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Spatiotemporal drivers of urban water pollution: Assessment of 102 cities across the Yangtze River Basin.
Zhao, Yi-Lin; Sun, Han-Jun; Wang, Xiao-Dan; Ding, Jie; Lu, Mei-Yun; Pang, Ji-Wei; Zhou, Da-Peng; Liang, Ming; Ren, Nan-Qi; Yang, Shan-Shan.
Afiliação
  • Zhao YL; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Sun HJ; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Wang XD; China Energy Conservation and Environmental Protection Group, Beijing 100082, China.
  • Ding J; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Lu MY; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Pang JW; China Energy Conservation and Environmental Protection Group, Beijing 100082, China.
  • Zhou DP; China Energy Conservation and Environmental Protection Group, CECEP Digital Technology Co., Ltd., Beijing 100089, China.
  • Liang M; China Railway Engineering Design and Consulting Group Co., Ltd., Beijing 100055, China.
  • Ren NQ; China Railway Engineering Design and Consulting Group Co., Ltd., Beijing 100055, China.
  • Yang SS; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Environ Sci Ecotechnol ; 20: 100412, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38560759
ABSTRACT
Effective management of large basins necessitates pinpointing the spatial and temporal drivers of primary index exceedances and urban risk factors, offering crucial insights for basin administrators. Yet, comprehensive examinations of multiple pollutants within the Yangtze River Basin remain scarce. Here we introduce a pollution inventory for urban clusters surrounding the Yangtze River Basin, analyzing water quality data from 102 cities during 2018-2019. We assessed the exceedance rates for six pivotal indicators dissolved oxygen (DO), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), biochemical oxygen demand (BOD), total phosphorus (TP), and the permanganate index (CODMn) for each city. Employing random forest regression and SHapley Additive exPlanations (SHAP) analyses, we identified the spatiotemporal factors influencing these key indicators. Our results highlight agricultural activities as the primary contributors to the exceedance of all six indicators, thus pinpointing them as the leading pollution source in the basin. Additionally, forest coverage, livestock farming, chemical and pharmaceutical sectors, along with meteorological elements like precipitation and temperature, significantly impacted various indicators' exceedances. Furthermore, we delineate five core urban risk components through principal component analysis, which are (1) anthropogenic and industrial activities, (2) agricultural practices and forest extent, (3) climatic variables, (4) livestock rearing, and (5) principal polluting sectors. The cities were subsequently evaluated and categorized based on these risk components, incorporating policy interventions and administrative performance within each region. The comprehensive analysis advocates for a customized strategy in addressing the discerned risk factors, especially for cities presenting elevated risk levels.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Environ Sci Ecotechnol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Environ Sci Ecotechnol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Holanda