Abstract
In this study, activated carbon (AC)-supported bimetallic materials (Fe.Ni/AC) were synthesized to activate persulfate for the degradation of 19 types of polycyclic aromatic hydrocarbons (PAHs) in contaminated soil. Fe.Ni/AC-activated persulfate exhibited a higher PAH removal efficiency in the soil remediation process compared to unactivated persulfate. After a 72 h treatment at 50 °C, the degradation efficiency for total PAHs was ∼86%. Based on this result, an innovative deep learning neural network model for predicting PAH degradation efficiency was developed. The correlation coefficients between the predicted and actual values were 0.5403 (21.5 °C) and 0.6125 (50 °C), indicating significant correlation. The degradation products of 2.6-ring PAHs could be divided into oxy-PAHs, ketones-PAHs, methyl phenols, and non-PAHs, among which monocyclic non-PAHs were the main degradation products. In addition, signals indicating unexpected intermediate products of PAHs such as environmentally persistent free radicals (EPFRs) were detected from the PAH-contaminated soil samples at different reaction times. The PAH-type radical with a g-factor of 2.0019 and oxygenic carboncentered radicals with a g-factor of 2.0036 were identified. This study shows that Fe.Ni/AC-activated persulfate is efficient for PAH removal and thus is suitable for the remediation of PAH-contaminated soils.
| Original language | English |
|---|---|
| Pages (from-to) | 1183-1192 |
| Number of pages | 10 |
| Journal | ACS ES and T Engineering |
| Volume | 1 |
| Issue number | 8 |
| DOIs | |
| State | Published - 13 Aug 2021 |
Keywords
- Deep learning
- Degradation pathways
- EPFRs
- PAHs
- Soil remediation
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