Horizontal and vertical variability of soil properties in a trace element contaminated area

Burgos, Pilar; Madejón, Engracia; Pérez‐de‐Mora, Alfredo; Cabrera, Francisco

doi:10.1016/j.jag.2007.04.001

Cited by 36 publications

(25 citation statements)

References 23 publications

(25 reference statements)

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“…Several topics were addressed: i) the sources of the elements [41] and the properties influencing their concentrations [56][57][58][59][60][61]; ii) the seasonal variations [62]; iii) the identification of areas with different contamination levels and consequently different risks for plant growth or human health [47,57,[63][64][65]; iv) the effect of different data processing procedures [66,67]; v) the relationship between Cd content in durum wheat grain and in soil [68]; vi) the effect of gypsum addition and water management (flooded vs. non-flooded) on the yield of marsh vegetation [69]; vii) the identification of deficiency of micronutrients [56].…”

Section: Geostatisticsmentioning

confidence: 99%

The role of chemometrics in single and sequential extraction assays: A Review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques

Giacomino

Abollino

Malandrino

et al. 2011

Analytica Chimica Acta

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Section: Geostatisticsmentioning

confidence: 99%

The role of chemometrics in single and sequential extraction assays: A Review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques

Giacomino

Abollino

Malandrino

et al. 2011

Analytica Chimica Acta

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“…Many researchers also found the similar results Results are presented as arithemic mean ± SD. Mean ± SD within a row for a certain land use type followed by different letters are significantly different at the 0.05 level n Number of samples (Burgos et al 2008;Krishna and Govil 2004;Romic and Romic 2003;Svendsen et al 2007). Moreover, there existed significant correlation between the Zn, Cu, Pb, Cd, Hg and As concentrations and soil organic matter (Table 4), which meant organic manures, such as pig and poultry litter, urban wastes, sewage sludge, etc., may be one of the main sources of these heavy metal pollutants, and organic matter helped to retain metals that entered the soils because of high affinity between metals and organic matter (Alloway 1995;Montagne et al 2007).…”

Section: Heavy Metal Concentrations In Soilsmentioning

confidence: 99%

“…The spatial distribution of metal concentrations is a useful visual aid to assess the possible sources of enrichment and to identify hot-spot area with high metal concentration (Burgos et al 2008;Carlon et al 2001;Ozan et al 2008;Romic and Romic 2003;Šichorová et al 2004). The spatial distribution of Zn, Cu, Pb, Cd, Hg and As in soils was analysed using GIS methods.…”

Section: Spatial Distribution Of Heavy Metals In Topsoilsmentioning

confidence: 99%

“…Usually the evidence for attributing the origin of soil pollution to a particular emitter is indirect and based on spatial gradients of decreasing pollutant concentrations with distance from the suspected source (Martley et al 2004;McMartin et al 2002). Moreover, vertical distribution of element concentrations with depth is a means of identifying geogenic and anthropogenic sources to some extent (Burgos et al 2008;Schulin et al 2007;Šichorová et al 2004;Sterckeman et al 2000).…”

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confidence: 99%

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Distribution of heavy metals in agricultural soils near a petrochemical complex in Guangzhou, China

Lü

Yin

et al. 2008

Environ Monit Assess

115

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The aim of the study was to investigate influence of an industrialized environment on the accumulation of heavy metals in agricultural soils. Seventy soil samples collected from surface layers (0-20 cm) and horizons of five selected pedons in the vicinity area of petrochemical complex in Guangzhou, China were analyzed for Zn, Cu, Pb, Cd, Hg and As concentrations, the horizontal and vertical variation of these metals were studied and geographic information system (GIS)-based mapping techniques were applied to generate spatial distribution maps. The mean concentrations of these heavy metals in the topsoils did not exceed the maximum allowable concentrations in agricultural soil of China with the exception of Hg. Significant differences between land-use types showed that Cu, Pb, Cd, Hg and As concentrations in topsoils were strongly influenced by agricultural practices and soil management. Within a radius of 1,300 m there were no marked decreasing trends for these element concentrations (except for Zn) with the increase of distance from the complex boundary, which reflected little influence of petroleum air emission on soil heavy metal accumulation. Concentrations of Zn, Cu, Pb, Cd, Hg and As in the five pedons, particularly in cultivated vegetable field and orchard, decreased with soil depth, indicating these elements mainly originated from anthropogenic sources. GIS mapping was a useful tool for evaluating spatial variability of heavy metals in the affected soil. The spatial distribution maps allowed the identification of hot-spot areas with high metal concentration. Effective measures should be taken to avoid or minimize heavy metal further contamination of soils and to remediate the contaminated areas in order to prevent pollutants affecting human health through agricultural products.

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“…With the rapid growth of computer technology and new statistical methods of analysis, such as geostatistics (Burgos et al 2008), geographical information systems (GIS) are becoming one of the most important tools for studying environmental geochemistry problems (Zhang and Selinus 1998). Although the distribution, chemistry and elemental interaction of heavy metals in soils have been largely studied (Udeigwe et al 2015;Jørgensen and Jensen 2009;Eze et al 2010), there is still the need for sitespecific study of elemental interactions in soils in view of the fact that soils under different climates and environmental conditions would behave differently.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

2018

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With a mining-driven economy, Botswana has experienced increased geochemical exploration of minerals around existing mining towns. The mining and smelting of copper and nickel around Selibe-Phikwe in the Central Province are capable of releasing heavy metals including Pb, Fe, Mn, Co, Ni and Cu into the soil environments, thereby exposing humans, plants and animals to health risks. In this study, turning bands co-simulation, a multivariate geostatistical algorithm, was presented as a tool for spatial uncertainty quantification and probability mapping of cross-correlated heavy metals (Co, Mn, Fe and Pb) risk assessment in a semiarid Cu-Ni exploration field of Botswana. A total of 1050 soil samples were collected across the field at a depth of $ 10 cm in a grid sampling design. Rapid elemental concentration analysis was done using an Olympus Delta Sigma portable X-ray fluorescence device. Enrichment factor, geoaccumulation index and pollution load index were used to assess the potential risk of heavy metals contamination in soils. The partially heterotopic nature of the dataset and strong correlations among the heavy metals favors the use of co-simulation instead of independent simulation in the probability mapping of heavy metal risks in the study area. The strong correlation of Co and Mn to iron infers they are of lithogenic origin, unlike Pb which had weak correlation pointing to its source in the area being of anthropogenic source. Manganese, Co and Fe show low enrichment, whereas Pb had high enrichment suggesting possible lead pollution. We, however, recommend that speciation of Pb in the soils rather than total concentration should be ascertained to infer chances of possible bioaccumulation, and subsequent health risk to human by chronic exposure.

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Horizontal and vertical variability of soil properties in a trace element contaminated area

Cited by 36 publications

References 23 publications

The role of chemometrics in single and sequential extraction assays: A Review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques

The role of chemometrics in single and sequential extraction assays: A Review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques

Distribution of heavy metals in agricultural soils near a petrochemical complex in Guangzhou, China

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

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