Source identification and contribution of land uses to the observed values of heavy metals in soil samples of the border between the Northern Ireland and Republic of Ireland by receptor models and redundancy analysis

Sakizadeh, Mohamad; Zhang, Chaosheng

doi:10.1016/j.geoderma.2021.115313

Cited by 33 publications

(10 citation statements)

References 78 publications

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“…In general, some arsenic-bearing sulfide and oxide rocks migrate into soil via weathering and rain erosion [ 46 ], and the As contents of natural soil range from 0.2 mg/kg to 40 mg/kg. Therefore, Factor 1 can be concluded to represent the parent material, i.e., a geological source [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

Pollution Assessment and Source Apportionment of Soil Heavy Metals in a Coastal Industrial City, Zhejiang, Southeastern China

Wang

Zhang

Cheng

et al. 2022

IJERPH

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In this research, Ningbo City, a typical industrial city in southeastern China, was selected as the study area, and the concentrations of 12 heavy metals (Cd, Cr, Ni, Pb, Zn, Cu, Hg, As, Co, V, Se, and Mn) were measured at 248 sampling points. Pollution index methods were used to assess the status of soil heavy metal contamination, and the Positive Matrix Factorization (PMF) model and Unmix model were integrated to identify and apportion the sources of heavy metal contamination. The results indicated that nearly 70% of the study area was polluted by heavy metals, and that Ni, Cr, and Zn were the main enriched heavy metals. The five sources identified using the PMF model were a geological source, an atmospheric deposition source, a transportation emissions source, a mixed source of agriculture and industry, and a mixed source of geology and industry. The four sources identified using the Unmix model were a mixed source of geology, agriculture, and industry (14.27%); a transportation emissions source (4.76%); a geological source (14.7%); and a mixed source of geology and industry (66.28%). These results have practical significance, as they can help to carry out pollution source risk assessment and give priority to the management of pollution source control.

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Section: Resultsmentioning

confidence: 99%

Pollution Assessment and Source Apportionment of Soil Heavy Metals in a Coastal Industrial City, Zhejiang, Southeastern China

Wang

Zhang

Cheng

et al. 2022

IJERPH

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“…Aydin et al 43 found significant Cu content in pesticides and phosphate rock (for phosphate fertilizer application) (see Table S7); fungicides and phosphorus fertilizers have a joint effect on Cu accumulation in agricultural soils. 26 Dong et al 44 and Giersz et al 45 also confirmed that the Hg contamination in soils mainly came from the application of Hg-containing pesticides and sludge fertilizers. With a similar analytical approach, the sources of soil HMs in ASA were interpreted as follows: (1) factor 1 corresponds to agricultural activities; (2) factor 2 corresponds to dry and wet deposition; and (3) factor 3 corresponds to geological sources.…”

Section: Hm Fractions In Soilsmentioning

confidence: 89%

“…In this study, the positive matrix factorization (PMF) model was used to calculate the precise contribution rates of HM pollution sources for paddy soils in eluvial and alluvial areas. The PMF model has been successfully applied to resolve the pollution source apportionment in air, soil, and sediment matrices . It not only can quantitatively analyze HMs but also has the advantages of not requiring measurement of the source component spectrum, non-negative elements in the decomposition matrix, and the use of standard deviations of data for optimization .…”

Section: Methodsmentioning

confidence: 99%

“…The PMF model has been successfully applied to resolve the pollution source apportionment in air, soil, and sediment matrices. 26 It not only can quantitatively analyze HMs but also has the advantages of not requiring measurement of the source component spectrum, non-negative elements in the decomposition matrix, and the use of standard deviations of data for optimization. 27 The specific algorithm of the PMF model is shown in Text S3.…”

Section: Positive Matrix Factorization Modelmentioning

confidence: 99%

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Bioavailability and Risk Variations of Heavy Metal(loid)s in Paddy Soils Developed from Eluvial and Alluvial Parent Materials in the Xitiaoxi Catchment, China

Zhang

Yuan

Cheng

et al. 2023

ACS Earth Space Chem.

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Heavy metals or metalloids (HMs) in agricultural soils at high geological background areas could inherit the characteristics of parent materials, which would affect HM accumulation in crops. However, the specific impact of different parent materials on the behavior of HMs was unclear. This study focused on the bioavailability and risk variations of HMs in paddy soils derived from eluvial and alluvial parent materials in the Xitiaoxi catchment based on the total concentration, fraction, and risk assessment of HMs. The results showed that the paddy soil in the alluvial area had a higher bioavailability and risk level of HMs than those in the eluvial area, due to the increased proportion of activated HMs in the alluvial paddy soil. The increase in activated HMs was associated with a decrease in soil acidity and fertility as well as intensified anthropogenic activities within the alluvial study area. The spatial distribution of HM risk in soil samples demonstrated that the risk level of HMs was related to the exposure of black shale in eluvial soil, while the risk level of HMs in alluvial soil was negatively correlated with distance from the riverside. The source apportionment results further indicated that the alluvial paddy soil had lower geological but higher anthropogenic sources, especially for Cd, Pb, and Cu. In summary, the alteration in the properties of parent materials during river transportation and more anthropogenic sources raised the bioavailability and risk of HMs in the alluvial paddy ecosystem. This study provided new insights into HM risk assessment and control in agricultural soils developed from different parent materials within a catchment with a high geological background.

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“…Multivariate statistical analysis, including principal component analysis (PCA) and correlation analysis (CA), is widely employed to identify pollution sources (Long et al, 2021). Quantitative receptor models, positive matrix factorization (PMF), UNMIX, and absolute principal component analysis‐multiple linear regression (APCS‐MLR) can identify and quantify pollutant sources in the atmosphere, sediments, waters, and urban soils (Mehr et al, 2017; Mohammad et al, 2016; Sakizadeh & Zhang, 2021; Shen et al, 2021). Recently, receptor models have been successfully applied in quantifying pollutant sources in farmland soils (Zhang, Yan, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk

et al. 2022

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The Karst Plateau is characterized by elevated heavy metals (HM), the farmland soils in the Karst Plateau area is especially vulnerable to HM pollution. To cope with soil HM pollution and conduct precaution in Karst Plateau, the key bottleneck is to understand the pollution levels, sources, and priority‐control of HM. Hence, geochemical baselines of HM in farmland soils were established to accurately evaluate the pollution characteristics. Pollution sources were identified with multivariate statistics, geostatistical methods, and receptor models. Priority‐control of HM were distinguished via health assessments with a Monte Carlo simulation. A remarkable accumulation of Pb, Sb, Zn, As, and Cd was observed. Hotspots of As, Cd, Pb, Sb, and Zn clustered in the southwestern region of Hezhang. Pb–Zn related activities, cement product activities, coal mining, and coal combustion were dominant sources. Both noncarcinogenic risk and carcinogenic risk followed the order: children>adult females>adult males. As, Cd, and Pb were found to be priority contaminants in farmland soils in Hezhang.

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Source identification and contribution of land uses to the observed values of heavy metals in soil samples of the border between the Northern Ireland and Republic of Ireland by receptor models and redundancy analysis

Cited by 33 publications

References 78 publications

Pollution Assessment and Source Apportionment of Soil Heavy Metals in a Coastal Industrial City, Zhejiang, Southeastern China

Pollution Assessment and Source Apportionment of Soil Heavy Metals in a Coastal Industrial City, Zhejiang, Southeastern China

Bioavailability and Risk Variations of Heavy Metal(loid)s in Paddy Soils Developed from Eluvial and Alluvial Parent Materials in the Xitiaoxi Catchment, China

Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk

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