Detecting air pollution episodes and exploring their impacts using low-cost sensor data and simultaneous community symptom and odor reports

Abstract As a team of community organizers and academic researchers, we conducted a community-based participatory exploration of industrial pollution impacts in Cherokee Forest, a fenceline community adjacent to an industrial park in Pascagoula, Mississippi. Using a derivative-based episode detection algorithm with low-cost uncalibrated sensor signal data sensitive to VOCs, ammonia, amine-series, and sulfurous odors, we identified frequent and intense pollution episodes within the community. According to wind data, these episodes came from the direction of the industrial park and often correlated with increased symptom and odor reports. Additionally, metals biomarker toenail sampling revealed elevated nickel levels in a subset of resident children, which is an industrial pollutant of concern in this community. The findings have supported Cherokee Concerned Citizens’ advocacy efforts to mobilize the community and engage with regulatory agencies. Our work demonstrates a transferable methodology for using low-cost sensors and community reports to document industrial pollution impacts in fenceline communities.