Antibiotics in coastal water and sediments of the East China Sea: Distribution, ecological risk assessment and indicators screening

Marine Pollution Bulletin 151 (2020) 110810

Contents lists available at ScienceDirect

Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Antibiotics in coastal water and sediments of the East China Sea: Distribution, ecological risk assessment and indicators screening

T

Feifei Lia,c, Lyujun Chenc,d, Weidong Chenb, Yingyu Baob, Yuhan Zhenga, Bei Huange, ⁎ Qinglin Mue, Donghui Wenb, Chuanping Fenga, a

School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China c School of Environment, Tsinghua University, Beijing 100084, China d Zhejiang Provincial Key Laboratory of Water Science and Technology, Zhejiang 314006, China e Zhejiang Provincial Zhoushan Marine Ecological Environmental Monitoring Station, Zhoushan 316021, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Antibiotics Coastal environment Ecological risk assessment

The distribution of 77 antibiotics in the coastal water and sediment from 3 bays of the East China Sea was investigated. There were 43 and 25 antibiotics detected with total concentrations of 30.8–2106.1 ng/L and 2.2–99.9 ng/g in water and sediment, respectively. Approximately 83.0% and 85.4% of the individual antibiotic concentrations were lower than 5.0 ng/L in water and 1.0 ng/g in sediment. Clindamycin (1.2–1507.9 ng/L, mean 183.8 ng/L) and erythromycin (ND–45.2 ng/g, mean 3.4 ng/g) were the most abundant in water and sediment, respectively. Ecological risk assessment revealed that the joint toxicity was enhanced when multiple antibiotics were present simultaneously. A decrease in the total antibiotic concentration and the ecological risk in water was observed from nearshore to offshore. Three antibiotics (sulfamethoxypyridazine, sulfamethoxazole and cinoxacin) were selected to be prioritized based on ecological risks for antibiotics monitoring and management of the coastal water in the East China Sea.

1. Introduction Large amounts of antibiotics were extensively and effectively used in clinics, agriculture, aquaculture and livestock (Sarmah et al., 2006; Kümmerer, 2009a). However, after being used by humans and animals, approximately 10%–90% of the used antibiotics might be excreted as either parent compounds or bioactive metabolites, and they could take several routes into natural ecosystems (Kümmerer, 2009b; Carvalho and Santos, 2016). Antibiotics have received increasing attention as emerging environmental contaminants because of their increasing consumption and the widespread occurrence in the environment. The occurrence of various classes of antibiotics in the environment, such as soils (Yi et al., 2019), sewage sludge, rivers, lakes (Carvalho and Santos, 2016) and even drinking water (Wang et al., 2016) has been widely reported. Intensive efforts have been devoted to examining the occurrence of three classes of antibiotics, sulfonamides (SAs), quinolones (QNs), and macrolides (MLs), due to their high consumption in China and their persistence in the environment compared to tetracyclines (TCs) and β-lactams (β-Ls) (Li et al., 2019). Although the major concern

about antibiotics has been associated with the development of resistance mechanisms by bacteria and the implications for human health, their sustained release to different environmental compartments and their bioactive properties also raise serious concerns about the toxicity of antibiotics to non-target organisms (Gonzalez-Pleiter et al., 2013; Van Boeckel et al., 2014; Klein et al., 2018). Some of the antibiotics in water have been proven to have high ecological risks for aquatic organisms (Bielen et al., 2017). For example, erythromycin (ETM), roxithromycin (RTM) and sulfamethoxazole (SMX) were identified as three priority antibiotics in surface water based on the environmental risk assessments (Bu et al., 2013). Similarly, ETM, amoxicillin (Amox), ciprofloxacin (CFX), ofloxacin (OFX), sulfadiazine (SDZ) and clarithromycin (CLM) were selected because of obvious ecological risks in European surface waters (Zhou et al., 2019). Aquatic environments affected by the discharge from pharmaceutical plants, aquaculture and sewage treatment plants have been proven to be high-risk areas of antibiotics (Ashbolt et al., 2013). All rivers run into the sea which was considered to be an important sink of many pollutants due to human activities, especially the offshore environment

Abbreviations: SAs, sulfonamides; MLs, macrolides; QNs, quinolones; TCs, tetracyclines; β-Ls, β-lactams; LMs, lincomycins; RQ, risk quotient; ECS, East China Sea ⁎ Corresponding author at: School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China. E-mail address: [email protected] (C. Feng). https://doi.org/10.1016/j.marpolbul.2019.110810 Received 16 October 2019; Received in revised form 3 December 2019; Accepted 7 December 2019 Available online 29 January 2020 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Oasis Hydrophilic-Lipophilic Balanced (HLB, 6 cm3, 200 mg) cartridges were purchased from Waters Co. (Milford, MA, USA). Glass fiber filters (GF/F, pore size 0.7 μm) were purchased from Whatman (Maidstone, England) and pyrolyzed at 450 °C for 4 h prior to use. Ultrapure water (resistivity 18.2 MΩ.CM) was prepared by a Milli-Q water purification system (Millipore, USA). Stock solutions of QNs, TCs, β-Ls and SAs standards were prepared in methanol, and MLs and LMs standards were prepared in a mixture of acetonitrile and water (1:3). All of the stock solutions were stocked in the dark at −20 °C. Highperformance liquid chromatography (HPLC) grade methanol, acetonitrile and formic acid were obtained from Fisher Scientific (USA). Disodium ethylenediamine tetracetate (Na2EDTA) and concentrated sulfuric acid (H2SO4) (guaranteed grade) were purchased from Tianjin Chemical Co., Ltd. (Tianjin, China). Unless otherwise indicated, the chemicals used in the analysis were analytical grade or above. Mcllvaine buffer (pH = 4.0) was prepared by dissolving 15 g of disodium hydrogen phosphate dihydrate and 13 g of citric acid monohydrate in 1 L of Milli-Q water.

(Zhang et al., 2013). Previous studies have shown that notable amounts of antibiotic residues were discharged into coastal areas due to intensive aquaculture activities, ambient wastewater discharge and runoff from farming. The median concentrations of most antibiotics were below 10 ng/L in the coastal water of China (Li et al., 2018b). In addition to harming aquatic organisms, exposure to antibiotics might induce resistance (Kummerer, 2004) and the horizontal transfer of resistance genes in field bacterial populations (Davison, 1999). However, possibly as a result of the difficulty to collect samples and a lack of attention, studies on antibiotics in offshore or marine environments are seriously insufficient, and current research on antibiotics in the marine environment mainly focused on seawater rather than sediment (Li et al., 2018b). However, some antibiotics (e.g., QNs and TCs) which were discharged into water could accumulate in the sediment phase because of their strong adsorption on particles and sediments (Gu and Karthikeyan, 2008; Mackay and Seremet, 2008). Antibiotics tightly adsorbed on sediments are persistent due to the poor fluidity of the sediment and a lack of oxygen, while antibiotics that are loosely bound to the sediment might desorb and enter the water again. Therefore, antibiotics in seawater and sediments should be highly regarded at the same time. The East China Sea (ECS) is a river-dominated epicontinental sea, linking the largest continent (Asian continent) and the northwestern Pacific. It plays key roles in China's fisheries, tourism, and marine aquaculture for humans (Liu et al., 2009). The coast of the ECS is one of the most developed regions in China, housing > 400 million inhabitants. With rapid economic development and industrialization, this region has become one of the most affected areas by the significantly increasing consumption of antibiotics (Zhang et al., 2015). On the other hand, the ECS mainly receives water from the Yangtze River, which is the largest and longest river in China, and it has the world's third largest water discharge and historically fourth largest sediment discharge (Zhao et al., 2018). The Yangtze River has been reported to be heavily contaminated by ETM (median concentration 17.3 ng/L) and TC (median concentration 14.8 ng/g) in its water and sediment, respectively (Li et al., 2018b). Regrettably, antibiotics were not intensively measured in both the coastal water and sediment of the ECS in previous studies, and the concentration datasets were even insufficient to compare with other seas in China and other countries (Li et al., 2018b). The detection of antibiotics in the offshore area would confirm their ubiquitous character and could lead to new insights into their persistence. This study aimed to (1) investigate the occurrence, geographical distribution and variation of antibiotics and provide data support for the status of antibiotic distribution in the offshore environment; (2) evaluate the ecological risk of antibiotics in coastal areas of the ECS and explore the potential hazards of current concentration levels of antibiotics in offshore waters to aquatic organisms; (3) identify antibiotics that need priority control and assist regulatory authorities in the development of management strategies for antibiotic pollution.

2.2. Study area and sample collection The samples were collected from the coastal environments of the ECS covering Hangzhou Bay (HB), Xiangshan Bay (XB) and Taizhou Bay (TB) (Fig. 1). A total of 13 seawater samples and 20 surface sediment samples covering coastal and offshore sites were collected in spring (April–May), 2018. All of the water (approximately 1 m below the surface) and surface sediment samples were collected using a stainless-steel grab bucket. The water samples were filtered through glass fiber filters immediately and adjusted to pH = 3.0–4.0 by adding concentrated H2SO4, and then transferred to a 5 L amber glass bottle which had been pre-cleaned and dried. All the water samples were kept at 4 °C, and the sediment samples were stored in a stainless-steel box and kept at −20 °C on the ship before further treatment and analysis in the laboratory. 2.3. Sample extraction and analysis The results of water quality and sediment physicochemical properties are shown in Table S2 and Table S3, respectively. Seawater temperature (T), pH, dissolved oxygen (DO), and salinity were measured in-situ by a pH meter with temperature measurement (Thermo Orion 868, United States), a DO meter (Thermo 3-star bench top, United States) and a salinity meter (Mettler Toledo SG3-ELK, Switzerland), respectively. The others were measured following the standard methods for seawater analysis (GB17378.4-2007). Suspended solids (SS) and chemical oxygen demand (COD) were measured using gravimetric and alkalescent permanganate titration, respectively. Ammonia (NH3-N), nitrite (NO2−-N), nitrate(NO3−-N) and active phosphorus (AP) were analyzed with a UV-VIS spectrophotometer (Shimadzu UV2401, Japan), i.e., indophenol blue, N-1-naphyl-ethylenediamine, alkaline potassium persulfate digestion-zinc cadmium reduction and ammonium molybdate, respectively. Metals content (Hg, Cu, Pb, Cd, As and Zn) and oil were measured by an ICP-MS (Agilent ICP-MS 7500ce, United States) and a molecular fluorescence spectrophotometer (Gangdong F-380, China), respectively. For sediment, total organic carbon (TOC), oil and S2− were analyzed according to standard methods for sediment analysis (GB17378.42007) using the K2Cr2O7 oxidation method, a molecular fluorescence spectrophotometer (Gangdong F-380, China) and methylene blue spectrophotometry (Shimadzu UV2401, Japan), respectively. Total phosphorus (TP) was measured using Mo-Sb colorimetry (Parker and Fudge, 1927) and total nitrogen (TN) was analyzed using the semimicro Macro Kjedahl method (Bradstreet, 1954). For metals content (Hg, Cu, Pb, Cd, As and Zn), air-dried sediment was digested according to standard methods for sediment analysis (GB17378.5-2007), and then measured using an ICP-MS (Agilent ICPMS 7500ce, United States).

2. Materials and methods 2.1. Antibiotic standards and chemicals The target antibiotics were selected mainly based on their usages in humans and animals in China, and most of them have been detected previously in water or sediments (Kim and Carlson, 2007; Zhang et al., 2014; Carvalho and Santos, 2016). A total of 77 native antibiotic standards belonging to 6 classes including 23 SAs, 9 MLs, 22 QNs, 9 TCs, 12 β-Ls and 2 LMs were purchased from Alta Scientific Co., Ltd. (Tianjin, China). The physicochemical properties of these antibiotics are listed in Table S1. Isotopically labeled internal standards including sulfamethoxazole-d4 (SMX-d4), tetracycline-d6 (TTC-d6), ciprofloxacin-d8 (CFX-d8), roxithromycin-d7 (RTM-d7) and cephalexin-d5 hydrate (CPX-d5) were obtained from Sigma-Aldrich Co. (St. Louis, MO, USA). 2

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Fig. 1. Location of sampling sites in the coastal water and sediments of the ECS.

chronic toxicity as follows.

Antibiotics in the water and sediment samples were extracted by a solid-phase extraction (SPE) method (Fig. S1), as described previously (Zhou et al., 2012). The target antibiotics were analyzed by ultra-highperformance liquid chromatography (UPLC; Waters ACQUITY H-Class, USA) coupled with a triple quadrupole mass spectrometer (MS, Waters Xevo TQ-S Micro, USA) equipped with an electrospray ionization (ESI) source in multiple reaction monitoring (MRM) mode. Five μL of the redissolved extract was injected into the chromatographic system. The target antibiotics were separated by a BEHC18 column (2.1 mm × 100 mm, 1.7 μm, Waters, USA) maintained at 40 °C. The flow rate of gradient elution was 0.3 mL/min with phase A (Milli-Q water with 0.1% (v/v) formic acid) and phase B (methanol). The detailed elution program is described in Table S4. Mass spectrometric analyses were operated in the positive ionization mode. Capillary voltage was set to 1.0 kV, and the temperature and flow rate of the desolvation gas were 500 °C and 1000 L/h, respectively.

PNEC = EC50 /AF

(1)

or

PNEC = LC50 /AF

(2)

where EC50 and LC50 are the median effective concentration and the median lethal concentration, respectively (Wollenberger et al., 2000). The levels of estimated risk could be divided into four categories: insignificant risk (RQ < 0.01), low risk (0.01 < RQ < 0.1), medium risk (0.1 < RQ < 1), and high risk (RQ > 1) (Hernando et al., 2006). 2.5. Statistical analysis Box-and-whisker plots of the antibiotic concentrations and detection frequencies were performed by using Origin 2018. Circos figures generated by R software (v3.5.2, R Foundation for Statistical Computing, Vienna, Austria) were used to visualize the distribution of antibiotic species among different bays. Relationships between antibiotic concentrations and environmental factors were evaluated by spearman correlation analysis using R software.

2.4. Ecological risk assessment Environmental risk assessment (ERA) methodologies for antibiotics have been developed based on the existing guidelines for other chemicals (e.g., industrial chemicals). The coastal water environments are complex aquatic systems of freshwater and saltwater, and data about the toxicity of antibiotics on marine organisms is insufficient. Data for freshwater algae (phototrophic level), Daphnia magna (invertebrates) and fish (vertebrates) were used to assess potential ecological effects (Du et al., 2017; Lu et al., 2018; Siedlewicz et al., 2018). The ecological risks presented by antibiotics in the aquatic environment were evaluated using the risk quotient (RQ) (European Commission, 2003). The individual RQ was calculated as a ratio of the measured environmental concentration (MEC) and predicted no-effect concentration (PNEC) (RQ = MEC/PNEC). PNEC values of acute and chronic toxicity were predicted using the ecological structure-activity relationship (ECOSAR) model (v2.0, U.S.EPA) by importing the chemical name and CAS number in this study. When more than one dataset on toxicity was obtained at the same nutrient level, the one indicating the strongest effect was used. The toxic data selected as the toxicological benchmarks for the calculation of the PNECs are shown in Table S5. PNEC values were calculated from the toxicity data using an assessment factor (AF) of 1000 for acute toxicity and an AF of 100 for

3. Results and discussion 3.1. Occurrence of antibiotics in water and sediments 3.1.1. Occurrence of antibiotic classes Concentrations and detection frequencies of antibiotics belonging to 6 classes in the water and sediment samples are listed in Table 1. Although the β-lactam antibiotics were highly consumed by humans in China and other countries (Zhang et al., 2015), they were not detected in water or sediment samples in this study. This can be explained by the clear fact that the chemical structures of all the β-lactam antibiotics have an unstable lactam ring causing them readily undergo hydrolysis shortly after excretion (Bu et al., 2013). The other 5 classes of antibiotics were detected in almost all the water and sediment samples. LMs presented the highest concentration, with a mean value of 215.6 ng/L in water which corroborated the prediction of a previous study (Zhang et al., 2015). QNs were the most abundant antibiotic in the sediment 3

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Table 1 Concentrations and detection frequencies of antibiotics in water and sediment samples from the ECS. Antibiotic classes

SAs QNs MLs LMs TCs a b

Sediment (ng/g)b

Water (ng/L) Min

Max

Mean

Median

Freqa

Min

Max

Mean

Median

Freqa

2.0 8.6 2.9 2.5 1.0

156.5 185.2 77.0 1688.4 6.5

39.3 54.2 33.6 215.6 2.5

15.2 48.9 14.8 29.8 2.0

100 100 100 100 100

0.0 0.4 0.6 0.1 0.0

25.3 45.1 60.3 19.4 1.8

3.0 7.3 5.2 2.6 0.6

0.6 1.3 1.8 0.2 0.5

95 100 100 100 95

Freq, detection frequency (%). Concentration of antibiotics in sediments refers to the concentration in dry sediments.

most to the total antibiotic burden of sediment.

samples, with a mean concentration of 7.3 ng/g. It was consistent with a previous study that the highest concentrations were predicted for QNs in sediment (Zhang et al., 2015). Similarly, QNs were proven to be dominant in the summer, with a mean concentration of 0.63 ng/g in the soils of the Yangtze River Delta (Zhao et al., 2018). QNs are known to have a large partition coefficient which enable them to easily migrate from water to sediment and result in a low biodegradability and a high environmental persistence (Chen et al., 2015).

3.2. Geographical distribution of antibiotic concentrations and compositions 3.2.1. Geographical variations of antibiotic concentrations in water and sediments The geographical variations of antibiotic concentrations in water (Fig. 3a) and sediment (Fig. 3b) in the ECS were presented. The highest total antibiotic concentration was detected in TB, which contributed 74.8% and 56.8% to the total antibiotic burden in the water and sediment samples of the 3 bays, respectively. This indicated that TB faced the most serious antibiotic contamination problem. The reason was based on the fact that the Taizhou Bay rim region is a gathering area for antibiotic production enterprises, and more antibiotic manufacturers are gathered here than in the areas around HB and XB. In water samples (Table S9), the levels of most antibiotic concentrations were comparable to those reported in the Bohai Sea (Zou et al., 2011), the Yellow Sea (Du et al., 2017), the South China Sea (Zhang et al., 2018), the Persian Gulf (Kafaei et al., 2018) and the southern Baltic Sea (Siedlewicz et al., 2018). However, they were much higher than those in sea water from the Antarctic (Hernández et al., 2019), which indicated intensive antibiotic discharge in populated areas. In general, the types of antibiotics detected were similar in each bay, and the total antibiotic concentrations decreased gradually with the distance from the shore increasing. Similar trends were also found in the South China Sea (Zhang et al., 2018), the Bohai Sea and the Yellow Sea (Zhang et al., 2013; Du et al., 2017). Previous research has shown that antibiotics were transported from land to the coast and from the coast to offshore (Zhang et al., 2018). Concentration reduction may be due to diffusion or adsorption, photolysis, hydrolysis and/or biodegradation in the transport process. Among the selected water sampling sites (Fig. 3a), the total antibiotic concentration was the highest at site TC1 (2106.1 ng/L), and CLIN at a concentration of 1508 ng/L, which accounted for 72.0% of the total antibiotic concentration of site TC1. In addition, the concentration of CLIN was 680.6 ng/L at site TC2, which was higher than that at other sampling sites. However, based on a large amount of detection data, most antibiotic concentrations detected in sea water ranged between 0.01 and 100 ng/L (Li et al., 2018b). CLIN was used only by humans and not at particularly high dosages. Therefore, the high concentration detected at site TC1 could be inferred because of the fact that there were 2 large CLIN antibiotic manufacturers with a high antibiotic concentration in the effluent close to the sampling point. In sediments (Table S10), the antibiotic concentrations were mostly comparable to those reported in the coastal environments of Dalian (Na et al., 2013), the Bohai Sea (Liu et al., 2016), Jiaozhou Bay of the Yellow Sea (Liu et al., 2018), Hailing Bay of the South China Sea (Chen et al., 2015), the southern Baltic Sea (Siedlewicz et al., 2018), and the Persian Gulf (Kafaei et al., 2018). The distribution of the total antibiotic concentration (Fig. 3b) seemed to be irregular, and the total antibiotic concentration did not increase or decrease with the offshore distance. The antibiotic concentrations of HB6 (99.9 ng/g) and TC0 (91.1 ng/g)

3.1.2. Occurrence of individual antibiotics Among the target list of 77 antibiotics, a total of 43 antibiotics were detected in water (Fig. 2a), and 39 (90.7%) of them were detected in more than half of the water samples. A total of 25 antibiotics were detected in the sediment samples (Fig. 2b), and 14 (56.0%) of them were detected in more than half of the samples. The average detection frequencies of the detected antibiotics were 82.3% and 55.2% in water (Table S6) and sediments (Table S7), respectively. The difference in the detection frequency of antibiotics in water and sediment was mainly due to that antibiotics were more diffusive in the water than that in the sediments. Noticeably, all the 25 antibiotics detected in the sediment samples were also detected in the water samples, implying the migration of antibiotics from water to sediment. Sediment-water distribution coefficients (Kd) of antibiotics were calculated when they were detected in > 50% of both water and sediment samples (Li et al., 2018a; Liang et al., 2013). The calculated Kd values (Table S8) ranged from 2.9 to 3813.7 L/kg in the ECS, which were mostly comparable to those reported in previous studies (Laak et al., 2006; Na et al., 2013; Li et al., 2018a; Li et al., 2018b; Li et al., 2019). For water samples, the total antibiotic concentration in the ECS was 30.8–2106.1 ng/L (mean 345.2 ng/L). Among the 43 antibiotics detected in water, approximately 83.0% of the concentrations were lower than 5.0 ng/L (Fig. 2a). Clindamycin (CLIN) (1.2–1507.9 ng/L, mean 183.8 ng/L) and lincomycin (LIN) (0.9–180.8 ng/L, mean 31.8 ng/L) were the top two antibiotics in water, and contributed 53.2% and 9.2% to the total antibiotic concentration, respectively. LIN was one of the top 5 antibiotics (Amox, florfenicol, LIN, penicillin and norfloxacin) used among the 36 most commonly and intensively used antibiotics in China (Zhang et al., 2015). In addition, LIN showed high hydrophilicity and low biodegradability in the water collected from the saline pond, making it more stable in seawater (t1/2 = 809 days) (Lei and Lai, 2018). For sediment samples, the total antibiotic concentrations ranged from 2.2–99.9 ng/g (mean 18.6 ng/g), and 85.4% of the detectable concentrations of 25 antibiotics were below 1.0 ng/g. ETM contributed the most (18.1%) to the total antibiotic burden at a mean concentration of 3.4 ng/g (ND–45.2 ng/g). Another study proved ETM had the highest mean concentrations of 0.12 ng/g and 1.41 ng/g in the summer and winter in soils of the Yangtze River Delta, respectively (Zhao et al., 2018). The result was consistent with the previous prediction that ETM was one of the chemicals with the maximum concentration in sediment (Zhang et al., 2015). CLIN (0.1–18.6 ng/L, mean 2.5 ng/L), which was the most abundant antibiotic in water, contributed the second (13.5%) 4

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Fig. 2. Box-and-whisker plots of the concentrations and detection frequencies of antibiotics in the water (a) and sediment samples (b) from the ECS. The numbers in brackets indicate the number of detected antibiotics/target antibiotics.

and antibiotic compositions in the other 2 bays were different from that in TB. ETM, RTM and tosufloxacin tosylate were the dominant antibiotics in XB. ETM, RTM and sulfamethoxypyridazine (SMP) were the dominant antibiotics in HB. It indicated TB had distinct types of antibiotic sources from HB and XB, and the antibiotic sources of HB and XB were similar. The dominance of SMP could be attributed to its widespread consumption and strong persistence in water. As previously reported, SMP was extensively used as an antibacterial agent in livestock and poultry farming and has been detected at high concentrations in the aquatic environments (Qin et al., 2018). For the sediment samples (Fig. 4b), CLIN was the main antibiotic, and the other important antibiotics (CNX, NDFX, MBFX, OFX, NFX, ERFX, OAO and FLU) mainly belonged to QNs in TB. In HB and XB, MLs (CLM, ETM and RTM) played a non-negligible role because of their hydrophobic and strong sorption affinity to the sediments, and they can easily enrich in sediments (Li et al., 2018a; Luo et al., 2011). The other important antibiotics (NDFX, MBFX, TSFX, OFX and ERFX) in HB and XB mainly belonged to QNs too. The consistency of dominant antibiotics in water and sediment in the 3 bays illustrated the migration of antibiotics from water to sediment. The reason for the high

were higher than those at other sites. It was inferred that antibiotics tended to accumulate at HB6 due to the complex impact by rivers input, complex ocean currents and the anoxic environment in the seabed (Xie et al., 2018; Yuan et al., 2019). However, antibiotics at TC0 were inferred from the land-based antibiotics plant which was the same as that in the water, and the concentration decreased from the interior of the bay to the outside as it get farther from the source of pollution. The antibiotics detected at site HB6 were mainly ETM contributing 45.2%, and SDZ and CLIN were the main antibiotics contributing 40.5% to the total antibiotic concentration at site TC0. ETM and SDZ with high Kd values (Li et al., 2018a) have a strong sorption ability to the sediments, resulting in their higher concentrations in the sediment. While, CLIN is easily soluble in the water and has a low sorption affinity to the soils and sediments, its high concentration in the sediment indicated the migration from water phase which is affected by complex environmental factors (Wang and Wang, 2015). 3.2.2. Comparison of antibiotic compositions in different bays For water samples, LMs (CLIN and LIN) was the major class of antibiotics detected in TB (Fig. 4a), while the dominant antibiotic species 5

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Fig. 3. Spatial distribution of antibiotic concentrations in water (a) and sediment (b) from the ECS.

NO3−). It was consistent with previous reports that the antibiotic distribution in the seawater was positively correlated with the values of COD and NO3− (Chen et al., 2015). Moreover, it was reported that the antibiotic concentrations were negatively correlated with salinity in the Bohai Sea (Zhang et al., 2013) and the Yellow Sea (Du et al., 2017). This correlation between antibiotics and environmental factors was particularly evident in two clusters including 18 antibiotics (CTC, TMP, ETC, SDX, SMZ, SCP, DCC, SPZ, SQX, SMX, SMP, FLU, CNX, OAO, LIN, SDZ, NAL and SMM). Some of these antibiotics such as CTC, SDX, SPZ, SMX, SMP, CNX and LIN are mainly used in clinics and some (ETC, SMZ, SCP, SQX, FLU and SMM) are often used in livestock and poultry farming. A variety of antibiotics mainly used in human production and life were

concentration proportion of QNs in sediments might due to the strong adsorption of QNs to sediment because of their lipophilicity and their large distribution coefficients (Kd) (Van Boeckel et al., 2014).

3.3. Correlation between antibiotic concentrations and water or sediment properties The correlations between the antibiotic concentrations and the water properties (Fig. 5a) revealed that the antibiotic concentrations in water were negatively obviously correlated with natural attribute indicators (pH and salinity), while they were positively correlated (P < 0.05) with some anthropogenic pollutants (oil, PO43−, COD and 6

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Fig. 4. Composition of antibiotics at different sampling sites in water (a) and sediments (b) from the ECS.

7

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Fig. 5. The spearman correlation between antibiotics and water (a) or sediment properties (b). Heavy metals included Hg, Cu, Pb, Cd, As and Zn.

simultaneously, which might increase the overall risk through the cocktail effect (Du et al., 2017). When summing up the RQ values of each antibiotic, more sampling sites were under acute and chronic toxicity risks in this study (Fig. 6). The geographical distribution of total ecological risks (Fig. S2) showed that the RQ values for HB and TB were generally greater than those for XB. In general, the acute and chronic risks reduced with the distance from the shore increasing which was consistent with the geographical distribution of antibiotic concentrations.

detected in the seawater, further, their concentrations were significantly positively correlated with anthropogenic pollution indicators and negatively correlated with natural attribute indicators, indicating that the antibiotics pollution in the ocean was acquired rather than innate. Previous studies have found that riverine inputs, sewage treatment plant effluents and mariculture were the main sources of antibiotics in the marine environments (Zou et al., 2011; Chen et al., 2015; Zhang et al., 2018). In sediment samples (Fig. 5b), the correlations between antibiotics and the other indexes were not as strong as those in the water. The cluster revealed a positive correlation between 8 antibiotics (FLU, LIN, NFX, CLIN, CNX, SMP, SDZ and OAO) and 4 other indexes (TOC, TN, heavy metals and oil). Previous studies have indicated that the antibiotic concentrations were positively correlated with the values of TOC (Liu et al., 2016) and TN (Li et al., 2018a). Some previous studies indicated that organic carbon in sediment was one of the most important impact factors to the sorption of antibiotics to sediment (Liu et al., 2016; Li et al., 2018a; Li et al., 2018b).

3.5. Indicator screening of antibiotics based on risk assessment Hundreds of antibiotics have been detected in the environment. However, the simultaneous quantification of numerous antibiotics is challenging because it requires efficient pretreatment, sensitive analytical instruments and techniques, and it is quite time-consuming and costly. To address these limitations, the identification of representative antibiotic indicators that can be used in the monitoring of antibiotics has been strongly encouraged by researchers and regulators. Because the potential hazard to aquatic organisms is an important environmental hazard of antibiotics, the indicators were screened based on risk assessment in this study. The 75th-percentile concentration of antibiotics was chosen as MEC to simulate a worst case scenario (Li et al., 2019). When the calculated RQ values of acute toxicity or chronic toxicity were > 0.01, the corresponding antibiotic will be selected because of its significant ecological risk according to ERA. One antibiotic (CNX) of acute toxicity and two antibiotics (SMX and SMP) of chronic toxicity were chosen for risk to fish, Daphnia magna, or green algae in this study (Fig. S3). As a result, SMX, SMP and CNX were selected as indicators for the monitoring and control of antibiotic pollution in the ECS. All of the three antibiotics were used for humans or animal husbandry, indicating that they were

3.4. Ecological risk assessment Due to the lack of a risk assessment system for antibiotics in sediment, this study only discussed the ecological risks of antibiotics in water. All individual antibiotics had insignificant acute and chronic toxicity to fish and algae. For Daphnia magna, 2 antibiotics (SMP and CNX) presented a low acute toxicity and 5 antibiotics (SDZ, SMX, SCP, SMP and SDO) presented a low chronic toxicity (Fig. 6). It may be due to PNECs of acute and chronic toxicity in Table S5. The PNECs of SMP, CNX, SDZ, SMX, SCP and SDO for Daphnia magna were lower than those for fish and algae, indicating that Daphnia magna was the most sensitive to the above antibiotics. Multiple antibiotic residues have been found to be present in water 8

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Insignificant risk Acute toxicity

MLs LMs TCs

High risk Chronic toxicity

SAs

QNs

MLs LMs TCs

Fish

HB1 HB2 HB4 HB5 HB10 XB1 XB2 XB3 XB5 TC1 TC3 TB1 TB2

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

Daphnid

HB1 HB2 HB4 HB5 HB10 XB1 XB2 XB3 XB5 TC1 TC3 TB1 TB2

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

Green Algae

SDZ SMO SMZ SMX SPD SIZ STZ TMP SMM SCP SMT SMP SPZ SDO SDX SQX PIP FLU OAO NAL GMFX ERFX NFX OFX ENOX TSFX MXFX CNX ATM RTM ETM TMCS CLM CLIN LIN DCC MCC MTC ETC CTC RQsum

QNs

Medium risk

SDZ SMO SMZ SMX SPD SIZ STZ TMP SMM SCP SMT SMP SPZ SDO SDX SQX PIP FLU OAO NAL GMFX ERFX NFX OFX ENOX TSFX MXFX CNX ATM RTM ETM TMCS CLM CLIN LIN DCC MCC MTC ETC CTC RQsum

SAs

Low risk

HB1 HB2 HB4 HB5 HB10 XB1 XB2 XB3 XB5 TC1 TC3 TB1 TB2

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

# # # # # # # # # # # # #

Fig. 6. Heat map of the RQ values of antibiotics for aquatic organisms in water samples from the ECS.

this area. All of the 3 screened antibiotics were used for humans or animal husbandry, indicating a source of human contamination. However, the representative antibiotics are tailored to local conditions and more researches are needed.

all from land-source pollution. SMX was the most concerned and studied antibiotic, it was positively detected in over 50% of 892 samples collected in 33 European countries, with a mean concentration of 192 ng/L, and they were comparable to those in China (up to 940 ng/L) (Bu et al., 2013). It was identified as one of the 3 priority antibiotics in surface water based on their environmental risk assessments (Bu et al., 2013).

Author contributions Feifei Li: Formal analysis, Methodology, Conceptualization, Writing - Original Draft, Writing - Review & Editing, Investigation. Lyujun Chen: Funding acquisition. Weidong Chen: Writing - Review & Editing. Yingyu Bao: Investigation. Yuhan Zheng: Investigation. Bei Huang: Resources. Qinglin Mu: Resources. Donghui Wen: Funding acquisition, Writing - Review & Editing. Chuanping Feng: Writing - Review & Editing, Conceptualization.

4. Conclusion In this study, we investigated the occurrence of antibiotics, explored the geographical variations of antibiotic concentrations and compositions, and evaluated the individual and joint ecological risks of antibiotics. Antibiotics were proven to be ubiquitous in the coastal marine environment and had certain ecological risks. In terms of concentrations, > 80% of the detectable concentrations of individual antibiotics were lower than 5.0 ng/L and 1.0 ng/g in the water and sediment samples, respectively. The most abundant antibiotics in water and sediment were CLIN and ETM, respectively. From the ecological risk perspective, all individual antibiotics have insignificant acute and chronic toxicity risks for fish and algae. However, two antibiotics (SMP and CNX) with low acute risks and 5 antibiotics (SDZ, SMX, SCP, SMP and SDO) with low chronic risks to Daphnia magna were observed. It was noteworthy that the joint toxicity was enhanced when multiple antibiotics were present simultaneously. Fortunately, both the concentrations and ecological risks of the antibiotics decreased with the increase of offshore distance. Based on the ecological risk of individual antibiotic, three representative antibiotics (SMX, SMP and CNX) were screened out from a wide range of species for antibiotic monitoring in

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the National Natural Science Foundation of China [grant numbers 51678003, 51938001]. The samples were collected by the technical staffs of Zhejiang 9

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

Provincial Zhoushan Marine Ecological Environmental Monitoring Station.

Li, S., Shi, W., Li, H., Xu, N., Zhang, R., Chen, X., Sun, W., Wen, D., He, S., Pan, J., He, Z., Fan, Y., 2018a. Antibiotics in water and sediments of rivers and coastal area of Zhuhai City, Pearl River estuary, south China. Sci. Total Environ. 636, 1009–1019. https://doi.org/10.1016/j.scitotenv.2018.04.358. Li, S., Shi, W., Liu, W., Li, H., Zhang, W., Hu, J., Ke, Y., Sun, W., Ni, J., 2018b. A duodecennial national synthesis of antibiotics in China's major rivers and seas (20052016). Sci. Total Environ. 615, 906–917. https://doi.org/10.1016/j.scitotenv.2017. 09.328. Li, Si, Zhang, Ruijie, Hu, Jingrun, Shi, Wanzi, Kuang, Yuzhu, Guo, Xiaoyu, Sun, Weiling, 2019. Occurrence and removal of antibiotics and antibiotic resistance genes in natural and constructed riverine wetlands in Beijing, China. Sci. Total Environ. 664, 546–553. https://doi.org/10.1016/j.scitotenv.2019.02.043. Liang, X., Chen, B., Nie, X., Shi, Z., Huang, X., Li, X., 2013. The distribution and partitioning of common antibiotics in water and sediment of the Pearl River Estuary, South China. Chemosphere 92, 1410–1416. https://doi.org/10.1016/j.chemosphere. 2013.03.044. Liu, P., Yu, Y., Liu, C., 2009. Studies on the situation of pollution and countermeasures of control of the oceanic environment in Zhoushan fishing ground-the largest fishing ground in China. Mar. Pollut. Bull. 23, 281–288. https://doi.org/10.1016/0025-326X (91)90688-O. Liu, X.H., Zhang, H.B., Li, L.Z., Fu, C.C., Chen, T., Huang, Y.J., Wu, L.H., Tang, J.H., Luo, Y.M., Christie, P., 2016. Levels, distributions and sources of veterinary antibiotics in the sediments of the Bohai Sea in China and surrounding estuaries. Mar. Pollut. Bull. 109 (1), 597–602. https://doi.org/10.1016/j.marpolbul.2016.05.033. Liu, Ke, Yin, Xiaofei, Zhang, Daolai, Yan, Dongyun, Cui, Lijuan, Zhu, Zhigang, Wen, Li, 2018. Distribution, sources, and ecological risk assessment of quinotone antibiotics in the surface sediments from Jiaozhou Bay wetland, China. Mar. Pollut. Bull. 129, 859–865. https://doi.org/10.1016/j.marpolbul.2017.10.010. Lu, Jian, Wu, Jun, Zhang, Cui, Zhang, Yuxuan, Lin, Yichen, Luo, Yongming, 2018. Occurrence, distribution, and ecological-health risks of selected antibiotics in coastal waters along the coastline of China. Sci. Total Environ. 644, 1469–1476. Luo, Y., Xu, L., Rysz, M., Wang, Y., Zhang, H., Alvarez, P.J.J., 2011. Occurrence and transport of tetracycline, sulfonamide, quinolone, and macrolide antibiotics in the Haihe River Basin, China. Environ. Sci. Technol. 45, 1827–1833. https://doi.org/10. 1021/es104009s. Mackay, A.A., Seremet, D.E., 2008. Probe compounds to quantify cation exchange and complexation interactions of ciprofloxacin with soils. Environ. Sci. Technol. 42, 8270–8276. https://doi.org/10.1021/es800984x. Na, Guangshui, Fang, Xiaodan, Cai, Yaqi, Ge, Linke, Zong, Humin, Yuan, Xiutang, Yao, Ziwei, Zhang, Zhifeng, 2013. Occurrence, distribution, and bioaccumulation of antibiotics in coastal environment of Dalian, China. Mar. Pollut. Bull. 69, 233–237. https://doi.org/10.1016/j.marpolbul.2012.12.028. Parker, F.W., Fudge, J.F., 1927. Soil phosphorus studies I the colorimetric determination of organic and inorganic phosphorus in soil extracts and the soil solution. Soil Sci. 24, 109–117. https://doi.org/10.1097/00010694-192708000-00004. Qin, Yanwen, Wen, Quan, Ma, Yingqun, Yang, Chenchen, Liu, Zhichao, 2018. Antibiotics pollution in Gonghu Bay in the period of water diversion from Yangtze River to Taihu Lake. Environ. Earth Sci. 77–419. https://doi.org/10.1007/s12665-018-7558-4. Sarmah, A.K., Meyer, M.T., Boxall, A.B.A., 2006. A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment. Chemosphere 65 (5), 725–759. https://doi.org/10.1016/j. chemosphere.2006.03.026. Siedlewicz, G., Białk-Bielińska, A., Borecka, M., Winogradow, A., Stepnowski, P., Pazdro, K., 2018. Presence, concentrations and risk assessment of selected antibiotic residues in sediments and near-bottom waters collected from the Polish coastal zone in the southern Baltic Sea -summary of 3 years of studies. Mar. Pollut. Bull. 129, 787–801. https://doi.org/10.1016/j.marpolbul.2017.10.075. Van Boeckel, T.P., et al., 2014. Global antibiotic consumption 2000 to 2010: an analysis of national pharmaceutical sales data. Lancet Infect. Dis. 14, 742–750. https://doi. org/10.1016/S1473-3099(14)70780-7. Wang, S.L., Wang, H., 2015. Adsorption behavior of antibiotic in soil environment: a critical review. Front. Environ. Sci. Eng. 9, 565–574. https://doi.org/10.1007/ s11783-015-0801-2. Wang, H., Wang, N., Wang, B., Zhao, Q., Fang, H., Fu, C., Tang, C., Jiang, F., Zhou, Y., Chen, Y., Jiang, Q., 2016. Antibiotics in drinking water in Shanghai and their contribution to antibiotic exposure of school children. Environ. Sci. Technol. 50, 2692–2699. https://doi.org/10.1021/acs.est.5b05749. Wollenberger, L., Halling-Sorensen, B., Kusk, K.O., 2000. Acute and chronic toxicity of veterinary antibiotics to Daphnia magna. Chemosphere 40 (7), 723–730. https://doi. org/10.1016/S0045-6535(99)00443-9. Xie, Y., Chen, L., Liu, R., Tian, J., 2018. AOX contamination in Hangzhou Bay, China: levels, distribution and point sources. Environ. Pollut. 235, 462–469. https://doi.org/ 10.1016/j.envpol.2017.12.089. Yi, Xinzhu, Lin, Chenghui, Ong, Eugene Jie Li, Wang, Mian, Zhou, Zhi, 2019. Occurrence and distribution of trace levels of antibiotics in surface waters and soils driven by non-point source pollution and anthropogenic pressure. Chemosphere 216, 213–223. https://doi.org/10.1016/j.chemosphere.2018.10.087. Yuan, Da, He, Zhen, Yang, Gui-Peng, 2019. Spatiotemporal distributions of halocarbons in the marine boundary air and surface seawater of the Changjiang estuary and its adjacent East China Sea. Mar. Pollut. Bull. 140, 227–240. https://doi.org/10.1016/j. marpolbul.2019.01.040. Zhang, R., Tang, J., Li, J., Zheng, Q., Liu, D., Chen, Y., Zou, Y., Chen, X., Luo, C., Zhang, G., 2013. Antibiotics in the offshore waters of the Bohai Sea and the Yellow Sea in China: occurrence, distribution and ecological risks. Environ. Pollut. 174 (5), 71–77. https://doi.org/10.1016/j.envpol.2012.11.008. Zhang, Q., Jia, A., Wan, Y., Liu, H., Wang, K., Peng, H., Dong, Z., Hu, J., 2014.

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpolbul.2019.110810. References Ashbolt, Nicholas J., Amézquita, Alejandro, Backhaus, Thomas, Borriello, Peter, Brandt, Kristian K., Collignon, Peter, Coors, Anja, Finley, Rita, Gaze, William H., Heberer, Thomas, Lawrence, John R., Larsson, D.G. Joakim, McEwen, Scott A., Ryan, James J., Schönfeld, Jens, Silley, Peter, Snape, Jason R., Van den Eede, Christel, Topp, Edward, 2013. Human health risk assessment(HHRA)for environmental development and transfer of antibiotic resistance. Environ. Health Perspect. 121, 993–1001. https:// doi.org/10.1289/ehp.1206316. Bielen, A., Simatovic, A., Kosic-Vuksic, J., Senta, I., Ahel, M., Babic, S., Jurina, T., Plaza, J.J.G., Milakovic, M., Udikovic-Kolic, N., 2017. Negative environmental impacts of antibiotic-contaminated effluents from pharmaceutical industries. Water Res. 126, 79–87. https://doi.org/10.1016/j.watres.2017.09.019. Bradstreet, R.B., 1954. Kjeldahl method for organic nitrogen. Anal. Chem. 26, 185–187. https://doi.org/10.1021/ac60085a028. Bu, Q.W., Wang, B., Huang, J., Deng, S.B., Yu, G., 2013. Pharmaceuticals and personal care products in the aquatic environment in China: a review. J. Hazard. Mater. 262, 189–211. https://doi.org/10.1016/j.jhazmat.2013.08.040. Carvalho, I.T., Santos, L., 2016. Antibiotics in the aquatic environments: a review of the European scenario. Environ. Int. 94, 736–757. https://doi.org/10.1016/j.envint. 2016.06.025. Chen, H., Liu, S., Xu, X.R., Liu, S.S., Zhou, G.J., Sun, K.F., Zhao, J.L., Ying, G.G., 2015. Antibiotics in typical marine aquaculture farms surrounding Hailing Island, South China: occurrence, bioaccumulation and human dietary exposure. Mar. Pollut. Bull. 90 (1–2), 181–187. https://doi.org/10.1016/j.marpolbul.2014.10.053. Davison, J., 1999. Genetic exchange between bacteria in the environment. Plasmid 42, 73–91. https://doi.org/10.1006/plas.1999.1421. Du, J., Zhao, H., Liu, S., Xie, H., Wang, Y., Chen, J., 2017. Antibiotics in the coastal water of the South Yellow Sea in China: occurrence, distribution and ecological risks. Sci. Total Environ. 595, 521–527. https://doi.org/10.1016/j.scitotenv.2017.03.281. European, 2003. Technical Guidance Document in Support of Commission Directive 93/ 67/EEC on Risk Assessment for New Notified Substances, Commission Regulation (EC) No. 1488/94 on Risk Assessment for Existing Substance, and Directive 98/8/EC of the European Parliament and of the Council Concerning the Placing of Biocidal Products on the Market. Part II. European Commission Joint Research Centre, pp. 100–102 (EUR 20418 EN/2). Gonzalez-Pleiter, M., Gonzalo, S., Rodea-Palomares, I., Leganes, F., Rosal, R., Boltes, K., Marco, E., Fernandez-Pinas, F., 2013. Toxicity of five antibiotics and their mixtures towards photosynthetic aquatic organisms: implications for environmental risk assessment. Water Res. 47, 2050–2064. https://doi.org/10.1016/j.watres.2013.01.020. Gu, C., Karthikeyan, K.G., 2008. Sorption of the antibiotic tetracycline to humicmineral complexes. J. Environ. Qual. 37, 704–711. https://doi.org/10.2134/jeq2007.0030. Hernández, F., Calısto-Ulloa, N., Gómez-Fuentes, C., Gómez, M., Ferrer, J., GonzálezRocha, G., Bello-Toledo, H., Botero-Coy, A.M., Boıx, C., Ibáñez, M., Montory, M., 2019. Occurrence of antibiotics and bacterial resistance in wastewater and sea water from the Antarctic. J. Hazard. Mater. 363, 447–456. https://doi.org/10.1016/j. jhazmat.2018.07.027. Hernando, M.D., Mezcua, M., Fernandez-Alba, A.R., Barcelo, D., 2006. Environmental risk assessment of pharmaceutical residues in wastewater effluents, surface waters and sediments. Talanta 69, 334–342. https://doi.org/10.1016/j.talanta.2005.09.037. Kafaei, Raheleh, Papari, Fatemeh, Seyedabadi, Mohammad, Sahebi, Soleyman, Tahmasebi, Rahim, Ahmadi, Mehdi, Sorial, George A., Asgari, Ghorban, Ramavandi, Bahman, 2018. Occurrence, distribution, and potential sources of antibiotics pollution in the water-sediment of the northern coastline of the Persian Gulf, Iran. Sci. Total Environ. 627, 703–712. https://doi.org/10.1016/j.scitotenv.2018.01.305. Kim, S.C., Carlson, K., 2007. Temporal and spatial trends in the occurrence of human and veterinary antibiotics in aqueous and river sediment matrices. Environ. Sci. Technol. 41, 50–57. https://doi.org/10.1021/es060737+. Klein, Eili Y., Boeckel, Thomas P. Van, Martinez, Elena M., Pant, Suraj, Gandra, Sumanth, Levin, Simon A., Goossensh, Herman, Laxminarayan, Ramanan, 2018. Global increase and geographic convergence in antibiotic consumption between 2000 and 2015. PNAS 115, E3463–E3470. https://doi.org/10.1073/pnas.1717295115. Kummerer, K., 2004. Resistance in the environment. J. Antimicrob. Chemother. 54, 311–320. https://doi.org/10.1093/jac/dkh325. Kümmerer, K., 2009a. Antibiotics in the aquatic environment-a review-part I. Chemosphere 75, 417–434. https://doi.org/10.1016/j.chemosphere.2008.11.086. Kümmerer, K., 2009b. Antibiotics in the aquatic environment-a review-part II. Chemosphere 75, 435–441. https://doi.org/10.1016/j.chemosphere.2008.12.006. Laak, T.L.T., Gebbink, W.A., Tolls, J., 2006. Estimation of soil sorption coefficients of veterinary pharmaceuticals from soil properties. Environ. Toxicol. Chem. 25, 933–941. https://doi.org/10.1897/05-229R.1. Lei, Ka-Hou, Lai, Hong-Thih, 2018. Effects of sunlight, microbial activity, and temperature on the declines of antibiotic lincomycin in freshwater and saline aquaculture pond waters and sediments. Environ. Sci. Pollut. Res. https://doi.org/10.1007/ s11356-018-3006-y.

10

Marine Pollution Bulletin 151 (2020) 110810

F. Li, et al.

1016/j.chemosphere.2018.07.162. Zhou, Li-Jun, Ying, Guang-Guo, Liu, Shan, Zhao, Jian-Liang, Chen, Feng, Zhang, RuiQuan, Peng, Fu-Qiang, Zhang, Qian-Qian, 2012. Simultaneous determination of human and veterinary antibiotics in various environmental matrices by rapid resolution liquid chromatography–electrospray ionization tandem mass spectrometry. J. Chromatogr. A 1244, 123–138. https://doi.org/10.1016/j.chroma.2012.04.076. Zhou, S., Paolo, C.D., Wu, X., Shao, Y., Seiler, T.-B., Hollert, H., 2019. Optimization of screening-level risk assessment and priority selection of emerging pollutants-the case of pharmaceuticals in European surface waters. Environ. Int. 128, 1–10. https://doi. org/10.1016/j.envint.2019.04.034. Zou, S.C., Xu, W.H., Zhang, R.J., Tang, J.H., Chen, Y.J., Zhang, G., 2011. Occurrence and distribution of antibiotics in coastal water of the Bohai Bay, China: impacts of river discharge and aquaculture activities. Environ. Pollut. 159, 2913–2920. https://doi. org/10.1016/j.envpol.2011.04.037.

Occurrences of three classes of antibiotics in a natural river basin: association with antibiotic resistant Escherichia coli. Environ. Sci. Technol. 48, 14317–14325. https:// doi.org/10.1021/es503700j. Zhang, Q.Q., Ying, G.G., Pan, C.G., Liu, Y.S., Zhao, J.L., 2015. Comprehensive evaluation of antibiotics emission and fate in the river basins of China: source analysis, multimedia modeling, and linkage to bacterial resistance. Environ. Sci. Technol. 49, 6772–6782. https://doi.org/10.1021/acs.est.5b00729. Zhang, R., Zhang, R., Yu, K., Wang, Y., Huang, X., Pei, J., Wei, C., Pan, Z., Qin, Z., Zhang, G., 2018. Occurrence, sources and transport of antibiotics in the surface water of coral reef regions in the South China Sea: potential risk to coral growth. Environ. Pollut. 232, 450–457. https://doi.org/10.1016/j.envpol.2017.09.064. Zhao, Fangkai, Chen, Liding, Yang, Lei, Li, Shoujuan, Sun, Long, Yu, Xinwei, 2018. Distribution, dynamics and determinants of antibiotics in soils in a peri-urban area of Yangtze River Delta, Eastern China. Chemosphere 211, 261–270. https://doi.org/10.

11