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

TY - JOUR

T1 - Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong

AU - Lee, Martha

AU - Brauer, Michael

AU - Wong, Paulina

AU - Tang, Robert

AU - Tsui, Tsz Him

AU - Choi, Crystal

AU - Cheng, Wei

AU - Lai, Poh-Chin

AU - Tian, Linwei

AU - Thach, Thuan-Quoc

AU - Allen, Ryan

AU - Barratt, Benjamin

PY - 2017/8/15

Y1 - 2017/8/15

N2 - 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 = 106 μg/m3, SD = 38.5, N = 95), b) NO (M = 147 μg/m3, SD = 88.9, N = 40), c) PM2.5 (M = 35 μg/m3, SD = 6.3, N = 64), and BC (M = 10.6 μg/m3, SD = 5.3, N = 76). Final LUR models had the following statistics: a) NO2 (R2 = 0.46, RMSE = 28 μg/m3) b) NO (R2 = 0.50, RMSE = 62 μg/m3), c) PM2.5 (R2 = 0.59; RMSE = 4 μg/m3), and d) BC (R2 = 0.50, RMSE = 4 μg/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.

AB - 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 = 106 μg/m3, SD = 38.5, N = 95), b) NO (M = 147 μg/m3, SD = 88.9, N = 40), c) PM2.5 (M = 35 μg/m3, SD = 6.3, N = 64), and BC (M = 10.6 μg/m3, SD = 5.3, N = 76). Final LUR models had the following statistics: a) NO2 (R2 = 0.46, RMSE = 28 μg/m3) b) NO (R2 = 0.50, RMSE = 62 μg/m3), c) PM2.5 (R2 = 0.59; RMSE = 4 μg/m3), and d) BC (R2 = 0.50, RMSE = 4 μg/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.

KW - Land use regression

KW - Air pollution

KW - GIS

KW - Exposure assessment

U2 - 10.1016/j.scitotenv.2017.03.094

DO - 10.1016/j.scitotenv.2017.03.094

M3 - Article

SN - 0048-9697

VL - 592

SP - 306

EP - 315

JO - Science of the Total Environment

JF - Science of the Total Environment

ER -