Assessments of surface water quality through the use of multivariate statistical techniques: A case study for the watershed of the Yuqiao Reservoir, China

Abstract

In light of the fact that water quality has been threatened by human activities, apportionments of potential pollution sources are essential for water pollution control. Multivariate methods were used to assess the water quality in the Yuqiao Reservoir and its surrounding rivers in northern China to identify potential pollution sources and quantify their apportionment. Fifteen variables at 10 sites were surveyed monthly in 2015–2016. The quality at this location was acceptable according to the water quality index (WQI), except for special parameters including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and chlorophyll (chlα). Cluster analysis (CA) grouped these datasets into three seasonal groups, July–September, December–March, and the remaining months. Principal component analysis/factor analysis (PCA/FA) identified seven factors that accounted for 79.7%–86.4% of the total variance, and the main sources included cities, rural districts, industries, weather, fertilizers, upstream areas, and vehicles. Absolute principal component scores and multiple linear regression (APCS–MLR) modeling results show that the hierarchical contribution of main pollution sources was ranked in the following order: upstream (26.6%) > urban district pollution source (21.5%) > vehicle emission pollution source (10.9%) in the flood season, upstream (22.3%) > rural district pollution (19.8%) > fertilizer erosion (15.8%) in the normal season, and upstream (26.4%) > urban district pollution (19.0%) > fertilizer erosion (18.8%) in the dry season. Sources from upstream and urban districts explained the most proportion. The matrix was also subjected to positive matrix factorization (PMF). A comparison of PMF and APCS–MLR results showed significant differences in the contribution of potential pollution sources. The APCS–MLR model performed better, as evidenced by a more robust R2 test. Measures should be discussed and implemented in managing upstream areas, sewage treatment facilities, and fertilizer and industrial application.

Phytoplankton community response to water physicochemical characteristics under seasonal variation at the Ubol Ratana Dam, Khon Kaen, Thailand

“…Several potentially toxic genera, such as Microcystis, Cylindrospermopsis, Planktotrix, Planktolyngbya, and Anabaena, were dominant throughout our study period (Barros et The ecology of phytoplankton in aquatic environments is affected by seasonal changes, leading to uctuations in species diversity, evenness, the Shannon-Wiener index, and cell density (Sun et al 2023;SureshKumar et al 2019). Temperature is a signi cant factor that regulates the growth of phytoplankton (Somdee et al 2013;Wang et al 2023). During the study, it was observed that species richness decreased in the summer.…”

“…Freshwater dams show variations in physicochemical characteristics based on hydrological cycles and water ow characteristics, which impact the diversity of organisms in freshwater ecosystems, including phytoplankton (Graco-Roza et al 2021). Furthermore, land-based pollution from industrial, agricultural, and urban areas is increasingly being discharged into freshwater reservoirs, resulting in decreased biodiversity in reservoirs (Wang et al 2023). The proliferation of organisms is typically caused by natural events and human activities, including water contamination with nutrients from riverine in ow, leaching, rainfall, aquaculture, and household, industrial, and agricultural activities (Bassem et al 2020;Ponnusamy et al 2023;Sidabutar et al 2020; Wang et al 2022).…”

Statistical Methods and Models for Pollutant Control in Municipal Surface Waters

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