Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes

• Main Authors: Edmund Seto, Graeme Carvlin, Elena Austin, Jeffry Shirai, Esther Bejarano, Humberto Lugo, Luis Olmedo, Astrid Calderas, Michael Jerrett, Galatea King, Dan Meltzer, Alexa Wilkie, Michelle Wong, Paul English
• Format: Article
• Language: English
• Published: MDPI AG 2019-09-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: air quality; sensors; community-engaged research; community-based participatory research; citizen science
• Online Access: https://www.mdpi.com/1660-4601/16/18/3268

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM_2.5 (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m^−3, are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.