Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data.

Traditional air quality data have a spatial resolution of 1 km or above, making it challenging to resolve detailed air pollution exposure in complex urban areas. Combining urban morphology, dynamic traffic emission, regional and local meteorology, physicochemical transformations in air quality models using big data fusion technology, an ultra-fine resolution modeling system was developed to provide air quality data down to street level. Based on one-year ultra-fine resolution data, this study investigated the effects of pollution heterogeneity on the individual and population exposure to particulate matter (PM and PM), nitrogen dioxide (NO), and ozone (O) in Hong Kong, one of the most densely populated and urbanized cities.

Implications of Mitigating Ozone and Fine Particulate Matter Pollution in the Guangdong-Hong Kong-Macau Greater Bay Area of China Using a Regional-To-Local Coupling Model.

Ultrahigh-resolution air quality models that resolve sharp gradients of pollutant concentrations benefit the assessment of human health impacts. Mitigating fine particulate matter (PM) concentrations over the past decade has triggered ozone (O) deterioration in China. Effective control of both pollutants remains poorly understood from an ultrahigh-resolution perspective.