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Record W3003696689 · doi:10.1039/c9em00455f

Investigating the presence and persistence of volatile methylsiloxanes in Arctic sediments

2020· article· en· W3003696689 on OpenAlexafffundabout
Dimitri Abrahamsson, Nicholas A. Warner, Liisa M. Jantunen, Annika Jahnke, Fiona Wong, Matthew MacLeod

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsToronto Public HealthEnvironment and Climate Change Canada
FundersSvenska Forskningsrådet FormasArcticNet
KeywordsArcticSedimentEnvironmental scienceEnvironmental chemistryWastewaterPersistence (discontinuity)OceanographyChemistryEnvironmental engineeringGeologyGeomorphology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.238
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2020
Admission routes3
Has abstractyes

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