An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils

Huang, Ying; Li, Tingqiang; Wu, Cheng-xian; He, Zhenli; Japenga, J.; Deng, Meihua; Yang, Xiaoe

doi:10.1016/j.jhazmat.2015.07.041

Cited by 269 publications

(62 citation statements)

References 38 publications

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“…Pb is an indicator element of traffic emission due to the utilization of engines and catalysts as well as the burning of fuels [46]. In addition, Huang et al [47] found that most Pb in the environment came from traffic…”

Section: Plos Onementioning

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

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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.

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Section: Plos Onementioning

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

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“…This makes scientists more convenient to identify and quantify the sources of lead in different environmental samples (e.g., atmospheric deposition (Gallon et al 2005), sediment (Dang et al 2015), and soil (Huang et al 2015)), as well as in organisms (Martinez-Haro et al 2011).…”

Section: Manuscript To Be Reviewedmentioning

confidence: 99%

Peer Review #2 of "Lead isotope trends and sources in the atmosphere at the artificial wetland (v0.1)"

2019

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“…In recent decades, heavy metal pollution of soils has become a globally recognized environmental issue 1, 3, 4 . To evaluate the potential risks to humans and the environment, there is a growing concern about the spatial distributions of soil heavy metals in the environment because an inaccurate estimation of soil heavy metal distributions will result in considerable bias in risk assessment 1 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China

Wang

Dai

et al. 2017

Sci Rep

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A suitable method and appropriate environmental variables are important for accurately predicting heavy metal distribution in soils. However, the classical methods (e.g., ordinary kriging (OK)) have a smoothing effect that results in a tendency to neglect local variability, and the commonly used environmental variables (e.g., terrain factors) are ineffective for improving predictions across plains. Here, variables were derived from the obvious factors affecting soil cadmium (Cd), such as road traffic, and were used as auxiliary variables for a combined method (HASM_RBFNN) that was developed using high accuracy surface modelling (HASM) and radial basis function neural network (RBFNN) model. This combined method was then used to predict soil Cd distribution in a typical area of Chengdu Plain in China, considering the spatial non-stationarity of the relationships between soil Cd and the derived variables based on 339 surface soil samples. The results showed that HASM_RBFNN had lower prediction errors than OK, regression kriging (RK) and HASM_RBFNNs, which didn’t consider the spatial non-stationarity of the soil Cd-derived variables relationships. Furthermore, HASM_RBFNN provided improved detail on local variations. The better performance suggested that the derived environmental variables were effective and HASM_RBFNN was appropriate for improving the prediction of soil Cd distribution across plains.

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An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils

Cited by 269 publications

References 38 publications

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

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

Peer Review #2 of "Lead isotope trends and sources in the atmosphere at the artificial wetland (v0.1)"

Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China

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