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. Urban air pollution has tremendous spatial variability at scalesranging from kilometers to meters due to unevenly distributed emissionsources, complex flow patterns, and photochemical reactions. However,high-resolution air quality information is not available through traditionalapproaches such as ground-based measurements and regional air quality models(with typical resolution &gt; 1 km). Here we develop a 10 mresolution air quality model for traffic-related CO pollution based on theParallelized Large-Eddy Simulation Model (PALM). The model performance isevaluated with measurements obtained from sensors deployed on a taxiplatform, which collects data with a comparable spatial resolution to ourmodel. The very high resolution of the model reveals a detailed geographicaldispersion pattern of air pollution in and out of the road network. Themodel results (0.92 ± 0.40 mg m−3) agree well with themeasurements (0.90 ± 0.58 mg m−3, n=114 502). The model hassimilar spatial patterns to those of the measurements, and the r2 valueof a linear regression between model and measurement data is 0.50 ± 0.07 during non-rush hours with middle and low wind speeds. A non-linearrelationship is found between average modeled concentrations and wind speedwith higher concentrations under calm wind speeds. The modeledconcentrations are also 20 %–30 % higher in streets that align with the winddirection within ∼ 20∘. We find that streets withhigher buildings downwind have lower modeled concentrations at thepedestrian level, and similar effects are found for the variability inbuilding heights (including gaps between buildings). The modeledconcentrations also decay fast in the first ∼ 50 m from thenearest highway and arterial road but change slower further away. This studydemonstrates the potential of large-eddy simulation in urban air qualitymodeling, which is a vigorous part of the smart city system and could informurban planning and air quality management.

atmospheric chemistry and physics

Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors