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，也是最主要的 地球上过早发病和死亡的环境风险因素。人体暴露在颗粒物空气中 污染在很大程度上是使用安装在人口稠密地区的固定室外监测仪的数据来估计的。 这些数据可能没有反映农村社区或弱势群体的暴露情况。此外，还有 证据表明，这些数据没有捕捉到给定个人的独特暴露，他可能花了大部分时间 他们在室内的时间以及全天在多个地点(例如，家、工作)之间的过渡。因此， 有必要提高我们对人类接触颗粒物的时间和程度的理解 空气污染与特定的污染源以及个人的行为或任务有关。 SBIR第二阶段B项目的目标是提高我们评估人类接触PM的能力(和 随后的健康结果)通过将实时传感器与基于过滤器的取样器(用于现场重力测量)相耦合 验证)在一个小型、轻便、安静、可穿戴的设备中。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 利用来自该设备的数据，我们将使用流行的、 开源平台(R统计软件项目)。验证设备性能并评估其 为了对研究和环境正义社区有所帮助，我们建议在农村地区进行实地示范 以及加利福尼亚州圣华金山谷的城市社区-该地区以遭受高水平的罚款而闻名 (PM2.5)和粗颗粒物(PM10)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.