Pattern recognition techniques for evaluating the spatial impact of copper mining on surface waters

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• 作者：O. N. Tiwari ; C. Devadoss ; M. Pradhan…
• 关键词：Copper mining ; Cluster analysis ; Spatial studies ; Surface water ; Principal component analysis
• 刊名：International Journal of Environmental Science and Technology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：14
• 期：1
• 页码：49-60
• 全文大小：
• 刊物主题：Environment, general; Environmental Science and Engineering; Environmental Chemistry; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution; Soil Science & Conservati
• 出版者：Springer Berlin Heidelberg
• ISSN：1735-2630
• 卷排序：14

文摘

In this study, multivariate statistical approaches, namely hierarchical cluster analysis (CA) and principal component analysis (PCA), were employed to understand the impact of copper mining on surface waters located in Central-East India. The data set generated consisted of nine parameters, namely pH, dissolved oxygen (DO), alkalinity, total dissolved solids, copper, iron, manganese, zinc and fluoride, collected in forty sampling points covering all seasons. As delineated by CA, the entire data set for both the surface waters was bifurcated into groups, namely Banjar River inclusion of seepage points (BRISP) and Banjar River exclusion of seepage points (BRESP), Son River inclusion of seepage points (SRISP) and Son River exclusion of seepage points (SRESP). Four latent factors were identified, namely copper, iron, fluoride and manganese, explaining 84.7 % of variance for BRISP, 71.9 % of variance for BRESP, 66.7 % of variance for SRISP and 68 % of variance for SRESP. The extensive application of PCA on BRISP, BRESP, SRISP and SRESP reveals that the main stream of both the rivers remains unaffected by mining operations when seepage points were excluded. Additionally, iron content is considerably significant throughout the stream due to the geogenic sources and it is considered as a major factor for the depletion of DO level in the streams. This study reveals the level of contamination in the studied surface waters and the effectiveness of multivariate statistical techniques for evaluation and interpretation of complex data matrix in understanding the spatial variations and identification of pollution sources.