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Kriging methods with auxiliary nighttime lights data to detect potentially toxic metals concentrations in soil
Zhen, Jinchun1,2; Pei, Tao1,3; Xie, Shuyun2
2019-04-01
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
Volume659Pages:363-371
Corresponding AuthorPei, Tao(peit@lreis.ac.cn)
AbstractThe spatial distribution of potentially toxic metals (PTMs) has been shown to be related to anthropogenic activities. Several auxiliary variables, such as those related to remote sensing data (e.g. digital elevation models, land use, and enhanced vegetation index) and soil properties (e.g. pH, soil type and cation exchange capacity), have been used to predict the spatial distribution of soil PTMs. However, these variables are mostly focused on natural processes or a single aspect of anthropogenic activities and cannot reflect the effects of integrated anthropogenic activities. Nighttime lights (NTL) images, a representative variable of integrated anthropogenic activities, may have the potential to reflect PTMs distribution. To uncover this relationship and determine the effects on evaluation precision, the NTL was employed as an auxiliary variable to map the distribution of PTMs in the United Kingdom. In this study, areas with a digital number (DN) >= 50 and an area > 30 km(2) were extracted from NTL images to represent regions of high-frequency anthropogenic activities. Subsequently, the distance between the sampling points and the nearest extracted area was calculated. Barium, lead, zinc, copper, and nickel concentrations exhibited the highest correlation with this distance. Their concentrations were mapped using distance as an auxiliary variable through three different kriging methods, i.e., ordinary kriging (OK), cokriging (CK), and regression kriging (RK). The accuracy of the predictions was evaluated using the leave-one-out cross validation method. Regardless of the elements, CK and RK always exhibited lower mean absolute error and root mean square error, in contrast to OK. This indicates that using the NTL as the auxiliary variable indeed enhanced the prediction accuracy for the relevant PTMs. Additionally, RK showed superior results in most cases. Hence, we recommend RK for prediction of PTMs when using the NTL as the auxiliary variable. (c) 2018 Elsevier B.V. All rights reserved.
KeywordSoil pollution Soil mapping Cokriging Regression kriging United Kingdom
DOI10.1016/j.scitotenv.2018.12.330
WOS KeywordHEAVY-METALS ; SPATIAL-DISTRIBUTION ; AGRICULTURAL SOILS ; RISK-ASSESSMENT ; URBAN SOILS ; WASTE-WATER ; HEALTH-RISK ; MULTIVARIATE ; REGRESSION ; GIS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFB0503604] ; National Key Research and Development Program of China[2016YFC0600501] ; National Natural Science Foundation of China (NSFC)[41525004] ; National Natural Science Foundation of China (NSFC)[41421001] ; National Natural Science Foundation of China (NSFC)[41872250] ; Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan[CUG170104]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000457293700037
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/49978
Collection中国科学院地理科学与资源研究所
Corresponding AuthorPei, Tao
Affiliation1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.China Univ Geosci, Fac Earth Sci, State Key Lab Geol Proc & Mineral Resources GPMR, Wuhan 430074, Hubei, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Zhen, Jinchun,Pei, Tao,Xie, Shuyun. Kriging methods with auxiliary nighttime lights data to detect potentially toxic metals concentrations in soil[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2019,659:363-371.
APA Zhen, Jinchun,Pei, Tao,&Xie, Shuyun.(2019).Kriging methods with auxiliary nighttime lights data to detect potentially toxic metals concentrations in soil.SCIENCE OF THE TOTAL ENVIRONMENT,659,363-371.
MLA Zhen, Jinchun,et al."Kriging methods with auxiliary nighttime lights data to detect potentially toxic metals concentrations in soil".SCIENCE OF THE TOTAL ENVIRONMENT 659(2019):363-371.
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