the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Nov 2022
Constraining emissions of volatile organic compounds from western US wildfires with WE-CAN and FIREX-AQ airborne observations
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 US at ~25 km resolution. Across three BB emission inventories widely used in CTMs, the total of 14 modeled BB VOC emissions in the western US agree with each other within 30–40 %. However, emissions for individual VOC differ by up to a factor of 5 (i.e., lumped ≥ C4 alkanes), driven by the regionally averaged emission ratios (ERs) among 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, 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 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. 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 model negative bias partially, though the back-of-the-envelope calculation and evaluation using longer-term ground measurements help increase the argument of the dry matter burned underestimation. The ERs of the 14 BB VOCs implemented in GEOS-Chem account for about half of the total 161 measured VOCs (~75 versus 150 ppb ppm-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 and unmodeled VOCs, we infer that BB contributed up to 10 % in 2019 and 45 % in 2018 (240 and 2040 GgC) of the total VOC primary emission flux in the western US during these two fire seasons, compared to only 1–10 % in the standard GEOS-Chem.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-1107', Anonymous Referee #1, 22 Dec 2022
Overall this is a useful addition to the literature on fire emissions of VOCs in the western US. After addressing my comments, it should be published:
Major comments:
- Lines 31-33: Why is the underestimation likely due to DM underestimates and not other causes?
- 206 - 224: Ideally all inventories will be used with the same vertical injection scheme no matter what the baseline in GEOS-Chem is. Can you clarify that you used the same across all three or four - either by putting all into the boundary layer or using the GFAS or QFED default schemes for all of them? Although the baseline in GEOS-Chem may use different approaches, that’s just because different researchers have used them for different purposes, not because that is a scientifically appropriate approach. Furthermore, these default schemes have changed over time and used to all emit into the boundary layer with the option to turn on the Fisher et al. (2014) scheme for all/any of them. It should not make much of a difference as you show later in the manuscript, but it would be better to ne consistent.
- Also the diurnal representation should really be standardized across the inventories no matter what is in the baseline GEOS-Chem - otherwise that’s another variable that’s not consistent across datasets, but which could be. It would likely make sense to apply the WRAP diurnal cycle to GFED for consistency sake.
- Line 288-289: How does this plot show that regionally averaged ERs make up the majority of disagreement and not DM burned? The back of the envelope calculation that you discuss seems to indicate a large DM underestimate.
Minor comments:
- Lines 57-58 - please add a citation.
- Line 70: the comma before “even though” should be a semicolon
- Line 92: missing a semicolon between “estimates” and “thus”
- Line 99: I’m not sure that I agree with the parenthetical “(more accurate)” here. Perhaps the top-down estimates match observations better in some regions, but it may be “correct” for the wrong reasons, such as how QFED scales up emissions to match AOD, which itself may be biased high for other reasons, such as optical property uncertainties.
- Figure S7: I’m fairly sure that with GFED4s, GEOS-Chem does include EFs for lumped alkanes and also alkenes per the van Der Werf spreadsheet that helps us calculate them. Please confirm.
- Line 324: “being” should be “is”
- Line 363: should be “Campaigns targeting plumes…” And adding “like WE-CAN” would also be helpful context here.
- Line 392: please don’t use “efficient” twice in one sentence.
- Figure 7: I’m fairly sure that GFED has EFs for acetaldehyde and xylenes. Please check and add.
Citation: https://doi.org/10.5194/egusphere-2022-1107-RC1 -
RC2: 'Comment on egusphere-2022-1107', Anonymous Referee #2, 07 Jan 2023
In this manuscript, the authors evaluated model predictions of volatile organic compound emissions from wildfires in the western United States. They performed GEOS-Chem simulations using different inventories and compared predicted VOC concentrations with those measured in 2 recent field campaigns, WE-CAN and FIREX-AQ. They found that the model systemically underpredicted concentrations regardless of the inventory used, and tripling one inventory can reconcile the model-measurement difference. There are more intricate differences when comparing individual VOC species, and the authors examined those in detail as well and proposed potential reasons. The manuscript is well written and easy to follow, and the results are significant and insightful. I recommend the manuscript be published. My comments are mostly suggestions and not required changes for publication.
In Line 146, the authors mentioned that xylenes in PTR could potentially be overestimated due to fragmentation in the mass spectrometer. I think this issue is very likely and should be explored further. The authors indicated that they would explore the measurement issue in Sect 4, but this issue was not specifically looked at in Sect 4. I wonder if the abundance of oxygenated aromatic compounds from BB emissions could factor into the overestimation of xylenes.
In Lines 355-360, the authors investigated why oxygenated compounds like formaldehyde, acetic acid are underestimated, even more so than other VOCs. While I agree that secondary in-plume production is very likely, I wonder if there could be other factors that cannot be ruled out. For example, these are all water soluble compounds. Could the wet deposition be overestimated in the model? Also, could there be production of these secondary species from other non-BB species? As mentioned earlier, this is more a suggestion for a deeper investigation, and I am just curious.
I noticed that there is always a jump in concentrations at two heights (530 hPa and 650 hPa) in the measured data. For some species, the model can reproduce this trend, but not always. Does this have to do with the injection heights? I am wondering if this will help diagnose the underestimation as well.
Line 421-425: I do not fully understand this. How would aromatic hydrocarbon/hydrocarbon relationship help diagnose the OH issue? Is this based on looking at the ratio of two hydrocarbons with different reactivities to look at OH exposure (i.e. hydrocarbon clock)? If not, can the hydrocarbon clock be used to assess OH exposure?
Minor comments:
Line 106: I cannot tell if it should be “though” or “through”. Both could potentially make sense.
Line 318: typo in xylene
Figure 1: it may be useful to indicate the ground site locations in the biomass burning panel too to give an idea of how far these ground sites are from the wildfires
Figure 2: for the species plotted with no bars, they are presumably not considered by each inventory? Might be helpful to indicate with n/a.
Citation: https://doi.org/10.5194/egusphere-2022-1107-RC2 -
EC1: 'Comment on egusphere-2022-1107 - Third reviewer comment', Holger Tost, 10 Jan 2023
This is a reviewer comment who due to technical issues was uploaded by the editor:
This is a nice paper that addresses a topic of great interest to atmospheric chemistry community. The authors use two aircraft campaigns, ground-based measurements and a nested model with three emission inventories to examine the biomass burning emissions in western US. They find a large underestimate of VOCs by current biomass burning emission inventories, which may have a large impact on ozone and aerosol air quality. The paper is well written and suited for ACP. I only have a few comments:
1. Dry Matter (DM) vs. Emission ratios. The authors argue that “the regionally averaged ERs dominate disagreement in emission estimates for most VOCs across the three inventories”. Looking at Figure 2, the difference in emission ratios seem to be small for some species. On the other hand, the authors say in Line 293 “these inventories agree on the amount of effective DM burned within 40 %”. It is unclear how much DM can account for the difference. It might be useful to add a plot for DM from different emission inventories.
2. Figure 4 vs. Figure 6. In Figure 4, the authors attribute the underestimate of OVOCs in their model to large secondary sources of OVOCs in biomass burning plumes that are missing in the model. However, it is shown in Figure 6 that observed OVOCs vs. CO slopes are well reproduced by their model. Are the authors assuming that the transects sampled here in Figure 6 have no secondary production of OVOCs? Some clarification is needed to reconcile Figures 4 and 6. Also it would be good to indicate the ages of those transected plumes.
3. It might be useful to point out the photochemical lifetimes of VOCs in the model. This will help to understand the impact of 3xGFAS in Figures 4 and 9.
4. What is the difference between QFED and GFAS despite that they both use FRP? This may help the reader to better understand the paper. Why are there missing VOC species in some inventories in Figures 2 and 7?
Citation: https://doi.org/10.5194/egusphere-2022-1107-EC1 - AC1: 'Comment on egusphere-2022-1107', Lixu Jin, 23 Mar 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-1107', Anonymous Referee #1, 22 Dec 2022
Overall this is a useful addition to the literature on fire emissions of VOCs in the western US. After addressing my comments, it should be published:
Major comments:
- Lines 31-33: Why is the underestimation likely due to DM underestimates and not other causes?
- 206 - 224: Ideally all inventories will be used with the same vertical injection scheme no matter what the baseline in GEOS-Chem is. Can you clarify that you used the same across all three or four - either by putting all into the boundary layer or using the GFAS or QFED default schemes for all of them? Although the baseline in GEOS-Chem may use different approaches, that’s just because different researchers have used them for different purposes, not because that is a scientifically appropriate approach. Furthermore, these default schemes have changed over time and used to all emit into the boundary layer with the option to turn on the Fisher et al. (2014) scheme for all/any of them. It should not make much of a difference as you show later in the manuscript, but it would be better to ne consistent.
- Also the diurnal representation should really be standardized across the inventories no matter what is in the baseline GEOS-Chem - otherwise that’s another variable that’s not consistent across datasets, but which could be. It would likely make sense to apply the WRAP diurnal cycle to GFED for consistency sake.
- Line 288-289: How does this plot show that regionally averaged ERs make up the majority of disagreement and not DM burned? The back of the envelope calculation that you discuss seems to indicate a large DM underestimate.
Minor comments:
- Lines 57-58 - please add a citation.
- Line 70: the comma before “even though” should be a semicolon
- Line 92: missing a semicolon between “estimates” and “thus”
- Line 99: I’m not sure that I agree with the parenthetical “(more accurate)” here. Perhaps the top-down estimates match observations better in some regions, but it may be “correct” for the wrong reasons, such as how QFED scales up emissions to match AOD, which itself may be biased high for other reasons, such as optical property uncertainties.
- Figure S7: I’m fairly sure that with GFED4s, GEOS-Chem does include EFs for lumped alkanes and also alkenes per the van Der Werf spreadsheet that helps us calculate them. Please confirm.
- Line 324: “being” should be “is”
- Line 363: should be “Campaigns targeting plumes…” And adding “like WE-CAN” would also be helpful context here.
- Line 392: please don’t use “efficient” twice in one sentence.
- Figure 7: I’m fairly sure that GFED has EFs for acetaldehyde and xylenes. Please check and add.
Citation: https://doi.org/10.5194/egusphere-2022-1107-RC1 -
RC2: 'Comment on egusphere-2022-1107', Anonymous Referee #2, 07 Jan 2023
In this manuscript, the authors evaluated model predictions of volatile organic compound emissions from wildfires in the western United States. They performed GEOS-Chem simulations using different inventories and compared predicted VOC concentrations with those measured in 2 recent field campaigns, WE-CAN and FIREX-AQ. They found that the model systemically underpredicted concentrations regardless of the inventory used, and tripling one inventory can reconcile the model-measurement difference. There are more intricate differences when comparing individual VOC species, and the authors examined those in detail as well and proposed potential reasons. The manuscript is well written and easy to follow, and the results are significant and insightful. I recommend the manuscript be published. My comments are mostly suggestions and not required changes for publication.
In Line 146, the authors mentioned that xylenes in PTR could potentially be overestimated due to fragmentation in the mass spectrometer. I think this issue is very likely and should be explored further. The authors indicated that they would explore the measurement issue in Sect 4, but this issue was not specifically looked at in Sect 4. I wonder if the abundance of oxygenated aromatic compounds from BB emissions could factor into the overestimation of xylenes.
In Lines 355-360, the authors investigated why oxygenated compounds like formaldehyde, acetic acid are underestimated, even more so than other VOCs. While I agree that secondary in-plume production is very likely, I wonder if there could be other factors that cannot be ruled out. For example, these are all water soluble compounds. Could the wet deposition be overestimated in the model? Also, could there be production of these secondary species from other non-BB species? As mentioned earlier, this is more a suggestion for a deeper investigation, and I am just curious.
I noticed that there is always a jump in concentrations at two heights (530 hPa and 650 hPa) in the measured data. For some species, the model can reproduce this trend, but not always. Does this have to do with the injection heights? I am wondering if this will help diagnose the underestimation as well.
Line 421-425: I do not fully understand this. How would aromatic hydrocarbon/hydrocarbon relationship help diagnose the OH issue? Is this based on looking at the ratio of two hydrocarbons with different reactivities to look at OH exposure (i.e. hydrocarbon clock)? If not, can the hydrocarbon clock be used to assess OH exposure?
Minor comments:
Line 106: I cannot tell if it should be “though” or “through”. Both could potentially make sense.
Line 318: typo in xylene
Figure 1: it may be useful to indicate the ground site locations in the biomass burning panel too to give an idea of how far these ground sites are from the wildfires
Figure 2: for the species plotted with no bars, they are presumably not considered by each inventory? Might be helpful to indicate with n/a.
Citation: https://doi.org/10.5194/egusphere-2022-1107-RC2 -
EC1: 'Comment on egusphere-2022-1107 - Third reviewer comment', Holger Tost, 10 Jan 2023
This is a reviewer comment who due to technical issues was uploaded by the editor:
This is a nice paper that addresses a topic of great interest to atmospheric chemistry community. The authors use two aircraft campaigns, ground-based measurements and a nested model with three emission inventories to examine the biomass burning emissions in western US. They find a large underestimate of VOCs by current biomass burning emission inventories, which may have a large impact on ozone and aerosol air quality. The paper is well written and suited for ACP. I only have a few comments:
1. Dry Matter (DM) vs. Emission ratios. The authors argue that “the regionally averaged ERs dominate disagreement in emission estimates for most VOCs across the three inventories”. Looking at Figure 2, the difference in emission ratios seem to be small for some species. On the other hand, the authors say in Line 293 “these inventories agree on the amount of effective DM burned within 40 %”. It is unclear how much DM can account for the difference. It might be useful to add a plot for DM from different emission inventories.
2. Figure 4 vs. Figure 6. In Figure 4, the authors attribute the underestimate of OVOCs in their model to large secondary sources of OVOCs in biomass burning plumes that are missing in the model. However, it is shown in Figure 6 that observed OVOCs vs. CO slopes are well reproduced by their model. Are the authors assuming that the transects sampled here in Figure 6 have no secondary production of OVOCs? Some clarification is needed to reconcile Figures 4 and 6. Also it would be good to indicate the ages of those transected plumes.
3. It might be useful to point out the photochemical lifetimes of VOCs in the model. This will help to understand the impact of 3xGFAS in Figures 4 and 9.
4. What is the difference between QFED and GFAS despite that they both use FRP? This may help the reader to better understand the paper. Why are there missing VOC species in some inventories in Figures 2 and 7?
Citation: https://doi.org/10.5194/egusphere-2022-1107-EC1 - AC1: 'Comment on egusphere-2022-1107', Lixu Jin, 23 Mar 2023
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Lixu Jin
Wade Permar
Vanessa Selimovic
Damien Ketcherside
Robert J. Yokelson
Rebecca S. Hornbrook
Eric C. Apel
I-Ting Ku
Jeffrey L. Collett Jr.
Amy P. Sullivan
Daniel A. Jaffe
Jeffrey R. Pierce
Alan Fried
Matthew M. Coggon
Georgios I. Gkatzelis
Carsten Warneke
Emily V. Fischer
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1856 KB) - Metadata XML
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Supplement
(1453 KB) - BibTeX
- EndNote
- Final revised paper