File(s) not publicly available
Using magnetic susceptibility for predicting hydrocarbon pollution levels in a petroleum refinery compound in Isfahan Province, Iran
journal contributionposted on 03.02.2020, 00:00 by S Ayoubi, MJ Samadi, H Khademi, M Shirvani, Yeboah Gyasi-Agyei
Nowadays through the world as well as in Iran, petroleum hydrocarbons have majority contribution in environmental ricks for human and other organisms. Therefore, this study evaluates hydrocarbon pollution effects on soil chemical properties, as well as soil magnetic susceptibility on a petroleum refinery compound in Isfahan province, Iran. It also examines the efficacy of using magnetic signatures to predict hydrocarbon pollution of soils. Two sites (polluted and unpolluted) with similar intrinsic soil properties and environmental attributes were selected. A total of 120 soil samples were collected at two depths of 0–10 and 10–30 cm. Laboratory analysis included measurement of electrical conductivity (EC), pH, aqua regial extractable Fe (FeA), dithionite extractable Fe (Fed), magnetic susceptibility at low frequency (χlf), total petroleum hydrocarbon (TPH), as well as powdery X-ray characterization. Polluted soils by petroleum hydrocarbons had significantly higher EC, χlf, FeA and Fed and lower pH values compared to the unpolluted soils. Positive significance (r = 0.88, p < .01) was obtained between TPH and magnetic susceptibility. Enhancement of magnetic susceptibility presumably attributed to formation of ferrimagnetic minerals such as magnetite because of degradation of hydrocarbon compounds. A multiple linear regression model was developed between magnetic susceptibility and TPH, and the results showed that the developed pedotransfer function could explain 86% of the variability of TPH in the studied area, and provided a reliable function to predict TPH in polluted soils, as a cost-effective and fast technique to be implemented in the polluted sites by petroleum hydrocarbons. It seems that inclusion of additional magnetic measures may improve the accuracy of the predictive model. © 2019 Elsevier B.V.