the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: Use of PM2.5 to CO ratio as a tracer of wildfire smoke in urban areas
- 1School of STEM, University of Washington, Bothell, WA, 98011, USA
- 2Department of Atmospheric Sciences, University of Washington, Seattle, WA, 98195, USA
- 3Washoe County Health District, Air Quality Management Division, Reno, NV, USA
- 1School of STEM, University of Washington, Bothell, WA, 98011, USA
- 2Department of Atmospheric Sciences, University of Washington, Seattle, WA, 98195, USA
- 3Washoe County Health District, Air Quality Management Division, Reno, NV, USA
Abstract. Wildfires, and the resulting smoke, are an increasing problem in many regions of the world. However, identifying the contribution of smoke to pollutant loadings in urban regions can be challenging at lower concentrations due to the presence of the usual array of anthropogenic pollutants. Here we propose a method using the difference in PM to CO emission ratios between smoke and typical urban pollution. For smoke, emission ratios of PM2.5 to CO are between 200–300 µg m−3 ppb−1, whereas typical urban sources have an emission ratio that is lower by a factor of 4–10. This gives rise to the possibility of using this ratio as an indicator of smoke extent. We use observations a regulatory surface monitoring sites in Sparks, NV, for the period of May–September 2018–2021. During this time, there were many smoke-influenced periods from numerous California wildfires that burned during this period. Using a PM / CO ratio of 30, we can split the data into smoke-influenced and no-smoke periods. We then develop a Monte Carlo simulation, tuned to local conditions, to derive a set of PM2.5 / CO values that can be used to identify smoke influence in urban areas. From the simulation, we find that a smoke enhancement ratio of 140 µg m−3 ppb−1 best fits the observations, which is significantly lower than the ratio observed in fresh smoke plumes. The most likely explanation for this difference is greater loss of PM2.5 during dilution and transport to warmer surface layers. We find that the PM2.5 / CO ratio in urban areas is an excellent indicator of smoke and should prove to be useful to identify biomass burning influence on the policy relevant concentrations of both PM2.5 and O3. Using the results of our Monte Carlo simulation, this ratio can also quantify the influence of smoke on urban PM2.5.
Daniel Jaffe et al.
Status: closed
-
RC1: 'Comment on acp-2022-138', Farren L. Herron-Thorpe, 05 Apr 2022
The manuscript is an excellent contribution to the scientific community for this subject. However, several technical corrections should be made for clarity and consistency. These are: 1) past work should be discussed in the past tense, not present tense; 2) PM2.5 should not be referred to as PM, since they are not equivalent (this problem appears in both text and figures); 3) "tracer" and "indicator" should not be used interchangeably. The authors should make sure the three listed items (above) are corrected for the entire manuscript. I have also suggested several technical corrections (below) that could help clarify the writing.
Title: change "a tracer" to "an indicator"
Abstract:
Line 9: remove commas
Line 10: change "lower" to "low"
Line 11: PM2.5
line 12: specify this is in regards to wildfire smoke
Line 14: "extent" is ambiguous; temporal or spatial extent?
Line 14: change to "We use observations at a regulatory surface monitor site..."
Line 15: remove "During this time,"
Line 16: PM to PM2.5; change to "ratio threshold of 30"
Line 19: "in fresh smoke plumes" - provide example range of values
Line 23: change to "...this ratio can also help quantify..."
1.Introduction:
Line 25: change to "... smoke impacts have become more prevalent due to..."
Line 30: change to "diameter less than"
Line 31: change to "...(VOCs) which include many toxic..."
Line 32: change "In addition, secondary" to "Furthermore, atmospheric"
Line 33: change "emissions" to "pollutants"
Line 34: New paragraph
Line 34: change "be transported" to "originate"
Line 40: change "markers" to "tracers"
Line 41: change to "... at surface sites and also have some anthropogenic..."
Line 43: change "anthropogenic" to something like "commercial, institutional, residential". Otherwise it is a redundant statement since industrial and vehicular are already anthropogenic
Line 43: change "in having" to "with"
Line 44: remove "For the US as a whole,"
Line 45: change to "... emission sources in the U.S., excluding..."
Line 46: change "report" to "reported"
Lines 43 to 64: this paragraph could be summarized in a table.
Line 65: change to "...smoke events should allow us to use the observed ratios to derive the smoke..."
Line 68: remove "recently"
Line 69: change "develop" to "developed"
Line 70: include a reference to what a Monte Carlo simulation is
2. Methods and data sources:
Line 73: remove "routine"
Line 74: remove (NV)
Line 80: preliminary data should be finalized and this sentence should be removed
Line 84: remove "Because there were some zerio and very low values for"
Line 84: change to "PM2.5 concentrations less than the DL were set..."
Line 86: change to "...we use the daily smoke polygons from..."
Line 87: change to "The smoke polygon product is created by expert image analysts that digitize smoke plume extent a few times per day based on analysis of GOES-16 and GOES-17 ABI True Color Imagery available during daylight hours."
3. Results:
Line 96-97: change to "Washoe County is located due east of the California-Nevada border, so smoke from fires in California..."
Line 98: remove comma at end of line
Table 1 caption last sentence: "ratios" should not be plural; "35" should include units
Line 111: change "(i)" to "(1)"
Line 115: change PM to PM2.5
Lines 116 and 117: both sentences should be changed to past tense
Line 117: remove "So, while"
Line 118: change to "... lower ratio, but the large PM2.5 concentrations..."
Line 120: correct PM to PM2.5 and remove "alone"
Line 121: correct PM to PM2.5; change "as well as using" to "and"; change "alone" to "separately".
Line 122: change "change" to "difference"; change "smoke influenced and non-smoke data" to "segregated data methods"
Line 123: remove "We note that"
Line 124: change "are" to "were"
Line 134: change "use" to "used"
Line 139: change "are only included" to "were non-zero"
Line 140: change "model" to "represent"
Line 145: change "For" to "During"
Line 147: remove "whereas"
Line 162: remove "and"
Line 165: include values for comparison
Line 167: remove "Here,"; change "is more" to "of R-urban was"
Line 168: change PM/CO to PM2.5/CO; change "this parameter" to "R-urban"
Line 169: include units for value of 20
Line 170: remove first occurrence of "and"
Line 172: change Emission to Emissions
Lines 174-176: This sentence need to be consistent with plural and singular usage; change "this approach" to "the Monte Carlo approach"
Line 177: change "remaining" to "rest"; move "for the rest of this analysis" to end of sentence (and remove comma).
Line 185: change "all had significant" to "all had a significant"
Line 191: change PM to PM2.5
4.Summary:
The summary should specify that the conclusions are specific to warm weather (or May-Sept) where RWC is not a factor. Other details about the monitoring details could also be shared. The final paragraph should have at least one instance of "smoke" changed to "wildfire smoke". Last sentence: change "propose" to "conclude".
Figure 2: change "sorted" to "segregated"; two graphs in one figure should be denoted as (a) and (b) on the graphs or referred to as top/bottom in the text; PM to PM2.5 in text
Figure 3: R-smoke should use subscript; "Monte Carlo" should come before "simulations"
Figure 4: R-smoke should use subscript
Figure 5: PM to PM2.5 (text and figure)
-
RC2: 'Comment on acp-2022-138', Anonymous Referee #2, 06 Apr 2022
This manuscript describes a new method to determine the relative contribution of smoke to observed PM2.5 during wildland fire smoke season. Following Liang et al, 2017, the authors use PM2.5/CO to categorize smoke and non-smoke influenced days. In contrast to the overhead HMS smoke product from satellite measurements that can misrepresent conditions on the ground due to inadequate (or nonexistent) retrieval of near-surface smoke concentrations, the PM2.5/CO method uses in-situ ground measurements typically present at regulatory surface monitoring sites. After determining the PM2.5/CO ratios for urban and smoke aerosol by comparing Monte Carlo simulations to observations, the authors estimate relative contribution of smoke to PM2.5 for smoke-influenced days, finding that indeed all the PM2.5 exceedance days during the period of study have high influence of smoke. Because the simulation is trained on local conditions, the values reported here may not be widely applicable, but the method can be applied to other sites to identify and estimate relative smoke influence. This manuscript describes the development of methods for interpretation of atmospheric data, but with a limited scope of one study location where all PM2.5 exceedance days are from smoke, so its publication as a Technical Note is appropriate. I recommend publication with minor revisions below:
- Discussion of previous work may be improved by description of the various units for normalized enhancement ratios (NERs).
- ΔPM2.5/ΔCO in g/g vs ug m-3 ppm-1: Inclusion of the scale factor may be appropriate.
- Ambient ug m-3 vs STP ug sm-3: Confirm that all values are reported at standard volume to compare like-to-like.
- PM2.5 vs PM1: Studies using Aerosol Mass Spectrometers (e.g. Kleinmann et al., 2020, and Garofalo et al., 2019) will report non-refractory PM1.
I acknowledge that choosing a convention will not have any bearing on the analysis, since this manuscript recommends performing the complete analysis for a particular location. Therefore, any definitions or units of PM will be consistent. However, uniformity in discussion of previous results and between the abstract and main text is appropriate.
- Ln 115: The authors state “Using the PM2.5/CO ratio to segregate the data, we find an improved correlation of PM and CO in the lower range of ratios, compared with using the HMS alone as an indicator (Figure 2).”
In Fig. 2, the R2 values for the smoke days indicated by HMS smoke and PM2.5/CO>30 for the entire range seem comparable, while the R2 values for the non-smoke days are less comparable, indicating the main difference between these methods is in the lower PM2.5 concentration range, well below the NAAQS. At these lower concentrations, the HMS smoke product is less likely to capture conditions at the surface and produces false negatives and positives for smoke-influence. To me, a major strength of the ratio method is the improved sensitivity and specificity in identifying smoke days at these lower concentrations. To highlight this, an SI figure explicitly showing the PM to CO correlations or an inset of PM2.5/CO vs CO that better shows this lower range would be helpful. Additionally, or alternatively, making the dots smaller or with some transparency might allow the reader to better see the differences in the two methods at low concentrations in Fig. 2. Table 2 indicates that only a net change of 2 days between methods, but it seems that more than 2 dots have changed color between Fig. 2a and 2b. Can you add how many days switch categorization (and in which direction)? I also suggest adding the NAAQS to Fig. 2 to show that both methods successfully identify exceedance days. Further explanation and slight tweaks to the figures for the low concentration data will further support the authors’ assertation that the ΔPM2.5/ΔCO method is generally a more robust indicator of surface smoke than satellite-based measurements.
A new development is the use of a Monte Carlo simulation to estimate PM2.5/CO ratios for smoke and urban influence separately in order to estimate the relative contribution of smoke to observed PM2.5.
- How sensitive are the Monte Carlo results to the chosen PM2.5 and CO backgrounds and how do they compare to the non-smoke days (from either and both methods) from the Sparks site in 2019?
- The ozone discussion is limited and the numbers in line 190 do not seem to match the numbers in Table 1.
Careful reading for grammatical errors and missing references (e.g. Briggs et al., 2016) will improve readability.
- Discussion of previous work may be improved by description of the various units for normalized enhancement ratios (NERs).
- AC1: 'Comment on acp-2022-138', D.A.J. Jaffe, 28 May 2022
Status: closed
-
RC1: 'Comment on acp-2022-138', Farren L. Herron-Thorpe, 05 Apr 2022
The manuscript is an excellent contribution to the scientific community for this subject. However, several technical corrections should be made for clarity and consistency. These are: 1) past work should be discussed in the past tense, not present tense; 2) PM2.5 should not be referred to as PM, since they are not equivalent (this problem appears in both text and figures); 3) "tracer" and "indicator" should not be used interchangeably. The authors should make sure the three listed items (above) are corrected for the entire manuscript. I have also suggested several technical corrections (below) that could help clarify the writing.
Title: change "a tracer" to "an indicator"
Abstract:
Line 9: remove commas
Line 10: change "lower" to "low"
Line 11: PM2.5
line 12: specify this is in regards to wildfire smoke
Line 14: "extent" is ambiguous; temporal or spatial extent?
Line 14: change to "We use observations at a regulatory surface monitor site..."
Line 15: remove "During this time,"
Line 16: PM to PM2.5; change to "ratio threshold of 30"
Line 19: "in fresh smoke plumes" - provide example range of values
Line 23: change to "...this ratio can also help quantify..."
1.Introduction:
Line 25: change to "... smoke impacts have become more prevalent due to..."
Line 30: change to "diameter less than"
Line 31: change to "...(VOCs) which include many toxic..."
Line 32: change "In addition, secondary" to "Furthermore, atmospheric"
Line 33: change "emissions" to "pollutants"
Line 34: New paragraph
Line 34: change "be transported" to "originate"
Line 40: change "markers" to "tracers"
Line 41: change to "... at surface sites and also have some anthropogenic..."
Line 43: change "anthropogenic" to something like "commercial, institutional, residential". Otherwise it is a redundant statement since industrial and vehicular are already anthropogenic
Line 43: change "in having" to "with"
Line 44: remove "For the US as a whole,"
Line 45: change to "... emission sources in the U.S., excluding..."
Line 46: change "report" to "reported"
Lines 43 to 64: this paragraph could be summarized in a table.
Line 65: change to "...smoke events should allow us to use the observed ratios to derive the smoke..."
Line 68: remove "recently"
Line 69: change "develop" to "developed"
Line 70: include a reference to what a Monte Carlo simulation is
2. Methods and data sources:
Line 73: remove "routine"
Line 74: remove (NV)
Line 80: preliminary data should be finalized and this sentence should be removed
Line 84: remove "Because there were some zerio and very low values for"
Line 84: change to "PM2.5 concentrations less than the DL were set..."
Line 86: change to "...we use the daily smoke polygons from..."
Line 87: change to "The smoke polygon product is created by expert image analysts that digitize smoke plume extent a few times per day based on analysis of GOES-16 and GOES-17 ABI True Color Imagery available during daylight hours."
3. Results:
Line 96-97: change to "Washoe County is located due east of the California-Nevada border, so smoke from fires in California..."
Line 98: remove comma at end of line
Table 1 caption last sentence: "ratios" should not be plural; "35" should include units
Line 111: change "(i)" to "(1)"
Line 115: change PM to PM2.5
Lines 116 and 117: both sentences should be changed to past tense
Line 117: remove "So, while"
Line 118: change to "... lower ratio, but the large PM2.5 concentrations..."
Line 120: correct PM to PM2.5 and remove "alone"
Line 121: correct PM to PM2.5; change "as well as using" to "and"; change "alone" to "separately".
Line 122: change "change" to "difference"; change "smoke influenced and non-smoke data" to "segregated data methods"
Line 123: remove "We note that"
Line 124: change "are" to "were"
Line 134: change "use" to "used"
Line 139: change "are only included" to "were non-zero"
Line 140: change "model" to "represent"
Line 145: change "For" to "During"
Line 147: remove "whereas"
Line 162: remove "and"
Line 165: include values for comparison
Line 167: remove "Here,"; change "is more" to "of R-urban was"
Line 168: change PM/CO to PM2.5/CO; change "this parameter" to "R-urban"
Line 169: include units for value of 20
Line 170: remove first occurrence of "and"
Line 172: change Emission to Emissions
Lines 174-176: This sentence need to be consistent with plural and singular usage; change "this approach" to "the Monte Carlo approach"
Line 177: change "remaining" to "rest"; move "for the rest of this analysis" to end of sentence (and remove comma).
Line 185: change "all had significant" to "all had a significant"
Line 191: change PM to PM2.5
4.Summary:
The summary should specify that the conclusions are specific to warm weather (or May-Sept) where RWC is not a factor. Other details about the monitoring details could also be shared. The final paragraph should have at least one instance of "smoke" changed to "wildfire smoke". Last sentence: change "propose" to "conclude".
Figure 2: change "sorted" to "segregated"; two graphs in one figure should be denoted as (a) and (b) on the graphs or referred to as top/bottom in the text; PM to PM2.5 in text
Figure 3: R-smoke should use subscript; "Monte Carlo" should come before "simulations"
Figure 4: R-smoke should use subscript
Figure 5: PM to PM2.5 (text and figure)
-
RC2: 'Comment on acp-2022-138', Anonymous Referee #2, 06 Apr 2022
This manuscript describes a new method to determine the relative contribution of smoke to observed PM2.5 during wildland fire smoke season. Following Liang et al, 2017, the authors use PM2.5/CO to categorize smoke and non-smoke influenced days. In contrast to the overhead HMS smoke product from satellite measurements that can misrepresent conditions on the ground due to inadequate (or nonexistent) retrieval of near-surface smoke concentrations, the PM2.5/CO method uses in-situ ground measurements typically present at regulatory surface monitoring sites. After determining the PM2.5/CO ratios for urban and smoke aerosol by comparing Monte Carlo simulations to observations, the authors estimate relative contribution of smoke to PM2.5 for smoke-influenced days, finding that indeed all the PM2.5 exceedance days during the period of study have high influence of smoke. Because the simulation is trained on local conditions, the values reported here may not be widely applicable, but the method can be applied to other sites to identify and estimate relative smoke influence. This manuscript describes the development of methods for interpretation of atmospheric data, but with a limited scope of one study location where all PM2.5 exceedance days are from smoke, so its publication as a Technical Note is appropriate. I recommend publication with minor revisions below:
- Discussion of previous work may be improved by description of the various units for normalized enhancement ratios (NERs).
- ΔPM2.5/ΔCO in g/g vs ug m-3 ppm-1: Inclusion of the scale factor may be appropriate.
- Ambient ug m-3 vs STP ug sm-3: Confirm that all values are reported at standard volume to compare like-to-like.
- PM2.5 vs PM1: Studies using Aerosol Mass Spectrometers (e.g. Kleinmann et al., 2020, and Garofalo et al., 2019) will report non-refractory PM1.
I acknowledge that choosing a convention will not have any bearing on the analysis, since this manuscript recommends performing the complete analysis for a particular location. Therefore, any definitions or units of PM will be consistent. However, uniformity in discussion of previous results and between the abstract and main text is appropriate.
- Ln 115: The authors state “Using the PM2.5/CO ratio to segregate the data, we find an improved correlation of PM and CO in the lower range of ratios, compared with using the HMS alone as an indicator (Figure 2).”
In Fig. 2, the R2 values for the smoke days indicated by HMS smoke and PM2.5/CO>30 for the entire range seem comparable, while the R2 values for the non-smoke days are less comparable, indicating the main difference between these methods is in the lower PM2.5 concentration range, well below the NAAQS. At these lower concentrations, the HMS smoke product is less likely to capture conditions at the surface and produces false negatives and positives for smoke-influence. To me, a major strength of the ratio method is the improved sensitivity and specificity in identifying smoke days at these lower concentrations. To highlight this, an SI figure explicitly showing the PM to CO correlations or an inset of PM2.5/CO vs CO that better shows this lower range would be helpful. Additionally, or alternatively, making the dots smaller or with some transparency might allow the reader to better see the differences in the two methods at low concentrations in Fig. 2. Table 2 indicates that only a net change of 2 days between methods, but it seems that more than 2 dots have changed color between Fig. 2a and 2b. Can you add how many days switch categorization (and in which direction)? I also suggest adding the NAAQS to Fig. 2 to show that both methods successfully identify exceedance days. Further explanation and slight tweaks to the figures for the low concentration data will further support the authors’ assertation that the ΔPM2.5/ΔCO method is generally a more robust indicator of surface smoke than satellite-based measurements.
A new development is the use of a Monte Carlo simulation to estimate PM2.5/CO ratios for smoke and urban influence separately in order to estimate the relative contribution of smoke to observed PM2.5.
- How sensitive are the Monte Carlo results to the chosen PM2.5 and CO backgrounds and how do they compare to the non-smoke days (from either and both methods) from the Sparks site in 2019?
- The ozone discussion is limited and the numbers in line 190 do not seem to match the numbers in Table 1.
Careful reading for grammatical errors and missing references (e.g. Briggs et al., 2016) will improve readability.
- Discussion of previous work may be improved by description of the various units for normalized enhancement ratios (NERs).
- AC1: 'Comment on acp-2022-138', D.A.J. Jaffe, 28 May 2022
Daniel Jaffe et al.
Daniel Jaffe et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 485 | 181 | 14 | 680 | 5 | 13 |
- HTML: 485
- PDF: 181
- XML: 14
- Total: 680
- BibTeX: 5
- EndNote: 13
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1