Combined Multivariate Statistical Techniques, Water Pollution Index (WPI) and Daniel Trend Test Methods to Evaluate Temporal and Spatial Variations and Trends of Water Quality at Shanchong River in the Northwest Basin of Lake Fuxian, China

Abstract

Understanding spatial and temporal variations in river water quality and quantitatively evaluating the trend of changes are important in order to study and efficiently manage water resources. In this study, an analysis of Water Pollution Index (WPI), Daniel Trend Test, Cluster Analysis and Discriminant Analysis are applied as an integrated approach to quantitatively explore the spatial and temporal variations and the latent sources of water pollution in the Shanchong River basin, Northwest Basin of Lake Fuxian, China. We group all field surveys into 2 clusters (dry season and rainy season). Moreover, 14 sampling sites have been grouped into 3 clusters for the rainy season (highly polluted, moderately polluted and less polluted sites) and 2 clusters for the dry season (highly polluted and less polluted sites) based on their similarities and the level of pollution during the two seasons. The results show that the main trend of pollution was aggravated during the transition from the dry to the rainy season. The Water Pollution Index of Total Nitrogen is the highest of all pollution parameters, whereas the Chemical Oxygen Demand (Chromium) is the lowest. Our results also show that the main sources of pollution are farming activities alongside the Shanchong River, soil erosion and fish culture at Shanchong River reservoir area and domestic sewage from scattered rural residential area. Our results suggest that strategies to prevent water pollutionat the Shanchong River basin need to focus on non-point pollution control by employing appropriate fertilizer formulas in farming, and take the measures of soil and water conservation at Shanchong reservoir area, and purifying sewage from scattered villages.

Adaptive capacity based water quality resilience transformation and policy implications in rapidly urbanizing landscapes

“…Our water quality monitoring dataset included 57 samples ( Considering the different units of those variables, the Water Comprehensive Pollution Index (WCPI) was used to analyze and monitor water quality for resilience assessment (Wang et al, 2008;Liu et al, 2011;Wang et al, 2015). This was calculated according to the Environmental Quality Standards for Surface Water (GB 3838-2002) (2002) as:…”

Developing an EFDC and Numerical Source-Apportionment Model for Nitrogen and Phosphorus Contribution Analysis in a Lake Basin

“…Another source-apportionment method based on statistical modeling calculates the proportion of pollutants in the water coming from pollution sources using the relationship between substances in statistical and monitoring data, including factor analyses [5][6][7], principal component analyses [8][9][10], cluster analyses [11][12][13][14][15], and multiple linear regression. This led to the hypothesis that there is no significant change in the composition of pollutants from generation to acceptor and that individual pollutant flux is proportional to pollutant concentration [16].…”

Spatial and Temporal Variations in Water Quality and Land Use in a Semi-Arid Catchment in Bolivia

“…The NSF-WQI approach focuses on the physicochemical and biological quality of water, whereas the IPI approach focuses on organic pollution. As reported by numerous authors [61][62][63][64][65], both these indices are very sensitive to the sampling period, that is, whether it is rainy or dry, and its effect on dilution or concentration of the parameters. Owing to the effect of air temperature on water quality parameters by influencing physical, biological, and chemical processes, time of sampling is important.…”