Different responses to soil petroleum contamination in monocultured and mixed plant systems

The role of plant composition should be considered during ecological risk assessment of soil petroleum contamination. To evaluate the influences of plant composition on phytotoxicity, petroleum degraders, and petroleum degradation, four treatments were arranged in the present study: unplanted, bristle grass only, alfalfa only, and bristle grass and alfalfa mixed planted in uncontaminated soil or petroleum contaminated soil (w/w, 1.0%). Petroleum contamination inhibited the growth of bristle grass and alfalfa significantly, and alfalfa growth inhibition was significantly alleviated when mixed planted with bristle grass (p < 0.05). MPN analysis indicated that the mixed plant treatment can gather the benefits of two species, and facilitate the development of alkane, total hydrocarbon and PAH degraders in contaminated soil, but not occur in uncontaminated soil. Compared with alfalfa only treatment, the degradation rates for total petroleum hydrocarbons (TPH) and aliphatic fraction were significantly increased in the mixed plant treatment (p < 0.05). However, the degradation of aromatic petroleum fraction was not received substantial improvement in the mixed plant treatment, despite containing an abundant PAH degraders. Overall, mixed plant cultivation had the significant influences on plant growth, microbial community and petroleum degradation in contaminated soils. The study provides valuable insights for vegetation restoration and remediation systems in petroleum contaminated sites of study area.

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R(2)=0.78, residual prediction deviation (RPD)=2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPD's of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF+VisNIR DRS system qualitatively separated contaminated soils from control samples.

CAPSULE

Fusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils.