The spatial–temporal exposure to traffic-related Particulate Matter emissions

Asjad Naqvi; Stefanie Peer; Johannes Müller; Markus Straub

doi:10.1016/j.trd.2023.103899

The spatial–temporal exposure to traffic-related Particulate Matter emissions

Asjad Naqvi^*, Stefanie Peer, Johannes Müller, Markus Straub

^*Corresponding author for this work

Publication: Scientific journal › Journal article › peer-review

Abstract

The paper simulates the spatial variations in the sources of, and exposure to, traffic-related PM10 emissions for the city of Vienna, Austria. Using an extended and calibrated MATSim micro-simulation model, we reproduce agent-level mobility patterns for a representative day. Street-level PM10 emissions, mostly from cars, are extrapolated for the entire city to estimate concentration and exposure levels at hourly intervals. We show that exposure levels exceed the recommended 50 μg/m³ threshold between peak travel hours at home, education, and work locations. Among different socioeconomic status (SES) groups, urban, single, 15 years and younger, and those living near the city center face high exposure levels, while car users, that cause a majority of the emissions, are relatively less exposed. Finally, we show that Shared Autonomous Electric Vehicles (SAEVs) reduce PM10 emissions, but the benefits are not homogeneously distributed across the different SES groups.

Original language	English
Article number	103899
Journal	Transportation Research Part D: Transport and Environment
Volume	123
DOIs	https://doi.org/10.1016/j.trd.2023.103899 https://doi.org/10.1016/j.trd.2023.103899
Publication status	Published - Oct 2023

Bibliographical note

Funding Information:
We gratefully acknowledge support from the Austrian Climate Research Programme (ACRP) Grant number KR17AC0K13731 (SimSAEV). All errors are our own.

Publisher Copyright:
© 2023

Keywords

Concentration versus exposure
MATSim model for Vienna, Austria
SES exposure inequality
Shared Autonomous Electric Vehicles
Traffic-related PM10 emissions

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Cite this

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title = "The spatial–temporal exposure to traffic-related Particulate Matter emissions",

abstract = "The paper simulates the spatial variations in the sources of, and exposure to, traffic-related PM10 emissions for the city of Vienna, Austria. Using an extended and calibrated MATSim micro-simulation model, we reproduce agent-level mobility patterns for a representative day. Street-level PM10 emissions, mostly from cars, are extrapolated for the entire city to estimate concentration and exposure levels at hourly intervals. We show that exposure levels exceed the recommended 50 μg/m3 threshold between peak travel hours at home, education, and work locations. Among different socioeconomic status (SES) groups, urban, single, 15 years and younger, and those living near the city center face high exposure levels, while car users, that cause a majority of the emissions, are relatively less exposed. Finally, we show that Shared Autonomous Electric Vehicles (SAEVs) reduce PM10 emissions, but the benefits are not homogeneously distributed across the different SES groups.",

keywords = "Concentration versus exposure, MATSim model for Vienna, Austria, SES exposure inequality, Shared Autonomous Electric Vehicles, Traffic-related PM10 emissions",

author = "Asjad Naqvi and Stefanie Peer and Johannes M{\"u}ller and Markus Straub",

note = "Funding Information: We gratefully acknowledge support from the Austrian Climate Research Programme (ACRP) Grant number KR17AC0K13731 (SimSAEV). All errors are our own. Publisher Copyright: {\textcopyright} 2023",

year = "2023",

month = oct,

doi = "10.1016/j.trd.2023.103899",

language = "English",

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journal = "Transportation Research Part D: Transport and Environment",

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AU - Naqvi, Asjad

AU - Peer, Stefanie

AU - Müller, Johannes

AU - Straub, Markus

N1 - Funding Information: We gratefully acknowledge support from the Austrian Climate Research Programme (ACRP) Grant number KR17AC0K13731 (SimSAEV). All errors are our own. Publisher Copyright: © 2023

PY - 2023/10

Y1 - 2023/10

N2 - The paper simulates the spatial variations in the sources of, and exposure to, traffic-related PM10 emissions for the city of Vienna, Austria. Using an extended and calibrated MATSim micro-simulation model, we reproduce agent-level mobility patterns for a representative day. Street-level PM10 emissions, mostly from cars, are extrapolated for the entire city to estimate concentration and exposure levels at hourly intervals. We show that exposure levels exceed the recommended 50 μg/m3 threshold between peak travel hours at home, education, and work locations. Among different socioeconomic status (SES) groups, urban, single, 15 years and younger, and those living near the city center face high exposure levels, while car users, that cause a majority of the emissions, are relatively less exposed. Finally, we show that Shared Autonomous Electric Vehicles (SAEVs) reduce PM10 emissions, but the benefits are not homogeneously distributed across the different SES groups.

AB - The paper simulates the spatial variations in the sources of, and exposure to, traffic-related PM10 emissions for the city of Vienna, Austria. Using an extended and calibrated MATSim micro-simulation model, we reproduce agent-level mobility patterns for a representative day. Street-level PM10 emissions, mostly from cars, are extrapolated for the entire city to estimate concentration and exposure levels at hourly intervals. We show that exposure levels exceed the recommended 50 μg/m3 threshold between peak travel hours at home, education, and work locations. Among different socioeconomic status (SES) groups, urban, single, 15 years and younger, and those living near the city center face high exposure levels, while car users, that cause a majority of the emissions, are relatively less exposed. Finally, we show that Shared Autonomous Electric Vehicles (SAEVs) reduce PM10 emissions, but the benefits are not homogeneously distributed across the different SES groups.

KW - Concentration versus exposure

KW - MATSim model for Vienna, Austria

KW - SES exposure inequality

KW - Shared Autonomous Electric Vehicles

KW - Traffic-related PM10 emissions

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DO - 10.1016/j.trd.2023.103899

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VL - 123

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