Mass concentration, optical depth and carbon composition of particulate matter in the major southern West African cities of Cotonou (Benin) and Abidjan (Côte d'Ivoire)
Bibliographic record
Abstract
Air quality degradation is a major issue in the large conurbations on the shore of the Gulf of Guinea. We present for the first time PM 2.5 time series collected in Cotonou, Benin, and Abidjan, Côte d'Ivoire, from February 2015 to March 2017. Measurements were performed in the vicinity of major combustion aerosol sources: Cotonou/traffic (CT), Abidjan/traffic (AT), Abidjan/landfill (AL) and Abidjan/domestic fires (ADF). We report the weekly PM 2.5 mass and carbonaceous content as elemental (EC) and organic (OC) carbon concentrations. We also measure the aerosol optical depth (AOD) and the Ångström exponent in both cities. The average PM 2.5 mass concentrations were 32 ± 32, 32 ± 24 and 28 ± 19 µg m −3 at traffic sites CT and AT and landfill site AL, respectively. The domestic fire site shows a concentration of 145 ± 69 µg m −3 due to the contribution of smoking and roasting activities. The highest OC and EC concentrations were also measured at ADF at 71 ± 29 and 15 ± 9 µg m −3 , respectively, while the other sites present OC concentration between 8 and 12 µg m −3 and EC concentrations between 2 and 7 µg m −3 . The OC ∕ EC ratio is 4.3 at CT and 2.0 at AT. This difference highlights the influence of two-wheel vehicles using gasoline in Cotonou compared to that of four-wheel vehicles using diesel fuel in Abidjan. AOD was rather similar in both cities, with a mean value of 0.58 in Cotonou and of 0.68 in Abidjan. The seasonal cycle is dominated by the large increase in surface mass concentration and AOD during the long dry season (December–February) as expected due to mineral dust advection and biomass burning activities. The lowest concentrations are observed during the short dry season (August–September) due to an increase in surface wind speed leading to a better ventilation. On the other hand, the high PM 2.5 ∕ AOD ratio in the short wet season (October–November) indicates the stagnation of local pollution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".