Spatial, sources and risk assessment of heavy metal contamination of urban soils in typical regions of Shenyang, China

Sun, Yuebing; Zhou, Qixing; Xie, Xuetong; Li, Rui

doi:10.1016/j.jhazmat.2009.09.074

Cited by 618 publications

(293 citation statements)

References 31 publications

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“…This indicates that soil toxic metals' contamination with these three elements is highly affected by human activities. These results are much in line with previous studies [16,[62][63][64].…”

Section: Crsupporting

confidence: 93%

“…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. Hotspots of Pb, Zn and Cu were, for example, found around the Doha City area (Figure 1), indicating that the soil contamination of these three elements was most likely affected by human activities [16,[62][63][64]. Hotspots of Cr and Ni soils were found in the same three locations, and all located in the northern part of Qatar (Figure 1), where the parent material is partly silty alluvium and land use is dominated by irrigated agriculture.…”

Section: General Statistics Of Toxic Metals Datamentioning

confidence: 95%

“…Hotspots of Cr and Ni soils were found in the same three locations, and all located in the northern part of Qatar (Figure 1), where the parent material is partly silty alluvium and land use is dominated by irrigated agriculture. This suggests that the Cr and Ni elements in soils can be linked to various processes of soil formation, including human interference [16,[62][63][64]. This variability in distribution affects the development of correlations between environmental parameters (natural and anthropogenic) and toxic metal pollution.…”

Section: General Statistics Of Toxic Metals Datamentioning

confidence: 99%

“…Many studies have reported accumulations of toxic metals (Zn, Pb and Cu) in urban soils, mainly derived from vehicle emissions and industrial consumption [16,[62][63][64]75]. Higher accumulations of these three elements have also been observed closer to highways, mainly from aerial deposition of metal particulates from motor vehicles.…”

Section: Predicted Maps Of Toxic Metals In Soilmentioning

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: Crsupporting

confidence: 93%

Section: General Statistics Of Toxic Metals Datamentioning

confidence: 95%

Section: General Statistics Of Toxic Metals Datamentioning

confidence: 99%

Section: Predicted Maps Of Toxic Metals In Soilmentioning

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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show abstract

“…Many indices and calculation methods, such as geoaccumulation index (Igeo), pollution index (PI), and integrated pollution index (IPI) have been proposed for quantifying the degree of metal enrichment or pollution in soils, sediments (Faiz et al 2009;Chen et al 2005;Sun et al 2010;Wei and Yang 2011;Lu et al 2009;Abanuz 2011).…”

Section: Soil Pollution Indicesmentioning

confidence: 99%

Heavy metals pollution in the soils of suburban areas in big cities: a case study

Taghipour

Mosaferi

Armanfar³

et al. 2013

Int. J. Environ. Sci. Technol.

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Soil pollution in agricultural areas surrounding big cities is a major environmental problem. Tabriz is the largest city in the northwest of Iran and the fourth largest city in the country. Soil samples were taken from 46 sites in the suburbs of the Tabriz city, and separate samples were taken from control site and analyzed. The results indicated that the mean pH value of the soil samples was 9.29, while the mean EC value was 354.33 ls/cm and the amount of TOC and TOM was 0.99 and 1.7 %, respectively. The mean concentrations of Cd, Pb, Cu, Cr, Ni, and Zn in the soil were determined to be 1. 61, 10.56, 101.25, 87.40, 38.73, and 98.27 mg/kg, respectively (dry weight). The concentrations of heavy metals (Cd, Pb, Cu, Cr, and Zn), with the exception of Ni, were higher than the concentrations of the same heavy metals at the control site. Despite these elevated concentrations, the concentrations of heavy metals were lower than the toxicity threshold limit of agricultural soils. The values of the pollution index revealed that the metal pollution level was Pb [ Cr [ Cu [ Zn [ Cd [ Ni, and the mean value of the integrated pollution index was determined to be 1.81, indicating moderate pollution. Nevertheless, there were some sites that were severely polluted by Cr (maximum values of 1,364 mg/kg). It was concluded that city probably has affected the surrounding agricultural area. Application of wastewater (municipal and industrial) as irrigation water, using of sludge as soil fertilizer, and atmospheric perceptions have been considered as main reasons of increased heavy metals concentrations found in the studied area.

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Development of an integrated modeling approach for identifying multilevel non-point-source priority management areas at the watershed scale

et al. 2014

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The identification of priority management areas (PMAs) at the large-basin scale is notably complex because of the random nature of watershed processes, which complicates the application of traditional deterministic PMAs. In this study, a multilevel PMA (ML-PMA) framework is designed as a new tool to pinpoint these sensitive areas, within a basin, that contribute the most to water quality deterioration. The main advantage of the ML-PMA framework is the wide availability of its supplementary tools and its complete framework, which integrates both watershed and river processes in addressing PMAs at the watershed scale. The watershed model, stream model, and a Markov chain approach are integrated to depict the dynamics of watershed processes and various water quality statutes. Based on the results of this study, the river migration process is vital for water quality degradation in the river network and significantly influenced the final PMA map. In addition, the proposed ML-PMA framework considers the impact of climatic conditions and hydrological properties and allows for a more cost-effective allocation of PMAs among different years. In the authors' view, the connectivity of PMAs in terms of flux distribution and propagation downstream on which the ML-PMA is based makes the ML-PMA framework particularly interesting for watershed non-point-source pollution control.

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Spatial, sources and risk assessment of heavy metal contamination of urban soils in typical regions of Shenyang, China

Cited by 618 publications

References 31 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

Heavy metals pollution in the soils of suburban areas in big cities: a case study

Development of an integrated modeling approach for identifying multilevel non-point-source priority management areas at the watershed scale

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