The objective of this work is to predict daily PM2.5 air quality in Dakar, Senegal using data from an automated measurement station integrated into a server using a data assimilation model. Initially, a 3-year data set was used to identify and validate an appropriate ARIMA data assimilation model. The data was split into an 80% training set and a 20% test set. The Augmented Dickey-Fuller (ADF) test was used to check the normality of the data series. Subsequently, we used the AutoArima method to determine the optimal model to represent the time series. Preliminary results show that a model with order (2,1,1) accurately represents the series. Additional analysis using model fit tests showed that the (3, 0, 1) model was most effective in representing and predicting the data. The statistical validation performance of this model demonstrates its capability to forecast PM2.5 concentrations for up to 72 hours (3 days), achieving correlation coefficients exceeding 80%. However, after three days, the predictions returned to background levels. In the final stage of the study, data from automatic stations were integrated into a server hosting the assimilation model to improve daily PM2.5 forecasts for Dakar. An interactive platform was developed to visualize measurements and forecasts over two days. The results show that by integrating the data with the assimilation model, predictions are significantly improved.

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.