Understanding the Impact of High-NO<sub><i>x</i></sub> Conditions on the Formation of Secondary Organic Aerosol in the Photooxidation of Oil Sand-Related Precursors
Bibliographic record
Abstract
Oil sands (OS) are an important type of heavy oil deposit, for which operations in Alberta, Canada, were recently found to be a large source of secondary organic aerosol (SOA). However, SOA formation from the OS mining, processing, and subsequent tailings, especially in the presence of NO x, remains unclear. Here, photooxidation experiments for OS-related precursors under high-NO x conditions were performed using an oxidation flow reactor, in which ∼95% of peroxy radicals (RO 2 ) react with NO. The SOA yields under high-NO x conditions were found to be lower than yields under low-NO x conditions for all precursors, which is likely due to the higher volatilities of the products from the RO 2 + NO pathway compared with RO 2 + HO 2 . The SOA yields under high-NO x conditions show a strong dependence on pre-existing surface area (not observed in previous low-NO x experiments), again attributed to the higher product volatilities. Comparing the mass spectra of SOA formed from different precursors, we conclude that the fraction of m / z > 80 ( F 80 ) can be used as a parameter to separate different types of SOA in the region. In addition, particle-phase organic nitrate was found to be an important component (9–23%) of OS SOA formed under high-NO x conditions. These results have implications for better understanding the atmospheric processing of OS emissions.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".