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 PublicationsJournal Article (refereed)

36 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 (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.
Original languageEnglish
Pages (from-to)306-315
Number of pages10
JournalScience of the Total Environment
Volume592
DOIs
Publication statusPublished - 15 Aug 2017
Externally publishedYes

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Soot
Air pollution
Land use
black carbon
Carbon black
atmospheric pollution
Nitric oxide
nitric oxide
land use
Nitric Oxide
modeling
Statistics
Nitrogen Dioxide
Air Pollutants
Particulate Matter
nitrogen dioxide
prediction
city
particulate matter
Railroad cars

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

Cite this

LEE, Martha ; BRAUER, Michael ; WONG, Pui Yun, Paulina ; TANG, Robert ; TSUI, Tsz Him ; CHOI, Crystal ; CHENG, Wei ; LAI, Poh Chin ; TIAN, Linwei ; THACH, Thuan Quoc ; ALLEN, Ryan ; BARRATT, Benjamin. / Land use regression modelling of air pollution in high density high rise cities : a case study in Hong Kong. In: Science of the Total Environment. 2017 ; Vol. 592. pp. 306-315.
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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 = 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.",
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",
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LEE, M, BRAUER, M, WONG, PYP, TANG, R, TSUI, TH, CHOI, C, CHENG, W, LAI, PC, TIAN, L, THACH, TQ, ALLEN, R & BARRATT, B 2017, 'Land use regression modelling of air pollution in high density high rise cities : a case study in Hong Kong', Science of the Total Environment, vol. 592, pp. 306-315. https://doi.org/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. / LEE, Martha; BRAUER, Michael; WONG, Pui Yun, Paulina; TANG, Robert; TSUI, Tsz Him; CHOI, Crystal; CHENG, Wei; LAI, Poh Chin; TIAN, Linwei; THACH, Thuan Quoc; ALLEN, Ryan; BARRATT, Benjamin.

In: Science of the Total Environment, Vol. 592, 15.08.2017, p. 306-315.

Research output: Journal PublicationsJournal Article (refereed)

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, 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

N1 - 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

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.

UR - http://commons.ln.edu.hk/sw_master/6142

U2 - 10.1016/j.scitotenv.2017.03.094

DO - 10.1016/j.scitotenv.2017.03.094

M3 - Journal Article (refereed)

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VL - 592

SP - 306

EP - 315

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

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