the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Jun 2025
Measurement report: Lessons learned from the comparison and combination of fine carbonaceous aerosol source apportionment at two locations in the city of Strasbourg, France
Abstract. Source apportionment analyses of carbonaceous aerosol were conducted at two neighboring urban sites in Strasbourg, France, during the winter of 2019/2020 using ACSMs (Aerosol Chemical Speciation Monitors; for non-refractory submicron aerosols), aethalometers (AE33; for equivalent Black Carbon – eBC) and filter-based offline chemical speciation. Positive Matrix Factorization (PMF) was applied to organic aerosols (OA) following two strategies: i) analyzing each site individually, ii) combining both sites into a single dataset. Both methods resolved five OA factors: hydrocarbon-like (HOA), biomass burning (BBOA), cooking-like (COA-like), oxygenated (OOA), and an amine-related OA (58-OA) factor. The latter factor, accounting for ~4 % of the total OA mass at each site, showed clear diel profiles and a distinct origin marked by specific wind directions, suggesting a unique local source, potentially linked to industrial emissions. The present study also highlights the challenge of attributing a cooking-only origin to the COA-like factor, which exhibited a diel cycle similar to biomass burning OA at the background site. The combined PMF analysis improved the apportionment of cooking emissions at nighttime, especially for the traffic site, compared to individual PMF analyses, but it did not enhance the other OA factors due to instrumental specificities (i.e., different fragmentation patterns) leading to differences in OA mass spectra between the two instruments. Overall, this study argues for careful inspection of instrumental peculiarities in ACSM and AE33 data treatment and provides hints to benefit from their use at various locations at the city scale. It also allows comparison between different types of PMF analyses, showing that combined PMF may not be appropriate for improving the consistency of OA factors in some cases such as the one presented here.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
(1411 KB) - BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-648', Anonymous Referee #2, 02 Jul 2025
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AC1: 'Reply on RC1', Hasna Chebaicheb, 19 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-648/egusphere-2025-648-AC1-supplement.pdf
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AC1: 'Reply on RC1', Hasna Chebaicheb, 19 Sep 2025
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RC2: 'Comment on egusphere-2025-648', Anonymous Referee #3, 25 Jul 2025
The study by Chebaicheb et. al, provides a standard report of fine aerosol measurements using a combination of ACSM/aethalometer instruments. In terms of the single vs. combined PMF comparison, a solid job has been done, which is more about the measurement technique. In terms of scientific contribution or novelty, I see an interesting discussion on "amine-related OA" factor. With the exception of this, however, the results are fairly standard compared to previously published similar work. For this reason, I recommend even further data exploitation in a situation when two ACSM/AE33 were measured in parallel within the same city. My comments are following.
- Can you present the concentration ratios of the time series between ACSMs to identify which species or m/z are significant for either station even if the distance between sites in not large?
- In context of previous comment, the concentration difference in m/z 60 (Fig.S14) looks like an interesting result if we take into account a fairly small distance between these two sites. It probably indicates some close source of fresh emissions from biomass burning. Is this plausible at Strasbourg? When you comparing the instruments against each other (Aug-Oct 2019, Fig.S1), how did the m/z60 comparison come out? If the pre-campaign comparison was the same for m/z60, I would add this figure to the main text and expand discussion on a possible specific source in the vicinity of Clemenceau site.
- L. 370, authors state: “A factor profile associated with amine-OA and a specific daily profile are consistent with an industrial source. This factor could therefore be associated with an industrial source of OA.” Can you be more specific and maybe even hypothesize about a specific industrial source of this factor? If you know wind direction (Fig.S16) and also that it is a local source, it might not be a problem to pick out something specific. It would be helpful for information if a similar source appears in other papers.
- In Fig.S4 is a comparison with OC and EC measured on the filters. The agreement is very good. Can you add a similar comparison of filters vs. ACSM concentrations for SO4, NO3 and NH4, and also the discussed comparison of Levoglucosan vs. eBCwb?
- 3: Can you set the same range on x-axis (time)? It would be better for visual comparing of time series with each other. I also suggest to add Fig.2 on top of Fig.3.
- S1 in supplement can be extended by intercomparison of aethalometers prior to the Strasbourg campaigns.
Citation: https://doi.org/10.5194/egusphere-2025-648-RC2 -
AC2: 'Reply on RC2', Hasna Chebaicheb, 19 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-648/egusphere-2025-648-AC2-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-648', Anonymous Referee #2, 02 Jul 2025
Chebaicheb et al. present a comparison of aerosol composition/concentration and positive matrix factorization (PMF) results from two quadrupole aerosol chemical speciation monitors (Q-ACSMs) that were stationed at two locations within Strasbourg, France, during a winter period in 2019/2020. The authors found that the Clemenceau site generally had, on average, higher PM1 than the Danube site due to differences in emissions; however, the composition between the two sites were generally similar (e.g., organics, sulfate, nitrate, ammonium, fossil fuel black carbon, and wood burning biomass burning). Running PMF with the dataset collected from each instrument similarly showed similar organic composition, with the largest difference in the hydrocarbon-like organic aerosol (HOA) at Danube vs Clemenceau. However, if PMF was conducted with the dataset with both instruments as one large dataset, large differences in the organic components occur between Danube than Clemenceau; however, the individual PMF determined for Clemenceau was similar to the combined PMF results for the same location.
As PMF is a tool often used for analysis for investigating sources of both particulate matter and gases, investigating potential sources of uncertainty in this tool is of value for the community of ACP. This paper could potentially also be published in AMT, as it is about the technique of PMF. Either way, the following comments need to be addressed prior to publication.
Minor
1) It was not clear until looking at the figure what orifice was used for both ACSMs to know what diameter cut-off the measurements correspond to (e.g., PM1 vs PM2.5).
2) How co-located were the AE33, Q-ACSM, and FIDAS 200? E.g., were they sampling from similar inlets for AE33 and Q-ACSM? Were the sampling heights similar for all three instruments? How close were the inlets for all three inlets?
3) Was there a dryer for any of the instruments?
4) How statistically different are the average values shown in Table 1? There is discussion about the percent differences in the concentrations; however, the average values fall within the standard deviation, which is assumed to be the spread in the observations and not the uncertainty of the measurements?
Major
1) Figure S3 does not make sense though it is needed, I believe, for the argument about potentially why the different ACSMs have different PMF results. How is it for both instruments and what is the average mass spectra being compared against? How does it impact total OA?
2) Figure S6 & Figure 3. Total PM2.5 is generally constant across an urban environment unless there is a very localized emission source, though that emission source maybe more impactful towards PM10 and PM0.1. However, though the PM2.5 (black line) looks generally similar between the two sites in Figure 3, there is very different slopes between the two ACSMs vs PM2.5 in Figure S6 (also, unclear which value is slope vs intercept). What is potentially leading to these differences, and what does it mean for the quantification of one instrument vs another?
3) The biggest concern and the least amount of discussion is with the combined PMF vs the individual PMF. From the discussion, it is not clear what is the "preferred," more accurate method? E.g., if there are multiple AMSs, ACSMs, or other measurements measuring the composition and concentration of PM, should they be combined into one dataset to conduct PMF for improved accuracy, or was the single PMF more accurate? Was whether one dataset was driving the results of the other dataset investigated? E.g., end points are determined, and then the results are determined from those end points. However, as the authors discuss, one location appeared to potentially have a mixed end-point as they called one of the results COA-like. Does it make sense for the Danube PMF results to have changed so much? I understand that this is a measurement report; however, the results of this paper has large implications for the general understanding and usage of PMF, particularly in how "certain" the results are and how to proceed when there are multiple measurements in one urban location. E.g., are there performance aspects or metrics that should be considered to determine if the PMF may be skewed due to unknown performance of one ACSM, especially if there are not multiple ACSMs to compare against or other external data to compare?
Citation: https://doi.org/10.5194/egusphere-2025-648-RC1 -
AC1: 'Reply on RC1', Hasna Chebaicheb, 19 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-648/egusphere-2025-648-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Hasna Chebaicheb, 19 Sep 2025
-
RC2: 'Comment on egusphere-2025-648', Anonymous Referee #3, 25 Jul 2025
The study by Chebaicheb et. al, provides a standard report of fine aerosol measurements using a combination of ACSM/aethalometer instruments. In terms of the single vs. combined PMF comparison, a solid job has been done, which is more about the measurement technique. In terms of scientific contribution or novelty, I see an interesting discussion on "amine-related OA" factor. With the exception of this, however, the results are fairly standard compared to previously published similar work. For this reason, I recommend even further data exploitation in a situation when two ACSM/AE33 were measured in parallel within the same city. My comments are following.
- Can you present the concentration ratios of the time series between ACSMs to identify which species or m/z are significant for either station even if the distance between sites in not large?
- In context of previous comment, the concentration difference in m/z 60 (Fig.S14) looks like an interesting result if we take into account a fairly small distance between these two sites. It probably indicates some close source of fresh emissions from biomass burning. Is this plausible at Strasbourg? When you comparing the instruments against each other (Aug-Oct 2019, Fig.S1), how did the m/z60 comparison come out? If the pre-campaign comparison was the same for m/z60, I would add this figure to the main text and expand discussion on a possible specific source in the vicinity of Clemenceau site.
- L. 370, authors state: “A factor profile associated with amine-OA and a specific daily profile are consistent with an industrial source. This factor could therefore be associated with an industrial source of OA.” Can you be more specific and maybe even hypothesize about a specific industrial source of this factor? If you know wind direction (Fig.S16) and also that it is a local source, it might not be a problem to pick out something specific. It would be helpful for information if a similar source appears in other papers.
- In Fig.S4 is a comparison with OC and EC measured on the filters. The agreement is very good. Can you add a similar comparison of filters vs. ACSM concentrations for SO4, NO3 and NH4, and also the discussed comparison of Levoglucosan vs. eBCwb?
- 3: Can you set the same range on x-axis (time)? It would be better for visual comparing of time series with each other. I also suggest to add Fig.2 on top of Fig.3.
- S1 in supplement can be extended by intercomparison of aethalometers prior to the Strasbourg campaigns.
Citation: https://doi.org/10.5194/egusphere-2025-648-RC2 -
AC2: 'Reply on RC2', Hasna Chebaicheb, 19 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-648/egusphere-2025-648-AC2-supplement.pdf
Peer review completion
Journal article(s) based on this preprint
Data sets
Lessons learned from the comparison and combination of fine carbonaceous aerosol source apportionment at two locations in the city of Strasbourg, France Hasna Chebaicheb et al. https://doi.org/10.5281/zenodo.14855186
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Hasna Chebaicheb
Mélodie Chatain
Olivier Favez
Joel F. de Brito
Vincent Crenn
Tanguy Amodeo
Mohamed Gherras
Emmanuel Jantzem
Caroline Marchand
Véronique Riffault
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1149 KB) - Metadata XML
-
Supplement
(1411 KB) - BibTeX
- EndNote
- Final revised paper
Chebaicheb et al. present a comparison of aerosol composition/concentration and positive matrix factorization (PMF) results from two quadrupole aerosol chemical speciation monitors (Q-ACSMs) that were stationed at two locations within Strasbourg, France, during a winter period in 2019/2020. The authors found that the Clemenceau site generally had, on average, higher PM1 than the Danube site due to differences in emissions; however, the composition between the two sites were generally similar (e.g., organics, sulfate, nitrate, ammonium, fossil fuel black carbon, and wood burning biomass burning). Running PMF with the dataset collected from each instrument similarly showed similar organic composition, with the largest difference in the hydrocarbon-like organic aerosol (HOA) at Danube vs Clemenceau. However, if PMF was conducted with the dataset with both instruments as one large dataset, large differences in the organic components occur between Danube than Clemenceau; however, the individual PMF determined for Clemenceau was similar to the combined PMF results for the same location.
As PMF is a tool often used for analysis for investigating sources of both particulate matter and gases, investigating potential sources of uncertainty in this tool is of value for the community of ACP. This paper could potentially also be published in AMT, as it is about the technique of PMF. Either way, the following comments need to be addressed prior to publication.
Minor
1) It was not clear until looking at the figure what orifice was used for both ACSMs to know what diameter cut-off the measurements correspond to (e.g., PM1 vs PM2.5).
2) How co-located were the AE33, Q-ACSM, and FIDAS 200? E.g., were they sampling from similar inlets for AE33 and Q-ACSM? Were the sampling heights similar for all three instruments? How close were the inlets for all three inlets?
3) Was there a dryer for any of the instruments?
4) How statistically different are the average values shown in Table 1? There is discussion about the percent differences in the concentrations; however, the average values fall within the standard deviation, which is assumed to be the spread in the observations and not the uncertainty of the measurements?
Major
1) Figure S3 does not make sense though it is needed, I believe, for the argument about potentially why the different ACSMs have different PMF results. How is it for both instruments and what is the average mass spectra being compared against? How does it impact total OA?
2) Figure S6 & Figure 3. Total PM2.5 is generally constant across an urban environment unless there is a very localized emission source, though that emission source maybe more impactful towards PM10 and PM0.1. However, though the PM2.5 (black line) looks generally similar between the two sites in Figure 3, there is very different slopes between the two ACSMs vs PM2.5 in Figure S6 (also, unclear which value is slope vs intercept). What is potentially leading to these differences, and what does it mean for the quantification of one instrument vs another?
3) The biggest concern and the least amount of discussion is with the combined PMF vs the individual PMF. From the discussion, it is not clear what is the "preferred," more accurate method? E.g., if there are multiple AMSs, ACSMs, or other measurements measuring the composition and concentration of PM, should they be combined into one dataset to conduct PMF for improved accuracy, or was the single PMF more accurate? Was whether one dataset was driving the results of the other dataset investigated? E.g., end points are determined, and then the results are determined from those end points. However, as the authors discuss, one location appeared to potentially have a mixed end-point as they called one of the results COA-like. Does it make sense for the Danube PMF results to have changed so much? I understand that this is a measurement report; however, the results of this paper has large implications for the general understanding and usage of PMF, particularly in how "certain" the results are and how to proceed when there are multiple measurements in one urban location. E.g., are there performance aspects or metrics that should be considered to determine if the PMF may be skewed due to unknown performance of one ACSM, especially if there are not multiple ACSMs to compare against or other external data to compare?