Geostatistical Simulation for the Assessment of Regional Soil Pollution. 区域土壤污染评估的地统计模拟方法

Abstract

Regional scale inventories of heavy metal concentrations in soil increasingly are being done to evaluate their global patterns of variation. Sometimes these global pattern evaluations reveal information that is not identified by more detailed studies. Geostatistical methods, such as stochastic simulation, have not yet been used routinely for this purpose in spite of their potential. To investigate such a use of geostatistical methods, we analyzed a data set of 14,674 copper and 12,441 cadmium observations in the topsoil of Flanders, Belgium, covering 13,522 km 2 . Outliers were identified and removed, and the distributions were spatially declustered. Copper was analyzed using sequential Gaussian simulation, whereas for cadmium we used sequential indicator simulation because of the large proportion (43%) of censored data. We complemented maps of the estimated values with maps of the probability of exceeding a critical sanitation threshold for agricultural land use. These sets of maps allowed the identification of regional patterns of increased metal concentrations and provided insight into their potential causes. Mostly areas with known industrial activities (such as lead and zinc smelters) could be delineated, but the effects of shells fired during the First World War were also identified.

Spatial Patterns of Heavy Metal Contamination by Urbanization

“…Low enrichment (PI <2) found for Cd and Cr is most likely due to diffuse deposition linked to the steel industry and to the burning of fossil fuels. In a regional study for the area of Flanders, the probability of Cd to exceed the sanitation value of 2 mg kg -1 (corresponding to a PI of 2.5) remained below 0.25 for most of the region of Ghent (Van Meirvenne and Meklit, 2009), confirming the results from this study.…”

Spatio‐temporal change of soil organic matter content of Jiangsu Province, China, based on digital soil maps

“…(2004), Zhao et al. (2005), Meirvenne & Meklit (2010), etc. This information can be effectively used by decision‐makers and land managers to understand where SOM change is credibly serious, for example, so that they are able to make a decision about how to manage land use.…”

“…The method of using stochastic simulation to derive spatiotemporal change of soil provided all possible spatial pattern of soil change that can be used to derive probability and CI of soil change, for example, Figure 6. This is just why stochastic simulation attracts so many researchers, for example, Juang et al (2004), Zhao et al (2005), Meirvenne & Meklit (2010), etc. This information can be effectively used by decision-makers and land managers to understand where SOM change is credibly serious, for example, so that they are able to make a decision about how to manage land use.…”

“…Besides, stochastic simulation has two other important advantages: honouring sample values and reproducing statistics of sample values (Goovaerts, 2001). These two advantages make stochastic simulation preferred to kriging in many applications, for example, Zhao et al (2005) and Meirvenne & Meklit (2010). Given this, it should be reasonable to further infer that stochastic simulation will honour and reproduce statistics of temporal change of soil at sampled locations.…”

“…These two advantages make stochastic simulation preferred to kriging in many applications, for example, Zhao et al. (2005) and Meirvenne & Meklit (2010). Given this, it should be reasonable to further infer that stochastic simulation will honour and reproduce statistics of temporal change of soil at sampled locations.…”