Development and application of a multi-scale modelling framework for urban high-resolution NO<sub>2</sub> pollution mapping

Abstract. Vehicle emissions have become a major source of air pollution in urban areas, especially for near-road environments, where the pollution characteristics are difficult to be captured by a single-scale air quality model due to the complex composition of the underlying surface. Here we developed a hybrid model CMAQ-RLINE_URBAN to quantitatively analyse the effects of vehicle emissions on urban roadside NO2 concentrations at a high spatial resolution of 50 m × 50 m. To estimate the influence of various street canyons on the dispersion of air pollutants, a Machine Learning-based Street Canyon Flow (MLSCF) scheme was constructed based on Computational Fluid Dynamic and ensemble learning methods. The results indicated that compared with the CMAQ model, the hybrid model improved the underestimation of NO2 concentration at near-road sites with MB changing from -10 μg/m3 to 6.3 μg/m3. The MLSCF scheme obviously increased concentrations at upwind receptors within deep street canyons due to changes in the wind environment caused by the vortex. In summer, the relative contribution of vehicles to NO2 concentrations in Beijing urban areas was 39 % on average, similar to results from CMAQ-ISAM model, but increased significantly with the decreased distance to the road centerline, especially reaching 75 % on urban freeways.