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  1. null (Ed.)
    Short-term exposure to fine particulate matter (PM2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3−7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3−21.5 μg/m3, n(normalized)-RMSE: 9−24%), the wildfire (n: 46, RMSE: 2.6−4.0 μg/m3; nRMSE: 13.1−22.9%), and the PCAP (n: 96, RMSE: 4.9−5.7 μg/m3; nRMSE: 20.2−21.3%). They also revealed dramatic geospatial differences in PM2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency’s AirNow’s visualizations. Complementing the PM2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users. 
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  4. Citizen scientist efforts, wherein members of the public who are not professional scientists participate in active research, have been shown to effectively engage the public in STEM fields and result in valuable data, essential to answering pressing research questions. However, most citizen scientist efforts have been centered in colleges of science, and a limited number have crossed into research areas important to chemical engineering fields. In this work we report on the results of a project to recruit high school and middle school students across Utah’s Salt Lake Valley as citizen scientists and potential engineering students who work in partnership with chemical engineering researchers in an effort to create a distributed online network of air quality sensors. Middle and high school students were trained by undergraduate mentors to monitor and maintain their own outdoor air quality sensor with the help of teaching materials that were co-developed with Breathe Utah, a local community group concerned with air quality. With the help of these tailored teaching modules, students learned about the science behind air quality research and the difficulties common to physical measurements to better prepare them to analyze their data. Once trained, students are expected to become semi-independent researchers in charge of monitoring and maintaining their piece of a larger air quality map. We describe in this work the hurdles inherent in citizen science engagement within a chemical engineering research program and the means to address them. We describe successful means of engaging classrooms, training citizen scientists, obtaining faculty buy-in within the confines of state curricular demands, and addressing school administration concerns. With this model, we have directly engaged over 1,000 high school and over 3,000 middle school students. The project has resulted in a growing network of citizen-maintained sensors that contributes to a real-time air quality map. Student scientists may also use the sensors to participate in active research or conduct science fair projects. Student response to this citizen scientist project, where it may be measured, has been enthusiastic and almost wholly positive. 
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  5. Using low-cost electronic components and building blocks, we have developed an effective teaching module where students design and test light-scattering, air-quality sensors to introduce them to chemical and environmental engineering research. This module has been successful in engaging the public, developing citizen scientists, and bridging gaps in understanding. To date, we have visited over 30 middle school and high school classrooms and over 1,000 students. 
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