Spatial analysis of surface water quality using multivariate statistical techniques and water quality index: Case study of Binh Duong Province, the largest industrial hub in Southern Vietnam

Abstract

Ensuring high‒quality water supply is essential for both domestic and manufacturing activities, particularly in Binh Duong Province (BDP), situated at the heart of Vietnam's southern key economic region, known for its dense population and numerous industrial parks. In this study, multivariate statistical analysis techniques, including Hierarchical Cluster Analysis (CA) and Principal Component Analysis (PCA), were employed to assess spatial variations in surface water quality (SWQ) along the Sai Gon and Dong Nai Rivers, which are the two primary water bodies in BDP. CA classified the 25 sampling sites into three groups (DN, SGDN1, SGDN2) and three outlying groups (RSG8, RSG10, and RDN7). Groups RDN7 and DN were deemed to have good surface water quality, while RSG8 exhibited moderate SWQ. Conversely, RSG10 and SGDN1 were classified as having bad and moderate surface water quality, respectively. The Kruskal‒Wallis test revealed significant spatial differences in all water quality parameters among the six clusters (p < 0.05). PCA identified two principal components (PCs) explaining 65.3% of the total variance, highlighting NO₃⁻, NO₂⁻, NH₄⁺, PO₄³⁻, COD, BOD₅, and coliform as major pollution sources in the area. The findings underscore the impact of untreated domestic and industrial sewage on water quality in the Sai Gon and Dong Nai Rivers. This study contributes valuable insights into water quality assessment using multivariate statistical methods and informs the formulation of effective public policies by local governments.