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Title:Fine particulate matter (PM2.5) exposure assessment among active daily commuters to induce behaviour change to reduce air pollution
Authors:ID Ilenič, Anja (Author)
ID Mauko Pranjić, Alenka (Author)
ID Zupančič, Nina (Author)
ID Milačič, Radmila (Author)
ID Ščančar, Janez (Author)
Files:URL URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S0048969723077471?via%3Dihub
 
.pdf PDF - Presentation file, download (2,57 MB)
MD5: 98184E1C7C65841CEB381FD39D3B3F9D
 
Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo ZAG - Slovenian National Building and Civil Engineering Institute
Abstract:Fine particulate matter (PM2.5), a detrimental urban air pollutant primarily emitted by traffic and biomass burning, poses disproportionately significant health risks at relatively limited exposure during commuting. Previous studies have mainly focused on fixed locations when assessing PM2.5 exposure, while neglecting pedestrians and cyclists, who often experience higher pollution levels. In response, this research aimed to independently validate the effectiveness of bicycle-mounted low-cost sensors (LCS) adopted by citizens, evaluate temporal and spatial PM2.5 exposure, and assess associated health risks in Ljubljana, Slovenia. The LCS quality assurance results, verified by co-location field tests by air quality monitoring stations (AQMS), showed comparable outcomes with an average percentage difference of 21.29 %, attributed to humidity-induced nucleation effects. The colder months exhibited the highest air pollution levels (μ = 32.31 μg/m3) due to frequent thermal inversions and weak wind circulation, hindering vertical air mixing and the adequate dispersion of pollutants. Additionally, PM2.5 levels in all sampling periods were lowest in the afternoon (μ = 12.09 μg/m3) and highest during the night (μ = 61.00 μg/m3) when the planetary boundary layer thins, leading to the trapping of pollutants near the surface, thus significantly affecting diurnal and seasonal patterns. Analysis of exposure factors revealed that cyclists were approximately three times more exposed than pedestrians. However, the toxicological risk assessment indicated a minimal potential risk of PM2.5 exposure. The collaborative integration of data from official AQMS and LCS can enhance evidence-based policy-making processes and facilitates the realignment of effective regulatory frameworks to reduce urban air pollution.
Keywords:air pollution monitoring, PM2.5 exposure, citizen science, bicycle-mounted low-cost sensors
Publication status:Published
Publication version:Version of Record
Publication date:07.12.2023
Publisher:Elsevier
Year of publishing:2023
Number of pages:str. 1-10
Numbering:Vol. 912, [article no.] 169117
PID:20.500.12556/DiRROS-18022 New window
UDC:54
ISSN on article:1879-1026
DOI:10.1016/j.scitotenv.2023.169117 New window
COBISS.SI-ID:177134851 New window
Copyright:© 2023 The Authors. Published by Elsevier B.V.
Note:Nasl. z nasl. zaslona; Opis vira z dne 12. 12. 2023;
Publication date in DiRROS:30.01.2024
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Downloads:280
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Record is a part of a journal

Title:Science of the total environment
Publisher:Elsevier
ISSN:1879-1026
COBISS.SI-ID:23110917 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P2-0273
Name:Gradbeni objekti in materiali

Funder:ARRS - Slovenian Research Agency
Project number:P1-0143
Name:Kroženje snovi v okolju, snovna bilanca in modeliranje okoljskih procesov ter ocena tveganja

Funder:ARRS - Slovenian Research Agency
Project number:P1-0008
Name:Paleontologija in sedimentarna geologija

Funder:Other - Other funder or multiple funders
Project number:22368
Name:EIT Urban Mobility RIS HUB Slovenia
Acronym:RHSLO2022

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:07.12.2023
Applies to:Text and Data Mining valid from 2024-02-01 Version of Record valid from 2023-12-07

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