CQUniversity
Browse

File(s) not publicly available

Using magnetic susceptibility for predicting hydrocarbon pollution levels in a petroleum refinery compound in Isfahan Province, Iran

journal contribution
posted on 2020-02-03, 00:00 authored by S Ayoubi, MJ Samadi, H Khademi, M Shirvani, Yeboah Gyasi-AgyeiYeboah 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.

History

Volume

172

Start Page

1

End Page

8

Number of Pages

8

ISSN

0926-9851

Publisher

Elsevier, Netherlands

Peer Reviewed

  • Yes

Open Access

  • No

Acceptance Date

2019-11-09

External Author Affiliations

Isfahan University of Technology, Iran

Era Eligible

  • Yes

Journal

Journal of Applied Geophysics

Usage metrics

    CQUniversity

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC