Journal of Geographical Sciences >
Three-dimensional delineation of soil pollutants at contaminated sites: Progress and prospects
Tao Huan (1989-), PhD, specialized in data mining and analysis of soil pollution. E-mail: taoh.11s@igsnrr.ac.cn |
Received date: 2022-04-25
Accepted date: 2022-06-12
Online published: 2022-10-25
Supported by
National Natural Science Foundation of China(42130713)
National Key R&D Program of China(2020YFC1807400)
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.
TAO Huan , LIAO Xiaoyong , CAO Hongying , ZHAO Dan , HOU Yixuan . Three-dimensional delineation of soil pollutants at contaminated sites: Progress and prospects[J]. Journal of Geographical Sciences, 2022 , 32(8) : 1615 -1634 . DOI: 10.1007/s11442-022-2013-6
Figure 1 The objectives of three-dimensional delineation of soil pollutants at contaminated sites |
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. |
Figure 2 Highly biased characteristics of soil boring at contaminated sites |
Figure 3 Spatial trend nonstationarity of concentration field influenced by the flow field of underground waterNote: The water table at A is higher than at B, C, and D, resulting in groundwater flow from A to C and D. |
Figure 4 Anisotropy nonstationarity of concentration field in horizontal and vertical directions |
Figure 5 Heterogeneous nonstationarity of concentration field of DNAPL influenced by soil textures |
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