From the journal Environmental Science: Atmospheres Peer review history

01-Dec-2022

Dear Dr FRY:

Manuscript ID: EA-ART-09-2022-000118
TITLE: Optical properties of biomass burning aerosol during the 2021 Oregon fire season: comparison between wild and prescribed fires

Thank you for your submission to Environmental Science: Atmospheres, published by the Royal Society of Chemistry. I sent your manuscript to reviewers and I have now received their reports which are copied below.

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Associate Editor, Environmental Sciences: Atmospheres

************

Reviewer 1

The manuscript is of interest, and provide a novel analysis of prescribed fires vs wild fires in the Oregon area. This analysis fits well within an ever-growing interest of improved parametrization of such plumes into GCMs, particularly under the clear expected increase of events and intensity in the upcoming years under a wide range of biomes. Whereas the text is clear, and analysis of optical properties and gas-phase (MCE and NOy) is sound, I do have strong reservation on the analytical techniques for aerosol loading, and derived results (namely MSE). Given the instrument used for measure aerosol loading has a theoretical low-size cutoff of (optical) diameter 0.25um, but in reality > 0.3um, there is likely a significant portion of mass loading that is not accurately quantified within the BB plumes. As shown in the same site by the 2016 study, average DPm of the regional plumes is 0.17um, thus having the PM1 fraction estimated here to be underreported by an unknown (and likely extremely variable) amount. This can explain some of the unexpected results of PM1 discussed in the manuscript (e.g. low MSE), by an underestimation of PM1 due to incompatible technique.
Given the issues discussed above, I recommend that the manuscript be revised, and if PM1 cannot be validated or assessed otherwise, that corresponding discussion (PM1/ MSE) to be removed from the manuscript, and focus be given on MAE, SSA, MCE and NOy, which in itself provide an interesting set of results.

Reviewer 2

The manuscript presents properties of biomass burning aerosol and gases measured from Mt. Bachelor observatory and separating fires by multiple properties including prescribed vs wildfires and provides explanations for the differences found. This study represents excellent contributions to the field and it’s within the scope of the journal. The manuscript is also very well written and referenced. See some comments below, which include clarification and potential additional analysis regarding the PM measurements and the FRP retrievals.

Comments by line:

98-108. The recent paper below would seem a good addition to the topic discussed in this paragraph:
https://pubs.acs.org/doi/full/10.1021/acs.est.2c03851

175-178. Aerosol mass being measured with an optical instrument complicates the analysis given the assumptions needed to be made to convert to mass concentration. Please add more details on how this conversion is made, if there is any potential saturation of the instrument at given concentrations, and include uncertainty due to changes in aerosol size (which are found in this work) and potential changes in refractive index (for instance, see articles below). I would tend to guess that uncertainty would be higher than +/-5% considering all of this. If the scattering to mass conversion factor used within the instrument is constant, I would encourage the authors to try to do a correction of this factor considering particle size (based on SAE), the particle size used to calibrate the instrument, and Mie Theory, given than the range measured is quite substantial (SAE varies from ~1.4 to ~2.2). This corrected data could be used to check if main the conclusions regarding ratios involving PM still hold.
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018JD028504
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019JD031372

Table S2. It would be great to add the name of the fires in the notes both for single and multiple fire events. Also, maybe you can mention in Table S1 and S2 caption that events 1-4 are PFs and the rest WFs

Table 2. It would be great to add ΔPM1 here (as in Table 1 without any ratio)

399-415. Reading this I realized that authors might be aggregating FRP differently than in previous work. Typically FRP for a single overpass is summed over the whole scene for a given time so the total FRP for the fire is obtained. If multiple overpasses/times are available, then the average/median of those would be taken (At least this is my understanding from Wiggins et al. 2020 and other papers). In this work it seems authors are taking the median of all pixels across space and time. I understand that this might make more sense for this study as it gives an idea of the median heat flux (energy per area) as all fire detections have roughly a similar area that could be better related to MCE. But for instance, I was confused that PM concentrations were lower for PF vs WF, while FRP had the opposite trend, as one would expect the same trend as FRP is directly proportional to emissions. Please make sure to clarify this in the text, maybe define what you are calling FRP with a different name. It would also be nice to report the total FRP to get an idea of the strength of the fires.

410-412. I would recommend the authors to repeat this analysis with GOES data anyways given the positive results of previous research. Timing of PF vs WF can be quite different, with VIIRS missing the peak time for WF which can bias results significantly. FRP is available from GOES-17 for 2021, I would encourage the authors to contact Chris Schmidt (chris.schmidt@ssec.wisc.edu) to get this dataset. Another option would be to get hourly gridded geostationary FRP and emissions from recently developed emissions inventories (https://www.sciencedirect.com/science/article/pii/S0034425722003431?via%3Dihub), contact Shobha Kondragunta (shobha.kondragunta@noaa.gov) for this. The latter would be more straight forward to avoid all processing steps needed when using the raw FRP data.

This text has been copied from the PDF response to reviewers and does not include any figures, images or special characters.

We thank the reviewers for their positive and constructive comments on our paper. To guide the
review process we have copied the reviewer comments in black text. Our responses are in
regular blue text. We have responded to all the reviewer comments and made alterations to our
paper (in bold text).
Reviewer #1
The manuscript is of interest, and provide a novel analysis of prescribed fires vs wild fires in the
Oregon area. This analysis fits well within an ever-growing interest of improved parametrization
of such plumes into GCMs, particularly under the clear expected increase of events and
intensity in the upcoming years under a wide range of biomes. Whereas the text is clear, and
analysis of optical properties and gas-phase (MCE and NOy) is sound, I do have strong
reservation on the analytical techniques for aerosol loading, and derived results (namely MSE).
Given the instrument used for measure aerosol loading has a theoretical low-size cutoff of
(optical) diameter 0.25um, but in reality > 0.3um, there is likely a significant portion of mass
loading that is not accurately quantified within the BB plumes. As shown in the same site by the
2016 study, average DPm of the regional plumes is 0.17um, thus having the PM1 fraction
estimated here to be underreported by an unknown (and likely extremely variable) amount. This
can explain some of the unexpected results of PM1 discussed in the manuscript (e.g. low MSE),
by an underestimation of PM1 due to incompatible technique. Given the issues discussed
above, I recommend that the manuscript be revised, and if PM1 cannot be validated or
assessed otherwise, that corresponding discussion (PM1/ MSE) to be removed from the
manuscript, and focus be given on MAE, SSA, MCE and NOy, which in itself provide an
interesting set of results.
We thank both reviewers for placing emphasis on the PM1 mass concentration measurement
method. Clarifications on the method and a discussion of its limitations have been added to
Section 2.3 of the manuscript:
“PM1 mass concentrations were measured with a Grimm 1.109 optical particle counter (OPC).
The OPC counts particles down to an optical diameter of 0.25 µm in 31 size bins, 11 of
which are under the 1 µm size cut. A lognormal distribution of particle count is assumed
based on the measured size distribution, which corrects for the unmeasured fraction of
particles smaller than 0.25 µm. After assuming spherical particles, the volume
distribution is converted to mass concentration using a constant density factor chosen
by the manufacturer by reference gravimetric PM measurements. The Grimm OPC has
been designated as a US EPA federal equivalent method (FEM) for measuring PM2.5 mass
concentrations. We note, however, that this conversion to mass concentration does not
account for potential changes in BB aerosol density and refractive index. While these
properties show little variation across replicate laboratory burns (Levin et al., 2010), there
is evidence that both aerosol density and refractive index can change with smoke age
(Saide et al., 2022). The OPC was factory calibrated prior to deployment with polydisperse
dolomite dust. Uncertainty based on flow rate and accuracy errors is estimated by the
manufacturer at ± 5%, although the true uncertainty of our PM1 measurement is likely
larger considering the above approximations. We acknowledge that these assumptions
cause our calculations involving OPC data to be less comparable to other studies which
employed filter-based or laser-induced incandescence methods to measure mass
concentration.”
We believe that this additional information clarifies the uncertainties associated with using OPC
data. The Laing et al. (2016) study mentioned by the reviewer also used PM1 mass
concentrations from the same Grimm OPC to calculate mass scattering efficiency (MSE). A key
difference between our study and that of Laing et al. (2016) is that we reported much larger
MSE values. After examining this difference we realized that calculating event-based MSE as an
enhancement ratio (Δscat/ΔPM1
) is inconsistent with other studies where MSE is simply defined
as the ratio of the scattering coefficient to the mass concentration (scat/PM1
) of an aerosol
population (e.g., Hand and Malm, 2006; Saide et al., 2022). Using the RMA regression method
to calculate enhancement ratios (as done by Laing et al., 2016) assumes that the two variables
are linearly related across the event time range, which is not always the case for scat and PM1
(see example figure below).
Time series of scat, PM1 and instantaneous MSE (scat/PM1
) during BB Event 23. The bottom
panel shows the non-linear relationship between scat and PM1 during the event time range.
Despite the high R
2 value, the RMA slope and the median scat/PM1
ratio yield very different
results for MSE.
Calculating instantaneous MSE as the ratio of scat/PM1
for each data point and then taking the
median across the BB event time range results in event-based MSE values that are lower and
more consistent with previous studies of aged smoke (3.3-7.4 m2
/g). Therefore, we have
updated the figures and text of Section 3.4 with these new values. Being analogous to MSE,
mass absorption efficiency (MAE) values were also re-calculated this way. With regards to why
2016 study reported lower MSE values calculated with the enhancement ratio method, we
emphasize that our highest MSE values result from extremely dense smoke (PM1 > 400 µg/m3
)
that has not been previously observed at MBO. The strong relationship between MSE and
smoke concentration is discussed in Section 3.4 and is an interesting result worthy of further
evaluation with more accurate mass concentration measurements.
Given that our study focuses on comparisons between BB events observed at MBO during a
single fire season, we have significantly rewritten Section 3.4 to improve clarity and to
emphasize our explanations of the observed MSE range while discouraging comparisons with
other studies that employed more reliable techniques to measure PM mass concentrations.
Reviewer #2
The manuscript presents properties of biomass burning aerosol and gases measured from Mt.
Bachelor observatory and separating fires by multiple properties including prescribed vs
wildfires and provides explanations for the differences found. This study represents excellent
contributions to the field and it’s within the scope of the journal. The manuscript is also very well
written and referenced. See some comments below, which include clarification and potential
additional analysis regarding the PM measurements and the FRP retrievals.
R2.1: 98-108. The recent paper below would seem a good addition to the topic discussed in this
paragraph: https://pubs.acs.org/doi/full/10.1021/acs.est.2c03851
This reference has been added to the discussion.
R2.2: 175-178. Aerosol mass being measured with an optical instrument complicates the
analysis given the assumptions needed to be made to convert to mass concentration. Please
add more details on how this conversion is made, if there is any potential saturation of the
instrument at given concentrations, and include uncertainty due to changes in aerosol size
(which are found in this work) and potential changes in refractive index (for instance, see
articles below). I would tend to guess that uncertainty would be higher than +/-5% considering
all of this. If the scattering to mass conversion factor used within the instrument is constant, I
would encourage the authors to try to do a correction of this factor considering particle size
(based on SAE), the particle size used to calibrate the instrument, and Mie Theory, given than
the range measured is quite substantial (SAE varies from ~1.4 to ~2.2). This corrected data
could be used to check if main the conclusions regarding ratios involving PM still hold.
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018JD028504
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019JD031372
Please see our response to Reviewer #1 for an explanation of the method used by the OPC to
convert particle counts to mass concentration and the corresponding uncertainty discussion
added to Section 2.3 of the manuscript.
Our highest measured PM1 mass concentration (~500 µg/m3
) is well within the
manufacturer-reported range of 0-15,000 µg/m3
for the Grimm OPC. The figure below shows the
strong correlation between PM1 and CO for the six-month study period, with no obvious
saturation effect at high smoke concentrations:
We are less concerned about potential biases in the OPC data due to changes in aerosol size
due to the thorough factory calibration procedure. According to the manual, the instrument was
calibrated to a reference Grimm OPC using polydisperse dolomite dust covering the full size
range of the OPC; 0.2 to >30 um. Each size channel of the reference Grimm OPC was
calibrated with NIST-traceable monodisperse polystyrene latex (PSL) spheres.
R2.3: Table S2. It would be great to add the name of the fires in the notes both for single and
multiple fire events. Also, maybe you can mention in Table S1 and S2 caption that events 1-4
are PFs and the rest WFs
The captions of Tables S1 and S2 have been updated to list which events are PFs vs. WFs. We
have revised our analysis of FRP (see responses to comments below) and Table S2 has been
updated to list the names of the sampled fires.
R2.4: Table 2. It would be great to add ΔPM1 here (as in Table 1 without any ratio)
ΔPM1 has been added to Tables 2 and S3.
R2.5: 399-415. Reading this I realized that authors might be aggregating FRP differently than in
previous work. Typically FRP for a single overpass is summed over the whole scene for a given
time so the total FRP for the fire is obtained. If multiple overpasses/times are available, then the
average/median of those would be taken (At least this is my understanding from Wiggins et al.
2020 and other papers). In this work it seems authors are taking the median of all pixels across
space and time. I understand that this might make more sense for this study as it gives an idea
of the median heat flux (energy per area) as all fire detections have roughly a similar area that
could be better related to MCE. But for instance, I was confused that PM concentrations were
lower for PF vs WF, while FRP had the opposite trend, as one would expect the same trend as
FRP is directly proportional to emissions. Please make sure to clarify this in the text, maybe
define what you are calling FRP with a different name. It would also be nice to report the total
FRP to get an idea of the strength of the fires.
This is a valid concern and we agree that the original method for aggregating FRP led to
potentially confusing results (i.e., higher FRP but lower emissions). We have restructured the
analysis to report maximum, mean, and median FRP for the entire duration of the source fires.
We also report the time-integrated fire radiative energy (FRE), which is the most representative
metric of the total emissions from a given fire over its entire lifecycle. This new analysis was
done using geostationary FRP data (from the Regional ABI and VIIRS fire Emissions (RAVE)
inventory) as suggested. Section 2.6 has been updated to describe the new methodology:
“Fire radiative power (FRP), defined as the rate at which an active fire is emitting radiative
energy, is a remote sensing metric proportional to the rates of fuel consumption and
smoke emission (Ichoku and Ellison, 2014; Kaiser et al., 2012). In this study we use
hourly 3 km gridded FRP from the Regional ABI and VIIRS fire Emissions (RAVE) product
(Li et al., 2022). RAVE fuses observations from the Advanced Baseline Imager (ABI)
aboard the Geostationary Operational Environmental Satellite R Series (GOES-R) with
Visible Infrared Imaging Radiometer Suite (VIIRS) observations aboard the Suomi
National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. A dynamic cloud
correction is applied to prevent both the underestimation of FRP under partial cloud
cover and the overestimation of FRP in grids with high cloud fractions, as burning
conditions under clear vs. cloudy skies are not the same (see details in Li et al., 2022).
We use the RAVE FRP product to contextualize MBO observations by identifying the
source fire for each BB event (Sect 2.4) and summing FRP observations across the fire
area for each hour that the fire was active (i.e., FRP > 0). We then calculate the mean and
median FRP (in MW) across the entire fire lifecycle (Table S2). We also estimate an
area-weighted FRP density (in MW km-2
) by dividing FRP observations by the total area
with FRP > 0 for a given fire. Given that several events were influenced by multiple fires
with variable transport times (Table 1), we use FRP to examine broad differences in fire
intensity between fire type (prescribed vs. wild) and ecoregion (Sect. 3.1) rather than comparing
the FRP of individual events.”
We have moved the corresponding discussion of FRP results to Section 3.1 (Overview of 2021
biomass burning events):
“The hourly RAVE FRP product (Sect. 2.6) revealed substantial differences in the duration
and intensity of the sampled fires. Notably, there was active FRP data for 3 days for PFs 1
and 4, and just 1 day for PFs 2 and 3, while the WFs remained active for an average of 30
days (maximum 51 days for the Middle Fork Complex). FRP statistics derived from the
RAVE product are listed in Table S2. The longer duration and larger area of the WFs
resulted in much larger total fire radiative energy (FRE; the time-integral of FRP), and
consequently much larger total emissions than the PFs. We constructed diurnal cycles of
FRP for each sampled fire based on the average FRP at each hour that the fire was active
(Fig. S1). The diurnal cycles show that the PFs were active only during the day, while
both eastern and western Cascades WFs remained active at night with peak FRP
occurring in the afternoon. Despite the smaller absolute FRP of the PFs, medians of
area-weighted FRP density revealed that the PFs burned at comparable intensity to the
western Cascades fires (8.84 ± 1.78 vs. 9.13 ± 2.23 MW km-2
). The two eastern Cascades
WFs (S-503 and Grandview fires) burned at the highest median and mean FRP density
(Table S2), which underscores the importance of ecoregion on fire behavior. We discuss
the potential causes of these differences in fire behavior (and resulting smoke properties
observed at MBO) in Section 3.2.”
We no longer attempt to correlate individual MBO plume observations with FRP since we are
aggregating FRP data for the entire fire lifecycle, not just at the time of measurement. We opted
for this method because we do not know with certainty the exact age of smoke solely based on
back trajectory analysis, especially for BB events that were influenced by multiple fires. Such
correlations would be more appropriate for aircraft campaigns sampling immediately downwind
of the fire as demonstrated by Wiggins et al., 2020. We also note that the Wiggins et al. study
correlated the rate of change in FRP with the rate of change of MCE and other smoke
properties, not the absolute FRP values by themselves. This would require consecutive samples
of the same smoke plume at different physical ages, which is difficult to accomplish using data
from a fixed observation site such as MBO.
R2.6: 410-412. I would recommend the authors to repeat this analysis with GOES data anyways
given the positive results of previous research. Timing of PF vs WF can be quite different, with
VIIRS missing the peak time for WF which can bias results significantly. FRP is available from
GOES-17 for 2021, I would encourage the authors to contact Chris Schmidt
(chris.schmidt@ssec.wisc.edu) to get this dataset. Another option would be to get hourly
gridded geostationary FRP and emissions from recently developed emissions inventories
(https://www.sciencedirect.com/science/article/pii/S0034425722003431?via%3Dihub), contact
Shobha Kondragunta (shobha.kondragunta@noaa.gov) for this. The latter would be more
straight forward to avoid all processing steps needed when using the raw FRP data
We thank the reviewer for this suggestion. As discussed above, we have repeated the analysis
with geostationary FRP data.
Other changes to the manuscript:
The CLAP measurement wavelengths were incorrectly reported in the submitted manuscript. All
figures and text have been updated in the new version to reflect the correct values (467, 528,
and 652 nm).
References:
Hand, J. L. and Malm, W. C.: Review of aerosol mass scattering efficiencies from ground-based
measurements since 1990, J. Geophys. Res., 112, D16203,
https://doi.org/10.1029/2007JD008484, 2007.
Ichoku, C. and Ellison, L.: Global top-down smoke-aerosol emissions estimation using satellite
fire radiative power measurements, Atmos. Chem. Phys., 14, 6643–6667,
https://doi.org/10.5194/acp-14-6643-2014, 2014.
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J.,
Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning
emissions estimated with a global fire assimilation system based on observed fire
radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012,
2012.
Laing, J. R., Jaffe, D. A., and Hee, J. R.: Physical and optical properties of aged biomass
burning aerosol from wildfires in Siberia and the Western USA at the Mt. Bachelor
Observatory, Atmos. Chem. Phys., 16, 15185–15197,
https://doi.org/10.5194/acp-16-15185-2016, 2016.
Levin, E. J. T., McMeeking, G. R., Carrico, C. M., Mack, L. E., Kreidenweis, S. M., Wold, C. E.,
Moosmüller, H., Arnott, W. P., Hao, W. M., Collett Jr., J. L., and Malm, W. C.: Biomass
burning smoke aerosol properties measured during Fire Laboratory at Missoula
Experiments (FLAME), Journal of Geophysical Research: Atmospheres, 115,
https://doi.org/10.1029/2009JD013601, 2010.
Li, F., Zhang, X., Kondragunta, S., Lu, X., Csiszar, I., and Schmidt, C. C.: Hourly biomass
burning emissions product from blended geostationary and polar-orbiting satellites for air
quality forecasting applications, Remote Sensing of Environment, 281, 113237,
https://doi.org/10.1016/j.rse.2022.113237, 2022.
Saide, P. E., Thapa, L. H., Ye, X., Pagonis, D., Campuzano-Jost, P., Guo, H., Schuneman, M. L.,
Jimenez, J.-L., Moore, R., Wiggins, E., Winstead, E., Robinson, C., Thornhill, L.,
Sanchez, K., Wagner, N. L., Ahern, A., Katich, J. M., Perring, A. E., Schwarz, J. P., Lyu,
M., Holmes, C. D., Hair, J. W., Fenn, M. A., and Shingler, T. J.: Understanding the
Evolution of Smoke Mass Extinction Efficiency Using Field Campaign Measurements,
Geophysical Research Letters, 49, e2022GL099175,
https://doi.org/10.1029/2022GL099175, 2022.
Wiggins, E. B., Soja, A. J., Gargulinski, E., Halliday, H. S., Pierce, R. B., Schmidt, C. C., Nowak,
J. B., DiGangi, J. P., Diskin, G. S., Katich, J. M., Perring, A. E., Schwarz, J. P., Anderson,
B. E., Chen, G., Crosbie, E. C., Jordan, C., Robinson, C. E., Sanchez, K. J., Shingler, T.
J., Shook, M., Thornhill, K. L., Winstead, E. L., Ziemba, L. D., and Moore, R. H.: High
Temporal Resolution Satellite Observations of Fire Radiative Power Reveal Link
Between Fire Behavior and Aerosol and Gas Emissions, Geophys. Res. Lett., 47,
https://doi.org/10.1029/2020GL090707, 2020.

14-Feb-2023

Dear Dr FRY:

Manuscript ID: EA-ART-09-2022-000118.R1
TITLE: Optical properties of biomass burning aerosol during the 2021 Oregon fire season: comparison between wild and prescribed fires

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Dr Nønne Prisle
Associate Editor, Environmental Sciences: Atmospheres

Reviewer 2

The authors worked hard on the new version of the manuscript considering comments from all reviewers. In my opinion the manuscript is now ready for publication