抗生素
抵抗性
抗生素耐药性
环丙沙星
恩诺沙星
化学
污水处理
渗滤液
环境化学
食品科学
生物技术
生物
微生物学
环境科学
环境工程
整合子
作者
Pinjing He,Zhuofeng Yu,Liming Shao,Yizhou Zhou,Fan Lü
标识
DOI:10.1016/j.jes.2019.04.004
摘要
Is our food safe and free of the crisis of antibiotics and antibiotic resistance (AR)? And will the derived food waste (FW) impose AR risk to the environment after biological treatment? This study used restaurant FW leachates flowing through a 200 tons-waste/day biological treatment plant as a window to investigate the fate of antibiotics and antibiotic-resistance genes (ARGs) during the acceptance and treatment of FW. Sulfonamides (sulfamethazine, sulfamethoxazole) and quinolones (ciprofloxacin, enrofloxacin, ofloxacin) were detected during FW treatment, while tetracyclines, macrolides and chloramphenicols were not observable. ARGs encoding resistance to sulfonamides, tetracyclines and macrolides emerged in FW leachates. Material flow analysis illustrated that the total amount of antibiotics (except sulfamethazine) and ARGs were constant during FW treatment processes. Both the concentration and total amount of most antibiotics and ARGs fluctuated during treatment, physical processes (screening, centrifugation, solid-liquid and oil-water separation) did not decrease antibiotic or ARGs concentrations or total levels permanently; the affiliated wastewater treatment plant appeared to remove sulfonamides and most ARGs concentrations and total amount. Heavy metals Ni, Co and Cu were important for disseminating antibiotics concentrations and MGEs for distributing ARGs concentrations. Humic substances (fulvic acids, hydrophilic fractions), C-associated and N-associated contents were essential for the distribution of the total amounts of antibiotics and ARGs. Overall, this study implied that human food might not be free of antibiotics and ARGs, and FW was an underestimated AR pool with various determinants. Nonetheless, derived hazards of FW could be mitigated through biological treatment with well-planned daily operations.
科研通智能强力驱动
Strongly Powered by AbleSci AI