Heavy metals in European soils: A geostatistical analysis of the FOREGS Geochemical database

Rodríguez‐Lado, Luis; Hengl, Tomislav; Reuter, H. I.

doi:10.1016/j.geoderma.2008.09.020

Cited by 289 publications

(150 citation statements)

References 36 publications

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Section: General Statistics Of Toxic Metals Datamentioning

confidence: 99%

“…These extreme values as outliers and these sampling points most likely represent hotspots. We decided to exclude those samples because the extreme values or the local hotspots might influence the prediction based on the regression modeling [16]. The significant variability in toxic metal content is related to the different intensity levels of agriculture, urbanization and industrialization in the different areas of Qatar.…”

Section: General Statistics Of Toxic Metals Datamentioning

confidence: 99%

“…For human and ecological risk assessment, the prediction of toxic metals accumulation in soils has been determined using a variety of geostatistical methods and environmental data at various scales [15][16][17][18][19][20][21][22]. Lado et al [16] used regression-kriging with a large number of auxiliary variables (1-km resolution) as predictors (e.g., land cover, geology, night-lights images) to model and map the distribution of eight toxic metals (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) for the topsoil in Europe.…”

Section: Introductionmentioning

confidence: 99%

“…Lado et al [16] used regression-kriging with a large number of auxiliary variables (1-km resolution) as predictors (e.g., land cover, geology, night-lights images) to model and map the distribution of eight toxic metals (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) for the topsoil in Europe. The cross-validation results showed that the overall accuracy of these maps was relatively low.…”

Section: Introductionmentioning

confidence: 99%

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Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

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After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0-30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R 2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R 2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals' monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.

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Section: General Statistics Of Toxic Metals Datamentioning

confidence: 99%

Section: General Statistics Of Toxic Metals Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

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“…A combination of multivariate statistics and geostatistical analysis is an advanced method for identifying pollution characteristics of heavy metals in soils and distinguishing their natural sources and anthropogenic inputs. This approach generally consists in performing principal component analysis (PCA) and mapping the scores with geostatistical tools (Rodríguez et al, 2006;López et al, 2008;Lado et al, 2008). Furthermore, spatially constrained multivariate analysis methods (MULTISPATI-PCA) were developed to consider the relationship among variables and their spatial structure together (Dray et al, 2008;Saby et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China

Wang

Qin

et al. 2012

Science of The Total Environment

432

121

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An extensive survey was conducted in this study to determine the spatial distribution and possible sources of heavy metals in the agricultural soils in Shunyi, a representative agricultural suburb in Beijing, China. A total of 412 surface soil samples were collected at a density of one sample per km 2 , and concentrations of As, Cd, Cu, Hg, Pb and Zn were analyzed. The mean values of the heavy metals were 7.85± 2.13, 0.136 ±0.061, 22.4 ±6.31, 0.073 ±0.049, 20.4 ±5.2, and 69.8 ±16.5 mg kg − 1 for As, Cd, Cu, Hg, Pb, and Zn, respectively, slightly higher than their background values of Beijing topsoil with the exception of Pb, but lower than the guideline values of Chinese Environmental Quality Standard for Soils. Multivariate and geostatistical analyses suggested that soil contamination of Cd, Cu and Zn was mainly derived from agricultural practices. Whereas, As and Pb were due mainly to soil parent materials, and Hg was caused by the atmospheric deposits from Beijing City. The identification of heavy metal sources in agricultural soils is a basis for undertaking appropriate action to protect soil quality.

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Geochemical Mapping of Ljubljana Urban and Suburban Area, Slovenia

Šajn¹,

Gosar²,

Bidovec³

et al. 2011

Mapping the Chemical Environment of Urban Areas

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Heavy metals in European soils: A geostatistical analysis of the FOREGS Geochemical database

Cited by 289 publications

References 36 publications

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China

Geochemical Mapping of Ljubljana Urban and Suburban Area, Slovenia

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