Following population growth and rapid urbanization, West African cities have become major sources of anthropogenic pollution. Additionally, Saharan dust has had a significant impact, representing a potentially toxic mix of sources for the population. This study characterizes the atmospheric composition and its sources in two African capitals, Bamako, Mali and Dakar, Senegal. TSP, PM₁₀ and PM_2.5 samples were collected during the dry season in 2009 when pollution levels were high: chemical analysis included organic carbon (OC), elemental carbon (EC), ions, and metals. PM_2.5 and PM₁₀ concentrations were 5–10 times and 3–8 times higher, respectively, than the 2005 WHO 24 h standards. Using PCA and PMF methodologies, five sources were identified in each city. In Bamako, traffic (motor vehicles and resuspended road dust) was the prevailing source of PM_2.5 and PM₁₀, accounting for 47% and 45%, respectively. Crustal dust was the second most important source (24–30%), followed by solid fuel combustion (16–13%) and secondary aerosols (10–16%). In Dakar, the following sources of PM_2.5 and PM₁₀ are identified: traffic (49%), mineral dust (16–25%), sea salts (15–20%) and industries (10–11%). Our study provides crucial information about the historical change in source characteristics in these two African cities, which can help for future mitigation strategies.

Model intercomparison studies often report a large spread in simulation results, but quantifying the causes of these differences is hindered by the fact that several processes contribute to the model spread simultaneously. Here we use the Multi‐Scale Infrastructure for Chemistry and Aerosols (MUSICA) version 0 to investigate the model resolution dependencies of simulated chemical species, with a focus on the differences between global uniform grid and regional refinement grid simulations with the same modeling framework. We construct two global (ne30 [∼112 km] and ne60 [∼56 km]) and two regional refinement grids over Korea (ne30x8 [∼14 km] and ne30x16 [∼7 km]). The grid resolution can change chemical concentrations by an order of magnitude in the boundary layer, and the importance increases as the species' reactivity increases (e.g., up to 50% and 1,000% changes for ethane and xylenes, respectively). The diurnal cycle of oxidants (OH, O₃, and NO₃) also varies with the grid resolution, which leads to different oxidation pathways of volatile organic compounds (e.g., the fraction of monoterpenes reacting with NO₃ in Seoul around midnight is 90% for ne30, but 65% for ne30x16). The models with high‐resolution grids usually do a better job at reproducing aircraft observations during the KORUS‐AQ campaign, but not always, implying compensating errors in the coarse grid simulations. For example, ozone is better reproduced by the coarse grid due to the artificial mixing of NO_x. When developing new chemical mechanisms and evaluating models over urban areas, the uncertainties associated with model resolution should be considered.

This study investigates the association between environmental and meteorological factors and asthma-related consultations among children in Dakar, Senegal. Retrospective hospital records of children aged 0–15 years who were treated for acute asthma exacerbations at the Albert Royer National Children’s Hospital (CHNEAR) were reviewed and analyzed for the 2011–2017 period. However, because of data limitations and data-quality considerations, the detailed analysis of environmental and meteorological factors was restricted to 2015, which recorded the highest annual number of asthma-related consultations (approximately 2000 recorded consultations). In 2015, asthma-related consultations exhibited marked seasonal variability, with higher numbers observed mainly from January to February and from May to July. These temporal patterns were investigated in relation to particulate matter concentrations (PM₁₀ and PM_2.5), aerosol optical depth (AOD), temperature, relative humidity, precipitable water, and precipitation. Gaseous pollutants (SO₂, NO₂, CO, and O₃) rarely exceeded the World Health Organization (WHO) short-term air quality guideline values and did not coincide with the main seasonal peaks in asthma consultations. Exploratory analyses showed positive associations between asthma consultations and aerosol-related indicators, particularly AOD and particulate matter. Because daily asthma consultations represent discrete count data, multivariate analyses were performed using Poisson generalized linear models (GLMs) with a log-link function. After adjustment for environmental variables and assessment of multicollinearity, AOD, PM₁₀, relative humidity, and precipitable water showed significant associations with asthma consultations, while precipitation and PM_2.5 displayed inverse associations. To evaluate delayed exposure–response relationships, single-lag Poisson GLMs were applied over lag periods ranging from 0 to 7 days. Significant lagged associations were identified between aerosol-related indicators and asthma consultations. PM_2.5 showed its strongest association at Lag 7 (RR = 1.042; 95% CI: 1.019–1.066; p < 0.001), while PM₁₀ was also associated at Lag 7 (RR = 1.010; 95% CI: 1.004–1.015; p < 0.001). AOD showed a significant delayed association at Lag 6 (RR = 2.841; p < 0.001). Meteorological conditions, particularly seasonal temperature variability and changes in atmospheric moisture, also coincided with variations in asthma consultations. Overall, these findings indicate that childhood asthma consultations in Dakar were associated with seasonal changes in aerosol loading, particulate matter exposure, and meteorological variability. Given that the detailed environmental and meteorological analysis was based on a single year and one referral hospital, the results should be interpreted as descriptive associations rather than evidence of causality. These results highlight the potential value of integrating environmental indicators and lag-based analyses into climate–health surveillance systems to improve preparedness for asthma exacerbation episodes among vulnerable populations.

Emissions from power plants contribute significantly to the overall greenhouse gas and air pollutant levels. Satellite observations of compounds such as NO2 and CO can help improve CO2 emission estimates, as proposed in the CORSO (CO2 Monitoring and Verification Support Research on Supplementary Observations) project. One of the key objectives of CORSO is to improve global and local capabilities in using observations of co-emitted species (NO2 and CO) to better estimate anthropogenic CO2 emissions. In this presentation, we will discuss a methodology developed to detect hotspots associated with power plants using NO2 data from the TROPOMI instrument on the Copernicus Sentinel-5P satellite, as well as observations from the Geostationary Environment Monitoring Spectrometer (GEMS) over Eastern and Southeast Asia. The final goal is to improve the accurate detection of hotspot locations to identify missing sources in emission inventories and refine their geolocation. It is important to note that the detectability of the anthropogenic signal from co-emitted species is generally much higher than that of CO2. Various statistical methods have been tested to identify high-probability hotspots in the NO2 tropospheric column densityfrom TROPOMI and GEMS. This includes an exploratory spatial data analysis for cluster detection (Getis-Ord Gi*), which evaluates each spatial variable’s neighborhood to determinewhether its values are significantly higher or lower than those in the surrounding area. The results indicate agreement between the hotspots identified through the Gi* method and the locations of power plants from the literature. These identified hotspot coordinates can be used to enhance the quanification of emissions and address mislocation in power plant emissions withinemission inventories.