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Analysis of exposure to fine particulate matter using passive data from public transport

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Analysis of exposure to fine particulate matter using passive data from public transport. / Trewhela, B.; Huneeus, N.; Munizaga, M. et al.
In: Atmospheric Environment, Vol. 215, 116878, 15.10.2019.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Trewhela, B, Huneeus, N, Munizaga, M, Mazzeo, A, Menut, L, Mailler, S, Valari, M & Ordoñez, C 2019, 'Analysis of exposure to fine particulate matter using passive data from public transport', Atmospheric Environment, vol. 215, 116878. https://doi.org/10.1016/j.atmosenv.2019.116878

APA

Trewhela, B., Huneeus, N., Munizaga, M., Mazzeo, A., Menut, L., Mailler, S., Valari, M., & Ordoñez, C. (2019). Analysis of exposure to fine particulate matter using passive data from public transport. Atmospheric Environment, 215, Article 116878. https://doi.org/10.1016/j.atmosenv.2019.116878

Vancouver

Trewhela B, Huneeus N, Munizaga M, Mazzeo A, Menut L, Mailler S et al. Analysis of exposure to fine particulate matter using passive data from public transport. Atmospheric Environment. 2019 Oct 15;215:116878. Epub 2019 Aug 12. doi: 10.1016/j.atmosenv.2019.116878

Author

Trewhela, B. ; Huneeus, N. ; Munizaga, M. et al. / Analysis of exposure to fine particulate matter using passive data from public transport. In: Atmospheric Environment. 2019 ; Vol. 215.

Bibtex

@article{6b31234676aa4f7ebadcc3e5477b96b3,
title = "Analysis of exposure to fine particulate matter using passive data from public transport",
abstract = "The city of Santiago experiences extreme pollution events during winter due to particulate matter and the associated health impact depends on the exposure to this pollutant, particularly to PM2.5. We present and apply a method that estimates the exposure of users of the public transport system of Santiago by combining smart card mobility data with measured surface concentrations from the monitoring network of Santiago and simulated concentrations by the CHIMERE model. The method was applied between July 20th and 24th of 2015 to 105,588 users corresponding to 12% of the frequent users of the public transport system and approximately 2% of the total population of Santiago. During those five days, estimated exposure based on measured concentrations varied between 44 and 75 μg/m3 while exposure based on simulated concentrations varied between 45 and 89 μg/m3. Furthermore, including socioeconomic conditions suggests an inverse relationship between exposure and income when measured concentrations are used, i.e. the lower the income the higher the exposure, whereas no such relationship is observed when using simulated concentrations. Although only exposure to PM2.5 was considered in this study, the method can also be applied to estimate exposure to other urban pollutant such as ozone.",
author = "B. Trewhela and N. Huneeus and M. Munizaga and A. Mazzeo and L. Menut and S. Mailler and M. Valari and C. Ordo{\~n}ez",
year = "2019",
month = oct,
day = "15",
doi = "10.1016/j.atmosenv.2019.116878",
language = "English",
volume = "215",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "PERGAMON-ELSEVIER SCIENCE LTD",

}

RIS

TY - JOUR

T1 - Analysis of exposure to fine particulate matter using passive data from public transport

AU - Trewhela, B.

AU - Huneeus, N.

AU - Munizaga, M.

AU - Mazzeo, A.

AU - Menut, L.

AU - Mailler, S.

AU - Valari, M.

AU - Ordoñez, C.

PY - 2019/10/15

Y1 - 2019/10/15

N2 - The city of Santiago experiences extreme pollution events during winter due to particulate matter and the associated health impact depends on the exposure to this pollutant, particularly to PM2.5. We present and apply a method that estimates the exposure of users of the public transport system of Santiago by combining smart card mobility data with measured surface concentrations from the monitoring network of Santiago and simulated concentrations by the CHIMERE model. The method was applied between July 20th and 24th of 2015 to 105,588 users corresponding to 12% of the frequent users of the public transport system and approximately 2% of the total population of Santiago. During those five days, estimated exposure based on measured concentrations varied between 44 and 75 μg/m3 while exposure based on simulated concentrations varied between 45 and 89 μg/m3. Furthermore, including socioeconomic conditions suggests an inverse relationship between exposure and income when measured concentrations are used, i.e. the lower the income the higher the exposure, whereas no such relationship is observed when using simulated concentrations. Although only exposure to PM2.5 was considered in this study, the method can also be applied to estimate exposure to other urban pollutant such as ozone.

AB - The city of Santiago experiences extreme pollution events during winter due to particulate matter and the associated health impact depends on the exposure to this pollutant, particularly to PM2.5. We present and apply a method that estimates the exposure of users of the public transport system of Santiago by combining smart card mobility data with measured surface concentrations from the monitoring network of Santiago and simulated concentrations by the CHIMERE model. The method was applied between July 20th and 24th of 2015 to 105,588 users corresponding to 12% of the frequent users of the public transport system and approximately 2% of the total population of Santiago. During those five days, estimated exposure based on measured concentrations varied between 44 and 75 μg/m3 while exposure based on simulated concentrations varied between 45 and 89 μg/m3. Furthermore, including socioeconomic conditions suggests an inverse relationship between exposure and income when measured concentrations are used, i.e. the lower the income the higher the exposure, whereas no such relationship is observed when using simulated concentrations. Although only exposure to PM2.5 was considered in this study, the method can also be applied to estimate exposure to other urban pollutant such as ozone.

U2 - 10.1016/j.atmosenv.2019.116878

DO - 10.1016/j.atmosenv.2019.116878

M3 - Journal article

VL - 215

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

M1 - 116878

ER -