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 (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.

Estimating daily surface NO<sub>2</sub> concentrations from satellite data – a case study over Hong Kong using land use regression models

“…The Dutch-Finnish Ozone Monitoring Instrument (OMI, Levelt et al, 2006) has been in continuous operation since 2004. OMI offers daily global coverage, with a local equatorial overpass time of approximately 13:45.…”

“…Also, because of their coarse spatial resolution, satellites are not capable of resolving fine-scale urban variation. For instance, modelled NO 2 VCDs at the same spatial footprint as the Ozone Monitoring Instrument (OMI; Levelt et al, 2006) over North American megacities were found to have a 20-30 % negative bias when compared to fine-scale models (Kim et al, 2016).…”

International Mind, Activities and Urban Places (iMAP) study: methods of a cohort study on environmental and lifestyle influences on brain and cognitive health

“…42 60-62 Air pollution surfaces for Barcelona have been developed within the European Study of Cohorts for Air Pollution Effects project, 63 and those for Hong Kong by Barratt and colleagues. [64][65][66] Traffic-related air pollution is operationalised as ambient NO 2 as well as traffic volumes and road density weighted by road type 67 within prespecified buffers around residences, workplaces and other visited locations. Participants are given the option of wearing a personal NO 2 monitor (Gradko, UK) for seven consecutive days (as part of a 7-day field assessment) and completing a short survey regarding their pollution-related housing characteristics and typical activities.…”

Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model

“…They also offer more advantages than other approaches because of the statistical relation between the pollutant concentration and the sites where pollutants are monitored, which could also be established with other factors such as population density, traffic, altitude, and weather conditions, amongst other aspects. This analysis has been used in Europe [18,19] and the United States to model air pollution exposure [20,21] and, more recently, in Asian and Middle Eastern countries like China [22,23], Iran [24], and India [25]. However, it has never been used in Mexico City.…”