Abstract
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.
Similar content being viewed by others
References
Antonopoulos VZ, Papamichail DM, Mitsiou KA (2001) Statistical and trend analysis of water quality and quantity data for the Strymon River in Greece. Hydrol Earth Syst Sci 5:679–692
APHA-AWWA-WPCF (1999) Standard methods for the examination of water and wastewater, 20th edn. American Public Health Association, Washington. Accessed on 20 Dec 2015. http://www.mwa.co.th/download/file_upload/SMWW_1000-3000.pdf
Astel A, Tsakovski S, Barbieri P, Simeonov V (2007) Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. Water Res 41:4566–4578
Beatriz H, Pardo R, Vega M, Barrado E, Fernandez JM, Fernandez L (2000) Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Res 34(3):807–816
Bhangu I, Whitfield PH (1997) Seasonal and long-term variations in water quality of the Skeena River at Usk, British Columbia. Water Res 31:2187–2194
Cooper DM, House WA, May A, Gannon B (2002) The phosphorus budget of the Thames catchment, Oxfordshire, UK: 1. Mass balance. Sci Total Environ 282:233–251
Davis SE, Reeder BC (2001) Spatial characterization of water quality in seven eastern Kentucky reservoirs using multivariate analyses. Aquat Ecosyst Health Manag 4:463–477
Dixon W, Chiswell B (1996) Review of aquatic monitoring program design. Water Res 30:1935–1948
Elhatip H, Hinis MA, Gulgahar N (2007) Evaluation of the water quality at Tahtali dam watershed in Izmir, Turkey by means of statistical methodology. Stoch Environ Res Risk Assess 22:391–400
Fan XY, Cui BS, Zhao H, Zhang ZM, Zhang HG (2010) Assessment of river water quality in Pearl River Delta using multivariate statistical techniques. Procedia Environ Sci 2:1220–1234
Homoncik SC, MacDonald AM, Heal KV, Dochartaigh BÉÓ, Ngwenya BT (2010) Manganese concentrations in Scottish groundwater. Sci Total Environ 408:2467–2473
Huang F, Wang XQ, Luo LP, Lou LP, Zhou ZQ, Wu JP (2010) Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Res 44:1562–1572
Lee JY, Cheon JY, Lee KK, Lee SY, Lee MH (2001) Statistical evaluation of geochemical parameter distribution in a ground water system contaminated with petroleum hydrocarbons. J Environ Qual 30:1548–1563
Liu CW, Lin KH, Kuo YM (2003) Application of factor analysis in the assessment of ground water quality in a Blackfoot disease area in Taiwan. Sci Total Environ 313:77–89
Mahbub H, Syed Munaf A, Walid A (2008) Cluster analysis and quality assessment of logged water at an irrigation project, eastern Saudi Arabia. J Environ Manag 86(1):297–307
Meglen RR (1992) Examining large databases: a chemometric approach using principal component analysis. Mar Chem 39:217–237
Ouyang Y (2005) Evaluation of River water quality monitoring stations by principal component analysis. Water Res 39:2621–2635
Pandey PK, Sharma R, Pandey M (2007) Toxic mine drainage from Asia’s biggest copper mine at Malanjkhand, India. Environ Geochem Health 29:237–248
Ragno G, De Luca M, Ioele G (2007) An application of cluster analysis and multivariate classification methods to spring water monitoring data. Microchem J 87(2):119–127. doi:10.1016/j.microc.2007.06.003
Ramadan Z, Song XH, Hopke PK (2000) Identification of sources of Phoenix aerosol by positive matrix factorization. J Air Waste Manag Assoc 50(8):1308–1320
Razmkhah H, Abrishamchi A, Torkian A (2010) Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: a case study on Jajrood River (Tehran, Iran). J Environ Manag 91(4):852–860
Reghunath R, Murthy TRS, Raghavan BR (2002) The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Res 36(10):2437–2442
Simeonov V, Stratis JA, Samara C, Zachariadis G, Voutsa D, Anthemidis A, Sofoniou M, Kouimtzis T (2003) Assessment of the surface water quality in Northern Greece. Water Res 37(17):4119–4124. doi:10.1016/S0043-1354(03)00398-1
Singh KP, Malik A, Mohan D, Sinha S (2004) Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study. Water Res 38(18):3980–3992
Singh KP, Malik A, Sinha S (2005) Water quality assessment and apportionment of pollution sources of Gomti River (India) using multivariate statistical techniques—a case study. Anal Chim Acta 538:355–374
Strain PM, Yeats PA (1999) The relationships between chemical measures and potential predictors of the eutrophication status of inlets. Mar Pollut Bull 38(12):1163–1170
Vega M, Pardo R, Barrado E, Deban L (1998) Assessment of seasonal and polluting effects on the quality of River water by exploratory data analysis. Water Res 32(12):3581–3592
Wang XL, Han JY, Xu LG, Zhang Q (2010) Spatial and seasonal variations of the contamination within water body of the Grand Canal, China. Environ Pollut 158:1513–1520
Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244
Wunderlin DA, Díaz MP, Amé MV, Pesce SF, Hued AC, Bistoni MA (2001) Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía River basin (Córdoba—Argentina). Water Res 35(12):2881–2894
Zhao J, Guo F, Lei K, Li Y (2011) Multivariate analysis of surface water quality in the Three Gorges area of China and implications for water management. J Environ Sci 23(9):1460–1471
Zitko V (1994) Principal component analysis in the evaluation of environmental data. Mar Pollut Bull 28(12):718–722
Acknowledgments
The cooperation and support rendered by Mr. Manish Gavai, Chief Manager (Civil), Hindustan Copper Limited, and Prof. Sudarshan Tiwari, Director, National Institute of Technology, Raipur, in effective planning and execution of the project are gratefully acknowledged. The support and trust reposed by Hindustan Copper Limited in this study are also gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Additional information
Editorial responsibility: M. Abbaspour.
Rights and permissions
About this article
Cite this article
Tiwari, O.N., Devadoss, C., Pradhan, M. et al. Pattern recognition techniques for evaluating the spatial impact of copper mining on surface waters. Int. J. Environ. Sci. Technol. 14, 49–60 (2017). https://doi.org/10.1007/s13762-016-1123-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13762-016-1123-z