An integrated computational and graphical approach for evaluating the geochemistry and health risks of nitrate-contaminated water for six age groups.

IF 1.2 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Johnbosco C Egbueri, Johnson C Agbasi, Mohamed ElKashouty, Mohd Yawar Ali Khan, Sani I Abba, Nazia Khan
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Abstract

Nitrate contamination in drinking water poses significant health risks, particularly in rapidly urbanizing areas of developing countries. This study presents an integrated computational and graphical approach to evaluate the geochemistry and health risks of nitrate-contaminated water for six age groups in Southeast, Nigeria. The research employed a detailed methodology combining water nutrient pollution index (WNPI), nitrate pollution index (NPI), water pollution index (WPI), geochemical plotting techniques, stoichiometry, and health risk computations. Water samples from several locations were analyzed for physicochemical parameters and nitrate concentrations. Results revealed predominantly acidic conditions and varying levels of nitrate contamination. Geochemical analysis indicated that silicate weathering and ion exchange processes were the primary influences on water chemistry. The WPI identified 14.29% of samples as "extremely polluted" (WPI > 1), while the WNPI classified 7.14% of samples as "moderately polluted" (WNPI > 1). However, the NPI categorized the samples as safe, indicating low nitrate inputs from anthropogenic sources. Health risk assessments indicated low-moderate risks, with the highest total hazard index of 0.839 for the 6-12 months age group; thus, higher vulnerability for infants. Oral exposure was found to be the dominant pathway, contributing over 99.90% to the total risk. This research provides crucial insights for achieving the Sustainable Development Goals (SDGs) related to water quality and public health protection. The integrated approach offers a robust framework for water resource management and interventions in risk-prone areas. Future research should focus on expanding the spatial coverage, incorporating sensitivity analyses, and exploring advanced technologies for real-time monitoring and predictive modeling of water quality.

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CiteScore
4.60
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0.00%
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