CPS: Synergy: A Layered Framework of Sensors, Models, Land-Use Information and Citizens for Understanding Air Quality in Urban Environments

空气质量差不仅与不良健康影响（如心律失常、肺癌、心脏病和死亡率增加）有关，而且还与一个地区的经济活力有关。这些问题在盐湖城 (SLC) 等城市尤为重要，那里的地形、气候和城市扩张共同造成了该国空气质量最差的情况。 像 SLC 这样的城市目前依靠分布在大片地理区域的少量昂贵传感器来测量空气质量，从而无法确定本地、社区级别的测量结果。与此同时，新的商品技术正在催生细粒度、基于社区的空气质量测量和交流策略。利用这两种方法，该项目将开发和部署一个密集、分布式和动态的空气质量网络物理框架（重点关注细颗粒物并使用 SLC 作为城市测试平台），以生成社区级别的空气质量估计值。 该框架包括一个低成本传感器网络，通过公民科学努力和创客套件方法进行托管和维护。这项研究将在三个领域带来新颖的发展：（i）传感器开发，重点是大幅降低成本和转向廉价、可穿戴、无源传感器； (ii) 将异构传感器测量结果与天气、地形和土地利用模式信息相结合的计算模型； (iii) 可视化界面设计，传达空间和时间上的空气质量估计值，以及相关的不确定性测量结果。每个领域都需要采用多学科方法，整合有关传感器网络、计算建模和数据意义构建的现有和新颖见解，并通过公民科学利用参与和相互联系的居民社区。

Poor air quality has been linked to not just adverse health effects such as increased incidence of cardiac arrhythmia, lung cancer, heart disease, and mortality, but also to the vitality of a region?s economy. These issues are particularly important in cities such as Salt Lake City (SLC), where topography, climate, and urban expansion combine to create some of the worst air quality episodes in the country. Cities like SLC currently rely on small numbers of expensive sensors placed across a large geographic area to measure air quality, making local, neighborhood-level measurements impossible to determine. Meanwhile, new commodity technologies are leading to fine-grained, community-based strategies for measuring and communicating air quality. Leveraging both of these approaches, this project will develop and deploy a dense, distributed, and dynamic air quality cyber-physical framework -- focusing on fine particulate matter and using SLC as an urban testbed -- to produce neighborhood-level estimates of air quality. The framework includes a network of low-cost sensors, hosted and maintained through a citizen science effort and maker-kit approach.This research will result in novel developments in three areas: (i) sensor development that focuses on dramatically reducing cost and a movement toward cheap, wearable, passive sensors; (ii) computational modeling that combines heterogeneous sensor measurements with information about weather, topography, and land use patterns; and (iii) visualization interface design that communicates air quality estimates over space and time, coupled with related uncertainty measurements. Each of these areas requires a multidisciplinary approach that integrates existing and novel insights about sensor networks, computational modeling, and sense-making of data, as well as leveraging an engaged and connected community of residents through citizen science.