From the journal Environmental Science: Atmospheres Peer review history

21-Nov-2022

Dear Dr Robinson:

Manuscript ID: EA-ART-10-2022-000140
TITLE: Wintertime spatial patterns of particulate matter in Fairbanks, AK during ALPACA 2022

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

Reviewer 1

Novelty and significance
The authors state that their approach is the first application of mobile monitoring in Fairbanks or a similar high-latitude city. They also show how meteorological data can be used to better interpret mobile measurements in such areas. The analysis is an example of policy-relevant science, useful for improving air quality in Fairbanks and as a prototype for field studies in montane areas subject to strong winter inversions. The study was not intended to generate new knowledge of atmospheric processes but is rather an interesting development and application of a method for identifying emission sources contributing to winter stagnation-driven air pollution episodes. Weather data have long been used with stationary pollution monitoring instruments to identify the direction, distribution, and location of air pollution sources. This manuscript extends such techniques to the interpretation of mobile monitoring measurements. The field study was well executed; the writing is clear and concise. The manuscript merits publication.

Results
Figure 5b: Please correct the caption to include AAE
Figure 6: I suggest swapping the independent and dependent variables. The intercept terms are due to lack of fit and are physically unrealistic: BC cannot be positive when PM2.5 mass concentration is zero. But PM2.5 can be nonzero when BC is zero. Alternatively, the authors might force the regression through zero. Either approach would yield a slope implying that the fraction of BC exceeds six percent, which would be more consistent with the relative proportions shown in Figures 3 and 5b (some of which indicate that BC is ~10% of PM2.5 mass).
Figure 9: The authors should take a victory lap for the agreement shown here! The agreement is impressive. Are the apparent darker shades of some circles simply overlapping symbols?

Discussion
It would be helpful if the authors could offer an informed opinion on measurements that could be added to enhance confidence in conclusions. These need not be mobile measurements but could include complementary stationary sampler measurements. For example, 14C measurements made on filter samples (e.g., from NCORE or downtown sites) could be fairly definitive in supporting the identification of biomass combustion.

Reviewer 2

This is a very interesting manuscript that reports on a unique dataset.
It's a great scientific contribution for air quality studies in such an under-researched geographical area.

However I found the manuscript too long, particularly the Introduction and Results. There are too many figures in the main text and Sup Material. It's off-putting to have to jump to the Sup Material to look for specific data/figures.

I found that the Instrument section should be better organised, as I had the feeling that the authors jumped back and forth between specific topics (calibration, validation, etc).

I consider that the following paragraph includes too much details in the Introductions. It distracts the reader. At this point of the narrative, the authors should be wrapping up the next and not adding new info:
"ADEC has previously conducted mobile sampling in FNSB.
Those results, published in the 2016 State Implementation
Plan17, revealed the existence of large spatial PM2:5 gradients
(>10 mg m-3) in different parts of FNSB during wintertime, including
in and around downtown Fairbanks. Resulting from these
mobile measurements, the “A Street” ADEC PM2:5 monitoring station
was established in 2019 about 1.1 miles east of the “NCORE”
site, due to concerns over high PM2:5 levels in residential areas
from woodsmoke emissions that weren’t fully captured by measurements
at NCORE."

"These differences are either dramatically
reduced or completely absent during periods with weaker SBI,
emphasizing the importance of meteorological conditions in interpreting
results from mobile sampling. Our results can inform
the community of Fairbanks, the mobile air pollution sampling literature,
and the high-latitude urban air pollution literature. Additionally,
this study illustrates a strategy for lower-cost mobile"

the "10 metrics" presented by Apte should not be generalised : who showed that 10 or more unique mobile lab visits were sufficient to characterize representative
concentrations in space.".
You can assess how representative your sampling sessions were. Please check the results presented by https://doi.org/10.1016/j.jhazmat.2021.127133 (Fig 2).

I don't understand this "over-sampling a la Apte et al.,"

I don't see the relevance for this in the Methods section Distracting "Figure S1 shows a time series of PM2:5 measurements made at the ALPACA house site with a MODULAIR-PM (QuantAQ, Inc.) sensor, with mobile sampling periods highlighted in red."

Moreover, your Supp FIgures are numbered 1, 2, 3, etc, not S1, S2.
This is part of results, not Methods "The average temperature difference between 25 m and 3 m (DT = T25m - T3m) over the 3-hours prior to the start of the drive
is the measure we use to bin the drives into inversion categories.
Figure S2a shows the distribution of DT for all drives in each of
January and February. January drives tended to have higher DT
values than February, but there is some overlap. The lowest average
DT across all drives was 0.37 ºC,"

unclear. how did you determine this ? "though our measurements indicated
that the very large majority (>97%) of all DustTrak-measured
PM was less than 2.5 μm,". The authors wrote that "The DustTrak was operated without any impactor"

This is a sensitive issue as the correction factor depends on the type of aerosol. The authors should issue a cautionary note if they don't have collocated gravimetry measurements to correct for the dusttrak overestimation. This statement is naïve ". We
apply the TSI-suggested 0.38 correction factor for ambient measurements
to the default DustTrak settings, which are calibrated
using Arizona Test Dust, which has a much higher density (2.65
g cm-3) than PM dominated by organic aerosol (typical density
1 - 1.5 g cm-3)."

confusing narrative. all instruments described in the same statement "The
manufacturer-stated lower particle detection range is 100nm, and
so the PM2:5 measurements we report here only account for particles
larger than this lower limit. Unlike PM2:5, our BC measurement
is not subject to a lower size cutoff, and our PN measurement
has a lower size cutoff of 5 nm."

Many important details were omitted in "2.2 Instrumentation and measurements". For example, what are the maximum measurable PN concentration?
What's the maximum attenuation reached in the microaethalometer?

confusing. Rewording required "We use l2 = 880 nm, and l1 = 375 nm in the above
equation, because these wavelengths span the full range over
which that the micro-aethelometer measures attenuation."


Moreover, the reason why AAEs was calculated should be disclosed before the equation is presented: "AAE indicates the wavelength-dependence of light absorption, which
can inform source apportionment given the characteristic AAE values of different aerosol types, such as brown carbon (BrC)."

AAE per se does not provide "source apportionment given the characteristic AAE". It's at best a proxy to indicate whether the fossil fuel or biomass combustion prevails.

I don't understand this or its relevance "Our AE-33 measurements required substantial averaging time to reduce noise, and so each measurement is averaged over
the length of each valve-state, as are microaethelometer measurements
in order to match."

I understand that particulates are seldom normally distributed, hence the median is a suitable measure of central tendency "All neighborhoods had median PN concentrations..." However, mean and SD should be provided (in a table) to allow comparison with other studies.

It's unclear which BC wavelength is presented "We summarize these neighborhood- and sub-neighborhoodlevel gradients in PM2:5, PN, and BC concentrations"

There's no discussion here as to the use of AAE and why the values 1.5 and 1.4 are mentioned. "For all residential neighborhoods, the median
AAE values are above 1.5 and there is little spread, as indicated
by the IQR, compared to weak inversions. Interestingly, the
Highway aggregation has median AAE lower than the residential
neighborhoods, and the least difference between the two inversion
conditions compared to the residential neighborhoods. This
likely reflects a stronger contribution from BC to the total light
absorbing PM for the Highway area. For all of the residential areas,
the high AAE values during strong inversion periods indicate
a strong contribution of BrC to light-absorbing PM."

relevance of 1.4? "Additionally, the majority (77%) of grid cells have AAE values below 1.4 during weak inversions."

how does the previous statement support this conclusion? "These results imply that the large majority of the spatial variability in both PM2:5 and BC is driven by residential emissions from home-heating, as opposed to vehicle emissions."

If you are trying to use AAE to make linkages, please provide suitable references about typical AAE values for biomass/fossil fuel emissions.

FIg 8 is interesting. However, elevation is only part of the story. Have the authors investigated relationships between traffic volume and particulates? For example, Fig 6 https://doi.org/10.1016/j.envpol.2016.07.027

Traffic counts would be a great asset.

Maybe I've missed this piece of info, but what hours do the measurements cover? "we aimed to accomplish the above objectives as best as possible within a 1.5
hour timeframe for each drive".

Also, have you measured at weekends? This would be an interesting way to segregate the week/weekend contribution to particulate concentrations.

I'm not familiar with the Arctic dynamics, but how do long-range transport of smoke from North America affect the air quality at Fairbanks? have this been taken into account?

Please, comment on self contamination from the tailpipe, particularly when the vehicle was idling at traffic lights or accelerating.

Note to editor/staff:
We have provided our responses in-line, separated by a blank line. It is easier to view the in-line responses with rich-text though, so we have also attached the document with the same content, but where our responses are in bold font, to make it easier to read.

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

ALPACA mobile - RSC Environmental Science Atmospheres - Reviews

REVIEWER REPORT(S):
Referee: 1

Comments to the Author
Novelty and significance
The authors state that their approach is the first application of mobile monitoring in Fairbanks or a similar high-latitude city. They also show how meteorological data can be used to better interpret mobile measurements in such areas. The analysis is an example of policy-relevant science, useful for improving air quality in Fairbanks and as a prototype for field studies in montane areas subject to strong winter inversions. The study was not intended to generate new knowledge of atmospheric processes but is rather an interesting development and application of a method for identifying emission sources contributing to winter stagnation-driven air pollution episodes. Weather data have long been used with stationary pollution monitoring instruments to identify the direction, distribution, and location of air pollution sources. This manuscript extends such techniques to the interpretation of mobile monitoring measurements. The field study was well executed; the writing is clear and concise. The manuscript merits publication.

Results
Figure 5b: Please correct the caption to include AAE

This change was made

Figure 6: I suggest swapping the independent and dependent variables. The intercept terms are due to lack of fit and are physically unrealistic: BC cannot be positive when PM2.5 mass concentration is zero. But PM2.5 can be nonzero when BC is zero. Alternatively, the authors might force the regression through zero. Either approach would yield a slope implying that the fraction of BC exceeds six percent, which would be more consistent with the relative proportions shown in Figures 3 and 5b (some of which indicate that BC is ~10% of PM2.5 mass).

Thank you for this suggestion—it brings up a good point that we overlooked. We have switched the axes for this plot (PM2.5 now on y, BC on x), and indeed the slope implies a higher fraction of BC to total PM2.5 during the strong inversion condition (9%), which we have changed accordingly

Figure 9: The authors should take a victory lap for the agreement shown here! The agreement is impressive. Are the apparent darker shades of some circles simply overlapping symbols?

We have clarified this — originally, we used a reduced opacity for the symbols to address some of the over-plotting that comes from having a lot of overlapping points, and so have added the following text to the figure caption: “Each symbol has reduced opacity, so darker shades indicate regions where there are overlapping data points in an effort to illustrate density of datapoints.”

Discussion
It would be helpful if the authors could offer an informed opinion on measurements that could be added to enhance confidence in conclusions. These need not be mobile measurements but could include complementary stationary sampler measurements. For example, 14C measurements made on filter samples (e.g., from NCORE or downtown sites) could be fairly definitive in supporting the identification of biomass combustion.

Our reading of this comment—which is appreciated—highlighted to us that we may have failed to fully emphasize in the introduction that a lot of previous work in Fairbanks has shown that there is a lot of wood-burning PM there in the wintertime, and that it typically represents the dominant PM source. There is variability in the estimation of the fraction of PM that is from woodsmoke across studies, but generally the studies show that it is the dominant source. We have tried to more fully emphasize this in the introduction, so that it is more clear that our paper is focusing on how different across space the woodsmoke might be, and not whether or not it is there in the first place.
We have also added a nod to the idea that unambiguous source-apportionment methods do exist, and that it would be nice to have those in support of our conclusions with the following sentence in the beginning paragraph of Discussions: “Radiocarbon ($^{14}$C) or other chemical composition-based (e.g. mass spectrometry) source-apportionment would offer more unambiguous source determinations.“ With an appropriate reference.

Referee: 2

Comments to the Author
This is a very interesting manuscript that reports on a unique dataset.
It's a great scientific contribution for air quality studies in such an under-researched geographical area.

We sincerely appreciate this thorough review. It took some effort to fully respond to these comments, but in the end they made this a better and more organized paper.

However I found the manuscript too long, particularly the Introduction and Results. There are too many figures in the main text and Sup Material. It's off-putting to have to jump to the Sup Material to look for specific data/figures.

Our intention with the supporting information was to put things there that are not at the core of the paper, but that someone who is really invested in the fine details of this paper could still have access to. Given that there is a criticism of there being too many figures, though without saying which figures are extraneous, we think it is appropriate to not overload the main paper with all figures, and use the SI in the way that we have. If there are specific figures in the main text that the reviewer feels are extraneous, or specific figures in the SI that should instead be in the main text, we are open to hearing those ideas.

I found that the Instrument section should be better organised, as I had the feeling that the authors jumped back and forth between specific topics (calibration, validation, etc).

We have tried to address this issue by adding specific sub-sections for each of the instruments. The Methods section is organized quite a bit differently now, for the better.

I consider that the following paragraph includes too much details in thed Introductions. It distracts the reader. At this point  of the narrative, the authors should be wrapping up the next and not adding new info:
"ADEC has previously conducted mobile sampling in FNSB.
Those results, published in the 2016 State Implementation
Plan17, revealed the existence of large spatial PM2:5 gradients
(>10 mg m-3) in different parts of FNSB during wintertime, including
in and around downtown Fairbanks. Resulting from these
mobile measurements, the “A Street” ADEC PM2:5 monitoring station
was established in 2019 about 1.1 miles east of the “NCORE”
site, due to concerns over high PM2:5 levels in residential areas
from woodsmoke emissions that weren’t fully captured by measurements
at NCORE.”

We have moved most of this information from the introduction to the “Mobile sampling results at ALPACA \& ADEC stationary monitoring locations.” We feel that it is important to acknowledge the previous mobile sampling work conducted in the area, but take the reviewer’s point that it was information overload in the introduction.

"These differences are either dramatically
reduced or completely absent during periods with weaker SBI,
emphasizing the importance of meteorological conditions in interpreting
results from mobile sampling. Our results can inform
the community of Fairbanks, the mobile air pollution sampling literature,
and the high-latitude urban air pollution literature. Additionally,
this study illustrates a strategy for lower-cost mobile”

We have trimmed this paragraph down to the following, based on the reviewer’s suggestion: “Herein, we present a mobile-based measurements of PM$_{2.5}$, particle number (PN), and light-absorbing PM (UV-PM and black carbon, BC). We find strong spatial pollution gradients both between neighborhoods and within neighborhoods, largely during periods with strong SBI, which emphasizes the importance of meteorological conditions in interpreting results from mobile sampling. Our results can inform the community of Fairbanks, the mobile air pollution sampling literature, and the high-latitude urban air pollution literature. ”


the "10 metrics" presented by Apte should not be generalised : who showed that 10 or more unique mobile lab visits were sufficient to characterize representative
concentrations in space.".
You can assess how representative your sampling sessions were. Please check the results presented by https://doi.org/10.1016/j.jhazmat.2021.127133 (Fig 2).

We agree that 10 visits is not immediately generalizable to all locations—In fact, we took care in this paragraph of our submission to specifically say that 10 visits indeed may not apply to our domain (“However, this rough guide of a 10$+$ visits threshold comes from data collected in Oakland, CA, and may not apply to FNSB.”). However, we have reworded this paragraph in an attempt to be extra explicit about this.
Additionally, we have added sections about representativeness to both methods and results that addresses the concern raised by the reviewer. In short, we show two things: 1) how many visits it took for the spread of possible cumulative mean values to cross a certain threshold that we think represents a measure of stability (answer: roughly 15, but up to 22 for PM2.5), and 2) whether, at 10 visits (which is arbitrary, but is useful in comparison to the Apte paper in addition to being “most of but not all” of the visits we made within a given inversion category) gives us enough confidence to be able to distinguish between the two inversion categories the grid cell mean value (answer: it does).
In our opinion, these results are important for the reader who is very interested in the details, but are outside of what we want the paper’s focus to be, and so the plots that came out of this analysis were put into the SI.

I don't understand this "over-sampling a la Apte et al.,"
Apte et al used a self-described “over-sampling” approach.

See above response—we have tried to rework this paragraph to be more explicit about the number of visits, and how we are referring to Apte et al.

I don't see the relevance for this in the Methods section Distracting "Figure S1 shows a time series of PM2:5 measurements made at the ALPACA house site with a MODULAIR-PM (QuantAQ, Inc.) sensor, with mobile sampling periods highlighted in red.”

We did not feel like this was a figure necessary for the main text. However, we do feel like there is value in the figure for the reader who really wants an extra level of detail, and thus decided to put it in the SI. The value is that mobile monitoring inherently lacks temporal coverage (and obviously stationary sampling lacks spatial coverage). Highlighting the periods of a stationary time series during which we did our mobile sampling illustrates the “snapshot” aspect of mobile sampling, during which we were able to capture spatial information while also illustrating temporal variability in PM2.5 at one location in the domain. As for whether this belong in Methods or not, the idea here is that we are trying to illustrate the experimental design and sampling periods, as opposed to showing this time series as a “Result.” We defer to the editor on whether they think this should be moved, but to us this makes sense to be in Methods for the reader that wants the extra detail.

Moreover, your Supp FIgures are numbered 1, 2, 3, etc, not S1, S2.

We have fixed this error—figures now have the correct numbering.

This is part of results, not Methods "The average temperature difference between 25 m and 3 m (DT = T25m - T3m) over the 3-hours prior to the start of the drive is the measure we use to bin the drives into inversion categories.
Figure S2a shows the distribution of DT for all drives in each of
January and February. January drives tended to have higher DT
values than February, but there is some overlap. The lowest average
DT across all drives was 0.37 ºC,”

We are using these auxiliary meteorological measurements to inform our data analysis. They act as a binning variable, and so with this section we are describing how that binning is done. As such, this is part of the methods of our data anlalysis, not a result in and of itself. Were this paper more focused on meteorology apart from air pollution, we would put this plot in Results (and additionally the plot would not be in SI, as it is here, appropriately, because it is in the details of part of our methods). To address the concern raised by this comment, we have highlighted a previous paper who has made similar temperature gradient differences with the following text, and re-organized the discussion of this issue by adding a sub-section that is focused solely on the temperature measurements. We have also removed the line about January and February, and placed that in the Results section, and are referring to the figure in question in the Results section.

unclear. how did you determine this ? "though our measurements indicated
that the very large majority (>97%) of all DustTrak-measured
PM was less than 2.5 μm,". The authors wrote that "The DustTrak was operated without any impactor”

Apologies for the confusion here, and we acknowledge this was not illustrated at all in the original submission. We have added a figure to the supporting information that (Figure S3) that illustrates how we came to this conclusion—which is a correlation plot between PM2.5 and total suspended particles. The slope illustrates that roughly 3% of particles are larger than 2.5 microns.

This is a sensitive issue as the correction factor depends on the type of aerosol. The authors should issue a cautionary note if they don't have collocated gravimetry measurements to correct for the dusttrak overestimation. This statement is naïve ". We
apply the TSI-suggested 0.38 correction factor for ambient measurements
to the default DustTrak settings, which are calibrated
using Arizona Test Dust, which has a much higher density (2.65
g cm-3) than PM dominated by organic aerosol (typical density
 1 - 1.5 g cm-3).”

We have reemphasized that we do not have collocated gravimetric measurements with the following text: “It should be noted that we do not have collocated gravimetric measurements of PM2.5 to compare against to definitively test this correction factor, however.”

confusing narrative. all instruments described in the same statement "The
manufacturer-stated lower particle detection range is 100nm, and
so the PM2:5 measurements we report here only account for particles
larger than this lower limit. Unlike PM2:5, our BC measurement
is not subject to a lower size cutoff, and our PN measurement
has a lower size cutoff of 5 nm.”

Given this and other comments from the reviewer, we have reorganized much of the information related to instrumentation and measurements in the Methods section. We have segregated information about each of the particulate measurements into its own section in an attempt to address this concern.

Many important details were omitted in "2.2 Instrumentation and measurements". For example, what are the maximum measurable PN concentration?

We have added this information, as well as other instrument details related to the Magic CPC. See the new “Particle number (Magic CPC)” subsection. This is part of our overall attempt to reorganize information related to instruments in a better way, per a handful of the reviewer’s comments.

What's the maximum attenuation reached in the microaethalometer?

See the new “Light-absorbing PM (micro-aethelometer)” subsection. This is part of our overall attempt to reorganize information related to instruments in a better way, per a handful of the reviewer’s comments.


confusing. Rewording required "We use l2 = 880 nm, and l1 = 375 nm in the above
equation, because these wavelengths span the full range over
which that the micro-aethelometer measures attenuation.”

See the new “Light-absorbing PM (micro-aethelometer)” subsection. This is part of our overall attempt to reorganize information related to instruments in a better way, per a handful of the reviewer’s comments.

Moreover, the reason why AAEs was calculated should be disclosed before the equation is presented: "AAE indicates the wavelength-dependence of light absorption, which
can inform source apportionment given the characteristic AAE values of different aerosol types, such as brown carbon (BrC).”

We have made this change.

AAE per se does not provide "source apportionment given the characteristic AAE". It's at best a proxy to indicate whether the fossil fuel or biomass combustion prevails.

Fully agree. We intend AAE to be used as a proxy measurement that informs our conclusions about sources, but is not definitive. In fact, the full sentence referred to here says that “(AAE) can inform source apportionment given characteristic AAE values of different aerosol types.” This wording is consistent with using AAE as a proxy. In general, we have reworked much of the Methods section related to AAE in order to respond to this and other comments from the reviewer.

I don't understand this or its relevance "Our AE-33 measurements required substantial averaging time to reduce noise, and so each measurement is averaged over
the length of each valve-state, as are micro-aethelometer measurements
in order to match.”

We have removed this sentence, given that it is extraneous and potentially confusing.

I understand that particulates are seldom normally distributed, hence the median is a suitable measure of central tendency "All neighborhoods had median PN concentrations..." However, mean and SD should be provided (in a table) to allow comparison with other studies.

We have added a large results table (Table S2) in the SI to address this issue. We refer to it at the end of the first subsection in Results.

It's unclear which BC wavelength is presented "We summarize these neighborhood- and sub-neighborhoodlevel gradients in PM2:5, PN, and BC concentrations”

See the new “Light-absorbing PM (micro-aethelometer)” subsection, which addresses this. Again, we have reworked much of the Methods section to address the very valid concerns over information completeness and organization brought up by the reviewer.

There's no discussion here as to the use of AAE and why the values 1.5 and 1.4 are mentioned. "For all residential neighborhoods, the median
AAE values are above 1.5 and there is little spread, as indicated
by the IQR, compared to weak inversions. Interestingly, the
Highway aggregation has median AAE lower than the residential
neighborhoods, and the least difference between the two inversion
conditions compared to the residential neighborhoods. This
likely reflects a stronger contribution from BC to the total light
absorbing PM for the Highway area. For all of the residential areas,
the high AAE values during strong inversion periods indicate
a strong contribution of BrC to light-absorbing PM.”

We have clarified in the methods section that 1.4 is a rough but useful delineation value for AAE, above which there is likely BrC that contributes to light absorption, and have given an appropriate citation. The sentence containing “AAE values are above 1.5 and there is little spread, as indicated by the IQR, compared to weak inversions,” is just a statement of fact, intended to summarize the top-line result from the AAE panel of Figure 5b. There is nothing special about 1.5 per se, it just is that all of the median values in the neighborhoods are above this number (and obviously higher than 1.4, which we have made clear is a roughly useful demarcation line). Similar to responses above, we have reworked information related to AAE in the Methods section” See the new “Light-absorbing PM (micro-aethelometer)” subsection, where we discuss characteristic AAE values with appropriate citations.

relevance of 1.4? "Additionally, the majority (77%) of grid cells have AAE values below 1.4 during weak inversions.”

Similar to previous response—see the new “Light-absorbing PM (micro-aethelometer)” subsection, where we discuss characteristic AAE values with appropriate citations.

how does the previous statement support this conclusion? "These results imply that the large majority of the spatial variability  in both PM2:5 and BC is driven by residential emissions from home-heating, as opposed to vehicle emissions.”

We have removed this sentence, and reworked the paragraph. The new paragraph reads as such:
“Based on the strong spatial correlation between PM$_{2.5}$ and BC during strong inversion conditions, there is likely a common source for the two pollutants. Additionally, because AAE values during strong inversion conditions are at their highest, especially where PM$_{2.5}$ concentrations are also high, it is likely that the common source driving variability is BrC-containing. And, because these high concentration and high AAE areas are largely in residential neighborhoods and not on highways, our results imply that the large majority of the spatial variability in both PM$_{2.5}$ and BC is driven by residential emissions from home-heating, as opposed to vehicle emissions. This is not to say that woodsmoke is the sole source of PM$_{2.5}$ or BC--clearly not, given some of the high BC concentration grid cells in the Highway category and lower AAE values--but that the spatial concentration gradients we observe for both pollutants are likely due to woodsmoke.”


If you are trying to use AAE to make linkages, please provide suitable references about typical AAE values for biomass/fossil fuel emissions.

Per a handful of the above responses, we have significantly reworked the section about the micro-aethelometer and our use of AAE in interpreting results. See the new “Light-absorbing PM (micro-aethelometer)” subsection) to address this and other concerns about our use of AAE.

FIg 8 is interesting. However, elevation is only part of the story. Have the authors investigated relationships between traffic volume and particulates? For example, Fig 6 https://doi.org/10.1016/j.envpol.2016.07.027

We agree—there is definitely an interesting relationship here between elevation and pollutant concentrations. We did not say that elevation is the sole causal variable (it is like the reviewer says, just part of the story), but we mean for this section to illustrate that there is a demonstrable difference in pollutant concentrations with elevation. We use a figure showing PN to illustrate, but also state (and illustrate by including the slopes) that the same issue is at play for BC and PM2.5, not just PN. To the point of the relationship between traffic and PM, certainly there are a large volume of studies illustrating the connection. Our investigation of that connection largely is through spatial analysis, where we see repeatedly (especially during strong SBI conditions) that there are numerous hotspots in residential areas on quiet streets, and that concentrations on the busiest streets in Fairbanks that we sampled on (aggregated into the “Highway” category) is substantially lower.

Traffic counts would be a great asset.

We agree, but do not have that information—at least in any quantitative way. However, as stated in the text, we saw the highest concentrations of all pollutants in slow, residential areas, NOT on the highways and arterial roads where there is significantly more traffic volume: “However, median concentrations in ``Highway'' are low compared to the rest of the domain. These are among the highest-volume roadways in Fairbanks (Steese Highway, Johansen Expressway), and their low concentrations relative to residential neighborhood areas imply that traffic emissions are not responsible for driving the spatial differences in PM that we observed.”

Maybe I've missed this piece of info, but what hours do the measurements cover? "we aimed to accomplish the above objectives as best as possible within a 1.5
hour timeframe for each drive”.

We illustrated this in multiple ways in our submission. First, the time series shown in Figure S1 shows clearly the periods during which all of the drives took place. Second, Figure S2c shows the distribution of our sampling across the hours of the day. The point of including Figure S2c was to illustrate that, at least qualitatively, the spread across time of day is roughly similar between the two inversion categories. We have added a table (Table S1) showing drive times and dates in the SI to address this issue.

Also, have you measured at weekends? This would be an interesting way to segregate the week/weekend contribution to particulate concentrations.

We did our driving on different days of the week, and table S1 now shows which day of the week each drive was. However, we do not feel like a weekend/weekday comparison is within the scope of this paper, given that we are limited by data. We did 9 total drives on weekends. Additionally, we have shown that the strength of the temperature inversion is very impactful on concentrations and spatial patterns. So, within those 9 total weekend drives, we would need to slice those additionally by inversion category (which gives n=3 and n=6 for weak and strong, respectively). This just isn’t enough data to be able to say anything about weekday/weekend effects on spatial patterns from our mobile dataset within each inversion category. Moreover, we can look (and have) at weekday vs. weekend and strong inversion vs. weak inversion for a single location using the full time series of the campaign from an instrument at one of the fixed-sites. When we do this, we see a strong inversion effect but not a strong weekday/weekend effect. This is only indicative for pollutant behavior at a single location, but still it suggests that there is not a strong impact. In any case, this is outside of the scope of what we are presenting in this paper.

I'm not familiar with the Arctic dynamics, but how do long-range transport of smoke from North America affect the air quality at Fairbanks?  have this been taken into account?

As stated in the introduction, the air quality issues in Fairbanks, as others have shown, are caused by local emissions and the lack of dispersion during wintertime. We have cited the ALPACA white paper, which discusses these issues most succinctly, in the first sentence of second paragraph of the introduction. Were long-range transport of woodsmoke impacting our dataset, we would not see the spatial gradients on small length scales that we observed in this paper.

Please, comment on self contamination from the tailpipe, particularly when the vehicle was idling at traffic lights or accelerating.

We did not see any noticeable increases in any of the pollutants we measured while stationary when we turned our vehicle’s engine on. We have a handful of situations where we were able to test this, as well as periods where we stopped on the route (to take notes, etc.), where no change in concentrations was noticed. As such, we did not feel it necessary to filter any of our data out for stopping at stoplights, or during accelerations. We have added the following language to the paper (last sentences in “Data analysis” subsection) to address this issue: “We assessed in post-processing whether self-sampling of our vehicle exhaust impacted or biases our dataset, and determined that it did not. As such, we have not filtered our dataset for stops or accelerations.”

07-Jan-2023

Dear Dr Robinson:

Manuscript ID: EA-ART-10-2022-000140.R1
TITLE: Wintertime spatial patterns of particulate matter in Fairbanks, AK during ALPACA 2022

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

Reviewer 2

I commend the authors for conducting a thorough revision. I'm looking forward to seeing this article published. I identified nevertheless a few editorial points that should be addressed in order to avoid confusion.

A proper word should be used for ~ in "~33,000,"; "pop. ~96,000" and elsewhere.

Please use an en-dash and not an hyphen in "(40-80%)"; "1 - 1.5 g cm-3" and elsewhere where ranges are shown.

I'm overly critical about this representation "light-absorbing PM (UV-PM and black
carbon, BC)". UV-PM is a loosely defined term and unrecognised in the scientific community. It probably stemmed from the first studies using the Aethalometer. Carbonaceous aerosols that absorb radiation in the UV rage should be refereed to as "brown carbon" (BrC).
This should be replaced with a more scientific description "the concentration of light-absorbing PM from the 375 nm measurement as UV-PM.". Please, check
"Olson et al., 2015. Investigation of black and brown carbon multiple-wavelength dependent light absorption from biomass and fossil fuel combustion source emissions"

The authors should endeavour to eradicate such misleading nomenclatures.

For example, the authors write " as BC and brown carbon (BrC)" without defining BrC and without linking BrC to UV-PM. I suggest "The microaethalometer signals at λ = 375 nm and 880 nm shall be referred to as BrC and BC, respectively"

I find some redundancy in "1.) route"; "2.) route", etc, as I think that the authors should use either a dot or a bracket.

This is confusing "has a response time of 1 Hz". Sampling rate (1 Hz) and response time do not share the same definitions. The response time can be defined as how quickly a sensor is able to react to changes in the measured variable, while sampling rate refers to the frequency that data are collected and stored. I have the feeling that the authors mean the latter.

space required between number and unit: "above 100nm"; "lowest ~10m above"

The instrument description should be harmonised as MODEL, MAKER, COUNTRY. They're presented in different forms.

poor scientific representation "1e6cm-3". consider "1 x 106" (6 obviously is superscripted). There are many other occurrences "PN concentrations above 4.5e4 cm-3."

no need to use quotation marks in “micro”-aethelometer
verb tense "We apply the TSI-suggested 0.38 correction factor". Applied?

confusing title "2.2.6 Temperature difference (DT)". difference between what?

there are many instances that hyphenation should be used: "All 1Hz mobile measurements"

the authors should either use a negative exponent or a slash to represent units, but not a mixture of both: "μg m-3"; "m/s". The whole manuscript should be harmonised.

Well done! I enjoyed reading your manuscript

REVIEWER REPORT(S):
Referee: 2

Comments to the Author
I commend the authors for conducting a thorough revision. I'm looking forward to seeing this article published. I identified nevertheless a few editorial points that should be addressed in order to avoid confusion.

A proper word should be used for ~ in "~33,000,"; "pop. ~96,000" and elsewhere.
We have removed all instances of “~” and replaced that symbol with “roughly,” “approximately,” or deleted it altogether.

Please use an en-dash and not an hyphen in "(40-80%)"; "1 - 1.5 g cm-3" and elsewhere where ranges are shown.
These changes were made.

I'm overly critical about this representation "light-absorbing PM (UV-PM and black
carbon, BC)". UV-PM is a loosely defined term and unrecognised in the scientific community. It probably stemmed from the first studies using the Aethalometer. Carbonaceous aerosols that absorb radiation in the UV rage should be refereed to as "brown carbon" (BrC). 
We have removed mention of “UV-PM” from the manuscript because we aren’t focusing on reporting concentrations of UV-PM. That sentence now refers alone to BC.

This should be replaced with a more scientific description "the concentration of light-absorbing PM from  the 375 nm measurement  as UV-PM.". Please, check
"Olson et al., 2015. Investigation of black and brown carbon multiple-wavelength dependent light absorption from biomass and fossil fuel combustion source emissions”
Similar to above, we have removed this, because we are only reporting concentrations of light-absorbing PM at 880 nm.

The authors should endeavour to eradicate such misleading nomenclatures. 
We have tried to be more clear about this situation, given that we haven’t tried to quantify BrC mass concentrations in this paper. Simply, we mean to use AAE to indicate when there is non-BC absorbing material in addition to BC. We have tried to make mentions of BrC more clear in this context

For example, the authors write " as BC and brown carbon (BrC)" without defining BrC and without linking BrC to UV-PM. I suggest "The microaethalometer signals at λ = 375 nm and 880 nm shall be referred to as BrC and BC, respectively”
We haven’t attempted to quantify Brown Carbon mass concentration, and don’t wish to report BrC concentrations, given the challenges and uncertainties with doing so. We have only presented BC concentrations and AAE values here, and so have removed ambiguity with respect to the mention of UV-PM in the previous draft. We have also replaced all instances of “micro-aethalometer” in the text with “microAeth,” because AethLabs doesn’t actually call the instrument this, and to be consistent with how we are referring to other instruments by their name (e.g. “DustTrak”).

I find some redundancy in "1.) route"; "2.) route", etc, as I think that the authors should use either a dot or a bracket.
We have changed “1.)” to “1)” both here and in another instance of this in the abstract.

This is confusing "has a response time of 1 Hz". Sampling rate (1 Hz) and response time do not share the same definitions. The response time can be defined as how quickly a sensor is able to react to changes in the measured variable, while sampling rate refers to the frequency that data are collected and stored. I have the feeling that the authors mean the latter.
We have changed this from “response time” to “sampling rate.”


space required between number and unit:  "above 100nm"; "lowest ~10m above”
We have fixed these instances and a few others that we found.

The instrument description should be harmonised as MODEL, MAKER, COUNTRY.  They're presented in different forms.
We have updated the text to this format.

poor scientific representation "1e6cm-3". consider "1 x 106" (6 obviously is superscripted). There are many other occurrences "PN concentrations above 4.5e4 cm-3.”
We have changed all instances of this in the text and figures to e.g. "1 x 106"

no need to use quotation marks in “micro”-aethelometer
We have changed all mentions to the instrument to its name from the manufacturer, “microAeth.”

verb tense "We apply the TSI-suggested 0.38 correction factor". Applied?
We changed this and another instance from “apply” to “applied.”

confusing title "2.2.6  Temperature  difference  (DT)". difference between what?
We have change this title to “Vertical temperature measurements”

there are many instances that hyphenation should be used: "All 1Hz mobile measurements”
We have changed all instances of “1Hz” to “1 Hz,” as there should be a space there and a few instances did not have one. We don’t think that a hyphen is appropriate here, but will leave this to the discretion of the editor. There are many instances of “1 Hz” that appear in Environmental Science: Atmospheres (e.g. https://pubs.rsc.org/en/content/articlepdf/2022/ea/d2ea00055e)

the authors should either use a negative exponent or a slash to represent units, but not a mixture of both: "μg m-3"; "m/s". The whole manuscript should be harmonised.
We have fixed this issue.

Well done! I enjoyed reading your manuscript
Thank you for the feedback, both with macro- and micro-level suggestions. We really appreciate it.

13-Jan-2023

Dear Dr Robinson:

Manuscript ID: EA-ART-10-2022-000140.R2
TITLE: Wintertime spatial patterns of particulate matter in Fairbanks, AK during ALPACA 2022

Thank you for submitting your revised manuscript to Environmental Science: Atmospheres. I am pleased to accept your manuscript for publication in its current form. I have copied any final comments from the reviewer(s) below.

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