Yanxu Zhang, Xingpei Ye, Shibao Wang, Xiaojing He, Lingyao Dong, Ning Zhang, Haikun Wang, Zhongrui Wang, Yun Ma, Lei Wang

Urban air pollution has tremendous spatial variability at scales ranged from kilometer to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution >1 km). Here we develop a ten-meter resolution air quality model for traffic-related CO pollution based on the parallelized large-eddy simulation model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92 ± 0.40 mg/m3) agree well with the measurements (0.90 ± 0.58 mg/m3, n = 114,502). The model has similar spatial patterns with that of the measurements, and the r2 value of 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-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20-30% higher in streets that align with the wind direction within ~20°. We find that streets with higher buildings in the downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ~50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.