• Title/Summary/Keyword: 지구화학

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Geochemistry of Groundwater in Limestone and Granite of Hwanggangri Fluorite Mineralized Area (황강리 형석 광화대내 석회암 및 화강암지역 지하수의 지구화학적 특성)

  • Hwang, Jeong
    • Journal of the Korean earth science society
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    • v.23 no.6
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    • pp.486-493
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    • 2002

  • Hydrogeochemical characteristics of groundwater from a limestone and granite area were studied in the Hwanggangri district, where important fluorite ore deposits are distributed. The geochemical properties of groundwater from limestone and granite are commonly characterized as Ca$^{2+}$-HCO$_3\;^-$ and (Ca$^{2+}$+Na$^+$)-HCO$_3\;^-$ type, respectively. Groundwater, contaminated by mine drainage water from the neighboring ore deposits, has not been observed yet. However, fluoride in groundwater exceeding the drinking water permission level is found in the wells located in a Cretaceous granite area. The concentrations of F in the groundwater show a positive relationship with the values of Na, HCO$_3$, Li and pH. This may suggest that the groundwater come from the decomposition of fluoride-bearing silicate minerals within highly differentiated granitic rocks.

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Application of Indicator Geostatistics for Probabilistic Uncertainty and Risk Analyses of Geochemical Data (지화학 자료의 확률론적 불확실성 및 위험성 분석을 위한 지시자 지구통계학의 응용)

  • Park, No-Wook
    • Journal of the Korean earth science society
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    • v.31 no.4
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    • pp.301-312
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    • 2010

  • Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.

  • https://doi.org/10.5467/JKESS.2010.31.4.301 인용 PDF KSCI