IGSNRR OpenIR
Method for identifying outliers of soil heavy metal data
Yang, Jun1,2; Wang, Jingyun1,2; Zheng, Yuanming3; Lei, Mei1; Yang, Junxing1; Wan, Xiaoming1; Chen, Tongbin1
2018-05-01
Source PublicationENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN0944-1344
Volume25Issue:13Pages:12868-12875
Corresponding AuthorYang, Jun(yangj@igsnrr.ac.cn)
AbstractArtificial errors in the experimental process may lead to some outliers, which reduce data quality and cause erroneous judgment in soil pollution assessment. Based on this, a method for detecting outliers of soil heavy metal data was proposed in this study. The As, Cd, and Pb concentrations of the soil in Beijing, China, were taken as samples to verify the validity of the method. Results showed that there were 8, 34, and 38 outliers for the As, Cd, and Pb concentrations in the Beijing soil, respectively. The result of re-analyzed revealed that 75.0, 76.5, and 92.1% of the As, Cd, and Pb outliers, respectively, were caused by artificial errors. After correcting, the interpolation accuracy for data was improved significantly. The mean relative error (MRE) of the As, Cd, and Pb outliers decreased by 48.0, 44.6, and 54.7%, while the mean square error of these outliers decreased by 34.2, 33.3, and 46.4%, respectively. The MRE values of the nearest neighboring points which were influenced by the outliers decreased by 5.2, 20.6, and 27.6%, while the mean square error of these points decreased by 5.3, 17.3, and 33.2%, respectively. To our knowledge, this is the first study on detecting outliers of soil heavy metal data. The method considers both spatial and numerical outliers, which avoids the limitation of single method, and can effectively improve the data quality of soil heavy metal concentrations with a finite sample size and analysis time.
KeywordSoil heavy metal Outlier data Checkout method Cross-validation Prediction accuracy
DOI10.1007/s11356-018-1555-8
WOS KeywordAGRICULTURAL SOILS ; CHINA ; RISK ; AREA ; CONTAMINATION ; GIS ; MULTIVARIATE ; STATISTICS ; POLLUTION ; HOTSPOTS
Indexed BySCI
Language英语
Funding ProjectNational Nature Science Foundation of China[41271478] ; 863 National Hi-tech Research and Development Project[2014AA06A513]
Funding OrganizationNational Nature Science Foundation of China ; 863 National Hi-tech Research and Development Project
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000431883500060
PublisherSPRINGER HEIDELBERG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/55056
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYang, Jun
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Ctr Environm Remediat, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China
Recommended Citation
GB/T 7714
Yang, Jun,Wang, Jingyun,Zheng, Yuanming,et al. Method for identifying outliers of soil heavy metal data[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2018,25(13):12868-12875.
APA Yang, Jun.,Wang, Jingyun.,Zheng, Yuanming.,Lei, Mei.,Yang, Junxing.,...&Chen, Tongbin.(2018).Method for identifying outliers of soil heavy metal data.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,25(13),12868-12875.
MLA Yang, Jun,et al."Method for identifying outliers of soil heavy metal data".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 25.13(2018):12868-12875.
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