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A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers.
Yang, Sihang; Liang, Manchun; Qin, Zesheng; Qian, Yiwu; Li, Mei; Cao, Yi.
Afiliação
  • Yang S; Institute of Public Safety Research, Department of Engineering Physics, Beijing Key Laboratory of City Integrated Emergency Response Science, Tsinghua University, Beijing, China.
  • Liang M; Institute of Public Safety Research, Department of Engineering Physics, Beijing Key Laboratory of City Integrated Emergency Response Science, Tsinghua University, Beijing, China. lmc@tsinghua.edu.cn.
  • Qin Z; Environmental Safety Business Division, Beijing GSafety Technology, Co., Ltd., Beijing, China.
  • Qian Y; Hefei Institute for Public Safety Research, Tsinghua University, Hefei, China.
  • Li M; Hefei Institute for Public Safety Research, Tsinghua University, Hefei, China.
  • Cao Y; Hefei Institute for Public Safety Research, Tsinghua University, Hefei, China.
Sci Rep ; 11(1): 8714, 2021 04 22.
Article em En | MEDLINE | ID: mdl-33888742
It's vital to explore critical indicators when identifying potential pollution sources of urban rivers. However, the variations of urban river water qualities following temporal and spatial disturbances were highly local-dependent, further complicating the understanding of pollution emission laws. In order to understand the successional trajectory of water qualities of urban rivers and the underlying mechanisms controlling these dynamics at local scale, we collected daily monitoring data for 17 physical and chemical parameters from seven on-line monitoring stations in Nanfeihe River, Anhui, China, during the year 2018. The water quality at tributaries were similar, while that at main river was much different. A seasonal ''turning-back" pattern was observed in the water quality, which changed significantly from spring to summer but finally changed back in winter. This result was possibly regulated by seasonally-changed dissolved oxygen and water temperature. Linear mixed models showed that the site 2, with the highest loads of pollution, contributed the highest (ß = 0.316, P < 0.001) to the main river City Water Quality Index (CWQI) index, but site 5, the geographically nearest site to main river monitoring station, did not show significant effect. In contrast, site 5 but not site 2 contributed the highest (ß = 0.379, P < 0.001) to the main river water quality. Therefore, CWQI index was a better index than water quality to identify potential pollution sources with heavy loads of pollutants, despite temporal and spatial disturbances at local scales. These results highlight the role of aeration in water quality controlling of urban rivers, and emphasized the necessity to select proper index to accurately trace the latent pollution sources.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article