Quantification of cooking organic aerosol in the indoor environment using aerodyne aerosol mass spectrometers
Bibliographic record
Abstract
The Aerodyne aerosol mass spectrometer (AMS) is used extensively to study the composition of non-refractory submicron aerosol composition during atmospheric field studies. During two recent studies of indoor environments, HOMEChem and ATHLETIC, the default ambient organic aerosol AMS quantification parameters resulted in a large discrepancy with co-located instruments while sampling cooking organic aerosol (COA). Instruments agreed within uncertainty estimates during all other sampling periods. Assuming a collection efficiency (CE) of unity, adjustments to the AMS relative ionization efficiency (RIE) were required to reach agreement with co-located instruments. The range of RIECOA observed (ATHLETIC: RIECOA = 4.26–4.96, HOMEChem: RIECOA = 4.70–6.50) was consistent with RIE measured in the laboratory for cooking-specific molecules. These results agree with prior AMS studies which have indicated that more oxidized outdoor ambient organic aerosol has a relatively constant RIE of 1.4 ± 0.3 while more reduced organics have higher RIE. The applicability of a higher RIE was considered for two ambient datasets, and agreement between the AMS and co-located instruments improved when an increased response factor (RIE × CE) was applied to positive matrix factorization-derived primary organic aerosol (POA). Based on the observations presented here and the literature, we recommend AMS users consider applying RIECOA=4.2 to source and indoor studies of COA and evaluate a higher POA response factor of the order of ∼1.5 in outdoor studies at urban background sites, and ∼2 at sites impacted by fresh sources. This study aims to improve AMS quantification methodology for reduced POA and highlights the importance of careful intercomparisons in field studies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".