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The rapid industrial and agricultural development as well as urbanization signi cantly affect the water environment, especially in subwatersheds where the contaminants/constituents present in the pollution sources are complex and the ow is unstable. Water quality assessment and quantitative identi cation of pollution sources are the primary prerequisites for the improvement of water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, which is an important pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis (PCA) was used to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment e ciency. Cluster analysis (CA) further illustrates the spatiotemporal distribution characteristics of river water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which veri es the reliability of the evaluation system. The PMF model identi ed ve and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The results of this study can provide corresponding theoretical support for pollutant control and water quality improvement, and avoid the ecological and health risks caused by the deterioration of water quality. IntroductionRiver-water quality is a serious problem, especially for urban managers and researchers worldwide. Clean rivers provide a safeguard for human health and the ecosystem and prevent the environmental, economic, and social risks associated with pollution (Liu et al. 2019a, Sener et al. 2017). However, with the increase in population, urbanization, and industrialization, pollution from industrial and domestic wastewaters as well as agricultural and urban runoffs has increased the load on surface water. Thus, water resource management faces serious challenges to reduce pollutants (Casillas-Garcia et al. 2021, Zhang et al. 2020a). In the sub-watersheds that lack supervision, the water quality is especially unstable, and the pollution sources are complicated. (Cheng et al. 2020, Huang et al. 2010. Therefore, rapid water quality assessment and accurate identi cation of pollution sources can help researchers develop strategies for water management and pollutant control (Zhang et al. 2022).Recently, China has mainly been relying on sampling analysis and automated monitoring stations to obtain water quality data. Various complex monitoring procedures are typically used, and additional physical and chemical parameters for wa...

No abstract

The rapid industrial and agricultural development as well as urbanization significantly affect the water environment, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for the improvement of water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018–2019 along the Laixi River, which is an important pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis (PCA) was used to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis (CA) further illustrates the spatiotemporal distribution characteristics of river water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The results of this study can provide corresponding theoretical support for pollutant control and water quality improvement, and avoid the ecological and health risks caused by the deterioration of water quality.