VOC speciation in the wildfire plume

Wildfires emit hundreds of reactive chemical species to the atmosphere, thus modifying incoming radiative transfer and changing atmospheric oxidant capacity. VOCs (Volatile Organic Compounds) are one of the critical chemical compounds to impact tropospheric ozone photochemistry. However, little information on wildfire VOC species is available in some biomass burning emission inventories, resulting in difficulty to be used for air quality forecasts. This study develops the lookup table of the wildfire VOC speciation ratios and investigates how VOC speciation affects ozone photochemistry during wildfire events. We first classify twenty VOC species (VOCi) to affect tropospheric ozone photochemistry. The VOCi and CO data are collected from aircraft field experiments such as We-CAN (https://www.eol.ucar.edu/ field_projects/ we-can/) and FIREX-AQ (https://csl.noaa.gov/projects/firex-aq/). The VOC speciation ratios are examined as a function of fire phase and plume age based on smoke flags, the modified combustion efficiency (MCE), flight height, and relationships between CO and CH3CN concentrations. The study shows that most wildfire VOC speciation ratios are distinguished between in-smoke and out-smoke cases. In general, the out-smoke case ratios are greater than those derived from National Emission Inventory Collaborative (NEIC) 2016v1 (NEIC, 2019). We also examine the VOC speciation ratios by different biomass burning types from global biomass burning inventories such as QFED (Darmenov and da Silva, 2013), GFED (Randerson et al., 2015), GBBEPx (Zhang et al., 2012), and FINN (Wiedinmyer et al., 2011). The finalized values are applied to the National Air Quality Forecasting Capability (NAQFC, Campbell et al., 2022) model and evaluated in the ozone photochemistry during the two field campaigns. The lookup table of the wildfire VOC speciation ratios will be worthwhile to utilize the bulk wildfire or biomass burning data with little information on the VOC speciation and to improve air quality forecasting capability.