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.