Estimating persistent oil contamination in tropical region using vegetation indices and random forest regression

• Published in: Ecotoxicology and environmental safety 2019-11, Vol.184, p.109654, Article 109654
• Main Authors: Lassalle, Guillaume, Credoz, Anthony, Hédacq, Rémy, Bertoni, Georges, Dubucq, Dominique, Fabre, Sophie, Elger, Arnaud
• Format: Article
• Language: English
• Subjects: Chlorophyll - metabolism; Environmental Engineering; Environmental Sciences; Machine Learning; Petroleum - analysis; Petroleum Pollution - analysis; Plant Leaves - chemistry; Plant Leaves - growth & development; Poaceae - chemistry; Poaceae - growth & development; Random forest; Reflectance spectroscopy; Soil - chemistry; Soil contamination; Soil Microbiology; Soil Pollutants - analysis; Spectrum Analysis; Total petroleum hydrocarbons; Tropical Climate; Vegetation indices
• ISSN: 0147-6513; 1090-2414
• DOI: 10.1016/j.ecoenv.2019.109654

The persistence of soil contamination after cessation of oil activities remains a major environmental issue in tropical regions. The assessment of the contamination is particularly difficult on vegetated sites, but promising advances in reflectance spectroscopy have recently emerged for this purpose. This study aimed to exploit vegetation reflectance for estimating low concentrations of Total Petroleum Hydrocarbons (TPH) in soils. A greenhouse experiment was carried out for 42 days on Cenchrus alopecuroides (L.) under realistic tropical conditions. The species was grown on oil-contaminated mud pit soils from industrial sites, with various concentrations of TPH. After 42 days, a significant decrease in plant growth and leaf chlorophyll and carotenoid contents was observed for plants exposed to 5–19 g kg−1 TPH in comparison to the controls (p  0.7). The same indices also succeeded in estimating the concentrations of TPH using random forest regression, at leaf and plant scales (RMSE = 1.46 and 1.63 g kg−1 and RPD = 5.09 and 4.44, respectively). Four out of the 33 indices contributed the most to the models (>75%). This study opens up encouraging perspectives for monitoring the cessation of oil activities in tropical regions. Further researches will focus on the application of our approach at larger scale, on airborne and satellite imagery. [Display omitted] •Vegetation reflectance was used for estimating persistent oil contamination in soil .•Changes in leaf chlorophyll and carotenoid contents varied with oil concentration .•Pigments were linked vegetation indices using elastic net regression .•Oil concentration was accurately estimated using random forest algorithm .•The approach provided consistent results at leaf and plant scales .