Using Multivariate Statistical Methods to Identify Key Surface Water Pollutants in the Dry Season in a Coastal Province, Vietnam

This study was conducted to analyze water quality in a coastal province of Vietnam. Multivariate statistical methods, namely cluster analysis (CA) and principal component (PCA) were utilized. Twelve parameters, pH, dissolved oxygen (DO), total suspended solids (TSS), biological demand (BOD), chemical (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), orthophosphate (P-PO43−), chloride (Cl−), iron (Fe), coliforms collected from ten locations the 2020 dry season. The results showed that surface polluted by TSS, organic matters, nutrients, salinity, compared national technical regulations on (QCVN 08-MT: 2015/BTNMT). Cluster classified original sampling into three groups due BOD, COD, N-NH4+, N-NO2−, coliforms, salinity. Principal revealed components (PCs) could explain 84.5% variance parameters area. Moreover, DO, N-NO3-, P-PO43−, Cl‑ key variables influenced findings this can provide useful information for policymakers developing programs management protection.