Soil risk assessment of heavy metal contamination near Oil Refinery area, Northeastern India

Reza, S. K.; Baruah, Utpal; Singh, Satnam

doi:10.5958/2230-732x.2014.01388.6

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…The results show that, even if metals are not the main concern related to soil contamination with petroleum products, they might contribute significantly to human health risk. Even if there is no certainty that heavy soil contamination is due to the crude oil contamination, the achieved results are in accordance with the results obtained by other authors that showed the importance of soil contamination with heavy metals by oil spills [55][56][57][58]. For instance, Asia et al [59] showed that exploration and production activities introduced a large amount of heavy metals into the soil and groundwater where such activities are carried out.…”

Section: Carcinogenic Risk Assessment For An Oil-contaminated Sitesupporting

confidence: 88%

Crude Oil Contaminated Sites: Evaluation by Using Risk Assessment Approach

2017

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Soils contaminated with toxic and persistent pollutants pose different and serious hazards to the environment and human health. Multidisciplinary procedures that are considering models for the prediction of risk for long-term exposure are needed. The present paper illustrates the operating mode and utility of an environmental software able toe0) integrates the dose-response modeling as a major part of hazard characterization in order to assess the quantitative risk from carcinogens. In this way, risk assessment is used as a method for the investigation of contaminated sites. The method is demonstrated on a Romanian field site, contaminated with petroleum products. For the assessment of human health risks from oil-contaminated sites, the contaminants of concern are considered as polycyclic aromatic hydrocarbons (PAHs), monocyclic aromatic hydrocarbons (MAHs), and heavy metals, chemicals known as human carcinogens. Quantitative risk calculations revealed an individual risk of 1.07 × 10 −5 for children and 6.89 × 10 −6 for adults. The paper represents a utility example of an environmental software solution that could be considered by decision-making factors in approving certain projects. The software tool is helpful for protecting the environment and human health.

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Section: Carcinogenic Risk Assessment For An Oil-contaminated Sitesupporting

confidence: 88%

Crude Oil Contaminated Sites: Evaluation by Using Risk Assessment Approach

2017

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“…Geostatistics is extensively used to assess the level of soil contamination and estimation of risk in contaminated sites by preserving the spatial distribution and uncertainty of the estimates. In addition, geostatistics and GIS provide useful tools for the study of spatial uncertainty and hazard assessment (McGrath et al 2004;Komnitsas and Modis 2006;Reza et al 2013Reza et al , 2014Reza et al , 2015b.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of Fe and Mn in paper mill effluent affected agricultural soils in Morigaon, Assam

Reza¹,

Baruah²,

Singh³

2019

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The spatial distribution of heavy metals (Fe and Mn) in the paper mill contaminated area of Jagiroad, Assam, India were investigated using statistics, geostatistics and GIS techniques. The total concentration of Fe and Mn were determined for 188 samples collected from the contaminated area. The mean concentration of Fe (7629 mg kg1) was high. The highest and the lowest standard deviation were observed in the Fe (1749) and pH (0.81), respectively. Analysis of the isotropic variogram indicated that the Fe semivariogram was well described with the Gaussian model, with the distance of spatial dependence being 1354 m, while Mn was well described with the spherical model, with the distance of spatial dependence being 833 m. The ordinary kriging estimates of Fe and Mn maps showed that high concentrations of these metals occured in the low-lying areas like bils (lakes). For both the investigated heavy metals the prediction of goodness (G) value was greater than zero. This indicates that spatial prediction is better than assuming mean of observed value as the property value for any unsampled location. Thus the geostatistical method was spatial variability of Fe and Mn.

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