Updating the Conceptual Model for Fine Particle Mass Emissions from Combustion Systems Allen L. Robinson
Bibliographic record
Abstract
Atmospheric transformations determine the contribution of emissions from combustion systems to fine particulate matter (PM) mass. For example, combustion systems emit vapors that condense onto existing particles or form new particles as the emissions are cooled and diluted. Upon entering the atmosphere, emissions are exposed to atmospheric oxidants and sunlight, which causes them to evolve chemically and physically, generating secondary PM. This review discusses these transformations, focusing on organic PM. Organic PM emissions are semi-volatile at atmospheric conditions and thus their partitioning varies continuously with changing temperature and concentration. Because organics contribute a large portion of the PM mass emitted by most combustion sources, these emissions cannot be represented using a traditional, static emission factor. Instead, knowledge of the volatility distribution of emissions is required to explicitly account for changes in gas-particle partitioning. This requires updating how PM emissions from combustion systems are measured and simulated from combustion systems. Secondary PM production often greatly exceeds the direct or primary PM emissions; therefore, secondary PM must be included in any assessment of the contribution of combustion systems to ambient PM concentrations. Low-volatility organic vapors emitted by combustion systems appear to be very important secondary PM precursors that are poorly accounted for in inventories and models. The review concludes by discussing the implications that the dynamic nature of these PM emissions have on source testing for emission inventory development and regulatory purposes. This discussion highlights important linkages between primary and secondary PM, which could lead to simplified certification test procedures while capturing the emission components that contribute most to atmospheric PM mass.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".