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

Yang, Jun; Wang, Jingyun; Qiao, Pengwei; Zheng, Yuqi; Yang, Jin‐yan; Chen, Tongbin; Lei, Mei; Wan, Xiaoming; Zhou, Xiaoyong

doi:10.1007/s11783-019-1216-2

Cited by 43 publications

(16 citation statements)

References 77 publications

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“…This result was quite different from other paddy soil which had heavy metals content was sequenced as Pb > Cd > Ni > As > Zn > Cu > Cr (Li et al, 2019). The content of heavy metal was usually governed by parent material, and acidic condition factors (Yang et al, 2020). As a whole, our result found the contents of all heavy metals in soft clay soil were still below the critical limit of soil based on Kabata-Pendias (2011).…”

Section: Discussioncontrasting

confidence: 83%

Characterization of physical, chemical and microstructure properties in the soft clay soil of the paddy field area

Rendana

Idris

Rahim

et al. 2021

Sains Tanah J. Soil Sci. Agroclimatology

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The soft clay soil has been categorized as infertile soil. The occurrence of soft clay soil in paddy field areas can decline soil quality and rice production. Therefore, to find the best technique for amending this soil, this study aimed to analyze the physical, chemical, and microstructure properties of the soft clay soil in the paddy field area. The soft clay soil samples were collected from two paddy blocks in Kedah, Malaysia. The physical and chemical properties of the soil were determined using the standard method in the laboratory. The microstructure properties were analyzed using Zeiss SUPRA 55VP microscopes. The results found that the soft clay soil was composed of silt – clay (> 90%) with the texture of silty clay. The soft clay soil was characterized by low values of organic matter (2.63-3.42%), pH (3.32-3.69), cation exchange capacity (6.89-8.72 cmolc kg-1), available P (0.14-0.41 mg kg-1), aggregate stability (16.53-17.78%), and hydraulic conductivity (0.17 cm hr-1). In contrast, it indicated high values of soil water content (42.24-43.21%), and exchangeable Na+ ions (2.48-2.50 cmolc kg-1). In addition, the analysis of heavy metals content revealed that their concentrations were below the critical level in the soil. The soft clay soil was largely governed by kaolinite minerals, and it had less compact structures with many large voids among soil aggregates. In conclusion, the quality of soft clay soil in the study area was poor with low physical and chemical parameters. The quality of the soil could be improved by the addition of soil amendments such as zeolite, cement, and other additive materials to absorb the excess water in the soil and increase the soil strength.

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Section: Discussioncontrasting

confidence: 83%

Characterization of physical, chemical and microstructure properties in the soft clay soil of the paddy field area

Rendana

Idris

Rahim

et al. 2021

Sains Tanah J. Soil Sci. Agroclimatology

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“…Category quantization refers to quantizing class variables by a specific nonlinear transformation and then iterating directly to find the optimal equation. CATREG model is a classical linear regression model applied with transformation variables (Yang and Wang et al, 2020).…”

Section: Catreg Modelmentioning

confidence: 99%

Analysis and Source Identification of Heavy Metals in Soils of Central Urban Area of Chongqing, China

Chen

Zhu

Cai

et al. 2021

Preprint

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In megacities, due to frequent human activities, large amounts of metals enter the soil indirectly or directly and eventually flow to people through the food chain. Therefore, the analysis and identification of soil heavy metal sources is an important part of revealing soil heavy metal pollution. The spatial content and potential sources of 11 heavy metals were analyzed from 342 surface soil samples collected from the central city of Chongqing in southwest China. The results showed that the main heavy metal elements under the first principal component loading were copper (Cu), nickel(Ni), zinc (Zn), manganese (Mn), cadmium (Cr),plumbum (Pb) and cadmium(Cd).The second principal component (F2) was mainly loaded with molybdenum (Mo), arsenic (As), mercury (Hg)and antimony (Sb), and the PCA-APCs receptor model of 11 heavy metals was constructed. The PCA-APCs receptor models of 11 heavy metals were constructed. The results of classification regression analysis confirmed the main sources of heavy metals. Population density mainly affected Cu (0.539), soil mainly affected Ni (0.411), Sb (0.493), Zn (0.472) and Mn (0.206), and water quality mainly affected As(0.453) and Mo (0.374). Air quality mainly affects Cd (0.332) and Cr (0.371), traffic activity mainly affects Hg (0.312), and slope mainly affects Pb (0.313). Hot spot analysis showed that heavy metals had a high degree of coincidence with environmental factors such as soil parent material, slope, soil type and traffic activities. The results of this study can be effectively used to make scientific decisions and strategies, and an effective strategy for prevention and control of soil heavy metal pollution should be formulated to protect the urban soil environmental quality.

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“…Pb is a heavy metal produced by the rapid economic development and greatly harmful to human body, especially to child. Human exposure to Pb occurs primarily through inhalation and ingestion, causing adverse health effects such as neurological and kidney diseases, even at low exposure levels [ 1 , 2 , 3 , 4 ], and Pb contamination in environmental compartments (rural soils, atmospheric dustfall, and food) has been partly documented in some articles [ 4 , 5 , 6 , 7 , 8 , 9 ]. Most investigations to date have focused on assessing contamination level and sources of Pb in soils and atmospheric particles [ 9 , 10 , 11 , 12 ], with few focusing on Pb contamination and its source apportionment in grains that are essential for metabolism.…”

Section: Introductionmentioning

confidence: 99%

“…Human exposure to Pb occurs primarily through inhalation and ingestion, causing adverse health effects such as neurological and kidney diseases, even at low exposure levels [ 1 , 2 , 3 , 4 ], and Pb contamination in environmental compartments (rural soils, atmospheric dustfall, and food) has been partly documented in some articles [ 4 , 5 , 6 , 7 , 8 , 9 ]. Most investigations to date have focused on assessing contamination level and sources of Pb in soils and atmospheric particles [ 9 , 10 , 11 , 12 ], with few focusing on Pb contamination and its source apportionment in grains that are essential for metabolism. Rice and wheat are the world’s two most important cereal crops, contributing 45% of digestible energy and 30% of total protein in the human diet, as well as a substantial contribution to livestock feed [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Pb Content, Risk Level and Primary-Source Apportionment in Wheat and Rice Grains in the Lihe River Watershed, Taihu Region, Eastern China

Lian

Zhou

Yang

et al. 2021

IJERPH

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This study detailed a complete research from Lead (Pb) content level to ecological and health risk to direct- and primary-sources apportionment arising from wheat and rice grains, in the Lihe River Watershed of the Taihu region, East China. Ecological and health risk assessment were based on the pollution index and US Environmental Protection Agency (EPA) health risk assessment model. A three-stage quantitative analysis program based on Pb isotope analysis to determine the relative contributions of primary sources involving (1) direct-source apportionment in grains with a two-end-member model, (2) apportionment of soil and dustfall sources using the IsoSource model, and (3) the integration of results of (1) and (2) was notedly first proposed. The results indicated that mean contents of Pb in wheat and rice grains were 0.54 and 0.45 mg/kg and both the bio-concentration factors (BCF) were <<1; the ecological risk pollution indices were 1.35 for wheat grains and 1.11 for rice grains; hazard quotient (HQ) values for adult and child indicating health risks through ingestion of grains were all <1; Coal-fired industrial sources account for up to 60% of Pb in the grains. This study provides insights into the management of grain Pb pollution and a new method for its source apportionment.

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

Cited by 43 publications

References 77 publications

Characterization of physical, chemical and microstructure properties in the soft clay soil of the paddy field area

Characterization of physical, chemical and microstructure properties in the soft clay soil of the paddy field area

Analysis and Source Identification of Heavy Metals in Soils of Central Urban Area of Chongqing, China

Pb Content, Risk Level and Primary-Source Apportionment in Wheat and Rice Grains in the Lihe River Watershed, Taihu Region, Eastern China

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