Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis
Zheng Zeng,
Wei-Ge Luo,
Zhe Wang and
Fa-Cheng Yi
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Zheng Zeng: College of Environment and Resources, Southwest University of Science and Technology, Mianyang 621010, China
Wei-Ge Luo: College of Environment and Resources, Southwest University of Science and Technology, Mianyang 621010, China
Zhe Wang: College of Environment and Resources, Southwest University of Science and Technology, Mianyang 621010, China
Fa-Cheng Yi: College of Environment and Resources, Southwest University of Science and Technology, Mianyang 621010, China
Sustainability, 2021, vol. 13, issue 2, 1-17
Abstract:
This work aimed to assess the water quality of the Tuojiang River Basin in recent years to provide a better understanding of its current pollution situation, and the potential pollution risks and causes. Water quality parameters such as dissolved oxygen (DO), ammonia–nitrogen (NH 3 -N), total phosphorus (TP), the permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), pH, and concentrations of various heavy metals were measured in the Tuojiang River, according to the national standards of the People’s Republic of China. Samples were collected between 2012 to 2018 at 11 national monitoring sites in the Tuojiang River Basin. The overall water pollution situation was evaluated with back propagation artificial neural network (BP-ANN) analysis. The pollution causes were analyzed considering both industrial wastewater discharge in the upper reaches and the current pollution situation. We found potential risks of excessive NH 3 -N, TP, Cd, Hg, and Pb concentrations in the Tuojiang River Basin. Moreover, corresponding water pollution control suggestions were given.
Keywords: Tuojiang River; water pollution; heavy metal pollution; BP-ANN evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:2:p:792-:d:480799
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