Environmental fate of pharmaceutical compounds and antimicrobial-resistant bacteria in hospital effluents, and contributions to pollutant loads in the surface waters in Japan

Science of the Total Environment 657 (2019) 476–484

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Environmental fate of pharmaceutical compounds and antimicrobial-resistant bacteria in hospital effluents, and contributions to pollutant loads in the surface waters in Japan Takashi Azuma ⁎, Kana Otomo, Mari Kunitou, Mai Shimizu, Kaori Hosomaru, Shiori Mikata, Mao Ishida, Kanae Hisamatsu, Ayami Yunoki, Yoshiki Mino, Tetsuya Hayashi Osaka University of Pharmaceutical Sciences, 4-20-1 Nasahara, Takatsuki, Osaka 569-1094, Japan

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• A year-round monitoring survey of urban hospital effluent in Japan was performed. • 58 pharmaceuticals and 6 drugresistant bacteria in hospital effluent were surveyed. • Drug-resistant bacteria were detected at 29 to 1805 CFU/mL. • The hospital pharmaceutical pollution load of STP influent varied from 0.1% to 15%. • Ozone treatment effectively removed the targeted pollutants.

a r t i c l e

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Article history: Received 25 September 2018 Received in revised form 28 November 2018 Accepted 28 November 2018 Available online 03 December 2018 Editor: Damia Barcelo Keywords: Antimicrobial resistance (AMR) Global action plan Hospital effluents Ozonation Pharmaceuticals and personal care products (PPCPs) River water

⁎ Corresponding author. E-mail address: [email protected] (T. Azuma).

https://doi.org/10.1016/j.scitotenv.2018.11.433 0048-9697/© 2018 Elsevier B.V. All rights reserved.

a b s t r a c t Environmental fate of 58 pharmaceutical compounds (PhCs) grouped into 11 therapeutic classes in the three different waters, hospital effluent, sewage treatment plant (STP) and river water, was estimated by combination of their quantitative concentration analysis and evaluation of their extent of contribution as loading sources. At the same time, distribution of six classes of antimicrobial-resistant bacteria (AMRB) in the same water samples was estimated by screening of individual PhC-resistant microbes grown on each specific chromogenic medium. The results indicate that 48 PhCs were detected ranged from 1 ng/L (losartan carboxylic acid) to 228 μg/L (acetaminophen sulfate) in hospital effluent, and contribution of the pollution load derived from hospital effluent to STP influent was estimated as 0.1% to 15%. On the other hand, contribution of STP effluent to river water was high, 32% to 60% for antibacterials, antipertensives and X-ray contrast media. In the cases for AMRB, detected numbers of colonies of AMRB in hospital effluent ranged from 29 CFU/mL to 1805 CFU/mL, and the estimated contribution of the AMRB pollution load derived from hospital effluent to STP influent was as low as 0.1% (levofloxacin and olmesartan) to 5.1% (N-desmethyl tamoxifen). Although the contribution of STPs as loading sources of PhCs and AMRB in surface waters was large, ozonation as an advanced water treatment system effectively removed a wide range of both PhCs and AMRB in water samples. These results suggest the importance of reducing environmental pollutant loads (not only at STPs but also at medical facilities) before being discharged into the surface waters, to both conserve water and keep the water environment safe. To our knowledge, this is the first report to show the distribution and contribution of AMRB from hospital effluent to the surface waters. © 2018 Elsevier B.V. All rights reserved.

T. Azuma et al. / Science of the Total Environment 657 (2019) 476–484

1. Introduction Recently, there has been extensive progress worldwide in research into wastewater from medical institutions such as hospitals (Rodriguez-Mozaz et al., 2015; Hocquet et al., 2016; Verlicchi, 2017). Because pharmaceutical compounds (PhCs) are used continuously in hospitals, a matter of importance is to make assessment of the pollution loads in wastewater from medical facilities and the risks posed to the river water environment (Ferrando-Climent et al., 2014; Oliveira et al., 2015; Verlicchi and Zambello, 2016). In addition, environmental risk assessment for aquatic ecosystems (Al Aukidy et al., 2014; Helwig et al., 2016) and development of treatment systems for hospital effluent (Verlicchi et al., 2015; Ferre-Aracil et al., 2016) become important. Detection of antimicrobial-resistant bacteria (AMRB) was pointed out in hospital effluent (Korzeniewska and Harnisz, 2013a; Ory et al., 2016; Tiedeken et al., 2017), as well as in sewage and river water (Rizzo et al., 2013; Czekalski et al., 2015; Huijbers et al., 2015; Sharma et al., 2016). Under these circumstances, the spread of AMRB in clinical sites is an urgent global issue, and it is now essential to take measures against this problem (Hocquet et al., 2016; Vikesland et al., 2017). According to a previous report (O'Neill, 2014), unless countermeasures are taken the number of fatalities worldwide from AMRB will reach 10 million a year by 2050; this exceeds the number of fatalities from cancer, which is currently the leading cause of death. In addition, the report pointed out that total economic losses caused by AMRB would exceed 100 trillion dollars in terms of global gross domestic product. The World Health Organization (WHO) declared a Global Action Plan on Antimicrobial Resistance (AMR) in 2015, and it is demanding that every country institutes a national action plan (WHO, 2015). In Japan, an Action Plan on Antimicrobial Resistance was established in 2016 to deter the spread of AMRB (The Government of Japan, 2016). In these action plans, the importance of conducting extensive research into the occurrence and risk assessment of AMRB flows into the water environment has been noted. In fact, in recent research, clusters of AMRB genes detected in river waters were of targeted ARGs conferring resistance to sulfonamides (sul 1, sul 2), tetracyclines (tetW, tetO), β-lactams (blaTEM), and quinolones (qnrS)/blaTEM (resistance to β-lactams), qnrS (reduced susceptibility to fluoroquinolones), ermB (resistance to macrolides), sulI (resistance to sulfonamides) and tetW (resistance to tetracyclines). These genes were shown to be derived mainly from STPs (Auguet et al., 2017; Parvez and Khan, 2018; Qiao et al., 2018); these AMRB genes were also detected in hospital effluent (Varela et al., 2016; Lamba et al., 2017; Timraz et al., 2017; Adelowo et al., 2018; Wang et al., 2018). These results emphasize the importance of conducting further studies of hospital effluent. Medical technology is highly developed in Japan, which has the second-highest consumption of PhCs in the world (Ministry of Health Labour and Welfare, Japan, 2013). In addition, urbanization in this country is advanced, and N99% of urban areas with high population density are sewered (Japan Sewage Works Association, 2017). Therefore, almost all wastewater, including household, industrial, and hospital effluents, is delivered to STPs via sewers. Several researchers have pointed out the indispensability of evaluating hospital effluent and devising countermeasures for preserving the surface waters, human health, and drinking water quality (Santos et al., 2013; Ferrando-Climent et al., 2014; Azuma et al., 2016). Nevertheless, research into hospital effluent in Japan has been limited (Verlicchi et al., 2015). Previous research in 2014–2015 reported the occurrence of 41 kinds of PhCs in hospital effluent in Japan and evaluated the contribution of hospital effluent to environmental discharges (Azuma et al., 2016). The main aim of our current research was to conduct a further detailed investigation of not only PhCs but also AMRB abundance in hospital effluent. A total of 58 PhCs were investigated, including several hospitalassociated PhCs not investigated in previous research; anticancer drugs; X-ray contrast media; and antihypertensives. All of these

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compounds are used at the highest rates among the PhCs by the local residents (Ministry of Health Labour and Welfare, Japan, 2017a) and show toxicity to aquatic ecosystems when discharged into the surface waters (Ferrando-Climent et al., 2014; Azuma et al., 2017a). Therefore, their pollutant loads in the hospital effluent are relatively high (Weissbrodt et al., 2009; Santos et al., 2013). Similarly, AMRB was also targeted on the basis of the recently reported WHO global priority list of AMRBs (WHO, 2017). The targets were carbapenem-resistant enterobacteriaceae (CRE) (Mortensen et al., 2016) (classified as being a critical priority); vancomycin-resistant enterococci (VRE) (Tacconelli and Cataldo, 2008) and methicillin-resistant Staphylococcus aureus (MRSA) (Pathare et al., 2016) (classified as high priority); and extendedspectrum β-lactamase (ESBL)-producing Enterobacteriaceae (Brolund, 2014), multi-drug-resistant Acinetobacter (MDRA) (Wachino and Arakawa, 2012), and multi-drug-resistant Pseudomonas aeruginosa (MDRP) (Marumo et al., 2013) (classified as the recommended focus of future R&D strategies). In this study, a year-round survey of these bacteria in hospital effluent was performed in an urban area of Japan. At the same time, a similar survey of an STP that treated sewage from a targeted hospital and river was conducted, and the contribution of the pollutant load from the hospital to STP influent and the contribution of the pollutant load from STP effluent to river water were estimated. Although presence of AMRB in the hospital wastewater was previously shown (Rizzo et al., 2013; Rodriguez-Mozaz et al., 2015), the results of the previous works were basically obtained by using molecular biological techniques to detect antimicrobial-resistant genes. Success in detection by the techniques of molecular biology does not necessary mean living of the target bacteria in the water environment. Therefore, present research was intended to show the presence of various kinds of living AMRB in the sampled waters by formation of differently colored growing colonies on the agar plates. 2. Materials and methods 2.1. Chemicals and reagents A total of 58 PhCs, grouped into 11 therapeutic classes—(1) antivirals; (2) antimicrobials; (3) anticancer agents; (4) psychotropics; (5) antihypertensives; (6) analgesics–antipyretics; (7) X-ray contrast media; (8) bronchodilators; (9) antipruritics; (10) herbal medicines; and (11) phytoestrogens—including their 20 metabolites, were investigated on the basis of previously detected levels and frequencies of detection in hospital effluent, sewage, and river water (Oliveira et al., 2015; Azuma et al., 2016; Tran et al., 2018). The names of the target compounds and the physicochemical properties of the target PhCs are listed in Table S1. All analytical standards were of high purity (N98%). Individual standard stock solutions at 10 mg/L were prepared in methanol and stored at −20 °C. All aqueous solutions were prepared with ultrapure water (18.2 MΩ·cm) provided by a Milli-Q purification system (MilliporeSigma, Watford, UK). Liquid chromatography – mass spectrometry (LC-MS)-grade solvents (methanol and acetone), formic acid, hydrochloric acid, ammonia, ascorbic acid, and sodium thiosulfate were purchased from Wako Pure Chemical Industries, Ltd. (Osaka, Japan). For estimation of the distribution of six classes of antimicrobialresistant bacteria (AMRB) in the same water samples, screening of individual PhC-resistant microbes grown on each specific chromogenic agar medium was applied (the chromogenic agar method). For detection of CRE, ESBL, MRSA, VRE, MDRA and MDRP, chromogenic agar media chromID CARBA, chromID ESBL, chromID MRSA, chromID VRE New, CHROMagar MDRA and CHROMagar MDRP were used respectively. The chromID series agar media were purchased from bioMérieux S.A., Marcy-l'Étoile, France, while two CHROMagar media were from Kanto Chemical Co., Inc., Tokyo, Japan. In addition, for separate detection of Escherichia coli and coliform group bacteria a similar specific agar medium, XM-G agar Nissui (Nissui Pharmaceutical Co., Ltd., Tokyo,

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Japan), was used. By using this medium E. coli and coliform group bacteria could be differentiated as different colored colonies; blue for the former and red to pinkish purple for the latter. 2.2. Sampling A survey was conducted in the Yodo River basin of Japan, which has a population of 17 million (13% of the Japanese population (Lake BiwaYodo River Water Quality Preservation Organization, Japan, 2017; Ministry of Health Labour and Welfare, Japan, 2017b)). Hospital effluent was collected from a core general hospital in Takatsuki City, the midpoint urban city between Kyoto and Osaka, and has the number of beds 480 and a mean number of patients 1200 patients/day. The flow rate of hospital effluent ranged from 426 to 503 m3/day with mean flow of 460 m3/day. The STP located in the lower reaches of the sewage system which connects the hospital. River water 1 km downstream of the site where the STP effluent entered the river was also sampled. The STP disposed of municipal sewage from 420,000 people and had one influent site and two effluent sites that differed in treatment. In one, the effluent was introduced from a conventional activated sludge (CAS) and a Step AO procedure was followed by chlorination (contact with 0.9 mg NaClO/L for 15 min) for disinfection. The other process consisted of partial CAS and Step AO followed by ozonation (contact with 8.6 mg ozone/L for 100 min). The annual average biochemical oxygen demand of the river water was 1.7 ± 0.4 mg/L. Sampling was conducted during 10–12 am on four different days representing the four seasons in 2016 and 2017, namely 11 July (summer), 24 November (autumn), 25 January (winter), and 17 April (spring). This sampling frequency was based on the findings of a previous report (Kumar et al., 2011; Azuma et al., 2016). Days of sampling were selected on rain-free days, and no rainfall N1 mm was observed during the 2 days before the sampling day (Japan Meteorological Agency, 2017). No unexpected disease epidemics occurred during the sampling years (Ministry of Health Labour and Welfare, Infectious Diseases Infectious Disease Surveillance Center, Japan, 2017). A stainless-steel pail sampler was used to collect 300-mL samples of hospital effluent, STP influent, STP effluent, and river water. Sterilized glass bottles were used to collect samples of hospital effluent, STP influent, STP effluent, and river water, and ascorbic acid (1 g/L) was immediately added as a quencher for residual chlorine and ozone, or sodium thiosulfate (0.5 g/L) was added as a preservative for PhCs (Kumar et al., 2009; Azuma et al., 2017b) and microbes (Zheng et al., 2017; Dunkin et al., 2018). There was insufficient space to settle composite samplers at the sampling location used for hospital effluent, and no power supply was available in the area. Similarly, a composite sampler could not be used at the STP. In addition, placement of any sampling equipment at the sampling location along the river was impossible by law. Therefore, identical manual sampling was adopted at a fixed sampling frequency. All water samples were immediately transported to the laboratory in a cooler box (within 1 to 2 h), and they were stored at 4 °C under darkness and processed within 12 h. 2.3. Analytical procedures 2.3.1. PhCs The concentrations of compounds in the water samples were determined by using a combination of solid phase extraction (SPE) and ultraperformance liquid chromatography – tandem mass spectrometry (UPLC-MS/MS), as described before (Azuma et al., 2016). Briefly, each water sample was filtered through a glass-fiber filter (GF/B, 1-μm pore size, Whatman, Maidstone, UK) and four sample solutions for analysis were then taken from the sample (10 mL each from hospital effluent and STP influent, and 30 mL each from STP effluent and river water). Four SPE cartridges (OASIS HLB, 200 mg; Waters Corp., Milford, MA, USA) were pre-conditioned for each sample by

being washed first with 3 mL of methanol and then with 3 mL of Milli-Q water adjusted to pH 3 with 1 N HCl. A known amount of each compound was spiked into two of the sample solutions for analysis to make a final concentration of 200 ng/L to account for matrix effects and loss during sample extraction (Prasse et al., 2010; Azuma et al., 2015). The solutions were then transferred to two different preconditioned SPE cartridges at a flow rate of 1 mL/min. Similarly, the two unspiked sample solutions were taken out and processed as above. All cartridges were washed with 6 mL of Milli-Q water preadjusted to pH 3 and then dried by a vacuum pump. Finally, the adsorbed PhCs were separately eluted with 3 mL of acetone and 3 mL methanol. The remaining sets of cartridges were eluted sequentially with 2 mL of 10% (v/v) formic acid in acetone, 2 mL of 10% (v/v) formic acid in methanol, and 2 mL of 5% ammonia-methanol (v/v). Each combined eluted solution was mildly evaporated to dryness under a gentle stream of nitrogen gas at 37 °C. The residue was solubilized in 200 μL of a 90:10 (v/v) mixture of 0.1% formic acid solution in methanol, and 10 μL of this solution was injected into the UPLC system coupled to a tandem quadrupole mass spectrometer (TQD, Waters Corp.) equipped with an electrospray ionization source; it was operated in positive and negative ion modes. Detailed information on the analytical parameters used for UPLC-MS/MS is given in Table S2. Six-point standard calibration curves were constructed for quantification; their values ranged between 0.5 and 200 ng/mL. Individual linear calibration curves for each compound were obtained in the concentration range from 0.5 to 200 ng/mL (r2 N 0.99) by selecting the weighting factor of 1/x. Quantification was performed by subtracting the blank data from the corresponding data given by the spiked sample solutions to account for matrix effects and losses during sample extraction (Prasse et al., 2010; Azuma et al., 2015). Recovery rates ranged from 51% (bortezomib acid) to 117% (glycitin) with exception of tamoxifen (45%) in the hospital effluent (Table S3). Similarly, recovery rates higher than 50% were attained in the most PhCs with several exceptions. These profiles were generally similar to those reported in previous studies of PhCs in river and sewage samples (Grabic et al., 2012; Petrović et al., 2014; Oliveira et al., 2015). The limits of detection (LOD) and limits of quantification (LOQ) were calculated as the concentrations at signal to noise ratios of 3 and 10, respectively, according to the methods applied to PhCs in water samples (Petrović et al., 2014; Schlüsener et al., 2015). These values are also listed in Table S3. 2.3.2. AMRB Detection of AMRB was made by using chromogenic agar media with following the manufacturer's protocol (bioMérieux, France, 2017) and the results published in the previous studies (Lamba et al., 2017; Haller et al., 2018). One milliliter of water was poured onto an agar plate, which was then incubated at 37 ± 1 °C for 24 h in the dark, and bacterial species were differentiated by colony color and morphology (bioMérieux, France, 2017; Hrenovic et al., 2017). Colonies on each medium were enumerated and the number of bacteria recovered expressed as colony forming units per mL (CFU/mL). The abundance of antimicrobial-resistant E. coli (CRE and ESBL) was defined as the number of resistant E. coli isolates growing on the chromID CARBA and ESBL plates (according to the manufacturer's protocol) (bioMérieux, France, 2017) and expressed as a percentage of the total number of E. coli growing on XM-G agar. 2.4. Calculation of mass loads and hospital effluent contributions to STP influent and the river water environment The mass load values of PhCs were calculated by multiplying the detected concentration (ng/L) by the mean daily flow rates of the hospital, STP and the river water (m3/day), while the corresponding values for AMRB were calculated by use of the numbers of colonies (CFU/mL) instead of concentration (ng/L). According to the flow rates on the sampling day (m3/day), the value at STP was available but the

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± 118 ng/L for phytoestrogens. The relatively high detected concentrations of analgesics–antipyretics, X-ray contrast media, and bronchodilators in hospital effluent were approximately similar to the values reported in this country and overseas (analgesics–antipyretics, 6 μg/L to 374 μg/L (Oliveira et al., 2015; Azuma et al., 2016); X-ray contrast media, 50 ng/L to 750 μg/L (Nielsen et al., 2013; Santos et al., 2013); bronchodilators, 22 μg/L to 325 μg/L (Oliveira et al., 2015; Azuma et al., 2016)). The results also showed that STP influents contained 35 mother PhCs similar to those observed in the hospital effluent. Although the range of their concentrations was very wide, the orders of magnitude of the detected concentrations were similar in hospital effluent. These results suggest that the usage of mother PhCs tended to be similar among hospitals and households. However, bicalutamide, tamoxifen, tegafur (anticancer), losartan, acetaminophen, loxoprofen (analgesics–antipyretic), and iomeprol and iopamidol (X-ray contrast medium) were detected at higher rates in the hospital effluents than in the STP influents. In addition, famciclovir and capecitabine were detected only in the hospital effluent, indicating the importance of deletion from hospital effluent into sewage (Franquet-Griell et al., 2015; Azuma et al., 2017a). The mother PhCs detected in the hospital effluents or STP influents, or both, were also distributed in the STP effluents and river waters; 24 compounds were detected in the STP effluents from the chlorinationbased system (several ng/L to 8.2 μg/L) and 23 compounds were detected in the river waters (several ng/L to 3.0 μg/L) (Table S3). Higher detection rates were observed for X-ray contrast media than for the other mother PhCs in the STP effluents: the average concentrations ranged from a low of 155 ng/L (iopromide) to a maximum of 8.2 μg/L (iohexol). The results support previous findings that the mother PhCs are relatively poorly removed by water treatment systems centered on biological treatment (Kümmerer, 2009; Michael et al., 2013). In contrast, in the effluent samples from the STP that used ozonation, the mean concentrations of most of the targeted PhCs ranged from nondetected to several tens of ng/L; they were roughly one-tenth to onehundredth of the concentrations detected in the STP effluent with chlorination after biological treatment. This result supported those of previous reports (Antoniou et al., 2013; Gomes et al., 2017; Park et al., 2017) indicating the effectiveness of ozonation in removing a wide range of PhCs, including recalcitrant compounds, from water samples. However, X-ray contrast media were recalcitrant to ozonation and remained in water samples after treatment: the average concentrations ranged from a low of 72 ng/L (iopromide) to a maximum of 1.5 μg/L

corresponding values at hospital effluent and river water were not available. In the latter two cases, only available data were the monthly flow rate for the hospital effluent from the hospital and one day mean river water flow rate per month provided by the Ministry of Land, Infrastructure, and Transport, Osaka, Japan. The deviation of the mean river flow rate was estimated as 10% (Azuma et al., 2015). Finally, the annual mean flow rates were given as 460 ± 35 m3/day for hospital effluent; 134,000 m3/day for STP (132,000 ± 5000 m3/day for CAS and Step AO followed by chlorination, and 2100 ± 20 m3/day for CAS and Step AO followed by ozonation), and 2,500,000 ± 400,000 m3/day for river water, respectively. The contribution of the mass load of PhCs in hospital effluent to STP influent was calculated by dividing the individual mass load of PhCs in the hospital effluent by that of the STP influent. The contribution of the mass load of PhCs in STP effluent to river water was calculated by dividing the individual mass load of PhCs in the STP effluent by that of river water (Santos et al., 2013; Ferrando-Climent et al., 2014; Oliveira et al., 2015). 3. Results and discussion 3.1. Occurrence of PhCs in hospital effluent, STP influent and effluent, and river water Quantitative concentration analysis of totally 58 PhCs grouped into 11 therapeutic classes with inclusion of 20 metabolites (Tables S1 and S2) was made and their concentrations in the hospital effluents and STP influents are shown in Fig. 1. And their individual concentrations together with those in the river waters are summarized in Table S4. To make the results and discussion clearly, description dealt with the mother PhCs was described separately from those dealt with their metabolites in this order. Firstly, the behaviors of the mother compounds were concerned. The results indicate that 38 mother PhCs were detected in hospital effluent at a wide range of concentrations from ng/L to μg/L levels (several ng/L to a maximum of 228 μg/L). The mean distributions of the mother PhCs in hospital effluent were 28 ng/L ± 36 ng/L for antivirals, 182 ng/L ± 388 ng/L for antibacterials, 52 ng/L ± 179 ng/L for anticancer agents; 1074 ng/L ± 2534 ng/L for pychotropic agents, 198 ng/L ± 308 ng/L for antihypertensives, 9 μg/L ± 16 μg/L for analgesics–antipyretics, 6.6 μg/L ± 14 μg/L for X-ray contrast media, 4.5 μg/L ± 4.0 μg/L for bronchodilators, 70 ng/L ± 63 ng/L for antipruritics, 345 ng/L ± 1245 ng/L for herbal medicines, and 65

a

b

c

d

479

e

f

g

h i j

k

1,000,000

Hospital effluent STP influent

100,000

a : Antiviral

Concentration (ng/L)

b : Antimicrobial c : Anticancer

10,000

d : Psychotropic e : Antihypertensive 1,000

f : Analgesic-antipyretic g : Bronchodilator h : Contrast media

100

i : Antipruritic j : Herbal medicine 10

Aciclovir Famciclovir Penciclovir Valaciclovir Az r mycin Cefdinir Ciprofloxacin Clarithromycin Levofloxacin Bicalutamide Bortezomib Bortezomib acid Capecitabine Cyclophosphamide Doxifluridine Etoposide Tamoxifen 4-Hydroxy tamoxifen N-Desmethyl tamoxifen 4-Hydroxy-N-desmethyl Tegafur Carbamazepine 2-Hydroxy carbamazepine 3-Hydroxy carbamazepine 10-Hydroxy carbamazepine 10,11-Dihydroxy Carbamazepine 10,11-epoxide Acridine Acridone Sulpiride Losartan Losartan carboxylic acid Olmesartan Olmesartan medoxomil Acetaminophen Acetaminophen glucuronide Acetaminophen sulfate Ethenzamide Ibuprofen Indomethacin Loxoprofen Loxoprofen alcohol Iohexol Iomeprol Iopamidol Iopromide Ioversol Caffeine Theophylline Crotamiton Berberine Puerarin Daidzein Daidzin Genistein Genistin Glycitein Glycitin

1

k : Phytoestrogen

Fig. 1. Occurrence and distribution of PhCs in hospital effluent and STP influent. (Therapeutic classes are shown as a to k in the figure and the key.).

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(iopamidol). These results support those of previous reports of the low rates of removal (10% to 42%) of X-ray contrast media by ozonation (Ternes et al., 2003; Seitz et al., 2008; Kovalova et al., 2013; Matsushita et al., 2016). The mother PhCs flowing into the surface waters after discharge in the STP effluents are exposed to photodegradation, biodegradation, and sorption onto the river sediments in the river flows. Some mother PhCs are rapidly attenuated by photodegradation and have half-lives ranging 0.5 min to 6 min for X-ray contrast media (Tian et al., 2014; Zhao et al., 2014; Allard et al., 2016) or from 2 min to 26 min for some antimicrobials, such as cipofloxacin and levofloxacin (Wang and Lin, 2014). However, many other PhCs are recalcitrant to these attenuation effects in the natural environment (Patrolecco et al., 2015; Azuma et al., 2017a; Blum et al., 2017). Next distribution of the metabolized compounds was analyzed. Within 20 metabolites bortezomib acid could not be detected in any water samples together with its mother compound, bortezomib. Similarly, 4-hydroxy tamoxifen and carbamazepine 10,11-epoxide were not detected. However, N-desmethyl tamoxifen (anticancer), olmesartan medoxomil (antihypertensive), acetaminophen glucuronide and acetaminophen sulfate (analgesics–antipyretic) were detected at higher rates in the hospital effluents than in the STP influents. Loxoprofen alcohol was exclusively detected in the hospital effluents and the STP influents similar to its mother compound, loxoprofen. Further, 4-hydroxy-N-desmethyl tamoxifen was only detected at a level of 0.3 ng/L in the hospital effluent, indicating the importance of deletion from hospital effluent into sewage similar to two mother compounds, famciclovir and capecitabine (Franquet-Griell et al., 2015; Azuma et al., 2017a) as described above. The distribution profiles of the metabolites in the STP effluents and the river waters were also similar. These results indicate that metabolites were distributed in the similar fashion to their own mother PhCs and their recalcitrant properties were also followed by their mother PhCs. This means that both compounds of the mother PhCs and their metabolites could be easily distributed in the surface waters at the times when they survived the treatments at STPs. Based on the results, the present results are briefly summarized as follows. The number of PhCs and their concentration detectable in the hospital effluents and the STP influents showed a tendency to be higher than the other water samples (the STP effluents and the river waters). Famciclovir, iopromide, berberine, daizin, genistin and glycitin were only detected in the hospital effluents, while cefdinir and puerarin were detected both in the hospital effluents and the STP influents. Both the mother loxoprofen and its metabolite, loxoprofen alcohol, were also detectable only in the hospital effluents and the STP influents. Therefore, these four compounds, cefdinir, loxoprofen, loxoprofen alcohol and puerarin, could be degraded completely by the treatment at STP. Majority of olmesartan medoxomil, acetaminophen and its two metabolites, acetaminophen glucuronide and acetaminophen sulfate, were also susceptible by the treatment at STP. However, a number of PhCs, penciclovir, N-desmethyl tamoxifen, 3-hydroxy carbamazepine and losartan carboxylic acid, were recalcitrant and remained in the STP effluents and distributed widely in the surface waters. Our results emphasize the importance of conducting further research into the environmental fates of chemicals in the surface waters and toxicity to ecosystems; a comprehensive risk assessment of the PhCs in the water environment is needed (Aus der Beek et al., 2016; Wilkinson et al., 2017). 3.2. Mass load-based analysis of the contribution of PhCs in hospital effluent to STP influent, and of STP effluent to the surface waters The contributions of PhCs derived from hospital effluent to STP influent on the basis of mass load are summarized in Table 1. A broad contribution profile was observed for targeted PhCs, with values ranging from ≤0.1% to a maximum of 15% (mean, 1.2% ± 2.7%). The results clearly

showed that contribution profiles varied depending on the class of PhCs. High mean contributions were frequently observed in the cases of bicalutamide (1.1%), N-desmethyl tamoxifen (15%), tegafur (1.6%), acetaminophen glucuronide (6.3%), iomeprol (3.9%), and iopamidol (6.3%). Low mean contributions were found for the other compounds. The results suggested that only about 12% of the PhCs were administered to hospital inpatients; most of the drugs were consumed by outpatients in their homes (Ort et al., 2010; Besse et al., 2012). Nevertheless, several studies have pointed out that, in some cases, N60% of pollutant loads entering STPs are from hospital effluent (Santos et al., 2013; Oliveira et al., 2015). Our results thus showed that the degree of pollutant loading of STP influent from hospital effluent varies from country to country (Santos et al., 2013; Oliveira et al., 2015). Next, the contribution of PhCs originating from STP effluent to river water was analyzed on the basis of mass load (Table S4). The estimated contributions ranged from 2% to 123% (mean, 26% ± 28%), suggesting that the contribution of STPs as loading sources of pollutants in surface waters was large. Particularly, the mean contributions of antibacterials, antihypertensives, and X-ray contrast media were high, at 60% ± 60%, 32% ± 16%, and 33% ± 19%, respectively. In this situation, the installation of water treatment systems in medical facilities would reduce pollution loads in rivers, as pointed out in evaluations of the effectiveness of hospital effluent treatment (Rodriguez-Mozaz et al., 2015; Hocquet et al., 2016; Verlicchi, 2017). 3.3. Occurrence of AMRB in hospital effluent, STP influent and effluent, and river water The distributions of AMRB in hospital effluent, STP influent and effluent, and river water are summarized in Fig. 2. In detail, the maximum and minimum values for each AMRB were 122 to 1350 CFU/mL for CRE, 132 to 560 CFU/mL for ESBL, 224 to 1805 CFU/mL for MDRA, 84 to 245 CFU/mL for MDRP, 29 to 472 CFU/mL for MRSA, and 283 to 482 CFU/mL for VRE in hospital effluent; and 206 to 653 CFU/mL for CRE, 825 to 1923 CFU/mL for ESBL, 174 to 1829 CFU/mL for MDRA, 41 to 978 CFU/mL for MDRP, 35 to 892 CFU/mL for MRSA, and 34 to 916 CFU/mL for VRE in STP influent. The detection of all targeted AMRB in both hospital effluent and STP influent points to the wide distribution of AMRB in today's society. The mean abundances of antimicrobial-resistant E. coli as percentages of the total number of E. coli in hospital effluent and STP influent were 6.75% ± 6.3% and 0.3% ± 0.1%, respectively, for CRE and 2.7% ± 2.2% and 2.2% ± 1.2% for ESBL (Fig. 3). Comparison of our sets of concentrations in hospital effluent and STP influent with data obtained in different countries (hospital effluent, 2 × 104 CFU/mL to 2 × 108 CFU/mL for CRE (Lamba et al., 2017; Haller et al., 2018; Wang et al., 2018) and 2 × 104 CFU/mL to 1 × 106 CFU/mL for ESBL (Haller et al., 2018); STP influent, 2 × 106 CFU/mL for CRE (Hrenovic et al., 2017)) revealed that the concentrations detected here were a magnitude of about onetenth to one-hundred-thousandth lower, indicating that the current rates of spread of AMRB vary among countries. On the other hand, the number of AMRB largely decreased in STP effluent after chlorination, and CRE and MDRP were not detected. These results suggest that some AMRB were effectively removed by the water treatment system at the STP. However, other AMRB remained after treatment and were detected in the range of 64 ± 107 CFU/mL for MDRA, 3 ± 3 CFU/mL for ESBL, 42 ± 52 CFU/mL for MRSA, 8 ± 16 CFU/mL for VRE. These results suggested the effectiveness of the chlorination-based water treatment system in treating target AMRB, but the effectiveness depended on the species, as indicated by previous research (e.g. 2 × 102 CFU/mL for ESBL in STP effluent) (Ojer-Usoz et al., 2014; Hrenovic et al., 2017). On the other hand, AMRB were not detected (N.D.), with the exception of MDRA (8 ± 9 CFU/mL), in STP effluent after ozonation. In the effluent samples from the STP that used ozonation, the mean concentrations of AMRB ranged from nondetected to several tens of CFU/mL—roughly one-tenth to one-

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481

Table 1 Mass loads and contributions of PhCs in hospital effluent to STP influent, by therapeutic class. Compound

Aciclovir Famciclovir Penciclovira Valaciclovir Azithromycin Cefdinir Ciprofloxacin Clarithromycin Levofloxacin Bicalutamide Bortezomib Bortezomib acidb Capecitabine Cyclophosphamide Doxifluridine Etoposide Tamoxifen 4-Hydroxy tamoxifenc N-Desmethyl tamoxifenc 4-Hydroxy-N-desmethyl tamoxifenc Tegafur Carbamazepine 2-Hydroxy carbamazepined 3-Hydroxy carbamazepined 10-Hydroxy carbamazepined 10,11-Dihydroxy carbamazepined Carbamazepine 10,11-epoxided Acridined Acridoned Sulpiride Losartan Losartan carboxylic acide Olmesartanf Olmesartan medoxomil Acetaminophen Acetaminophen glucuronideg Acetaminophen sulfateg Ethenzamide Ibuprofen Indomethacin Loxoprofen Loxoprofen alcoholh Iohexol Iomeprol Iopamidol Iopromide Ioversol Caffeine Theophylline Crotamiton Berberine Puerarin Daidzeini Daidzin Genisteinj Genistin Glyciteink Glycitin N.D.: Not detected, N.A.: Not available. a Metabolite of famciclovir. b Metabolite of bortezomib. c Metabolite of tamoxifen. d Metabolite of carbamazepine. e Metabolite of losartan. f Metabolite of olmesartan medoxomil. g Metabolite of acetaminophen. h Metabolite of loxoprofen. i Metabolite of daidzin. j Metabolite of genistin. k Metabolite of glycitin.

Therapeutic class

Antiviral

Antimicrobial

Anticancer

Psychotropic

Antihypertensive

Analgesic-antipyretic

X-ray contrast media

Bronchodilator Antipruritic Herbal medicine Phytoestrogen

Mass load (g/day)

Contribution of hospital effluent (% of total STP influent)

Hospital effluent Mean (SD)

STP influent Mean (SD)

Mean (SD)

Max

Min

0.03 (0.02) 0.01 (0.01) b0.01 b0.01 0.05 (0.11) 0.01 (0.02) 0.01 (0.01) 0.2 (0.3) 0.2 (0.1) 0.2 (0.2) N.D. N.D. b0.01 N.D. N.D. b0.01 0.02 (0.02) N.D. 0.1 (0.2) b0.01 0.02 (0.01) 0.02 (0.01) N.D. b0.01 N.D. N.D. N.D. 0.01 (0.01) N.D. 1.0 (1.6) 0.2 (0.2) 0.1 (0.2) 0.04 (0.03) 0.03 (0.05) 7.8 (9.6) 6.1 (10.2) 28.0 (51.0) 0.02 (0.03) 0.4 (0.4) 0.03 (0.01) 9.2 (14.0) 1.4 (1.7) 0.8 (0.6) 6.6 (5.0) 7.8 (3.7) 0.03 (0.06) b0.01 2.6 (1.1) 1.6 (1.0) 0.03 (0.03) 0.02 (0.02) 0.3 (0.6) 0.6 (0.3) 0.02 (0.03) 0.4 (0.1) 0.1 (0.1) 0.3 (0.3) 0.01 (0.02)

322 (126) N.D. 10(4) 1(1) 75 (79) 6 (4) 7 (2) 79 (35) 151 (74) 11 (6) N.D. N.D. N.D. 4 (7) N.D. 19 (37) 1 (1) N.D. 1 (2) N.D. 3 (6) 25 (23) 11 (21) 18 (12) N.D. 73 (102) N.D. 3 (2) 1 (1) 250 (182) 26 (16) 24 (8) 100 (15) 3 (5) 1260 (298) 7 (13) 3242 (5661) 11 (14) 407 (613) 49 (37) 1584 (1646) 976 (1085) 1329 (1157) 504 (840) 571 (668) 16 (25) 13 (15) 2214 (1393) 488 (331) 195 (159) 6 (5) 112 (85) 398 (303) 10 (12) 461 (336) 8 (8) 303 (276) 7 (7)

b0.1 N.A. 0.1 (0.1) 0.9 (1.4) 0.1 (0.1) 0.1 (0.2) 0.2 (0.2) 0.3 (0.5) 0.1 (0.1) 1.1 (0.9) N.A. N.A. N.A. N.A. N.A. b0.1 0.9 (0.1) N.A. 15 (N.A.) N.A. 1.6 (2.1) 0.6 (1.1) N.A. b0.1 N.A. N.A. N.A. 0.6 (0.4) N.A. 0.4 (0.6) 0.8 (0.9) 0.6 (1.1) b0.1 0.5 (0.8) 0.5 (0.5) 6.3 (N.A.) 0.9 (0.8) 0.2 (0.2) 0.4 (0.5) 0.1 (0.1) 0.4 (0.3) 0.3 (0.5) 0.1 (0.2) 3.9 (2.5) 6.3 (6.5) 0.5 (0.7) 0.1 (0.1) 0.2(0.1) 0.4 (0.2) b0.1 0.3 (0.2) 0.2 (0.3) 0.2 (0.1) 0.2 (0.2) 0.1 (0.1) 0.6 (0.7) 0.1 (0.1) 0.2 (0.2)

b0.1 N.A. 0.3 2.4 0.2 0.3 0.4 1.1 0.1 2.3 N.A. N.A. N.A. N.A. N.A. b0.1 1.0 N.A. 15 N.A. 3.1 2.3 N.A. 0.1 N.A. N.A. N.A. 1.0 N.A. 1.3 1.8 2.3 0.1 1.1 1.3 6.3 2.0 0.4 0.9 0.2 0.8 1.1 0.4 5.9 13.1 1.0 0.2 0.3 0.6 0.0 0.5 0.6 0.3 0.4 0.2 1.1 0.1 0.4

b0.1 N.A. N.A. N.A. N.A. N.A. b0.1 b0.1 b0.1 0.3 N.A. N.A. N.A. N.A. N.A. b0.1 0.8 N.A. 15 N.A. 0.1 N.A. N.A. N.A. N.A. N.A. N.A. 0.1 N.A. b0.1 N.A. b0.1 b0.1 N.A. b0.1 6.3 0.3 N.A. b0.1 b0.1 0.1 N.A. b0.1 0.8 0.2 b0.1 N.A. b0.1 0.2 b0.1 0.2 N.A. 0.1 b0.1 0.1 0.1 0.1 N.A.

482

T. Azuma et al. / Science of the Total Environment 657 (2019) 476–484 1,000,000 Max 75%

Bacteria counts (CFU/mL)

100,000

Median 25%

10,000

Min

1,000 River

100

STP effluent STP effluent (Ozonation)

10

STP influent *

1

*

CRE

ESBL

* * *

MDRA

MDRP

*

*

*

MRSA

VRE

E. coli

*

Hospital effluent

Coliform group

( * : Not detected)

Fig. 2. Distribution of AMRB in hospital effluent and STP influent. (AMRB, antimicrobial-resistant bacteria; CRE, carbapenem-resistant enterobacteriaceae; ESBL, extended-spectrum βlactamase; MDRA, multi-drug-resistant Acinetobacter; MDRP, multi-drug-resistant Pseudomonas aeruginosa; MRSA, methicillin-resistant Staphylococcus aureus; VRE, vancomycin-resistant enterococci; and E. coli, Escherichia coli.)

hundredth of the concentrations detected in the effluent from the STP with chlorination after biological treatment. This result indicates the effectiveness of ozonation for removal of a wide range of AMRB (and PhCs) from water samples (see Section 3.1). AMRB (with the exception of MDRP) were detected in the river water samples. The mean numbers of the AMRB were 2 ± 3 CFU/mL for CRE, 4 ± 3 CFU/mL for ESBL, 32 ± 36 CFU/mL for MDRA, 24 ± 24 CFU/mL for MRSA, and 1 ± 2 CFU/mL for VRE. Detection of these AMRB in the surface waters indicates that there is a potential risk for transmission to, and spread of infection between, human beings (Le Page et al., 2017; Pepper et al., 2018). Therefore, in addition to improving the installation of sewerage systems, it is important to introduce effective removal of AMRB in water treatment processes to decrease their levels in the water environment. The previous related works were obtained by using molecular biological techniques. Detection of DNA fragments whose base sequences are specific for each AMRB is not necessary indicating the presence of its mother bacteria. In the present research, however, the presence of the various type living AMRB in the sampled waters was evidenced by formation of individual colonies on the agar plates. Previous studies have detected CRE and ESBL, or their genes, in hospital effluent, STP, and river water (Korzeniewska and Harnisz, 2013b; Ojer-Usoz et al., 2014; Vikesland et al., 2017; Qiao et al., 2018). The target microbes evaluated here include several newly investigated AMRB (MDRA, MDRP, MRSA, and VRE). To our knowledge, this is the first report to show the distribution and contribution of AMRB from hospital effluent through to the surface waters. Our findings should help to give a comprehensive

Composition ratios of drug-resistant E. coli in total E. oli (%)

15 ESBL

understanding of the environmental risks associated with AMRB and PhCs in hospital effluent. 3.4. Mass load-based analysis of the contributions of AMRB from hospital effluent to STP influent, and from STP effluent to the surface waters The contributions of AMRB originating from hospital effluent to STP influent were analyzed on the basis of mass load (Table 2). The contributions of AMRB derived from hospital effluent to STP influent on the basis of mass load ranged from 0.1% to a maximum of 5.1% (mean, 0.5% ± 0.4%). High mean contributions were observed in the case of VRE (1.4%), CRE and MDRA (0.5%). The results clearly suggest that not only hospitals, but also households, are sources of AMRB pollution in Japan. Although the degree of pollutant loading from hospital effluent to STP influent varies from country to country (Santos et al., 2013; Oliveira et al., 2015), as already mentioned in Section 3.2, our results support the need for further, conclusive, research by taking regional customs, bias, and unknown factors into consideration. Finally, the contribution of AMRB originating from STP effluent to river water was analyzed on the basis of mass load (Table S5). The estimated contribution ranged from several percent to 62% (average, 5.9% ± 6.3%), suggesting that the contribution of STPs as loading sources of AMRB pollutants in surface waters is large. In particular, high mean values were observed for MRSA and MDRA, the contributions of which were 17% ± 30% and 11% ± 10%, respectively. These findings indicate the importance of reducing environmental pollutant concentrations before discharge into the surface waters to keep the water environment safe. Further research that expands the number of target hospitals to clarify the occurrence and pollution load of PhCs in hospital effluents is required in future, together with the

CRE Table 2 Composition ratios of AMRB in each type of water sample.

10

Compound

5

0

Mean mass load (CFU/day)

Hospital effluent

*

River

*

*

STP effluent

*

*

STP effluent (Ozonation)

STP influent

Hospital effluent

Fig. 3. Composition ratios of AMRB in each type of water sample. (CRE, carbapenemresistant enterobacteriaceae; ESBL, extended-spectrum β-lactamase; E. coli, Escherichia coli; and STP, sewage treatment plant.)

CRE ESBL MDRA MDRP MRSA VRE Escherichia coli Coliform group

11

3.0 × 10 1.9 × 1011 3.8 × 1011 6.4 × 1010 7.2 × 1010 1.5 × 1011 2.5 × 1013 2.7 × 1013

Contribution of hospital effluent (% of total STP influent)

STP influent 13

5.3 × 10 1.6 × 1014 1.3 × 1014 3.9 × 1013 3.6 × 1013 4.7 × 1013 8.0 × 1015 1.9 × 1016

Mean

Max

0.5 0.1 0.4 0.5 0.4 1.5 0.3 0.1

0.7 0.2 0.5 0.7 1.0 5.1 0.6 0.3

Min 0.2 b0.1 0.2 b0.1 0.1 0.1 b0.1 b0.1

AMRB, antimicrobial-resistant bacteria; CRE, carbapenem-resistant enterobacteriaceae; ESBL, extended-spectrum β-lactamase; MDRA, multi-drug-resistant Acinetobacter; MDRP, multi-drug-resistant Pseudomonas aeruginosa; MRSA, methicillin-resistant Staphylococcus aureus; and VRE, vancomycin-resistant enterococci.

T. Azuma et al. / Science of the Total Environment 657 (2019) 476–484

development of innovative water treatment systems that target pathogenic microbes. The introduction of additional advanced water treatment systems—not only ozonation, but also membrane (Maletz et al., 2013; Verlicchi et al., 2015), electrochemical (Nakano et al., 2013; Verlicchi et al., 2015), and disinfection (Abreu et al., 2013) treatments —looks to be effective in removing large amounts of pollutant loads (Kovalova et al., 2013; Al Aukidy et al., 2014; Isidori et al., 2016; Verlicchi, 2017). These results should be of value in improving our understanding of environmental pollution problems associated with PhCs and AMRB in the surface waters. Our findings will help enhance the effectiveness of introducing advanced wastewater treatment systems, not only at STPs but also at medical facilities, to reduce the discharge of pollutants into rivers and keep the surface waters safe. 4. Conclusions The distributions of PhCs and AMRB from hospital effluent through to the river water environment and the contribution of hospital effluent to environmental discharge was estimated. 48 PhCs at concentrations ranging from several ng/L to 228 μg/L in hospital effluent was detected, and the contribution of the pollution load derived from hospital effluent to STP influent was estimated at 0.1% to 15%. AMRB concentrations in the hospital effluent ranged from 29 to 1805 CFU/mL, and the estimated contribution of the pollution load derived from hospital effluent to STP influent was 0.1% to 5.1%. The overall data including hospital effluents, STP influents and effluents, and river water, indicate that seven PhCs (ciprofloxacin, levofloxacin, iohexol, iomeprol, iopamidol, caffeine, and crotamiton) and a group of AMRB (MDRA) could be detected at the most frequent levels; 100% frequency for the former and 92% for the latter. Ozonation was effective in removing a wide range of both PhCs and AMRB in water samples. These findings suggest the importance of reducing the concentrations of environmental pollutants, not only at STPs but also at medical facilities, before the discharge of effluent into the surface waters. This should minimize the environmental pollution posed by PhCs and AMRB in the water environment. Acknowledgments We thank the staff of the hospitals and STPs for sampling the water. We acknowledge the Sumitomo Foundation, Japan (153018), Maeda Engineering Foundation, Japan and the Ministry of Education, Culture, Sports, Science and Technology of Japan (16K16218) for funding in the form of research grants and scholarships. We also thank the Promoting Academic Exchange Board of Osaka University of Pharmaceutical Sciences for supporting our collaborative research. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.11.433. References Abreu, A.C., Tavares, R.R., Borges, A., Mergulhão, F., Simões, M., 2013. Current and emergent strategies for disinfection of hospital environments. J. Antimicrob. Chemother. 68, 2718–2732. Adelowo, O.O., Caucci, S., Banjo, O.A., Nnanna, O.C., Awotipe, E.O., Peters, F.B., et al., 2018. Extended spectrum beta-lactamase (ESBL)-producing bacteria isolated from hospital wastewaters, rivers and aquaculture sources in Nigeria. Environ. Sci. Pollut. Res. 25, 2744–2755. Al Aukidy, M., Verlicchi, P., Voulvoulis, N., 2014. A framework for the assessment of the environmental risk posed by pharmaceuticals originating from hospital effluents. Sci. Total Environ. 493, 54–64. Allard, S., Criquet, J., Prunier, A., Falantin, C., Le Person, A., Yat-Man Tang, J., et al., 2016. Photodecomposition of iodinated contrast media and subsequent formation of toxic iodinated moieties during final disinfection with chlorinated oxidants. Water Res. 103, 453–461.

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