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Frontiers of Environmental Science & Engineering

Front. Environ. Sci. Eng.    2020, Vol. 14 Issue (3) : 37
Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China
Jun Yang1,2(), Jingyun Wang1,2, Pengwei Qiao4, Yuanming Zheng3, Junxing Yang1,2, Tongbin Chen1,2, Mei Lei1,2, Xiaoming Wan1,2, Xiaoyong Zhou1
1. Center for Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
4. Beijing Key Laboratory of Remediation of Industrial Pollution Sites, Environmental Protection Research Institute of Light Industry, Beijing 100048, China
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• 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.

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     
Corresponding Author(s): Jun Yang   
Just Accepted Date: 30 December 2019   Issue Date: 11 February 2020
 Cite this article:   
Jun Yang,Jingyun Wang,Pengwei Qiao, et al. Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China[J]. Front. Environ. Sci. Eng., 2020, 14(3): 37.
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Jun Yang
Jingyun Wang
Pengwei Qiao
Yuanming Zheng
Junxing Yang
Tongbin Chen
Mei Lei
Xiaoming Wan
Xiaoyong Zhou
Fig.1  The basic  information of the study area. (a) Spatial distribution of river systems and terrain; (b) Spatial distribution of traffic.
Independent variables Type Categories
Soil parent material Nominal 1= Ordovician, 2= alluvial-diluvial, 3= alluvial, 4= Changcheng-Jixian of Proterozoic, 5= Archean
Soil type Nominal 1= alluvial soil, 2= skeleton soil, 3= cinnamon soil, 4= demeasdow soil
Land use type Nominal 1= clean area, 2= vegetable plot, 3= grassland, 4= paddy field, 5= orchard, 6= wheat land
Slope (°) Numeric ?
Traffic activity (m) Numeric ?
Sewage irrigation Ordinal 1= Liangshui river sewage irrigation area, 2= Xinfeng river sewage irrigation area, 3= no sewage irrigation
Industrial production (million yuan/km2) Numeric ?
Population density (people/km2) Numeric ?
Tab.1  Selected  independent variables
Fig.2  Influence factors  of soil heavy metals. (a) Soil parent material; (b) Soil types; (c) Slope; (d) Land use types; (e) Road distance; (f) Sewage irrigation; (g) Industrial activity; (h) Population density.
Fig.3  Spatial  distribution of samples.
Influence factor As Cd Cr Cu Ni Pb Zn
Beta Sig Beta Sig Beta Sig Beta Sig Beta Sig Beta Sig Beta Sig
Soil parent material 0.124 0 0.147 0 0.133 0 0.258 0 0.278 0 0.244 0 0.156 0
Soil type 0.178 0 0.111 0 0.078 0.001 0.183 0 0.094 0.007 0.126 0 0.158 0
Land use type 0.124 0 0.189 0 0.22 0 0.121 0 0.129 0 0.1 0 0.097 0
Topography ?0.161 0 0.185 0 ?0.242 0 0.164 0.206 ?0.17 0.128 0.067 0.272 0.056 0.45
Traffic activity ?0.046 0.295 0.161 0 0.045 0.496 0.05 0.388 ?0.062 0.162 ?0.017 0.855 0.077 0.185
Sewage irrigation ?0.045 0.251 ?0.073 0.067 ?0.038 0.214 ?0.177 0 ?0.041 0.228 ?0.139 0 ?0.173 0
Industrial activity 0.013 0.732 0.077 0.013 0.032 0.374 0.115 0.001 0.018 0.599 0.147 0 0.168 0
Population density 0.015 0.685 ?0.006 0.845 0.022 0.482 0.004 0.912 0.01 0.743 0.098 0.003 0.092 0.008
Tab.2  CATREG  coefficients and significance test of different factors
Influence factor As Cd Cr Cu Ni Pb Zn
Soil parent material 0.214 0.133 0.087 0.347 0.532 0.386 0.182
Soil type 0.373 0.126 0.026 0.147 0.130 0.087 0.141
Land use type 0.288 0.266 0.418 0.108 0.144 0.104 0.097
Topography 0.033 0.280 0.414 0.083 0.112 0.051 0.042
Traffic activity 0.046 0.154 0.018 0.007 0.040 0.007 0.032
Sewage irrigation 0.033 0.018 0.022 0.213 0.036 0.110 0.177
Industrial activity 0.006 0.024 0.011 0.092 0.002 0.174 0.241
Population density 0.007 0.000 0.004 0.001 0.003 0.082 0.089
Tab.3  Importance of  different factors
Heavy metal Interaction Sig.
As Land use type–sewage irrigation 0.003
Population density–sewage irrigation 0.030
Cd Soil parent material–soil type 0.021
Land use type–traffic activity 0.026
Industrial activity–population density 0.003
Pb Land use type–traffic activity 0.002
Industrial activity–sewage irrigation 0.047
Zn Land use type–traffic activity 0.001
Industrial activity–traffic activity 0.042
Tab.4  Interaction of different factors
Fig.4  Spatial corresponding distribution of soil heavy metal hotspots and influence factors. (a) The Ni hotspots and soil parent material; (b) The Zn hotspots and industrial activity; (c) The Zn hotspots and population density; (d) The Cu hotspots and industrial activity.
Heavy metal Heavy metal content (mg/kg) Background value (mg/kg) (Heavy metal content-Background value)/Background value
As 7.18a 7.81a ?
Cd 0.150a 0.145a 3.45%
Cr 35.94a 31.1b 15.56%
Cu 24.04a 19.7b 22.03%
Ni 27.67a 27.9a ?
Pb 28.86a 25.1b 14.98%
Zn 65.37a 59.6b 9.68%
Tab.5  Assessment of  soil heavy metal pollution in Beijing (n = 844)
Method Characteristic Limitation
Factor analysis Using fewer factors to represent many primitive variables, data dimensionality reduction is realized, and factor variables are more interpretable by rotation. Meaning of factor cannot be completely determined, there is a lack of information, and conclusions are mainly speculative.
Geostatistics The spatial distribution and variation of target elements can be seen intuitively by considering both numerical and spatial location (Journel and Huijbregts, 1978). Limited to map overlay, lack of objective data, and pollution sources are from qualitative speculation.
Stable isotope tracer Main sources of soil pollutants are objectively, accurately, and quantitatively estimated (Zhang et al., 2018b). It cannot identify more than three kinds of pollution sources.
Geographical detector Both qualitative and quantitative data are available, and factor interaction can be analyzed by this method. Data must be transformed to categorical data.
Data classification is based on prior knowledge, and subjectivity is strong. Different classifications will result in different conclusions.
This method It can handle not only categorical data but also process numerical data.
It can objectively and quantitatively identify the effects of various factors on heavy metals.
There is a need for a certain amount of sample data.
Tab.6  Comparison of  different identification methods
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