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Smell Pittsburgh: Engaging Community Citizen Science for Air Quality

Published: 08 November 2020 Publication History

Abstract

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 Smell Pittsburgh, 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 https://smellpgh.org.

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cover image ACM Transactions on Interactive Intelligent Systems
ACM Transactions on Interactive Intelligent Systems  Volume 10, Issue 4
Special Issue on IUI 2019 Highlights
December 2020
274 pages
ISSN:2160-6455
EISSN:2160-6463
DOI:10.1145/3430697
Issue’s Table of Contents
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Publication History

Published: 08 November 2020
Online AM: 07 May 2020
Accepted: 01 December 2019
Revised: 01 October 2019
Received: 01 August 2019
Published in TIIS Volume 10, Issue 4

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  1. Community citizen science
  2. air quality
  3. community empowerment
  4. machine learning
  5. push notifications
  6. regression analysis
  7. smell
  8. survey
  9. sustainable HCI
  10. system
  11. visualization

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