A novel representation of time-resolved particle emissions from pyrolyzing wood

Biomass burning is responsible for emitting 90 % of total primary organic aerosols into the atmosphere. Products from pyrolysis are released to the atmosphere when gas -phase reactions are unable to oxidize them, and even narrow windows of release can produce a large fraction of the overall particle emissions. This work introduces an approach to predict particle emission during high -emitting periods of biomass burning. We trained a regression -based machine learning model to predict particle emission using data from pyrolysis experiments covering seven wood types under controlled conditions. The model considered experimental mass -loss rate (MLR) of the wood, wood density, and heating conditions as features for prediction of measured particle -phase real-time emissions. After training, the experimental mass -loss rate was replaced with modeled MLR from a two-dimensional finite volume model of pyrolysis to predict particle emission rate using only wood properties and the boundary conditions of pyrolysis. The hybrid model explains 80 % of the variance in particle emission and has comparable error metrics with the machine learning model that relies on experimental MLR. Errors are greatest during the initial transient where pyrolysis is occurring near the surface.