Application And Study Of Several Geostatistical Methods In Soil Spatial Information Processing At County Scale

Geostatistics has been evovling with applied sciences such as mining. In two recent decades, it has become more and more popular in soil science, environmental science, ecology, meteorology and even in economics and human science. It is through these applications that geostatistics obtained many new ideas for its futher development. Indeed, scientists in the fields of soil and environmental sciences have investigated geostatistics for various application perspectives, for example, soft data integration, stochastic simulation and spatial uncertainty assessment. However, further study is needed to address some important issues in both methodology and applications in soil and environmental sciences. This study explored the following seven subtopics:â– Whether this is an effective geostatistical method better than previously used residual kriging to integrating categorical information into the geostatistical mapping of soil properties.â– Whether categorical data can be combined into stochastic simulation and thus reduce the spatial uncertainty of simulated results of soil attributes.â– Whether we can obtain the spatial distribution maps of the absolute contributions of pollutant sources from multiple data sets of samples in different spatial positions.â– Whether data transformation conducted in previous stochastic simulations can be avoid; and if it is feasible then how to assess the spatial uncertainty of soil properties using such method.â– Because kriging has the smoothing effect and the division of pollution probability thresholds is usually subjective, whether there is a more objective delineation method to map polluted areas.â– How to assess the spatial uncertainty of ecological risks that account for the spatial heterogeneity and uncertainty of pollutants in ecological risk assessment study.â– How to map effectively the spatial distribution of the availability of soil macronutrients.We conducted some explorations in developing geostatistcs and expanding its application breadth and depth. This dissertation achieved the following major results:(1) The area-and-point kriging was introduced to the field of predictive soil mapping, and it was proven to be a better method for precision agriculture and environmental management.Mapping the spatial distribution of soil nutrient contents from sample data received much attention in recent decade. Accurately mapping soil nutrients purely based on sample data, however, is difficult due to the sparsity and high cost of samples. Land use types usually influence the contents of soil nutrients at local level and it is desirable to integrate such information into the predictive mapping. The area-and-point kriging (AAPK) method, which was proposed recently, may provide an interpolation technique for such purpose. This study mapped the soil total nitrogen (TN) distribution of Hanchuan County, China, using AAPK with402point sample data and land use information. Ordinary kriging (OK) and residual kriging (RK) were compared to evaluate the performance of AAPK. Results showed that:(1) land use types had important impacts on the spatial distribution of soil TN;(2) measured data at the135validation locations had stronger correlation with the predicted data by AAPK than with those by RK and OK, and the mean error and root mean square error with AAPK were lower than those with RK and OK; and (3) AAPK generated smaller error variances than RK and OK did. This means AAPK represents an effective method for increasing the interpolation accuracy of soil TN.(2) We developed a new geostatistical stochastic simulation method by combining categorical land use data and sequential Gaussian simulation, and applied it to a case study. This may improve the application value of sequential Gausian simulation.Geostatistics is often used to characterize the spatial variability of soil properties. However, simulated realization maps by stochastic geostatistical algorithms can represent the spatial distribution more realistically than the kriged optimal map. Because land use types usually influence the local content level of soil nitrogen, it is desirable to integrate land use information into the geostatistical stochastic simulation of soil nitrogen. In this study, the data of TN contents at402sampling sites and the categorical information of land use maps were integrated together for performing the sequential Gaussian simulation incorporating land use information (SGSLU). A comparison of SGSLU with ordinary kriging (OK) and sequential Gaussian simulation (SGS) in their performances was conducted, and135validation samples were used to assess the improvement of SGSLU over SGS in prediction quality and uncertainty reduction of soil TN contents. Results showed that the validation data were more correlated with the optimal prediction (i.e., E-type estimates) data of SGSLU than with those of OK and of SGS, and the mean error and root mean square error with the optimal prediction of SGSLU were lower than those with OK and SGS. Further, according to accuracy plots and the goodness statistic G, SGSLU performed better in uncertainty modeling than SGS did. We conclude that land use types have important impacts on the spatial distribution of soil TN, and SGSLU is an effective method for increasing the prediction accuracy and reducing the uncertainty in soil TN prediction. The differences among realizations represent the spatial uncertainty of soil TN prediction and such knowledge will be helpful to evaluate the delineation of soil TN deficiency and abundance areas for agricultural and environmental management.(3) We introduced the principal component analysis/absolute principal component scores (PCA/APCS) model to the field of the source apportionment of pollution sources and developed a more effective method for source apportionment through combined gestatstics and PCA/APCS.At present, principal component analysis is the most often used method in the field of identifying pollution sources. Source apportionment is further quantitative at the basis of PCA. PCA/APCA has the advantage of not requiring the knowledge in the number of sources and source characteristics in advance, thus being widely used in source apportionment studies. Source apportionment is an underutilized technique in soil science. The application of PCA to environmental data has experienced significant setbacks because its outcomes are correlated with but not proportional to source contributions. Consequently, PCA results can detect latent sources only qualitatively and cannot be used directly for source apportionment. With the PCA/APCA method, it is possible to determine quantitatively the loading of each variable from each source, and the contribution of that source to the total pollutant concentration. However, the results of the PCA/APCA lack visual effect, and are not conductive to identification of the locations of the pollution sources (such as hidden pollution source). In order to facilitate understanding the spatial distribution of the absolute contribution of each pollution source and identifying the specific pollution sources, geostatistics and PCA/APCS were combined together, that us, kriging is used to interpolate the absolute contribution of each source obtained from PCA/APCS. The purpose of this study is to propose an integrated approach for source appointment of soil pollution source. At the same time, two case studies were conducted according to the different characters of the data set and whether multiple data sets should be used in source apportionment of a single pollution matter.(4) Direct sequential simulation (DSS) was introduced for uncertainty assessment of soil properties, which may help to expand the application scope of DSS.Sequential Gaussian simulation and sequential indicator simulation are the most often used method to simulate the soil and environmental properties. A data transformation process must be carried out in advance when using the two simulation technologies; this process will inevitably lead to the reduction of simulation accuracy. The recently emerged direct sequential simulation (DSS) method overcomes this weakness. In this study, the spatial uncertainty assessment of the soil total nitrogen was performed. Ordinary kriging was used as a reference method to illustrate the advantages of the DSS method in uncertainty assessment.(5) We simulated the risk costs of delineating pollution areas by combing the method of sequential Gaussian simulation with transfer functions, and at the meantime a method of delineating pollution areas based on the standard of minimum expected costs was proposed.As kriging interpolation has the smoothing effect, it is not appropriate to use the kriged results as the standard data for delineating pollution areas. In addition, as the setup of probability thresholds is usually subjective in estimating threshold exceedance probabilities, such delineation scheme lacks scientific basis. Geostatistical simulated realization maps can represent the spatial heterogeneity of the studied spatial variable more realistically than the kriged optimal map because they overcome the smoothing effect of interpolation. The difference among realizations indicates spatial uncertainty. These realizations may serve as input data to transfer functions to further evaluate the resulting uncertainty in impacted dependent variables. In this study, sequential Gaussian simulation was used to simulate the spatial distribution of soil nickel (Ni) in the study area. Simulated realizations were then imported into transfer functions to calculate the health risk costs caused by Ni polluted areas being ignored in remediation due to underestimation of the Ni contents and the remediation risk costs caused by unnecessary remediation of unpolluted areas due to overestimation of the Ni contents. The uncertainty about the input Ni content values thus propagated through these transfer functions, leading to uncertain responses in health risk costs and remediation risk costs. The spatial uncertainties of the two forms of risk costs were assessed based on the response realizations. Because the risk of exposure of soil Ni to humans and animals is generally greater in contaminated arable lands than in industrial and residential lands, the effect of land use types was also taken into account in risk cost estimation. Most of the south part of the study area was delineated as contaminated according to the minimum expected cost standard. This study shows that sequential Gaussian simulation and transfer functions are valuable tools for assessing risk costs of soil contamination delineation and associated spatial uncertainty.(6) An integrated approach was proposed by combing sequential Gaussian simulation and Hakanson potential ecological risk index.Potential ecological risk index was introduced to assess the degree of heavy metal pollution in soils, which was originally introduced by Hakanson (1980), according to the toxicity of heavy metals and the response of the environment. At present, potential ecological risk index (RI) is one of the most commonly used tools for ecological risk assessment. However, while the spatial distribution of heavy metals is heterogeneous the spatial heterogeneity was seldomly accounted for in previous literature related to ecological risk study. In this study, a comprehensive method for spatial analysis of ecological risks was proposed through combining stochastic simulation and Hakanson potential ecological risk index. Sequential Gaussian simulation (SGS) is usually used to describe joint realizations of pollutants. The objective of this study is to generate a number of realizations in the studied region, which can effectively reflect the uncertainty resulting from heterogeneity, and feed them into the model of the potential ecological risk index, so that the spatial uncertainty of the ecological risks resulting from the spatial uncertainty of pollutants could be quantified. The E-type estimates of the Hakanson potential ecological risk indexes could be obtained through summing the E-type estimates of the potential ecological risk factors for signal metals.(7) An application of geostatistics in the spatial distribution pattern of N, P and K availability ratios was performed.Many studies on soil N, P and K. were conducted in the past several decades. Most of them, however, were focused on the total contents or available contents of these elements in agricultural fields, and few characterized their variability in soils. In this study, the controlling factors on the spatial variability of soil N, P and K availability ratios were determined using multivariate statistics;and geostatistcs were adopted to map the spatial distribution patterns of the soil available contents and total contents of the macronutrients, and then N, P and K availability ratios were obtained from the result of the geostatistics analysis.