Quantitative source apportionment, risk assessment and distribution of heavy metals in agricultural soils from southern Shandong Peninsula of China

Liu, Haiwei; Zhang, Yan; Yang, Jingli; Wang, Haiyun; Li, Yile; Shi, Yi; Li, Decheng; Holm, Peter E.; Ou, Qingyu; Hu, Wenyou

doi:10.1016/j.scitotenv.2020.144879

Cited by 148 publications

(70 citation statements)

References 33 publications

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“…Three carcinogenic risk levels were listed based on the CR values: CR<1×10 -6 indicates no carcinogenic risk; 1×10 -6 < CR<1×10 -4 indicates a generally acceptable risk; CR> 1×10 -4 indicates an unacceptable risk (Liu et al, 2021).…”

Section: Health Risk Assessmentmentioning

confidence: 99%

“…where Ci is the measured concentration of element i in soil, Bi is the geochemical background value of element i obtained from the China National Environmental Monitoring Centre (CNEMC); the background values of Ni, Pb, Cd, Cu and As in Yunnan province were 42.5, 40.6, 0.218, 46.3 and 18.4 mg kg -1 , respectively (CNEMC, 1990). Seven levels were distinguished based on the Igeo value: <0, 0-1, 1-2, 2-3, 3-4, 4-5, ≥5, representing uncontaminated, uncontaminated to moderately contaminated, moderately contaminated, moderately to heavily contaminated, heavily contaminated, heavily to extremely contaminated and extremely contaminated, respectively (Liu et al, 2021).…”

Section: Geo-accumulation Indexmentioning

confidence: 99%

“…where NIPI is the composite pollution index in soil; PImax is the maximum PI value of all elements; PIave is the average PI value for all elements. There were five levels distinguished based on the PI and NIPI values: <0.7, 0.7-1, 1-2, 2-3, ≥3, representing no pollution, precautionary pollution, low pollution, moderate pollution and severe pollution, respectively (Liu et al, 2021).…”

Section: Pollution Index and Nemerow Integrated Pollution Indexmentioning

confidence: 99%

“…Geostatistical models based on geographic information systems (GIS) (Yu et al, 2021) and multivariate statistical methods, e.g. principal component analysis (PCA), cluster analysis (CA) and correlation analysis are often used to qualitatively identify the origin of elements in soil (Liu et al, 2021). In addition, quantitative methods mostly involve receptor models, such as the chemical mass balance (CMB) model (Jiang et al, 2017) or positive matrix factorization (PMF), etc.…”

Section: Introductionmentioning

confidence: 99%

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Distribution and risk assessment of heavy metals in soil in a commonly used mining area

Xu¹,

Dai²,

Skuza³

et al. 2021

Preprint

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This study investigated the spatial, vertical and fraction distribution, multivariate statistical analysis, as well as assessed pollution levels and health risks posed by heavy metals at the Wenshan mining area. There were 6 sampling points at the site, from which a total of 32 samples were collected. The eastern and south-eastern parts of the site were heavily polluted, while the western and north-western parts were less contaminated. As pollution was particularly severe in the topsoil, with the mean value of 503.31 mg kg− 1 being more than 25-fold higher than the risk screening value. As content in all layers was greater than the standard risk screening value in the three typical profiles. The proportion of various Ni, Cu and As fractions changed slightly with depth at all three sampling points, and the residual fraction percentage accounted for more or nearly 80% of the total concentration. In addition, a significantly negative correlation (p < 0.05) was detected between Ni and As (-0.85), indicating a different source of these two elements. Principal component analysis demonstrated that two PCs, i.e. PC1, attributed to human activities, e.g. mining activities and transport, and PC2, defined as natural sources, were responsible for 80.632% of the total variance. As posed severe ecological and unacceptable health risks, i.e. non-carcinogenic and carcinogenic risks both for adults and children. Therefore, a comprehensive understanding of the characteristics of mining pollutants and improved emission management in the mining process are essential to protect the environment and health of local residents.

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Section: Health Risk Assessmentmentioning

confidence: 99%

Section: Geo-accumulation Indexmentioning

confidence: 99%

Section: Pollution Index and Nemerow Integrated Pollution Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distribution and risk assessment of heavy metals in soil in a commonly used mining area

Xu¹,

Dai²,

Skuza³

et al. 2021

Preprint

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“…Due to the differences in soil parent material, climate conditions and human interference, the sources of heavy metals vary greatly, so it is necessary to detect the specific sources of heavy metals in the site. For example, Liu (2021) found that the heavy metals Hg, Cd, Cu, Pb and Zn in agricultural soils of southern Shandong Peninsula in China came from coal burning, agricultural practices, and industrial activities. Aguilera (2021) explained that the main sources of heavy metals Pb, Zn, Cu and Cr in street dust in Mexico City are human activities, possibly due to vehicle traffic.…”

Section: Introductionmentioning

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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Contamination, risk assessment and source apportionment of the heavy metals in the soils of apple orchard in Qixia City, Shandong Province, China

Hua

Zhuo

Kang

et al. 2022

Stoch Environ Res Risk Assess

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Quantitative source apportionment, risk assessment and distribution of heavy metals in agricultural soils from southern Shandong Peninsula of China

Cited by 148 publications

References 33 publications

Distribution and risk assessment of heavy metals in soil in a commonly used mining area

Distribution and risk assessment of heavy metals in soil in a commonly used mining area

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

Contamination, risk assessment and source apportionment of the heavy metals in the soils of apple orchard in Qixia City, Shandong Province, China

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