Download Smell Report Data

We take every precaution to protect any personally identifiable data. All information shown on the public map visualization for Smell MyCity is anonymized and location data is skewed to protect your privacy. Personal contact information you enter in the settings tab is only made available to the relevant local regulatory agency. Our backend database only contains anonymous User ID's created by your app service (Apple or Google Play).

This data is in a CSV format. You can open it in Google Sheets, Excel, or any text editor. Each smell report is a unique line item. Each field in the line is separated by commas. Data fields include:

  • Date and time of smell report
    • You can choose to download the data in UTC time or your local time
    • Your local time will be the time stamp on the machine you use to download the data
    • The date/time will be formatted as follows: 06/01/2016 01:47:52 -04:00
    • In the example above, the "-04:00" at the end of the string indicates time zone
  • Smell value (a number 1 through 5, with 5 being the most severe)
  • Skewed latitude and longitude coordinates of smell report location
    • To protect user privacy, actual locations are NOT shown
    • lat/lon coordinates are randomly assigned, within a short radius of the actual location
  • Zip code of smell report location
  • Smell description and symptoms linked to odor (if provided)
Select a participating city or enter ZIP codes:
Enter ZIP codes, seperated by comma:
Select a timezone:
Select a time range:
Download data in csv format:

Smell Data Analysis and Research

In the Pittsburgh region, using the smell reports and the air quality data from local agency monitoring stations, we developed a statistical model to predict upcoming smell events and send push notifications to inform communities. The dataset and the code are publicly available on GitHub. Our analysis indicated that smell events in Pittsburgh are related to the joint effect of wind directions and hydrogen sulfide readings. This research shows that engaging residents in documenting their experiences with pollution odors can help identify local air pollution patterns. To learn more, check our research paper below:

Yen-Chia Hsu, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting-Hao (Kenneth) Huang, and Illah Nourbakhsh. 2020. Smell Pittsburgh: Engaging Community Citizen Science for Air Quality. ACM Transactions on Interactive Intelligent Systems. 10, 4, Article 32. DOI: Preprint: