An appraisal to hydrochemical characterization, source identification, and potential health risks of sulfate and nitrate in groundwater of Bemetara district, Central India

Mohit Kumar,Mukesh Kumar Sharma, Devendra Singh Malik

Environmental monitoring and assessment(2023)

引用 0|浏览11
暂无评分
摘要
Gypsum-enriched aquifers (GEA) and intensive agriculture regions (IAR) in semi-arid regions are responsible for very high amounts of sulfate and nitrate in many groundwater systems of the world, respectively. However, in such regions, the problem of nitrate pollution and its associated health risk has been increasing and emerging as a global issue. However, along with nitrate, sulfate contamination and its potential health risks are often neglected worldwide in these regions. Therefore, considering sulfate along with nitrate as a significant threat to water quality in such regions, this study aimed to characterize hydrochemistry, factors controlling groundwater quality, and assessment of risk to human health. To accomplish this objective, 116 groundwater samples were collected over pre-monsoon (PRM) and post-monsoon (POM) (2019) seasons in Bemetara district. As per Bureau of Indian standards (BIS) for drinking, SO 4 2− (28 and 19%) and NO 3 − (7 and 35%) exceeded the permissible limits in PRM and POM seasons, respectively; thereby, groundwater was not suitable for drinking. SO 4 2− and NO 3 − pollution sources were identified and mainly attributed to gypsum dissolution and agricultural activities as well as domestic sewage discharge, respectively. In addition, SO 4 2− and NO 3 − risk assessment results show that total 20% to 46% of all samples surpassed the permissible limit (HQ = 1) of risk to children and adults, over both seasons. To ensure drinking water security in this region, sustainable management of agricultural activities and treatment should be done to reduce the potential health risks due to SO 4 2− and NO 3 − .
更多
查看译文
关键词
Hydrochemistry,Water quality,Non-carcinogenic risk,Sulfate pollution,Source identification,Bemetara district
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要