Source Apportionment and Geostatistics: An Outstanding Combination for Describing Metals Distribution in Soil

Schaefer, Kristin; Einax, Jürgen W.

doi:10.1002/clen.201400459

Cited by 22 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PMF has been widely applied to source apportionment of pollutants in the atmosphere [ 58 ], water [ 59 ], and sediments [ 60 ]. There are also some cases in which source apportionment was successfully used to determine the concentrations of heavy metals in soils via PMF [ 11 , 61 ], even though this failed to successfully apportion the contributions from three sources in the northwest corner of Zhejiang Province, China. This was attributed to the high skewness or outliers in the soil contamination data and the influence of the distance from each contributing source [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

Source Identification of Heavy Metals in Surface Paddy Soils Using Accumulated Elemental Ratios Coupled with MLR

Chen

Weng

et al. 2021

IJERPH

View full text Add to dashboard Cite

Source identification of heavy metals in agricultural soils using small sample sizes, simple experimental procedures, and convenient analysis is urgently required. This study employed a simple source identification model using a visual comparison via radar plots, cluster analysis, principal component analysis, and a multiple linear regression model to determine the source of heavy metal pollution in soil samples from the Chang-Zhu-Tan urban agglomeration area of China. The elemental compositions of major pollution sources (atmospheric deposition, organic fertilizer, irrigation water, and tailings) were compared with soil samples from 11 study locations and the model was used to determine the relative contribution of different pollution sources at each sample site. The results showed that the model successfully calculated the contribution of different pollution sources at each site based on the pollution characteristics and contaminant transport rules of the region. The proposed method overcomes the requirement for extensive data and complex experimental procedures. Furthermore, the model can determine the source of heavy metal contamination in single or small plots, which is important for the prevention and control of heavy metal soil pollution and remediation at the plot scale.

show abstract

Section: Resultsmentioning

confidence: 99%

Source Identification of Heavy Metals in Surface Paddy Soils Using Accumulated Elemental Ratios Coupled with MLR

Chen

Weng

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Although most literature suggests that Fe can be found virtually everywhere, its excesses in higher levels in soil and the environment may be traceable to a point source (e.g., iron and steel industries). Reitner and Thiel 65 , Alloway 57 and Schafer and Einax 68 hinted that the increased Fe concentrations in the environment and soil might be due to nearby industrial point sources producing iron-based substances such as iron and steel production, machine making, cast iron, wrought iron, and alloy as a significant source of Fe pollution in the environment. This correlates to the current situation in the study area, as evidenced by the presence of metal and steel industries.…”

Section: Resultsmentioning

confidence: 99%

A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example

Agyeman

John

Kebonye

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Unhealthy soils in peri-urban and urban areas expose individuals to potentially toxic elements (PTEs), which have a significant influence on the health of children and adults. Hundred and fifteen (n = 115) soil samples were collected from the district of Frydek Mistek at a depth of 0–20 cm and measured for PTEs content using Inductively coupled plasma—optical emission spectroscopy. The Pearson correlation matrix of the eleven relevant cross-correlations suggested that the interaction between the metal(loids) ranged from moderate (0.541) correlation to high correlation (0.91). PTEs sources were calculated using parent receptor model positive matrix factorization (PMF) and hybridized geostatistical based receptor model such as ordinary kriging-positive matrix factorization (OK-PMF) and empirical Bayesian kriging-positive matrix factorization (EBK-PMF). Based on the source apportionment, geogenic, vehicular traffic, phosphate fertilizer, steel industry, atmospheric deposits, metal works, and waste disposal are the primary sources that contribute to soil pollution in peri-urban and urban areas. The receptor models employed in the study complemented each other. Comparatively, OK-PMF identified more PTEs in the factor loadings than EBK-PMF and PMF. The receptor models performance via support vector machine regression (SVMR) and multiple linear regression (MLR) using root mean square error (RMSE), R square (R2) and mean square error (MAE) suggested that EBK-PMF was optimal. The hybridized receptor model increased prediction efficiency and reduced error significantly. EBK-PMF is a robust receptor model that can assess environmental risks and controls to mitigate ecological performance.

show abstract

“…The information obtained can be used to develop suggestions for the prevention and control of pollution around polluted industrial land. Several studies have been performed on source apportionment and geostatistics [ 34 , 35 , 36 ], focusing on clarifying the sources. However, this study emphasizes the characteristics of the soil pollution around industries of concern by extracting certain factors via PMF and estimating the spatial distributions using CK with the homology of the elements.…”

Section: Discussionmentioning

confidence: 99%

Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China

Chen

Lei

Zhang

et al. 2022

IJERPH

View full text Add to dashboard Cite

Soil heavy metal pollution is frequent around areas with a high concentration of heavy industry enterprises. The integration of geostatistical and chemometric methods has been used to identify sources and the spatial patterns of soil heavy metals. Taking a county in southwestern China as an example, two subregions were analyzed. Subregion R1 mainly contained nonferrous mining, and subregion R2 was affected by smelting. Two factors (R1F1 and R1F2) associated with industry in R1 were extracted through positive matrix factorization (PMF) to obtain contributions to the soil As (64.62%), Cd (77.77%), Cu (53.10%), Pb (75.76%), Zn (59.59%), and Sb (32.66%); two factors (R2F1 and R2F2) also related to industry in R2 were extracted to obtain contributions to the As (53.35%), Cd (32.99%), Cu (53.10%), Pb (56.08%), Zn (67.61%), and Sb (42.79%). Combined with PMF results, cokriging (CK) was applied, and the z-score and root-mean square error were reduced by 11.04% on average due to the homology of heavy metals. Furthermore, a prevention distance of approximately 1800 m for the industries of concern was proposed based on locally weighted regression (LWR). It is concluded that it is necessary to define subregions for apportionment in area with different industries, and CK and LWR analyses could be used to analyze prevention distance.

show abstract

Source Apportionment and Geostatistics: An Outstanding Combination for Describing Metals Distribution in Soil

Cited by 22 publications

References 27 publications

Source Identification of Heavy Metals in Surface Paddy Soils Using Accumulated Elemental Ratios Coupled with MLR

Source Identification of Heavy Metals in Surface Paddy Soils Using Accumulated Elemental Ratios Coupled with MLR

A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example

Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China

Contact Info

Product

Resources

About