Source Apportionment and Quality Assessment of Surface Water Using Principal Component Analysis and Multiple Linear Regression statistics
Principal component analysis (PCA) and multiple linear regressions (MLR) analysis were applied on the data set of surface water quality for source identification of pollution and their contribution on the variation of water quality. Results revealed that, most of the water quality parameters were f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Environment Conservation Journal
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/5052/ http://umpir.ump.edu.my/id/eprint/5052/ http://umpir.ump.edu.my/id/eprint/5052/1/paper-Source_ECJ_14%283%299-16.pdf |
| Summary: | Principal component analysis (PCA) and multiple linear regressions (MLR) analysis were applied on the data set of
surface water quality for source identification of pollution and their contribution on the variation of water quality. Results revealed that, most of the water quality parameters were found to be toxic compare to the national standard of Malaysia. PCA identified the sources as, ionic groups of salts, soil erosion and agricultural runoff, organic and nutrient pollutions from domestic wastewater, industrial sewage and wastewater treatment plants. MLR investigated the R= 0.968 and R2=0.934 and it was highly significant (p<0.01). |
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