Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques

This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and...

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Veröffentlicht in:Marine pollution bulletin 2016-05, Vol.106 (1-2), p.292-300
Hauptverfasser: Ismail, Azimah, Toriman, Mohd Ekhwan, Juahir, Hafizan, Zain, Sharifuddin Md, Habir, Nur Liyana Abdul, Retnam, Ananthy, Kamaruddin, Mohd Khairul Amri, Umar, Roslan, Azid, Azman
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Sprache:eng
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Zusammenfassung:This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and southern regions) are observed according to level of concentrations via the application of chemometric techniques. Principal component analysis (PCA) demonstrates that Cu and Cr dominate the source apportionment in northern region with a total variance of 57.62% and is identified with mining and shipping activities. These are the major contamination contributors in the Straits. Land-based pollution originating from vehicular emission with a total variance of 59.43% is attributed to the high level of Pb concentration in the southern region. The results revealed that one state representing each cluster (northern and southern regions) is significant as the main location for investigating heavy metal concentration in the Straits of Malacca which would save monitoring cost and time. The monitoring of spatial variation and source of heavy metals pollution at the northern and southern regions of the Straits of Malacca, Malaysia, using chemometric analysis. •Heavy metals are non-biodegradable in marine systems.•Determination of heavy metal concentration using chemometric statistical techniques•From backward stepwise regression, four variables should high spatial variation.•Monitoring of heavy metal concentration using only one state from each cluster
ISSN:0025-326X
1879-3363
DOI:10.1016/j.marpolbul.2015.10.019