场地土壤污染物含量三维刻画的研究进展

doi:10.11821/dlxb202203005

地理学报 ›› 2022, Vol. 77 ›› Issue (3): 559-573.doi: 10.11821/dlxb202203005

场地土壤污染物含量三维刻画的研究进展

陶欢¹^,²(), 廖晓勇¹(), 曹红英¹, 赵丹³, 侯艺璇¹

1.中国科学院地理科学与资源研究所,北京 100101
2.中国科学院大学,北京 100049
3.生态环境部环境规划院,北京 100012

收稿日期:2020-11-03 修回日期:2022-01-20 出版日期:2022-03-25 发布日期:2022-05-25
通讯作者: 廖晓勇(1977-), 男, 湖南人, 研究员, 博士生导师, 主要从事土壤污染评估与修复研究。E-mail: liaoxy@igsnrr.ac.cn
作者简介:陶欢(1989-), 男, 湖南人, 博士, 主要从事土壤环境数据挖掘与分析研究。E-mail: taoh.11s@igsnrr.ac.cn
基金资助:
国家自然科学基金项目(42130713);国家重点研发计划(2020YFC1807400)

Research progress of three-dimensional delineation of soil pollutants at contaminated sites

TAO Huan¹^,²(), LIAO Xiaoyong¹(), CAO Hongying¹, ZHAO Dan³, HOU Yixuan¹

1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Chinese Academy of Environmental Planning, Beijing 100012, China

Received:2020-11-03 Revised:2022-01-20 Published:2022-03-25 Online:2022-05-25
Supported by:
National Natural Science Foundation of China(42130713);National Key R&D Program of China(2020YFC1807400)

摘要/Abstract

摘要：

污染场地精准修复与再开发利用是改善人居环境、建设美丽中国所面临的重要课题。场地土壤污染物含量三维刻画是精准修复与再开发利用的基础。然而,场地环境是一个耦合地上地下多要素的复杂三维系统,使得基于离散稀疏土壤钻井样点和空间统计推断模型的场地土壤污染物含量刻画结果存在着较大的不确定性。本文梳理了场地土壤污染物含量三维精细刻画的目的和钻井布设方式、常用的三维刻画模型和相关案例,分析了土壤钻井数据的“稀疏偏性”特征对刻画结果的影响,总结了“非平稳浓度场”条件下三维土壤污染物含量插值模型的研究现状和存在问题。在此基础上,从多情景、非平稳、非线性、多源数据融合、多类模型耦合和复合污染刻画6个方面,展望了场地地下“黑箱”环境土壤污染物含量精细刻画的研究趋势。

关键词: 污染场地, 土壤污染, 三维刻画模型, 稀疏偏性, 非平稳

Abstract:

The precision remediation and redevelopment of contaminated sites are crucial for improving the living environment and constructing a beautiful China. Three-dimensional delineation of soil pollutants at contaminated sites is the basis and prerequisite for precise remediation and redevelopment. However, a contaminated site is a three-dimensional complex system coupling multiple spatial elements above- and under-ground. The complexity incurs significant uncertainties about the three-dimensional delineation of soil pollutants calculated by sparse borehole data and spatial statistical and inference models. In this study, we first systematically reviewed the objectives of three-dimensional delineation of fine soil pollutants, the sampling strategies for soil drilling, the commonly used models for delineating soil pollutants, and the relevant cases on applying these models at contaminated sites. We then summarized the effects of soil borehole data and three-dimensional models on the soil pollutants' delineation results from sparse skewed characteristics and non-stationary conditions, respectively. The present research status and related issues on correcting sparse skewed characteristics and non-stationary conditions were analyzed. Finally, based on the problems and challenges, we proposed the three-dimensional delineation of soil pollutants in the underground "black box" for future research from the following six priority areas: multi-scenarios, non-stationarity, non-linearity, multi-source data fusion, multiple model coupling, and the delineation of co-contaminated sites.

Key words: contaminated sites, soil pollution, 3D delineation model, sparse and skewed characteristics, non-stationarity

陶欢, 廖晓勇, 曹红英, 赵丹, 侯艺璇. 场地土壤污染物含量三维刻画的研究进展[J]. 地理学报, 2022, 77(3): 559-573.

TAO Huan, LIAO Xiaoyong, CAO Hongying, ZHAO Dan, HOU Yixuan. Research progress of three-dimensional delineation of soil pollutants at contaminated sites[J]. Acta Geographica Sinica, 2022, 77(3): 559-573.

图/表 7

图1

表1

表2

空间统计模型在场地管理中的应用案例

介质	刻画方法模型	软件工具	污染物类型	功能	案例地点	文献来源
土壤	Ordinary kriging	MVS/EVS^$	有机污染物	③ ⑥ ⑦	中国重庆化工厂	[7]
土壤	Ordinary/Indicator kriging	MVS/EVS^$	有机污染物	③ ⑦	中国北京焦化厂	[13]
土壤	Moran's I, LISA	Open GeoDa^W	有机污染物	⑤ ⑥	中国北京焦化厂	[28]
土壤	Ordinary kriging	MVS/EVS^$	有机污染物	①	中国河北某化工厂	[29]
土壤	Ordinary kriging	MVS/EVS^$	有机污染物	③ ⑥	中国江苏氯苯厂	[47]
土壤	Kriging, IDW, Nearest neighbor	MVS/EVS^$	有机污染物	③ ⑥ ⑦	中国山东皮革厂	[52]
土壤	Ordinary kriging	MVS/EVS^$	有机污染物	④ ⑦	中国上海化工厂	[55]
土壤	Ordinary kriging	Voxler^$	重金属	③	中国上海化工厂	[56]
土壤	Ordinary kriging, Conditional simulations	GS+^$, ArcGIS^$	重金属	④ ⑧	中国某铁合金厂	[57]
土壤	Point/Block kriging, Exploratory, Variography	ArcGIS^$	重金属	② ⑤	美国乔治亚州垃圾填埋场	ITRC
土壤	IDW, Ordinary kriging	ArcGIS^$	重金属	③ ⑤	日本福岛核电站	ITRC
土壤	IDW, Ordinary kriging	MVS/EVS^$	重金属	③ ⑦ ⑨	美国伊利诺伊州冶炼厂	ITRC
底泥	Natural neighbor	MATLAB^$	重金属	③	美国威斯康星州射击场	[50]
底泥	Exploratory, Variography, Point/Block kriging	ArcGIS^$	有机污染物	①	美国新泽西州码头	ITRC
底泥	Variogram, Conditional simulations	ISATIS^$	有机污染物	④ ⑤ ⑦	加拿大魁北克市码头	ITRC
地下水	Regression, Delaunay mesh, Sampling algorithm	MAROS^W	有机污染物	①	美国加利福尼亚州危险废物处理厂	ITRC
地下水	Penalized splines, Delaunay	GWSDAT^W	有机污染物	⑥	美国新泽西州石油化工厂	ITRC
地下水	Voronoi/Delaunay	MAROS^W	有机污染物和重金属复合	① ⑥	美国德克萨斯州冶炼厂	ITRC
地下水	Kriging, Iterative thinning, Quasi-genetic optimization	GTS^W	有机污染物	① ⑨	美国内布拉斯加洲	ITRC
地下水	Ordinary kriging	MVS/EVS^$	有机污染物	① ③ ⑧	科威特战地	[51]

表2

图2

图3

图4

图5

参考文献 90

[1]	Li Fasheng, Yan Zengguang. Vocabulary Handbook of Contaminated Sites. Beijing: Science Press, 2009.
	[李发生, 颜增光. 污染场地术语手册. 北京: 科学出版社, 2009.]
[2]	Fang Chuanglin, Zhou Chenghu, Gu Chaolin, et al. Theoretical analysis of interactive coupled effects between urbanization and eco-environment in mega-urban agglomerations. Acta Geographica Sinica, 2016, 71(4): 531-550. doi: 10.11821/dlxb201604001
	[方创琳, 周成虎, 顾朝林, 等. 特大城市群地区城镇化与生态环境交互耦合效应解析的理论框架及技术路径. 地理学报, 2016, 71(4): 531-550.]
[3]	Liao Xiaoyong, Chong Zhongyi, Yan Xiulan, et al. Urban industrial contaminated sites: A new issue in the field of environmental remediation in China. Environmental Science, 2011, 32(3): 784-794.
	[廖晓勇, 崇忠义, 阎秀兰, 等. 城市工业污染场地: 中国环境修复领域的新课题. 环境科学, 2011, 32(3): 784-794.]
[4]	Jiang Lin, Zhong Maosheng, Liang Jing, et al. Application and benefit evaluation of tiered health risk assessment approach on site contaminated by benzene. Environmental Science, 2013, 34(3): 1034-1043.
	[姜林, 钟茂生, 梁竞, 等. 层次化健康风险评估方法在苯污染场地的应用及效益评估. 环境科学, 2013, 34(3): 1034-1043.]
[5]	Cao G Z, Yang L, Liu L X, et al. Environmental incidents in China: Lessons from 2006 to 2015. Science of the Total Environment, 2018, 633: 1165-1172. doi: 10.1016/j.scitotenv.2018.03.271
[6]	Liao Q, Deng Y P, Shi X Q, et al. Delineation of contaminant plume for an inorganic contaminated site using electrical resistivity tomography: Comparison with direct-push technique. Environmental Monitoring and Assessment, 2018, 190(4): 187. DOI: 10.1007/s10661-018-6560-3. doi: 10.1007/s10661-018-6560-3
[7]	Liu G, Niu J J, Guo W J, et al. Assessment of terrain factors on the pattern and extent of soil contamination surrounding a chemical industry in Chongqing, Southwest China. CATENA, 2017, 156: 237-243. doi: 10.1016/j.catena.2017.04.005
[8]	Tao H, Liao X Y, Zhao D, et al. Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics. Geoderma, 2019, 354(15): 113878. DOI: 10.1016/j.geoderma.2019.07.036. doi: 10.1016/j.geoderma.2019.07.036
[9]	Goovaerts P. Geostatistics in soil science: State-of-the-art and perspectives. Geoderma, 1999, 89(1/2): 1-45. doi: 10.1016/S0016-7061(98)00078-0
[10]	Xie Y F, Chen T B, Lei M, et al. Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis. Chemosphere, 2011, 82(3): 468-476. doi: 10.1016/j.chemosphere.2010.09.053
[11]	Li J, Heap A D. Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 2014, 53: 173-189.
[12]	Liao Y L, Li D Y, Zhang N X. Comparison of interpolation models for estimating heavy metals in soils under various spatial characteristics and sampling methods. Transactions in GIS, 2018, 22(2): 409-434. doi: 10.1111/tgis.2018.22.issue-2
[13]	Tao Huan, Liao Xiaoyong, Yan Xiulan, et al. Uncertainty analysis and pollution volumetric calculation of soil BaP contents in a contaminated site. Geographical Research, 2014, 33(10): 1857-1865. doi: 10.11821/dlyj201410007
	[陶欢, 廖晓勇, 阎秀兰, 等. 某污染场地土壤苯并(a)芘含量的三维估值及不确定性分析. 地理研究, 2014, 33(10): 1857-1865.]
[14]	Li K B, Goovaerts P, Abriola L M. A geostatistical approach for quantification of contaminant mass discharge uncertainty using multilevel sampler measurements. Water Resources Research, 2007, 43(6): w06436. DOI: 10.1029/2006WR005427. doi: 10.1029/2006WR005427
[15]	Juang K W, Liao W J, Liu T L, et al. Additional sampling based on regulation threshold and kriging variance to reduce the probability of false delineation in a contaminated site. Science of the Total Environment, 2008, 389(1): 20-28. doi: 10.1016/j.scitotenv.2007.08.025
[16]	Volchko Y, Kleja D B, Back P E, et al. Assessing costs and benefits of improved soil quality management in remediation projects: A study of an urban site contaminated with PAH and metals. Science of the Total Environment, 2020, 707: 135582. DOI: 10.1016/j.scitotenv.2019.135582. doi: 10.1016/j.scitotenv.2019.135582
[17]	Gao B B, Liu Y, Pan Y C, et al. Error index for additional sampling to map soil contaminant grades. Ecological Indicators, 2017, 77: 129-138. doi: 10.1016/j.ecolind.2017.02.011
[18]	Van Meirvenne M, Goovaerts P. Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold. Geoderma, 2001, 102(1/2): 75-100. doi: 10.1016/S0016-7061(00)00105-1
[19]	Jiang Chengsheng, Wang Jinfeng, Cao Zhidong. A review of geo-spatial sampling theory. Acta Geographica Sinica, 2009, 64(3): 368-380.
	[姜成晟, 王劲峰, 曹志冬. 地理空间抽样理论研究综述. 地理学报, 2009, 64(3): 368-380.]
[20]	Brus D J, de Gruijter J J. Random sampling or geostatistical modelling? Choosing between design-based and model based sampling strategies for soil (with discussion). Geoderma, 1997, 80: 1-40. doi: 10.1016/S0016-7061(97)00072-4
[21]	Wang J F, Stein A, Gao B B, et al. A review of spatial sampling. Spatial Statistics, 2012, 2: 1-14. doi: 10.1016/j.spasta.2012.08.001
[22]	Chadalavada S, Datta B, Naidu R. Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site. Environmental Monitoring and Assessment, 2011, 173(1): 929-940. doi: 10.1007/s10661-010-1435-2
[23]	Brus D J, Yang L, Zhu A X. Accounting for differences in costs among sampling locations in optimal stratification. European Journal of Soil Science, 2019, 70(1): 200-212. doi: 10.1111/ejss.12731
[24]	Wang J F, Haining R, Cao Z D. Sample surveying to estimate the mean of a heterogeneous surface: Reducing the error variance through zoning. International Journal of Geographical Information Science, 2010, 24(4): 523-543. doi: 10.1080/13658810902873512
[25]	Zhao Yishu, Liao Xiaoyong, Li You, et al. Occurrence characteristics and health risks of PAHs on the surface of buildings and devices in the coking plant. Environmental Science, 2019(11): 4870-4878.
	[赵一澍, 廖晓勇, 李尤, 等. 焦化厂建构筑物和生产设施表面PAHs的赋存特征及健康风险. 环境科学, 2019(11): 4870-4878.]
[26]	Grauer-Gray J, Hartemink A E. Raster sampling of soil profiles. Geoderma, 2018, 318: 99-108. doi: 10.1016/j.geoderma.2017.12.029
[27]	Pan Y C, Ren X H, Gao B B, et al. Global mean estimation using a self-organizing dual-zoning method for preferential sampling. Environmental Monitoring & Assessment, 2015, 187(3): 187-121.
[28]	Liu G, Bi R T, Wang S J, et al. The use of spatial autocorrelation analysis to identify PAHs pollution hotspots at an industrially contaminated site. Environmental Monitoring and Assessment, 2013, 185(11): 9549-9558. doi: 10.1007/s10661-013-3272-6
[29]	Tao Huan, Liao Xiaoyong, Yan Xiulan, et al. Methodological investigation on dynamically adding samples for drilling design in contaminated site investigation. Acta Scientiae Circumstantiae, 2017, 37(4): 1461-1468.
	[陶欢, 廖晓勇, 阎秀兰, 等. 污染场地调查动态追补钻井点位的方法研究. 环境科学学报, 2017, 37(4): 1461-1468.]
[30]	Verstraete S, Van Meirvenne M. A multi-stage sampling strategy for the delineation of soil pollution in a contaminated brownfield. Environmental Pollution, 2008, 154(2): 184-191. pmid: 18068880
[31]	Marchant B P, McBratney A B, Lark R M, et al. Optimized multi-phase sampling for soil remediation surveys. Spatial Statistics, 2013, 4: 1-13. doi: 10.1016/j.spasta.2012.11.001
[32]	Kang X Y, Kokkinaki A, Kitanidis P K, et al. Improved characterization of DNAPL source zones via sequential hydrogeophysical inversion of hydraulic-head, self-potential and partitioning tracer data. Water Resources Research, 2020, 56(8): e2020WR027627. DOI: 10.1029/2020WR027627. doi: 10.1029/2020WR027627
[33]	Troldborg M, Nowak W, Lange I V, et al. Application of Bayesian geostatistics for evaluation of mass discharge uncertainty at contaminated sites. Water Resources Research, 2012, 48(9): w09535. DOI: 10.1029/2011WR011785. doi: 10.1029/2011WR011785
[34]	Chen X Y, Murakami H, Hahn M S, et al. Three-dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data. Water Resources Research, 2012, 48(6): w06501. DOI: 10.1029/2011 WR010675. doi: 10.1029/2011 WR010675
[35]	Minasny B, McBratney A B. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 2006, 32(9): 1378-1388. doi: 10.1016/j.cageo.2005.12.009
[36]	Minasny B, McBratney A B, Walvoort D J J. The variance quadtree algorithm: Use for spatial sampling design. Computers & Geosciences, 2007, 33(3): 383-392. doi: 10.1016/j.cageo.2006.08.009
[37]	Matheron G. Principles of geostatistics. Economic Geology, 1963, 58: 1246-1266. doi: 10.2113/gsecongeo.58.8.1246
[38]	Veronesi F, Corstanje R, Mayr T. Mapping soil compaction in 3D with depth functions. Soil and Tillage Research, 2012, 124: 111-118. doi: 10.1016/j.still.2012.05.009
[39]	Liu F, Zhang G L, Sun Y J, et al. Mapping the three-dimensional distribution of soil organic matter across a subtropical hilly landscape. Soil Science Society of America Journal, 2013, 77(4): 1241-1253. doi: 10.2136/sssaj2012.0317
[40]	Lacoste M, Minasny B, McBratney A, et al. High resolution 3D mapping of soil organic carbon in a heterogeneous agricultural landscape. Geoderma, 2014, 213: 296-311. doi: 10.1016/j.geoderma.2013.07.002
[41]	Zhang Y K, Ji W J, Saurette D D, et al. Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging. Geoderma, 2020, 366: 114253. DOI: 10.1016/j.geoderma.2020.114253. doi: 10.1016/j.geoderma.2020.114253
[42]	Šichorová K, Tlustoš P, Száková J, et al. Horizontal and vertical variability of heavy metals in the soil of a polluted area. Plant Soil and Environment, 2004, 50(12): 525-534. doi: 10.17221/PSE
[43]	Poggio L, Gimona A. National scale 3D modelling of soil organic carbon stocks with uncertainty propagation: An example from Scotland. Geoderma, 2014, 232: 284-299.
[44]	Brus D J, Yang R M, Zhang G L. Three-dimensional geostatistical modeling of soil organic carbon: A case study in the Qilian Mountains, China. CATENA, 2016, 141: 46-55. doi: 10.1016/j.catena.2016.02.016
[45]	Krivoruchko K, Gribov A. Evaluation of empirical Bayesian kriging. Spatial Statistics, 2019, 32: 100368. DOI: 10.1016/j.spasta.2019.100368. doi: 10.1016/j.spasta.2019.100368
[46]	Gribov A, Krivoruchko K. Empirical Bayesian kriging implementation and usage. Science of the Total Environment, 2020, 722: 137290. DOI: 10.1016/j.scitotenv.2020.137290. doi: 10.1016/j.scitotenv.2020.137290
[47]	Ren L X, Lu H W, He L, et al. Characterization of monochlorobenzene contamination in soils using geostatistical interpolation and 3D visualization for agrochemical industrial sites in southeast China. Archives of Environmental Protection, 2016, 42(3): 17-24. doi: 10.1515/aep-2016-0025
[48]	Liu G, Niu J J, Zhang C, et al. Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies. Environmental Science and Pollution Research, 2015, 22(24): 20121-20130. doi: 10.1007/s11356-015-5122-2
[49]	Jones N L, Davis R J. Three-dimensional characterization of contaminant plumes. Transportation Research Record, 1996, 1526(1): 177-182.
[50]	Perroy R L, Belby C S, Mertens C J. Mapping and modeling three dimensional lead contamination in the wetland sediments of a former trap-shooting range. Science of the Total Environment, 2014, 487: 72-81. doi: 10.1016/j.scitotenv.2014.03.102
[51]	Yihdego Y, Al-Weshah R A. Gulf war contamination assessment for optimal monitoring and remediation cost-benefit analysis, Kuwait. Environmental Earth Sciences, 2016, 75(18): 1234. DOI: 10.1007/s12665-016-6025-3. doi: 10.1007/s12665-016-6025-3
[52]	Men Xiaoye, Yang Zongzheng, Liu Xiao, et al. Application of 3-D spatial interpolation technique to analyzing the distribution of TPH contamination in a field-site. Journal of Safety and Environment, 2017, 17(2): 713-718.
	[门晓晔, 杨宗政, 刘肖, 等. 基于三维空间插值技术的某场地中总石油烃污染分布确定. 安全与环境学报, 2017, 17(2): 713-718.]
[53]	Jones N L, Davis R J, Sabbah W. A comparison of three-dimensional interpolation techniques for plume characterization. Ground Water, 2003, 41(4): 411-419. doi: 10.1111/gwat.2003.41.issue-4
[54]	Pannecoucke L, Le Coz M, Freulon X, et al. Combining geostatistics and simulations of flow and transport to characterize contamination within the unsaturated zone. Science of the Total Environment, 2020, 699: 134216. DOI: 10.1016/j.scitotenv.2019.134216. doi: 10.1016/j.scitotenv.2019.134216
[55]	Guo Guanlin, Wang Xiang, Guan Liang, et al. Site-specific spatial distribution of VOC/SVOC and determination of the remediation boundary. Acta Scientiae Circumstantiae, 2009, 29(12): 2597-2605.
	[郭观林, 王翔, 关亮, 等. 基于特定场地的挥发/半挥发有机化合物(VOC/SVOC)空间分布与修复边界确定. 环境科学学报, 2009, 29(12): 2597-2605.]
[56]	Li Xiaoxuan, Zhang Bin, Wan Zhengmao, et al. Application of Golden Software Voxler in the investigation and health risk assessment for contaminated site. Science Technology and Engineering, 2017, 17(8): 317-323.
	[李晓璇, 张斌, 万正茂, 等. Golden Software Voxler在污染场地调查与风险评估方面的应用. 科学技术与工程, 2017, 17(8): 317-323.]
[57]	Jiang Shijie, Wang Jinsheng, Zhai Yuanzheng, et al. Determination of the volume of soil requiring remediation in contaminated sites based on conditional simulation. Acta Scientiae Circumstantiae, 2016, 36(7): 2596-2604.
	[蒋世杰, 王金生, 翟远征, 等. 基于条件模拟的污染场地土壤修复量的确定研究. 环境科学学报, 2016, 36(7): 2596-2604.]
[58]	Schnabel U, Tietje O, Scholz R W. Uncertainty assessment for management of soil contaminants with sparse data. Environmental Management, 2004, 33(6): 911-925. pmid: 15517687
[59]	Campbell J E, Moen J C, Ney R A, et al. Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks. Environmental Pollution, 2008, 152(2): 267-273. pmid: 17706329
[60]	Haskard K A, Lark R M. Modelling non-stationary variance of soil properties by tempering an empirical spectrum. Geoderma, 2009, 153(1/2): 18-28. doi: 10.1016/j.geoderma.2009.07.006
[61]	Cuba M A, Leuangthong O, Ortiz J M. Detecting and quantifying sources of non-stationarity via experimental semivariogram modeling. Stochastic Environmental Research and Risk Assessment, 2012, 26(2): 247-260. doi: 10.1007/s00477-011-0501-9
[62]	Fuentes I, Padarian J, Iwanaga T, et al. 3D lithological mapping of borehole descriptions using word embeddings. Computers & Geosciences, 2020, 141: 104516. DOI: 10.1016/j.cageo.2020.104516. doi: 10.1016/j.cageo.2020.104516
[63]	Liu Geng, Niu Junjie, Zhang Chao, et al. Spatial distribution prediction of surface soil Pb in a battery contaminated site. Environmental Science, 2014, 35(12): 4712-4719.
	[刘庚, 牛俊杰, 张朝, 等. 某铅酸蓄电池污染场地表层土壤重金属Pb空间分布预测研究. 环境科学, 2014, 35(12): 4712-4719.]
[64]	Xu C D, Wang J F, Li Q X. A new method for temperature spatial interpolation based on sparse historical stations. Journal of Climate, 2018, 31(5): 1757-1770. doi: 10.1175/JCLI-D-17-0150.1
[65]	Franssen H J W M H, Van Eijnsbergen A C, Stein A. Use of spatial prediction techniques and fuzzy classification for mapping soil pollutants. Geoderma, 1997, 77: 243-262. doi: 10.1016/S0016-7061(97)00024-4
[66]	Saito H, Goovaerts P. Geostatistical interpolation of positively skewed and censored data in a dioxin-contaminated site. Environmental Science & Technology, 2000, 34: 4228-4235. doi: 10.1021/es991450y
[67]	Journel A G, Deutsch C V. Rank order geostatistics: A proposal for a unique coding and common processing of diverse data. Geostatistics Wollongong, 1997, 96(1): 174-187.
[68]	Juang K W, Lee D Y, Ellsworth T R. Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site. Journal of Environmental Quality, 2001, 30(3): 894-903. pmid: 11401278
[69]	Deutsch C V, Journel A G. GSLIB, Geostatistical Software Library and User's Guide. New York: Oxford University Press, 1998.
[70]	Wu C F, Wu J P, Luo Y M, et al. Spatial interpolation of severely skewed data with several peak values by the approach integrating kriging and triangular irregular network interpolation. Environmental Earth Sciences, 2011, 63(5): 1093-1103. doi: 10.1007/s12665-010-0784-z
[71]	Tobler W R. A computer movie simulating urban growth in the Detroit region. Economic Geography, 1970, 46(2): 234-240. doi: 10.2307/143141
[72]	Anselin L. Local indicators of spatial association: LISA. Geographical Analysis, 1995, 27(2): 93-115. doi: 10.1111/gean.1995.27.issue-2
[73]	MacDonald L A. Sub-surface migration of an oil pollutant into aquifers. Plymouth: University of Plymouth, 2000.
[74]	Myers D E. To be or not to be stationary? That is the question. Mathematical Geology, 1989, 21(3): 347-362. doi: 10.1007/BF00893695
[75]	Sampson P D, Guttorp P. Nonparametric-estimation of nonstationary spatial covariance structure. Journal of the American Statistical Association, 1992, 87: 108-119. doi: 10.1080/01621459.1992.10475181
[76]	Wadoux A M J C, Brus D J, Heuvelink G B M. Accounting for non-stationary variance in geostatistical mapping of soil properties. Geoderma, 2018, 324: 138-147. doi: 10.1016/j.geoderma.2018.03.010
[77]	Ge Y, Jin Y, Stein A, et al. Principles and methods of scaling geospatial Earth science data. Earth Science Reviews, 2019, 197: 102897. DOI: 10.1016/j.earscirev.2019.102897. doi: 10.1016/j.earscirev.2019.102897
[78]	Ma Y X, Minasny B, McBratney A, et al. Predicting soil properties in 3D: Should depth be a covariate? Geoderma, 2021, 383: 114794. DOI: 10.1016/j.geoderma.2020.114794. doi: 10.1016/j.geoderma.2020.114794
[79]	Eriksson M, Siska P P. Understanding anisotropy computations. Mathematical Geology, 2000, 32(6): 683-700. doi: 10.1023/A:1007590322263
[80]	Lark R M. Kriging a soil variable with a simple nonstationary variance model. Journal of Agricultural, Biological, and Environmental Statistics, 2009, 14(3): 301-321. doi: 10.1198/jabes.2009.07060
[81]	Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective. Acta Geographica Sinica, 2017, 72(1): 116-134. doi: 10.11821/dlxb201701010
	[王劲峰, 徐成东. 地理探测器: 原理与展望. 地理学报, 2017, 72(1): 116-134.]
[82]	Marchant B P, Newman S, Corstanje R, et al. Spatial monitoring of a non-stationary soil property: Phosphorus in a Florida water conservation area. European Journal of Soil Science, 2009, 60: 757-769. doi: 10.1111/j.1365-2389.2009.01158.x
[83]	Gao B B, Wang J F, Fan H M, et al. A stratified optimization method for a multivariate marine environmental monitoring network in the Yangtze River estuary and its adjacent sea. International Journal of Geographical Information Science, 2015, 29(8): 1332-1349. doi: 10.1080/13658816.2015.1024254
[84]	Liu Y L, Chen Y Y, Wu Z H, et al. Geographical detector-based stratified regression kriging strategy for mapping soil organic carbon with high spatial heterogeneity. CATENA, 2021, 196: 104953. DOI: 10.1016/j.catena.2020.104953. doi: 10.1016/j.catena.2020.104953
[85]	Quach A N O, Tabor L, Dumont D, et al. A machine learning approach for characterizing soil contamination in the presence of physical site discontinuities and aggregated samples. Advanced Engineering Informatics, 2017, 33: 60-67. doi: 10.1016/j.aei.2017.05.002
[86]	Zhu D, Cheng X M, Zhang F, et al. Spatial interpolation using conditional generative adversarial neural networks. International Journal of Geographical Information Science, 2020, 34(4): 735-758. doi: 10.1080/13658816.2019.1599122
[87]	Samui P, Sitharam T G. Site characterization model using artificial neural network and kriging. International Journal of Geomechanics, 2010, 10(5): 171-180. doi: 10.1061/(ASCE)1532-3641(2010)10:5(171)
[88]	Padarian J, Minasny B, McBratney A B. Using deep learning for digital soil mapping. Soil, 2019, 5(1): 79-89. doi: 10.5194/soil-5-79-2019
[89]	Shlomi S, Michalak A M. A geostatistical framework for incorporating transport information in estimating the distribution of a groundwater contaminant plume. Water Resources Research, 2007, 43(3): w03412. DOI: 10.1029/2006WR005121. doi: 10.1029/2006WR005121
[90]	Boudreault J P, Dubé J S, Marcotte D. Quantification and minimization of uncertainty by geostatistical simulations during the characterization of contaminated sites: 3-D approach to a multi-element contamination. Geoderma, 2016, 264: 214-226. doi: 10.1016/j.geoderma.2015.10.019

先验知识情景		钻井布点方式
历史钻井样点(地理空间)	辅助资料信息(特征空间)	钻井布点方式
无	无	系统或随机布点
无	有	地理或特征空间无偏均匀布点,判断布点
有	无	地理空间加密布点
有	有	地理空间加密布点或特征空间无偏均匀布点

场地土壤污染物含量三维刻画的研究进展

Research progress of three-dimensional delineation of soil pollutants at contaminated sites

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 90

相关文章 4

Metrics

本文评价

[1]	温庆志, 孙鹏, 张强, 姚蕊. 非平稳标准化降水蒸散指数构建及中国未来干旱时空格局[J]. 地理学报, 2020, 75(7): 1465-1482.
[2]	顾西辉, 张强, 孔冬冬. 中国极端降水事件时空特征及其对夏季温度响应[J]. 地理学报, 2016, 71(5): 718-730.
[3]	黄婕, 高路, 陈兴伟, 陈莹, 刘梅冰. 东南沿海前汛期降水极值变化特征及归因分析[J]. 地理学报, 2016, 71(1): 152-165.
[4]	王振中, 张友梅. 衡山自然保护区森林土壤中动物群落研究[J]. 地理学报, 1989, 44(2): 205-213.