Investigation of the Soan River Water Quality Using Multivariate Statistical Approach
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2020/6644796 |
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| author | Zakaullah Naeem Ejaz |
| author_facet | Zakaullah Naeem Ejaz |
| author_sort | Zakaullah |
| collection | DOAJ |
| description | 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 of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management. |
| format | Article |
| id | doaj-art-c3172ae2890d45489a160dbd0195df2d |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-c3172ae2890d45489a160dbd0195df2d2025-02-03T06:45:51ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2020-01-01202010.1155/2020/66447966644796Investigation of the Soan River Water Quality Using Multivariate Statistical ApproachZakaullah0Naeem Ejaz1Department of Civil Engineering, University of Engineering and Technology, Taxila, PakistanDepartment of Civil Engineering, University of Engineering and Technology, Taxila, PakistanEvaluating 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 of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management.http://dx.doi.org/10.1155/2020/6644796 |
| spellingShingle | Zakaullah Naeem Ejaz Investigation of the Soan River Water Quality Using Multivariate Statistical Approach International Journal of Photoenergy |
| title | Investigation of the Soan River Water Quality Using Multivariate Statistical Approach |
| title_full | Investigation of the Soan River Water Quality Using Multivariate Statistical Approach |
| title_fullStr | Investigation of the Soan River Water Quality Using Multivariate Statistical Approach |
| title_full_unstemmed | Investigation of the Soan River Water Quality Using Multivariate Statistical Approach |
| title_short | Investigation of the Soan River Water Quality Using Multivariate Statistical Approach |
| title_sort | investigation of the soan river water quality using multivariate statistical approach |
| url | http://dx.doi.org/10.1155/2020/6644796 |
| work_keys_str_mv | AT zakaullah investigationofthesoanriverwaterqualityusingmultivariatestatisticalapproach AT naeemejaz investigationofthesoanriverwaterqualityusingmultivariatestatisticalapproach |