Development and application of a multi-scale modeling framework for urban high-resolution NO2 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 capture 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 analyze the effects of vehicle emissions on urban roadside NO2 concentrations at a high spatial resolution of 50m×50m. To estimate the influence of various street canyons on the dispersion of air pollutants, a machine-learning-based street canyon flow (MLSCF) scheme was established based on computational fluid dynamics and two machine learning methods. The results indicated that compared with the Community Multi-scale Air Quality (CMAQ) model, the hybrid model improved the underestimation of NO2 concentration at near-road sites with the mean bias (MB) changing from -10 to 6.3µgm-3. The MLSCF scheme obviously increased upwind concentrations 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 the CMAQ-ISAM (Integrated Source Apportionment Method) model, but it increased significantly with the decreased distance to the road centerline, especially on urban freeways, where it reached 75%.