Source apportionment of heavy metals in agricultural soil based on PMF: A case study in Hexi Corridor, northwest China

Guan, Qingyu; Wang, Feifei; Xu, Cailing; Pan, Ninghui; Lin, Junqi; Zhao, Rui; Yang, Yanyan; Luo, Haiping

doi:10.1016/j.chemosphere.2017.10.151

Cited by 441 publications

(152 citation statements)

References 73 publications

Supporting

Mentioning

148

Contrasting

Unclassified

Order By: Relevance

“…Higher metal levels in the ecosystem and human beings could pose potential ecological and health risks, including the inhibition of growth and development, physiological disorders, and neurotoxicity that is related to ecological receptors and human beings (Iwegbue & Martincigh, 2018;. Combined with the sampling sites, the accumulation of As (metalloid) in soil might be attributed to the application of herbicides/pesticides, preservations, and arseniccontaining wastes (Guan et al, 2018;Xia et al, 2011). Its levels exceeded around twice (inside > outside) as much as the background values, and 0.5-0.8 times (inside) and 0-0.4 times (outside) as much as the screening values, but were lower than the intervention values.…”

Section: Characteristics Of Metal Pollutionmentioning

confidence: 99%

“…The higher concentrations inside of the campus were mainly distributed in the active, commercial, and greening areas in the northern area of the campus. Combined with the sampling sites, the accumulation of As (metalloid) in soil might be attributed to the application of herbicides/pesticides, preservations, and arseniccontaining wastes (Guan et al, 2018;Xia et al, 2011). Outside of the campus, As (metalloid) and Cd (heavy metal) were attributed to traffic and industrial sources.…”

Section: Characteristics Of Metal Pollutionmentioning

confidence: 99%

See 1 more Smart Citation

Metal status in soils within a developing education park: Potential risk of land development

Teng

2019

Land Degrad Dev

View full text Add to dashboard Cite

Many countries have developed university/college towns with specific characteristics (temporal, spatial, structural, and functional aspects). During the land development, there were obvious changes in soil metal levels and distribution patterns attributed to anthropogenic activities, thus having induced comparable results in university/college towns due to similar anthropogenic activities. Until now, specific studies on the metal status in university/college towns, especially the variation inside and outside of a campus were rarely involved. Herin we report on 109 soil samples around a new campus within a developing education park, in which the levels, spatial distributions, and possible sources of (alkali, alkaline-earth, transition, and main group) metals and metalloids (Cd, Cr, As, Pb, Zn, Cu, Mn, Ba, Ni, Ti, Be, Bi, Sb, Sn, Co, V, and Zr) were systematically investigated. The Nemerow pollution indexes, spatial analysis, principal components analysis, and cluster analysis were calculated and analyzed. The results showed that the level of most metals within the campus with different functional areas (college, activity, and greening areas) was 1-1.3-times as much as that outside in four directions and had greater variations. Moreover, the level of most metals was 1-1.5-times as much as their background value and they distributed in the areas under specific developments and might mainly be attributed to anthropogenic sources. These findings provide the knowledge about effects of land development and anthropogenic activities on soil metal status that could be used by university/college towns in the similar context around the world. KEYWORDS education park, land development, metal variation, university/college town

show abstract

Section: Characteristics Of Metal Pollutionmentioning

confidence: 99%

Section: Characteristics Of Metal Pollutionmentioning

confidence: 99%

Metal status in soils within a developing education park: Potential risk of land development

Teng

2019

Land Degrad Dev

View full text Add to dashboard Cite

show abstract

“…The accumulation of heavy metals in soils not only leads to a decline in the production and quality of agricultural yield but also poses a serious threat to human health through the food chain, as their detrimental impact appears after several years of exposure [1]. Thus, heavy metal pollution directly influences the quality and safety of agriculture products by affecting the soil environmental quality and safety [2][3], which is not only key to the sustainable development of farmland resources and land conservation but also the basis of national food security.…”

Section: Introductionmentioning

confidence: 99%

“…The short non-negative constraint is another remarkable downside of the PCA, APCS and CMB methods [20]. Many studies were carried out using the PMF model and valuable results were obtained [1], [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Ecological risk assessment and source identification of heavy metal pollution in vegetable bases of Urumqi, China, using the positive matrix factorization (PMF) method

et al. 2020

View full text Add to dashboard Cite

Heavy metal pollution is a widespread problem and strongly affects human health through the food chain. In this study, the overall pollution situation and source apportionment of heavy metals in soil (Hg, Cd, As, Pb, Ni, Zn, Cu and Cr) were evaluated using various methods including geo-accumulation index (I geo), potential ecological risk index (RI) and positive matrix factorization combined with Geographical Information System (GIS) to quantify and identify the possible sources to these heavy metals in soils. The results of I geo showed that this farmland top soil moderate contaminated by Hg, other selected elements with noncontamination level. And the average RI in the top soil was 259.89, indicating a moderate ecological risk, of which Hg and Cd attributed 88.87% of the RI. The results of the PMF model showed that the relative contributions of heavy metals due to atmospheric depositions (18.70%), sewage irrigations (21.17%), soil parent materials (19.11%), industrial and residential coal combustions (17.43%) and agricultural and lithogenic sources (23.59%), respectively. Of these elements, Pb and Cd were came from atmospheric deposition. Cr was attributed to sewage irrigations. As was mainly derived from the soil parent materials. Hg originated from industrial and residential coal combustions, and most of the Cu, Zn and Ni, except for Pb, were predominantly derived from agricultural and lithogenic sources. These results are important in considering management plans to control the aggravation of heavy metal pollution and ultimately to protect soil resources in this region. In addition, this study enhances the understanding of heavy metal contamination occurrence in agroecosystem that helps predicting and limiting the potential of heavy metal exposure to people and ecosystem.

show abstract

“…The toxicity and carcinogenicity of Sb have also been confirmed [7]. The enrichment of these harmful metals in soil changes the soil's physical and chemical properties, affects plant growth, and threatens people's health through different means [8][9][10], such as Pb exposure, which will adversely affect fetal neurodevelopment [11] and continue during the course of life [12], while affecting several key human organ systems, including the cardiovascular system [13,14], renal system [15,16], and hepatic system [17,18].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

et al. 2019

View full text Add to dashboard Cite

The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12–64%. The prediction ability of the model of the four metals is Cr > Co > Sb > Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.

show abstract

Source apportionment of heavy metals in agricultural soil based on PMF: A case study in Hexi Corridor, northwest China

Cited by 441 publications

References 73 publications

Metal status in soils within a developing education park: Potential risk of land development

Metal status in soils within a developing education park: Potential risk of land development

Ecological risk assessment and source identification of heavy metal pollution in vegetable bases of Urumqi, China, using the positive matrix factorization (PMF) method

Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

Contact Info

Product

Resources

About