A Study Of The Heavy Metal's Spatial Variation Of Zhuji Lipu Copper Mining Area

This research was carried on the heavy metal polluted areas(0.478km2) around No.4sap of ZhuJi LiPu copper mining area in ZheJiang province, which includs farmlands,vegetable plots, the wild and mountainous regions. It takes the method of geostatisticsbased on GIS to study the spatial variation rules of heavy metal polluted areas in coppermining area on the wolds.Using the GPS positioning system, the sampling distance was controlled between50-100 meters. 49 soil samples were totally gathered, and were compared with a controlmountain soil sample that was gathered from 3000 meters away. The events of soil analysisinclude the entire quantity of Cu, Zn,Pb,Ni,As and Cr, as well as the pH values, the organicmatter, soil particle size and the effective quantity of Cu and Zn in the soil.The research analyzes the spatial variation by using the GeostatisticalAnalyst moduleof ArcGIS. The events include: (1) Calculate semi-variance function and choose the fittestmodel and parameters, (2) Choose an appropriate Kriging through cross-validation, (3)Draw up the spatial distributional map of heavy metal(the entire quantity and effectivequantity) pollution, and analyze the rules of spatial variation and the influential factorsthrough the spatial interpolation. The research indicated that GeostatisticalAnalyst modulein ArcGIS can analyze the spatial variation characteristics of heavy metal pollution incopper mining area comparatively accurately.The result indicated that:(1)The content of As,Cr, Cu, Zn, Ni, Pb in the soil around No.4 sap of LiPu Cumining area presented a log-nomal distribution. Using the Geometric mean value and thestandard deviation as the typical value, there is obvious accumulation in As, Cu, Zn,Ni andPb.(2)The fact that there was high relevance between As and Cu, As and Zn, As and Ni,As and Pb, Cu and Zn, Cu and Ni, Cu and Pb, Zn and Ni, Zn and Pb, Pb and Ni presentedhigh homology between them. The fact that there was low relevance between Cr and As,Cr, Cu, Zn, Ni, Pb presented that the accumulation of Cr was unique and its accumulationfrom extraneous source was little. As, Cu, Zn, Ni and Pb were the elements with the mostpollution.(3)The spatial variation rules of these heavy metals are: Co/(C+Co):Cu＞Zn＞Pb＞As ＞Ni＞Cr. Because human activity slackens the elements' spatial relevance,theCo/(C+Co)ratioes of Pb,Cu and As respectively achieved 92.6014%,86.6664% and76.0163%, belonging to the weak degree spatial correlation. And the ratios of other threekinds of heavy metals were between 25%～75%, belonging to the medium degree spatialcorrelation. The fact that the ratio of Cr was lower indicated that the content of Cr in thesoil was mainly influenced by soil formers.(4)Aider analyzing with GeostatisticalAnalyst module in ArcGIS, the error ratios ofspatial interpolation of each kind of model can be obtained. The results shows: The errorratio of Zn was are higher, the one of Ni,As,Cr, Cu,Pb was lower.(5)According to the suitable parameters based on the Kdging interpolation principleand Semi-variance function,using GeostatisticalAnalyst module,this article carded on thespatial variation interpolation, a chart about spatial distribution of these six kinds of heavymetals content was drawn. The chart can reflect the content's scope of the six kinds ofheavy metals in different regions. It can provide the foundation basis for the pollutioncontrolling and management of the region.(6)The spatial variability rules of the heavy metals' effective quantity and the soil'sphysical and chemiscal natures:Co/(C+Co):effective quantity of Cu＞pH value＞organicmatter＞effective quantity of Zn＞organic matter＞Soil particle size.The Co/(C+Co)ratios ofCu's effective quantity,pH values and Zn's effective quantity achieved86.596%,82.8339%,80.8711%,and they belong to the weak degree spatial correlation. Thefacts above indicated that there was low relevance between effective quantity ofCu, effective quantity of Zn and pH values. And the Co/(C+Co)ratios of organic matter andsoil particle size were between 25%～75%, belonging to the medium degree spatialcorrelation.(7)Based on Kriging interpolation theory and Semi-variance function fitparameters,and though using GeostatisticalAnalyst module carded on the spatial variationinterpolation, a chart about spatial distribution of copper's and zinc's effective quantitycontent was drawn. The chart can reflect the content's range of effective quantity of Cu andZn in different regions clearly.