Limits...
Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

Huo XN, Li H, Sun DF, Zhou LD, Li BG - Int J Environ Res Public Health (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Beijing Academy of Agriculture and Forestry, Beijing 100089, China. hxnsky@126.com

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.

Show MeSH
The distribution of soil samples at three levels.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3367293&req=5

ijerph-09-00995-f001: The distribution of soil samples at three levels.

Mentions: To investigate the pollution status of heavy metals in Beijing agricultural areas, a large-scale soil sampling project was conducted after the crop harvest in the autumn of 2006. According to the agricultural land distribution and land use type maps of Beijing, a non-uniform distribution of the stratified sampling technique was adopted to collect samples and ensure the representativeness of samples. The sampling strategy was divided into three steps to collect a total of 1,018 samples. First, 231 soil samples were collected from the entire study area, with uniform sampling being the low sampling density (C). Secondly, another 360 soil samples were added from areas with more agricultural soils to create the medium sampling density (M). Third, 427 soil samples were further collected on the basis of the two previous samplings and the agricultural soils to make a high sampling density (F). Figure 1 shows the distribution of soil samples at the three sampling densities.


Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

Huo XN, Li H, Sun DF, Zhou LD, Li BG - Int J Environ Res Public Health (2012)

The distribution of soil samples at three levels.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3367293&req=5

ijerph-09-00995-f001: The distribution of soil samples at three levels.
Mentions: To investigate the pollution status of heavy metals in Beijing agricultural areas, a large-scale soil sampling project was conducted after the crop harvest in the autumn of 2006. According to the agricultural land distribution and land use type maps of Beijing, a non-uniform distribution of the stratified sampling technique was adopted to collect samples and ensure the representativeness of samples. The sampling strategy was divided into three steps to collect a total of 1,018 samples. First, 231 soil samples were collected from the entire study area, with uniform sampling being the low sampling density (C). Secondly, another 360 soil samples were added from areas with more agricultural soils to create the medium sampling density (M). Third, 427 soil samples were further collected on the basis of the two previous samplings and the agricultural soils to make a high sampling density (F). Figure 1 shows the distribution of soil samples at the three sampling densities.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Beijing Academy of Agriculture and Forestry, Beijing 100089, China. hxnsky@126.com

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.

Show MeSH