Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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Application of Hot Spot Analysis for Interpreting Soil Heavy-Metal Concentration Data in Abandoned Mines

폐금속 광산의 토양 중금속 오염 조사 자료 해석을 위한 핫스팟 분석의 적용

  • LEE, Chae-Young (Dept. of Transport Big Data, The Korea Transport Institute) ;
  • KIM, Sung-Min (Dept. of Energy Engineering, Kangwon National University) ;
  • CHOI, Yo-Soon (Dept. of Energy Resources Engineering, Pukyong National University)
  • 이채영 (한국교통연구원 교통빅데이터연구본부) ;
  • 김성민 (강원대학교 에너지공학부) ;
  • 최요순 (부경대학교 에너지자원공학과)
  • Received : 2019.03.15
  • Accepted : 2019.03.28
  • Published : 2019.06.30
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Abstract

In this study, a hotspot analysis was conducted to suggest a new method for interpreting soil heavy-metal contamination data of abandoned metal mines according to statistical significance level. The spatial autocorrelation of the data was analyzed using the Getis-Ord $Gi{\ast}$ statistic in order to check whether soil heavy metal contamination data showing abnormal values appeared concentrated or dispersed in a specific space. As a result, the statistically significant data showing abnormal values in the mine area could be classified as follows: (1) the contamination degree and the hotspot value (z-score) were both high, (2) the contamination degree was high but the z-score was low, (3) the contamination degree was low but the z-score was high and (4) the contamination degree and the z-score were both low. The proposed method can be used to interpret the soil heavy metal contamination data according to the statistical significance level and to support a rational decision for soil contamination management in abandoned mines.

본 연구에서는 핫스팟 분석을 통해 폐금속 광산의 토양 중금속 오염 조사 자료를 통계적 유의수준에 따라 해석할 수 있는 새로운 방법을 제시하였다. 이상 값을 나타내는 토양 중금속 오염 조사 자료들이 특정한 공간에 집중 또는 분산되어 나타나는지를 확인하기 위해 자료들의 공간적 자기상 관성을 Getis-Ord $Gi{\ast}$ 통계량을 이용하여 분석하였다. 그 결과 폐금속 광산지역에서 이상 값을 나타내는 자료들이 통계적으로 얼마나 유의미하게 집중되어 있는지 확인할 수 있었다. 각각의 자료들이 가지는 중금속 원소별 오염도 값과 Getis-Ord $Gi{\ast}$ 통계량 계산 결과를 이용하여 자료들을 (1) 오염도와 집중도가 모두 높은 것, (2) 오염도는 높으나 집중도가 낮은 것, (3) 오염도는 낮으나 집중도가 높은 것, (4) 오염도와 집중도가 모두 낮은 것 중 하나의 유형으로 분류할 수 있었다. 이러한 분류 결과를 활용하면 토양 중금속 오염 조사자료를 통계적 유의수준에 따라 해석할 수 있으며, 폐광산 지역의 토양오염 관리와 관련하여 합리적인 의사결정을 지원할 수 있으리라 판단된다.

Keywords

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FIGURE 1. Distribution of soil concentration survey data in the study area

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FIGURE 2. Distribution of Arsenic concentration in soils

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FIGURE 3. Procedure of hot spot analysis for interpreting survey data of soil heavy-metal concentrations

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FIGURE 4. Spatial autocorrelations of (a) raw data, (b) square root transformed data and (c) log transformed data.

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FIGURE 5. Graph of statistical significance for interpreting As contamination in soils

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FIGURE 6. Results of hotspot analysis using (a) raw data, (b) square root transformed data and (c) Log transformed data

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FIGURE 6. Continued

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FIGURE 7. Result showing the statistical significance for interpreting As contamination in soils

TABLE 1. Summary of geology for mines

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TABLE 2. Number of contamination sources at the study area

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TABLE 3. Descriptive statistics of arsenic concentration at the study area.

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TABLE 4. Descriptive statistics of Gi* z-scores determined by hotspot analysis

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