Influencing factors identification and the nested structure analysis of heavy metals in soils in entire city and surrounding the multiple pollution sources.

Identifying the sources of pollutants and analyzing the nested structure of heavy metals is vital for the prevention and control of soil pollution. However, there is a lack of research on comparison the main sources and the nested structure at different scales. In this study, two spatial extent scales were taken as the research objects, the results showed that, (1) the point exceeding standard rate of As, Cr, Ni, and Pb is higher at the entire city scale; (2) As and Pb, while Cr, Ni, and Zn, have weaker spatial variability at the entire scale and surrounding the pollution sources, respectively; (3) the contribution of the larger structure of Cr and Ni, while Cr, Ni, and Zn, at the entire scale and surrounding the pollution sources, respectively, is bigger to the total variability. The representation of semivariogram is better when the general spatial variability is weaker and the contribution of the smaller structure is lower; (4) various factors with different influencing distance could lead to nested structure even at a small extent spatial scale. The results provide a basis for the determination of remediation and prevention objectives at different spatial scales.

[1]  David Taylor,et al.  A synthesis framework using machine learning and spatial bivariate analysis to identify drivers and hotspots of heavy metal pollution of agricultural soils. , 2021, Environmental pollution.

[2]  Yaling Gou,et al.  An effective method for determining the optimal sampling scale based on the purposes of soil pollution investigations and the factors influencing the pollutants. , 2021, Journal of hazardous materials.

[3]  S. Ussher,et al.  The characteristics of atmospheric particles and metal elements during winter in Beijing: Size distribution, source analysis, and environmental risk assessment. , 2021, Ecotoxicology and environmental safety.

[4]  Yaling Gou,et al.  Quantitative analysis of the main sources of pollutants in the soils around key areas based on the positive matrix factorization method. , 2021, Environmental pollution.

[5]  Ruimin Liu,et al.  Temporal variations of levels and sources of health risk associated with heavy metals in road dust in Beijing from May 2016 to April 2018. , 2020, Chemosphere.

[6]  Deyi Hou,et al.  VIRS based detection in combination with machine learning for mapping soil pollution. , 2020, Environmental pollution.

[7]  Lei Wen,et al.  Probing Energy-Related CO2 Emissions in the Beijing-Tianjin-Hebei Region Based on Ridge Regression Considering Population Factors , 2020 .

[8]  Zhou Shi,et al.  Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory. , 2020, Journal of hazardous materials.

[9]  G. He,et al.  How does environmental concern influence public acceptability of congestion charging? Evidence from Beijing , 2020 .

[10]  Zehang Sun,et al.  Quantitative source apportionment of heavy metal(loid)s in the agricultural soils of an industrializing region and associated model uncertainty. , 2020, Journal of hazardous materials.

[11]  Yongzhang Zhou,et al.  Heavy metal(loid)s in the topsoil of urban parks in Beijing, China: Concentrations, potential sources, and risk assessment. , 2020, Environmental pollution.

[12]  Yuan-ming Zheng,et al.  Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China , 2020, Frontiers of Environmental Science & Engineering.

[13]  G. Christakos,et al.  Spatial variability assessment of La and Nd concentrations in coastal China soils following 1000 years of land reclamation , 2019, Journal of Soils and Sediments.

[14]  Chao Nie,et al.  Spatio-Temporal Variability and the Factors Influencing Soil-Available Heavy Metal Micronutrients in Different Agricultural Sub-Catchments , 2019, Sustainability.

[15]  Yanyan Li,et al.  Comprehensive Evaluation and Source Apportionment of Potential Toxic Elements in Soils and Sediments of Guishui River, Beijing , 2019, Water.

[16]  Sucai Yang,et al.  Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector. , 2019, The Science of the total environment.

[17]  Ruimin Liu,et al.  Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system. , 2019, The Science of the total environment.

[18]  R. Choudhari,et al.  Assessment of air pollution caused by illegal e-waste burning to evaluate the human health risk. , 2019, Environment international.

[19]  Xiuduan Chen,et al.  Contamination characteristics and source apportionment of heavy metals in topsoil from an area in Xi'an city, China. , 2018, Ecotoxicology and environmental safety.

[20]  Sucai Yang,et al.  Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing , 2018, Environmental Science and Pollution Research.

[21]  Ruimin Liu,et al.  Pollution characteristics, risk assessment, and source apportionment of heavy metals in road dust in Beijing, China. , 2018, The Science of the total environment.

[22]  B. Marschner,et al.  The power of Random Forest for the identification and quantification of technogenic substrates in urban soils on the basis of DRIFT spectra. , 2017, Environmental pollution.

[23]  Nianliang Cheng,et al.  Comparisons of two serious air pollution episodes in winter and summer in Beijing. , 2017, Journal of environmental sciences.

[24]  Lanlan Guo,et al.  Deposited atmospheric dust as influenced by anthropogenic emissions in northern China , 2017, Environmental Monitoring and Assessment.

[25]  G. Zeng,et al.  Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, Lianyuan, China. , 2017, Environmental pollution.

[26]  G. Yuan,et al.  The emerging source of polycyclic aromatic hydrocarbons from mining in the Tibetan Plateau: Distributions and contributions in background soils. , 2017, The Science of the total environment.

[27]  Zirui Liu,et al.  [Concentration Characteristics and Sources of Trace Metals in PM2.5 During Wintertime in Beijing]. , 2017, Huan jing ke xue= Huanjing kexue.

[28]  Hongbin Cao,et al.  Source apportionment and health risk assessment of heavy metals in soil for a township in Jiangsu Province, China. , 2017, Chemosphere.

[29]  Wenji Zhao,et al.  Spatial variation and provenance of atmospheric trace elemental deposition in Beijing , 2016 .

[30]  Meie Wang,et al.  Spatial pattern of heavy metals accumulation risk in urban soils of Beijing and its influencing factors. , 2016, Environmental pollution.

[31]  L. Zeng,et al.  Positive matrix factorization as source apportionment of soil lead and cadmium around a battery plant (Changxing County, China) , 2014, Environmental Science and Pollution Research.

[32]  Yang Liu,et al.  Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils. , 2013, Journal of hazardous materials.

[33]  Meie Wang,et al.  Vegetative cover and PAHs accumulation in soils of urban green space. , 2012, Environmental pollution.

[34]  M. Shao,et al.  The interpolation accuracy for seven soil properties at various sampling scales on the Loess Plateau, China , 2012, Journal of Soils and Sediments.

[35]  Sun Danfeng,et al.  Multi-scale spatial structure of heavy metals in agricultural soils in Beijing , 2010, Environmental monitoring and assessment.

[36]  Xiaoying Zheng,et al.  Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China , 2010, Int. J. Geogr. Inf. Sci..

[37]  H. Hagendorfer,et al.  Analyses of platinum group elements in mosses as indicators of road traffic emissions in Austria , 2006 .

[38]  Jianguo Wu Effects of changing scale on landscape pattern analysis: scaling relations , 2004, Landscape Ecology.

[39]  F. Yu,et al.  Scale-dependent spatial heterogeneity of vegetation in Mu Us sandy land, a semi-arid area of China , 2002, Plant Ecology.

[40]  R. Webster,et al.  Coregionalization of trace metals in the soil in the Swiss Jura , 1994 .

[41]  Jianshu Lv Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils. , 2019, Environmental pollution.

[42]  Bing Li,et al.  Spatial distribution of soil cadmium and its influencing factors in peri-urban farmland: a case study in the Jingyang District, Sichuan, China , 2016, Environmental Monitoring and Assessment.

[43]  Chaosheng Zhang,et al.  Relationships between heavy metal concentrations in soils and reclamation history in the reclaimed coastal area of Chongming Dongtan of the Yangtze River Estuary, China , 2014, Journal of Soils and Sediments.

[44]  Lifang Ma,et al.  Geological Atlas of China , 2002 .