Vu et al.: Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

Vu, B.N., Bi, J., Wang, W., Huff, A., Kondragunta, S., Liu, Y. (2022). Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California. Remote Sensing of Environment, 271, 112890.

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Particulate matter from wildland fire smoke can traverse hundreds of kilometers from where they originated and result in excess morbidity and mortality. However, estimating PM2.5 levels in and around wildland fires poses many challenges including lack of monitors at or near smoke plumes and temporally coverage since monitors generally obtain daily measurements yet smoke plumes continually change throughout the day due to wind. In this paper, we estimated total PM2.5 concentration during the Camp Fire episode, one of the deadliest wildland fires in California history. We used a random forest (RF) model to calibrate hourly regulatory monitor measurements (AQS maintained by the EPA) and low-cost sensors (PurpleAir) to Geostationary Operational Satellite-16 (GOES-16)’s AOD and fire spot detection variables, High-Resolution Rapid Refresh (HRRR) meteorology, and ancillary variables including land-use, distance to road, and a convolutional layer calculated for each hour. We tried three separate approaches: 1) an AQS-only model; 2) AQS + weighted PurpleAir model; and 3) AQS + weighted PurpleAir + SMOTE model. Synthetic Minority-Oversampling Technique (SMOTE) was applied to enhance and bolster the number of extremely high observations and improve model performance. The AQS-only model achieved and out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and a spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. The AQS + weighted PurpleAir model’s OOB and spatial and temporal CV R2 (RMSE) was 0.86 (9.52 μg/m3), 0.75 (14.93 μg/m3), and 0.79 (11.89 μg/m3), respectively. The AQS + weighted PurpleAir + SMOTE model’s OOB and spatial and temporal CV R2 (RMSE) was 0.92 (10.44 μg/m3), 0.84 (12.36 μg/m3), and 0.85 (14.88 μg/m3), respectively. Results from this study indicate that increased measurements as well as the application of SMOTE not only improves model performance but also residual errors, and may aid in epidemiological studies investigating intense and acute exposure to wildland fire smoke.     

 

Density Scatter Plot
Density Scatter Plots of spatial and temporal CV from left to right: Top (spatial CVs for AQS-only, AQS + weighted PurpleAir, AQS + weighted PurpleAir + SMOTE); Bottom (temporal CVs for AQS-only, AQS + weighted PurpleAir, AQS + weighted PurpleAir + SMOTE)

 

Blowout Maps
Prediction Maps at noon on November 16th, 2018. Blowouts from left to right: AQS-only model, AQS + weighted PurpleAir, AQS + weight PurpleAir + SMOTE.

 

Comparison to true-color composite
Comparison of prediction maps to true-color composites from MODIS instruments.

Stowell et al.: Asthma exacerbation due to climate change-induced wildfire smoke in the Western US

Climate change and human activities have drastically altered the natural wildfire balance in the Western US and increased population health risks due to exposure to pollutants from fire smoke. Using dynamically downscaled climate model projections, we estimated additional asthma emergency room visits and hospitalizations due to exposure to smoke fine particulate matter (PM2.5) in the Western US in the 2050s. Under the ICLUS A2 population scenario, we estimated the smoke-related asthma events could increase at a rate of 15.1 visits per 10 000 persons in the Western US, with the highest rates of increased asthma (25.7–41.9 per 10 000) in Idaho, Montana, Oregon, and Washington. Estimated healthcare costs of smoke-induced asthma exacerbation to be over $1.5 billion during a single future fire season. Open access article link here.