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 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
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 6.3 µg m−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 %.
