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Atmospheric pollution is pointed out by the World Health Organization as the responsible for approximately 7 million premature deaths per year, and different approaches are investigated worldwide to reduce the high concentrations observed in urban areas. Regional-scale concentrations are influenced by different sources of pollutants, such as residences, industries and road traffic. Particularly road traffic is one of the important sources of regulated and emerging pollutants, including fine and ultra-fine particles, nitrogen dioxide and black carbon. Nevertheless, the traffic emission rate of each pollutant depends on the vehicle characteristics: the vehicle type, fuel and the vehicle manufacturing year. Urban mobility is, then, a key aspect to determine the total amount of traffic emissions, and choices related to the available modes of transport in a city may have an important role in urban air quality. This study investigates the influence of five extreme mobility scenarios on pollutant emissions and concentrations in urban air quality. In each scenario very strong limitations on road traffic are adopted: (i) a limited electrification of private and commercial vehicles and a shift to soft mobility, (ii) a significant increase in the number of users per car and reduced use of private cars car, (iii) a total electrification of the fleet (Paris-region target for 2030), (iv) limits on private vehicles circulation in specific areas, and (v) the implementation of all these measures simultaneously. For this, the regional-scale model CHIMERE is employed to calculate the concentrations of multi-pollutants with a 1 km x 1 km spatial resolution. A special focus is given to the emerging pollutants black carbon and ultra-fine particles. In this study CHIMERE is coupled with the chemical module SSH-aerosol, which enables the representation of aerosol dynamics with the state-of-art modules available in the literature. Simulations are performed in Paris during summer 2022. This study shows the potentialities of an air-quality modeling approach to understand trends in concentrations according to scenarios aiming to reduce population exposure to atmospheric pollution.
Lugon et al. (Sat,) studied this question.