Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55296
Title: Physicochemical assessment of groundwater quality at Kashipur (Uttarakhand) industrial areas
Authors: Nandi, S
Sharma, A
Ahmed, S
Teotia, D
Keywords: Physicochemical parameters;Treatment of groundwater quality;Quantitative pH modeling;Kashipur (Uttarakhand) industrial belt
Issue Date: Aug-2020
Publisher: NISCAIR-CSIR, India
Abstract: The quality of groundwater has been degrading due to municipal sewage, industrial pollutants, fertilizers, herbicides, and pesticides. These dangerous pollutants enter into the deeper soil layers, infiltrate some aquifers, and decrease the gradation of groundwater. Other problems are associated with the leakage of sewer, faulty septic tank cleaning, landfill leachates, throwing of garbage into the river, pond and soil pollution. In coastal areas, salt water intrudes into fresh-water aquifers due to intensive pumping of fresh groundwater. In the present study, the city Kashipur in the state of Uttarakhand has been chosen due to big industrial settlements. The industrial wastes contain many highly harmful elements which destroy the quality of groundwater in the adjacent areas. Therefore, it is our target to test the groundwater quality of Kashipur industrial areas. To analyze the groundwater and to assess the impact of groundwater pollution of Kashipur area, an attempt has been made in the present study to test the physicochemical parameters including pH, total hardness, alkalinity, chloride, fluoride, sulphate, iron, zinc, copper and heavy metal atoms such as lead, arsenic etc. Imbalance of these parameters may degrade the quality of groundwater and may be deleterious to the health of individual and society in context of drinking, agriculture and industrial purposes. Physiochemical treatment of groundwater samples collected in summer, rainy season and post-monsoon were compared and analyzed by regression modeling making a quantitative correlation between pH and other parameters including total hardness, alkalinity, chloride, sulfate, fluoride and copper using multiple linear regression methods.
Page(s): 1486-1494
URI: http://nopr.niscair.res.in/handle/123456789/55296
ISSN: 2582-6727 (Online); 2582-6506 (Print)
Appears in Collections:IJMS Vol.49(08) [August 2020]

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