Arman Nor Zaiha, Aris Azmi, Salmiati Salmiati, Rosli Ainul Syarmimi, Foze Mohd Faiz, Talib Juhaizah
Center for Environmental Sustainability and Water Security (IPASA), Research Institute for Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia.
Research Institute for Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia.
Environ Sci Pollut Res Int. 2025 Jan;32(4):1766-1782. doi: 10.1007/s11356-024-35692-7. Epub 2025 Jan 2.
In the Johor River Basin, a comprehensive analysis was conducted on 24 water environmental parameters across 33 sampling sites over 3 years, encompassing both dry and wet seasons. A total of 396 water samples were collected and analyzed to calculate the Water Quality Index (WQI). To further assess water quality and pinpoint potential pollution sources, multivariate techniques such as principal component analysis (PCA) and cluster analysis (CA), alongside spatial analysis using inverse distance weighted (IDW) interpolation, were employed. According to the National Water Quality Standard, most of the analyzed physicochemical components fall within Classes II and III, albeit with varying concentrations. However, certain sites exhibited levels of BOD, TSS, and nutrients such as total nitrogen (TN) and total phosphorus (TP) that exceeded the threshold level of water quality standards, signaling pollution from diverse sources. Notably, all trace elements, with the exception of copper (Cu) and nickel (Ni), remained within the acceptable limits set by WHO guidelines and the National Water Quality Standard. PCA revealed parameter groupings linked to factors such as soil erosion, salinity, wastewater discharge, and fecal contamination, which are key determinants of water quality. The cluster analysis categorized the 33 sampling sites into three distinct clusters, each reflecting the geological setting and varying levels of pollution. The IDW-based spatial distribution indicated significant water quality degradation as the river flows downstream, particularly in regions experiencing rapid agricultural, industrial, and residential development. These activities contribute to the breakdown of organic matter and the release or overflow of wastewater into nearby river systems. This study highlights the effectiveness of integrating data-driven methodologies for surface water quality assessment, offering valuable insights for sustainable watershed management.
