Top-Down Estimation of Particulate Matter Emissions from Extreme Tropical Peatland Fires Using Geostationary Satellite Fire Radiative Power Observations.

Extreme fires in the peatlands of South East (SE) Asia are arguably the world's greatest biomass burning events, resulting in some of the worst ambient air pollution ever recorded (PM > 3000 µg·m). The worst of these fires coincide with El Niño related droughts, and include huge areas of smouldering combustion that can persist for months. However, areas of flaming surface vegetation combustion atop peat are also seen, and we show that the largest of these latter fires appear to be the most radiant and intensely smoke-emitting areas of combustion present in such extreme fire episodes. Fire emissions inventories and early warning of the air quality impacts of landscape fire are increasingly based on the fire radiative power (FRP) approach to fire emissions estimation, including for these SE Asia peatland fires. "Top-down" methods estimate total particulate matter emissions directly from FRP observations using so-called "smoke emission coefficients" [; g·MJ], but currently no discrimination is made between fire types during such calculations. We show that for a subset of some of the most thermally radiant peatland fires seen during the 2015 El Niño, the most appropriate is around a factor of three lower than currently assumed (~16.8 ± 1.6 g·MJ vs. 52.4 g·MJ). Analysis indicates that this difference stems from these highly radiant fires containing areas of substantial flaming combustion, which changes the amount of particulate matter emitted per unit of observable fire radiative heat release in comparison to more smouldering dominated events. We also show that even a single one of these most radiant fires is responsible for almost 10% of the overall particulate matter released during the 2015 fire event, highlighting the importance of this fire type to overall emission totals. Discriminating these different fires types in ways demonstrated herein should thus ultimately improve the accuracy of SE Asian fire emissions estimates derived using the FRP approach, and the air quality modelling which they support.