Literature DB >> 35189208

Sulfonamide and tetracycline in landfill leachates from seven municipal solid waste (MSW) landfills: Seasonal variation and risk assessment.

Yangqing Wang1, Yu Lei1, Xi Liu2, Liyan Song3, Naima Hamid4, Rui Zhang2.   

Abstract

Antibiotics have received increased attention as emerging contaminants due to their toxicity and potential risk. Landfills serve as one of the important reservoirs of antibiotics. The antibiotics in landfills leaching to nearby environment by leachate may threat ecosystem health. The present study aimed to evaluate the levels of tetracyclines (TCs) and sulfonamides (SAs) in seven Chinese Municipal Solid Waste (MSW) landfill leachates over two years (2017-2018). Seven target antibiotics, TC, oxytetracycline (OTC), doxycycline (DXC), sulfonamide sulfadiazine (SD), sulfamerazine (SM), sulfamethazine (SMX), and sulfamethoxazole (SMT), were detected in 56 landfill leachate samples. Among these, SMT had the highest mean concentration at 654 ng/L (n = 45), followed by OTC (219.58 ng/L, n = 47), and SD (209.98 ng/L, n = 49). The temporal trend showed that antibiotic concentrations were higher in 2017 than in 2018. Furthermore, physicochemical properties were significantly correlated with SAs (p < 0.05), whereas no significant correlation was found for TCs. Seasonal variation analysis revealed that antibiotic levels were higher in spring and winter compared to summer and fall seasons, which might be attributed to the higher waterfall levels in these seasons. Risk assessment revealed that SAs (SM, SMX, SMT) are associated with high risk, and the RQs follow the order of: SMX > SMT > SM. In contrast, TCs had insignificant risk. The findings of this two-year comprehensive monitoring project have produced positive results regarding antibiotic pollution at landfill sites, which can be applied to antibiotics management in landfill and further ensure public health.
Copyright © 2022 Elsevier B.V. All rights reserved.

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Keywords:  Antibiotics; Landfill and landfill leachate; Risk assessment; Seasonal variation

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Year:  2022        PMID: 35189208     DOI: 10.1016/j.scitotenv.2022.153936

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long Short-Term Memory Model.

Authors:  Bingchun Liu; Ningbo Zhang; Lingli Wang; Xinming Zhang
Journal:  Int J Environ Res Public Health       Date:  2022-05-28       Impact factor: 4.614

  1 in total

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