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Wildland fire smoke contains large amounts of PM 2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM 2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM 2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM 2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM 2.5 during the Camp Fire episode. • Incorporation of PurpleAir low-cost sensors expands spatial modeling domain. • Application of SMOTE reduces model underestimation of wildland fire PM 2.5. • Our model (R2 = 0.92) can estimate hourly PM 2.5 during the Camp Fire episode. [ABSTRACT FROM AUTHOR]

Epidemiologic studies have used aerosol optical depth (AOD)-PM 2.5 as a proxy for ambient PM 2.5 in urban and rural areas, even though its validation with air monitors has only occurred in urban areas. The contribution of elevated AOD-PM 2.5 on respiratory-cardiovascular true positive (TP) cases, exposed to high PM 2.5 , and true negative (TN) controls, not exposed to elevated PM 2.5 , was evaluated in 72 Community Multiscale Air Quality (CMAQ) grids with (urban) and without (rural) air monitors. The odds ratio (OR) algorithm and the newly developed beta (ẞ) algorithm were used to evaluate the reliability and validity of TP cases, and TN controls in grids with and without air monitors. Four experimental AOD-PM 2.5 fused surfaces and four health outcomes were evaluated. Only the linear predictor (ẞ) algorithm reliably and correctly identified TP cases and TN controls, with probabilities ~1.00. The OR algorithm only identified TN controls, with probabilities ~1.00, and significantly overestimated the percentage of TP cases. Regression analyses demonstrated that the OR algorithm's accuracy could be improved if the number of cases for all health outcomes was increased 50.8% in all grids and 73.9% in grids without monitors. Since the number and percentage of TP cases and TN controls were similar in grids with and without air monitors, this outcome suggests that the AOD-PM 2.5 and health outcome concentration-response function evaluated in grids with monitors also holds in grids without air monitors. The possible use of AOD-PM 2.5 fused surfaces, as another epidemiologic tool, to assess elevated ambient PM 2.5 concentration levels to respiratory-cardiovascular hospital events in rural areas is discussed. Contribution of AOD-PM 2.5 Bayesian Surfaces to Respiratory-Cardiovascular Hospital Events in Urban and Rural Areas in Baltimore, Maryland, USA: New Analytical Method Correctly Identified True Positive Cases and True Negative Controls Figure Caption: New analytical method correctly detected case-control status of participants exposed to PM 2.5 in the case-crossover study. [Display omitted] • Accuracy of respiratory-cardiovascular case-control evaluated in AOD-PM 2.5 study. • OR method overestimated true positive cases and correctly detected true negative controls. • The New Beta algorithm accurately detected true positive cases and true negative controls. • The new method confirmed the correct classification cases and controls in urban and rural areas. [ABSTRACT FROM AUTHOR]