Environ Sci Pollut Res DOI 10.1007/s11356-016-6733-y
RESEARCH ARTICLE
Environmental and human health risks of antimicrobials used in Fenneropenaeus chinensis aquaculture production in China Ming Sun 1,2 & Zhiqiang Chang 2 & Paul J. Van den Brink 3,4 & Jian Li 2,5 & Fazhen Zhao 2,5 & Andreu Rico 3,6
Received: 26 August 2015 / Accepted: 20 April 2016 # Springer-Verlag Berlin Heidelberg 2016
Abstract This study aimed to quantify the environmental fate of antimicrobials applied in Fenneropenaeus chinensis aquaculture production in China and to assess their potential risks for surrounding aquatic ecosystems, for the promotion of antimicrobial resistance in target and non-target bacteria and for consumers eating shrimp products that contain antimicrobial residues. For this, we first used the results of an environmental monitoring study performed with the antimicrobial sulfamethazine to parameterize and calibrate the ERA-AQUA model, a mass balance model suited to perform risk assessments of veterinary medicines applied in aquaculture ponds. Next, a scenario representing F. chinensis production in China was built and used to perform risk assessments for 21 antimicrobials which are regulated for aquaculture in China. Results of the model calibration showed a good correspondence between the predicted and the measured sulfamethazine concentrations, with differences within an order of magnitude. Results
of the ecological risk assessment showed that four antimicrobials (levofloxacin, sarafloxacin, ampicillin, sulfadiazine) are expected to have adverse effects on primary producers, while no short-term risks were predicted for invertebrates and fish exposed to farm wastewater effluents containing antimicrobial residues. Half of the evaluated antimicrobials showed potential to contribute to antimicrobial resistance in bacteria exposed to pond water and farm effluents. A withdrawal period of three weeks is recommended for antimicrobials applied via oral administration to F. chinensis in order to comply with the current national and international toxicological food safety standards. The results of this study indicate the need to improve the current regulatory framework for the registration of aquaculture antimicrobials in China and suggest compounds that should be targeted in future aquaculture risk assessments and environmental monitoring studies.
Responsible editor: Philippe Garrigues Electronic supplementary material The online version of this article (doi:10.1007/s11356-016-6733-y) contains supplementary material, which is available to authorized users. * Jian Li
[email protected]
1
Fishery College, Ocean University of China, Qingdao 266003, People’s Republic of China
2
Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, People’s Republic of China
3
Alterra, Wageningen University and Research centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands
4
Department of Aquatic Ecology and Water Quality Management, Wageningen University, Wageningen University and Research centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands
5
Function Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, No. 1 Wenhai Road, Aoshanwei Town, Jimo, Qingdao, People’s Republic of China
6
IMDEA Water Institute, Science and Technology Campus of the University of Alcalá, Avenida Punto Com 2, P.O. Box 28805, Alcalá de Henares, Madrid, Spain
Environ Sci Pollut Res
Keywords Aquaculture . Human health . Ecological risk assessment . Shrimps . Environmental modelling . Antimicrobial resistance
Introduction Coinciding with the rapid population growth of the 20th century, there has been a sharp increase in demand for seafood products, including finfish and shellfish. The global aquaculture industry is primarily dominated by production facilities located in a few Asian countries, with 88 % of the global production in volume taking place in Asia (FAO 2014). China alone accounts for approximately 62 % of the global aquaculture production. Shrimp production is the pillar of the aquaculture industry in China and has achieved a great development thanks to the improvement of science and technology, the expansion of the cultivation area and improved management practices (Xie and Yu 2007). The shrimp Fenneropenaeus chinensis is one of the highvalue aquaculture species produced in China, mainly cultured in the coastal areas of the Bohai Sea and the Yellow Sea (north China). The species is well suited for aquaculture because of its rapid growth, resistance to low temperatures, high nutritional properties and good adaptation to artificial culture conditions (Li et al. 2006). It is also a major ingredient for the local people’s diet because of its nutritional benefits and tradition (Xue et al. 1997). F. chinensis is mainly produced in monoculture and polyculture systems established in earthen ponds, which rely on regular water exchange from surface water supplies. F. chinensis cultured under such conditions are highly vulnerable to stress produced by water quality fluctuations and can easily be infected by naturally occurring microorganisms. Particularly, infestations with Vibrio anguillarum and Aeromonas hydrophila have been reported to be the main causes of mortality in F. chinensis (Zhang et al. 2003; Qin et al. 2006). Antimicrobials are extensively used for the prevention and treatment of such bacterial disease outbreaks (Zhang et al. 2005; Sun et al. 2013; Rico et al. 2013a). Because of the widespread distribution of shrimp farms in the coastal areas of China and their relatively intensive production practices, the Chinese shrimp industry has been considered an important source of antimicrobials into the environment and the evaluation of their environmental fate and spread has recently become an important issue (Liang et al. 2010; Yang et al. 2010; Zhou et al. 2011; Zou et al. 2011; Rico et al. 2014; Chen et al. 2015). For instance, Chen et al. (2015) monitored antimicrobial concentrations in selected aquaculture farms in Hilling Island rearing shrimp larvae and adults and detected six different antimicrobial substances in water samples collected in aquatic ecosystems surrounding the aquaculture farms, with erythromycin H2O showing the highest residue concentrations (7.7–16.6 ng L−1). Zou et al.
(2011) investigated the residual concentrations of several antimicrobials in the surroundings of Bohai Bay (North-East China), showing concentrations in the range of 2.3 to 6800 ng L−1. The majority of the environmental monitoring studies evaluating aquaculture-borne antimicrobial pollution is based on samples collected in water bodies after significant dilution and cannot be considered good representatives of worst-case environmental exposure situations. The human and environmental health problems associated to the use of antimicrobials in shrimp production systems are threefold (Rico et al. 2012a). First, residue concentrations in aquaculture wastewater effluents can be released into surrounding aquatic ecosystems and might be responsible for adverse effects on non-target aquatic organisms due to their biologically active properties (Heberer 2002; Fent et al. 2006; Sarmah et al. 2006), principally on non-target microorganisms such as photosynthetic cyanobacteria (Rico et al. 2014) or other non-target microbial species. Second, antimicrobial residues accumulated in harvested shrimps can be taken up by the consumers, leading to possible human health consequences such as anaphylactic reaction, thrombocytopenia, and liver and kidney damages (Chang et al. 2012). Third, antimicrobial residues close to or exceeding minimal inhibitory concentrations (MICs) of bacteria can contribute to the selection of resistance genes in different environments (Gullberg et al. 2011), including the aquaculture pond and downstream river ecosystems. Several studies have shown that monitored bacteria in the environment surrounding (semi)intensive aquaculture sites contain an increasing number of antibiotic resistance genes (Rhodes et al. 2000; Sørum 2006; Gao et al. 2012) as compared to unimpacted sites. The spread of antibiotic-resistant bacteria in aquaculture environments, the increase of antibiotic resistance in shrimp pathogens, and the potential of being transmitted by horizontal gene transfer to bacteria of the terrestrial environment, including human pathogens (Sørum 2006), raises concerns about the sustainability of the current production methods. There is a need to better understand the contribution of the food industry to this problem and to implement measures capable of reducing the discharge of antimicrobial residues and resistance genes into the environment (Pruden et al. 2013). To date, the extent to which antimicrobial pollution from shrimp farm effluents contributes to antimicrobial resistance in the environment is rather unknown as monitored levels have not been compared to resistance thresholds. Some studies have compared concentrations of antimicrobials measured in sewage treatment plants with predicted no effect concentrations (PNECs) for antimicrobial resistance developed from MIC data distributions for pathogenic bacteria (Tello et al. 2012; Bengtsson-Palme and Larsson 2016). For instance, Bengtsson-Palme and Larsson (2016) found that resistance PNECs were exceeded in 28 % of the cases for which monitoring data was available. Concentrations in aquaculture
Environ Sci Pollut Res
effluents are in the order of magnitude of those monitored in sewage treatment plants or even higher (Rico et al. 2014; Andrieu et al. 2015) and may therefore have a similar contribution to the selection of antimicrobial resistance genes in the environment. There are several laws and regulations that manage the Chinese aquaculture industry in order to control the potential food safety and environmental risks caused by antimicrobial use in aquaculture production. These are the Fisheries Law of the People’s Republic of China (SCNPC 2000), the Agricultural Product Quality Safety Law (SCNPC 2009), and the animal medicine management regulations (SCNPC 2004). The Fisheries Law mentions that the use of antimicrobials in Chinese aquaculture Bshould be appropriate,^ and the wastewater effluent discharges Bshould not pollute the surrounding aquatic area,^ but lacks a pertinent, explicit statement on how this can be evaluated and avoided. Concerning regulations on food safety, guidelines of the Ministry of Agriculture of the People’s Republic of China were issued in 2002 (MAPRC 2002), which list maximum residue limits (MRLs) of antimicrobials in edible fish/shrimp tissue. Although some based guidance exists regarding the prevention of environmental damage by aquaculture contaminants (Tieyu et al. 2005), environmental standards for antimicrobial pollution have not been proposed. Furthermore, the authorized antimicrobials for use in Chinese aquaculture have not been appropriately evaluated regarding their environmental pollution hazard and associated risks, particularly those related to antimicrobial resistance. The main objective of the present study was to assess the potential environmental and human health risks posed by the use of antimicrobials in F. chinensis production in China. For this, we used the ERA-AQUA model, a dynamic mass balance model that is able to predict the environmental fate and risks of veterinary medicinal substances applied to pond aquaculture production systems (Rico et al. 2013b). In this study, we first used the outcomes of an experimental trial performed with the antimicrobial sulfamethazine in F. chinensis ponds to calibrate and evaluate the chemical exposure outcomes generated by the ERA-AQUA model. Next, a scenario representing F. chinensis production in China was built and used to calculate the antimicrobial fate after use in aquaculture and to perform a risk assessment for the antimicrobials that are currently regulated under the Newly Organized Fishery Drugs Manual of China (Yang 2005). The endpoints considered in the risk assessment were ecological risks for non-target aquatic organisms, resistance promotion risks in bacteria exposed to antimicrobial residues in the aquaculture environment, and human health risks for consumers potentially consuming shrimp products that contain antimicrobial residues. The modelling scenarios and outcomes provided by this study will contribute to prioritize antimicrobial compounds regarding their environmental pollution and risk and can be used for the development
of future science-based regulations and environmental monitoring studies in China.
Materials and methods The ERA-AQUA model The ERA-AQUA model is a cost-effective tool that was developed to determine the potential environmental and human health risks posed by aquaculture medicines applied in aquaculture ponds (Rico et al. 2012b, 2013b). The risk characterization is based on the comparison of predicted exposure concentrations (exposure assessment) and predicted safe concentrations (effect assessment) for different endpoints, including aquatic ecosystems and the human population consuming aquaculture products. The exposure assessment is performed by combining information representing the aquaculture production scenario (i.e., environmental characteristics of the aquaculture pond, characteristics of the cultured species, aquaculture management practices; e.g., Table 1), chemical use data, and physicochemical properties of the compounds under study. Differential equations are used by the model to predict chemical concentrations in different compartments of the aquaculture pond, including the pond water, pond sediment, cultured species, and watercourse receiving pond effluent discharges. The effect assessment is performed by combining (eco)toxicological information and food safety threshold concentrations for the studied compound. A detailed explanation of the equations used by the ERA-AQUA model is provided in Rico et al. (2012a, 2012b), and examples of its application for environmental and human health risk assessment can be found in Rico et al. (2013b) and Rico and Van den Brink (2014). Model calibration and evaluation The ERA-AQUA model calibration was performed based on a previously performed field experiment that evaluated the environmental fate and concentration dynamics of the antimicrobial sulfamethazine (SM2) applied to F. chinensis experimental ponds in Changyi, Shandong province, China (Sun et al. 2015). In this study, 12 earthen ponds were randomly distributed into 4 groups (3 ponds in each group). At the beginning of the experiment, one group was set as control and the other 3 groups received a treatment of an SM2 with a dose of 50, 100, or 150 mg kg−1 of shrimp body weight daily. The shrimps in the treatment groups were fed with medicated feed twice per day for 5 consecutive days, and then the medicated feed was replaced by antimicrobial-free feed for the remaining days of the experiment. The dosages used in this study were based on recommended dosages for antimicrobials used in China aquaculture and prescribed dosages provided by the antimicrobial
Environ Sci Pollut Res Table 1
Parameters of the aquaculture pond scenarios used to perform the ERA-AQUA model simulations
Scenario parameters
Scenario used for the model calibration and evaluation
Scenario representing F. chinensis production in China
Value
Reference
Value
Reference
Pond area (m2)
250
Sun et al. (2015)
3502
Li et al. (2006)
Pond water depth (m) Mass concentration of suspended solids in pond water (kg L−1) Mass fraction of organic matter in suspended solids (g g−1) Top sediment layer depth (m) Mass fraction of organic matter in sediment (−) Sediment porosity (v v−1) Sediment bulk density (g cm−3)
1.5 0.001 0.02
Sun et al. (2015) This study This study
1.5 0.001 0.02
Zhang (2012) This study This study
0.05 0.012 0.39 1.61
Sun et al. (2015) This study Based on values for sand Calculated
0.05 0.012 0.39 1.61
Assumption This study This study This study
22
Sun et al. (2015)
25
Zhang (2012)
Organism weight at the start of the simulation period (kg) Maximum organism weight (kg) Cultured species density at stocking (kg · m−2) Lipid fraction of cultured species (−) Mortality fraction during the culture period (−)
0.008 0.02 0.046 0.06 0.2
This study Calibrating parameter Sun et al. (2015) Chen et al. (2010) Sun et al. (2015)
0.00004 0.05 0.0002 0.06 0.33
Zhang (2012) Calibrating parameter Zhang (2012) Chen et al. (2010) Zhang (2012)
Feed input to the cultured species Daily specific feeding rate, SFR [(kg food kg−1 BW−1) day−1] Organism’s weight at which SFR was determined (kg)
0.02 0.008
Sun et al. (2015) Sun et al. (2015)
0.02 0.008
ADCC,(2005) ADCC,(2005)
0.1 2 0.2
Tian et al. (2004) Ziaei-Nejad et al. (2006) Calibrating parameter
0.1 2 0.2
Tian et al. (2004) Ziaei-Nejad et al. (2006) Calibrating parameter
0 0 0
No water exchange No water exchange No water exchange
4 20 7
Rico et al. (2014) Zhang (2012) Zhang (2012)
30
Sun et al. (2015)
120
Zhang (2012)
Aquaculture pond
Average water temperature (°C) Cultured species
Lipid fraction of feed (−) Feed conversion rate in the cultured species [kg food (kg BW)−1] Fraction of eaten feed (−) Effluent discharge management teffluent (h) Water discharge per event (%) Time interval between discharge events (day) Duration of the simulation period Duration (day)
producing company. During this experiment, sulfamethazine concentrations in water, in the top 5-cm sediment layer and in different organs and tissues of the cultured shrimp (i.e., plasma, hepatopancreas, gill, muscle, stomach, intestine, carapace) where measured daily during the treatment period as well as on day 1, 2, 3, 4, 5, 7, and 10 after the last antimicrobial application. A full description of the sampling techniques and analytical methods used for the SM2 quantification can be found in Sun et al. (2015). The measured concentrations of SM2 at different dosages are shown in Tables S1, S2, and S3. The experimental ponds used in the field experiment represent a F. chinensis production scenario in the last few months of the culture cycle. The information regarding the pond and species characteristics obtained during this study was used to build a model scenario that was implemented in the ERA-AQUA model. For example, the pond area was
250 m2, the water depth was 1.5 m, the mean water temperature was 22 °C, and the culture species density was 45.5 g m−2 (see Table 1 for a complete list of input parameter values). The scenario was complemented with the inspection and analysis of water, sediment, and shrimp samples taken during the experimental period. A gravimetric method was used to measure the mass concentration of suspended solids in pond water, and the mass fraction of organic matter in suspended solids and in sediment was analyzed according to Davis (1974). For each measurement, three replicates were evaluated. The sediment porosity was assumed to be 0.39, according to the value of a sandy sediment texture (Guber et al. 2006), and the sediment bulk density (1.61 g cm−3) was calculated according to the equation porosity = 1 − (bulk density / soil particle density) (Chesworth 2008), in which a typical value of 2.65 g cm−3 was used to characterize the soil particle density. The average
Environ Sci Pollut Res
shrimp weight was 8 g at the start of the simulation period and 12 g at the end of the simulation period (n = 30). In order to calibrate the shrimp mass balance calculated by the ERAAQUA model with measured values, a maximum organism weight of 20 g was calculated using the Von Bertalanffy asymptotic growth model described in Kooijman (2000). The lipid content of F. chinensis was set to 6 % based on the value reported in the literature (Chen et al. 2010), as well as the lipid fraction of food and the feed conversion ratio (Table 1). The feeding rate was set to 2 % of the body weight of the cultured organisms, as was calculated based on measurements made during the experimental period. During the experimental period, there was no water exchange, so the average daily water exchange in the model scenario was set to zero (Table 1). The scenario assumed that water percolation, rainfall rate, and evaporation were negligible and were set to zero too, so the water volume was kept constant during the simulation period. Default values were used for partial water layer diffusion resistance to and from water, partial water layer diffusion resistance to and from food, lipid layer permeation resistance and for the water absorption-excretion coefficient (Rico et al. 2012b). Because shrimps only started feeding when the feed was sunk into the pond sediment and also have a relatively slow feeding behaviour (as compared to fish), it is possible that a large proportion of the antimicrobial substance that is administered is dissolved into the water layer and is therefore not available for shrimp consumption. The fraction of applied antimicrobial that is actually eaten by the shrimp was considered as the main calibrating parameter for this scenario and was estimated by calculating the fraction of eaten antimicrobial that minimizes the difference between the modeled and measured sediment-shrimp exposure concentration ratio, yielding a value of 0.2 (Table 1). Physicochemical properties for SM2 needed for the ERAAQUA simulations were collected from online databases, chemical evaluation reports, and the peer-reviewed literature. For a detailed description of the antimicrobial-related parameters used for the model calculations, see the Supplemental Data file (Table S4). The dissipation rates of SM2 in water and sediment and the pharmacokinetics data in shrimp were based on the data obtained from Sun et al. (2015). The residual concentrations of SM2 in shrimp, water, and sediments used for calculating the half-life were recalculated taking into account the calculated recovery of the analytical method (Sun et al. 2015). The concentrations in the shrimp were calculated to represent the whole body concentrations, based on the measured concentration in the different tissue and organs, and their relative weight. The relative weight proportion of plasma, hepatopancreas, gill, muscle, intestine, stomach, and carapace was 11.51, 10.07, 4.31, 64.46, 1.01, 4.32, and 4.32 %, respectively. The biological half-life of the drug in the cultured species was 1.41 days, and the half-life degradation of the substance in water and sediment was 2.17 and 2.15 days,
respectively (Table 1). Finally, the ERA-AQUA model was run using the SM2 model scenario, and the predicted shrimp (whole-body residue), water and sediment concentrations were plotted against those measured by Sun et al. (2015) for the evaluation of the model output. Antimicrobial use data Information on antimicrobials potentially used in F. chinensis in China were collected from the new organized fishery drugs manual (Yang 2005), which was compiled from the technical information issued by the Chinese administrative department for fisheries, technology research, and promotion institution for aquaculture. The dataset contained a total number of 21 compounds belonging to five antimicrobial classes (Table 2). Antimicrobials that are reported to be banned by the Chinese regulation were not evaluated, assuming that they are not used or that their use will be ceased in the near future. The information collected included names of the main antimicrobial ingredients, dosages, modes, and frequencies of application (Table 2). When the recommended dosage and the duration of the treatment were reported as a numeric interval, the highest value was conservatively chosen for the risk assessment calculations. Physicochemical and pharmacokinetics data Physicochemical properties for the evaluated antimicrobials (e.g., molar mass, octanol-water sorption coefficient, sorption coefficient to organic carbon, half-life degradation in water, and sediment etc.) were collected from online databases (e.g., http://sitem.herts.ac.uk/aeru/vsdb/index.htm), chemical evaluation reports, and the peer-reviewed literature (Table S4). Data on biological half-lives in F. chinensis were selected from pharmacokinetics studies of antimicrobials in shrimp studies. When there were no data available in the open literature, the biological half-lives were obtained following the approach described in Rico and Van den Brink (2014). Toxicity data for aquatic organisms, resistance thresholds, and acceptable daily intake values Toxicity data for the antimicrobial ingredients evaluated in this study was collected for primary producers, invertebrates, and fish from the open literature and toxicity databases (e.g., www.epa.gov/ecotox). When experimental data was not available, toxicity values were derived from the US EPA quantitative structure-activity relationship (QSARs) stored in the ECOSAR v1.11 program (http://www.epa.gov/oppt/ newchems/tools/21ecosar.htm). The endpoint used for the calculation of acute predicted no effect concentrations (PNECs) for primary producers were the EC50 for algal growth or yield, calculated after an exposure period of 3 to 4 days. Priority was given to toxicity data for cyanobacteria
Environ Sci Pollut Res Table 2
Antimicrobial treatments used in F. chinensis and evaluated in this study
Antimicrobial
Treatment dose and duration
Ampicillin (BL) Amoxicillin (BL) Chlortetracycline (T) Doxycycline (T) Enrofloxacin (Q) Florfenicol (AP) Flumequine (AI) Gentamycin (AI) Kanamycin (AI) Levofloxacin (Q) Neomycin (AI)
20 20 50 60 50 20 20 70 50 50 40
mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 7 mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 5 mg (kg body weight)−1, daily for 5
days days days days days days days days days days days
Antimicrobial
Treatment dose and duration
Norfloxacin (Q) Ofloxacin (Q) Oxolinic acid (Q) Oxytetracycline (T) Pipemidic acid (Q) Sarafloxacin (Q) Sulfadiazine (S) Sulfamethazine (S) Tetracycline (T) Thiamphenicol (AP)
50 mg (kg body weight)−1, daily for 5 days 50 mg (kg body weight)−1, daily for 5 days 20 mg (kg body weight)−1, daily for 7 days 120 mg (kg body weight)−1, daily for 10 days 50 mg (kg body weight)−1, daily for 7 days 50 mg (kg body weight)−1, daily for 5 days 100 mg (kg body weight)−1, daily for 6 days 200 mg (kg body weight)−1, daily for 6 days 150 mg (kg body weight)−1, daily for 5 days 60 mg (kg body weight)−1, daily for 5 days
BL Beta-lactams, T Tetracyclines, Q Quinolones, AP Amphenicols; AI: aminoglycosides
species (e.g., Microcystis aeruginosa) which have a high sensitivity to these compounds (Lützhøft et al. 1999; HallingSørensen 2000). If no cyanobacteria toxicity data was available, toxicity values were obtained for Chlorella vulgaris or Scenedesmus subspicatus. The acute endpoints for the calculation of PNECs for invertebrates were the EC50 for Daphnia magna calculated for mortality or immobilization after an exposure period of 1 to 4 days. The acute endpoints for fish were based on the LC50 obtained after an exposure period of 1 to 7 days for any of the standard test species (Brachydanio rerio, Cyprinus carpio, Gasterosteus aculeatus, Lepomis macrochirus, Oncorhynchus mykiss, Oryzas latipes, Pimephales pomelas, or Poecilia reticulata). The lowest value was taken when more than one EC50 was available for the same species. The resulting dataset of ecotoxicity data for the evaluated compounds is presented in Table S5. The ecological PNEC was calculated by dividing the obtained EC50/ LC50 values by an assessment factor (AF) of 100 according to the international guidance document for the risk assessment of veterinary medicines (VICH 2004). Data on resistance thresholds for 14 antimicrobials were obtained from the study by Bengtsson-Palme and Larsson (2016). In their study, resistance PNECs were derived by dividing the predicted lowest MIC value obtained from the database of the European Committee on Antimicrobial Susceptibility Testing (EUCAST; www.eucast.org) by an assessment factor of 10 to account for the difference between the growth inhibitory concentration and the resistance selective concentration. The rationale behind using the lowest MIC values is that concentrations that inhibit growth of some bacterial species will be selective at the community level and thus represent upper boundaries for minimum selective concentrations. The calculated PNECs were rounded down to the closest concentration in the EUCAST testing scale and the lowest MIC concentrations used for their derivation were previously corrected for the
number of species tested as described in Bengtsson-Palme and Larsson (2016). Antimicrobial acceptable daily intake (ADI) values for humans were collected from the literature or regulatory reports (see Table S6), such as the ADI list made by the Australian government (2014). Scenario for F. chinensis production in China The aquaculture pond scenario for F. chinensis was generated based on a combination of literature data, monitoring results from pond water and sediment as described above, and a few assumptions. The modeled scenario represents a commercial pond production system over a grow-out period of 4 months (shrimp body weight at the start of the simulation period 0.04 g) with an area of 3502 m2, a water depth of 1.5 m, and an initial cultured species density of 6 individuals m−2, based on available literature describing typical production characteristics (Li et al. 2006; Zhang 2012). Depth of the top sediment layer in the aquaculture pond was assumed to be 5 cm. The suspended solid concentration in water and the organic matter content of suspended solids and sediments was based on the results obtained from the experimental study described in the BModel calibration and evaluation^ section. Feed application characteristics and other basic species parameters (e.g., lipid content) were obtained from the literature (Table 1). A maximum shrimp weight of 47 g was set as calibrating parameter to achieve an individual shrimp weight of 21 g at the end of a grow-out period of 4 months. The same calibration value of 0.2 for the fraction of applied antimicrobial that is actually eaten by the shrimp was also used here in the model. Mortality during the culture cycle was assumed to be 33 % based on the data reported by Zhang (2012). Pond water replacement was set to 20 cm every 7 days as described in Zhang (2012), with a duration of the effluent discharge of 4 h based on the values reported for Litopenaeus vannamei
Environ Sci Pollut Res
production in China (Rico and Van den Brink 2014). Untreated effluents were assumed to be directly discharged into the environment. Concentrations in the effluents were conservatively chosen for the risk assessment (no dilution was assumed). The full list of input parameter values representing the F. chinensis aquaculture production scenario for China is shown in Table 1. Environmental fate and risk calculations Separate runs were performed for the ecological and antimicrobial resistance risk assessments and for the human health risk assessment, with different administration timing of the antimicrobials. For the environmental risk assessment, antimicrobials were applied in the middle of the simulation period (day 60) assuming a realistic worst-case situation. These simulations were also used to evaluate the environmental fate and to determine the main processes contributing to the dissipation of the antimicrobials from the aquaculture ponds. The consumer risk assessment was performed for several application times in the last month of the culture cycle (assuming a worstcase situation), which were calculated taking into account a harvest time corresponding to days 5, 10, 15, and 20 after the last antimicrobial administration. The ecotoxicological and the resistance risks of the applied antimicrobials for the aquatic ecosystems surrounding F. chinensis farms were assessed by following a risk quotient (RQ) approach. RQs were calculated by dividing the peak predicted environmental concentration of the ecological and the resistance PNECs, respectively. RQs for consumers were estimated by dividing the estimated daily intake (EDI) of the antimicrobial in humans by the acceptable daily intake (ADI) values. The EDI was calculated according to the following equation: EDI ¼
PCC harvest 0:001 cons bw
With, PCCharvest: predicted antimicrobial concentration in the cultured species at harvest; cons: daily consumption of the cultured species; 0.001: correction factor to convert from μg kg−1 to mg kg−1; bw: consumer’s body weight. The daily consumption of the cultured species (cons) was assumed to be of 45 g (World Health Organization 2011), and the consumer’s body weight (bw) was assumed to be 60 kg (World Health Organization 2011).
Results and discussion Model calibration and evaluation The predicted and measured SM2 concentration dynamics in the shrimp, pond water, and sediment are shown in
Fig. 1. Results showed a good correspondence between the calculated and the measured SM2, with maximum differences within an order of magnitude. The largest discrepancies between the modeled and the measured concentrations were observed in the sediment compartment during the treatment period. Such differences can be explained by the fact that the ERA-AQUA model assumes uneaten medicated feed and feces being deposited on top of the sediment compartment, thus considering them part of it and neglecting dissolution of the antimicrobial mass contained in them directly into the pond water. Also, the model does not include a time lag between the antimicrobial intake and the egestion time, whereas in reality, the time to egestion may be of one to a few days (Tian et al. 2004). Overall, the model showed that predicted concentrations in the whole shrimp and in the water compartments are in agreement or slightly higher than the measured concentrations, indicating that the ERA-AQUA predictions can (conservatively) be used to perform risk evaluations. Residual concentrations and fate of antimicrobials The predicted peak antimicrobial concentrations in pond water, sediments, and in shrimps at the moment of harvest are shown in Table S7. Sulfadiazine showed the highest predicted pond water concentration (2846 ng L−1), followed by levofloxacin (2110 ng L − 1 ), sulfamethazine (1908 ng L −1 ), gentamicin (1680 ng L −1), tetracycline (1627 ng L−1), oxytetracycline (1430 ng L−1), oxolinic acid (1300 ng L−1), sarafloxacin (1237 ng L−1), and flumequine (1160 ng L−1). The concentrations of other antimicrobials ranged from 46.5 to 577 ng L−1. Sulfonamides such as sulfadiazine and sulfamethazine are hydrophilic substances with relatively slow degradation in surface waters (Kolpin et al. 2002), which explains their high predicted concentration in pond water. Discrepancies between the results calculated here and those shown by other studies can be explained by the different application methods and aquaculture management practices. For example, in southern China, OTC was detected in shrimp larvae ponds at a concentration (15,163 ng L−1) 10 times higher than the predicted concentration in our study, which might be explained by the typical application of this antimicrobial directly to water when shrimp larvae have not achieved a sufficient size to feed and receive antimicrobial treatments from pelleted feed. The concentration of oxytetracycline in pond sediments was the highest (601 ng g−1), followed by tetracycline (420 ng g−1), sulfamethazine (238 ng g−1), sulfadiazine (228 ng g−1), chlortetracycline (141 ng g−1), and doxycycline (110 ng g−1), whereas the maximum sediment concentration for the remaining antimicrobials ranged from 2.65 to 79 ng g−1, which were higher than some of the values reported
Environ Sci Pollut Res
A
30 Concentraon (μg·g-1)
Fig. 1 Measured (symbols) and predicted (lines) SM2 concentration dynamics in a. F. chinensis (whole body residue), b pond water, and c pond sediment. SM2 was applied at a daily dose of 50, 100, and 150 mg · (kg bw)−1 for a period of 5 consecutive days
25
150 mg·(kg·bw)-1
20
100 mg·(kg·bw)-1 50 mg·(kg·bw)-1
15 10 5 0
0
Concentraon (μg·L-1)
4
6
8
10
12
14
16
2
4
6
8
10
12
14
16
2
4
B
1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
Concentraon (μg·kg-1)
2
180 160 140 120 100 80 60 40 20 0
0
C
0
in previous studies (e.g., Chen et al. 2015). This can be explained by the different environmental conditions and aquaculture practices, such as organic acids (Zhang and Dong 2008), pH (Tolls 2001), minerals (Zhang and Huang 2007), drug administration method, and dosages (Sun et al. 2014, 2015). The high predicted concentration of oxytetracycline and tetracycline were probably related to their high sorption capacity to organic matter, with organic carbon sorption coefficients of 103,000 and 40,000 L kg−1, respectively (Table S4). As shown in Fig. 2, the amount of antimicrobials applied in F. chinensis aquaculture that is released into the environment via wastewater discharge ranged from 0.2 to 32 % of the total applied amount. The antimicrobials with the highest potential for environmental pollution
6 8 10 Days aer first anbioc applicaon
12
14
16
were oxolinic acid, levofloxacin, and sarafloxacin. In the majority of the cases, the main dissipation process in the pond environment was sediment degradation, whereas degradation in water was the second most important. Exceptions were the highly hydrophilic substances such as flumequine, kanamycin, neomycin, pipemidic acid, and thiamphenicol, for which degradation in the water compartment was found to be the main dissipation process (Fig. 2). The mass balance also shows that a high percentage of the applied antimicrobial mass might remain in the pond sediments at the end of the culture cycle, indicating that disposal of sludge or sediments from shrimp ponds into the environment can significantly contribute to antimicrobial discharge and contamination.
Environ Sci Pollut Res Amoxicillin (18) Ampicillin (18) Chlortetracycline (45) Doxycycline (54) Enrofloxacin (12) Florfenicol (18) Flumequine (25) Gentamycin (63) Kanamycin (45) Levofloxacin (45) Neomycin (36) Norfloxacin (45) Ofloxacin (12) Oxolinic acid (25) Oxytetracycline (208) Pipemidic acid (12) Sarafloxacin (22) Sulfadiazine (107) Sulfamethazine (249) Tetracycline (135) Thiamphenicol (54) 0%
20%
% waste water discharge % degradation in water %remaining in water %remaining in species
40%
60%
80%
100%
% degradation in sediment %biotransformation %remaining in sediment
Fig. 2 Results of the mass balance including the main processes accounting for the dissipation of antimicrobials at the end of the culture cycle in the Chinese F. chinensis scenario. Numbers next to the antimicrobial name indicate the mass applied during the whole antimicrobial treatment (g)
Ecological risk assessment The inspection of the ecotoxicity data already indicated that primary producers are generally the most sensitive group to antimicrobials compared to invertebrates and fish (Table S5). Results of the environmental risk assessment showed that only four antimicrobial applications resulted in an RQ higher than 1 for algae (Fig. 3), while no risks were calculated for invertebrates and fish. In this study, the cyanobacterium Microcystis aeruginosa was chosen as the preferred test organism to evaluate the potential environmental risks of antimicrobials to primary producers, because it has been found to be up to two orders more sensitive to antimicrobials than green algae (Lützhøft et al. 1999; Halling-Sørensen 2000) and it is the Fig. 3 Calculated risk quotients for primary producers for antimicrobials used in F. chinensis in China and predicted maximum environmental concentrations (μg L−1). Letters next to the antimicrobial name indicate the species used for the PNEC calculation. M = Microcystis aeruginosa, L = Lemna gibba, N = algae species were not available, G = green algae
Levofloxacin (M) Sarafloxacin (M) Ampicillin (M) Sulfadiazine (M) Oxolinic acid (M) Ofloxacin (M) Oxytetracycline (M) Thiamphenicol (M) Norfloxacin (M) Enrofloxacin (M) Sulfamethazine (M) Flumequine (M) Tetracycline (M) Kanamycin (N) Neomycin (L) Doxycycline (L) Chlortetracycline (M) Pipemidic acid (N) Florfenicol (G) Amoxicillin (G) Gentamycin (N)
preferred species for the regulatory risk assessment of antimicrobials (VICH 2004). Levofloxacin showed the highest potential risk, with an RQ of 32 at the predicted environmental concentration of 2.5 μg L−1 in our study. Robinson et al. (2005) reported an EC50 value of levofloxacin to Lemna minor of 51 (8.6–94) μg L−1. Therefore, an RQ of 4.9 was calculated for L. minor at the predicted environmental concentration (2.5 μg L−1), indicating that higher aquatic plants might also be affected by peak levofloxacin concentrations in shrimp wastewater discharge points. Sarafloxacin showed the second highest risk, with a calculated RQ of 8.29, followed by ampicillin and sulfadiazine. Lützhøft et al. (1999) reported EC50 values of sulfadiazine to M. aeruginosa, R. salina, and S. capricornutum of 0.135, 403, and 7.8 mg L−1, respectively. In our study, sulfadiazine showed an RQ of 2.71 for M. aeruginosa, which was much higher than the calculated RQs for R. salina and S. capricornutum (0.00092 and 0.045 respectively), which suggests that there is no potential risk of sulfadiazine to eukaryotic algae such as cryptophycean and green algae. Whether the competition between green algae and cyanobacteria can be affected by low, environmental concentrations of antimicrobials has not yet been investigated and remains an important topic to evaluate the possible effects of aquaculture-borne antimicrobial pollution on microalgal communities in aquatic ecosystems. Due to the simultaneous application of antimicrobials and the clustered characteristics of some of the most intensive aquaculture producing areas of China, antimicrobials can cooccur in the aquatic environment, and therefore the risk of mixtures of antimicrobials may be important to be assessed. For cyanobacteria, it has been demonstrated that the combined toxicity of several antimicrobials may result in effects other than the expected additive effects. For example, combinations of amoxicillin-norfloxacin and amoxicillin-levofloxacin showed strong antagonism at low exposure levels and synergistic toxic effects at high exposure levels, while amoxicillin2.5
0.059 1.9 0.57 1.5 0.55
1.2 0.34 0.57 0.045 1.8
0.01
1.5 0.093
0.18 1.3
3.7 1.7
1.2
0.32 0.29 0.22
0.1
1
Risk Quotients
10
100
Environ Sci Pollut Res
tetracycline and levofloxacin-tetracycline could result in synergistic toxic effects (González-Pleiter et al. 2013). Some studies have also shown that quinolones affect synergistically green algae (Backhaus et al. 2000; Christensen et al. 2006; Yang et al. 2008; González-Pleiter et al. 2013). Further research must be dedicated to evaluate antimicrobial use practices in clustered, intensive aquaculture areas in order to identify relevant antimicrobial mixtures that have to be addressed in refined risk assessments. Another important factor influencing the toxicity of the evaluated antimicrobials to primary producers is the pH of the exposed aquatic ecosystems. The pH levels of water fluctuate in some aquatic ecosystems daily and seasonally, depending on the eutrophic status and the contamination with acidifying substances such as mine or industrial drainages (Nieto et al. 2007). Most antimicrobials are ionisable substances at environmentally relevant pHs, and higher toxicity is expected at a high pH for bases and at a low pH for acids (Rendal et al. 2011). Several studies have demonstrated that changes in water pH could influence the bioavailability, uptake, and toxicity of ionisable pharmaceuticals for invertebrates (Kim et al. 2010; Meredith-Williams et al. 2012). Further experiments should focus on assessing the effects of pH on the toxicity of antimicrobials to algae and to evaluate whether the safety factors used in this study and proposed by regulatory guidance documents (e.g., VICH 2004) are sufficient to account for such toxicity differences. In this study, we assumed that the dissipation of the antimicrobials in the effluent discharge point was fast due to the water flow, sorption to sediments and biodegradation processes. This, added to the discontinuous effluents discharge into the environment (once every 7 days), resulted in low timeweighted average concentrations and very low calculated chronic risk quotients (all below one, data not shown). However, sediments nearby aquaculture effluent discharge points can be considered as an important environmental compartment accumulating antimicrobial residues (Andrieu et al. 2015) and should be further investigated by, for example, conducting long-term ecotoxicological tests including sediment microbial communities, periphytic algae, and benthic invertebrates.
2005). It is expected that the selective pressure exerted by antimicrobial residues in the pond environment is affecting non-target and target bacteria indistinctively, thus compromising the efficacy of some antimicrobials to effectively treat bacterial disease outbreaks. As for the natural environment, it must be taken into account that dilution of the shrimp farm effluents in the surrounding aquatic ecosystems can occur, but direct risks for some antimicrobials will prevail when dilution factors are about or below 10. Furthermore, the discharge of farm effluents and the aquatic persistence of these antimicrobials is, in most cases, long enough (days to weeks) to exert a chronic selective pressure on microbial communities in the environment. Although a relationship between overall antimicrobial use and the increasing prevalence of bacterial resistance has been demonstrated in many studies (Samuelsen et al. 1992; Avendaño-Herrera et al. 2008; Hoa et al. 2011; Buschmann et al. 2012; Muziasari et al. 2016), the relationship between the environmental occurrence of certain antimicrobials and resistance levels found in the environment is not the same in all cases (Takasu et al. 2011). The bioavailability of different antimicrobials to bacteria can be largely influenced by the physicochemical properties of the compound, such as its sorption capacity to decomposing organic material, and the variation in environmental conditions influencing sorptiondesorption dynamics (e.g., pH, salinity; Xu and Li 2010). Furthermore, the bacterial susceptibility to exposure largely depends on their biological traits (e.g., non-aggregated vs biofilm aggregated bacteria). Uncertainties on the actual exposure of benthic bacteria to antimicrobials prevented us from calculating RQs using predicted sediment concentrations. It must be noted, however, that predicted exposure levels in this compartment were higher and exposure durations longer than in the pond water, suggesting that higher growth inhibition and selective pressure could take place when the sediment compartment is taken into account. Additionally, other biocides used in aquaculture and metals from waste feeds may contribute to the selection of antibiotic resistance genes (Pal et al. 2014), complicating our predictive capacity between antimicrobial applications and resistance gene dynamics at the population and community levels.
Antimicrobial resistance risk assessment
Consumer risk assessment
The calculated resistance RQs for the 14 antimicrobials with available PNEC values are shown in Figure 4. RQs were higher than 1 for seven antimicrobials, and higher than 10 for enrofloxacin, ampicillin, and levofloxacin. High percentages of resistance to ampicillin and other antimicrobials similar to those showing high RQs (e.g., enrofloxacin) have been monitored among bacteria isolated from shrimp ponds in India, including those that are a major threat to F. chinensis such as V. anguillarum or A. hydrophila (Vaseeharan et al.
The results of the human health risk assessment of antimicrobials are shown in Table 3. Exceedance of the EDI for consumers was observed for four antimicrobials when a withdrawal period of 5 days was used, while only ofloxacin showed a RQ higher than 1 after a withdrawal period of 10 days. Therefore, a withdrawal period of about 2 weeks after the end of the antimicrobial treatment is recommended for antimicrobials added via oral administration in order to prevent possible short-term toxicological risks for consumers.
Environ Sci Pollut Res Fig. 4 Calculated resistance risk quotients for antimicrobials used in F. chinensis in China and predicted maximum environmental concentrations (μg L−1)
1.9
Enrofloxacin
3.7
Ampicillin
2.5
Levofloxacin
1.5
Flumequine
1.5 1.3
Oxytetracycline Ofloxacin
1.8
Gentamycin
0.55
Tetracycline Florfenicol Amoxicillin Kanamycin Neomycin Norfloxacin Doxycycline
0.57 0.045 0.32 0.29 0.059 0.22
0.1
1
10
100
Risk Quotients
The predicted shrimp concentration of amoxicillin 15 days after the administration was 200 μg kg−1, which was four times higher than the MRL (50 μg kg−1) set by both the Chinese government and the EU (Love et al. 2011). For florfenicol and flumequine, the predicted shrimp concentration was 290 and 240 μg kg−1, respectively, which was higher than the MRL (100 μg kg−1) set by EU (Love et al. 2011), but lower than the MRLs (1000 μg kg−1 for florfenicol and Table 3 Results of the consumer’s risk assessment for antimicrobials used in F. chinensis in China
Antimicrobial
500 μg kg−1 for flumequine) set by the Chinese government. After a withdrawal period of 20 days, there was no risk calculated for the trade of shrimp to the EU for all the listed antimicrobials, i.e., no exceedances of MRLs. This indicates that a withdrawal period of at least 3 weeks is needed to comply with the food safety standards currently used by importing countries, as well as to protect local consumer’s health. It should be noted, however, that the uptake and
5 days
10 days
15 days
20 days
EDI
RQ
EDI
RQ
EDI
RQ
EDI
RQ
Amoxicillin
0.94
1.34
0.38
0.55
0.16
0.23
0.068
