Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory‐constructed samples
| Autor: | Nuwan K. Wijewardane, Toni Miao, Natasha Sihota, David C. Weindorf, Yufeng Ge, Thomas P. Hoelen |
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| Rok vydání: | 2020 |
| Předmět: |
Environmental Engineering
Field (physics) Soil test Reflectance spectroscopy Soil science Sample (statistics) 010501 environmental sciences Management Monitoring Policy and Law 01 natural sciences Soil chemistry.chemical_compound Partial least squares regression Soil Pollutants Petroleum Pollution Waste Management and Disposal 0105 earth and related environmental sciences Water Science and Technology 04 agricultural and veterinary sciences Pollution Hydrocarbons Petroleum chemistry Soil water Oil spill 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science |
| Zdroj: | Journal of Environmental Quality. 49:847-857 |
| ISSN: | 1537-2537 0047-2425 |
| DOI: | 10.1002/jeq2.20102 |
| Popis: | Accurate quantification of petroleum hydrocarbons (PHCs) is required for optimizing remedial efforts at oil spill sites. While evaluating total petroleum hydrocarbons (TPH) in soils is often conducted using costly and time-consuming laboratory methods, visible and near-infrared reflectance spectroscopy (Vis-NIR) has been proven to be a rapid and cost-effective field-based method for soil TPH quantification. This study investigated whether Vis-NIR models calibrated from laboratory-constructed PHC soil samples could be used to accurately estimate TPH concentration of field samples. To evaluate this, a laboratory sample set was constructed by mixing crude oil with uncontaminated soil samples, and two field sample sets (F1 and F2) were collected from three PHC-impacted sites. The Vis-NIR TPH models were calibrated with four different techniques (partial least squares regression, random forest, artificial neural network, and support vector regression), and two model improvement methods (spiking and spiking with extra weight) were compared. Results showed that laboratory-based Vis-NIR models could predict TPH in field sample set F1 with moderate accuracy (R2 > .53) but failed to predict TPH in field sample set F2 (R2 |
| Databáze: | OpenAIRE |
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