Smell Pittsburgh: Engaging Community Citizen Science for AirQuality

Yen Chia Hsu, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting Hao (Kenneth) Huang, Illah Nourbakhsh

Research output: Contribution to journalArticlepeer-review

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 http://smellpgh.org.

Original languageEnglish (US)
Article number3369397
JournalACM Transactions on Interactive Intelligent Systems
Volume10
Issue number4
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Artificial Intelligence

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