Data Management Practices for Low-Cost Air Quality Sensors in Madison

Decades of research have demonstrated the harmful health effects of air pollution, with certain populations more at risk of illness due to higher exposure, underlying health vulnerabilities, or both. With support from a federal grant, the City of Madison is installing dozens of air quality sensors throughout the city to deliver information about when and where high levels of particulate matter pollution occur. Using ground-level data from the sensor network, coupled with satellite data, the city will be better equipped to identify potential sources of pollution and the neighborhoods most affected. With this knowledge, the city will partner with community organizations to provide greater awareness, education, and action to protect community health and address disparities. 

For this capstone, EAP students Cecilia Orth, Jing Ling Tan, Lizzy Kysela, and Nilanjan Biswas developed a research and engagement “roadmap” for the City of Madison to utilize this sensor network. This project proposed a suite of best practices for a Low-Cost Sensor (LCS) program, with a focus on the linkages between analyzing data from the low-cost sensors and engaging the community. 

The team recommends that LCS programs focus on the entire data lifecycle: from gathering to analyzing to communicating data. From interviews with 14 experts, they highlight the importance of practices such as centralizing data warehousing, tailoring analyses to identified community needs, and maintaining two-way communication channels with the public. These recommendations are tailored to the City of Madison but also speak to broader themes that cut across air quality monitoring and environmental justice in any community.