Spatiotemporal Variations in Physicochemical and Biological Properties of Surface Water Using Statistical Analyses in Vinh Long Province, Vietnam

In this study, spatiotemporal fluctuations in surface water quality in Vinh Long province, Vietnam, were conducted using entropy weighting, water quality index (WQI), and multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), and discriminant analysis (DA). The samples collected at 63 monitoring locations in March, June, and September were measured for 15 parameters. Compared to the Vietnamese standard, surface water was contaminated with organic matters, nutrients, microorganisms, and salinity. DA identified the most typical parameters (pH, turbidity, TSS, EC, DO, Cl-, E. coli, coliform) in distinguishing temporal variations in water quality with greater than 75% of the correction. CA group 63 sampling sites into 22 clusters representing different land use patterns. WQI determined the worst water quality was found in the agricultural areas. Based on the results of entropy weighting, EC, coliform, N-NH4+, BOD, N-NO3-, and Fe had significantly controlled surface water quality. Four principal components obtained from PCA explained 66.45% of the variance, suggesting the influences of geohydrological factors and anthropogenic activities, such as domestic, market area, agriculture, and industry. The findings of this study can provide useful information for authorities to evaluate the effectiveness of monitoring systems and plan for water quality management strategies.

Automated summary

This study investigated the spatiotemporal fluctuations in surface water quality in Vinh Long province, Vietnam using entropy weighting, water quality index (WQI), and multivariate statistical techniques. The results showed that the surface water in the area was contaminated with organic matters, nutrients, microorganisms, and salinity. Different parameters were identified as key indicators for distinguishing temporal variations in water quality. Cluster analysis revealed different land use patterns, while WQI indicated poor water quality in agricultural areas. Principal component analysis suggested the influences of geohydrological factors and anthropogenic activities on water quality.