Characterization of spatial patterns in river water quality using chemometric techniques
Water pollution has become a growing threat to human society and natural ecosystem in recent decades, increasing the need to better understand the variabilities of pollutants within aquatic systems. This study presents the application of two chemometric techniques, namely, cluster analysis (CA) and...
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Penerbit Universiti Kebangsaan Malaysia
2014
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2-s2.0-84907663510 Baharuddin N.; Saim N.; Zain S.M.; Juahir H.; Osman R.; Aziz A. Characterization of spatial patterns in river water quality using chemometric techniques 2014 Sains Malaysiana 43 9 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907663510&partnerID=40&md5=c06647ab1f20ae8432101eeec38dbbdd Water pollution has become a growing threat to human society and natural ecosystem in recent decades, increasing the need to better understand the variabilities of pollutants within aquatic systems. This study presents the application of two chemometric techniques, namely, cluster analysis (CA) and principal component analysis (PCA). This is to classify and identify the water quality variables into groups of similarities or dissimilarities and to determine their significance. Six stations along Kinta River, Perak, were monitored for 30 physical and chemical parameters during the period of 1997-2006. Using CA, the 30 physical and chemical parameters were classified into 4 clusters; PCA was applied to the datasets and resulted in 10 varifactors with a total variance of 78.06%. The varifactors obtained indicated the significance of each of the variables to the pollution of Kinta River. © 2014, Penerbit Universiti Kebangsaan Malaysia. All rights resaerved. Penerbit Universiti Kebangsaan Malaysia 1266039 English Article |
author |
Baharuddin N.; Saim N.; Zain S.M.; Juahir H.; Osman R.; Aziz A. |
spellingShingle |
Baharuddin N.; Saim N.; Zain S.M.; Juahir H.; Osman R.; Aziz A. Characterization of spatial patterns in river water quality using chemometric techniques |
author_facet |
Baharuddin N.; Saim N.; Zain S.M.; Juahir H.; Osman R.; Aziz A. |
author_sort |
Baharuddin N.; Saim N.; Zain S.M.; Juahir H.; Osman R.; Aziz A. |
title |
Characterization of spatial patterns in river water quality using chemometric techniques |
title_short |
Characterization of spatial patterns in river water quality using chemometric techniques |
title_full |
Characterization of spatial patterns in river water quality using chemometric techniques |
title_fullStr |
Characterization of spatial patterns in river water quality using chemometric techniques |
title_full_unstemmed |
Characterization of spatial patterns in river water quality using chemometric techniques |
title_sort |
Characterization of spatial patterns in river water quality using chemometric techniques |
publishDate |
2014 |
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Sains Malaysiana |
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43 |
container_issue |
9 |
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url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907663510&partnerID=40&md5=c06647ab1f20ae8432101eeec38dbbdd |
description |
Water pollution has become a growing threat to human society and natural ecosystem in recent decades, increasing the need to better understand the variabilities of pollutants within aquatic systems. This study presents the application of two chemometric techniques, namely, cluster analysis (CA) and principal component analysis (PCA). This is to classify and identify the water quality variables into groups of similarities or dissimilarities and to determine their significance. Six stations along Kinta River, Perak, were monitored for 30 physical and chemical parameters during the period of 1997-2006. Using CA, the 30 physical and chemical parameters were classified into 4 clusters; PCA was applied to the datasets and resulted in 10 varifactors with a total variance of 78.06%. The varifactors obtained indicated the significance of each of the variables to the pollution of Kinta River. © 2014, Penerbit Universiti Kebangsaan Malaysia. All rights resaerved. |
publisher |
Penerbit Universiti Kebangsaan Malaysia |
issn |
1266039 |
language |
English |
format |
Article |
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scopus |
collection |
Scopus |
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1809677911902388224 |