Naphthenic acids: identification of structural properties that influence acute toxicity
Bibliographic record
Abstract
The oil sands of north-eastern Alberta, Canada contain the second largest oil reserve on Earth, with an estimated 173.2 billion barrels of recoverable bitumen. The primary method for the extraction of bitumen from oil sand has been the Clark hot water extraction, a procedure that uses 80°C water and sodium hydroxide. The alkaline water used for the extraction is recycled, resulting in the release and concentration of naphthenic acids (NAs) from the sediment. NAs, a natural constituent of oil sand, have been identified as the principal toxic components of oil sands process-affected water (OSPW). Field studies have demonstrated that microbial degradation of lower molecular weight (MW) NAs leads to decreased acute toxicity, although the mechanism by which lower MW NAs would elicit a greater toxic response has not been described. Prior to the start of toxicity testing in this project, a time-efficient method for the extraction, purification, and concentration of NAs from OSPW was developed. In place of using ultrafiltration, diethylamoniethyl (DEAE) cellulose was used to remove humic-like materials from the organic acid extract, shortening the time required by approximately 90%. Kugelrohr distillation was used to separate a mixture of methylated NAs by differences in boiling point. Higher boiling NAs had a greater MW, as confirmed by electrospray ionization mass spectrometry (ESI-MS). Subsequent toxicity tests with 'Vibrio fischeri' using the Microtox assay revealed that toxicity decreased from the lowest MW fraction (41.9 ± 95% confidence interval (CI) of 5.5 mg L-1) to the highest (64.9 ± 95% CI of 14.5 mg L-1). Proton nuclear magnetic resonance (1H NMR) analysis of the Kugelrohr distilled methylated NA fractions revealed an increasing ratio of methyl ester hydrogen atoms to remaining aliphatic hydrogen atoms from the lowest MW fraction (0.130) to the highest MW fraction (0.214). These results, coupled with analysis by ESI-MS, indicate greater carboxylic acid content within NAs of higher MW and a greater degree of cyclicity. To investigate the influence of multiple carboxylic acid groups as well as increased MW on NA toxicity, eight NA-like surrogates (four mono-carboxylic; four di-carboxylic) were assayed in 'V. fischeri' and ' Daphnia magna.' The acute toxicity of the NA-like surrogates increased with higher MW, however, the toxicity significantly decreased with the presence of an additional carboxylic acid group. These results suggest that acute toxicity of NAs is dependent upon hydrophobicity, therefore the probable mode of action is narcosis, also referred to as membrane disruption. To determine the practicality of utilizing a (Quantitative) Structure-Activity Relationship ((Q)SAR) model to predict NA toxicity, the eight NA-like surrogates were entered into USEPA's ECOSAR model. The model's predicted toxicities were similar to observed toxicities in 'V. fischeri' and 'D. magna' assays, indicating that the model has potential to serve as a prioritization tool for identifying NA structures likely to have an increased toxicity. The ECOSAR model predicted increased toxic potency for NAs of equal MW containing fewer carbon rings. Furthermore, NA structures with a linear grouping of carbon rings had a greater predicted toxic potency than structures containing carbon rings in a clustered grouping.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".