Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China

doi:10.1007/s11783-019-1216-2

	Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China
	Yang, Jun 3,4; Wang, Jingyun 3,4; Qiao, Pengwei 2; Zheng, Yuanming 1; Yang, Junxing 3,4; Chen, Tongbin 3,4; Lei, Mei 3,4; Wan, Xiaoming 3,4; Zhou, Xiaoyong 3
刊名	FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING
	2020-01-22
卷号	14 期号:3 页码:14
关键词	Soil Heavy metal Influencing factor Categorical regression Identification method
ISSN号	2095-2201
DOI	10.1007/s11783-019-1216-2
通讯作者	Yang, Jun(yangj@igsnrr.ac.cn)
英文摘要	Identifying the factors that influence the heavy metal contents of soil could reveal the sources of soil heavy metal pollution. In this study, a categorical regression was used to identify the factors that influence soil heavy metals. First, environmental factors were associated with soil heavy metal data, and then, the degree of influence of different factors on the soil heavy metal contents in Beijing was analyzed using a categorical regression. The results showed that the soil parent material, soil type, land use type, and industrial activity were the main influencing factors, which suggested that these four factors were important sources of soil heavy metals in Beijing. In addition, population density had a certain influence on the soil Pb and Zn contents. The distribution of soil As, Cd, Pb, and Zn was markedly influenced by interactions, such as traffic activity and land use type, industrial activity and population density. The spatial distribution of soil heavy metal hotspots corresponded well with the influencing factors, such as industrial activity, population density, and soil parent material. In this study, the main factors affecting soil heavy metals were identified, and the degree of their influence was ranked. A categorical regression represents a suitable method for identifying the factors that influence soil heavy metal contents and could be used to study the genetic process of regional soil heavy metal pollution. (C) Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
资助项目	National Natural Science Foundation of China[41771510] ; National Natural Science Foundation of China[41271478] ; Science and Technology Service Network Initiative (STS) from the Chinese Academy of Sciences[KFJ-STS-ZDTP-007]
WOS关键词	MULTIVARIATE STATISTICAL-ANALYSIS ; AGRICULTURAL SOILS ; SURFACE SOILS ; HEALTH-RISK ; SOURCE IDENTIFICATION ; SOURCE APPORTIONMENT ; STREAM SEDIMENTS ; ORGANIC-MATTER ; FARMLAND SOILS ; WASTE-WATER
WOS研究方向	Engineering ; Environmental Sciences & Ecology
语种	英语
出版者	HIGHER EDUCATION PRESS
WOS记录号	WOS:000521343100001
资助机构	National Natural Science Foundation of China ; Science and Technology Service Network Initiative (STS) from the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/133502]
专题	中国科学院地理科学与资源研究所
通讯作者	Yang, Jun
作者单位	1.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China 2.Environm Protect Res Inst Light Ind, Beijing Key Lab Remediat Ind Pollut Sites, Beijing 100048, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Ctr Environm Remediat, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Yang, Jun,Wang, Jingyun,Qiao, Pengwei,et al. Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China[J]. FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING,2020,14(3):14.
APA	Yang, Jun.,Wang, Jingyun.,Qiao, Pengwei.,Zheng, Yuanming.,Yang, Junxing.,...&Zhou, Xiaoyong.(2020).Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China.FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING,14(3),14.
MLA	Yang, Jun,et al."Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China".FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING 14.3(2020):14.