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
State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
China Energy Conservation and Environmental Protection Group, Beijing 100082, China.
Environ Sci Ecotechnol. 2024 Mar 11;20:100412. doi: 10.1016/j.ese.2024.100412. eCollection 2024 Jul.
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 (NH-N), chemical oxygen demand (COD), biochemical oxygen demand (BOD), total phosphorus (TP), and the permanganate index (COD) 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.
对大型流域进行有效管理需要精准确定主要指标超标以及城市风险因素的时空驱动因素,这为流域管理者提供了关键见解。然而,对长江流域内多种污染物进行全面考察的研究仍然稀缺。在此,我们引入了长江流域周边城市群的污染清单,分析了2018 - 2019年期间102个城市的水质数据。我们评估了六个关键指标的超标率:每个城市的溶解氧(DO)、氨氮(NH-N)、化学需氧量(COD)、生化需氧量(BOD)、总磷(TP)和高锰酸盐指数(COD)。通过随机森林回归和SHapley值附加解释(SHAP)分析,我们确定了影响这些关键指标的时空因素。我们的结果表明,农业活动是所有六个指标超标问题的主要成因,因此将其确定为该流域的主要污染源。此外,森林覆盖率、畜牧业、化工和制药行业,以及降水和温度等气象因素,对各指标的超标情况有显著影响。此外,我们通过主成分分析划定了五个核心城市风险成分,即(1)人为和工业活动,(2)农业活动和森林覆盖范围,(3)气候变量,(4)畜牧业养殖,以及(5)主要污染行业。随后,根据这些风险成分对各城市进行了评估和分类,并将各地区的政策干预措施和行政绩效纳入其中。综合分析主张针对已识别的风险因素制定定制化策略,特别是对于风险水平较高的城市。