Constructing an Understanding of Low-Cost Air Quality Monitoring

Project Partner: QuantAQ, Summer 2020

Project Summary: Air pollution is a major problem in cities throughout the world, but decision-makers have limited opportunities to improve air quality without understanding where and when air pollution is at its worst. QuantAQ builds low-cost air quality sensors to serve municipal air quality monitoring needs. While low-cost monitoring offers a unique opportunity to increase the number of monitoring sites, one tradeoff may be the provision of lower-quality data relative to reference sensors. Taylor developed tools to assist in the acquisition of AirNow air quality reference data and managed existing in-house data from low-cost sensors. The internship opportunity improved Taylor’s ability to work with large datasets and his comfort with Python computing language. Going forward, Taylor has continued his partnership with QuantAQ to assess the accuracy and precision of their low-cost sensor observations versus other reference air quality sensors, which would improve the applications of low-cost air quality monitoring and provide useful data to decision-makers and clients.

Project Deliverables: 

• Data Munging Tools – Quickly and efficiently munge air quality data into a single file.
• AirNow API Wrapper – Efficiently scrape data from AirNow in a developer-friendly package.
• Market Research – Building an understanding of existing low-cost air quality networks
• AirNow API Scraping tool blog post – Explaining the above (download here)