Groundwater quality appraisal by improved set pair analysis with game theory weightage and health risk estimation of contaminants for Xuecha drinking water source in a loess area in Northwest China
By Rui Tian, Jianhua Wu. Human and Ecological Risk Assessment, 2019
87 citations recorded by the source corpus.
Abstract The current study was carried out to investigate the hydrochemistry of groundwater in Xuecha drinking water source by statistical analysis and graphical approaches, and to evaluate the groundwater quality using improved set pair analysis (SPA) model with game theory for weight determination. The non-carcinogenic health risks and carcinogenic health risks of nitrate and Cr6+ through oral exposure pathway for adults and children were also estimated, respectively. For this study, 39 groundwater samples were collected from Luohe and Huanhe aquifers in the study area for physicochemical analysis. Groundwater in the Huanhe aquifer shows poorer quality than that in the Luohe aquifer with greater hardness and higher salinity. The Piper diagram and Gibbs diagrams show that the Huanhe groundwater is mainly SO4·Cl–Ca·Mg, HCO3–Na, and HCO3–Ca·Mg types, and rock weathering is the leading factor in groundwater chemistry evolution, and groundwater evaporation is also a noticeable factor. All Luohe groundwater samples are SO4·Cl–Na type and are mainly regulated by the weathering of rocks and water–rock interactions, but less affected by evaporation compared to the Huanhe groundwater. Most of the Huanhe groundwater is of excellent to fair quality, and more than half the Luohe water samples are of poor quality. Children in the area are facing higher health risks. Carcinogenic risks caused by Cr6+ in this study area are higher than the acceptable limit for adult and children, and higher severity is observed in the Huanhe groundwater. For the non-cancer risk, greater threat is from the Luohe groundwater.
Subfields: Environmental Engineering, Geochemistry and Petrology, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/66aa94f824336fef70c6d964e61795e15c0d0c4a
Citations in the corpus
- The concept and future prospects of soil health (2020, unknown): “Emerging data analysis tools such as machine learning 6 , deep learning, artificial neural networks 101 or game theory 102 should be explored more fully in order to quantify the effect of soil health indicators as well as prioritize soil functions such as water quality or food production.”