School of Geography and Ocean Sciences, Yanbian University, 977 Park Road, Hunchun, Jilin, China.
Key Laboratory of Wetland Ecological Functions and Ecological Security, 977 Park Road, Hunchun, Jilin, China.
Environ Sci Pollut Res Int. 2023 Oct;30(47):104852-104869. doi: 10.1007/s11356-023-29390-z. Epub 2023 Sep 15.
Agricultural production, urbanization, and other anthropogenic activities, the major causes of surface water pollution in China, have dramatically altered hydrological processes and nutrient cycles. Identifying and quantifying the key factors affecting water quality are essential for the better prevention and management of water pollution. However, due to the limitations of traditional statistical analysis methods, it is difficult to evaluate the spatial changes and interactions of influencing factors on water quality. In addition, research on a national scale is difficult, as it involves large-scale and long-term water quality monitoring work. In this study, we collected and collated the monthly average concentrations of four water quality parameters, dissolved oxygen, ammonia nitrogen, chemical oxygen demand, and total phosphorous, based on data from 1547 water quality monitoring stations in China. The combined pollution level of the water quality was assessed using the water quality index. Based on the water quality characteristics, water quality monitoring sites in the dry and wet seasons were grouped using k-means clustering. Eleven environmental factors were evaluated using geodetector software, including six human factors and five natural factors. The results showed that there are high-risk areas for water quality pollution in the eastern and southeastern coastal regions of China in both the dry and wet seasons and that surface water pollution in China is highly spatial heterogenous in both the dry and wet seasons. Among the anthropogenic factors, urban land area is the main factor of water quality pollution in the dry season, and the explanation rate of spatial heterogeneity of integrated water quality pollution index is 20.3%. The number of poultry farms and the area of farmland explained 12.4% and 12.1% of the integrated water quality pollution index in the wet season. The nonlinear relationship between these three anthropogenic and natural factors and their interaction exacerbated water quality pollution. Based on this analysis, we identified the key factors affecting surface water quality in China during the dry and wet seasons, evaluated the achievements of the water environmental protection policies in China in recent years, and proposed future management measures for the effective prevention and control of water quality pollution in high-risk areas.
农业生产、城市化和其他人为活动是中国地表水环境污染的主要原因,这些活动极大地改变了水文过程和养分循环。识别和量化影响水质的关键因素对于更好地预防和管理水污染至关重要。然而,由于传统统计分析方法的局限性,很难评估影响因素对水质的空间变化和相互作用。此外,由于涉及大规模和长期的水质监测工作,在全国范围内进行研究是困难的。
在这项研究中,我们根据中国 1547 个水质监测站的数据,收集和整理了四个水质参数(溶解氧、氨氮、化学需氧量和总磷)的月平均浓度。采用水质指数法评价水质综合污染水平。根据水质特征,利用 k-均值聚类法将枯水期和丰水期的水质监测站点进行分组。利用地理探测器软件评估了 11 种环境因素,包括 6 个人为因素和 5 个自然因素。
结果表明,中国东部和东南部沿海地区在枯水期和丰水期都存在高风险的水质污染区,中国地表水在枯水期和丰水期都具有高度的空间异质性。在人为因素中,城市土地面积是枯水期水质污染的主要因素,综合水质污染指数的空间异质性解释率为 20.3%。丰水期,家禽养殖场数量和耕地面积分别解释了综合水质污染指数的 12.4%和 12.1%。这三个人为和自然因素及其相互作用的非线性关系加剧了水质污染。
基于此分析,我们确定了中国在枯水期和丰水期影响地表水水质的关键因素,评估了中国近年来水环境保护政策的成效,并提出了未来在高风险地区有效预防和控制水质污染的管理措施。