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1.
Environ Monit Assess ; 154(1-4): 439-49, 2009 Jul.
Article in English | MEDLINE | ID: mdl-18597177

ABSTRACT

Understanding regional variations of soil heavy metals and their anthropogenic influence are very important for environmental planning. In this study, 286 surface soil samples were collected in Fuyang county, and the 'total' metals for copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd) and nickel (Ni) were measured in 2005. Statistic analysis showed that Cu, Zn, Pb and Cd had been added by exterior factors, and Ni was mainly controlled by natural factors. The combination of multivariate statistical and geostatistical analysis successfully grouped three groups (Cu, Zn and Pb; Cd; and Ni) of heavy metals from different sources. Through pollution evaluation, it was found that 15.76% of the study area for Cu, Zn and Pb, and 46.14% for Cd suffered from moderate or severe pollution. Further spatial analysis identified the limestone mining activities, paper mills, cement factory and metallurgic activities were the main sources for the concentration of Cu, Zn, Pb and Cd in soils, and soil Ni was mainly determined by the parent materials.


Subject(s)
Environmental Monitoring , Metals, Heavy/analysis , Soil Pollutants/analysis , Soil/analysis , China , Multivariate Analysis , Risk Assessment
2.
Environ Pollut ; 156(3): 1260-7, 2008 Dec.
Article in English | MEDLINE | ID: mdl-18455844

ABSTRACT

Recent enhanced urbanization and industrialization in China have greatly influenced soil Cu content. To better understand the magnitude of Cu contamination in soil, it is essential to understand its spatial distribution and estimate its values at unsampled points. However, Kriging often can not achieve satisfactory estimates when soil Cu data have weak spatial dependence. The proposed classification and regression tree method (CART) simulated Cu content using environmental variables, and it had no special data requirements. The Cu concentration classes estimated by CART had accuracy in attribution to the right classes of 80.5%, this is 29.3% better than ordinary Kriging method. Moreover, CART provides some insight into the sources of current soil Cu contents. In our study, low soil Cu accumulation was driven by terrain characteristic, agriculture land uses, and soil properties; while high Cu concentration resulted from industrial and agricultural land uses.


Subject(s)
Copper/analysis , Environmental Pollution/statistics & numerical data , Soil Pollutants/analysis , Soil/analysis , Agriculture , China , Decision Trees , Industry , Metallurgy , Mining , Refuse Disposal , Sewage
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