Estimating Human Exposure to Air Pollution in High Rise Cities: The Hong Kong D3D Study

High density high rise cities have become a more prominent feature globally and air quality is a significant public health risk in many of these cities. As the spatial resolution of exposure assessment at ground level improves, there is an increased need to better understand vertical variation in air pollution, or risk overestimating the exposure of those who live on higher residential floors. Results will be presented from the Hong Kong D3D Study, which aimed to combine (i) a bespoke two dimensional air pollution model, (ii) a comprehensive vertical pollutant monitoring campaign within urban street canyons and (iii) a travel behavior survey to create a dynamic three dimensional (3D) population exposure model. A staged approach was used to quantify mortality risk estimates for an elderly cohort of 66,000 Hong Kong residents.The results from the study provided the first evidence that considering air pollution exposure in a dynamic 3D landscape would benefit epidemiological studies. Higher hazard ratios and a greater number of significant associations were found between mortality and pollutant exposures that would not have found had standard 2D exposure models been used. Dynamic models can also identify differential exposures between population subtypes. Improved urban building design appears to be stimulating the dispersion of local transport related air pollutant emissions in street canyons. Conversely, infiltration factors found in naturally ventilated buildings were high and residences provided little protection from ambient air pollution.This study demonstrated the benefits of dense sensor networks for characterizing human exposure in epidemiological studies. However, shortcomings of sensor technology were also found, where certain sensor types fell short of the required accuracy and precision to detect subtle gradients in pollution levels.