SPATIAL CHARACTERIZATION AND IDENTIFICATION SOURCES OF POLLUTION USING MULTIVARIATE ANALYSIS AT TERENGGANU RIVER BASIN, MALAYSIAMultivariate Analysis at Terengganu River Basin, Malaysia (original) (raw)
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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.
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.
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, NH3-NL, PO4 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.
2015
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, NH3-NL, PO4 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 ...
Spatial Water Quality Assessment Of Langat River Basin (Malaysia) Using Environmetric Techniques
2011
This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data. © 2010 The Author(s).
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...
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.
Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia
Environmental Monitoring and Assessment, 2014
This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km 2 , from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6 % of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.
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...
International Journal on Advanced Science, Engineering and Information Technology, 2021
Citarum River is one of the most important rivers in Indonesia. Around 16 million people interrelate with this river, covers 12,000 Km2 of the watershed, supplies water for irrigation of 420,000 hectares of rice fields, provides 80% of water need for the city of Jakarta- the capital of Indonesia. Unfortunately, Citarum was also known as one of the most polluted rivers in the world. Although there is much attention to this river nowadays, there is still no analysis to determine the latent contributing factors of water quality cluster distribution. This study aims to provide spatial water quality on the Citarum River Bandung Regency. This study can help the government decide on how to manage the water quality of Citarum and all socio-cultural factors involved in polluting the river. Open Data can also use the data and result for further research. Assessment of Citarum water quality is done through the application of multivariate statistical approaches. The data set comprises one-month...