Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques

Date

2017-11-09

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Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Department

Type

Article

ISSN

0048-9697

Format

Free to read from

Citation

RK Douglas, S Nawar, MC Alamar, et al., (2018) Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques. Science of The Total Environment, Volumes 616–617, March 2018, pp. 147-155

Abstract

Petroleum hydrocarbons contamination in soil is a worldwide significant environmental issue which has raised serious concerns for the environment and human health (Brevik and Burgess, 2013). Petroleum hydrocarbons encompass a mixture of short and long-chain hydrocarbon compounds. However the difference between the term petroleum hydrocarbons (PHC) as such and the term total petroleum hydrocarbons (TPH) should be noted. PHC typically refer to the hydrogen and carbon containing compounds that originate from crude oil, while TPH refer to the measurable amount of petroleum-based hydrocarbons in an environmental matrix and thus to the actual results obtained by sampling and chemical analysis (Coulon and Wu, 2017). TPH is thus a method-defined term. Among a range of techniques, gas chromatography is preferred for the measurement of hydrocarbon contamination in environmental samples, since it allows to detect a broad range of hydrocarbons and can provide both sensitivity and selectivity depending on the detector and hyphenated configuration used (Brassington et al., 2010; Drozdova et al., 2013). However, GC-based techniques can be time consuming and expensive and do not allowed rapid and broad scale analysis of petroleum contamination on-site (Okparanma and Mouazen, 2013; Okparanma et al., 2014).

Description

Software Description

Software Language

Github

Keywords

Total petroleum hydrocarbons, vis-NIR spectroscopy, Chemometric methods, Partial least squares regression, Random forest regression

DOI

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

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