Science of the Total Environment 476–477 (2014) 266–275

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Seasonal variation and sediment–water exchange of antibiotics in a shallower large lake in North China Dengmiao Cheng, Xinhui Liu ⁎, Liang Wang, Wenwen Gong, Guannan Liu, Wenjun Fu, Ming Cheng State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Antibiotics in different environmental compartments were monitored seasonally. • Antibiotics showed significant seasonal variations in water and sediment matrix. • Sediments may act as a second source of antibiotics to aquatic environment.

a r t i c l e

i n f o

Article history: Received 22 July 2013 Received in revised form 5 January 2014 Accepted 5 January 2014 Available online 25 January 2014 Keywords: Antibiotics Seasonal variation Sediment–water exchange Baiyangdian Lake

a b s t r a c t The occurrence of four antibiotics, including oxytetracycline (OTC), tetracycline (TC), norfloxacin (NOR) and ofloxacin (OFL), in surface water, overlying water, pore water and sediment samples were studied in the Baiyangdian Lake from February to November in 2009. The total concentrations of these antibiotics ranged among 17.73–281.82, 22.98–258.45, 22.43–198.95 ng L−1 and 131.65–750.27 ng g−1 in surface water, overlying water, pore water and sediments, respectively. Seasonal variation might be impacted by the frequency of different pattern of antibiotics and the water temperatures of different seasons, where the higher concentrations appeared at different seasons. In addition, the regions with significant sewage discharge or human agricultural activities exhibited high concentrations of antibiotics in water and sediments. The highest accumulation rates of the four antibiotics ranged from 11.27 to 29.71%, which indicated that these compounds exhibited strong adsorption to the sediment. However, higher concentrations of antibiotics in pore water and even overlying water may result in the release of these compounds from the sediment acting as a secondary contaminant source in a certain time period, especially for TC. The pseudo-partitioning values of fluoroquinolones (FQs) ranged from 4493 to 47,093 L kg−1 and were much higher than those of tetracyclines (TCs), which ranged from 277 to 1880 L kg−1 indicating that the FQs are prone to accumulation in the sediment. © 2014 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Corresponding author at: School of Environment, Beijing Normal University, Beijing 100875, China. Tel./fax: +86 10 58802996. E-mail address: [email protected] (X. Liu). 0048-9697/$ – see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.scitotenv.2014.01.010

Antibiotics as ionic organic contaminants (IOCs) have been widely used for several decades in both humans and livestock (Kümmerer, 2009). The consumption has gradually increased due to global

D. Cheng et al. / Science of the Total Environment 476–477 (2014) 266–275

economic development. For China, it has been estimated that more than 25,000 tons of antibiotics are used each year (Gao et al., 2012), and except for disease treatment, a large portion of these antibiotics were used by livestock producers (Mellon et al., 2001). Several types of antibiotics have been detected in the aquatic environment, especially fluoroquinolones (FQs) and tetracyclines (TCs) (Leung et al., 2012; Li et al., 2012; Shimizu et al., 2013; Zou et al., 2011). These two compounds are frequently detected in both surface water and sediment and exhibit relatively high pseudo-partitioning coefficients that are calculated from the antibiotic concentrations in sediment divided by the corresponding concentrations in surface water compared to sulfonamides (SAs) and macrolides (MCs) (Gong et al., 2012; Kim and Carlson, 2007; Li et al., 2012; Liang et al., 2013; Zhang et al., 2011). The environmental effects caused by the widespread use of antibiotics have resulted in much attention due to their consumption and excretion. These antibiotics and their corresponding metabolites are released to the environment after consumption by humans and livestock (McArdell et al., 2003). For antibiotics used to treat humans, previous studies have shown that sewage treatment plants (STPs) are the main component of discharges to the environment, due to the partial elimination during the purification process (Chang et al., 2010; Jia et al., 2012). When the manure is applied to agricultural fields, the soil may be contaminated by livestock treated with antibiotics, and overflow and drain flow can pollute rivers, streams and ditches (Jacobsen et al., 2004; Wei et al., 2011). In addition, the usage of antibiotics in aquaculture is another important source of antibiotics in the environment (Lalumera et al., 2004). Currently, antibiotic resistance in bacteria has become a new challenge for infection control worldwide (Wellington et al., 2013). Antibiotics were initially designed to produce biological effects and interact with specific biological systems. However, the continual exposure of the bacterial community, even at small concentrations of antibiotics or active metabolites, could lead to the emergence or persistence of antibiotic-resistant bacteria in the natural aquatic environment, which poses a potential threat to human health (Schwartz et al., 2003). In the long term, humans will be threatened by infections caused by exposure to bacteria (Wellington et al., 2013). In addition, a variety of antibiotics have been detected in the sediment matrix which is an important habitat for bacteria and sequesters more pollutants (Kim and Carlson, 2007; Li et al., 2012). These results also indicate that sediment can potentially act as a significant secondary source of antibiotics that can be released into the water if the aquatic environment changes. Many studies have indicated that changes in the aquatic environment (e.g., water volume, flow and physicochemical properties such as water temperature and pH) substantially contribute to the ad/desorption behaviour of pharmaceuticals and other contaminants in sediment (Gong et al., 2012; Kümmerer, 2009; Rosen et al., 2010; Westerhoff et al., 2005). Therefore, it is important to explore the antibiotic partitioning behaviour between the water phase and sediment to predict and control the risk of aquatic organisms and humans which have been directly or indirectly exposed to a contaminated aquatic environment (Chau, 2005). It has been confirmed that hydrophobic organic contaminants (HOCs), such as PCDD/Fs, PCBs and OCPs, participates in sediment– water exchange in natural aquatic environments (Dai et al., 2013; Dalla Valle et al., 2003). For HOCs, it is generally believed that sediment–water exchange includes two main processes: (i) diffusion of dissolved HOCs between sediment and water phases and (ii) particle-phase HOC deposition and resuspension (Lun et al., 1998). However, the sediment–water exchange of IOCs has been rarely studied and is urgently needed for a better understanding of the environmental behaviour of IOCs. In recent years, there has been an abundance of research focused on determining the presence of antibiotics in environmental waters (Yan et al., 2013; Zhang et al., 2013). These studies typically focus on the lateral distribution of antibiotics via short-term monitoring. However, antibiotics will continue to move within a water column (i.e., a river or

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lake) until they are removed by attenuation processes, such as sorption or degradation. Less is understood about the vertical distribution and exchange between the sediment and water phase of antibiotics in a natural water system. Long-term continuous monitoring combined with multiple media vertical sampling is necessary to better understand the behaviour of antibiotics in a natural aquatic environment. Motivated by our previous study (Dai et al., 2013), our hypothesis in this paper is that distributions of antibiotics will vary depending on sampling locations and time due to different human factors and environmental conditions (e.g., water temperature, volume, and physical disturbance) in the Baiyangdian Lake. Therefore, the major objectives of this study were (1) to investigate the seasonal and spatial behaviours of antibiotics between sediments and the water phase and (2) to understand the exchange and partitioning behaviours of different antibiotics between water and sediment in aquatic environment. Two classes of antibiotics were included in the study, including ofloxacin and norfloxacin (FQs) and oxytetracycline and tetracycline (TCs) (Table 1). 2. Material and methods 2.1. Site description and sample collection As the largest shallow freshwater lake in North China, Baiyangdian Lake (Fig. 1) is located approximately 200 km southwest of Beijing and covers more than 366 km2 with an average depth of approximately 2–4 m. This lake consists of more than 100 small and shallow lakes linked by thousands of ditches and large areas of reed marshes. Currently, there are more than 243,000 people living in 39 villages scattered in and around it. Ten different sampling events were conducted from February to November 2009 (February in frozen period) at six sampling sites (Sites 1–6, given in Fig. 1 and Supporting information Table S3) representing slightly polluted, urban, and agriculturally influenced areas in the Baiyangdian Lake in North China. Water and sediment samples were collected according to our previously published protocols (Dai et al., 2013). Surface water samples were collected approximately 0.5 m below the water surface using a stainless-steel submersible pump. Overlying water samples were collected approximately 0.2 m off the bottom using a peristaltic pump to ensure that the surface sediments would not be disturbed. In situ pore water samples were collected using a Rhizon in situ sampler (RISS, Rhizosphere Research Products, Wageningen). All of the water samples were stored in pre-cleaned amber glass bottles. Sediments (0–5 cm deep) were collected at the corresponding water sampling sites using a stainless steel static gravity corer. The top surface layer (approximately 1 cm) was carefully removed with a stainless steel spoon, and stored in aluminium containers. All of the samples were stored in a cooler during sampling events and were immediately transported to the laboratory. Sediment samples were freeze-dried using a vacuum freeze-drier (FD-1A, China), and water samples were stored at − 20 °C. Freeze-dried sediments were ground by an agate mortar and sieved through a 100 mesh sieve, wrapped in pre-cleaned aluminium foil, sealed in plastic bags and stored at −20 °C prior to extraction. 2.2. Chemicals and materials Four antibiotic compounds (Table 1) (oxytetracycline (OTC), tetracycline (TC), norfloxacin (NOR) and ofloxacin (OFL)) were purchased from Dr. Ehrenstorfer (Augsburg, Germany). HPLC grade methanol, acetonitrile and formic acid were purchased from Fisher Science Co. The internal standard (i.e., diuron-d6) was acquired from Cambridge Isotope Laboratories, USA. Unless otherwise indicated, the other chemicals used in this study were of analytical grade and used without further purification. Separate stock solutions (1000 mg L−1) of individual compounds and internal standards were prepared by dissolving an appropriate amount of each

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Table 1 Properties and primary usage of selected antibiotics in this study. pKa

Log Kow

Water solubility (mg L−1)

Primary usage

OTC

3.3/7.3/9.1

−1.22

1000

Human Cattle, sheep, swine

Tetracycline

TC

3.3/7.7/9.7

−1.33

230–52,000

Human Cattle, sheep, swine

Norfloxacin

NOR

6.22/8.51

−1.0 to –1.7

400–161,000

Human fish, cattle, sheep, swine

Ofloxacin

OFL

6.10/8.28

−0.02

28,300

Human

Classes

Antibiotics

Acronym

Tetracyclines (TCs)

Oxytetracycline

Fluoroquinolones (FQs)

Structure

substance in methanol, which were then stored at −20 °C and further diluted prior to use. Solid-phase extraction (SPE) cartridges (Oasis HLB, 500 mg/6 cm3) were purchased from Waters (Milford, MA, USA). Glass fibre filters (GF/F, 47 mm/0.45 μm) were obtained from Whatman (Maidstone, UK). Milli-Q water from a Milli-Q Advantage A10 system (Millipore, USA) was used when ultrapure water was required. 2.3. Sample preparation and analysis The detailed pre-treatment process is summarised in the Supporting information. The extracted antibiotics were analysed by liquid chromatography–electrospray ionisation tandem mass spectrometry (LC–ESI-MS/MS), using the multiple reaction monitoring (MRM) model with unit mass resolution. The LC separation was performed using a Waters 2695 HPLC separation module (Milford, MA, USA) equipped with a Zorbax Bonus-RP column (2.1 mm i.d. × 150 mm, particle size 5 μm). Optimum separation was accomplished by gradient elution with eluent A (0.1% formic acid in ultrapure water) and eluent B (acetonitrile with 0.1% formic acid). The tandem MS analyses were performed on a Micromass Quattro triple–quadrupole mass spectrometer equipped with a Z-spray electrospray interface. The analyses were performed in positive ion mode for all of the compounds. The precursor mass, product ion, and optimised tandem mass spectrometry parameters for the analysis are also described in the Supporting information (Table S1). 2.4. Quality assurance and quality control In this paper, an internal standard (diuron-d6) was used for quantifying the concentrations of target compounds based on the use of a relative response factor (RRF) (Zhang and Zhou, 2007). In addition, a recovery study and limit of quantification (LOQ) were conducted for quality assurance purposes. The detailed procedures of the recovery study and LOQ determination are described in the Supporting information (Table S2). The recovery ranges of TC, OTC, NOR and OFL were 87–97, 82–93, 84–92 and 84–89%, respectively, in water and 78–89, 70–86, 85–95 and 68–83% respectively, in the sediment samples. The LOQ ranged from 0.4 to 1.5 ng L− 1 and from 1.1 to 3.4 ng g− 1 for water and sediment samples, respectively. All of the samples were analysed in triplicate, and the relative standard deviation was less than 20%.

2.5. Statistical analysis To examine the spatial and temporal effects of the measured concentrations of the four compounds, Friedman's test (Kim and Carlson, 2007) was employed. The significance of the results was determined based on an approximate P-value of 0.05 obtained from Friedman's test. In addition, Pearson's correlation analysis (SPSS 16.0 for Windows, SPSS Inc.) was used to examine the correlation between the physicochemical parameter and the antibiotic concentration as well as between different pseudo-partitioning coefficients.

3. Results and discussion 3.1. Concentrations of antibiotics in water and sediment phases All of the antibiotics were detected in 100% of the samples including surface, overlying and pore water. The mean concentrations of OTC, TC, and NOR, which were not statistically significantly different (P N 0.05), ranged from 25.95 to 31.60, 23.28 to 30.74 and 18.86 to 24.54 ng L−1 in surface, overlying and pore water, respectively (Table 2). However, the calculated mean values of OFL, which were very low compared to the other antibiotics ranged from 3.67 to 4.33 ng L−1 in the different water samples. The levels of selected antibiotics in surface water were at intermediate concentrations (Chen et al., 2013; Gao et al., 2012; Luo et al., 2011), and agreed with results from a recent study for residue levels in the surface water of Baiyangdian Lake (Li et al., 2012). However, no reference data on the antibiotic residual levels in overlying and pore water have been reported in previous studies. Therefore our results could not be compared with other areas. The concentrations of antibiotics in sediment samples are summarised in Table 2. NOR exhibited with the highest mean concentration of 274.76 ng g−1, followed by OFL, TC and OTC with mean concentrations of 39.73, 25.71 and 15.66 ng g−1, respectively. These results suggested that NOR and OFL had stronger tendencies to accumulate in sediments than TCs (Yang et al., 2010). In addition, NOR and OFL levels were consistent with results from a previous study of the Baiyangdian Lake (mean: NOR, 267 ng g− 1; OFL, 21.0 ng g−1) (Li et al., 2012). In comparison to other areas, the concentrations of antibiotics were also at a medium level in sediments (Gao et al., 2012; Kim and Carlson, 2007; Yang et al., 2010).

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Fig. 1. Six sampling sites (S1–S6) in the Baiyangdian Lake and its geographical location.

3.2. Temporal and spatial behaviours of antibiotics 3.2.1. Seasonal variation The concentrations of antibiotics were significantly different from season to season for surface water (Table S4). In general, the highest

concentrations of TCs, which are extensively used as veterinary drugs and feedstuff additives, detected at each sampling site showed an increasing trend with time from late summer to fall, and the highest concentrations of them were detected under low temperatures in autumn (Sept–Oct, Fig. 2a, b). One reason for this trend is the higher TC usage

Table 2 Summary of measured concentration in the surface water (ng L−1), overlying water (ng L−1), pore water (ng L−1) and sediment (ng g−1, dw) samples collected from the Baiyangdian Lake. Antibiotics

OTC TC NOR OFL Total a

Surface water

Overlying water

Pore water

Sediment

Meana

Max

Min

Meana

Max

Min

Meana

Max

Min

Meana

Max

Min

27.17 25.95 31.60 4.33 89.05

90.30 85.19 97.00 9.43 281.92

4.64 8.07 3.00 2.02 17.73

23.28 27.08 30.74 3.92 85.02

70.38 90.00 92.00 6.07 258.45

6.26 9.96 5.00 1.76 22.98

18.86 24.54 21.43 3.67 68.5

55.34 85.11 51.80 6.70 198.95

6.72 9.25 4.66 1.80 22.43

15.66 25.71 274.76 39.73 355.86

35.40 93.36 550.00 71.51 750.27

4.28 4.78 103.97 18.62 131.65

Averaged value of all measurements in different times and locations.

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rates in fall. This result likely occurred because TCs are commonly administered to livestock as prevention and treatment of most respiratory infection and diarrhoea (Matsui et al., 2008), and usage tends to increase in fall and even winter in north China when the animals are most vulnerable to these diseases, which has been validated by many studies (Table S5) (Ben et al., 2013; Chen et al., 2012; Pan et al., 2011). According to the literature, another reason is that the degradation of antibiotics is lower in fall. Photo- and bio-degradation play two important roles in the process of natural elimination of antibiotics, and the temperature has been demonstrated to significantly affect the degradation rate of TCs (Doll and Frimmel, 2003; Loftin et al., 2008; Tore Lunestad et al., 1995). Photo- and bio-degradation are less effective in the lower temperature. Consequently, the concentrations of TCs would be higher in this season and region. In addition, traditional planting patterns of winter wheat result in excessive manure application and flood irrigation in late September or early October leading to releases of TCs from manure into surface water via overflow and drain flow (Table S5) (Chang et al., 2010; Chen et al., 2011; Müller, 2000). The highest concentration of NOR was observed in summer instead of autumn (Fig. 2c). This result was not surprising because NOR is typically used in human and veterinary medicine to treat and prevent diarrhoea and other intestinal infections that occur more frequently due to the high ambient temperature of summer (Chen et al., 2012; Sarmah et al., 2006). Therefore, the heavy use of NOR especially its overuse in aquaculture far exceeded the degradation and dilution rates of NOR in the aquatic environment (Wiwattanapatapee et al., 2002). Significant high concentrations of NOR were found in summer in aquaculture farms with highest concentration up to 5400 ng L−1 in water samples (Table S5) (Zou et al., 2011). The high residuals of NOR in aquatic commercial animals (23.8 ng g−1) confirmed that a large amount of NOR was consumed by aquaculture in Baiyangdian Lake (Li et al., 2012). For OFL, which are only used in human, the seasonal variation was smaller than that observed for the other 3 compounds due to a relatively small and constant input into Baiyangdian Lake (Fig. 2d). Therefore, degradation and dilution accounted for the lower OFL concentrations detected in summer (Shao et al., 2012). In addition, 30 and 70.2 million m3 of fresh water (from Angezhuang Reservoir and Yellow River, respectively) were allocated to the lake by the local government to improve the hydrological and ecological conditions of the lake in July and October 2009, respectively, which might result in dilution of antibiotic concentrations in the surface water, and all of the antibiotic concentrations should be decreased. However, the concentrations of some drugs slightly increased in the beginning month of water supplement and decreased in the next month, especially for OFL that had a smaller seasonal variation; this result was unexpected. One explanation is that the release of those compounds are increased during that month due to the accumulation of sewage and other waste in dried-up water diversion canals of water transfer project and the residues of antibiotics in the water body itself. Recent study had indicated the presence of antibiotics in Yellow River (mean: NOR, 115 ng L−1; OFL, 114 ng L− 1) (Xu et al., 2009a). In addition, the researchers had assessed water pollution risk for water transfer in Baiyangdian Lake and the result indicated that the risk of pollutants from towns and villages along the line of water transfer project to the channel water body is at a high level with the probability of 0.373 (Liu et al., 2009b). No concentration comparison was made between water diversion canal and the Baiyangdian Lake of our studied region in the present study but additional research is critical to quantify the impact of water transfer project on antibiotic transport. Statistical analysis also indicated that there were significant seasonal variations for both overlying and pore water, except for OFL in overlying water (Table S4). The seasonal variations of these compounds in both overlying and pore water were similar to those in surface water, but the ranges for the ratio of the highest concentrations to the lowest concentrations from February to November with respect to each sampling point (1.2–5.8 in overlying water and 1.3–2.7 in pore water) were less

than that in surface water (1.6–18.4). The continuing reduction of ratios and ratio scopes indicated that the effect of seasonal changes was gradually reduced from surface to pore water. A similar trend was observed in sediment samples for these antibiotics, especially for TC (Table S4). This result may be due to the strong sorption characteristics of the 2 classes of antibiotics to the sediment and their accumulation at higher concentrations in water, especially for FQs (Li et al., 2012; Yang et al., 2010). In addition, for most of the sampling points, an increase in the concentration of antibiotics to different degrees occurred in sediment during November compared to February and half of the increase rate of antibiotics was greater than 10% (Fig. 2, Fig. S1), reflecting possible net accumulation depending on the exposure time of these antibiotics to the sediment. 3.2.2. Spatial distribution For water samples, all of the compounds exhibited significant differences in concentrations at least once at different sampling sites within serial sampling periods (Table S6). Baiyangdian Lake as a semi-closed water body has a low mobility and a slow replacement rate (Chu et al., 2012; Lv et al., 2013). Therefore, the spatial heterogeneity of antibiotics will largely depend upon extraneous input at different sampling sites and partly contribute to the effect of closer sampling points. Site 1 was the most contaminated with the highest total average concentration (TAC) of 4 antibiotics up to 160.88, 156.16 and 153.23 ng L−1 in surface, overlying, and pore water, respectively. Site 1 was located at the entrance of the Fuhe River (Fig. 1), and the highest concentrations of antibiotics in the water phase at this site might be caused by the high rates of contaminant influx into the lake through the Fuhe River. It has been reported that the Fuhe River, which runs through the city of Baoding, receives a huge amount of domestic wastewater (250,000 m3 d−1) from Baoding City (Dai et al., 2013). Therefore, the sewage discharge from Baoding City, which has over 10 million residents, is a main source of antibiotics in Baiyangdian Lake (Li et al., 2012). This result can be confirmed by the fact that the concentration of antibiotics in the surface water from the Fuhe River (TAC, 1841 ng L−1) was significantly higher than those in the Baiyangdian Lake (TAC, 15.9–432 ng L−1) (Li et al., 2012). This result agrees with previous studies, which indicated that the major pathway for the release of antibiotics is wastewater from urban areas (Zhang et al., 2012). Many studies have reported that the levels of other chemical pollutants, such as organochlorine compounds, polycyclic aromatic hydrocarbons and perfluorinated compounds, are much higher in the Fuhe River than in the Baiyangdian Lake (Hu et al., 2010; Shi et al., 2012). In addition, a significant decreasing trend in antibiotic levels was observed in water samples with increasing distances from the Fuhe River (site 1). For example, the TAC of the 4 compounds in sites 3, 4 and 5 decreased from 101.87 to 51.82 ng L− 1 in surface water (Fig. 1), which further indicated that the Fuhe River was a possible source of the antibiotics in Baiyangdian Lake (Shi et al., 2012). The levels of antibiotics in sites 2–4 (TAC, 66.02–109.27 ng L−1) in surface water were approximately two fold higher than those in sites 5 and 6 (TAC, 33.92–51.82 ng L−1). This large difference may be directly related to the contamination sources in sites 5 and 6 in Baiyangdian Lake, where there was little disturbance by human activity in the middle of the larger lakes. However, sites 2–4 were located in densely populated lakeside areas (Fig. 1) and wastewater continuously discharged into Baiyangdian Lake from the local residents. Human activity (e.g., agricultural and aquaculture production) could also contribute to the large difference. In addition, sites 3 and 4 were located in a well-known tourism location, and the nearby region has a more dense population than that of the other regions. For sediments, all four compounds also exhibited significant differences in levels at different sampling sites (Table S6) and a similar trend was observed in water samples. The highest mean concentrations (range: 30.74–443.14 ng g−1) of the 4 antibiotics in sediment were measured at site 1, which is the region by influence of wastewater from Baoding City. This result suggested that the antibiotic concentrations in

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The dissolved organic carbon (DOC) content in the water phase and total organic carbon (TOC) in sediments were also investigated in this study, and their values ranged from 7.22 to 22.08 mg L−1 in the water samples and 0.69 to 2.46% in the sediments, during the sampling periods (Table S3). Previous research has shown that the distribution of HOCs, such as PCBs, was related the DOC content in the water column and to the TOC content in sediment (Iwata et al., 1995). However, the distribution of antibiotics did not show any correlation with corresponding DOC and TOC (statistical data of correlation analysis not shown), which is most likely due to the less lipophilic and more soluble nature of the IOCs relative to HOCs. This result suggested that a complex adsorption mechanism contributed to the interaction between IOCs and dissolved organic matter (DOM), which include hydrogen bonding and

sediments can be highly influenced by municipal sewage from the Fuhe River because antibiotics associated with suspended solids in sewage can settle into the lake sediment after the river water mixes with the lake water. A similar result has been reported in a previous study (Liu et al., 2009a). In addition, OTC, TC and NOR as well as typical veterinary antibiotics exhibited higher mean concentrations (range: 14.23– 335.82 ng g−1) in agriculture and residential regions (i.e., sites 2–4) leading to the release of these compounds into the sediment from both livestock manure and residual feed (Li et al., 2012; Zhao et al., 2012). For OFL used to only treat humans, the variations in the mean concentrations (range: 36.03–40.83 ng g−1) among the sampling sites were not apparent (Fig. 2d), which is primarily due to continuous sewage discharge from urban and lakeside residents.

a 120

Sediment (TC)

Concentration (ng L-1)

Concentration (ng g-1)

120 100 80 60 40 20

80 60 40 20

0

0 Feb Mar Apr May Jun Jul AugSep Oct Nov

Feb Mar Apr May Jun Jul AugSep Oct Nov 120

Overlying water (TC)

Concentration (ng L-1)

Concentration (ng L-1)

120

Pore water (TC)

100

100 80 60 40 20

Surface water (TC)

100 80 60 40 20 0

0

Feb Mar Apr May Jun Jul AugSep Oct Nov

Feb Mar Apr May Jun Jul AugSep Oct Nov

S1

S2

S3

S4

S5

S6

b 120

Sediment (OTC) Concentration (ng L-1)

Concentration (ng g-1)

60 50 40 30 20 10 0

100 80 60 40 20 0

Feb Mar Apr May Jun Jul AugSep Oct Nov

Overlying water (OTC)

100 80 60 40 20

Feb Mar Apr May Jun Jul AugSep Oct Nov 120

Concentration (ng L-1)

Concentration (ng L-1)

120

Pore water (OTC)

0

Surface water (OTC)

100 80 60 40 20 0

Feb Mar Apr May Jun Jul AugSep Oct Nov

S1

S2

S3

Feb Mar Apr May Jun Jul AugSep Oct Nov

S4

S5

S6

Fig. 2. Spatial and temporal variation of TC (a), OTC (b), NOR (c) and OFL (d) in sediment, pore water, overlying water and surface water of the Baiyangdian Lake. The bars are the mean concentrations (water phase: ng L−1, sediment: ng g−1) of antibiotics in every sampling site.

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600

120

Sediment (NOR)

Concentration (ng L-1)

Concentration (ng g-1)

c 500 400 300 200 100

80 60 40 20

0

0

Feb Mar Apr MayJun Jul AugSep Oct Nov

Feb Mar Apr MayJun Jul AugSep Oct Nov 120

Overlying water (NOR)

Concentration (ng L-1)

Concentration (ng L-1)

120

Pore water (NOR)

100

100 80 60 40 20

Surface water (NOR)

100 80 60 40 20

0

0

Feb Mar Apr MayJun Jul AugSep Oct Nov

S1

S2

S3

Feb Mar Apr MayJun Jul AugSep Oct Nov

S4

S5

S6

d Sediment (OFL)

Concentration (ng L-1)

Concentration (ng g-1)

12 70 60 50 40 30 20 10

8 6 4 2 0

0 Feb Mar Apr May Jun Jul AugSep Oct Nov

12

Overlying water (OFL)

Concentration (ng L-1)

Concentration (ng L-1)

12 10 8 6 4 2 0

Pore water (OFL)

10

Feb Mar Apr MayJun Jul AugSep Oct Nov

Surface water (OFL)

10 8 6 4 2 0

Feb Mar Apr May Jun Jul AugSep Oct Nov

S1

S2

S3

Feb Mar Apr May Jun Jul AugSep Oct Nov

S4

S5

S6

Fig. 2 (continued).

cationic exchange (Carmosini and Lee, 2009; Sun et al., 2010), compared to the hydrophobic distribution between HOCs and DOM. However, further study is needed to clarify the specific reasons. 3.3. Sediment–water exchange The vertical distribution of monthly concentrations of the four antibiotics in the water–sediment system is shown in Fig. 3. It is assumed that there “quasi-equilibrium” existed for the organic compounds in the sediment and water phase and that the exchange might occur across the water–sediment interface due to the breakage of the “quasiequilibrium” (Maria and Maria, 2008). In an aquatic environment, the pore water could fill the interface hole and act as a bridge between the overlying water and sediment, which plays a vital role in the

transport processes, reactions in the sediment matrix and exchange across the sediment–water interface (Jurado et al., 2007). An accumulation rate (AR) was defined to interpret the trend in the antibiotic contamination: AR (%) = [(Cs − Cs, Feb) / Cs, Feb] × 100, where Cs, Feb is the initial median concentration of an antibiotic in the sediment (i.e., the median concentration in February (ng g−1)) and Cs is the monthly median concentration of an antibiotic in the sediment (ng g−1). In most cases, the changes in the AR values were consistent with the antibiotic concentrations in surface water and exhibited adsorption exchange in the sediment (Fig. 3), especially during the months when a large amount of antibiotics were introduced into the surface water (e.g., the highest AR value of TC: 23.55% in November, NOR: 7.38% in August and OFL: 6.24% in September). These observations could be associated with the diffusive transfer of antibiotics from the water phase into

120 100

40

80

30

60

20

40

10

20 0

0

35 30 25 20 15

NOR

250 200 75 60 45 30 15 0

40 32 24 16

*

8 0

* *

-8 -16 -24

Feb Mar Apr MayJun Jul AugSep Oct Nov 40 32 24 16 8 0 -8 -16 -24 -32 -40

Feb Mar Apr MayJun Jul AugSep Oct Nov

Concentration (ng/L or ng/g)

300

Sediment Pore Water Overlying Water Surface Water

TC

10

Feb Mar Apr MayJun Jul AugSep Oct Nov

350

Sediment Pore Water Overlying Water Surface Water

Accumulation Rate (%)

140

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OFL

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Concentration (ng/L or ng/g)

D. Cheng et al. / Science of the Total Environment 476–477 (2014) 266–275

Feb Mar Apr MayJun Jul AugSep Oct Nov

Fig. 3. Seasonal changes of four antibiotics in surface water, overlying water, pore water and sediments in Baiyangdian Lakes. The bar is the median concentration (water phase: ng L−1, sediment: ng g−1) of antibiotic in all six sampling sites. The asterisk (*) indicated that the sediment was acting as a contaminant source to the overlying water during that month.

the sediment because there was a gradient decrease (diffusive potential) in the antibiotic concentrations from the surface water to pore water (Fig. 3) (Santschi et al., 1990). A similar result was also observed in a previous study, which showed that different concentrations of organic contaminants between the water column and sediment can affect their transfer trend (Dai et al., 2013; Tolls, 2001). In addition, a progressive decrease in the TC concentrations in the sediment was observed as the TC concentrations in both the overlying water and pore water decreased from March to May, which indicates that the sediment is acting as a secondary source of antibiotics for the overlying water (Fig. 3b). Most studies have shown that TCs are not readily degraded at a lower temperature and in an anaerobic environment (Arikan et al., 2007; Doi and Stoskopf, 2000; Ingerslev et al., 2001; Kühne et al., 2000). Therefore, the changes in the concentrations of TCs in the sediment were primarily due to its adsorption or desorption during that time in conjunction with other factors. The main factor was that the period from March to May involved lower levels of drug consumption compared to the prime seasons in Baiyangdian Lake. Another factor was the water temperature variation. As the water temperature increased from spring to summer, strong photolysis of antibiotics in surface water further increased the concentration disparity between the surface water and overlying water (Karthikeyan and Meyer, 2006). In addition, vertical convection accelerated the transport of antibiotics from overlying water to surface water. All of these factors collectively accelerated the diffusive transfer of antibiotics from the sediment to the surface water. The convectional phenomenon can be supported by the gradually uniform temperature throughout the water column during the period from March to August (Fig. S2 and Table S3). This result agreed well with the results reported by Ma and Jing (2006), which indicated that the water temperature distribution in Baiyangdian Lake is vertically stratified. This stratification disappears and the water temperature is nearly the same in August, and then, the stratification appears again in September. The final factor was associated with the high resuspension of bottom sediments caused by intensive fishing, or shipping activity from relatively quiescent environments (frozen period: February) to a dynamic environment, and this process favours a high sediment– water exchange (Xu et al., 2009b). Therefore, anthropogenic disturbance will have a potential impact on the remobilisation of sediment bound antibiotics in this lake. However, an obvious source of the antibiotics

could not be determined due to the absence of the requisite conditions as discussed above. Relatively stable behaviour occurred in different environmental compartments, except for surface water, from October to November (Fig. 3). This result may also be associated with the generation of stable thermal stratification (Fig. S2). 3.4. Pseudo-partitioning coefficient calculation To obtain a quantitative understanding of the relationship of antibiotics between the sediment and water phase, the pseudo-partitioning coefficient (kd, s) was used to calculate according to the following equation: kd, s = Cs / Cw, where Cs is the average concentration in the sediment (ng g− 1), and Cw is the corresponding average concentration in the water phase (ng L−1) (Kim and Carlson, 2007; Li et al., 2012). The kd, s-values corresponding to sediment/surface water (kd, s (sw)), sediment/overlying water (k d, s (ow) ), and sediment/pore water (k d, s (pw)) in the Baiyangdian River are reported in Table S6. Previous research has determined that the kd, s for TCs ranged from 290 to 31,170 L kg−1, whereas those of FQs ranged from 310 to 9360 L kg−1 (Kim and Carlson, 2007; Li et al., 2012; Tolls, 2001; Zhang et al., 2011). Our result indicated that the pseudo-partitioning values in Baiyangdian Lake ranged from 277 to 1800 L kg− 1 for TCs and from 4493 to 47,093 L kg− 1 for FQs. Our result also indicated that the kd, s (sw)-values for TCs agreed with previous reports, but those of FQs were determined to exhibit a much higher partitioning (Table S7), which suggested that FQs were strongly adsorbed on sediments compared to TCs and other antibiotics, such as sulfonamides (SAs) and macrolides (MCs), which promoted FQs persistence in aquatic environments (Li et al., 2012). The high kd, s-values of FQs are most likely related to the physicochemical properties of sediment and pH of water (Gong et al., 2012). Our previous work suggested that physicochemical properties, such as clay content, free Fe oxides, free Al oxides, Ca content, Al content and organic matter, and pH played significant roles in governing the partitioning characteristics between soil and water for different types of antibiotics. These characteristics also indicated that the high kd, s-values for FQs was significantly affected by the content of organic matter, in addition to the other three common physicochemical properties (i.e., pH, clay content, and free Fe oxides)

D. Cheng et al. / Science of the Total Environment 476–477 (2014) 266–275

1300

400

20000

700

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Stratified

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1200

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TC

1200

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OTC

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274

6000 0 FebMar AprMayJun Jul AugSepOctNov

kd,s(pw)

kd,s(ow)

FebMar AprMayJun Jul AugSepOctNov

kd,s(sw)

: destratification period

Fig. 4. Spatiotemporal trends of kd,s values in the Baiyangdian River.

of soil (Gong et al., 2012). Because the content of organic matter in the sediment in Baiyangdian Lake was higher than other sampling watersheds (Table S3), the high distribution of FQs in the sediment contributed to the organic matter (Kim and Carlson, 2007; Liang et al., 2013). In addition, spatiotemporal trends of the kd, s values in the Baiyangdian River shown in Fig. 4, and a significant relationship between kd, s (sw)–kd, s (ow) and kd, s (ow)–kd, s (pw) were observed (Table S8). As the vertical depth from surface water to sediment in sampling area varied, the kd, s-values of OTC, TC, NOR, and OFL ranged from 277 to 1800, 768 to 1227, 4493 to 47,093 and 5925 to 12,465 L kg−1, respectively. The vertical profiles of the kd, s-values exhibited obvious differences during the cooler months (October–March) and similarities in the hottest month (August, kd, s (sw) ≈ kd, s (ow) ≈ kd, s (pw), shown in Fig. 4). According to our results, we recommend using kd, s (pw) as a more desirable value for indicating the sorption characteristics compared to kd, s (sw) in aquatic environments. Two explanations may be possible: (1) sediments are in closer contact with the pore water than overlying and surface water and (2) kd, s (pw) exhibits smaller fluctuations than both kd, s (sw) and kd, s (ow). However, taking into account that the in-situ sampling method of pore water is an expensive, timeconsuming process, the sampling time should be selected during the destratification period (August, Fig. 4), and only surface water and sediments are collected to determine the kd, s (sw) due to the similarity between kd, s (ow) and kd, s (pw) throughout the year. 4. Conclusions This paper provides the first systematic seasonal data on the distribution of four typical antibiotics in the water column and corresponding sediment from the Baiyangdian Lake. The results of the statistical analysis of spatial and temporal variations suggested that concentrations of the target compounds were significantly different depending on the sampling location and time period for different water compartment and sediment samples. In general, the temporal trends indicated stable accumulations of antibiotics in the sediment. However, the vertical distribution suggests that the sediment may also be acting as a secondary source of some antibiotics under specific conditions. Therefore, it is necessary to further investigate the behaviours of antibiotics at the water– sediment interface to confirm their fate in aquatic environments, as well as their risk to public health.

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