Source Apportionment of Agricultural Soil Heavy Metals Based on PMF Model and Multivariate Statistical Analysis

Liu, Hong; Anwar, Shazma; Fang, Liqiang; Chen, Linhua; Xu, Weizhou; Xiao, Linlin; Zhong, Beilong; Liu, Dan

doi:10.1080/15275922.2022.2047838

Cited by 24 publications

(13 citation statements)

References 37 publications

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“…The findings (Table 2) showed that CVs of As, Na, and Sr indicated a high variability, while those for Fe, K, Mn, Mg, and Ca indicated low variability. Previous studies showed that human influences have a significant impact on the concentration of metals when the mean concentration value of metals in the surface soil is higher than the reference value and the CV is more than 0.2 (102,104). Overall, there was more variation and spatial dispersion in the concentration of metals in the recreational soil of the study region than in agricultural soil, and it is expected that anthropogenic activities have an impact on this concentration.…”

Section: Descriptive Statistics Of Metal Concentrations In Soilmentioning

confidence: 99%

“…In addition, there is a major concern for As in Mexican soils with rigorous monitoring in place to restrict potential contamination. The ratio of standard deviation to mean is known as the coefficient of variation (CV), and it can be used to analyse the distribution of metal concentrations in the soil as well as their variability (102). According to the study of Wilding (103), the coefficient of variation is classified into three different levels: low (CV< 0.16), medium (0.16< CV< 0.36), and high (CV > 0.36).…”

Section: Descriptive Statistics Of Metal Concentrations In Soilmentioning

confidence: 99%

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Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

et al. 2022

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The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil samples. After implementing PMF to analyze the soils, it was possible to differentiate the variations in factors linked to the contaminants, farming impacts, and the reference soil geochemistry. The soil in the two studied locations included high concentrations of As, Ca, Mg, K, Sr, Mn, and Fe, including variations in their spatial compositions, which were caused by direct mining activities, the movement and deposition of smelting waste, and the extensive use of irrigated contaminated groundwater for irrigation. The four possible factors were identified for soil pollution including industrial, transportation, agricultural, and naturogenic based on the PMF and geostatistical analysis. The spatial distribution of metal concentrations in the soil was also presented using a geographical information system (GIS) interpolation technique. The identification of metal sources and contamination risk mapping presents a significant role in minimizing pollution sources, and it may be performed in regions with high levels of soil contamination risk.

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Section: Descriptive Statistics Of Metal Concentrations In Soilmentioning

confidence: 99%

Section: Descriptive Statistics Of Metal Concentrations In Soilmentioning

confidence: 99%

Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

et al. 2022

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“…Moreover, the coefficients of variation did not exceed 0.5, suggesting little impact of human activities on the concentrations of Cr and Ni. Liu et al and Lv et al also noted that the concentrations of Cr and Ni were primarily controlled by natural factors, such as parent soil material and soil-forming process (Liu et al 2022;Lv et al 2014). Thus, Factor 2 is defined as a natural source.…”

Section: Spatial Distributions Of Heavy Metalsmentioning

confidence: 99%

Characteristics of soil heavy metal pollution and health risk assessment in urban parks at megacity of central China

Chen

et al. 2022

Preprint

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In this study, we compared the concentrations of the heavy metals Cd, Cr, Cu, Zn, Ni, and Pb in the surface soils of urban parks in Wuhan, Hubei Province with those in the surface soils of urban parks worldwide. The soil contamination data were assessed using enrichment factors, and spatial analysis of heavy metals using inverse distance weighting and quantitative analysis of heavy metal sources with a positive definite matrix factor (PMF) receptor model. Further, a probabilistic health risk assessment of children and adults using Monte Carlo simulation was performed. The average Cd, Cr, Cu, Zn, Ni, and Pb concentrations in the surface soils of urban parks were 2.52, 58.74, 31.39, 186.28, 27.00, and 34.89 mg·kg−1, respectively, which exceeded the average soil background values in Hubei. From the inverse distance spatial interpolation map, heavy metal contamination was primarily observed to be present to the southwest of the main urban area. The PMF model resolved four sources: mixed traffic and industrial emission, natural, agricultural, and traffic sources, with relative contributions of 23.9%, 19.3%, 23.4%, and 33.4%, respectively. The Monte Carlo health risk evaluation model demonstrated negligible noncancer risks for both adult and child populations, whereas the health effects of Cd and Cr on children were a concern for cancer risks.

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“…However, it cannot determine the source components, contribution rates, or contribution values. Liu et al used PMF to determine the pollution sources in a farmland in Wenzhou, Zhejiang Province, and combined it with multivariate statistical methods, which demonstrated the feasibility of PMF models for source analysis [ 29 ]. Lv used APCS/MLR and PMF, and both provided three identical sources and were consistent with the mapping of parent material distribution [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Source Apportionment of Potentially Toxic Elements in Baoshan Soils Employing Combined Receptor Models

Dong

Zhang

Yang

et al. 2023

Toxics

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Arable soils are crucial for national development and food security; therefore, contamination of agricultural soils from potentially toxic elements (PTEs) is a global concern. In this study, we collected 152 soil samples for evaluation. Considering the contamination factors and using the cumulative index and geostatistical methods, we investigated the contamination levels of PTEs in Baoshan City, China. Using principal component analysis, absolute principal component score-multivariate linear regression, positive matrix factorization, and UNMIX, we analyzed the sources and quantitatively estimated their contributions. The average Cd, As, Pb, Cu, and Zn concentrations were 0.28, 31.42, 47.59, 100.46, and 12.36 mg/kg, respectively. The Cd, Cu, and Zn concentrations exceeded the corresponding background values for Yunnan Province. The combined receptor models showed that natural and agricultural sources contributed primarily to Cd and Cu and As and Pb inputs, accounting for 35.23 and 7.67% pollution, respectively. Industrial and traffic sources contributed primarily to Pb and Zn inputs (47.12%). Anthropogenic activities and natural causes accounted for 64.76 and 35.23% of soil pollution, respectively. Industrial and traffic sources contributed 47.12% to pollution from anthropogenic activities. Accordingly, the control of industrial PTE pollution emissions should be strengthened, and awareness should be raised to protect arable land around roads.

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Source Apportionment of Agricultural Soil Heavy Metals Based on PMF Model and Multivariate Statistical Analysis

Cited by 24 publications

References 37 publications

Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

Characteristics of soil heavy metal pollution and health risk assessment in urban parks at megacity of central China

Quantitative Source Apportionment of Potentially Toxic Elements in Baoshan Soils Employing Combined Receptor Models

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