Author: Bryan

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.

Science Direct: Link

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.

Vu et al.: The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru

Vu, B. N., Tapia, V., Ebelt, S., Gonzales, G. F., Liu, Y., Steenland, K. (2021). The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru. Environmental Research, 199, 111226. 

Elsevier: Link 

Lima, Peru is one of the most populated and polluted cities in South America. However, epidemiological studies pertaining to air pollution in Lima, Peru have been limited by the lack of quality and consistent historical ground measurements. With the recent development of exposure estimates created by Vu et al.’s satellite-driven machine learning model, we conduct a time-series study to investigate the association between PM2.5 and asthma emergency department (ED) visits between 2010 to 2016. Results from this study showed that from the 103,974 cases of asthma during the study period across Lima, there was a 3.7% increase in ED visits for every 6.02 µg/m3 (IQR, interquartile range) increase in PM2.5 for same day exposure. When stratified by age,  children ages 18 and younger saw a 4.5% increase in ED visits for every IQR increase in PM2.5 while adults ages 19-64 saw a 6.0% increase in ED visits for every IQR increase in PM2.5. The elderly population saw a 16.0% decrease in ED visits for every IQR increase in PM2.5; however, this age group had a small population size and the models may not be robust. Results from this study provides additional literature on the use of satellite-derived exposure estimates in epidemiologic studies conducted in low- and middle-income countries.

 

 

 

Vu et al.: Developing an Advanced PM2.5 Exposure Model in Lima, Peru

We developed a machine learning model to estimate daily PM2.5 levels in Lima Peru. This is the first advanced model to incorporate satellite remote sensing data as well as variables from chemical transport and forecast models to predict daily PM2.5 levels in South America. Our results indicates that concentrations are low in the coast and rises with elevation up to the Andes Mountains due to prevailing coastal winds. This model also provides historical daily levels for epidemiological studies in a rapidly developing urban center.

 

 

Vu, B.N.; Sánchez, O.; Bi, J.; Xiao, Q.; Hansel, N.N.; Checkley, W.; Gonzales, G.F.; Steenland, K.; Liu, Y. Developing an Advanced PM2.5 Exposure Model in Lima, Peru. Remote Sens. 2019, 11, 641. https://doi.org/10.3390/rs11060641