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Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

Author

Listed:
  • Xiao-Ni Huo

    (Beijing Academy of Agriculture and Forestry, Beijing 100089, China
    Department of Environmental Engineering, Taiyuan University, Taiyuan 030009, China)

  • Hong Li

    (Beijing Academy of Agriculture and Forestry, Beijing 100089, China)

  • Dan-Feng Sun

    (Department of Land Resources and Management, College of Natural Resources and Environment Science, China Agricultural University, Beijing 100193, China)

  • Lian-Di Zhou

    (Beijing Academy of Agriculture and Forestry, Beijing 100089, China)

  • Bao-Guo Li

    (Department of Land Resources and Management, College of Natural Resources and Environment Science, China Agricultural University, Beijing 100193, China)

Abstract

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

Suggested Citation

  • Xiao-Ni Huo & Hong Li & Dan-Feng Sun & Lian-Di Zhou & Bao-Guo Li, 2012. "Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China," IJERPH, MDPI, vol. 9(3), pages 1-23, March.
    • Handle: RePEc:gam:jijerp:v:9:y:2012:i:3:p:995-1017:d:16741
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    References listed on IDEAS

    as
    1. Xiao-Ni Huo & Wei-Wei Zhang & Dan-Feng Sun & Hong Li & Lian-Di Zhou & Bao-Guo Li, 2011. "Spatial Pattern Analysis of Heavy Metals in Beijing Agricultural Soils Based on Spatial Autocorrelation Statistics," IJERPH, MDPI, vol. 8(6), pages 1-16, June.
    2. Luc Anselin, 2001. "Spatial Effects in Econometric Practice in Environmental and Resource Economics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 705-710.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Binwu Wang & Hong Li & Danfeng Sun, 2014. "Social-Ecological Patterns of Soil Heavy Metals Based on a Self-Organizing Map (SOM): A Case Study in Beijing, China," IJERPH, MDPI, vol. 11(4), pages 1-21, March.
    2. Wei-Chih Lin & Yu-Pin Lin & Johnathen Anthony & Tsun-Su Ding, 2015. "Avian Conservation Areas as a Proxy for Contaminated Soil Remediation," IJERPH, MDPI, vol. 12(7), pages 1-20, July.
    3. Katarzyna Kocur-Bera & Jacek Rapiński & Monika Siejka & Przemysław Leń & Anna Małek, 2023. "Potential of an Area in Terms of Pro-Climate Solutions in a Land Consolidation Project," Sustainability, MDPI, vol. 15(12), pages 1-25, June.
    4. Junxiao Wang & Xiaorui Wang & Shenglu Zhou & Shaohua Wu & Yan Zhu & Chunfeng Lu, 2016. "Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations," IJERPH, MDPI, vol. 13(10), pages 1-12, September.
    5. Katarzyna Kocur-Bera & Szymon Czyża, 2023. "Socio-Economic Vulnerability to Climate Change in Rural Areas in the Context of Green Energy Development—A Study of the Great Masurian Lakes Mesoregion," IJERPH, MDPI, vol. 20(3), pages 1-24, February.

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