| Literature DB >> 30698906 |
Enru Wang1, Qian Li2, Hao Hu3, Fuli Peng2, Peng Zhang2, Jianjun Li2.
Abstract
Based on recent water quality data collected from 763 monitoring sections nationwide, this study examined the concentration of major pollutants in China's major rivers. A spatial autocorrelation analysis confirmed that river pollution was spatially uneven and clustered. While pollution of surface water was a nationwide concern, most serious water pollution happened in the Huai, Hai, Yellow, and Liao river Basins in Northern China. The results of the spatial regression analysis showed that GDP per capita, surface water stock, population, and economic structure were all significantly correlated with surface water pollution, with population having strongest impact, followed by level of economic development. By investigating the common characteristics shared by the "hotspot" cities where serious water pollution occurred, this study recommended a regional or basin approach to assessing water quality and controlling river pollution that cuts across jurisdiction boundaries. While China has made considerable progress in improving water productivity, there is still enormous potential in water conservation. It is also imperative to restructure local economy and develop water-efficient, less polluting industries and services. PRACTITIONER POINTS: River pollution in China was spatially uneven and clustered. Most serious water pollution happened in the Huai, Hai, Yellow, and Liao river basins in Northern China. GDP per capita, surface water stock, population, and economic structure correlated with surface water pollution, with population having strongest impact. A regional or basin approach was recommended to assess water quality and controlling river pollution across jurisdiction boundaries. It is also imperative to restructure local economy and develop water-efficient, less polluting industries and services.Entities:
Keywords: China; river basins; spatial autocorrelation; spatial regression; water pollution; water quality
Mesh:
Year: 2019 PMID: 30698906 DOI: 10.1002/wer.1044
Source DB: PubMed Journal: Water Environ Res ISSN: 1061-4303 Impact factor: 1.946