW (45 ± 27%) and VFCW (28 ± 27%). The high water solubility (11.24 g L−1 at 25 °C) and high hydrophilicity (Log Kow = 0.89; Log Dow = 0.79) do not favor its adsorption to the substrate in CWs, although its molecular weight is moderate (278.33 g mol−1) (Table 6). Similarly, although its sorption capacity is moderate (Log Koc = 2.28), due to its neutral or anionic nature (Table 6), its binding to biomass (sorption) is also likely to be negligible (Dan et al. 2013). This is evident by its removal in HFCW using different substrate materials (oyster shell, zeolite, medical stone, and ceramic), and it was below the limit of detection in substrate media (Chen et al. 2016a) (Figure 4 and Table S9). Analogous to that, Liu et al. (2014), reported its low concentration in the soil (0.8–27 μg kg−1) and oyster shell (1.2–10 μg kg−1) of CWs. However, high water solubility, low Log Kow, and neutral form favor its removal by plant uptake and biodegradation. This is made explicit by the major contribution of biodegradation pathways (31%) to its total removal efficiency of 52% in HFCW (Chen et al. 2016a) (Figure 4 and Table S9) and slight difference in removal efficiency (∼4.5%) in planted and unplanted FWSCW (>99% and 95%, respectively) (Xian et al. 2010) (Table S1).

Sulfamethoxazole

The removal efficiency of sulfamethoxazole was moderate in HCW (61 ± 31%) and FWSCW (54 ± 29%) and VFCW (54 ± 29%) but lower in HFCW (43 ± 24%). Adsorption to the substrate cannot be considered its removal mechanism due to its high water solubility (3.94 g L−1 at 25 °C) and high hydrophilicity (Log Kow = 0.89), although its molecular weight is moderate (253.28 g mol−1) (Table 6). Additionally, due to its neutral or anionic form (Table 6), its binding to biomass is likely to be minimal, although it has moderate sorption capacity (Log Koc = 2.41) (Dan et al. 2013). This can be seen by non-significant difference in its removal efficiency between hydroponic system and FWSCW (planted and gravel bed) (38% and 35%, respectively) (Hijosa-Valsero et al. 2011a). Similarly, Zhu & Chen (2014) reported its slight sorption to the sludge (19–43 μg kg−1) in HCW. Its high water solubility, hydrophilic character, and neutral form (Table 6) suggest its uptake by the plants in CWs. This is made explicit by its better removal efficiency in the planted compared with the unplanted FWSCW (92% and 73%, respectively) (Xian et al. 2010) (Table S1), and planted compared with unplanted HFCW (71% and 46%, respectively) (Hijosa-Valsero et al. 2011a) (Table S2). However, in planted and unplanted VFCW, its complete removal (100%) was achieved (Button et al. 2019) (Table S3) and in planted and unplanted HCW its removal efficiency was 58% and 61%, respectively (Hijosa-Valsero et al. 2011a) (Table S4), which indicates that in planted CWs direct uptake by the plants is minimal due to its low Log Kow, but the plants also support biodegradation (Choi et al. 2016; Liang et al. 2018). This is evident by the major contribution of biodegradation pathways (68%) to its total removal of 71% in hydroponic systems (Choi et al. 2016) (Figure 3 and Table S8). In unplanted CWs, the removal may be because the substrates provide a surface area suitable for the growth of microorganisms and the formation of biofilm for biodegradation (Dan et al. 2013; Choi et al. 2016). This is obvious by its higher removal in biotic systems (73%) compared with abiotic systems (67%) during a soil adsorption experiment under biotic and abiotic conditions (Choi et al. 2016). Furthermore, Choi et al. (2016) investigated the possibility of photodegradation and observed 23% of its removal by this process in a photodegradation experiment (Figure 3 and Table S8). Thus, a slightly higher increase in the removal efficiency by unplanted HCW (FWS on top of HFF) compared with planted HCW indicates that photodegradation might contribute to its removal (Hijosa-Valsero et al. 2011a).

Lipid regulators

Gemfibrozil

The removal efficiency of gemfibrozil was higher in HCW (95%), moderate in HFCW (58 ± 23%) but low in VFCW (45 ± 9%) and FWSCW (12 ± 2%). Its high hydrophobicity (Log Kow = 4.77), low water solubility (4.964 mg L−1 at 25 °C), moderate molecular weight (250.33 g mol−1), high sorption coefficient (Log Koc = 2.636), and anionic form under neutral conditions (pH = 7) (Table 6) favors its adsorption onto soil particles following complexation with metal ions. However, its adsorption to substrate as one of its removal pathways has not been reported by any of the reviewed studies which investigated its removal (Tables S1–S4). Furthermore, its low to moderate removal in most of the cases indicates its lower retention due to the development of negatively charged biofilms on substrate surfaces over time (Dordio et al. 2009a, 2010), which might obstruct its binding to biomass. Similarly, due to its anionic nature, uptake by the plants cannot be considered one of its dominant removal mechanisms in CWs. This might be the reason that Nuel et al. (2018) did not observe its uptake by any of the studied plants in FWSCW (Salix alba, Callitriche palustris, Carex caryophyllea, Juncus effusus, Iris pseudacorus). However, its higher removal efficiency was observed in planted compared with unplanted HFCW (58 ± 18% and 49 ± 13%, respectively) (Table S2) and planted and unplanted VFCW (50 ± 11% and 40 ± 3%, respectively) (Table S3) by Zhang et al. (2018b). The higher removal in planted systems might be due to the positive effects of plants' presence such as biodegradation. Several studies attributed its removal to biodegradation (Table 4), which can be seen by its higher removal efficiency in summer compared with winter (42 ± 25% and 32 ± 15%, respectively) due to the enhancement in biodegradation in the warm season (Zhang et al. 2018b).

CONCLUSIONS

CWs have been investigated for the treatment of wastewater for traditional parameters as well as the removal of PhCs. The large number of published studies provided the foundation of this comprehensive assessment, which is based on critical review of the literature and statistical analysis of data gathered from peer-reviewed studies. Thus, a novel database was compiled in this study, which included influent and effluent concentrations, removal efficiency, and removal rate of PhCs from the information of 260 CWs that were reported in 66 peer-reviewed journal publications with case studies from 19 countries for the removal of 148 PhCs grouped into 33 categories according to their therapeutic classes and 25 TPs. Additionally, the environmental risk posed by a number of PhCs and the contribution of CWs to their risk reduction was evaluated. Finally, the role of physicochemical properties of PhCs was examined, and the available evidence from experimental studies on the major removal mechanisms was comprehensively and critically assessed and summarized. The following specific conclusions were inferred from this research.

A very high range of variability in the influent and effluent concentrations, and removal efficiencies exist among the studied PhCs across the case study regions around the world. While CWs could effectively remove most of the PhCs and their metabolites, some cases of negative removal also occur. However, despite large variability, CWs have demonstrated their capability to effectively and efficiently remove a large number of PhCs from wastewater (e.g., 96 out of 113 compounds or 85% of the sample show a positive removal efficiency). An in-depth analysis of the 34 most widely studied PhCs indicated a moderate to high potential of CWs for the removal of monensin, ofloxacin, oxytetracycline, sulfapyridine, caffeine, salicylic acid, atenolol, furosemide, doxycycline, codeine, diltiazem, acetaminophen, naproxen, ibuprofen, metoprolol, and sulfadiazine.

There is an encouraging trend in studies related to ecological risk assessment posed by PhCs, which should be continued to enhance the available knowledge. The available evidence revealed that CWs could considerably reduce the environmental risk posed by PhCs. However, the risk is not fully eliminated by CWs, although significantly reduced in most cases. Based on this analysis, and on data from several countries, a number of PhCs could be classified under the high risk category: oxytetracycline, ofloxacin, sulfamethoxazole, erythromycin, sulfadiazine, gemfibrozil, ibuprofen, acetaminophen, salicylic acid, sulfamethazine, naproxen, and clarithromycin. These high risk PhCs could be considered for regulatory monitoring, water quality standard formulation, and control purposes. For instance, the EU watch list of four PhCs (azithromycin, clarithromycin, erythromycin, and diclofenac) could be enhanced by considering these PhCs. Since this analysis is based on several countries across the world, these assessments could also inform the WHO's global guidelines on PhCs. However, it is recognized that in-depth studies are essential for a specific water and environmental context to establish risk and consequent monitoring and management actions.

The most widely reported mechanism for PhCs' removal in CWs is biodegradation (aerobic and anaerobic). The other notable mechanisms are adsorption/sorption, plant uptake (planted CWs), and photodegradation (FWSCW). Most readily biodegradable PhCs and the ones removed via several mechanisms are likely to demonstrate the highest removal efficiencies. In contrast, most of the PhCs reveal moderate to low removal efficiency when one of their most dominant removal mechanisms is adsorption, photodegradation, or plant uptake. The role of physicochemical properties of PhCs is pivotal in the removal processes. Among the studied properties, Log Koc, Log Dow, and molecular weight could (together) explain about 65% of the variance in the removal efficiency. Thus, these three factors could be seen as important predictors of removal efficiency of PhCs, and therefore, could contribute to a screening process for potential removal of PhCs by CWs. Finally, an optimal design of CWs and other wastewater treatment technologies must be underpinned by the evidence-based scientific knowledge on the compound-specific variability in removal efficiency, complexity of governing physicochemical properties and removal mechanisms of the targeted PhCs in a specific environmental context.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this paper is available online at https://dx.doi.org/10.2166/wh.2020.213.

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Supplementary data