Uncertainties from biomass burning aerosols in air quality models obscure public health impacts in Southeast Asia

• Main Authors: Marvin, Margaret R., Palmer, Paul I., Yao, Fei, Latif, Mohd Talib, Khan, Md Firoz
• Format: Article
• Published: Copernicus Gesellschaft MBH 2024
• Subjects: Q Science (General); QE Geology; S Agriculture (General)
• Online Access: http://eprints.um.edu.my/45409/; https://doi.org/10.5194/acp-24-3699-2024
• Institution: Universiti Malaya

Models suggest that biomass burning causes thousands of premature deaths annually in Southeast Asia due to excessive exposure to particulate matter (PM) in smoke. However, measurements of surface air quality are sparse across the region, and consequently estimates for the public health impacts of seasonal biomass burning, are not well constrained. We use the nested GEOS-Chem model of chemistry and transport (horizontal resolution of 0.25 degrees x 0.3125 degrees ) to simulate atmospheric composition over Southeast Asia during the peak burning months of March and September in the moderate burning year of 2014. Model simulations with GEOS-Chem indicate that regional surface levels of PM 2.5 (fine particulate matter with a diameter <= 2.5 mu m) greatly exceed World Health Organization guidelines during the burning seasons, resulting in up to 10 000 premature deaths in a single month. However, the model substantially underestimates the regional aerosol burden compared to satellite observations of aerosol optical depth (AOD) (20 %-52 %) and ground-based observations of PM (up to 54 %), especially during the early burning season in March. We investigate potential uncertainties limiting the model representation of biomass burning aerosols and develop sensitivity simulations that improve model-measurement agreement in March (to within 31 %) and increase the estimated number of PM 2.5 -related premature deaths that month by almost half. Our modifications have a much smaller impact on the same metrics for September, but we find that this is due to canceling errors in the model. Compared to PM 2.5 simulated directly with GEOS-Chem, PM 2.5 derived from satellite AOD is less sensitive to model uncertainties and may provide a more accurate foundation for public health calculations in the short term, but continued investigation of uncertainties is still needed so that model analysis can be applied to support mitigation efforts. Further reduction of uncertainties can be achieved with the deployment of more aerosol measurements across Southeast Asia.