To what extent is the description of streets important in estimating local air-quality? A case study over Paris

Abstract. Modelling atmospheric composition at street level is challenging because pollutant concentration within street canyons depends on both local emissions and the transport of polluted air masses from remote areas. Therefore, regional-scale modelling and local applications must be combined to provide accurate simulations of the atmospheric composition at street locations. In our study, we compare two strategies: i) a subgrid-scale approach embedded in the chemistry-transport model or ii) the street-network model MUNICH. In both cases, the regional-scale chemistry-transport model CHIMERE provides the urban background concentrations, and the meteorological model WRF, coupled with CHIMERE, is used to provide meteorological fields. Simulation results for NO_x, NO₂, and PM_2.5 concentrations over the city of Paris from both modelling approaches are compared with in-situ measurements in traffic air-quality stations. At stations located in downtown areas, with low traffic emissions, the street-network model MUNICH exhibits superior performance compared to the Subgrid approach for NO_x concentrations, while comparable results are obtained for NO₂. However, significant discrepancies between the two methods are observed for all analyzed pollutants at stations heavily influenced by road traffic. These stations are typically located near highways, where the bias between the two approaches can reach 58 %. The Subgrid approach's ability to estimate accurate emissions data is limited, leading to potential underestimation or overestimation of gas and fine particle concentrations based on the emission heterogeneity it handles. The performance of MUNICH appears to be highly sensitive to the friction velocity, a parameter influenced by the anthropogenic heat flux used in the WRF model. Street dimensions do contribute to the performance disparities observed between the two approaches, yet emissions remain the predominant factor.