Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia (original) (raw)
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Assessment of water quality status using univariate analysis at Klang and Juru River, Malaysia
Journal of Fundamental and Applied Sciences, 2018
Klang River and Juru River which today suffers from water quality problems resulting from anthropogenic and geomorphology quality status from 2009 until 2013. 10 monitoring stations covering along Klang River and Juru River were selected. Six water quality parameters analyzed based on in analysis were carried out according to the univariate analysis to obtain WQI level. The result indicated the impact of various anthropogenic and geomorphology activities contributed higher values of BOD, COD, SS Juru River still on stable condition. upstream area triggered by pollutants from waste products of development activities inclusion of a high amount of pollutants of Klang River and Juru River.
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Use of Multivariate Analytical Methods in Assessment of River Water Quality
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Assessment of Water Quality Data Using Functional Data Analysis for Klang River Basin, Malaysia
Research Square (Research Square), 2023
Rivers are subject to different sources of pollution. Continuous monitoring of river water quality provides an important basis for the authorities to take appropriate action. Water quality monitoring stations located within the river basin can provide necessary water quality data to establish any changes observed in the river water quality. It is important to highlight lower water quality status at speci c monitoring stations so that immediate action can be taken. Similarly, it is an utmost important to ensure water quality at monitoring stations close to water catchment areas always at an acceptable level. This study aims to identify such monitoring stations using descriptive and functional data analysis. The approaches were applied to water quality data collected by the Department of Environment Malaysia at 16 stations in the Klang River basin from January 2013 to December 2016. Speci cally, the functional boxplot was applied to identify the monitoring station with outlying properties. We identi ed many occasions when water quality deteriorated or improved largely due to the increase of COD, BOD and TSS. In addition, three stations close to two main catchment areas and forest reserve showed consistently good water quality. These indicate that the surrounding areas of the stations at the upstream of the rivers are still protected from uncontrolled pollution sources. The study is critical for the authority to understand the overall pattern of water quality data at each station so that action can be planned locally to preserve good river water quality.
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Statistical techniques such as multivariate analysis of variance (MANOVA) and discriminate analysis (DA) were used to analyze the data obtained from three different locations (rivers, lake, drinking water supply). Seven parameters were measured pH, temperature, TSS, COD, BOD, turbidity and E. coli to investigate the pollution status. MANOVA showed a strong significant difference. While discriminate analysis (DA) explained the differences between different locations with the use of two functions. The first function showed 98.4% total variation, mainly due to E. coli, turbidity, BOD, temperature and COD. Even as the second function recorded 1.6% total variation, mainly due to E. coli, COD, temperature and pH. DA was also used to determine the relative contribution for all parameters in differentiating between river, lake and tap water. The results also showed strong correlations between COD and suspended solids. BOD with temperature, COD and pH. Turbidity with pH, temperature, COD and...
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The aim of this study is to assess the spatial variability and to determine the main contamination sources in surface water quality of the Nhue River, Viet Nam by using multivariate statistical analysis techniques, including principal component analysis (PCA) and cluster analysis (CA). Eight water quality parameters were measured at 21 sites along the Nhue River and its tributaries during irrigated periods from 2016 to 2019. The spatial variability of water quality in the Nhue River and its tributaries was determined separately from cluster analysis. The result determined two tributaries, including Yen Xa Canal (NT9 monitoring site) and To Lich River (NT3 monitoring site) leading to severe pollution at To Bridge (N4 monitoring site) region in the Nhue River. The PCA determined a reduced number of two principal components that explained 47.75% of the total variation in the data. The first PC indicated that water temperature (WT) and pH are the dominant polluting factors which are att...
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A collective set of data over five years (2003 to 2007) in Klang River, Selangor were studied in attempt to assess and determine the contributions of sources affecting the water quality. A precise technique of multiple linear regressions (MLR) were prepare as an advance tool for surface water modeling and forecasting. Likewise, principle component analysis (PCA) was used to simplify and understand the complex relationship among water quality parameters. Nine principle components were found responsible for the data structure provisionally named as soil erosion, anthropogenic input, surface runoff, fecal waste, detergent, urban domestic waste, industrial effluent, fertilizer waste and residential waste explains 72% of the total variance for all the data sets. Meanwhile, urban domestic pollution accounted as the highest pollution contributor to the Klang River. Thus, the advancement of receptor model was applied in order to identify the major sources of pollutant at Klang River. Result showed that the use of PCA as inputs improved the MLR model prediction by reducing their complexity and eliminating data collinearity where R value in this study is 0.75 and the model indicates that 75% 2 variability of WQI explained by the five independent variables used in the model. This assessment presents the importance and advantages poses by multivariate statistical analysis of large and complex databases in order to get improved information about the water quality and then helps to reduce the sampling time and cost for reagent used prior to analyses.