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

Highlights

A method was proposed to identify the main influence factors of soil heavy metals.

The influence degree of different environmental factors was ranked.

Parent material, soil type, land use and industrial activity were main factors.

Interactions between some factors obviously affected soil heavy metal distribution.

Abstract

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.

Keywords

Soil / Heavy metal / Influencing factor / Categorical regression / Identification method

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 41771510 and 41271478) and the Science and Technology Service Network Initiative (STS) from the Chinese Academy of Sciences (No. KFJ-STS-ZDTP-007). In addition, the authors would like to thank professor Yucheng Chen of South-west University for helping us to analyze the data.

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