SPATIOTEMPORAL VARIATION AND POLLUTION SOURCE APPORTIONMENT OF SURFACE WATER IN AN GIANG PROVINCE USING MULTIVARIATE STATISTICS)

Abstract

This study applies multivariate statistical methods, including Cluster Analysis (CA) and Principal Component Analysis (PCA), to assess variation patterns and identify the main pollution sources affecting surface water quality in An Giang province. The analysis was performed on a dataset of 10 water quality parameters (temperature, pH, DO, COD, BOD₅, TSS, N-NH₄⁺, N-NO₃^-, P-PO₄^3- and coliform), collected from 62 locations from 2021 to 2023. The Cluster Analysis results indicated that the monitoring network could be effectively optimized by reducing the number of monitoring sites from 62 to 46 and adjusting the sampling frequency from six to four times per year, while still ensuring representativeness for the entire area. Meanwhile, Principal Component Analysis identified that the governing factors of water quality degradation are primarily organic, nutrient and microbiological pollution. The variation in these parameters originates from socioeconomic activities such as urban wastewater, intensive agriculture and aquaculture, in addition to natural processes within the catchment area. The findings of this study provide a crucial scientific basis to support managers in redesigning the monitoring network and prioritizing pollution control measures, thus contributing to the sustainable management of water resources.