Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China
Frontiers of Environmental Science and Engineering, ISSN: 2095-221X, Vol: 14, Issue: 3
2020
- 50Citations
- 20Captures
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Metrics Details
- Citations50
- Citation Indexes50
- 50
- CrossRef27
- Captures20
- Readers20
- 20
Article Description
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. [Figure not available: see fulltext.]
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85078111450&origin=inward; http://dx.doi.org/10.1007/s11783-019-1216-2; https://link.springer.com/10.1007/s11783-019-1216-2; https://dx.doi.org/10.1007/s11783-019-1216-2; https://link.springer.com/article/10.1007/s11783-019-1216-2; http://sciencechina.cn/gw.jsp?action=cited_outline.jsp&type=1&id=6791151&internal_id=6791151&from=elsevier
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