Naphthenic acids degradation and toxicity mitigation in tailings wastewater systems and aquatic environments: A review
Bibliographic record
Abstract
Naphthenic acids, NAs (classical formula C(n)H(2n+z)O(2), where n is the carbon numbers, z represents zero or negative even integers), found in oil sands process waters (OSPWs), are toxic to aquatic environments depending upon several factors such as pH, salinity, molecular size and chemical structure of NAs. Among various available methods, biodegradation seems to be generally the most cost-effective method for decreasing concentrations of NAs (n ≤ 21) and reducing their associated toxicity in OSPW, however the mechanism by which the biodegradation of NAs occurs are poorly understood. Ozonation is superior over biodegradation in decreasing higher molecular weight alkyl branched NAs (preferentially, n ≥ 22, -6 ≥ z ≥ -12) as well as enabling accelerated biodegradation and reducing toxicity. Photolysis (UV at 254 nm) is effective in cleaving higher molecular weight NAs into smaller fragments that will be easier for microorganisms to degrade, whereas photocatalysis can metabolize selective NAs (0 ≥ z ≥ -6) efficiently and minimize their associated toxicity. Phytoremediation is applicable for metabolizing specific NAs (O(2), O(3), O(4), and O(5) species) and minimizing their associated toxicities. Petroleum coke (PC) adsorption is effective in reducing the more structurally complex NAs (preferentially 12 ≥ n ≥ 18 and z = -10, -12) and their toxicity in OSPWs, depending upon the PC content, pH and temperature. Several factors have influence on the degradation of NAs in OSPWs and aquatic environments, which include molecular mass and chemical structure of NAs, sediment structure, temperature, pH, dissolved oxygen, nutrients, and bacteria types.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".