Source identification and global implications of black carbon
Bibliographic record
Abstract
Black carbon (BC) is one of the short-lived air pollutants that contributes significantly to aerosol radiative forcing and global climate change. It is emitted by the incomplete combustion of fossil fuels, biofuels, and biomass. Urban environments are quite complex and thus, the use of mobile jointly with fixed monitoring provides a better understanding of the dynamics of BC distribution in such areas. The present study addresses the measurement of BC concentration using real-time mobile and ambient monitoring in Barranquilla, an industrialized urban area of the Colombian Caribbean. A microaethalometer (MA200) and an aethalometer (AE33) were used for measuring the BC concentration. The absorption Ångström exponent (AAE) values were determined for the study area, for identifying the BC emission sources. The results of the ambient sampling show that vehicle traffic emissions prevail; however, the influence of biomass burning was also observed. The mean ambient BC concentration was found to be 1.04 ± 1.03 μg/m3 and varied between 0.5 and 4.0 μg/m3. From the mobile measurements obtained in real traffic conditions on the road, a much higher average value of 16.1 ± 16.5 μg/m3 was measured. Many parts of the city showed BC concentrations higher than 20 μg/m3. The spatial distribution of BC concentration shows that vehicle emissions and traffic jams, a consequence of road and transport infrastructure, are the factors that most affect the BC concentration. A comparison of results obtained from two aethalometers indicates that the concentrations measured by MA200 are 9% lower than those measured by AE33. The AAE obtained was found to vary between 1.1 and 1.6, indicating vehicular emissions as the most crucial source. In addition, it was observed that the BC concentration on working days was 2.5 times higher than on the weekends in the case of mobile monitoring and 1.5 times higher in the case of ambient monitoring.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".