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One of the primary challenges associated with evaluating smoke models is the availability of observations. The limited density of traditional air quality monitoring networks makes evaluating wildfire smoke transport challenging, particularly over regions where smoke plumes exhibit significant spatiotemporal variability. In this study, we analyzed smoke dispersion for the 2018 Pole Creek and Bald Mountain Fires, which were located in central Utah. Smoke simulations were generated using a coupled fire-atmosphere model, which simultaneously renders fire growth, fire emissions, plume rise, smoke dispersion, and fire-atmosphere interactions. Smoke simulations were evaluated using PM 2.5 observations from publicly accessible fixed sites and a semicontinuously running mobile platform. Calibrated measurements of PM 2.5 made by low-cost sensors from the Air Quality and yoU (AQ&U) network were within 10% of values reported at nearby air quality sites that used Federal Equivalent Methods. Furthermore, results from this study show that low-cost sensor networks and mobile measurements are useful for characterizing smoke plumes while also serving as an invaluable data set for evaluating smoke transport models. Finally, coupled fire-atmosphere model simulations were able to capture the spatiotemporal variability of wildfire smoke in complex terrain for an isolated smoke event caused by local fires. Results here suggest that resolving local drainage flow could be critical for simulating smoke transport in regions of significant topographic relief. Plain Language Summary Smoke forecasts for wildfires in central Utah were evaluated using low-cost air quality sensors and measurements from an instrument attached to a public transit train car. Preliminary results from this study suggest that calibrated low-cost sensors can measure pollutant concentrations during wildfire smoke events within 10% of values measured by traditional air quality stations. A unique benefit of low-cost sensor and mobile measurement networks is that they can delineate the edges of smoke plumes and are useful for identifying small-scale processes that effect smoke plume dispersion. Smoke forecasts from a weather prediction model were able to capture the timing of a smoke plume, which inundated the Salt Lake Valley during the morning of 15 September 2018. However, local observations indicated that forecasted smoke was overpredicted by a factor of 2. Smoke forecast errors could potentially be attributed to fire growth errors in the fire spread model used within the weather prediction model.

Short-term exposure to fine particulate matter (PM 2.5 ) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM 2.5 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 PM 2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM 2.5 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/m 3 , n(normalized)-RMSE: 14.9−24%), the wildfire (n: 46, RMSE: 2.6−4.0 μg/m 3 ; nRMSE: 13.1−22.9%), and the PCAP (n: 96, RMSE: 4.9−5.7 μg/m 3 ; nRMSE: 20.2−21.3%). They also revealed dramatic geospatial differences in PM 2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency's AirNow visualizations. Complementing the PM 2.5 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 PM 2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users.