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Social-Ecological Patterns of Soil Heavy Metals Based on a Self-Organizing Map (SOM): A Case Study in Beijing, China

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  • Binwu Wang

    (College of Resources and Environmental Sciences, China Agricultural University, No. 2 Yuan Ming Yuan west Road, Beijing 100193, China
    Key Laboratory of Agricultural Land Quality, Ministry of Land and Resources, No. 2 Yuan Ming Yuan west Road, Beijing 100193, China)

  • Hong Li

    (Institute of Agricultural Integrated Development, Beijing Academy of Agriculture and Forestry Sciences, No. 9 Shu Guang Hua Yuan Middle Road, Beijing 100097, China)

  • Danfeng Sun

    (College of Resources and Environmental Sciences, China Agricultural University, No. 2 Yuan Ming Yuan west Road, Beijing 100193, China
    Key Laboratory of Agricultural Land Quality, Ministry of Land and Resources, No. 2 Yuan Ming Yuan west Road, Beijing 100193, China)

Abstract

The regional management of trace elements in soils requires understanding the interaction between the natural system and human socio-economic activities. In this study, a social-ecological patterns of heavy metals (SEPHM) approach was proposed to identify the heavy metal concentration patterns and processes in different ecoregions of Beijing (China) based on a self-organizing map (SOM). Potential ecological risk index (RI) values of Cr, Ni, Zn, Hg, Cu, As, Cd and Pb were calculated for 1,018 surface soil samples. These data were averaged in accordance with 253 communities and/or towns, and compared with demographic, agriculture structure, geomorphology, climate, land use/cover, and soil-forming parent material to discover the SEPHM. Multivariate statistical techniques were further applied to interpret the control factors of each SEPHM. SOM application clustered the 253 towns into nine groups on the map size of 12 × 7 plane (quantization error 1.809; topographic error, 0.0079). The distribution characteristics and Spearman rank correlation coefficients of RIs were strongly associated with the population density, vegetation index, industrial and mining land percent and road density. The RIs were relatively high in which towns in a highly urbanized area with large human population density exist, while low RIs occurred in mountainous and high vegetation cover areas. The resulting dataset identifies the SEPHM of Beijing and links the apparent results of RIs to driving factors, thus serving as an excellent data source to inform policy makers for legislative and land management actions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:4:p:3618-3638:d:34603
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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. 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.
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    Cited by:

    1. Ekaterini Hadjisolomou & Konstantinos Stefanidis & George Papatheodorou & Evanthia Papastergiadou, 2018. "Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps," IJERPH, MDPI, vol. 15(3), pages 1-16, March.

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