Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale

Wang, Qi; Xie, Zhenghui; Li, Fangbai

doi:10.1016/j.envpol.2015.06.040

Cited by 133 publications

(38 citation statements)

References 40 publications

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“…pC2 includes Cd and pb with opposite loadings, which can be identified as a tracer of anthropogenic pollution sources for Cd and pb. it is documented that the essential origins of Cd and pb were anthropogenic counting industrial and mining activities (Wang et al, 2015;Zhou et al, 2015;Wen et al, 2015), which was comparable with our conclusions. pCa results are consistent with correlations matrices pointing out that most of metal variability in ait ammar soils can be explained by entirely natural inputs by parent materials.…”

Section: Determining Geochemical Relations Between Metals and Phosphosupporting

confidence: 93%

Assessment of metals contamination and ecological risk in ait Ammar abandoned iron mine soil, Morocco

Nouri¹,

Haddioui²

2016

Ekológia (Bratislava)

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The present study is an attempt to assess the pollution intensity and corresponding ecological risk of phosphorus and metals including Cd, Cr, Cu, Zn, Pb and Fe using various indices like geo-accumulation index, enrichment factor, pollution and ecological risk index. In all, 20 surface soil samples were collected from the Ait Ammar iron mine of Oued Zem city, province of Khouribga, in central Morocco. The concentrations of heavy metals in soil samples were used to assess their potential ecological risks. According to the results of potential ecological risk index (RI), pollution index (PI), geo-accumulation index (Igeo), enrichment factor (EF), potential contamination index (Cp), contaminant factor (Cf) and degree of contamination (Cd), based on the averages, considerable pollution of metals in soils of study area was observed. The consequence of the correlation matrix and principal component analysis (PCA) indicated that Fe, Cu, Zn, Cr and P mainly originated from natural sources and Cd and Pb are mostly derived from anthropogenic sources. The results showed that these metals in soil were ranked by severity of ecological risk as Pb > Cd > Cu > Cr > Zn, based on their single-element indexes. In view of the potential ecological risk (RI), soils from all soil samples showed a potential ecological risk. These results will provide basic information for the improvement of soil environment management and heavy metal pollution prevention in Ait Ammar.

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Section: Determining Geochemical Relations Between Metals and Phosphosupporting

confidence: 93%

Assessment of metals contamination and ecological risk in ait Ammar abandoned iron mine soil, Morocco

Nouri¹,

Haddioui²

2016

Ekológia (Bratislava)

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“…y de diferentes vías, como el desgaste del material parental, el vertido de aguas residuales sin tratamiento, la aplicación de biosólidos como abono, el uso de fertilizantes inorgánicos y plaguicidas, el depósito atmosférico, etc. (Wang et al, 2015). Los asentamientos humanos cercanos al área de estudio no cuentan con un relleno sanitario, por lo tanto, los residuos domiciliarios son depositados en usados para controlar las enfermedades de la caña de azúcar son elaborados a base de compuestos orgánicos de Hg (Yadav et al, 2010).…”

Section: Concentraciones De Mp En Suelos Y Sedimentosunclassified

Metales pesados en suelos y sedimentos de la zona cañera del sur de Quintana Roo, México

et al. 2017

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In order to determine the heavy metals content and distribution in an area cultivated with sugarcane in the Río INTRODUCCIÓNMéxico se encuentra dividido en 37 regiones hidrológicas que a su vez se agrupan en 13 regiones hidrológico-administrativas. Las aguas del estado de Quintana Roo se ubican en la región hidrológica-Metales pesados en suelos y sedimentos de la zona cañera del sur de Quintana Roo, México

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“…The elevated spatial diverseness of heavy metals in soils, the wide arrayness of pollution causes and improper long-term monitoring information are the issues for scientists to evaluate the multi-source pollution of heavy metals from agricultural soils on a local level; searching appropriate approaches to tackle this issue is important. For solving all these issues, modeling approaches serve as important techniques in investigation and apportionment of heavy metal source [18]. These techniques required a minute sample size, and consequently time and work are cost-efficient [19].…”

Section: Introductionmentioning

confidence: 99%

Comparison of different approaches for modeling of heavy metal estimations

2019

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Soil contamination by heavy metals is very important for environmental scientists due to the greater mobilization of metals and the possibility of contamination of groundwater. One of the most effective tools for evaluating the risk is the combination of experimentation with computer modeling. Modeling techniques are important in assessing the potential risks associated with heavy metals in the environment. Determination of models that can precisely evaluate the heavy metals in soils is an important need of agricultural researches, which could eradicate the weaknesses in the measurement of heavy metals in soils. The purpose of the present study is to test and compare different models according to their suitability for describing the estimation of heavy metals. The models used in this study were multilayer perceptron neural network (MLP), M5 model tree (M5) and bagging approach (BM5P). The data from 164 sampling sites from Neyshabur and Mashhad plains were taken in this study. The inputs combination according to feature selection-based correlation was used to feed the models. To model soil heavy metals, soil attributes, namely sand, silt, clay (as texture fractions), organic carbon, pH and available phosphorus, were entered in some models. To evaluate the performance of various techniques used in this study, several statistical indexes, including the correlation coefficient, root-mean-square error, Nash-Sutcliffe coefficient, Willmott's index (d) and mean absolute error, were assessed. Comparison of different models for Fe, Cu, Mn and Zn indicated that MLP is the most suitable method for estimations of Fe and Mn, whereas BM5P and M5P are the most suitable models for determinations of Cu and Zn, respectively. This study concluded that machine learning models can be successfully applied to the rapid prediction of soil heavy metals using soil variables.

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Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale

Cited by 133 publications

References 40 publications

Assessment of metals contamination and ecological risk in ait Ammar abandoned iron mine soil, Morocco

Assessment of metals contamination and ecological risk in ait Ammar abandoned iron mine soil, Morocco

Metales pesados en suelos y sedimentos de la zona cañera del sur de Quintana Roo, México

Comparison of different approaches for modeling of heavy metal estimations

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