Volume 16 Issue 2
Jun.  2023
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Article Navigation >  Water Science and Engineering  > 2023  >  16(2): 165-174
Kai Chen, Qi-meng Liu, Wei-hua Peng, Yu Liu, Zi-tao Wang. 2023: Source apportionment of river water pollution in a typical agricultural city of Anhui Province, eastern China using multivariate statistical techniques with APCS– MLR. Water Science and Engineering, 16(2): 165-174. doi: 10.1016/j.wse.2022.12.007
Citation: Kai Chen, Qi-meng Liu, Wei-hua Peng, Yu Liu, Zi-tao Wang. 2023: Source apportionment of river water pollution in a typical agricultural city of Anhui Province, eastern China using multivariate statistical techniques with APCS– MLR. Water Science and Engineering, 16(2): 165-174. doi: 10.1016/j.wse.2022.12.007
shu

Source apportionment of river water pollution in a typical agricultural city of Anhui Province, eastern China using multivariate statistical techniques with APCS– MLR

doi: 10.1016/j.wse.2022.12.007

  • a State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Huainan 232001, China;

  • b School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China;

  • c Key Laboratory of Mine Water Resource Utilization of Anhui Higher Education Institutes, Suzhou University, Suzhou 234000, China;

  • d Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining 810008, China

Funds:

This work was supported by the 2021 Graduate Science Research Project of the Anhui Higher Education Institutions (Grant No. YJS20210375), the Natural Science Research Project of Universities in Anhui Province (Grant No. KJ2020ZD64), the Natural Science Foundation of Anhui Province (Grant No. 2008085MD122), the Outstanding Young Talents in Higher Education Institutions of Anhui Province (Grant No. ZD2021134), and the Research Development Foundation of Suzhou University (Grant No. 2021fzjj28).

  • Received Date: 2021-12-22
  • Accepted Date: 2022-12-30
  • Rev Recd Date: 2022-12-18
    • Available Online: 2023-05-11
    Abstract
  • The deterioration of the surface water environment has become a serious challenge for water resources management due to increasing anthropogenic disturbance. Water resources protection requires control of potential pollution sources. In this study, 99 water samples were collected from a river in a typical agricultural city of Anhui Province in eastern China, and these samples were analyzed in terms of pH, electrical conductivity, and the concentrations of F-, Cl-, SO42-, Na+, K+, Mg2+, Ca2+, As, Cr, Cu, Zn, and Pb. Cluster analysis, co-occurrence network analysis, and principal component analysis/factor analysis were conducted to qualitatively identify the potential sources of river water pollution in the study area. An absolute principal component score-multiple linear regression receptor model was used to quantitatively evaluate the contribution of each source to water quality parameters. The results showed that all observed water quality indices met the quality criteria specified in the Chinese drinking water standards, except for pH, ρ(F-), ρ(SO42-), and ρ(As). The heat map showed that the frequent recharge of pollutants from the tributaries during the wet season was the main reason for the deterioration of water quality. Five sources of river water pollution were identified, and their contribution ratios in a descending order were as follows: the geogenic process (24%) > agricultural activities (21%) > poultry farming sources (17%) > domestic pollution (9%) > transportation pollution (5%). Therefore, controlling pollution from agricultural activities, strengthening the regulation of livestock farming, and improving the sewage network are the recommended strategies for improving the quality of surface water resources in this area.

     

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