Application of stable isotope of water and a Bayesian isotope mixing model (SIMMR) in groundwater studies: a case study of the Granvillebrook and Kingtom dumpsites

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作者
Abdul Aziz Sankoh
Cynthia Laar
Nana Sarfo Agyemang Derkyi
Ronnie Frazer-Williams
机构
[1] University of Energy and Natural Resources,Department of Environmental Engineering Management
[2] Fourah Bay College,Department of Chemistry
[3] University of Sierra Leone,undefined
[4] National Nuclear Research Institute,undefined
[5] Ghana Atomic Energy Commission,undefined
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关键词
Bayesian isotope mixing model; SIMMR model; Groundwater salinity; Geochemical analysis; Dumpsite leachate;
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摘要
The increase in groundwater salinity of the two major dumpsites in Sierra Leone has been a major concern for stakeholders. Therefore, this study employed geochemical and stable water isotope analyses to investigate the factors controlling groundwater salinity. The proportional sources of the groundwaters were also evaluated using the Bayesian isotope mixing model. The geochemical analysis showed that the groundwater chemistry in the Granvillebrook dumpsite is controlled by water–rock interaction and evaporation while that of the Kingtom is dominated by water–rock interaction and precipitation. The biplot of deuterium (δ2H) versus oxygen (δ18O) composition relative to the global meteoric water line confirms that the groundwaters of the study areas are of meteoric origin. The linear plot of electrical conductivity versus δ18O depicts that mineralization is the major factor impacting the groundwater salinity in the study areas. The stable isotope mixing model in R (SIMMR) suggests that 96.5% of the groundwaters in the study areas are recharged by precipitation while only 3.5% originated from surface water. The SIMMR model also depicts that groundwaters in the Granvillebrook dumpsite have been bridged by leachate (33.0%) and domestic wastewater (15.2%) while for the Kingtom dumpsite, 13% and 21.5% are contaminated by leachate and domestic wastewaters. Contrary to other previous studies, this research confirms the feasibility of using the Bayesian isotope mixing model to quantify the factors influencing groundwater salinity.
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