Abstract
Sagar, a district in central India, hosts a large population of people dependent on groundwater (without further purification) for their daily needs. However, water borne diseases, primarily kidney stones, are prevalent in this district. Therefore, we collected a large number of drinking water samples (227) in both pre-monsoon and post-monsoon seasons by dividing the entire area into 6 × 6 km2 grids. A total of 19 drinking water quality parameters were assessed and analyzed for each sample and compared with the Bureau of Indian Standards BIS 2012 (IS: 10500). The correlation coefficients of total dissolved solids (TDS), electrical conductivity and salinity were found to align perfectly with the correlation coefficients of total hardness, calcium hardness and magnesium hardness. Similarly, the total alkalinity and phenolphthalein alkalinity were correlated with bicarbonate and carbonate ion concentrations, respectively. Ion Selective electrodes (ISE), UV–vis Spectrophotometer, and volumetric methods were used for the analyses. The water quality was found to be mainly influenced by geological factors, which play an important role in controlling hardness, TDS and chloride and sulfate ion concentrations. The water quality index indicates that about 89% of the samples were in good condition. However, total hardness (TH) and nitrate ion concentration were the issues at large. Geological analysis was performed using ArcGIS 10.0 to mark the spatiality of drinking water quality through distribution maps, which revealed that the predominance of permeable calcium-rich rocks is the prime reasons behind the water hardness.
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•Physico-chemical parameters of 227 drinking water samples were determined in pre- and post-monsoon season.•The significant components affecting water quality were obtained using principle component analysis.•Major factors affecting water quality were determined using factor analysis.•To find out the average water quality of Sagar district, the method of water quality index was used.•Geospatial analysis was used to derive a pictorial model of water quality and find the key factors behind water quality.