<p>Accurate sub-city fine particulate matter (PM_2.5) estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM_2.5, yet most cities globally lack the monitoring density necessary for sub-city-scale estimates. To estimate spatiotemporal variability in PM_2.5, we use machine learning (Random Forests; RFs) and concurrent PM_2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM_2.5 measurements from the Environmental Protection Agency&rsquo;s (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM_2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74&ndash;0.84 for CEAMS; R2=0.68&ndash;0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM_2.5 measurements. We found that the most important predictors of PM_2.5 were factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors were less important than spatiotemporal predictors because temporal variability exceeded spatial variability in our dataset. Finally, although concurrent AOD was an important predictor in our RF model for hourly PM_2.5, it did not improve model performance with high statistical significance. Regardless, we found that low-cost PM_2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM_2.5 over wintertime Denver. We also explored how the RF model performance and interpretation changes based on different model configurations and data processing.</p>