Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong

Abstract

Land use regression (LUR) is a common method of predicting spatial variability of air pollution to estimate exposure. Nitrogen dioxide (NO2), nitric oxide (NO), fine particulate matter (PM2.5), 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) NO2 (Mean=106μg/m3, SD=38.5, N=95), b) NO (M=147μg/m3, SD=88.9, N=40), c) PM2.5 (M=35μg/m3, SD=6.3, N=64), and BC (M=10.6μg/m3, SD=5.3, N=76). Final LUR models had the following statistics: a) NO2 (R2=0.46, RMSE=28μg/m3) b) NO (R2=0.50, RMSE=62μg/m3), c) PM2.5 (R2=0.59; RMSE=4μg/m3), and d) BC (R2=0.50, RMSE=4μg/m3). Traditional LUR predictors such as road length, car park density, and land use types were included in most models. The NO2 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 PM2.5 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.