Abstract
The impact of biomass burning (BB) on the atmospheric burden of volatile organic compounds (VOCs) is highly uncertain. Here we apply the GEOS-Chem chemical transport model (CTM) to constrain BB emissions in the western USA at ∼25km resolution. Across three BB emission inventories widely used in CTMs, the inventory-inventory comparison suggests that the totals of 14 modeled BB VOC emissions in the western USA agree with each other within 30%-40%. However, emissions for individual VOCs can differ by a factor of 1-5, driven by the regionally averaged emission ratios (ERs, reflecting both assigned ERs for specific biome and vegetation classifications) across the three inventories. We further evaluate GEOS-Chem simulations with aircraft observations made during WE-CAN (Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption and Nitrogen) and FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality) field campaigns. Despite being driven by different global BB inventories or applying various injection height assumptions, the model-observation comparison suggests that GEOS-Chem simulations underpredict observed vertical profiles by a factor of 3-7. The model shows small to no bias for most species in low-/no-smoke conditions. We thus attribute the negative model biases mostly to underestimated BB emissions in these inventories. Tripling BB emissions in the model reproduces observed vertical profiles for primary compounds, i.e., CO, propane, benzene, and toluene. However, it shows no to less significant improvements for oxygenated VOCs, particularly for formaldehyde, formic acid, acetic acid, and lumped ≥C3 aldehydes, suggesting the model is missing secondary sources of these compounds in BB-impacted environments. The underestimation of primary BB emissions in inventories is likely attributable to underpredicted amounts of effective dry matter burned, rather than errors in fire detection, injection height, or ERs, as constrained by aircraft and ground measurements. We cannot rule out potential sub-grid uncertainties (i.e., not being able to fully resolve fire plumes) in the nested GEOS-Chem which could explain the negative model bias partially, though back-of-the-envelope calculation and evaluation using longer-term ground measurements help support the argument of the dry matter burned underestimation. The total ERs of the 14 BB VOCs implemented in GEOS-Chem only account for half of the total 161 measured VOCs (∼75 versus 150ppbppm-1). This reveals a significant amount of missing reactive organic carbon in widely used BB emission inventories. Considering both uncertainties in effective dry matter burned (×3) and unmodeled VOCs (×2), we infer that BB contributed to 10% in 2019 and 45% in 2018 (240 and 2040GgC) of the total VOC primary emission flux in the western USA during these two fire seasons, compared to only 1%-10% in the standard GEOS-Chem.
| Original language | English |
|---|---|
| Pages (from-to) | 5969-5991 |
| Number of pages | 23 |
| Journal | Atmospheric Chemistry and Physics |
| Volume | 23 |
| Issue number | 10 |
| DOIs | |
| State | Published - May 31 2023 |
Funding
This study was supported by NASA (grant no. 80NSSC20M0166); NSF (EPSCoR Research Infrastructure grant no. 1929210 and AGS grant nos. 2144896 and 1950327); Montana NASA EPSCoR Research Initiation funding; and the NOAA Climate Program Office's Atmospheric Chemistry, Carbon Cycle and Climate program (grant no. NA20OAR4310296). The 2018 WE-CAN field campaign was supported by the US NSF (AGS grant no. 1650275, University of Montana; grant no. 1650786, Colorado State University; grant no. 1650288, University of Colorado Boulder; grant no. 1650493, University of Wyoming; grant no. 1652688, University of Washington; and grant no. 1748266, University of Montana) and NOAA (grant no. NA17OAR4310010, Colorado State University, and grant no. NA16OAR4310100, University of Montana). The Mt. Bachelor Observatory was supported by the NSF (grant no. AGS-1447832) and NOAA (contract no. RA-133R-16-SE-0758). This material was also based upon work supported by the NCAR, which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. The authors acknowledge high-performance computing resources and support from Cheyenne ( https://doi.org/10.5065/D6RX99HX ) provided by the NCAR Computational and Information Systems Laboratory, sponsored by the NSF, and the University of Montana's Griz Shared Computing Cluster (GSCC). We also thank Joel A. Thornton, Teresa L. Campos, Glenn S. Diskin, Dirk Richter, Patrick R. Veres, Joshua P. Schwarz, and Donald R. Blake for providing other WE-CAN and FIREX-AQ measurements used in this work.
| Funders | Funder number |
|---|---|
| 1650275 | |
| National Aeronautics and Space Administration | 80NSSC20M0166 |
| National Oceanic and Atmospheric Administration | NA20OAR4310296 |
| 1929210, 1950327, 2144896 | |
| Colorado State University Pueblo | 1650288 |
| University of Colorado Boulder | 1650493 |
| University of Wyoming | 1652688 |
| 1650786 |