Validation of the UPAS+: A filter-based air sampler coupled with a suite of real-time sensors

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Particulate matter air pollution is a major contributor to the global burden of disease and the leading environmental risk factor for premature morbidity and mortality on the planet. Human exposure to particulate air pollution is largely estimated using data from stationary outdoor monitors installed in heavily populated areas. These data might not reflect exposures in rural communities or vulnerable subpopulations. Further, there is evidence that these data do not capture the unique exposure of a given individual, who likely spends most of their time indoors and transitions between multiple locations (e.g., home, work) throughout the day. Therefore, a need exists to improve our understanding of how the timing and magnitude of human exposure to particulate air pollution relates to specific sources as well as individual behaviors or tasks. The goal of this SBIR Phase IIB project is to improve our ability to assess human exposure to PM (and subsequent health outcomes) by coupling a real-time sensor with a filter-based sampler (for in-situ gravimetric validation) in a small, lightweight, quiet, wearable device. This coupling will improve our ability to estimate time- and location-resolved PM exposure for outdoor, indoor, and personal exposure monitoring. To help users harness the data from this device, we will develop data analysis and visualization software using a popular, open-source platform (the R Statistical Software Project). To validate device performance and to evaluate its usefulness to the research and environmental justice communities, we propose a field demonstration in rural and urban communities in the San Joaquin Valley of California—an area known to suffer from high levels of fine (PM2.5) and coarse (PM10) particulate matter pollution.