From the journal Environmental Science: Atmospheres Peer review history

04-Apr-2022

Dear Dr Vance:

Manuscript ID: EA-ART-03-2022-000025
TITLE: Assessment of PM2.5 concentrations, transport, and mitigation in indoor environments using low-cost air quality monitors and a portable air cleaner.

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

This study investigated the concentrations and transport of PM from cooking activities indoors using low-cost PM sensors and evaluated the effects of portable air cleaners (PAC) on indoor PM mitigation. The best PM sensors were selected by assessing five commercially available low-cost sensors, through comparing measurement results to an Optical Particle Sizer. Results show that the transport time of cooking PM from the kitchen to the bedroom ranged from as short as 1 min to 45 min, depending on the layout of the homes (e.g., the distance between the kitchen and the bedroom, and the presence of physical barriers such as doors). The use of PAC was found to be an effective measure for reducing PM concentration. And it is more effective to place PAC in the kitchen during the daytime and in the bedroom overnight considering personal PM exposure. Overall, this is a comprehensive study regarding the transport and mitigation of PM indoors. The manuscript is well written. And the results are valuable to the literature. I would like to recommend its publication to Environmental Science: Atmospheres, subjective to minor revisions.

1. Page 9 line 3: This study focuses on PM2.5. The OPS measured particles with a size of 300-10000 nm. What was the size distribution of PM under cooking and background conditions? OPS cannot measure particles smaller than 0.3 um. Would OPS underestimate the results?

2. Table 1: Please also list the PM detection technique for the low-cost sensors. Are they based on light scattering?

3. AV2 was the most accurate sensor as demonstrated in this study. However, it seems that the data of AV2 is not included in Figure S1 and Figures S2a-b?

4. Figure 1: some of the sensors were placed right next to the PAC? Some were placed a bit farther away. Did the authors intend to do so? Would the placement make any difference on the results?

5. Page 14 line 5: To be clearer, please change “C/COPS” to “CAQM/COPS”

6. Page 14 lines 8-9: The authors demonstrated that no sensor drift was observed over the course of the measurements. How was this conclusion made? Did the authors compare the changes of slope and intercept of Table S4 at different phases? Please clarify.

7. Tables S3 and S4: Why are there intercepts of the correlations? Does it mean that there were small particles (e.g., < 300 nm) that AQM could measure but OPS could not?

PA sensors have a “PM1” mode. Does it suggest that they are more sensitive to smaller particles, so that they have greater intercepts in Tables S3 and S4?

Same as Comment 1, what was size distribution of indoor PM during the measurements? Did small particles (< 300 nm) make up a significant fraction of indoor PM? If smaller particles are concern, will it make PA a better sensor over AV?

8. Page 15 line 6 of the first paragraph: “The R2 values for all AQMs were higher during background periods than cooking periods.”

Could it be just that ambient particles have larger sizes so that they can be more accurately and represented captured by OPS?

9. Page 16 line 13-15: “Similar factor mentioned as L/K ratio in Wan et al. has been used to compare PM levels in the kitchen and living rooms due to indoor cooking in 12 different sites.”

Change “PM levels in the kitchen and living rooms” to “PM levels in the living rooms and kitchen”

10. Figure 4: What does the brown shaded region in Figure 4c represent?

11. Page 19 lines 3-4 of the first paragraph: “Figure 4 also shows that PAC deployment in the bedroom (Phase 3) did not affect median concentrations in the kitchen.”

I get confused. According to Table S2, Phases 1, 2, and 3 were performed on different days. Factors such as cooking materials, cooking behaviors, household ventilation (especially when the windows were open) might vary from day to day. How could the authors obtain nearly the same diurnal trends of PM for Phases 1 and 3? Also, besides the researchers, were there any other inhabitants staying in the houses during the time periods of the three phases? The presence of other inhabitants and their everyday activities could also lead to PM diurnal variation.

12. Page 19 lines 9-10: “According to the inhabitants of Home 3, the high PM2.5 concentrations observed overnight in kitchen of Home 3 during could be…”

Incomplete sentence, missing information after “during”

13. Page 20 and Page 21 last sentences: there sentences are about nighttime analyses. Will it be better to move them to section 3.3.2?

14. Page 22 lines 9-10: Remove “-” before “53%”.

15. Page 22 lines 12-13: “This could be because Home 1 had an extracting range hood…”

Should it be Home 2, instead of Home 1?

16. Page 22 lines 15-16: “Therefore, the exposure values calculated for the bedroom areas may be
from other sources, such as outdoor infiltration.”

I am not sure if outdoor infiltration is a significant source. It seems that indoor concentrations were much higher than those outdoors (Figure S3) for all Homes 1-4, although the outdoor concentrations were measured at a different site. In addition, there could also be outdoor infiltration at kitchen sites. Was there any resident in the bedroom at night? Could it be any PM sources from resident activities in the bedroom?

17. Page 24 first sentence: “The overnight PM2.5 exposure values during Phase 1 were one order of magnitude lower as compared to daytime periods, thereby suggesting the role of outdoor infiltration in PM2.5 exposure levels indoors even though its contribution is much lower than indoor cooking, likely due to low ambient PM2.5 levels during this study.”

Same to Comment 16. I don’t think the conclusion regarding the role of outdoor infiltration PM2.5 exposure levels indoors can be made. The much smaller PM2.5 exposure values overnight are likely due to the reason that there were barely cooking activities at night.

18. Page 24 last sentence of the paragraph: “A moderate reduction in mean exposure (~35%) was also observed for Home 4 and Home 2…”

According to Figure 6, comparing Phase 2 to Phase 1, no reduction but an increase in the mean exposure was observed for Home 2.

19. Page 25 lines 9-12: “For homes that did not have an effective control strategy (Home 3 and Home 1), the reductions in mean exposure values were usually greater as compared to the other two homes with extracting range hood and open windows for higher air exchange rates.”

According to Tables S5 and S6, this is only valid for the observations during Phase 2. To clarify, maybe the authors can revise the sentence as: “…values were usually greater as compared to the other two homes with extracting range hood and open windows for higher air exchange rates (e.g., observations in Phase 2)”

Reviewer 2

The manuscript utilizes low-cost sensors to evaluate the transport of particulate matter (PM) from kitchen to bedroom area in four residences and the effectiveness of using a portable air cleaner to reduce indoor PM exposure. I have some major questions about the data interpretation, as given below.

Major comments:
(1) Instrument inter-comparison: Are the low-cost sensors based on passive sampling? If so, would they respond slower to variations of PM concentration, compared with the reference OPC? I suggest the authors to determine the response time of sensors using the time series data, and then determine at what time resolution the measured concentration should be compared (now 1 min). This distinction might help explain higher R2 during background periods than that in cooking periods, which is featured with highly variable concentrations (Page 15, the first paragraph).
(2) Figure 3b: the Cbedroom/Ckitchen ratios in the four homes shown in Figure 3b are not so consistent with the daytime exposure of bedroom and kitchen presented in Figure 5 (No PAC). For example, Home 2 exhibited the lowest Cbedroom/Ckitchen in Figure 3b, whereas Home 3 exhibited lowest ratio in term of exposure level in Figure 5. There is long discussion why some homes have higher values of Cbedroom/Ckitchen than the others (section 3.1), I wonder if such discussion makes sense given the totally differing picture shown in Figure 5. Maybe instead of focusing on the differences among the four homes, it is better to highlight their common feature. That is, the bedroom concentration/exposure was overall not much lower than that in the kitchen, despite the particle emission occured in the kitchen.
(3) Daytime exposure analysis (Page 20): “With no PAC was used, the average (+/- standard error) daytime PM2.5 concentration in all four homes was 10.3+/-0.2 ug/m3 in the kitchen…” If the standard error means standard variation here, please double check the number. Based on Figure 5, I would expect a much larger variation.

Specific comments:
(1) Page 4 Abstract: “over a period of nine months” is a bit misleading. It might be better to state “more than 9 weeks at each home”.
(2) Page 11 last paragraph: There seem to be some errors on home numbers in the last two sentences, please double check.
(3) Page 16 first paragraph: suggest to revise “effective air exchange rates” to “effective particle loss rate”
(4) Page 18 second paragraph: an extracting fume hood and open windows in the kitchen area can only reduce kitchen concentration, it is unclear how they can lead to lower Cbedroom/Ckitchen.
(5) Figure 4: Given the large variability of data (most likely not following normal distribution), I would suggest to use shaded region to show interquartile range, instead of standard error.
(6) Page 22 second paragraph: Home 2 has an extracting range hood instead of Home 1.
(7) Page 24: It seems unfair to me to directly compare overnight PM2.5 exposure with daytime exposure to infer emission sources of PM2.5. The defined daytime period is 2 times longer than the night period.

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

Response to reviewers’ comments
Manuscript ID: EA-ART-03-2022-000025
TITLE: Assessment of PM2.5 concentrations, transport, and mitigation in indoor
environments using low-cost air quality monitors and a portable air cleaner.
We thank the reviewers for reading our manuscript and for their reviews, comments, and
suggestions. We believe that these comments led to an overall improvement of our manuscript.
Below, the original reviewer comments are shown in italic blue font, while our replies are in
ordinary typeface. Newly added text is indented and in red.
Reviewer #1
This study investigated the concentrations and transport of PM from cooking activities indoors
using low-cost PM sensors and evaluated the effects of portable air cleaners (PAC) on indoor
PM mitigation. The best PM sensors were selected by assessing five commercially available
low-cost sensors, through comparing measurement results to an Optical Particle Sizer. Results
show that the transport time of cooking PM from the kitchen to the bedroom ranged from as
short as 1 min to 45 min, depending on the layout of the homes (e.g., the distance between the
kitchen and the bedroom, and the presence of physical barriers such as doors). The use of PAC
was found to be an effective measure for reducing PM concentration. And it is more effective to
place PAC in the kitchen during the daytime and in the bedroom overnight considering personal
PM exposure. Overall, this is a comprehensive study regarding the transport and mitigation of
PM indoors. The manuscript is well written. And the results are valuable to the literature. I
would like to recommend its publication to Environmental Science: Atmospheres, subjective to
minor revisions.
We thank the reviewer for their time, comments and appreciation of this work. The reviewer has
made valuable points that we address in the responses below. We feel that these revisions and
additions have strengthened our paper.
1. Page 9 line 3: This study focuses on PM2.5. The OPS measured particles with a size of
300-10000 nm. What was the size distribution of PM under cooking and background conditions?
OPS cannot measure particles smaller than 0.3 um. Would OPS underestimate the results?
Since the lower particle size cutoff for all the sensors used in this study, including both the OPS
and the low-cost PM sensors is 0.3 μm, the chances for underestimation of the results is unlikely.
However, we acknowledge here that the D50 cutoff is not the same across each instrument
primarily due to differences in sampling flow rates and measurement chamber geometry.
Additionally, we are aware of anecdotal evidence that some nephelometers may “over count” PM
1
concentrations when there are relatively high concentrations of ultrafine PM. To further address
this comment we have added the size detection limit for each sensor in the Table S1.
Additionally, we understand that the measurements both by OPS and by low-cost sensors are
likely to underestimate actual PM2.5 concentrations because they miss potential PM mass
contributions from particles < 300 nm, especially from indoor cooking. To address this point, we
aded the text below to section 2.1:
Because all particle instruments used in this study are limited to particles > ~0.3 µm in
diameter, their measurements are likely to underestimate actual PM2.5 concentrations
because they miss potential PM mass contributions from particles < 300 nm, which may
be important indoors, especially during some indoor cooking activities. As such, all PM2.5
concentrations reported in this work should be interpreted as PM0.3-2.5.
2. Table 1: Please also list the PM detection technique for the low-cost sensors. Are they
based on light scattering?
Per the reviewer's suggestion, we added a PM detection technique column to Table 1.
3. AV2 was the most accurate sensor as demonstrated in this study. However, it seems that the
data of AV2 is not included in Figure S1 and Figures S2a-b?
We thank the reviewer for pointing out this discrepancy. We have updated the plots (Figure S1
and Figure S2) with AV2 data.
4. Figure 1: some of the sensors were placed right next to the PAC? Some were placed a bit
farther away. Did the authors intend to do so? Would the placement make any difference on the
results?
Due to differences in the kitchen layouts, the PAC couldn't be placed right next to the sensors in
some of the homes. However, we ensured that the sensors and PAC were still placed in close
proximity to each other so as to avoid creating large spatial gradients.
5. Page 14 line 5: To be clearer, please change “C/COPS” to “CAQM/COPS”
We have replaced the C/COPS
term with CAQM/COPS
throughout the manuscript for better clarity.
6. Page 14 lines 8-9: The authors demonstrated that no sensor drift was observed over the
course of the measurements. How was this conclusion made? Did the authors compare the
changes of slope and intercept of Table S4 at different phases? Please clarify.
We have edited the following line for better clarity:
2
The resulting CAQM/COPS values were in the range of 1-4 for most of the AQMs during
both cooking and background periods for different collocation phases in all the homes
and did not change over time. Therefore, we did not observe any significant sensor
measurement drift within the timeframe of this study.
7. Tables S3 and S4: Why are there intercepts of the correlations? Does it mean that there
were small particles (e.g., < 300 nm) that AQM could measure but OPS could not? PA sensors
have a “PM1” mode. Does it suggest that they are more sensitive to smaller particles, so that
they have greater intercepts in Tables S3 and S4? Same as Comment 1, what was the size
distribution of indoor PM during the measurements? Did small particles (< 300 nm) make up a
significant fraction of indoor PM? If smaller particles are concern, will it make PA a better
sensor over AV?
Since all of the AQMs used in this study have a theoretical lower particle size cutoff of 0.3 μm,
we can not comment whether the AQMs were able to measure particles smaller than 300 nm
more accurately as compared to OPS. Unfortunately, we were not able to make measurements of
PM < 0.3 μm for this study due to instrument availability and logistics of deployment into
homes. Moreover, the PM1mode of PurpleAir (PA) theoretically takes into account the
concentrations for particles in the 0.3-0.5 μm and 0.5-1 μm size bins, therefore we can not say
that PA would outperform AirVisual for measuring particles below 300 nm given its own
specifications.
8. Page 15 line 6 of the first paragraph: “The R2 values for all AQMs were higher during
background periods than cooking periods.”Could it be just that ambient particles have larger
sizes so that they can be more accurately and represented captured by OPS?
This question is connected to question 7 and we refer to it for our reasoning as to why we do not
believe that counting particles outside its specified range would constitute a higher measurement
quality. The response of low-cost sensors when deployed in ambient conditions depends mainly
upon the aerosol physical properties as well as chemical composition. Therefore, we believe that
the higher values of R2 during background periods is primarily due to rather stable conditions in
indoor environments during no-activity periods inside the homes. We included the discussion
below to this paragraph for further clarity:
During background periods, particles are likely to have penetrated from outdoors and are
more likely to match low-cost sensor calibration inputs. Infiltrated particles are also less
unlikely to suffer strong temporal and spatial gradients. During cooking periods, particle
concentration, size distribution, optical properties, and chemical composition are likely to
change quickly, creating strong temporal and spatial gradients. Sudden changes in these
parameters may have led to deviation of response linearity.
3
9. Page 16 line 13-15: “Similar factor mentioned as L/K ratio in Wan et al. has been used to
compare PM levels in the kitchen and living rooms due to indoor cooking in 12 different sites.”
Change “PM levels in the kitchen and living rooms” to “PM levels in the living rooms and
kitchen”
We have made the change as per the reviewer’s suggestion. We have edited the following line
accordingly:
Similar factor mentioned as L/K ratio in Wan et al.67 has been used to compare PM levels
due to indoor cooking in the living rooms and kitchen of 12 different homes.
10. Figure 4: What does the brown shaded region in Figure 4c represent?
The brown shaded region represents the overlap of standard error values between Phase 1 and
Phase 3. We have updated the figure caption to include this information:
Figure 4. Median PM2.5 concentrations in the kitchen area during the day for different
phases in Homes 1-4 are shown in panels a, b, c, and d, respectively. The shaded region
represents standard error. The brown shaded region represents the overlap between Phase
1 and Phase 3. Note that the y axis is different for each panel.
11. Page 19 lines 3-4 of the first paragraph: “Figure 4 also shows that PAC deployment in the
bedroom (Phase 3) did not affect median concentrations in the kitchen.” I get confused.
According to Table S2, Phases 1, 2, and 3 were performed on different days. Factors such as
cooking materials, cooking behaviors, household ventilation (especially when the windows were
open) might vary from day to day. How could the authors obtain nearly the same diurnal trends
of PM for Phases 1 and 3? Also, besides the researchers, were there any other inhabitants
staying in the houses during the time periods of the three phases? The presence of other
inhabitants and their everyday activities could also lead to PM diurnal variation.
We concur with the reviewer that the different phases were performed on different days which
could lead to some inherent variability due to different factors as evidenced by higher standard
error values during daytime periods when the cooking activities were taking place. However, the
overlap in the median concentration data signifies that the main source of PM concentrations in
the kitchen area were cooking activities that were being performed at regular intervals during the
day thereby leading to a clear diurnal pattern especially in case of Home 1 and Home 3. For
further clarification, we have edited the following line accordingly:
The median concentrations for Phase 1 and Phase 3 in all the homes also exhibit the same
diurnal pattern and an overlap to a certain extent. This shows that PAC deployment in the
bedroom (Phase 3) did not affect concentrations in the kitchen in a significant manner.
4
12. Page 19 lines 9-10: “According to the inhabitants of Home 3, the high PM2.5
concentrations observed overnight in kitchen of Home 3 during could be…”Incomplete sentence,
missing information after “during”
We have completed the sentence.
13. Page 20 and Page 21 last sentences: there sentences are about nighttime analyses. Will it
be better to move them to section 3.3.2?
We feel that this information should be provided in the background of Figure 4 for better clarity.
14. Page 22 lines 9-10: Remove “-” before “53%”.
We have replaced the dash with a colon.
15. Page 22 lines 12-13: “This could be because Home 1 had an extracting range hood…”
Should it be Home 2, instead of Home 1?
We thank the reviewer for pointing out this mistake. Home 1 has been replaced with Home 2.
16. Page 22 lines 15-16: “Therefore, the exposure values calculated for the bedroom areas
may be from other sources, such as outdoor infiltration.” I am not sure if outdoor infiltration is a
significant source. It seems that indoor concentrations were much higher than those outdoors
(Figure S3) for all Homes 1-4, although the outdoor concentrations were measured at a different
site. In addition, there could also be outdoor infiltration at kitchen sites. Was there any resident
in the bedroom at night? Could it be any PM sources from resident activities in the bedroom?
The median concentrations in Figure 5 during nighttime periods never reached zero level during
periods of No PAC phase in the kitchen area, which suggests that there were background
concentrations attributable to outdoor infiltration. Since the study was carried out in
non-smoking households we do not believe there were additional sources of fine PM due to
resident activities. To further address this comment regarding the residents in the bedrooms, we
have added additional information regarding the occupancy in the bedroom areas for all the
homes in the methods section.
The bedroom areas in Homes 2 and 3 were mostly unoccupied throughout the day
whereas in the case of other two homes, the bedroom areas were inhabited.
17. Page 24 first sentence: “The overnight PM2.5 exposure values during Phase 1 were one
order of magnitude lower as compared to daytime periods, thereby suggesting the role of outdoor
infiltration in PM2.5 exposure levels indoors even though its contribution is much lower than
indoor cooking, likely due to low ambient PM2.5 levels during this study.” Same to Comment 16.
I don’t think the conclusion regarding the role of outdoor infiltration PM2.5 exposure levels
5
indoors can be made. The much smaller PM2.5 exposure values overnight are likely due to the
reason that there were barely cooking activities at night.
The contribution of outdoor infiltration to indoor exposure cannot be neglected in this study
because the building envelope in all homes wasn't perfectly sealed from outdoors. A recent study
by Bi et al.1
investigated the role of outdoor infiltration in 41 different residential homes in
California using data from PurpleAir monitors and reported that the average infiltration factor
was around 0.26. Therefore, we can not neglect the contribution of outdoor infiltration towards
driving the indoor exposure values in this study.
1 Bi, Jianzhao, et al. "Characterizing outdoor infiltration and indoor contribution of PM2. 5 with
citizen-based low-cost monitoring data." Environmental Pollution 276 (2021): 116763.
18. Page 24 last sentence of the paragraph: “A moderate reduction in mean exposure (~35%)
was also observed for Home 4 and Home 2…” According to Figure 6, comparing Phase 2 to
Phase 1, no reduction but an increase in the mean exposure was observed for Home 2.
Thanks for pointing out this discrepancy. We have edited the following line accordingly:
A moderate reduction in mean exposure (~35%) was also observed for Home 4 where the
average exposure value during nighttime periods was calculated to be 8 µg m-3 h.
19. Page 25 lines 9-12: “For homes that did not have an effective control strategy (Home 3
and Home 1), the reductions in mean exposure values were usually greater as compared to the
other two homes with extracting range hood and open windows for higher air exchange rates.”
According to Tables S5 and S6, this is only valid for the observations during Phase 2. To clarify,
maybe the authors can revise the sentence as: “…values were usually greater as compared to the
other two homes with extracting range hood and open windows for higher air exchange rates
(e.g., observations in Phase 2)”
We have made the change as per the reviewer’s suggestion. We have edited the following line
accordingly:
For homes that did not have an effective control strategy (Home 3 and Home 1), the
reductions in mean exposure values were usually greater as compared to the other two
homes with extracting range hood and open windows for higher air exchange rates,
especially during Phase 2 of deployment.
Reviewer #2
(1) Instrument inter-comparison: Are the low-cost sensors based on passive sampling? If so,
would they respond slower to variations of PM concentration, compared with the reference
OPC? I suggest the authors to determine the response time of sensors using the time series data,
6
and then determine at what time resolution the measured concentration should be compared
(now 1 min). This distinction might help explain higher R2 during background periods than that
in cooking periods, which is featured with highly variable concentrations (Page 15, the first
paragraph).
All the low-cost sensors used in this study were based on active sampling and the time resolution
of the real time data provided by each sensor’s manufacturer is located in Table 1. Because all
sensors average data to relatively high time resolutions (10 - 300 s) and cooking activities may
have led to spatial gradients in the home, a distance of a few inches between sensors could affect
their response time. For this reason, we chose to line up all datasets to a 60 s time resolution and
refrain from performing a response time analysis. A more controlled environment with well
known flow patterns would be required for this.
(2) Figure 3b: the Cbedroom/Ckitchen ratios in the four homes shown in Figure 3b are not so
consistent with the daytime exposure of bedroom and kitchen presented in Figure 5 (No PAC).
For example, Home 2 exhibited the lowest Cbedroom/Ckitchen in Figure 3b, whereas Home 3
exhibited lowest ratio in term of exposure level in Figure 5. There is long discussion why some
homes have higher values of Cbedroom/Ckitchen than the others (section 3.1), I wonder if such
discussion makes sense given the totally differing picture shown in Figure 5. Maybe instead of
focusing on the differences among the four homes, it is better to highlight their common feature.
That is, the bedroom concentration/exposure was overall not much lower than that in the kitchen,
despite the particle emission occured in the kitchen.
We concur with the reviewer that the values for the Cbedroom/Ckitchen factor were not consistent with
the exposure values; however that is likely due to the fact that the former calculations were done
for a cooking period (90 minutes of duration) whereas the latter analysis was performed for
longer periods of time.
We also agree that the discussion regarding the overall bedroom exposure being much lower than
that in the kitchen should be highlighted more in the manuscript so in that regard we have added
the following lines in the conclusion section:
The exposure analysis performed in this study suggests that PAC use is an important
intervention strategy for reducing personal PM2.5 exposure, especially in indoor
environments where cooking is the main source of PM2.5
. When no PAC was being used
in all the homes, the bedroom exposure values were also comparable to the exposure at
the kitchen location in all the homes. During daytime (6:00 am- 10:00 pm), PAC use in
the bedroom or kitchen area yielded 30-90% reductions in PM2.5 exposure in three of the
four homes. Daytime exposure results also suggest that using a PAC in the kitchen results
in lower exposure values in both the bedroom and kitchen areas. During overnight
periods, PAC use resulted in the lowest exposure values in all homes, with a reduction in
7
mean exposure values by 30-90% or 4-25 µg m-3 h as compared to not using a PAC in the
bedroom.
(3) Daytime exposure analysis (Page 20): “With no PAC was used, the average (+/- standard
error) daytime PM2.5 concentration in all four homes was 10.3+/-0.2 ug/m3 in the kitchen…” If
the standard error means standard variation here, please double check the number. Based on
Figure 5, I would expect a much larger variation.
We assume that by standard variation, the reviewer is referring to standard deviation. We
calculated the standard error values instead of the standard deviation values (which were higher
than one order of magnitude in some cases due to large variability in the dataset). The standard
deviation measures the discrepancy of data to the mean, while the standard error measures the
discrepancy of the sample mean from the true population mean.
Specific comments:
(1) Page 4 Abstract: “over a period of nine months” is a bit misleading. It might be better to
state “more than 9 weeks at each home”.
We have edited the statement as per reviewer’s suggestion, which now reads:
In this study, we deployed multiple low-cost air quality monitors (AQMs) to investigate
the transport of kitchen-generated fine particulate matter (PM2.5) into the bedrooms of
four homes of different sizes over a period of more than nine weeks at each home.
(2) Page 11 last paragraph: There seem to be some errors on home numbers in the last two
sentences, please double check.
Thanks for pointing out this discrepancy. We have edited the following line accordingly:
Homes 1, 3, and 4 were apartments while Home 2 was a single-family detached home.
Homes 3 and 4 were located on the first floor whereas Home 1 was located on the ground
floor.
(3) Page 16 first paragraph: suggest to revise “effective air exchange rates” to “effective
particle loss rate”
Thank you for this comment. The following line has been edited in the manuscript:
We also calculated the first-order decay rate associated with each cooking event to
compare the effective particle loss rates (including deposition losses) in the kitchen areas
for different homes, as shown in Figure S7.
8
(4) Page 18 second paragraph: an extracting fume hood and open windows in the kitchen area
can only reduce kitchen concentration, it is unclear how they can lead to lower
Cbedroom/Ckitchen.
The control measures in the kitchen area will prevent the concentrations from reaching the
bedroom area which will result in lower values of Cbedroom/Ckitchen values since these values take
into account time-averaged concentration over 90 minutes from a peak occurrence corresponding
to a cooking event. To address this comment, we have added a few supporting arguments in the
manuscript:
It is also interesting to note that the median values of CBedroom/CKitchen for homes which had
an extracting fume hood (Home 2) and open windows in the kitchen area during cooking
periods (Home 4) were lower than that value for the other homes. This could be due to
the fact that these control measures prevented the majority of the kitchen concentrations
from reaching the bedroom area, thereby lowering the time-averaged concentrations
(calculated for 90 minutes post peak). These results indicate the effectiveness of such
control measures in reducing PM exposure due to indoor cooking in both the kitchen and
bedroom areas. This is expanded in greater detail in the next section.
(5) Figure 4: Given the large variability of data (most likely not following normal
distribution), I would suggest to use shaded region to show interquartile range, instead of
standard error.
We purposefully chose the shaded region to represent standard error because these values were
comparable to the median concentrations.
(6) Page 22 second paragraph: Home 2 has an extracting range hood instead of Home 1.
Home 1 has been replaced with Home 2.
(7) Page 24: It seems unfair to me to directly compare overnight PM2.5 exposure with
daytime exposure to infer emission sources of PM2.5. The defined daytime period is 2 times
longer than the night period.
We concur with the reviewer that due to the difference in time periods daytime exposure values
will always be greater than nighttime exposure therefore we have edited this line to include this
caveat:
The overnight PM2.5 exposure values during Phase 1 were one order of magnitude lower as
compared to daytime periods, thereby suggesting the role of outdoor infiltration in PM2.5
exposure levels indoors even though its contribution is much lower than indoor cooking, likely
due to low ambient PM2.5
levels during this study. However, it is important to mention that the
time period for daytime exposure was twice that of nighttime periods (16 h vs 8 h).

29-Apr-2022

Dear Dr Vance:

Manuscript ID: EA-ART-03-2022-000025.R1
TITLE: Assessment of PM2.5 concentrations, transport, and mitigation in indoor environments using low-cost air quality monitors and a portable air cleaner.

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Reviewer 2

The authors have addressed most of my comments nicely. The only response I still have a different opinion is regarding to Specific comment (5):
Standard error is parametric statistics, how could it be comparable with median (nonparametric statistics)? If the lines show median PM2.5 concentrations, it makes more sense to plot interquartile range using the shades.