Abstract
In the present work, the pollution levels of airborne particles in the city of Ouagadougou have been assessed through two campaigns of in situ measurements of PM2.5 and PM10 concentrations. These measures were performed using a portable device (AEROCET 531S) on roadside sites and on sites located in administrative services, secondary schools and outlying districts. Since in situ measurements don’t give any indication of the pollution that could be due to a new source (industrial plant, road, etc.) and how it is dispersed spatially and temporally, thus pollutant emissions from road traffic and the dispersion of these pollutants are obtained by a modelling chain that couples COPERT IV emission model under Pollemission code with MUNICH dispersion model. As results, the average 24-hour concentrations are 87±16 and 951±266 μg/m3 for the PM2.5 and PM10, respectively, in 2018 dry season. They are 29±5 and 158±43 μg/m3, respectively, in 2018 rainy season and, 22±9 and 187±67 μg/m3 in 2019 rainy season, respectively. These results show that independently of the season (dry or rainy), the PM10 concentrations are high and exceed the World Health Organization Air Quality Guidelines (WHO AQG) and European 24-hour threshold value of 50 μg/m3. Concerning the PM2.5 concentrations in dry season, they are high and exceed the WHO AQG and European 24-hour threshold value of 25 μg/m3. The results obtained concerning the modeling of the dispersion of PM air pollution due to road traffic in the city of Ouagadougou are partially satisfactory. The modeled maximum values of the exhaust emission factors are very high while the modeled concentrations are underestimated compared to the measured concentrations The estimated emission factors range between 0 and 400 g/km/h and between 0 and 303 g/km/h for exhaust PM2.5 and resuspension PM2.5, respectively. They vary between 0 and 2 997 g/km/h for resuspension PM10. Concerning the modelled concentrations, they are between 0 and 238 μg/m3 for PM2.5 and between 0 and 2 072 μg/m3 for PM10. These results can be improved with updated traffic and road characterization data.
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