[Coregionalization, spatial-correlation and spatial-factor analysis of soil available heavy metals in a typical region of the Yangtze River Delta].
School of Geosranhic and Oceaneraohic Sciences, Nanjing University, Nanjing 210093, China.Huan jing ke xue= Huanjing kexue / [bian ji, Zhongguo ke xue yuan huan jing ke xue wei yuan hui "Huan jing ke xue" bian ji wei yuan hui.] 01/2008; 28(12):2758-65.
The method of factorial kriging based on the theory of coregionalization is developed by the combination of multi-statistics, geostatistics and GIS. Soil available heavy metals of 126 topsoil samples in Kunshan city, a typical region of Yangtze River Delta, were analyzed, and the spatial distribution pattern was investigated by the method of factorial kriging. Based on the analysis of multi-scale spatial structure characteristics of available heavy metals, we discussed the pollution source and cause of this spatial distribution by means of spatial scale-correlation analysis and spatial principal component analysis. Our results show that all the available heavy metals distribute normally or lognormally with great variability, and the contamination of available Cd is the biggest. The available heavy metals are categorized into three spatial scales, i.e. nugget, short-range (15 km) and long-range (40 km), respectively, and a linear model of coregionalization comprising these three spatial scales is fitted to the experimental auto-and cross-variograms of the soil available heavy metals. Significant relationship is found between Cd and Zn in the three scales. The spatial correlation of available heavy metals in short-range and long-rang are stronger than it in nugget, while the long-rang has more obvious negative correlation than the other two spatial scales. The results of spatial principal component analysis show the pollution sources are different in the three spatial scales. The kriging interpolation method was applied to work out the distribution maps of first and second principal component of available heavy metal, which indicate that available heavy metal concentrations in the soils are closely related to their industry activity, sewage irrigation and soil characteristics.
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ABSTRACT: Total mercury (Hg(T)) and bioavailability Hg (Hg(HCl)) concentrations in soil were determined in five districts in Wuhu urban area. Spatial pattern of soil Hg concentration was generated through kriging technology. Results showed that Hg concentration in soil ranged from 0.024 to 2.844 mg kg( -1) with an average of 0.207 mg kg( -1). Hg concentration in soil appeared to have a block distribution and decreased from downtown to surrounding district. And Hg concentrations appeared to have a medium scale spatial auto correlation, strongly affected by human activity. The maximal Hg average concentration (0.332 mg kg( -1)) in soil appeared in Jinghu district, where the high intensity of human activities is. Second highest Hg average concentration (0.263 mg kg( -1)) in soil appeared in development district, where the intensive industrial activities are. Bioavailability Hg concentration in soil ranged from 2.6 to 4.9 μg kg( -1) with an average of 3.8 μg kg( -1), which had a ratio of 0.28~6.44% to total Hg. The ratios of bioavailability Hg to total Hg in vegetable soil were bigger than those of park soil. Correlation analysis showed that total Hg, organic matter, total phosphorus, and bioavailability Hg concentrations in soil were significantly positively correlated. Hg concentration in vegetable ranged from 2.7 to 15.2 μg kg( -1) with an average of 6.5 μg kg( -1). Hg concentration in vegetable was positively correlated with Hg(HCl) concentration in soil. According to the calculation on hazard quotient (HQ) for children, inhalation of Hg vapor from soil is the main exposure pathway, in which HQ is 2.517 × 10( -2), accounting for 80.3% of the four exposure pathways. Hazard index (HI) of the four exposure pathways is lower than the "safe" level of HI = 1; therefore, exposure of soil Hg exhibited little potential health risk to children in Wuhu urban area.Environmental Monitoring and Assessment 10/2010; 179(1-4):255-65. DOI:10.1007/s10661-010-1733-8 · 1.68 Impact Factor
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