
Three-dimensional delineation of soil pollutants at contaminated sites: Progress and prospects
TAO Huan, LIAO Xiaoyong, CAO Hongying, ZHAO Dan, HOU Yixuan
Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (8) : 1615-1634.
Three-dimensional delineation of soil pollutants at contaminated sites: Progress and prospects
The precision remediation and redevelopment of contaminated sites are crucial issues for improving the human settlement and constructing a beautiful China. Three-dimensional delineation of soil pollutants at contaminated sites is a prerequisite for precision remediation and redevelopment. However, a contaminated site is a three-dimensional complex system coupling multiple spatial elements above- and under-ground. The complexity incurs high uncertainties about the three-dimensional delineation of soil pollutants based on sparse borehole and spatial statistics and inference models. This paper first systematically reviewed the objectives of fine three-dimensional delineation of soil pollutants, the sampling strategies for soil boring, the commonly used models for delineating soil pollutants, and the relevant cases of applying these models at contaminated sites. We then summarized the effects of borehole data and three-dimensional models on soil pollutants’ delineation results from biased characteristics and nonstationary conditions. The present research status and related issues on correcting the biased characteristics and nonstationary conditions were analyzed. Finally, based on the problems and challenges, we suggested the three- dimensional delineation of soil pollutants in the underground “black box” for future research from the following six priority areas: multi-scenarios, nonstationary, non-linearity, multi-source data fusion, multiple model coupling, and the delineation of co-contaminated sites.
contaminated sites / soil pollution / three-dimensional delineation model / sparse and biased / nonstationarity {{custom_keyword}} /
Table 1 The strategies of borehole layout in different scenarios of prior knowledge at contaminated sites |
Scenarios of prior knowledge | Strategies of borehole layout | |
---|---|---|
Historical soil boring data in geographic space | Auxiliary variable information in feature space | |
No | No | Systematic or random sampling |
No | Yes | Even sampling in geographic or feature space, judgmental sampling, or purposive sampling |
Yes | No | Densify sampling in geographic space |
Yes | Yes | Densify sampling in geographic space or even sampling in feature space |
Table 2 Summary of case studies on the application of spatial statistics to the management of contaminated sites |
Pollution medium | Delineation method | Software tools1) | Pollutant types | Function2) | Location of case | Reference |
---|---|---|---|---|---|---|
Soil | Ordinary kriging | MVS/EVS$ | Organic pollutants | (3), (6), (7) | A chemical plant in Chongqing, China | Liu et al., 2017 |
Soil | Ordinary/Indicator kriging | MVS/EVS$ | Organic pollutants | (3), (7) | Beijing Coking Plant, China | Tao et al., 2014 |
Soil | Moran’s I, LISA | Open GeoDaΩ | Organic pollutants | (5), (6) | Beijing Coking Plant, China | Liu et al., 2013a |
Soil | Ordinary kriging | MVS/EVS$ | Organic pollutants | (1) | A chemical plant in Hebei, China | Tao et al., 2017 |
Soil | Ordinary kriging | MVS/EVS$ | Organic pollutants | (3), (6) | A chlorobenzene plant in Jiangsu, China | Ren et al., 2016 |
Soil | Kriging, IDW, Nearest neighbor | MVS/EVS$ | Organic pollutants | (3), (6), (7) | A leather factory in Shandong, China | Men et al., 2017 |
Soil | Ordinary kriging | MVS/EVS$ | Organic pollutants | (4), (7) | A chemical plant in Shanghai, China | Guo et al., 2009 |
Soil | Ordinary kriging | Voxler$ | Heavy metals | (3) | A chemical plant in Shanghai, China | Li et al., 2017 |
Soil | Ordinary kriging, Conditional simulations | GS+$, ArcGIS$ | Heavy metals | (4), (8) | A ferroalloy factory, China | Jiang et al., 2016 |
Soil | Point/Block kriging, Exploratory, Variography | ArcGIS$ | Heavy metals | (2), (5) | Georgia landfill, US | ITRC |
Soil | IDW, Ordinary kriging | ArcGIS$ | Heavy metals | (3), (5) | Fukushima nuclear power plant, Japan | ITRC |
Soil | IDW, Ordinary kriging | MVS/EVS$ | Heavy metals | (3), (7), (9) | A smelter in Illinois, US | ITRC |
Sediment | Natural neighbor | MATLAB$ | Heavy metals | (3) | A shooting range in Wisconsin, US | Perroy et al., 2014 |
Sediment | Exploratory, Variography, Point/block kriging | ArcGIS$ | Organic pollutants | (1) | New Jersey Pier, US | ITRC |
Sediment | Variogram, Conditional simulations | ISATIS$ | Organic pollutants | (4), (5), (7) | Quebec City Pier, Canada | ITRC |
Groundwater | Regression, Delaunay mesh, Sampling algorithm | MAROSΩ | Organic pollutants | (1) | California Hazardous Waste Treatment Plant, US | ITRC |
Groundwater | Penalized splines, Delaunay | GWSDATΩ | Organic pollutants | (6) | New Jersey Petrochemical Plant, US | ITRC |
Groundwater | Voronoi/Delaunay | MAROSΩ | combined pollutants | (1), (6) | A smelter in Texas, US | ITRC |
Groundwater | Kriging, Iterative thinning, Quasi-genetic optimization | GTSΩ | Organic pollutants | (1), (9) | Nebraska, US | ITRC |
Groundwater | Ordinary kriging | MVS/EVS$ | Organic pollutants | (1), (3), (8) | Battlefield, Kuwait | Yihdego et al., 2016 |
Notes: Available for software tools, Ω represents open access, $ represents premium; Function list, (1) borehole layout, (2) mean concentration estimation, (3) 3D delineation of pollutants distribution, (4) partion of remediation boundaries, (5) hotpot identification, (6) spatial pattern analysis, (7) estimation of polluted soil volumes, (8) uncertainty evaluation, and (9) spatio-temporal pattern exploration. |
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Mapping the spatial distribution of contaminants in soils is the basis of pollution evaluation and risk control. Interpolation methods are extensively applied in the mapping processes to estimate the heavy metal concentrations at unsampled sites. The performances of interpolation methods (inverse distance weighting, local polynomial, ordinary kriging and radial basis functions) were assessed and compared using the root mean square error for cross validation. The results indicated that all interpolation methods provided a high prediction accuracy of the mean concentration of soil heavy metals. However, the classic method based on percentages of polluted samples, gave a pollution area 23.54-41.92% larger than that estimated by interpolation methods. The difference in contaminated area estimation among the four methods reached 6.14%. According to the interpolation results, the spatial uncertainty of polluted areas was mainly located in three types of region: (a) the local maxima concentration region surrounded by low concentration (clean) sites, (b) the local minima concentration region surrounded with highly polluted samples; and (c) the boundaries of the contaminated areas.Copyright © 2010 Elsevier Ltd. All rights reserved.
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