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Article

Occurrence, Comparison and Priority Identification of Antibiotics in Surface Water and Sediment in Urbanized River: A Case Study of Suzhou Creek in Shanghai

by
Xuhui Li
,
Yuan Yuan
,
Dou Zhang
,
Xiao Li
,
Dehuan Li
and
Xiangrong Wang
*
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(14), 8757; https://doi.org/10.3390/su14148757
Submission received: 1 June 2022 / Revised: 13 July 2022 / Accepted: 16 July 2022 / Published: 18 July 2022
(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)
Browse Figures
Versions Notes

Abstract

:
Antibiotics in water have attracted increasing attention due to their potential threat to aquatic ecosystems and public health. Most previous studies have focused on heavily polluted environments, while ignoring urbanized rivers with high population density. Taking Suzhou Creek in Shanghai as an example, this study attempted to explore the antibiotic pollution characteristics of typical urbanized rivers. Further, it screened out priority antibiotics so as to provide reference for the regular monitoring of antibiotics in urban surface water in the study’s later stage. Four classes of 27 antibiotics in surface water samples and sediment samples were detected and analyzed by SPE-UPLC-MS/MS under both wet season and dry season. Results demonstrate that the total amount of antibiotics detected reached 1936.9 ng/L and 337.3 ng/g in water samples and sediment samples, respectively. Through Pearson correlation analysis, it can be shown that there is a very significant correlation between a variety of antibiotics in water and sediment. The results of ecological risk assessment based on risk quotient (RQ) show that certain antibiotics presented high and medium risk to the surrounding ecosystem. Finally, the priority antibiotics selected by optimized priority screening method were EM, SPD, CLR and RTM. Therefore, we have proven that the antibiotics being discharged in urbanized rivers show different types of antibiotics, while presenting a toxicological risk to certain species.

1. Introduction

Antibiotics are widely used by humans in their daily lives, as well as in crops and animal medicine to treat diseases and promote growth [1]. Between 2000 and 2015, Antibiotic consumption, expressed in defined daily doses (DDD), increased 65% in 76 countries [2]. In 2013, the use of antibiotics in China was 162,000 tons, this figure is more than nine times that of the United States and more than 150 times that of the United Kingdom in the same year [3]. Antibiotics cannot be completely absorbed or utilized by the receptor, 10–90% of antibiotics may be excreted in the form of identical active compounds into the water environment and sediments [4]. Antibiotics will be released into natural aquatic systems in different ways, such as sewage treatment plant discharge, pharmaceutical wastewater and domestic sewage [5,6]. Discharged antibiotics have chemical stability and bioaccumulation ability, which can make them exist and accumulate in surface water and sediment for a long time, and then pose significant risks to aquatic organisms, human health and ecological environments [7]. Studies have discussed the antibiotic residue level in coral reef fishes [8], vegetables [9] and drinking water [10]. Moreover, ARGs can also be horizontally transferred and amplified in the environment through genetic mechanisms under certain conditions [11].
Many studies have focused on the pollution characteristics of antibiotics around the world [9,12,13,14,15,16]. China is the largest country in the production and consumption of antibiotics, its emissions and potential risks have led to multiple studies in different environmental zones in China, including reservoirs [17], aquaculture farms [18], oceans [19], coastlines [20], lakes [21], upstream and downstream of sewage treatment plants [22], rivers [23], sediments [24] and topsoil of first tier cities [25]. Previous studies have mostly taken clearly polluted sewage receiving water bodies as their research objects to analyze the occurrence and ecological risk of this pollution. However, there are few studies about urbanized rivers, which are densely populated and ecologically fragile, while even fewer people rank antibiotics in regard to the above areas. Rapid urbanization and population increase mean more residents and more drug abuse, which leads to a lot of pollution of urbanized rivers and brings more potential risks to the public [26,27]. Therefore, it is necessary to study antibiotic pollution in urbanized rivers.
Suzhou Creek is 53 km long, and is located in the Yangtze River Delta (TRD). YRD is an area with one of the most developed economies, the highest population density and the most serious antibiotic pollution in China [28]. As a typical urbanized river, Suzhou Creek connects Taihu Lake, Huangpu River and the East China Sea which has a developed marine aquaculture. Its water environment not only affects aquatic organisms and radiated residents, but also makes the greatest contribution to the antibiotic pollution of Huangpu River [29]. The population density of Shanghai is about 3627 people/km2 while Suzhou Creek passes through the downtown area. Therefore, extensive and intensive human activities produce a large number of clinical antibiotics, which may dominate the antibiotic pollution in Suzhou Creek, resulting in an extremely significant impact [24,30], and its water pollution has become the main pressure affecting ecologically fragile coastal areas [31]. In comparison with other regions, Suzhou Creek covers a smaller area with a considerably larger population density. This provides an excellent opportunity to study the antibiotics distribution in urbanized areas.
Water environments are the main source of antibiotic pollution [28]. However, most previous studies have only detected the occurrence of antibiotics in surface water during certain seasons. Due to poor sediment mobility and hypoxia, it is difficult for antibiotics adsorbed on sediments to migrate further [32], while some antibiotics will desorb and enter the water again, and some antibiotics will combine or decompose, synthesize maternal antibiotics, and even produce more toxic products [33]. Therefore, simultaneously studying the occurrence and ecological risk level of antibiotics in sediment and surface water in different seasons has important theoretical significance for understanding the migration, transformation, risk management and control of antibiotics in complex environments. The detection requires efficient pre-treatment, precision instruments and professional analysis technology. Moreover, quantifying antibiotics is very challenging, and not only takes a long time, but also requires great expenses. Therefore, it is necessary to sort the antibiotics detected in the environment and screen out the priority monitoring antibiotics. Existing antibiotic priority screening methods either focus on the ecological risk [34,35], or their steps are cumbersome and their methods difficult to implement [36]. The high requirements for data sources and the incomplete consideration of indicators have led to the failure of systematic and convenient antibiotic screening. Therefore, a simple and efficient screening method is urgently needed.
Under both wet season and dry season, 27 typical antibiotics from four types of antibiotic were selected as target pollutants for sampling and analysis in this study according to previous studies in Shanghai [10,37,38], and finally the concentration data of 14 antibiotics were obtained through SPE-UPLC-MS/MS, including fluoroquinolones (QNs), sulfonamides (SAs) and macrolides (MCs), which were the most widely used antibiotics in China [39]. This study intends to solve three problems: (1) the occurrence status of antibiotics in surface water and sediment in Suzhou Creek under different seasons and compare it with other study areas in the world; (2) the distribution of antibiotics in surface water and sediment and its ecological risk assessment; and (3) determination of the identification priority antibiotics in Suzhou Creek using an optimized priority screening method.

2. Materials and Methods

2.1. Sample Collection

Surface water samples and sediment samples were collected in both May (wet season) in 2021 and January (dry season) in 2022 at 21 river sampling sites (P1–P21) in Shanghai, China, as Figure 1 shows. Both samples were collected at the same sampling site. The selection of sampling sites mainly refers to previous studies in Shanghai and the main tributaries, at the same time, the sampling sites are distributed in the downstream of industrial land, residential areas and agricultural land. The distance of each sampling site varies from 1 km to 5 km. Suzhou Creek passes through the city center, and the river radiation area has a high population density. The specific coordinates of sampling points are shown in Supplementary Table S1. All water samples were collected about 0.5 m below the water surface with a cylindrical sampler and stored in a pre-cleaned brown glass bottle (2.5 L) with screw cap. Surface water samples were stored at 4 °C. A grab sampler was used to collect sediment samples, and polyethylene plastic bags were used to store sediment samples. Water samples were transported to the laboratory with sediment samples within 24 h for analysis. The concentrations of all detected antibiotics in surface water were in ng/L level, the concentrations of all detected antibiotics in sediment were in ng/g level.

2.2. Chemicals and Reagents

Twenty-seven typical antibiotics were screened, including 11 SAs (sulfamethoxazole (SMX), sulfisoxazole (SIZ), sulfadiazine (SDZ), sulfamerazine (SMR), sulfachloropyridazine (SCP), sulfathiazole (SAZ), trimethoprim (TMP), sulfapyridine (SPD), sulfamonomethoxine (SMM), sulfadimethoxypyrimidine (SMM), sulfamethazine (SMZ)), 6 MCs (erythrocin (EM), roxithromycin (RTM), clarithromycin (CLR), azithromycin (AZM), tylosin (TYL), spiramycin (SPI)), 6 QNs (norfloxacin (NOR), enrofloxacin (ENR), ciprofloxacin (CIP), difloxacin (DIF), pefloxacin (PEF), ofloxacin (OFL)), 4 TCs (oxytetracycline (OTC), chlorotetracycline (CTC), tetracycline (TC), and doxycycline (DOX)).
In the experiment, HPLC-grade acetonitrile was acquired from Thermofisher (Waltham, MA, USA), LCMSMS-grade formic acid from Sigma-Aldrich (Shanghai, China), ultrapure water from Millipore (Burlington, MA, USA), and the filler of solid phase extraction column was copolymer of divinylbenzene and N-vinyl pyrrolidone (HLB, 500 mg/6 mL, Waters, Milford, MA, USA).

2.3. Sample Pretreatment

Appropriate amounts of Na2HPO4, Na2EDTA and citric acid were weighed to prepare, respectively, 0.2 mol/L solution, mixed citric acid solution with Na2HPO4 solution at 8:5 (V/V) to prepare McIlvaine solution, and then mixed McIlvaine and Na2EDTA solution at 1:1 (V/V) to prepare 0.1 mol/L EDTA-McIlvaine extraction solution, which was adjusted to pH 3.0 with H3PO4. For Acetonitrile, about 0.1% H3PO4 was added and adjusted to pH 3.0. After freeze-drying, the sediment sample was ground through a 2 mm sieve, 1 g of the ground sample was added to 10 mL EDTA-McIlvaine buffer, vortexed and mixed for 30 s, ultrasonic for 10 min, centrifugation at 12,000 R/min for 3 min. The supernatant was transferred to another container for repeated extraction of the residue with organic mixed extract. The extracts were combined, diluted with ultrapure water to 200 mL, and passed through 0.5 mL 45 μm fiber filter membrane, adjusted to pH 3.0 with H3PO4.
The solid phase extraction column with 10 mL methanol and 10 mL ultrapure water was activated to ensure the infiltration of the column head. The extract passed through a solid phase extraction column under the action of natural gravity and was pumped dry by vacuum pump. After the column was dried, the column was eluted with 10 mL 2% (volume fraction) of ammonia and methanol at a flow rate of about 3 mL/min (about 1 drop/s). The eluent was collected, blown via nitrogen in a 35 °C water bath until nearly dry, the volume fixed to 1 mL with acetonitrile/water (V/V = 10/90), ultrasonic or vortexed until completely dissolved and passed through 0.22 μm organic filter membrane before being transferred to brown 2 mL sample bottle for test. A blank test was conducted according to the same operation method.

2.4. Analytical Methods

In this study, the target antibiotics were analyzed by ultra-high performance liquid chromatography triple quadrupole mass spectrometry. The sample was on C18 column (particle size 1.7 μm, with a column of 100 mm length and an inner diameter of 2.1 mm) and was separated at a constant temperature of 40 °C. The flow rate was 0.4 mL/min and the injection volume was 10 μL. Mobile phase A was 5 mmol/L ammonium formate-0.1% formic acid and mobile phase B is 5 mmol/L ammonium formate-0.1% formic acid acetonitrile. The specific gradient elution conditions of liquid chromatography are recorded in Supplementary Table S2 in the attachment.
Mass spectrometry includes electrospray ionization (ESI), capillary voltage of 2.5 KV, ion source temperature of 150 °C, desolvent temperature of 600 °C, desolvent gas flow of 1000 L/h, cone hole gas flow of 50 L/h, collision gas of argon, desolvent gas of N2, mobile phase a of 5 mmol/L ammonium formate-0.1% formic acid acetonitrile, mobile phase B of 5 mmol/L ammonium formate-0.1% formic acid-water. The elution method was gradient elution. 0.0–0.5 min, 5% A, 0.5–5 min, 5–20% A, 5–8 min, 20–90% A, 8–10 min, 90% A, 10–13 min, 5% A. Supplementary Table S3 shows specific monitoring ion pair information and other information.

2.5. Quality Analysis and Control

An appropriate amount of standard was accurately weighed and put into a 1 mg/mL standard solution with acetonitrile, in which quinolones were dissolved in acetonitrile and a small amount of 0.1% formic acid water. The standard solutions were accurately absorbed and diluted with acetonitrile into a 1 mg/L mixed standard solution, and then stored at 4 °C. The mixed standard solutions were accurately absorbed and prepared with the initial mobile phase, with concentrations of 0.005 μg/L, 0.01 μg/L, 0.05 μg/L, 0.1 μg/L, 0.5 μg/L, 1 μg/L, 5 μg/L, 10 μg/L series of standard solutions.
The prepared series of standard solutions were taken and determined for both the quantitative ion peak area and concentration for linear regression analysis. During the experiment, one laboratory blank and solvent blank were analyzed in every 10 samples as controls, and the spiked recovery was determined. The target antibiotics in each blank sample were lower than the detection limit. Analysis of field blank and procedure blank showed that there was no pollution in the extraction and sampling process. Six samples with different concentrations were prepared after adding standard solution. After pretreatment, the recovery and relative standard deviation (RSD) of six parallel determinations were calculated. The results show that the relative standard deviation ranged from 0.672% to 15.5%, and the recovery ranged from 56.2% to 128.0%.

2.6. Pseudo Distribution Coefficient

The resistance genes may be influenced by antibiotics accumulated in sediment, so it is very important to quantify the distribution behavior of antibiotics between water and sediment. Although there is no balance between the content of antibiotics in water and sediment in aquatic ecosystems, the pseudo distribution coefficient (Kd) is still an important index to indicate the distribution behavior of antibiotics affected by all environmental factors in a water–sediment system. The Kd value is calculated according to the formula: Kd = Cs/Cw, where Cs represents the antibiotic concentration in sediments, Cw represents the antibiotic concentration in surface water.

2.7. Environmental Risk Assessment

Some antibiotics widely exist in the water environment of Shanghai [38] with different levels of pollution. According to the European technical guidance document on risk assessment, the environmental risk of antibiotics in aquatic environment was assessed based on risk quotient [34,35,40]. In order to evaluate the environmental risk of antibiotics, the following formula was used to calculate the risk quotient (RQ) for evaluation, including the use of the data of freshwater algae (phototrophic level), Daphnia magna (invertebrates) and fish (vertebrates).
RQ = MEC/PNEC
PNEC = (EC50 or LC50 or NOEC)/AF
where MEC is the measured environmental concentration and PNEC is the predicted no-effect concentration. EC50(LC50) is the median effective or lethal concentrations obtained from the existing studies and ECOSAR database, and no observed effect concentration (NOEC) represents chronic toxicity [41]. When more than one toxicity dataset is obtained at the same nutritional level, the dataset with the strongest effect is used based on the worst-case consideration. AF is the safety factor of acute toxicity and chronic toxicity, which are 1000 and 100, respectively [42]. Generally, The RQ values were classified into four risk levels, including insignificant risk(RQ < 0.01), low risk(0.01 < RQ < 0.1), median risk(0.1 < RQ < 1), and high risk(RQ > 1) [43].

2.8. Priority Antibiotics Screening Method

In order to comprehensively determine the priority types of antibiotics to be monitored, on the basis of the identification method which is easy to implement and combines the occurrence of antibiotics with ecological risk [29,44], the screening conditions are added and improved, including the following four conditions.
(1)
High frequency of detection (>80%)
The high detection frequency indicates that the antibiotic is representative and universal in the whole study area with greater potential harm to aquatic organisms. High frequency of detection was considered if levels were over 80%.
(2)
Strong positive correlation with total antibiotic concentration (SUM) (p < 0.05) or high concentration antibiotics
If there is a strong positive correlation between an antibiotic and sum, such antibiotic will have a strong indication of the total antibiotic concentration. Pearson correlation coefficient (R) between single antibiotic concentration and total antibiotic concentration was calculated. Only a robust (r > 0.6) and statistically significant (p < 0.05) correlation will be considered as strong correlation. The ratio of each antibiotic concentration to the total antibiotic concentration was calculated, and then the average value was obtained. Antibiotic with a proportion greater than the average is defined as high concentration antibiotic.
(3)
Significant risk of acute or chronic toxicity to aquatic organisms (RQ > 0.01)
This restriction mainly considers the acute or chronic ecological risks to aquatic animals and plants. When the RQ value is greater than or equal to 0.01, the antibiotic is selected, which indicates that the antibiotic studied may cause potential adverse effects on aquatic organisms in the short or long term. Antibiotics are considered to meet constraints as long as they have acute or chronic ecological risks.
(4)
Detection frequency is over 30% in sediment
This constraint is designed to give priority to the monitoring of antibiotics with certain detection frequency in sediment. Any antibiotic with 30% detection frequency or more would be selected. Adding this restriction allows us to take into account the enrichment of antibiotics in the sediment environment, and then more accurately screen out antibiotics with great potential pollution hazards.
Antibiotics meeting the above four constraints will be selected as priority antibiotics, and regular monitoring is recommended.

3. Results and Discussion

3.1. Occurrence of Selected Antibiotics

3.1.1. Antibiotics in Surface Water

The histogram of target antibiotic concentration in surface water in each sampling site of Suzhou Creek is shown in Figure 2. The detected antibiotics in study area belong to SAs, QNs and MCs classes. The occurrence of various antibiotics in aquatic systems will be affected by the chemical stability and distribution characteristics brought by their special structure. SAs have high solubility and chemical stability in water [45], while MCs are easy to be adsorbed by sediments [46]. QNs are easily photodegradable [47]. Tetracyclines (TCs) are widely used to protect animal health, are added to animal feed at sub therapeutic levels to prevent infection and act as growth promoters [48]. TCs are the most widely used veterinary drugs and food additives in aquaculture and animal husbandry in China. There is no large-scale aquaculture and animal husbandry in the center of Shanghai, so this may be one of the reasons why relevant types of antibiotics are not detected in this study. This also indicates that the main sewage types in the study area do not include aquaculture wastewater. The concentration was relatively constant, meanwhile, there was little difference in antibiotic composition and distribution at different sampling points.
The detection frequency of antibiotics in the study area is summarized in Table 1. Twenty-seven antibiotics were under detection, 12 antibiotics with different detection rates were detected in water samples. The total concentrations of antibiotics detected in surface water in the two seasons were 425.2 ng/L and 1734.7 ng/L. The detection frequency of antibiotics was 5–100%. SPD was the main antibiotic detected, and the detection rate was 100%. There was a large gap of concentration between SPD and other detected antibiotics, followed by RTM, CLR and SMX. Similarly, the detection rate was also 100% in the sampling area. The detection concentrations of OFL, TMP and SMM were the lowest, and the range of the detection rate was 35–100%. The concentration of antibiotics detected in the dry season was generally higher than in the wet season, and five kinds of antibiotics, such as SDZ, SMR, SCP, EM and AZM, were detected, in which the concentration of EM was the highest among all antibiotics.
The heavy precipitation and the strong fluidity of the river in the wet season will dilute the antibiotics in the river, thus reducing the antibiotic concentration there [49]. The high temperature and strong light in May accelerated the degradation of antibiotics in water [50]. Some antibiotics are concentrated in the sediments, and, because little light reaches these sediments it is not easy for photodegradation to occur, resulting in the accumulation of some antibiotics there [18]. Finally, the demand for drugs in winter is generally higher than in summer, which leads to a higher level of antibiotic pollution. Among them, SMX and SPD, which are often used as clinical drugs, have higher concentrations in winter, and EM, which is often used as a household drug, is detected in winter and shows extremely high pollution concentrations. Therefore, compared with the dry season, the wet season has lower antibiotic concentration and fewer antibiotic species [51,52].
Sulfonamide antibiotics and macrolide antibiotics were the most abundant antibiotics in the detection results of surface water, accounting for 99.9% of the total antibiotic concentration. SAs have high water solubility and chemical stability [53], so these antibiotics are very easy to retain and diffuse in water. As shown in Figure 2, the largest concentration of antibiotics detected in the surface water was SPD. SPD is mainly used to treat human diseases and belongs to the SA class. SMX is one of the 14 SAs required to be tested preferentially by the U.S. Food and Drug Administration. SMX has been widely detected in many surface water environments in China [54]. TMP is a kind of SA synergist, which is often used in combination with SA antibiotics to enhance their therapeutic effect. It can be seen from this detection data that the concentration distribution trend of these two antibiotics was consistent, so they were frequently detected in Suzhou Creek. TCs were not detected in this study, maybe because of their strong chelating agent, their low solubility and stability in water [55], and how prone they are to photodegradation [56]. Similarly, the detection frequency and concentration of TCs were low according to previous studies on Shanghai [29].
Quinolone antibiotics, which are easily photolyzed, are widely used in China because of their affordable price and their lack of cross resistance with other drugs [57], OFL was the only quinolone antibiotic detected in this study, with an average concentration of 0.32 ng/L. It has been detected in many surface water environments, such as the rivers in Zhuhai [34], where the concentration level was 2–3 orders of magnitude higher than the results of this study. MCs are often used as clinical drugs and pet drugs, at the same time, they are the third most dominant antibiotics in water [58]. In this study, four major macrolide antibiotics RTM, CLR, AZM and EM were detected in surface water. These four antibiotics are widely used all over the world. For example, CLR is one of the most commonly detected antibiotics in some developed countries in Europe [59]. In this study, the detection concentration and frequency of macrolide antibiotics are generally average, but are the lowest compared with other study areas [54]. Among these, the detection concentration of EM was higher than that of all other antibiotics, and its average value reached 51.9 ng/L. This is because this kind of antibiotic is a typical representative of the same type of antibiotic, which is widely used because of its easy availability, low price and wide application range.

3.1.2. Antibiotics in Sediment

Eight antibiotics were detected in sediment (Figure 3). The detection frequency of antibiotics in sediment was 6–100% (Table 2). AZM had the highest detection concentration in the two phases, and the detection rate was 100%. EM had a significantly increased detection concentration in the dry season. There was a large gap between the two antibiotics and other detection antibiotics. The concentrations of SPD, RTM and CLR were the second, and these three antibiotics had very high detection frequency. The detection concentrations of SMZ, STZ and SMX were the lowest, and the detection rate was between 6–47%. These three kinds of antibiotics were not detected in the dry season. SAs and MCs were the most abundant antibiotics in the results, accounting for 50% of the total antibiotics in sediment samples respectively. Similar to the results of surface water samples, EM, SMX, SPD, CLR and RTM in these two classes of antibiotics were also detected in sediments, which indicate that these antibiotics were widely used in the study area. In this study, the antibiotic pollution level in sediment did not change significantly with seasons, which was different from the general pattern because the concentration distribution of antibiotics in the sediment environment of urbanized rivers was not easily affected by seasonal changes. At the same time, the sediment dredging project was carried out constantly in the study area in dry season, which eventually led to the inconsistency between this study and the general pattern.
MC, as an important type of clinical drug, has been reported to mainly exist as a cation and adsorbed on solid particles owing to its high Kow [60,61]. It can eventually remain stable in sediment [58], something which can explain their high detection frequency there in sediment environments. On the contrary, SA showed a lower concentration due to its strong hydrophilicity. AZM was the antibiotic with the largest concentration in sediment, and AZM belongs to the same type of antibiotic as MCs. STZ and EM detected in sediment belongs to antibiotics banned by China’s aquaculture industry in aquatic food and bait animals. The commonly used antibiotics in aquaculture belong to TCs and QNs [62], and these antibiotics have strong adsorption on sediments and particles [63]. These antibiotics were not detected in sediment in the study area. It can be seen that the type of sewage source in the study area is very likely to exclude aquaculture wastewater.

3.1.3. Comparison of Antibiotics in Different Studies

Figure 4 and Table S4 show the concentration comparison of various selected antibiotics in some surface water environments at home and abroad. In order to obtain more comparative data, this study selected the data measured in wet season for comparison. Specifically, 7 antibiotics including OFL, SMX, TMP, SPD, SMM, RTM and CLR were involved. The other concentrations of antibiotics were either collected from the studies or calculated using the data provided in the studies (Supplementary Table S4). The concentrations those studies reported as ‘not detected’ or ‘below detection limit’ were assigned not detected [64]. As an urbanized river, Suzhou Creek receives a smaller amount of sewerage from husbandry and aquaculture, and there are relatively few pollution sources. Moreover, the water body of the river has large fluidity and a strong dilution effect. Therefore, compared with other domestic rivers, lakes and other surface water bodies, the antibiotic pollution concentration in Suzhou Creek is relatively low. Overall, the relatively different concentration levels indicate that the frequency, quantity and treatment level of the same drug are different in different regions.
Antibiotic abuse is most common in developing countries, such as isakavagu nakkavagu in India [65], which is located in Hyderabad, one of the world’s largest hubs for the bulk drug industry. OFL concentration detected in this river reaches 10,000 ng/L. The BTH region is the capital economic circle of China, with a large population density and rapid economic development. OFL with a concentration of 11,735 ng/L was measured in Wangyang River in this region [62]. The PRD is the largest urban agglomeration with population and area in the world. Quite high concentrations of OFL were also detected in Hong Kong [66] and Pearl River [34], the antibiotic concentrations in both regions were 3–4 orders of magnitude higher than those in this study. Compared with the Huangpu River in Shanghai [38] which also belongs to the YRD, the detected concentration of OFL in this study was low and one order of magnitude less than that in Huangpu River [22].
Compared with other river basins in China, the concentrations of SA and MC in the study area were generally 1–2 orders of magnitude less, and the pollution level was similar to that of many rivers in Japan [67]. SMM was detected only in some areas [68]. The concentrations of SMX, SPD and SMM in the study area were low in general, similar to the Yellow River [69], Barigui River [13] and La Poudre River [70]. Barigui River, which is located in Curitiba, one of the most developed and populous cities in Brazil, has similar characteristics with the study area. SMX has the highest detection rate of all the above surface water areas, and is selected as the representative antibiotic of spatial variation of antibiotics in China [28]. Haihe River [71] and Wangyang River [62] located in the above densely populated region BTH have detected high concentrations of SMX. Antibiotic resistance can be produced when antibiotics reach a concentration of 100 ng/L, while the antibiotic concentration detected in the above rivers is as high as 1000 ng/L. Charmoise River belongs to the Seine River basin, which is characterized by a high population density within the Paris conurbation and the basin itself receives large amounts of domestic effluents [72]. The Nairobi river basin in Kenya [73] and the rivers of Durban in South Africa [74] are both located in informal settlements that are characterized by overcrowding and poor sanitation, moreover, the former is exposed to diseases such as HIV/AIDS. The rivers of Kumasi lie in Ghana [9], a low and middle-income country where the public has easy and multiple access to antibiotics. SMX has been detected in all the mentioned watersheds with high concentrations of 1435 ng/L, 13765 ng/L, 2561 ng/L and 2861 ng/L, respectively. TMP is often used with SMX, so it has a relatively high detection rate. TMP with concentrations above 1000 ng/L was detected in Wangyang River [62], Ravi River [75] and Kenya [73].
RTM is one of the most concerning antibiotics [28]. The average concentration of RTM in this study is much lower than that in German [76] and Australian rivers [77], and the concentration range is equivalent to that in Chenhu Lake [78] and Songhua River [79]. The detected concentration of CLR in other watersheds is low, basically at the level of 1–10 ng/L, such as the Jialing River in Chongqing [54] and the Chester River in the United States [16], but the Ter River detected a high concentration of 97 ng/L [80].
Figure 5 and Table S5 list the occurrence of eight antibiotics in other different areas in wet season. Similar to the above-mentioned circumstance, this study selected the data measured in wet season for comparison (Supplementary Table S5). It is worth noting that the classes of antibiotic measured in this study area are SAs and MCs, with low concentrations, similar to aquaculture farms [18], while the main antibiotics in other sediment environments are QNs and TCs [81]. SMX was the most frequently detected antibiotic in SAs. Except for Michigan Lake [40], SMX was detected in other areas, and reached the highest concentration of 118.76 ng/g in Honghu Lake [82] and 115.35 ng/g in Dongting Lake [82]. It can be seen that the content of SMX in lake sediments in China is relatively high [24], while the SMX pollution in river sediments was relatively light, such as at Fenhe River [83] and Liaohe River [84]. SMZ was detected in the topsoil of Beijing and Shanghai [25], and the concentration was close to the results of this study with the same order of magnitude. A high concentration of 59 ng/g was measured in the Haihe River [85].
The detection frequency of MC antibiotics was low, but some studies have detected high concentrations and have shown that these are the main pollutants in sediments [21,23]. For example, 5620 ng/g and 2582 ng/g of RTM were detected in Haihe River [85] and Wangyang River [62], respectively. In contrast, less than 10 ng/g of RTM was detected in this study, similar to the Huangpu River [11] and La Poudre River [70]. It is worth mentioning that the Suzhou Creek flows into the Huangpu River, the antibiotic concentration of Suzhou Creek dominated the antibiotic pollution of Huangpu River to a certain extent [29]. The concentration levels of other MC antibiotics (CLR and AZM) detected in this study were basically consistent with those in Jianghan Plain [86] and Chenhu Lake [78] at a low level.
As Figure 6 shows, compared with the concentration of antibiotics reported in Shanghai, the concentration of antibiotics detected in Suzhou Creek is low, and detected antibiotics have been discussed in previous studies on Shanghai as well. Notably, the concentrations of those antibiotics in water from this study were significantly lower than those reported from previous studies [87,88]. Further, the concentration of these antibiotics showed a decreasing trend, the reason for which may be that the government has carried out the regulation works in relevant river sections in 2020. Compared with other densely populated watersheds, Suzhou Creek shows a low concentration of antibiotic pollution as well [89,90].

3.2. Correlation Analysis and Pseudo Distribution Coefficient

3.2.1. Pearson Correlation Analysis

The Pearson correlation coefficient method was used to analyze the occurrence relationship of antibiotics in both sediment and surface water in Suzhou Creek. As shown in Figure 7, it can be seen that there is a very significant positive correlation between multiple pairs of antibiotics in the water body (p < 0.01), such as SPD and SMX, CLR and RTM, EM and CLR. There is also a very significant positive correlation between many pairs of antibiotics in sediments, such as EM and RTM, CLR and EM, SPD and EM. This suggests that these antibiotics may have the same source and have the same occurrence trajectory [11].

3.2.2. Pseudo Distribution Coefficient

The pseudo distribution coefficient [70], that is, the ratio of the measured concentration in sediments to the corresponding concentration in surface water, is used to describe the dynamic changes of antibiotics in sediment and water [91]. Considering the difference of detection frequency between water and sediment, antibiotics with a detection frequency greater than 50% in both surface water and sediment were selected to calculate Kd value. Four kinds of antibiotics were detected in wet season, and five kinds of antibiotics were detected in dry season.
The Kd value of SMX in Supplementary Table S6 and AZM in Supplementary Table S7 are relatively high, indicating that mentioned antibiotics are easier to adsorb on the sediment. In other words, compared with the aquatic environment, SMX and AZM are easier to affect the ecological balance of the sediment [92]. The Kd values of the rest of the antibiotics are low, which means that they are more likely to exist in surface water. Generally speaking, the difference of Kd can be attributed to the physicochemical properties of antibiotics (e.g., solubility, molecular structure and hydrophobicity) and the properties of surface water and sediment [32,93,94,95].

3.3. Ecological Risk Assessment

The RQ values of most antibiotics in a water body are between 0.01 and 1 [66,96,97], indicating a certain risk to the water environment. Different types of aquatic organisms may be exposed to numerous types of antibiotics in the environment. The acute toxicity of antibiotics was mainly for Daphnia and green algae [42], while Daphnia was more sensitive to chronic toxicity of antibiotics compared to both algae and fish [98,99,100]. Therefore, timely monitoring and accurate risk characterization of these pollutants should be carried out. Considering the worst case, the PNEC value of the most sensitive species in the biological grade is selected to calculate RQ in this study. Specific acute and chronic toxicity data are shown in Supplementary Table S8. The results of this study are consistent with previous research, and shared similar trends as in previous research.
In general, the risk of all antibiotics detected in Suzhou Creek to fish, daphnia and algae was either low or insignificant. Compared with other regions with the same characteristics of high population density and wide antibiotic consumption, the ecological risk posed by antibiotics in Suzhou Creek was obviously at a low level [34,44,85]. Compared with previous studies in the same region, the results were basically consistent [29]. As shown in Figure 8, the acute toxicity of antibiotics in wet season was mainly against algae, while the chronic toxicity was mainly against daphnia. Due to being in different layers of the food chain, algae are very sensitive to antibiotics [44], daphnia has low tolerance to antibiotics [98], and fish are least sensitive to antibiotic toxicity [42]. Specifically, in terms of chronic toxicity, daphnia are more sensitive to SAs. In terms of acute toxicity, algae are more sensitive to MCs. For example, CLR was at median risk and had a detection rate of 100%, while SMX and TMP, belonging to SA, had a relatively low detection rate and low risk.
More kinds and higher concentrations of antibiotics were detected in the dry season, which also led to a more serious ecological risk level than in the wet season (Figure 9). Among these, macrolide antibiotics presented a high level of ecological risk. For example, CLR, which was different from the wet season, presented a median risk to algae, while EM presented a high risk. It was also the only antibiotic with high risk in this study. These kinds of antibiotics are commonly used in clinical and family medicine, so the results of the risk quotient demonstrates that the antibiotic pollution in the study area is composed mainly of human antibiotics and that the harm of human antibiotics varies according to the different types of aquatic organisms.

3.4. Identification of the Priority Antibiotics

Sediment is both the source and sink of antibiotic pollutants. Some antibiotics adsorbed on the sediment are not easily able to migrate further, while other antibiotics will be released into surface water and become part of suspended particulate matter for secondary pollution and diffusion [78]. The sediment plays the role of adsorbing antibiotics between surface water and groundwater. With the increase of water depth, antibiotics will be captured by sediments, with some antibiotics even penetrating into groundwater, causing more serious pollution [78]. In addition, antibiotic residues in sediments can not only reflect their migration regularity, but also reflect the past antibiotic use in the region [101]. Based on previous results [29,35,44], a new screening rule was added under the requirements of easy operation and accuracy, which was based on the occurrence of antibiotics in sediment. The rule takes the detection rate as the screening basis and includes the antibiotics that meet the restrictions into the screening process. SPD and RTM were finally selected as the priority antibiotics for antibiotic pollution monitoring in Suzhou Creek in wet season. Specific screening conditions and screening process are shown in Supplementary Table S9. Finally, SPD, EM and CLR were selected as the priority antibiotics for antibiotic pollution monitoring in Suzhou Creek in dry season. Specific screening conditions and screening process are shown in Supplementary Table S10.
In Europe, spiramycin (SRM) and amoxicillin (AMOX) are included in the list of 12 high ecological risk drugs, and there are six antibiotics among 17 medium ecological risk drugs [35]. According to the ecological risk assessment, Erythromycin(EM), roxithromycin (RTM) and sulfamethoxazole (SMX) have been identified as three priority antibiotics in China’s surface water [5]. Similar to the results of this study, RTM poses median risk to aquatic organisms in Haihe river [85], and RTM is identified as a pollutant of high concern; SPD has been reported as the main antibiotic that brings ecological risk to Poyang Lake [42]. It can be seen that the selected priority antibiotics are representative. It is worth noting that EM and CLR have been added to the observation list of the European Community in the field of water policy [102].

4. Conclusions

Antibiotics are ubiquitous in surface water all over the world, often detected in the range of ng/L and ng/g, and pose a potential threat to the ecological environment. This paper studied the antibiotic pollution status of Suzhou Creek located in a densely populated urban environment. Compared with other typical surface water environments around the world, the pollution level of this area is fairly low, and the main types of antibiotics are SAs and MCs used mostly in clinics and families. Pearson correlation analysis showed that there was a very significant correlation between multiple groups of antibiotics, which means that these antibiotics are likely to share a similar emission trajectory. The results of environment risk assessment show that only EM belongs to high risk level, and the other antibiotics generally belong to median risk, low risk and no risk level.
Taking into account the occurrence in the sediment and using the optimized priority screening methods, CLR and RTM were selected as priority antibiotics in wet season, while SPD, EM and CLR were selected in dry season. Due to the different structure, concentration and use of each antibiotic, it is necessary to continuously monitor them and optimize the priority screening rules in order to adapt to the antibiotic pollution with high sensitivity and rapid change. Though the selected area of this study is small, it may be a starting point, which is helpful to understand the environmental pollution caused by antibiotics in urbanized rivers, to further consider their impact on natural water bodies, and to provide reference for water quality management and antibiotic pollution control in urban areas.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/su14148757/s1, Table S1. Coordinates of sampling sites in Suzhou Creek. Table S2. Gradient elution conditions of liquid chromatography. Table S3. Monitoring ion pairs and collision energy of target compounds. Table S4 Comparison of antibiotic occurrence in surface water at home and abroad. Table S5. Comparison of target antibiotic concentration range in sediment in different regions. Table S6. Pseudo partition coefficient of antibiotics in wet season. Table S7. Pseudo partition coefficient of antibiotics in dry season. Table S8. Summary of acute and toxicity data obtained by ECOSAR and other studies. Table S9. Criteria for the priority antibiotics in Suzhou Creek in wet season. Table S10. Criteria for the priority antibiotics in Suzhou Creek in dry season.

Author Contributions

Conceptualization, X.L. (Xuhui Li) and X.W.; methodology, X.L. (Xuhui Li) and X.W.; software, X.L. (Xuhui Li); validation, X.W.; formal analysis, X.L. (Xuhui Li) and X.W.; investigation, X.L. (Xuhui Li); resources, X.W.; data curation, X.L. (Xuhui Li) and X.W.; writing—original draft preparation, X.L. (Xuhui Li); writing—review and editing, X.L. (Xuhui Li), Y.Y., D.Z., X.L. (Xiao Li), D.L. and X.W.; visualization, X.L. (Xuhui Li); supervision, X.W.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (grant numbers 2016YFC0502705); the National Social Science Major Foundation of China (grant number 14ZDB140), and Major Program of Social Science Foundation of National Education Ministry of China (20JZD058).

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Locations of sampling sites in Suzhou Creek. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge), P18 (Guangfu road), P19 (North-south viaduct), P20 (Fujianlu bridge), P21 (Garden Bridge).
Figure 1. Locations of sampling sites in Suzhou Creek. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge), P18 (Guangfu road), P19 (North-south viaduct), P20 (Fujianlu bridge), P21 (Garden Bridge).
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Figure 2. Concentrations of the antibiotics detected in the surface water samples from the 21 sites in Suzhou Creek: (a) detected antibiotics from each site in wet season; (b) detected antibiotics from each site in dry season. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge), P18 (Guangfu road), P19 (North-south viaduct), P20 (Fujianlu bridge), P21 (Garden Bridge).
Figure 2. Concentrations of the antibiotics detected in the surface water samples from the 21 sites in Suzhou Creek: (a) detected antibiotics from each site in wet season; (b) detected antibiotics from each site in dry season. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge), P18 (Guangfu road), P19 (North-south viaduct), P20 (Fujianlu bridge), P21 (Garden Bridge).
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Figure 3. Concentrations of the antibiotics detected in the sediment samples from the 21 sites in Suzhou Creek: (a) detected antibiotics from each site in wet season; (b) detected antibiotics from each site in dry season. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge), P18 (Guangfu road), P19 (North-south viaduct), P20 (Fujianlu bridge), P21 (Garden Bridge).
Figure 3. Concentrations of the antibiotics detected in the sediment samples from the 21 sites in Suzhou Creek: (a) detected antibiotics from each site in wet season; (b) detected antibiotics from each site in dry season. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge), P18 (Guangfu road), P19 (North-south viaduct), P20 (Fujianlu bridge), P21 (Garden Bridge).
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Figure 4. Comparison of target antibiotics concentrations in surface water in different regions: (a) comparison with domestic research; (b) comparison with abroad research.
Figure 4. Comparison of target antibiotics concentrations in surface water in different regions: (a) comparison with domestic research; (b) comparison with abroad research.
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Figure 5. Comparison of target antibiotic concentrations in sediment in different regions.
Figure 5. Comparison of target antibiotic concentrations in sediment in different regions.
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Figure 6. Comparison of antibiotic concentrations in surface water between this study and previous results from Shanghai.
Figure 6. Comparison of antibiotic concentrations in surface water between this study and previous results from Shanghai.
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Figure 7. Pearson correlation analysis of antibiotic concentrations in surface water and sediment in Suzhou Creek: (a) correlation analysis of antibiotics in wet season; (b) correlation analysis of antibiotics in dry season.
Figure 7. Pearson correlation analysis of antibiotic concentrations in surface water and sediment in Suzhou Creek: (a) correlation analysis of antibiotics in wet season; (b) correlation analysis of antibiotics in dry season.
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Figure 8. Heat map of RQ values of antibiotics in surface water in wet season. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge).
Figure 8. Heat map of RQ values of antibiotics in surface water in wet season. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge).
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Figure 9. Heat map of RQ values of antibiotics in surface water in dry season. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge), P18 (Guangfu road), P19 (North-south viaduct), P20 (Fujianlu bridge), P21 (Garden Bridge).
Figure 9. Heat map of RQ values of antibiotics in surface water in dry season. P1 (Wetland Park), P2 (Fishing center), P3 (Golf Club), P4 (Wusong River Bridge), P5 (Shuangyang port), P6 (Huajiang bridge), P7 (Tongpu road), P8 (Qilianshan bridge), P9 (Sightseeing dock), P10 (Ludinglu bridge), P11 (Gubeilu bridge), P12 (Kaixuanlu bridge), P13 (ECUPL), P14 (Wuninglu bridge), P15 (Zhenpinglu bridge), P16 (Changhualu bridge), P17 (Changshoulu bridge), P18 (Guangfu road), P19 (North-south viaduct), P20 (Fujianlu bridge), P21 (Garden Bridge).
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Table
Table 1. Detection frequency and concentration of antibiotics in surface water samples.
Table 1. Detection frequency and concentration of antibiotics in surface water samples.
CompoundsWet Season (ng/L)Dry Season (ng/L)
Freq.MedianMeanMaxFreq.MedianMeanMax
OFL82%0.30.40.4
SMX100%0.20.20.3100%2.42.42.9
TMP35%0.10.10.1100%0.30.30.4
SPD100%7.98.410.4100%21.822.630.6
SMM82%0.10.10.1100%0.50.50.6
RTM100%1.71.72.1100%1.51.73.7
CLR100%1.31.31.7100%1.11.22.3
SDZ 100%0.50.60.9
SMR 5%000.4
SCP 100%0.40.40.6
EM 100%52.951.969.5
AZM 71%1.31.01.9
Table
Table 2. Detection frequency and concentration of antibiotics in sediment samples.
Table 2. Detection frequency and concentration of antibiotics in sediment samples.
CompoundsWet Season (ng/g)Dry Season (ng/g)
Freq.MedianMeanMaxFreq.MedianMeanMax
EM53%0.10.10.386%1.42.17.8
RTM100%0.40.51.795%0.30.51.4
CLR100%0.50.51.795%0.30.51.9
AZM100%9.79.819.9100%1.82.74.5
SPD100%1.41.84.738%00.10.5
SMX47%00.32.7
SMZ18%000.2
STZ6%000.3
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Li, X.; Yuan, Y.; Zhang, D.; Li, X.; Li, D.; Wang, X. Occurrence, Comparison and Priority Identification of Antibiotics in Surface Water and Sediment in Urbanized River: A Case Study of Suzhou Creek in Shanghai. Sustainability 2022, 14, 8757. https://doi.org/10.3390/su14148757

AMA Style

Li X, Yuan Y, Zhang D, Li X, Li D, Wang X. Occurrence, Comparison and Priority Identification of Antibiotics in Surface Water and Sediment in Urbanized River: A Case Study of Suzhou Creek in Shanghai. Sustainability. 2022; 14(14):8757. https://doi.org/10.3390/su14148757

Chicago/Turabian Style

Li, Xuhui, Yuan Yuan, Dou Zhang, Xiao Li, Dehuan Li, and Xiangrong Wang. 2022. "Occurrence, Comparison and Priority Identification of Antibiotics in Surface Water and Sediment in Urbanized River: A Case Study of Suzhou Creek in Shanghai" Sustainability 14, no. 14: 8757. https://doi.org/10.3390/su14148757

APA Style

Li, X., Yuan, Y., Zhang, D., Li, X., Li, D., & Wang, X. (2022). Occurrence, Comparison and Priority Identification of Antibiotics in Surface Water and Sediment in Urbanized River: A Case Study of Suzhou Creek in Shanghai. Sustainability, 14(14), 8757. https://doi.org/10.3390/su14148757

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