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Record W4407921709 · doi:10.5194/acp-25-2423-2025

Characterization of atmospheric water-soluble brown carbon in the Athabasca oil sands region, Canada

2025· article· en· W4407921709 on OpenAlexafffundabout
Dane Blanchard, Mark Gordon, Duc Huy Dang, Paul A. Makar, Julian Aherne

Bibliographic record

VenueAtmospheric chemistry and physics · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change CanadaYork UniversityTrent University
FundersEnvironment and Climate Change Canada
KeywordsOil sandsCharacterization (materials science)Carbon fibersEnvironmental scienceAtmospheric sciencesGeologyEnvironmental chemistryGeochemistryMineralogyChemistryArchaeologyAsphaltGeographyMaterials scienceComposite number

Abstract

fetched live from OpenAlex

Extensive industrial operations in the Athabasca oil sands region (AOSR) (Alberta, Canada) are a suspected source of water-soluble brown carbon (WS-BrC), a class of light-absorbing organic aerosols capable of altering atmospheric solar-radiation budgets. However, the current understanding of WS-BrC across the AOSR is limited, and the primary regional sources of these aerosols are unknown. During the summer of 2021, active filter-pack samplers were deployed at five sites across the AOSR to collect total suspended particulate matter for the purpose of evaluating WS-BrC. Ultraviolet–visible spectroscopy and fluorescence excitation–emission matrix (EEM) spectroscopy, complemented by parallel factor analysis (PARAFAC) modelling, were employed for sample characterization. Aerosol absorbance was comparable between near-industry and remote field sites, suggesting that industrial WS-BrC exerted limited influence on regional radiative forcing. The combined EEM–PARAFAC method identified three fluorescent components (fluorophores), including one humic-like substance (C1) and two protein-like substances (C2 and C3). Sites near oil sands facilities and sample exposures receiving atmospheric transport from local industry (as indicated by back-trajectory analysis) displayed increased C1 and C3 fluorescence; moreover, both fluorophores were positively correlated with particulate elements (i.e. vanadium and sulfur) and gaseous pollutants (i.e. nitrogen dioxide and total reduced sulfur), indicative of oil sands emissions. The C2 fluorophore exhibited high emission intensity at near-field sites and during severe wildfire smoke events, while positive correlations with industry indicator variables suggest that C2 likely reflected both wildfire-generated and anthropogenic WS-BrC. These results demonstrate that the combined EEM–PARAFAC method is an accessible and cost-effective tool that can be applied to monitor industrial WS-BrC in the AOSR.

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)0.000
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 score0.042
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.163
Teacher spread0.161 · 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

Citations3
Published2025
Admission routes3
Has abstractyes

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