Characterization of Atmospheric Organic Carbon and Element Carbon of PM2.5 during the Long Dry Period in Cotonou, Benin
Bibliographic record
Abstract
In the African countries, air pollution appears as a public health problem. The health consequences of this pollution are currently causing concern among the population and decision-makers. Air quality degradation is a major issue in the large conurbations on the shore of the Gulf of Guinea. In this study, daily atmospheric PM2.5 and carbonaceous aerosol (organic carbon (OC) and elemental carbon (EC)) concentrations were measured at Dantokpa site in Cotonou, Benin, Southern West Africa during the long dry period (December 2016 to March 2017). We analyzed the mass concentrations and carbonaceous species of PM2.5. The average PM2.5 concentration was 69.20 µg·m−3, while OC and EC concentrations were 34.39 ± 12.62 μg·m−3 and 10.82 ± 7.89 μg·m−3, respectively. Total carbon (TC) accounted for 65.35% of the PM2.5. Strong correlation between OC and EC was found during the long dry period, suggesting the contributions of similar sources. We have also studied the correlations between OC-EC and PM2.5. We found that OC was highly correlated with PM2.5 (R = 0.94) while EC was moderately correlated with PM2.5 (R = 0.77), suggesting that carbonaceous aerosols and PM2.5 shared major sources at Dantokpa site during the long dry seasons.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".