Investigating the presence and persistence of volatile methylsiloxanes in Arctic sediments
Bibliographic record
Abstract
Volatile methylsiloxanes (VMS) have been identified as contaminants of emerging concern in aquatic systems. Here, we report on the presence of VMS in sediment and wastewater from Arctic regions in 2014 to 2016 and model their persistence in Adventfjorden in Longyearbyen, Svalbard. Total concentrations of VMS in sediment were dominated by D4 and D5 and ranged from 0.0024 to 1.7 ng g-1 at Svalbard (Longyearbyen), from 4.0 to 43 ng g-1 in Greenland (Nuuk) and from 0.19 to 21 ng g-1 in the Canadian Archipelago. Concentrations in wastewater samples from Svalbard ranged from 12 to 156 ng L-1. Large variability in reported values of the partition ratio between organic carbon and water (KOC) and enthalpy of sorption (ΔHOC; often estimated from enthalpy of phase change between octanol and water, ΔHOW) of VMS has resulted in high uncertainty in evaluating persistence in aquatic systems. We evaluated previously reported KOC and ΔHOC values from the literature in predicting measured VMS concentrations in sediment and wastewater in scenarios using a fugacity-based multimedia model for VMS concentrations in Svalbard. We tested two different model scenarios: (1) KOC and ΔHOW measurements for three cyclic VMS previously reported by Kozerski et al. (Environ. Toxicol. Chem., 2014, 33, 1937-1945) and Xu and Kropscott (Environ. Chem., 2014, 33, 2702-2710) and (2) the KOC and ΔHOC measurements from Panagopoulos et al. (Environ. Sci. Technol., 2015, 49, 12161-12168 and Environ. Sci. Technol. Lett., 2017, 4(6), 240-245). Concentrations of VMS in sediment predicted from concentrations in wastewater in scenario 2 were in good agreement with measured concentrations, whereas in scenario 1, predicted concentrations were 2 to 4 orders of magnitude lower. Such large discrepancies indicate that the differences in the predicted concentrations are more likely to be attributed to KOC and ΔHOC than to uncertainty in environmental parameters or emission rates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".