The comparison between in-situ monitored data and modelled results of nitrogen dioxide (NO(2)): case-study, road networks of Kigali city, Rwanda

The incomplete combustion of fossil fuels from petrol, natural gas, and fuel oil in the engine of vehicles contributes to air quality degradation through traffic-related air pollutant emissions. The Real-time affordable multi-pollutant (RAMPs) monitors were installed in Kigali, the capital of Rwanda, to fill the gap in air quality datasets. Using RAMPs, this is the first air quality modelling research in Rwanda aiming to report the concentration of NO(2) by comparing In-situ monitored data and modelled results. We targeted NO(2) emissions from 27 road networks of Kigali to address the impacts of traffic emissions on air quality over 2021. The American Meteorological Society and Environmental Protection Agency regulatory models (AERMOD and ISCST3) were used for simulation. Statistical indexes include fractional bias (FB), the fraction of the prediction within the factor of two of the observations (FAC2), normalized mean square error (NMSE), geometric mean bias (MG), and geometric variance (VG) used to assess models' reliability. Monitoring shows the annual mean of 16.07 μg/m(3), 20.35 μg/m(3), and 15.46 μg/m(3) at Mont-Kigali, Gacuriro, and Gikondo-Mburabuturo stations, respectively. Modelling shows the daily mean of 111.77 μg/m(3) and annually mean of 50.42 μg/m(3) with AERMOD and daily mean of 200.26 μg/m(3) and annually mean of 72.26 μg/m(3) with ISCST3. The FB, NMSE, and FAC2 showed good agreement, while MG and VG showed moderate agreement with AERMOD. The FB, NMSE, and MG showed moderate agreement, while FAC2 and VG disagreed with ISCST3. Traffic and urban residential emissions were identified as potential sources of NO(2). Results indicated that Kigali residents are exposed to a significant level of NO(2) exceeding World Health Organisation limits. Findings will help track the effectiveness of Rwanda’s recently executed pollution-control policy, suggest evidence based on the recommendations to reduce NO(2), and use further dispersion models to support ground-based observations to improve public health.