Modeling groundwater quality over a humid subtropical region using numerical indices, earth observation datasets, and X-ray diffraction technique: a case study of Allahabad district, India
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Water is undoubtedly the vital commodity for all living creatures and required for well-being of the human society. The present work is based on the surveys and chemical analyses performed on the collected groundwater samples in a part of the Ganga basin in order to understand the sources and evolution of the water quality in the region. The two standard indices such as water quality index and synthetic pollution index for the classification of water in the region are computed. The soil and sediment analysis are carried out with the help of X-ray diffractometer (XRD) for the identification of possible source of ions in water from rock and soil weathering. The dominant minerals which include quartz, muscovite, plagioclase, and orthoclase are reported in the area. The study further utilizes the multivariate statistical techniques for handling large and complex datasets in order to get better information about the groundwater quality. The following statistical methods such as cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA) are applied to handle the large datasets and to understand the latent structure of the data. Through FA/PCAs, we have identified a total of 3 factors in pre-monsoon and 4 factors in post-monsoon season, which are responsible for the whole data structure. These factors explain 77.62 and 82.39 % of the total variance of the pre- and post-monsoon datasets. On the other hand, CA depicted the regions that have similar pollutants origin. The average value of synthetic pollution index of groundwater during pre-monsoon is 9.27, while during post-monsoon, it has been recorded as 8.74. On the other hand, the average values of water quality index of groundwater during pre-monsoon and post-monsoon seasons are found as 217.59 and 233.02, respectively. The study indicates that there occurs an extensive urbanization with gradual vast development of various small- and large-scale industries, which is responsible for degradation in water quality. The overall analysis reveals that the agricultural runoff, waste disposal, leaching, and irrigation with wastewater are the main causes of groundwater pollution followed by some degree of pollution from geogenic sources such as rock and soil weathering, confirmed through XRD analysis.
KeywordsWater quality index XRD Synthetic pollution index Ganga basin Remote sensing and GIS Multivariate analysis
Authors are grateful to School of Environmental Sciences, Jawaharlal Nehru University and University Grant Commission, New Delhi, Grant No. (F. No. 42-74/2013(SR) for their technical and financial support, respectively. The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NASA or the authors’ affiliated institutions.
- APHA (1998). Standard methods for the examination of water and wastewater (20th ed.). Washington, DC: American Public Health Association Inc.Google Scholar
- Banerjee, R., & Srivastava, P. K. (2014). Remote sensing based identification of painted rock shelter sites: Appraisal using advanced wide field sensor, neural network and field observations. In P. K. Srivastava, S. Mukherjee, M. Gupta, & T. Islam (Eds.), Remote sensing applications in environmental research (pp. 195–212). Society of Earth Scientists Series. Berlin: Springer. doi: 10.1007/978-3-319-05906-8_11.
- Bennett, P., & Siegel, D. (1987). Increased solubility of quartz in water due to complexing by organic compounds.Google Scholar
- Bishop, Y. M. M., Fienberg, S. E., & Holland, P. W. (1975). Discrete multivariate analysis theory and practice (557 p). Cambridge, Massachusetts: MIT Press.Google Scholar
- Chamley, H. (1989). Clay formation through weathering. In Clay sedimentology (pp. 21–50). Berlin: Springer.Google Scholar
- Chang, K. (2002). Introduction to geographic information systems. New York.Google Scholar
- Johnston, K., Ver Hoef, J. M., Krivoruchko, K. & Lucas, N. (2001). Using ArcGIS geostatistical analyst, vol. 380. Esri Redlands.Google Scholar
- Kaurish, F. W., & Younos, T. (2007). Developing a standardized water quality index for evaluating surface water quality. JAWRA, 43(2), 533–545.Google Scholar
- Krishna, A. K., Satyanarayanan, M., & Govil, P. K. (2009). Assessment of heavy metal pollution in water using multivariate statistical techniques in an industrial area: a case study from Patancheru, Medak District, Andhra Pradesh, India. Journal of Hazardous Materials, 167(1), 366–373.CrossRefGoogle Scholar
- Merchant, J. W. (1994). GIS-based groundwater pollution hazard assessment: A critical review of the DRASTIC model. Photogrammetric Engineering and Remote Sensing, 60(9), 1117–1128.Google Scholar
- Mitas, L., & Mitasova, H. (1999). Spatial interpolation. Geographical Information Systems: Principles, Techniques, Management and Applications, 1, 481–492.Google Scholar
- Moore, D. M., & Reynolds, R. C, Jr. (1989). X-ray diffraction and the identification and analysis of clay minerals. Oxford: Oxford University Press.Google Scholar
- Patel, D. P., Dholakia, M. B., Naresh, N., & Srivastava, P. K. (2011). Water harvesting structure positioning by using geo-visualization concept and prioritization of mini-watersheds through morphometric analysis in the Lower Tapi Basin. Journal of the Indian Society of Remote Sensin,1–14.Google Scholar
- Singh, S. K., Singh, C. K., Kumar, K. S., Gupta, R., & Mukherjee, S. (2009). Spatialtemporal monitoring of groundwater using multivariate statistical techniques in Bareilly district of Uttar Pradesh, India. Journal of Hydrology and Hydromechanics, 57(1), 45–54.Google Scholar
- Singh, S. K., Srivastava, P. K., & Pandey, A. C. (2013a). Fluoride contamination mapping of groundwater in northern India integrated with geochemical indicators and GIS. Water Science and Technology: Water Supply. doi: 10.2166/ws.2013.160.
- Snow, J. (1856). Cholera and the water supply in the south districts of London in 1854. T. Richards.Google Scholar
- Srivastava, P. K., Singh, S. K., Gupta, M., Thakur, J. K., & Mukherjee, S. (2013). Modeling impact of land use change trajectories on groundwater quality using remote sensing and GIS. Environmental Engineering and Management Journal, 12(12), 2343–2355.Google Scholar
- Stallard, R., & Edmond, J. (1987). Geochemistry of the Amazon: 3. Weathering chemistry and limits to dissolved inputs. Journal of Geophysical Research: Oceans (1978–2012), 92(C8), 8293–8302.Google Scholar
- Sun, H., Bergstrom, J. C., & Dorfman, J. H. (1992). Estimating the benefits of groundwater contamination control. Southern Journal of Agricultural Economics, 24, 63.Google Scholar
- Tiwari, T., & Mishra, M. (1985). A preliminary assignment of water quality index of major Indian rivers. Indian Journal of Environmental Protection, 5(4), 276–279.Google Scholar
- Vasanthavigar, M., Srinivasamoorthy, K., Vijayaragavan, K., Ganthi, R. R., Chidambaram, S., Anandhan, P., et al. (2010). Application of water quality index for groundwater quality assessment: Thirumanimuttar sub-basin, Tamilnadu, India. Environmental Monitoring and Assessment, 171(1–4), 595–609.CrossRefGoogle Scholar
- Waller, R. M. (2001). Ground water and the rural homeowner. US Geological Survey (USGS).Google Scholar