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

Martha LEE; Michael BRAUER; Pui Yun, Paulina WONG; Robert TANG; Tsz Him TSUI; Crystal CHOI; Wei CHENG; Poh Chin LAI; Linwei TIAN; Thuan Quoc THACH; Ryan ALLEN; Benjamin BARRATT

doi:10.1016/j.scitotenv.2017.03.094

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

Martha LEE
, Michael BRAUER
, Pui Yun, Paulina WONG
, Robert TANG
, Tsz Him TSUI
, Crystal CHOI
, Wei CHENG
, Poh Chin LAI
, Linwei TIAN
, Thuan Quoc THACH
, Ryan ALLEN
, Benjamin BARRATT

Research output: Journal Publications › Journal Article (refereed) › peer-review

155 Citations (Scopus)

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 (PM_2.5), 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=147 μg/m³, SD=88.9, N=40), c) PM_2.5 (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) PM_2.5 (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 PM_2.5 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.

Original language	English
Pages (from-to)	306-315
Number of pages	10
Journal	Science of the Total Environment
Volume	592
Early online date	17 Mar 2017
DOIs	https://doi.org/10.1016/j.scitotenv.2017.03.094
Publication status	Published - 15 Aug 2017
Externally published	Yes

Bibliographical note

Corrigendum to this article was published in December 2017, Science of The Total Environment, 603/604, 832-833. doi: 10.1016/j.scitotenv.2017.04.225

Publisher Copyright:
© 2017 Elsevier B.V.

Funding

This study was supported by the Health Effects Institute, Boston, USA under Research Agreement 4941-RFA13-1. M. Lee was supported in part by funding from a Natural Sciences and Engineering Research Council of Canada CREATE-Atmospheric Aerosol Program fellowship at The University of British Columbia.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities
SDG 15 Life on Land

Keywords

Air pollution
Exposure assessment
GIS
Land use regression

Access to Document

10.1016/j.scitotenv.2017.03.094

Cite this

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title = "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=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=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.",

keywords = "Air pollution, Exposure assessment, GIS, Land use regression",

author = "Martha LEE and Michael BRAUER and WONG, \{Pui Yun, Paulina\} and Robert TANG and TSUI, \{Tsz Him\} and Crystal CHOI and Wei CHENG and LAI, \{Poh Chin\} and Linwei TIAN and THACH, \{Thuan Quoc\} and Ryan ALLEN and Benjamin BARRATT",

note = "Corrigendum to this article was published in December 2017, Science of The Total Environment, 603/604, 832-833. doi: 10.1016/j.scitotenv.2017.04.225 Publisher Copyright: {\textcopyright} 2017 Elsevier B.V.",

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doi = "10.1016/j.scitotenv.2017.03.094",

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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, Pui Yun, 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

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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=106μg/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=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.

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=106μg/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=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.

KW - Air pollution

KW - Exposure assessment

KW - GIS

KW - Land use regression

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DO - 10.1016/j.scitotenv.2017.03.094

M3 - Journal Article (refereed)

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SN - 0048-9697

VL - 592

SP - 306

EP - 315

JO - Science of the Total Environment

JF - Science of the Total Environment

ER -

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

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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Corrigendum to “Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong" [Sci. Total Environ. 592 (2017) 306–315] (S004896971730606X) (10.1016/j.scitotenv.2017.03.094)

Cite this