Observations of volatile organic compounds during ARCTAS – Part 1: Biomass burning emissions and plume enhancements
Bibliographic record
Abstract
Abstract. Mixing ratios of a large number of volatile organic compounds (VOCs) were observed by the Trace Organic Gas Analyzer (TOGA) on board the NASA DC-8 as part of the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. Many of these VOCs were observed concurrently by one or both of two other VOC measurement techniques on board the DC-8: proton-transfer-reaction mass spectrometry (PTR-MS) and whole air canister sampling (WAS). A comparison of these measurements to the data from TOGA indicates good agreement for the majority of co-measured VOCs. The ARCTAS study, which included both spring and summer deployments, provided opportunities to sample a large number of biomass burning (BB) plumes with origins in Asia, California and Central Canada, ranging from very recent emissions to plumes aged one week or more. For this analysis, identified BB plumes were grouped by flight, source region and, in some cases, time of day, generating 40 individual plume groups, each consisting of one or more BB plume interceptions. Normalized excess mixing ratios (EMRs) to CO were determined for each of the 40 plume groups for up to 19 different VOCs or VOC groups, many of which show significant variability, even within relatively fresh plumes. This variability demonstrates the importance of assessing BB plumes both regionally and temporally, as emissions can vary from region to region, and even within a fire over time. Comparisons with literature confirm that variability of EMRs to CO over an order of magnitude for many VOCs is consistent with previous observations. However, this variability is often diluted in the literature when individual observations are averaged to generate an overall regional EMR from a particular study. Previous studies give the impression that emission ratios are generally consistent within a given region, and this is not necessarily the case, as our results show. For some VOCs, earlier assumptions may lead to significant under-prediction of emissions in fire emissions inventories. Notably, though variable between plumes, observed EMRs of individual light alkanes are highly correlated within BB emissions. Using the NCAR master mechanism chemical box model initialized with concentrations based on two observed scenarios, i.e., fresh Canadian BB and fresh Californian BB, both plumes are expected to experience primarily decreases in oxygenated VOCs during the first 2.5 days, such that any production in the plumes of these compounds is less than the chemical loss. Comparisons of the modeled EMRs to the observed EMRs from BB plumes estimated to be three days in age or less indicate overall good agreement and, for most compounds, no significant difference between BB plumes in these two regions.
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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".