Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics

doi:10.1016/j.geoderma.2019.07.036

	Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics
	Tao, Huan 3,4,5; Liao, Xiaoyong 3; Zhao, Dan 1; Gong, Xuegang 3,5; Cassidy, Daniel P.2
刊名	GEODERMA
	2019-11-15
卷号	354 页码:9
关键词	Co-contaminated sites Arsenic PAHs Backpropagation (BP) neural networks Geostatistics Nemerow pollution index
ISSN号	0016-7061
DOI	10.1016/j.geoderma.2019.07.036
通讯作者	Liao, Xiaoyong(liaoxy@igsnrr.ac.cn)
英文摘要	The delineation of contamination at a co-contaminated site is vital for designing remedial strategy and estimating costs. Backpropagation (BP) neural networks and the Nemerow pollution index (NPI) with 3-D kriging were combined in this study to delineate contaminant plumes, analyze the spatial distribution of pollutants in different layers and visualize them in three dimensions, quantify polluted areas and pollution levels, and identify hotspots of the contaminants of concern (COCs). The results of a comprehensive assessment performed using BP networks and NPI were compared. The analysis of the volumes of soil contaminated with specific COCs in different soil layers showed that arsenic (As) hardly migrated downgradient whereas benzo [a] pyrene (BaP) had a strong tendency to migrate. The tendency of fluorene (FLE), naphthalene (NAP), and phenanthrene (PHE) to migrate was between that for As and BaP. The volumes of earth contaminated with of all five of these COCs generally decreased with increasing pollution levels. The volumes of PHE, FLE, and NAP at different pollution levels exhibited similar trends, and most contaminated areas were safe. The volume of BaP at high pollution levels was markedly greater than that for the other four COCs, and the volume of As at low pollution levels was also notably greater than that for the other four pollutants. (3) The spatial patterns performed by the NPI and BP network comprehensive assessment methods were similar. However, BP networks can overcome the deficiencies of NPI, which are amplification of the effect of heavily polluted elements and narrowing the separability of polluted and unpolluted areas. After comparing performance and performing a cost-benefit analysis, we propose a model that integrates BP networks and geostatistics to delineate soil contaminant plumes at co-contaminated sites.
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040302]
WOS关键词	VERTICAL VARIABILITY ; SPATIAL-DISTRIBUTION ; POLLUTION ; REMEDIATION ; IDENTIFICATION ; RISK
WOS研究方向	Agriculture
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000486133300006
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/69575]
专题	中国科学院地理科学与资源研究所
通讯作者	Liao, Xiaoyong
作者单位	1.Chinese Acad Environm Planning, Ctr Environm Risk & Damage Assessment, Beijing 100012, Peoples R China 2.Western Michigan Univ, Dept Geol & Environm Sci, Kalamazoo, MI 49008 USA 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing Key Lab Environm Damage Assessment & Reme, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 4.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Tao, Huan,Liao, Xiaoyong,Zhao, Dan,et al. Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics[J]. GEODERMA,2019,354:9.
APA	Tao, Huan,Liao, Xiaoyong,Zhao, Dan,Gong, Xuegang,&Cassidy, Daniel P..(2019).Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics.GEODERMA,354,9.
MLA	Tao, Huan,et al."Delineation of soil contaminant plumes at a co-contaminated site using BP neural networks and geostatistics".GEODERMA 354(2019):9.