River pollution impact human health, environment and the sustainable development. This study was conducted to identify spatial patterns and the main parameters affecting the water pollution within nine monitoring stations in the Muda River basin (Malaysia) over a 16-year database (1998–2013). Environmetric techniques were applied to the dataset. These combined Cluster Analysis, Discriminant Analysis, and Multiple Linear Regression. The Cluster Analysis showed that the monitoring stations divided into two separate groups based on similarities features of water quality while Discriminant Analysis validated these groups. Furthermore, the Multiple Linear Regression analysis showed that the significant parameters contributing to variability the Water Quality Index was biological oxygen demand, chemical oxygen demand and ammonia nitrogen. This was due to the point-source pollution, particularly from rubber factory. Therefore, the results provided information to support future water polluti.....

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The river water quality worldwide has declined progressively as an implication of improper land-use practices. In this study, the Geographic Information System and Multiple Linear Regression analysis were used to determine the river water quality that is associated with various effects of land use, particularly in the north-western part of Peninsular Malaysia. The study employed secondary water quality data consisted of 22 water quality variables at four different buffer zones: 500, 1000, 1500 and 2000 m. The land use data were sorted into four land use categories: agricultural, forestry, urban areas, and others. Out of 88 regression models examined, only eight significant models were obtained. The analysis indicates a weak association between the water quality and land use for nitrates (0.08< R2 < 0.14,p< 0.05) and calcium (0.10 < R 2< 0.17, p< 0.05) within all the investigated zones. Interestingly, although the catchment area is covered by 55 % of agricultural land and 35 % of the fo.....

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