The influence of biomass burning on the global distribution of selected non-methane organic compounds.
Bibliographic record
Abstract
Forests fires are a significant source of chemi- cals to the atmosphere including numerous non-methane or- ganic compounds (NMOCs). We report airborne measure- ment of hydrocarbons, acetone and methanol from > 500 whole air samples collected over Eastern Canada, includ- ing interceptions of several different boreal biomass burn- ing plumes. From these and concurrent measurements of carbon monoxide (CO) we derive fire emission ratios for 29 different organic species relative to the emission of CO. These range from 8.9± 3.2 ppt ppb 1 CO for methanol to 0.007± 0.004 ppt ppb 1 CO for cyclopentane. The ratios are in good to excellent agreement with literature values. Us- ing the GEOS-Chem global 3-D chemical transport model (CTM) we show the influence of biomass burning on the global distributions of benzene, toluene, ethene and propene (species which are controlled for air quality purposes and sometimes used as indicative tracers of anthropogenic ac- tivity). Using our observationally derived emission ratios and the GEOS-Chem CTM, we show that biomass burning can be the largest fractional contributor to observed ben- zene, toluene, ethene and propene levels in many global lo- cations. The widespread biomass burning contribution to at- mospheric benzene, a heavily regulated air pollutant, sug- gests that pragmatic approaches are needed when setting air quality targets as tailpipe and solvent emissions decline in developed countries. We subsequently determine the ex- tent to which the 28 global-status World Meteorological Or- ganisation - Global Atmosphere Watch stations worldwide are influenced by biomass burning sourced benzene, toluene, ethene and propene as compared to their exposure to anthro- pogenic emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".