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Our team is developing a platform for low-cost air quality sensors that may be used for personal exposure monitoring, stationary continuous monitoring, or mobile applications (e.g., monitoring via drones). This project aims to improve not only calibration methods for these sensors, but also method of detecting de-calibration and ways to leverage separate data sets (e.g., local traffic) through machine learning. Overall this will result in a more robust and useful sensing system that will support air quality and public health investigations. Additionally, features like an associated app and database will make the platform more user friendly. The MetaSense monitors have been deployed in Los Angeles as part of a larger campaign to examine community questions around urban oil and gas development and we hope to utilize them for more community-based research applications in the future.

Project URL:

Geographic Scope: California and Colorado

Project Status: Complete - not recruiting volunteers

Participation Tasks: Learning, Measurement, Observation, Site selection and/or description,

Start Date: 2015-01-01

Project Contact:

Federal Government Sponsor:

NSF logo

Other Federal Government Sponsor: None

Fields of Science: Computers and technology, Health and medicine

Intended Outcomes: Research development, Civic and community, Individual learning,