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

Date Received: Sep 20, 2021

Date Accepted: Jun 27, 2022

Date Published: Jun 27, 2022

DOI: https://doi.org/10.31817/vjas.2022.5.2.05

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Issue: Vol. 5 No. 2 (2022): Vietnam Journal of Agricultural Sciences

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NATURAL RESOURCES AND ENVIRONMENT

How to Cite:

Giao, N., & Nhien, H. (2022). Using Multivariate Statistical Methods to Identify Key Surface Water Pollutants in the Dry Season in a Coastal Province, Vietnam. Vietnam Journal of Agricultural Sciences, 5(2), 1480–1491. https://doi.org/10.31817/vjas.2022.5.2.05

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

Nguyen Thanh Giao (*) 1   , Huynh Thi Hong Nhien 1

  • Corresponding author: ntgiao@ctu.edu.vn
  • 1 College of Environment and Natural Resources, Can Tho University, Can Tho 900000, Vietnam

    • Keywords

    Bac Lieu, coastal water quality, organic matters, principal component analysis

    Abstract


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

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