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Int J Environ Res Public Health. 2014 Mar 31;11(4):3618-38. doi: 10.3390/ijerph110403618.

Social-ecological patterns of soil heavy metals based on a self-organizing map (SOM): a case study in Beijing, China.

Author information

1
College of Resources and Environmental Sciences, China Agricultural University, No. 2 Yuan Ming Yuan west Road, Beijing 100193, China. binw_w@cau.edu.cn.
2
Institute of Agricultural Integrated Development, Beijing Academy of Agriculture and Forestry Sciences, No. 9 Shu Guang Hua Yuan Middle Road, Beijing 100097, China. lihsdf@sina.com.
3
College of Resources and Environmental Sciences, China Agricultural University, No. 2 Yuan Ming Yuan west Road, Beijing 100193, China. sundf@cau.edu.cn.

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.

PMID:
24690947
PMCID:
PMC4025005
DOI:
10.3390/ijerph110403618
[Indexed for MEDLINE]
Free PMC Article

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