IVL Swedish Environmental Research Institute

ivl.se
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Citizen-operated mobile low-cost sensors for urban PM2.5 monitoring: field calibration, uncertainty estimation, and application
IVL Swedish Environmental Research Institute.
2023 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 95, p. 104607-104607, article id 104607Article in journal (Refereed) Published
Abstract [en]

Research communities, engagement campaigns, and administrative agents are increasingly valuing low-cost air-quality monitoring technologies, despite data quality concerns. Mobile low-cost sensors have already been used for delivering a spatial representation of pollutant concentrations, though less attention is given to their uncertainty quantification. Here, we perform static/on-bike inter-comparison tests to assess the performance of the Snifferbike sensor kit in measuring outdoor PM2.5 (Particulate Matter < 2.5 μm). We build a network of citizen-operated Snifferbike sensors in Kristiansand, Norway, and calibrate the measurements using Machine

Learning techniques to estimate the concentrations of PM2.5 along the city roads. We also propose a method to estimate the minimum number of PM2.5 measurements required per road segment to assure data representativeness. The co-location of three Snifferbike kits (Sensirion SPS30) at the monitoring station showed a RMSD of 7.55 μg m−3. We approximate that one km h−1 increase in the speed of the bikes will add 0.03 - 0.04 μg m−3 to the Standard Deviation of the Snifferbike PM2.5 measurements. We estimate that at least 27 measurements per road segment are required (50 m here) if the data are sufficiently dispersed over time. We recommend calibrating the mobile sensors when they coincide with reference monitoring stations.

Place, publisher, year, edition, pages
Göteborg: IVL Svenska Miljöinstitutet AB , 2023. Vol. 95, p. 104607-104607, article id 104607
Keywords [en]
Air quality monitoring, Particulate matters (PM2.5), Mobile low-cost sensors, Uncertainty analysis, Machine Learning, Snifferbike, Citizen science
Keywords [sv]
Luftkvalitet, medborgarforskning
National Category
Meteorology and Atmospheric Sciences
Identifiers
URN: urn:nbn:se:ivl:diva-4211DOI: 10.1016/j.scs.2023.104607Local ID: A2655OAI: oai:DiVA.org:ivl-4211DiVA, id: diva2:1772239
Funder
NordForsk, 95326
Note

A-rapport, A2655

Available from: 2023-06-21 Created: 2023-06-21 Last updated: 2023-06-21

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Hassani, AmirhosseinWatne, Ågot K.
By organisation
IVL Swedish Environmental Research Institute
In the same journal
Sustainable cities and society
Meteorology and Atmospheric Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 61 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf