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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 PM2.5observations from publicly accessible fixed sites and a semicontinuously running mobile platform. Calibrated measurements of PM2.5made 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. 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. Low‐cost sensors that are calibrated for smoke compares favorably to air quality stations that abide by Federal Equivalent MethodsNighttime drainage flow from nearby mountains can confine smoke plumes to the center of mountain valleys and advect in smoke free airHigh density, low‐cost air quality sensors can provide key insights on interactions between smoke plumes and local‐scale meteorology