Ecotoxicological risk assessment and source apportionment of antibiotics in the waters and sediments of a peri-urban river.


Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
20 Aug 2020
Historique:
received: 26 02 2020
revised: 27 04 2020
accepted: 28 04 2020
pubmed: 16 5 2020
medline: 11 7 2020
entrez: 16 5 2020
Statut: ppublish

Résumé

Antibiotics have been widely used in the past decades and caused global public health concerns due to the growing problems of antimicrobial resistance. The peri-urban rivers are always receiving massive wastes containing antibiotics and appear to be a reservoir of antibiotics and antibiotic resistance genes in the environment. To prevent and control the pollution of antibiotics, it is essential to correctly identify the potential sources of antibiotics in peri-urban rivers. Currently, systematic knowledge on risk characteristics and source apportionment of antibiotics in peri-urban rivers is still lacking. In the study, we addressed this problem and focused on exploring the ecotoxicological risk and potential sources of antibiotics in a peri-urban river in Beijing (Chaobai River). To this end, the waters and sediments were collected from the river, as well as the potential source types including domestic sewage, WWTP effluent, chicken manure, pig manure and cattle manure. The occurrence and concentration levels of 16 antibiotics in the waters and sediments of the river were comprehensively characterized, as well as the correlation of antibiotics with environmental factors. Then, risk quotients and mixture risk quotients were used to assess the ecotoxicological risk of single compound and the mixture toxicity of antibiotics, respectively. The synergistic effects of antibiotic mixtures were also analyzed. Further, positive matrix factorization was employed to apportion the potential sources of antibiotics based on the multilinear engine (ME-2) algorithm. The target antibiotics were widely detected in the peri-urban river and several antibiotics posed moderate ecotoxicological risks on aquatic organisms. Apportionment analysis identified four potential sources of antibiotics in the waters of Chaobai River, including domestic sewage (31.5%), chicken waste (26.4%), WWTP effluent (22.2%) and a mix source (20.0%). Additionally, WWTP effluent (~58%) and sewage effluent (41%) were apportioned as the main contributors of antibiotics in the sediments.

Identifiants

pubmed: 32413658
pii: S0048-9697(20)32645-0
doi: 10.1016/j.scitotenv.2020.139128
pii:
doi:

Substances chimiques

Anti-Bacterial Agents 0
Water Pollutants, Chemical 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

139128

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Yuxin Zhang (Y)

Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education, College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing 100875, China.

Haiyang Chen (H)

Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education, College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing 100875, China. Electronic address: chen.haiyang@bnu.edu.cn.

Lijun Jing (L)

Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education, College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing 100875, China.

Yanguo Teng (Y)

Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education, College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing 100875, China.

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Classifications MeSH