ASSESSMENT OF SURFACE WATER QUALITY USING MULTIVARIATE STATISTICAL TECHNIQUES IN THE TERENGGANU RIVER BASIN (original) (raw)
Related papers
Surface Water Quality Assessment of Terengganu River Basin Using Multivariate Techniques
Surface stream water is truly encountering sullying that undermines human wellbeing, biological community and plants/creatures life. The study investigates the spatial variation with the aim to identify the surface water pollution using multivariate statistical techniques. Thirty water quality parameters were extracted from 2003-2007 monitoring stations by Department of Environment Malaysia. The spatial variation of the water quality, identification of the prospective pollution sources and the explanation of huge complicated water quality data sets were assessed using multivariate statistical techniques which includes cluster analysis (CA), discriminant analysis (DA) and principal component analysis/factor analysis (PCA/FA). The revealed that thirteen sampling stations were grouped by CA into two major classes: Low Pollution Source (LPS) and Moderate Pollution Source (MPS) and each group show similar water quality characteristics. DA through standard mode, backward stepwise mode and forward stepwise mode rendered correct assignation of 83.03%, 81.55% and 80.81% with four significant variables (BOD, conductivity, NO3 and Zn) as the most significant. Indeed, DA reduces the data and produces good result for the spatial variation of the river. PCA identifies variables liable for water quality variation. Moreover, PCA revealed cumulative variance of 73.62% of the overall variance, each having greater than >1 eigenvalue. PCA suggested the major variations in river are attributed to domestic waste, agricultural activities and industrial activities this represent (anthropogenic activities) and erosion as well as runoff indicating (natural processes). Thus, this study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for Effective River water quality management.
Surface stream water is truly encountering sullying that undermines human wellbeing, biological community and plants/creatures life. The study investigates the spatial variation with the aim to identify the surface water pollution using multivariate statistical techniques. Thirty water quality parameters were extracted from 2003-2007 monitoring stations by Department of Environment Malaysia. The spatial variation of the water quality, identification of the prospective pollution sources and the explanation of huge complicated water quality data sets were assessed using multivariate statistical techniques which includes cluster analysis (CA), discriminant analysis (DA) and principal component analysis/factor analysis (PCA/FA). The revealed that thirteen sampling stations were grouped by CA into two major classes: Low Pollution Source (LPS) and Moderate Pollution Source (MPS) and each group show similar water quality characteristics. DA through standard mode, backward stepwise mode and forward stepwise mode rendered correct assignation of 83.03%, 81.55% and 80.81% with four significant variables (BOD, conductivity, NO3 and Zn) as the most significant. Indeed, DA reduces the data and produces good result for the spatial variation of the river. PCA identifies variables liable for water quality variation. Moreover, PCA revealed cumulative variance of 73.62% of the overall variance, each having greater than >1 eigenvalue. PCA suggested the major variations in river are attributed to domestic waste, agricultural activities and industrial activities this represent (anthropogenic activities) and erosion as well as runoff indicating (natural processes). Thus, this study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for Effective River water quality management.
Journal of Chemistry, 2017
The present study evaluated the spatial variations of surface water quality in a tropical river using multivariate statistical techniques, including cluster analysis (CA) and principal component analysis (PCA). Twenty physicochemical parameters were measured at 30 stations along the Batang Baram and its tributaries. The water quality of the Batang Baram was categorized as “slightly polluted” where the chemical oxygen demand and total suspended solids were the most deteriorated parameters. The CA grouped the 30 stations into four clusters which shared similar characteristics within the same cluster, representing the upstream, middle, and downstream regions of the main river and the tributaries from the middle to downstream regions of the river. The PCA has determined a reduced number of six principal components that explained 83.6% of the data set variance. The first PC indicated that the total suspended solids, turbidity, and hydrogen sulphide were the dominant polluting factors whi...
Multivariate Statistical techniques including cluster analysis, discriminant analysis, and principal component analysis/factor analysis were applied to investigate the spatial variation and pollution sources in the Terengganu river basin during 5 years of monitoring 13 water quality parameters at thirteen different stations. Cluster analysis (CA) classified 13 stations into 2 clusters low polluted (LP) and moderate polluted (MP) based on similar water quality characteristics. Discriminant analysis (DA) rendered significant data reduction with 4 parameters (pH, NH 3 -NL, PO 4 and EC) and correct assignation of 95.80%. The PCA/FA applied to the data sets, yielded in five latent factors accounting 72.42% of the total variance in the water quality data. The obtained varifactors indicate that parameters in charge for water quality variations are mainly related to domestic waste, industrial, runoff and agricultural (anthropogenic activities). Therefore, multivariate techniques are important in environmental management.
The aims of this research are to assess water quality by organic and nutrient matters and identifying the environmental pressures, examine the impact of the loads to Nhu Y River, Thua Thien-Hue Province. Five stations were sampled at Nhu Y River, the research had monitoring of water quality parameters such as Temperature (Temp), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD 5), Chemical Oxygen Demand (COD), Nitrate (NO 3-) and Phosphate (PO 4 3-). The research used multivariate statistical techniques such as correlation analysis, principal component analysis (PCA) and cluster analysis (CA) to assess water quality. The correlation analysis shown a strong positive correlation exists between water quality parameters such as TempDO and BOD 5 COD (p<0.01). The PCA technique was applied to water quality data sets, which was obtained from Nhu Y River and the results show that the indices which has changed water quality. The results of the PCA using a varimax rotation technique were illustrated with two principal components (PC) and accounts for 62.207% of the overall total variance. The first PC accounted for 40.873% of the total variance, which was loaded with Temp, DO, BOD 5 and COD. The second PC consists of NO 3-and PO 4 3-which accounts for 21.334% of the total variance, it can be due to the discharge of agricultural activities. Similarly, the CA has identified two major clusters involving: BOD 5 , COD, Temp, DO (the first cluster) and NO 3-, PO 4 3-(the second cluster).
International Journal of Environmental Science and Development, 2020
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...
Jurnal Teknologi, 2015
The spatial water quality data (281x22) obtained from 12 sampling stations located along the Terengganu River and its main tributaries were evaluated with environmetric methods. Principal component analysis was used to investigate the origin of each variable due to land use and human activities based on the three clustered regions obtained from the hierarchical agglomerative cluster analysis. Six principal components (PCs) were obtained, where six varimax factor (VF) of values more than 0.70 that considered strong loading are discussed. The possible pollution sources identified are of anthropogenic sources, mainly municipal waste, surface runoff, agricultural runoff, organic pollution and urban storm runoff. As a conclusion, the application of environmetric methods could reveal important information on the spatial variability of a large and complex river water quality data in order to control pollution sources.
The spatial water quality data (281x22) obtained from 12 sampling stations located along the Terengganu River and its main tributaries were evaluated with environmetric methods. Principal component analysis was used to investigate the origin of each variable due to land use and human activities based on the three clustered regions obtained from the hierarchical agglomerative cluster analysis. Six principal components (PCs) were obtained, where six varimax factor (VF) of values more than 0.70 that considered strong loading are discussed. The possible pollution sources identified are of anthropogenic sources, mainly municipal waste, surface runoff, agricultural runoff, organic pollution and urban storm runoff. As a conclusion, the application of environmetric methods could reveal important information on the spatial variability of a large and complex river water quality data in order to control pollution sources.
Investigation of the Soan River Water Quality Using Multivariate Statistical Approach
International Journal of Photoenergy, 2020
Evaluating the quality of river water is a critical process due to pollution and variations of natural or anthropogenic origin. For the Soan River (Pakistan), seven sampling sites were selected in the urban area of Rawalpindi/Islamabad, and 18 major chemical parameters were examined over two seasons, i.e., premonsoon and postmonsoon 2019. Multivariate statistical approaches such as the Spearman correlation coefficient, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the water quality of the Soan River based on temporal and spatial patterns. Analytical results obtained by PCA show that 92.46% of the total variation in the premonsoon season and 93.11% in the postmonsoon season were observed by only two loading factors in both seasons. The PCA and CA made it possible to extract and recognize the origins of the factors responsible for water quality variations during the year 2019. The sampling stations were grouped into specific clusters on the basis ...
Pertanika Journal of Science and Technology
In Pasir Gudang, an accelerated industry-based economy has caused a tremendous increase and diversity of water contamination. The application of multivariate statistical techniques can identify factors that influence water systems and is a valuable tool for managing water resources. Therefore, this study presents spatial evaluation and the elucidation of inordinate complex data for 32 parameters from 25 sampling points spanning 20 rivers across Pasir Gudang, summing up to 1500 observations between 2015-2019. Hierarchical cluster analysis with the K-means method grouped the rivers into two main clusters, i.e., proportionately low polluted rivers for Cluster 1 (C1) and high polluted rivers for Cluster 2 (C2), based on the similitude of water quality profiles. The discriminant analysis applied to the cluster resulted in a data reduction from 32 to 7 parameters (Cl, Cd, S, OG, temperature, BOD, and pH) with a 99.5% correct categorization in spatial analysis. Hence, element complexity wa...