Smell Pittsburgh: Community-empowered mobile smell reporting system

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

Research output: Contribution to conferencePaperpeer-review

7 Scopus citations

Abstract

Urban air pollution has been linked to various human health considerations, 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. 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.

Original languageEnglish (US)
Pages65-79
Number of pages15
DOIs
StatePublished - Jan 1 2019
Event24th ACM International Conference on Intelligent User Interfaces, IUI 2019 - Marina del Ray, United States
Duration: Mar 17 2019Mar 20 2019

Conference

Conference24th ACM International Conference on Intelligent User Interfaces, IUI 2019
CountryUnited States
CityMarina del Ray
Period3/17/193/20/19

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction

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