Abstract
The geostatistical technique of Kriging has extensively been used for the investigation and delineation of soil heavy metal pollution. Kriging is rarely used in practical circumstances, however, because the parameter values are difficult to decide and relatively optimal locations for further sampling are difficult to find. In this study, we used large numbers of assumed actual polluted fields (AAPFs) randomly generated by unconditional simulation (US) to assess the adjusted total fee (ATF), an assessment standard developed for balancing the correct treatment rate (CTR) and total fee (TF), based on a traditional strategy of systematic (or uniform) grid sampling (SGS) and Kriging. We found that a strategy using both SGS and Kriging was more cost-effective than a strategy using only SGS. Next, we used a genetic algorithm (GA) approach to find optimal locations for the additional sampling. We found that the optimized locations for the additional sampling were at the joint districts of polluted areas and unpolluted areas, where abundant SGS data appeared near the threshold value. This strategy was less helpful, however, when the pollution of polluted fields showed no spatial correlation.
Highlights
Soil heavy metal pollution is becoming an increasingly severe global environmental problem, especially in developing countries such as China, due to the toxic, non-biodegradable, and persistent nature of heavy metals (HMs) [1]-[5]
When we deal with a real-world polluted site, we invariably struggle to solve complex issues such as the number of samples to be taken, the actual site of sampling, the remediation method to use, and where the additional samples should be located in order to trade off the total fee (TF: fees for investigation, analysis, transportation, remediation, etc.) and correct treatment rate (CTR: the percentage of the area or volume of treated polluted soil and the total polluted soil before treatment)
HMs tend to accumulate in the soil layers rich in organic matter and clay at depths from 0 to 50 cm, because HMs have a high affinity with organic matter and clay that prevents them from rapidly leaching [49] [50]
Summary
Assumed Actual Polluted Fields (AAPFs) Generated by Unconditional Simulation (US) Methods based on conditional simulation (CS) generate a series of realizations ( probable solutions) of pollution distribution through original sampling data from one polluted field. We randomly generated a series of AAPFs. we used the sampling data (logarithm) on each AAPF to generate the realization by Kriging. US honors the overall mean, variance, and spatial correlation while disregarding observations at specific sampling locations [40]. An AAPF generated by US keeps a certain spatial correlation and can show the concentration (logarithm data) at every single location. The parameters of a semivariogram based on the exponential model to generate AAPFs are set as follows: sill = 1; correlation scale = 30 m; nugget = 0; mean = 1.
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