A large contribution of anthropogenic organo-nitrates to secondary organic aerosol in the Alberta oil sands
Bibliographic record
Abstract
The oil sands industry in Alberta, Canada, represents a large anthropogenic source of secondary organic aerosol (SOA). Atmospheric emissions from oil sands operations are a complex mixture of gaseous and particulate pollutants. Their interaction can affect the formation and characteristics of SOA during plume dispersion, but their chemical evolution remains poorly understood. Oxidative processing of organic vapours in the presence of NO x can lead to particulate organo-nitrate (pON) formation, with important impacts on the SOA budgets, the nitrogen cycle and human health. We provide the first direct field evidence, from ground- and aircraft-based real-time aerosol mass spectrometry, that anthropogenic pON contributed up to half of SOA mass that was freshly produced within the emission plumes of oil sands facilities. Using a top-down emission-rate retrieval algorithm constrained by aircraft measurements, we estimate the production rate of pON in the oil sands region to be ∼15.5 t d −1 . We demonstrate that pON formation occurs via photo-oxidation of intermediate-volatility organic compounds (IVOCs) in high-NO x environments, providing observational constraints to improve current SOA modelling frameworks. Our ambient observations are supported by laboratory photo-oxidation experiments of IVOCs from bitumen vapours under high-NO x conditions, which demonstrate that pON can account for 30 %–55 % of the observed SOA mass depending on the degree of photochemical ageing. The large contribution of pON to freshly formed anthropogenic SOA illustrates the central role of pON in SOA production from the oil and gas industry, with relevance for other urban and industrial regions with significant anthropogenic IVOC and NO x emissions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".