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

糟糕的空气质量不仅与心律失常、肺癌、心脏病和死亡率增加等不良健康影响有关，还与一个地区的活力有关？S经济。这些问题在盐湖城(SLC)等城市尤为重要，这些城市的地形、气候和城市扩张共同造成了美国一些最糟糕的空气质量事件。像SLC这样的城市目前依赖于分布在大片地理区域的少量昂贵的传感器来测量空气质量，这使得当地、社区层面的测量无法确定。与此同时，新的大宗商品技术正在导致测量和交流空气质量的细粒度、基于社区的战略。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--专注于细颗粒物，并将SLC用作城市试验床--以产生社区级别的空气质量估计。该框架包括一个低成本传感器网络，通过公民科学努力和制造商套件的方法进行托管和维护。这项研究将在三个领域产生新的发展：(I)传感器开发，侧重于显著降低成本，并转向廉价、可穿戴的被动传感器；(Ii)计算建模，将不同类型的传感器测量与天气、地形和土地利用模式的信息相结合；以及(Iii)可视化界面设计，在空间和时间上交流空气质量估计，以及相关的不确定性测量。这些领域中的每一个都需要一种多学科方法，整合关于传感器网络、计算建模和数据感知的现有和新见解，并通过公民科学利用参与和连接的居民社区。

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.