Smell Pittsburgh: Engaging Community Citizen Science for Air Quality

<jats:p> Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed <jats:bold> <jats:italic>Smell Pittsburgh/jats:italic /jats:bold , a system that enables community members to report odors and track where these odors are frequently concentrated. All smell report data are publicly accessible online. These reports are also sent to the local health department and visualized on a map along with air quality data from monitoring stations. This visualization provides a comprehensive overview of the local pollution landscape. Additionally, with these reports and air quality data, we developed a model to predict upcoming smell events and send push notifications to inform communities. We also applied regression analysis to identify statistically significant effects of push notifications on user engagement. Our evaluation of this system demonstrates that engaging residents in documenting their experiences with pollution odors can help identify local air pollution patterns and can empower communities to advocate for better air quality. All citizen-contributed smell data are publicly accessible and can be downloaded from <jats:italic> <jats:bold> /jats:italic . /jats:p

© Hsu 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'ACM Transactions on Interactive Intelligent Systems',



Work Title Smell Pittsburgh: Engaging Community Citizen Science for Air Quality
Open Access
  1. Yen-Chia Hsu
  2. Jennifer Cross
  3. Paul Dille
  4. Michael Tasota
  5. Beatrice Dias
  6. Randy Sargent
  7. Ting-Hao (Kenneth) Huang
  8. Illah Nourbakhsh
License In Copyright (Rights Reserved)
Work Type Article
  1. Association for Computing Machinery (ACM)
Publication Date December 3, 2020
Publisher Identifier (DOI)
  1. 10.1145/3369397
  1. ACM Transactions on Interactive Intelligent Systems
Deposited September 09, 2021




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Work History

Version 1

  • Created
  • Added 2005.06111-2.pdf
  • Added Creator Yen-Chia Hsu
  • Added Creator Jennifer Cross
  • Added Creator Paul Dille
  • Added Creator Michael Tasota
  • Added Creator Beatrice Dias
  • Added Creator Randy Sargent
  • Added Creator Ting-Hao (Kenneth) Huang
  • Added Creator Illah Nourbakhsh
  • Published
  • Updated
  • Updated