Airborne Measurements of Formaldehyde In Biomass Burning and Urban Plumes In Central Africa Using Laser Induced Fluorescence
Bibliographic record
Abstract
Formaldehyde is a key intermediate in photochemical oxidation of volatile organic compounds in the troposphere and is also directly emitted by a range of sources, including biomass burning and fuel combustion. Airborne measurements of formaldehyde have therefore been used to investigate oxidation in biomass burning (BB) plumes intercepted during the Methane Observations and Yearly Assessments (MOYA) campaign. The MOYA campaign took place January/February 2019 in Uganda and Zambia and mixing ratios of formaldehyde were obtained using the University of Leeds formaldehyde Laser Induced Fluorescence (LIF) instrument. A range of air masses were intercepted including multiple near-field biomass burning (BB) plumes, with up to 140 ppb of formaldehyde observed, and urban emission plumes from the capital city of Kampala in Uganda, where up to 7 ppb of formaldehyde was measured. Formaldehyde emission factors have been calculated for Ugandan BB (1.20 ± 0.23 g kg-1) which agree well with literature (1.23 ± 0.65 g kg-1) for Savannah combustion. Production of formaldehyde as a function of plume age has also been investigated in order to discriminate direct emission from photochemical formation in BB plumes. BB plumes were also intercepted during other aircraft campaigns several days downwind of emission such as a plume transported from Canadian wildfires observed in the North Atlantic during ACSIS-5/ARNA-1 where levels of up to 18.30 ppb were detected, indicative of sustained photochemical oxidation within the plume. Comparison of urban, near-field BB and far-field BB plumes provides a variety of environments and photochemical ages to test our understanding of combustion oxidation processes.
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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 | 0.000 | 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) | 0.000 | 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".