Long-term exposure to ambient PM and BC was associated with an elevated risk of cardiovascular mortality. Despite far higher air pollution exposure concentrations, HRs per unit increase in PM were similar to those from recent comparable studies in North America.

Land use regression (LUR) is a common method of predicting spatial variability of air pollution to estimate exposure. Nitrogen dioxide (NO), nitric oxide (NO), fine particulate matter (PM), and black carbon (BC) concentrations were measured during two sampling campaigns (April-May and November-January) in Hong Kong (a prototypical high-density high-rise city). Along with 365 potential geospatial predictor variables, these concentrations were used to build two-dimensional land use regression (LUR) models for the territory. Summary statistics for combined measurements over both campaigns were: a) NO (Mean=106μg/m, SD=38.5, N=95), b) NO (M=147μg/m, SD=88.9, N=40), c) PM (M=35μg/m, SD=6.3, N=64), and BC (M=10.6μg/m, SD=5.3, N=76). Final LUR models had the following statistics: a) NO (R=0.46, RMSE=28μg/m) b) NO (R=0.50, RMSE=62μg/m), c) PM (R=0.59; RMSE=4μg/m), and d) BC (R=0.50, RMSE=4μg/m). Traditional LUR predictors such as road length, car park density, and land use types were included in most models. The NO prediction surface values were highest in Kowloon and the northern region of Hong Kong Island (downtown Hong Kong). NO showed a similar pattern in the built-up region. Both PM and BC predictions exhibited a northwest-southeast gradient, with higher concentrations in the north (close to mainland China). For BC, the port was also an area of elevated predicted concentrations. The results matched with existing literature on spatial variation in concentrations of air pollutants and in relation to important emission sources in Hong Kong. The success of these models suggests LUR is appropriate in high-density, high-rise cities.

Urbanization is known to cause noticeable changes in the properties of local climate. Studies have shown that urban areas, compared to rural areas with less artificial surfaces, register higher local temperatures as a result of Urban Heat Islands (UHIs). Hong Kong is one of the most densely populated cities in the world and a high proportion of its population residing in densely built high-rise buildings are experiencing some degrees of thermal discomfort. This study selected Mong Kok and Causeway Bay, two typical urban communities in Hong Kong, to gather evidence of microclimate variation and sources of thermal discomfort. UHIs were estimated from 58 logging sensors placed at strategic locations to take temperature and humidity measurements over 17 consecutive days each in the summer/hot and winter/cool periods. By employing geographic information and global positioning systems, these measurements were geocoded and plotted over the built landscape to convey microclimate variation. The empirical data were further aligned with distinct environmental settings to associate possible factors contributing to UHIs. This study established the existence and extent of microclimate variation of UHI within urban communities of different environmental configuration and functional uses. The findings provided essential groundwork for further studies of UHI effects to inform sources of local thermal discomfort and better planning design to safeguard environmental health in public areas.

COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres. A historical dataset on mobility was used to calculate the generalized activity space and space–time prism of individuals during a pre-pandemic period. Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk. These risk surfaces were weighed and integrated into a “risk map of superspreading environment” (SE-risk map) at the city level. Overall, the proposed method can estimate empirical hot spots of superspreading environment with statistical accuracy. The SE-risk map of Hong Kong can pre-identify areas that overlap with the actual disease clusters of bar-related transmission. Our study presents first-of-its-kind research that combines data on facility location and human mobility to identify superspreading environment. The resultant SE-risk map steers the investigation away from pure human focus to include geographic environment, thereby enabling more differentiated non-pharmaceutical interventions and exit strategies to target some places more than others when complete city lockdown is not practicable.