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
Distribution maps of heavy metals based on the nested model of  and  (a) Cr, (b) Ni, (c) Zn, (d) Hg.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

ijerph-09-00995-f005: Distribution maps of heavy metals based on the nested model of and (a) Cr, (b) Ni, (c) Zn, (d) Hg.

Mentions: Figure 5 shows the spatial distribution of heavy metal concentrations interpolated using the optimality model, that is the nested model and . Heavy metals concentrations are separated into classes according to the background values of soil heavy metals of Beijing and their multiples to highlight the spatial differences of different classes.


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)

Distribution maps of heavy metals based on the nested model of  and  (a) Cr, (b) Ni, (c) Zn, (d) Hg.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

ijerph-09-00995-f005: Distribution maps of heavy metals based on the nested model of and (a) Cr, (b) Ni, (c) Zn, (d) Hg.
Mentions: Figure 5 shows the spatial distribution of heavy metal concentrations interpolated using the optimality model, that is the nested model and . Heavy metals concentrations are separated into classes according to the background values of soil heavy metals of Beijing and their multiples to highlight the spatial differences of different classes.

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