Impact of irreversible sorption of phthalate acid esters on their sediment quality criteria
Bibliographic record
Abstract
The equilibrium partitioning (EqP) method has been applied to establish sediment quality criteria (SQC); however, it does not consider the nonlinear irreversible sorption of many organic contaminants. In this research, the sorption and desorption of two phthalate esters (PAEs), dimethyl phthalate (DMP) and di(2-ethylhexyl) phthalate (DEHP), in four natural sediments collected from the Yangtze River and the Yellow River were studied; the impact of irreversible sorption of DMP and DEHP on SQC has been evaluated. Based on the reversible and irreversible biphasic sorption model, the values of maximum irreversible sorption capacity (q(max)(irr)) were 125.19 μg g(-1)-337.37 μg g(-1) for DMP and 515.87 μg g(-1)-591.40 μg g(-1) for DEHP. The q(max)(irr) value was positively related to the organic carbon and black carbon contents, cation exchange capacity, and surface area of the sediments. The values of the irreversible sorption coefficient K(oc)(irr) for both DEHP and DMP in the four sediments approximated to a constant of 10(6.46 ± 0.38), which was 1-2 orders of magnitude higher than their reversible sorption coefficient K(oc)(irr). The values of SQC for PAEs based on the EqP method were modified by involving the irreversible sorption. The modified SQC of DEHP could be 2 to 20 times higher than the value predicted by the EqP method, and the assessment results for DEHP contamination in the sediments with the modified SQC were more reasonable than those with the non-modified SQC. It indicated that the current SQC based on the EqP method may be unnecessarily strict for specific organic compounds and the irreversible sorption should be taken into account.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".