• Energy Research

Carbonaceous emissions from wildfires are a dynamic mixture of gases and particles that have important impacts on air quality and climate. Emissions that feed atmospheric models are estimated using burned area and fire radiative power (FRP) methods that rely on satellite products. These approaches show wide variability and have large uncertainties, and their accuracy is challenging to evaluate due to limited aircraft and ground measurements. Here, we present a novel method to estimate fire plume-integrated total carbon and speciated emission rates using a unique combination of lidar remote sensing aerosol extinction profiles and in situ measured carbon constituents. We show strong agreement between these aircraft-derived emission rates of total carbon and a detailed burned area-based inventory that distributes carbon emissions in time using Geostationary Operational Environmental Satellite FRP observations (Fuel2Fire inventory, slope = 1.33 ± 0.04, r2 = 0.93, and RMSE = 0.27). Other more commonly used inventories strongly correlate with aircraft-derived emissions but have wide-ranging over- and under-predictions. A strong correlation is found between carbon monoxide emissions estimated in situ with those derived from the TROPOspheric Monitoring Instrument (TROPOMI) for five wildfires with coincident sampling windows (slope = 0.99 ± 0.18; bias = 28.5%). Smoke emission coefficients (g MJ-1) enable direct estimations of primary gas and aerosol emissions from satellite FRP observations, and we derive these values for many compounds emitted by temperate forest fuels, including several previously unreported species.

Measurement-based estimates of greenhouse gas (GHG) emissions from complex industrial operations are challenging to obtain, but serve as an important, independent check on inventory-reported emissions. Such top–down estimates, while important for oil and gas (O&G) emissions globally, are particularly relevant for Canadian oil sands (OS) operations, which represent the largest O&G contributor to national GHG emissions. We present a multifaceted top–down approach for estimating CO₂ emissions that combines aircraft-measured CO₂/NOₓ emission ratios (ERs) with inventory and satellite-derived NOₓ emissions from Ozone Monitoring Instrument (OMI) and TROPOspheric Ozone Monitoring Instrument (TROPOMI) and apply it to the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. Historical CO₂ emissions were reconstructed for the surface mining region, and average top–down estimates were found to be >65% higher than facility-reported, bottom–up estimates from 2005 to 2020. Higher top–down vs. bottom–up emissions estimates were also consistently obtained for individual surface mining and in situ extraction facilities, which represent a growing category of energy-intensive OS operations. Although the magnitudes of the measured discrepancies vary between facilities, they combine such that the observed reporting gap for total AOSR emissions is ≥(31 ± 8) Mt for each of the last 3 years (2018–2020). This potential underestimation is large and broadly highlights the importance of continued review and refinement of bottom–up estimation methodologies and inventories. The ER method herein offers a powerful approach for upscaling measured facility-level or regional fossil fuel CO₂ emissions by taking advantage of satellite remote sensing observations.