Abstract
Understanding the spatiotemporal patterns of water quality is crucial because it provides essential information for water pollution control. The spatiotemporal variations in water quality for the Nanxi River in the Taihu watershed of China were evaluated by a water quality index (WQI) and multivariate statistical techniques; additionally, the potential sources of contamination were identified. The data set included 22 water quality parameters collected during the monitoring period from 2015 to 2020 for 14 monitoring stations. WQI assessment revealed that approximately 85% of monitoring stations were classified as “medium-low” water quality, and most showed continuous improvement in water quality. Cluster analysis divided the 14 monitoring stations into three clusters (low contamination, medium contamination and high contamination). Discriminant analysis identified pH, petroleum, volatile phenol, chemical oxygen demand, total phosphorus, F, S, fecal coliform, SO4, Cl, NO3-N, total hardness, NO2-N and NH3 as important parameters affecting spatial variations. Factor analysis identified four potential contamination source types: nutrient, organics, feces and oil. This study demonstrated the usefulness of multivariate statistical techniques in assessing large data sets, identifying contamination source types, and better understanding spatiotemporal variations in water quality to restore and protect water resources.
Highlights
Accepted: 27 February 2022Deterioration of the water environment is a prominent problem in worldwide watershed management and seriously threatens the security of the water ecological environment [1]
total and nitrogen (TN) is the sum of NO3 -N, NO2 -N, NH3 -N and organic nitrogen, which is the main indicator of water eutrophication
The concentration levels of CODMn, BOD5 and chemical oxygen demand (COD) deserve attention because these parameters represent the levels of biological, chemical and organic contamination in surface water, respectively
Summary
Deterioration of the water environment is a prominent problem in worldwide watershed management and seriously threatens the security of the water ecological environment [1]. Natural factors (such as climate, topography, geology and soil) and human activities (such as urbanization, industrial production and agricultural practice) affect the surface water quality of an area [2–5]. Dynamic spatiotemporal assessment of water quality can be used to analyze water contamination problems, identify potential contamination source types, and provide information support and reference to effectively manage water resources [3]. To effectively prevent and control surface water contamination, reliable water quality data for in-depth research is necessary. Considering the spatiotemporal variation in the physicochemical and biological characteristics of surface water, a long-term monitoring plan to accurately assess water quality should be developed [9]. Environmental protection departments in China have established sound water quality monitoring networks and continuous water quality monitoring procedures that monitor the physical properties
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