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

颗粒物空气污染是全球疾病负担的主要贡献者， 这是地球上过早发病和死亡的环境风险因素。人体暴露于颗粒空气 污染主要是利用安装在人口稠密地区的固定户外监测器的数据来估计的。 这些数据可能并不反映农村社区或脆弱亚群的接触情况。此外， 有证据表明，这些数据没有捕捉到特定个体的独特暴露，他们可能花费了大部分时间， 他们在室内的时间和在多个位置之间的转换（例如，工作，工作（一整天）。因此，我们认为， 需要提高我们对人类暴露于颗粒物的时间和程度的理解， 空气污染与特定的来源以及个人行为或任务有关。 该SBIR第IIB阶段项目的目标是提高我们评估人类暴露于PM的能力（以及 随后的健康结果），通过耦合实时传感器与基于过滤器的采样器（用于原位重量分析 验证）在小型、轻量、安静的可穿戴设备中。这种耦合将提高我们估计时间的能力- 以及用于室外、室内和个人暴露监测的位置分辨PM暴露。帮助用户 利用这个设备的数据，我们将开发数据分析和可视化软件，使用流行的， R统计软件项目（R Statistical Software Project）验证器械性能并评价其 有用的研究和环境正义社区，我们建议在农村实地示范 加州圣华金河谷的城市社区--该地区因高罚款而闻名 （PM2.5）和粗（PM10）颗粒物污染。

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.