Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong

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

Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

9 Citations (Scopus)

Abstract

Background: Epidemiological studies typically use subjects’ residential address to estimate individuals’ air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks. Objectives: To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong. Methods: A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component. Results: Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM2.5, BC, and NO2 were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses. Conclusions: The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than ‘true’ exposures.
Original languageEnglish
Pages (from-to)100-108
Number of pages9
JournalEnvironment International
Volume113
DOIs
Publication statusPublished - 1 Apr 2018

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pollution exposure
travel behavior
atmospheric pollution
land use
infiltration
exposure
residential location
activity pattern
health risk
student

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TANG, Robert ; TIAN, Linwei ; THACH, Thuan Quoc ; TSUI, Tsz Him ; BRAUER, Michael ; LEE, Martha ; ALLEN, Ryan ; YUCHI, Weiran ; LAI, Poh Chin ; WONG, Pui Yun, Paulina ; BARRATT, Benjamin. / Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong. In: Environment International. 2018 ; Vol. 113. pp. 100-108.
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title = "Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong",
abstract = "Background: Epidemiological studies typically use subjects’ residential address to estimate individuals’ air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks. Objectives: To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong. Methods: A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component. Results: Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM2.5, BC, and NO2 were 13{\%}, 39{\%} and 14{\%} higher, respectively for subjects aged below 18, and 3{\%}, 18{\%} and 11{\%} higher, respectively for working adults. Exposures of females were approximately 4{\%} lower than those of males. Dynamic exposures were around 20{\%} lower than ambient exposures at residential addresses. Conclusions: The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than ‘true’ exposures.",
author = "Robert TANG and Linwei TIAN and THACH, {Thuan Quoc} and TSUI, {Tsz Him} and Michael BRAUER and Martha LEE and Ryan ALLEN and Weiran YUCHI and LAI, {Poh Chin} and WONG, {Pui Yun, Paulina} and Benjamin BARRATT",
year = "2018",
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doi = "10.1016/j.envint.2018.01.009",
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TANG, R, TIAN, L, THACH, TQ, TSUI, TH, BRAUER, M, LEE, M, ALLEN, R, YUCHI, W, LAI, PC, WONG, PYP & BARRATT, B 2018, 'Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong', Environment International, vol. 113, pp. 100-108. https://doi.org/10.1016/j.envint.2018.01.009

Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong. / TANG, Robert; TIAN, Linwei; THACH, Thuan Quoc; TSUI, Tsz Him; BRAUER, Michael; LEE, Martha; ALLEN, Ryan; YUCHI, Weiran; LAI, Poh Chin; WONG, Pui Yun, Paulina; BARRATT, Benjamin.

In: Environment International, Vol. 113, 01.04.2018, p. 100-108.

Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

TY - JOUR

T1 - Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong

AU - TANG, Robert

AU - TIAN, Linwei

AU - THACH, Thuan Quoc

AU - TSUI, Tsz Him

AU - BRAUER, Michael

AU - LEE, Martha

AU - ALLEN, Ryan

AU - YUCHI, Weiran

AU - LAI, Poh Chin

AU - WONG, Pui Yun, Paulina

AU - BARRATT, Benjamin

PY - 2018/4/1

Y1 - 2018/4/1

N2 - Background: Epidemiological studies typically use subjects’ residential address to estimate individuals’ air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks. Objectives: To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong. Methods: A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component. Results: Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM2.5, BC, and NO2 were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses. Conclusions: The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than ‘true’ exposures.

AB - Background: Epidemiological studies typically use subjects’ residential address to estimate individuals’ air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks. Objectives: To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong. Methods: A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component. Results: Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM2.5, BC, and NO2 were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses. Conclusions: The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than ‘true’ exposures.

UR - http://commons.ln.edu.hk/sw_master/6659

U2 - 10.1016/j.envint.2018.01.009

DO - 10.1016/j.envint.2018.01.009

M3 - Journal Article (refereed)

VL - 113

SP - 100

EP - 108

JO - Environmental International

JF - Environmental International

SN - 0160-4120

ER -