Relationship between biodegradation and sorption of phthalate esters and their metabolites in natural sediments
Bibliographic record
Abstract
Regulatory evaluations of commercial chemicals in Canada, the United States, the European Union, and other countries aim to identify biodegradation rates of chemicals in natural soils and sediments. However, commonly used biodegradation testing methods are limited in their capacity to determine biodegradation rates under natural environmental conditions. As a result, widely varying biodegradation rates have been reported for many very hydrophobic substances. This variability causes difficulties in regulatory evaluations, potentially leading to chemical misclassification. In the present study, the authors developed a model of the relationship between biodegradation, sorption, and hydrophobicity, and tested the model in experiments that measured the biodegradation rates of a range of di-phthalate esters (DPEs) and mono-phthalate esters (MPEs) in natural sediments. The results indicate that DPEs and MPEs have the inherent capacity to be quickly degraded by microbes in sediments at a common rate, but that DPE biodegradation rates in natural sediments decrease with increasing phthalate ester sorption to sediments. The results show that inherently biodegradable substances that are subject to a high degree of sorption can be expected to exhibit long half-lives in natural sediments. The model provides a potential methodology for assessing biodegradation rates in natural sediments from inherent biodegradation rates measured in screening tests by accounting for chemical sorption. The present study indicates that a reduced rate of biodegradation is due to a reduced fraction of freely dissolved chemical concentration in the interstitial water, and that the environmental significance of sorption-reduced biodegradation rates needs to be viewed in the context of risk in chemical evaluations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".