Bioremediation of Petroleum Hydrocarbons in the Subsurface
Bibliographic record
Abstract
Abstract Due to human activity and, to a lesser extent, natural processes, petroleum hydrocarbons continue to pollute the environment. These contaminants of concern can be found globally and their remediation is key to restoring affected sites to safe and functional status. Conventional treatment of sites contaminated with petroleum hydrocarbons relies heavily on remediation approaches that are often financially prohibitive or may be technically impractical and that sometimes produce undesirable by-products. Using microbes that occur in nature (if not always at the site), can be a viable treatment with distinct advantages. Understanding the environment, contaminants, and natural biological processes occurring are key aspects for effective application of remediation techniques that rely on biological processes. Whether by stimulating the native microbial community, or, secondarily, by augmenting the native community with known degrader populations to degrade the target compounds, bioremediation is a practical, effective, and sustainable natural solution to a wide array of contamination around the globe. This chapter explores approaches to bioremediation of both soil and groundwater contaminated by petroleum hydrocarbons, describing how the approaches work and the benefits and challenges associated with them. It focuses on the use of aerobic and anaerobic microbial bioremediation, phytoremediation, and mycoremediation to address petroleum hydrocarbons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".