Strategies for Bioremediation of Soil from an Industrial Site Exposed to Chlorinated and Nitroaromatic Compounds
Bibliographic record
Abstract
Abstract As technological advances allow the development of new products, the number of synthetic chemical compounds released into the soil, surface water and groundwater increases, posing a threat to the environment. Therefore, treatability studies to improve bioremediation strategies (biostimulation and bioaugmentation) were applied to samples of soil containing nitro and chlorinated aromatic compounds from a former chemical manufacturing site in Brazil. Native microorganisms were stimulated to degrade compounds including dichloroanilines, dichloronitrobenzenes, 2‐chloronitrobenzene, and 1,2‐chlorobenzene, through oxygen exposure and pH (6.0‐8.4) and moisture content (13‐23%) adjustments. For the inoculation of soil samples, a culture enriched from site groundwater was developed. The aeration alone stimulated the indigenous microbes to degrade some of the compounds. However, reinoculation with an enriched culture and moisture content adjustment increased the attenuation rates by 3.6 and 1.4 times, respectively. The pH values in the range of 7.6 and 8.4 seem not to harm microbes' activity and moisture content higher than 16% is recommended to enhance biodegradation. Based on the findings, it is likely that natural attenuation is happening in aerobic zones at the site. Results indicate both bioremediation strategies (biostimulation and bioaugmentation through reinoculation with enriched culture mainly composed of organisms from the Diaphorobacter genus) are promising strategies to enhance bioremediation. However, considering the applicability of the strategies on a field scale, further experiments will broaden the understanding of biodegradability of compounds, such as their inhibitory effects when in higher concentration (>150 mg/kg), individually or combined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".