Urban air pollution features large spatial and temporal variations due to the high heterogeneity in emissions and ventilation conditions, which render the pollutant distributions in complex urban terrains difficult to measure. Current urban air pollution models are not able to simulate pollutant dispersion and distribution at a low computational cost and high resolution. To address this limitation, we have developed the urban terrain air pollution (UTAP) dispersion model to investigate, at a spatial resolution of 5 m and a temporal resolution of 1 h, the distribution of the local traffic-related NOx concentration at the pedestrian level in a 1 × 1 km2 area in Baoding, Hebei, China. The UTAP model was shown to be capable of capturing the local pollution variations in a complex urban terrain at a low computational cost. We found that the local traffic-related NOx concentration along or near major roads (10–200 μg m−3) was 1–2 orders of magnitude higher than that in places far from roads (0.1–10 μg m−3). Considering the background pollution, the NO and NO2 concentrations exhibited similar patterns with higher concentrations in street canyons and lower concentrations away from streets, while the O3 concentration exhibited the opposite behavior. Sixty percent of the NOx concentration likely stemmed from local traffic when the background pollution level was low. Both the background wind speed and direction substantially impacted the overall pollution level and concentration variations, with a low wind speed and direction perpendicular to the axes of most streets identified as unfavorable pollutant dispersion conditions. Our results revealed a large variability in the local traffic-related air pollutant concentration at the pedestrian level in the complex urban terrain, indicating that high-resolution computationally efficient models such as the UTAP model are required to accurately estimate the pollutant exposure of urban residents.
All Science Journal Classification (ASJC) codes
- Health, Toxicology and Mutagenesis
- Human exposure
- Spatial heterogeneity
- Street canyon