| Alongside air,water and the biota,the soil is of central significance in ecosystem research as it is the place where many kinds of interactions take place between minerals,air,water and the living environment.Heavy metals in soil originate either from weathering of parent material and/or from numerous external contaminating sources.In unpolluted regions,parent materials are the primary source of trace elements.Human activities,such as non-point source agriculture activities,release of emissions from nearby point sources such as smelters,and traffic may also affect the chemical composition of soil.In urban areas,deposition of pollutants emitted to the atmosphere from point sources such as residential heating,industrial facilities and mobile sources such as traffic are the primary sources of soil pollution.Various aspects must be considered by the society to provide a sustainable environment, including a soil clean of heavy metal pollution.The first among them is to identify environments(or areas) in which anthropogenic loading of heavy metals puts ecosystems and their inhabitants at health risk.Based on the soil sampling point data, DEM,soil map,and land use map,the present study explored the interwined influences of these factors on the heavy metal concentrations in soils,and evaluated the quality of amble land considering the complex influencing factors.(1) To understand the spatial distribution of soil heavy metals contents,and identify pollution sources,multivariate analysis,geostatistical methods and spatial analysis have been developed and widely applied in soil systems.But these methods could not efficiently simulate the spatial distribution of heavy metals which are greatly influenced by human activities.However,a prior requirement of these methods is to quantify the spatial autocorrelation between properties at different locations so that the information from samples can be weighted into an estimator of the values at unsampled locations.As a new modeling approach,the data mining technology(fuzzy comprehensive judgment and decision tree models) has been shown to have high predictive accuracy and Geographic information systems(GIS) have increasingly become a valuable management tool,providing an effective infrastructure for managing,analyzing,and visualizing disparate datasets related to soils,topography,land-use and land cover.So integration of the data mining technology approach with a GIS offers a potential solution in meeting this challenge.(2) The Fuyang County is assumed as representative to counties in the Yangtze Delta where the economic development has witnessed an unprecedented rapid growth since economic reform in 1978 and also heavily contaminated by industrial waste, mining,vehicular emissions and so on.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.The simulated semivariogram for the raw Zn data of Fuyang presented a horizontal line,denoting soil Zn content was greatly influenced by exterior factors. Thus,the Box-Cox transformation was used to obtain normally distributed transformed soil Zn content.The experimental semivariogram suggested that the Box-Cox transformed Zn contents are best fitted to a Gaussian model dominated by a long-range structure.The nugget,sill and range of this semivariogram is 0.21,0.43, 9508 m,respectively,and the determination coefficient(r~2) is 0.72.So in this study, the classification and regression tree(CART) has been integrated with GIS to predict the spatial distribution of soil heavy metals contents in Fuyang County.The overall CART accuracy of assigning samples to the right Zn classes is 89.39%and 87.18%,the Kappa coefficient is 0.8296 and 0.8018 respectively for training data and test data.This is a great improvement compared to ordinary Kriging method in ArcGIS.The total accuracy of assigning kriged estimates of Zn classes to measured values is 41.79%,and the corresponding Kappa coefficient is 0.2584.The main reason for increased accuracy might be that Zn content in this study area is greatly influenced by human activities leading to localized sharp variations and hotspots which are smoothed over by Kriging with a long range variogram.Certainly,the method of CART decreases the measurement scale of the raw data to a lower level for the classification of the target.But decision makers and spatial planners require information on soil quality for different purposes:to locate areas suitable for organic(ecologically clean) farming and agro-tourism;to select sites suitable for conversion of agricultural to non-agricultural land,particularly for urbanization;setting up protection zones for groundwater pumped for drinking water; to estimate costs of remediation of contaminated areas and similar.So these classifications are however useful when detailed concentrations are not required.Of course,CART can not replace Kriging to predict heavy metals concentrations at unsampled points.The two methods have their own respective advantages and disadvantages in simulation the spatial distribution of soil heavy metals concentrations.(3) In this study,the fuzzy comprehensive judgment method also has been integrated with GIS to predict the spatial distribution of soil heavy metals contents in Fuyang County.First,different township's membership was achieved in the Fuyang region by fuzzy comprehensive judgment.There were five membership degrees: unpolluted,light-degree,moderate,heavy-degree and severely polluted respectively. With different membership we have had different method to simulate the spatial distribution of soil heavy metals contents.Decision tree classification and kriging interpolation were both used.For example,the Huyuan and Changlv town's membership is 1,so they were polluted most lightly.Then the ordinary Kriging method was taken to simulate the spatial distribution of soil heavy metals contents for the spatial autocorrelation of the two towns were not severely destroyed by human activities.If the spatial autocorrelation of the towns was severely destroyed,i.e,the towns' membership is high(4 or 5),and then the decision tree model would be considered.The Divide-And-Conquer method has brought us a satisfied result of soil heavy metals contents prediction.Most towns of the Fuyang County could achieve a 95%or even higher assessment accuracy of assessment. (4) There are two primary advantages for the integration:â‘ Spatial representation is critical to environmental problem solving,but GIS currently lack the predictive and related analytic capabilities necessary to examine complex problems;â‘¡Modelling tools typically lack sufficiently flexible GIS-like spatial analytic components and are often inaccessible to potential users less expert than their makers. The developed system seamlessly links ArcObjects and the data mining models, automating the transfer of parameters and data,and graphically displaying the analysis results.The system also removes the margin for error intrinsic to any manual process.Successful implementation of the data mining models involves the integration of GIS,multiple databases,and visualization tools for extraction of the needed model input parameters and for analysis and visualization of the simulated results.In this study,the developed system provided an approach for assessing the spatial distribution of soil heavy metals contents with high predictive accuracy,and to present model predictions over space for further application and investigation.
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