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  • 标题:Multivariate Chemometric Approach on the Surface Water Quality in Langat Upstream Tributaries, Peninsular Malaysia
  • 本地全文:下载
  • 作者:Mohammad Zahirul Haque ; Sahibin Abd Rahim ; Md. Pauzi Abdullah
  • 期刊名称:Journal of Environmental Science and Technology
  • 印刷版ISSN:1994-7887
  • 电子版ISSN:2077-2181
  • 出版年度:2016
  • 卷号:9
  • 期号:3
  • 页码:277-284
  • DOI:10.3923/jest.2016.277.284
  • 出版社:Asian Network for Scientific Information
  • 摘要:The small tributaries to upstream Langat of Peninsular Malaysia play an important role to water quality in downstream. This study was carried out to investigate the indicator pollution and identify the potential sources of pollutants using multivariate chemometric techniques. Sampling campaign was conducted on monthly basis from January-June, 2015, duly interval dry and rainy seasons at six stations. Hierarchical cluster analysis (HACA) was employed on temporal and spatial dataset. Temporal dataset were grouped into two clusters on the basis of rainfall before collecting samples; the months of January, March and June formed one cluster and February, April and May appeared in the other. Spatial dataset were grouped into three clusters namely less polluted, medium polluted and polluted sites. Factor Analysis (FA) and Principal Component Analysis (PCA) were applied to identify the significant sources of pollutants, which resulted in five latent factors amounting to 73.0% of the total variance in data sets. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are related to physicochemical parameters and nutrients from both nonpoint and point sources. The nonpoint sources include plantation area, weathering of sedimentary rock and natural vegetation and point sources include mainly domestic wastewater. Thus, this study illustrates the water quality assessment, identification of pollution factors and temporal/spatial variations in water quality for the surface water of upstream tributaries to implement effective river water quality management with multivariate statistical techniques for analysis and interpretation of complex data sets.
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