Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam

Abstract

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Assessment of Water Quality and Identification of Pollution Risk Locations in Tiaoxi River (Taihu Watershed), China

“…The CA enabled us to categorize sampling locations based on water quality, so that in future studies, the number of sampling locations can be minimized for cost-effective monitoring of water quality in Tiaoxi River by choosing a few locations from each cluster based on the distance distribution and pollution levels in those locations. Previous studies have reported that a similar strategy has been successfully applied in water quality monitoring programs elsewhere [12,13,61,62], and the Tiaoxi River Taihu catchment is therefore similarly amenable to this rational approach. the number of sampling locations can be minimized for cost-effective monitoring of water quality in Tiaoxi River by choosing a few locations from each cluster based on the distance distribution and pollution levels in those locations.…”

“…the number of sampling locations can be minimized for cost-effective monitoring of water quality in Tiaoxi River by choosing a few locations from each cluster based on the distance distribution and pollution levels in those locations. Previous studies have reported that a similar strategy has been successfully applied in water quality monitoring programs elsewhere [12,13,61,62], and the Tiaoxi River Taihu catchment is therefore similarly amenable to this rational approach. …”

Comparison of seven water quality assessment methods for the characterization and management of highly impaired river systems

“…The axes determined by PCA lie along the directions of maximum variance. PCA provides an objective way of calculating indices so that variation in the data can be accounted for as concisely as possible (Phung et al 2015). It provides information on the most meaningful parameters, which describes the whole data set interpretation, provides data reduction, and summarizes the statistical correlation among water quality constituents with minimum loss of the original information (Helena et al 2000).…”

Water Quality Evaluation of the Tien River by Means of Water Quality Index (Wqi) and Statistical Techniques

“…Additionally, One-way Analysis of Variance (ANOVA) was used to consider the significance of the mean differences among groups of monitoring sites and seasonal factors. Spearman correlation analysis (SCA) was used to assess the relationships among dependent and independent variables (physicochemical parameters and WQI) [2,3,5,6].…”

“…Namely, nine Physicochemical parameters, including DO, BOD 5 , COD, NH 4 + -N, PO 4 3--P; TSS, pH, temperature and total coliforms, were selected to calculate WQI. In the WQI, number ranges from 0 to 100, corresponding to different bands of colors, signifies better water quality when it is higher [2,5,7].…”