Modeling Anthropogenic Aerosol Sources and Secondary Organic Aerosol Formation: A Wintertime Study in Central Europe

Wiedenhaus, Hanna; Schrödner, Roland; Wolke, Ralf; Luttkus, Marie L.; Arora, Shubhi; Poulain, Laurent; Lhotka, Radek; Vodička, Petr; Schwarz, Jaroslav; Pokorna, Petra; Ondráček, Jakub; Ždímal, Vladimir; Herrmann, Hartmut; Tegen, Ina

doi:https://doi.org/10.5194/egusphere-2025-1225

Preprints

https://doi.org/10.5194/egusphere-2025-1225

Preprints

30 Apr 2025

| 30 Apr 2025

Modeling Anthropogenic Aerosol Sources and Secondary Organic Aerosol Formation: A Wintertime Study in Central Europe

Hanna Wiedenhaus, Roland Schrödner, Ralf Wolke, Marie L. Luttkus, Shubhi Arora, Laurent Poulain, Radek Lhotka, Petr Vodička, Jaroslav Schwarz, Petra Pokorna, Jakub Ondráček, Vladimir Ždímal, Hartmut Herrmann, and Ina Tegen

Abstract. Anthropogenic aerosol particles remain a signiﬁcant air quality concern in Central Europe, particularly during winter months. This study employs the COSMO-MUSCAT chemistry transport model to investigate particulate matter sources, with a focus on emissions from residential heating. The model results are compared with winter measurements from sites in Germany and the Czech Republic, where solid fuels are commonly used for heating. A non-reactive tagging method tracking primary organic matter (OM) reveals a high contribution from residential heating. Although the magnitude and temporal changes of the model results mostly agree with total OM values at two measuring stations, it appears to underestimate measurements at a site in the central Czech Republic. This underestimation is partly attributed to the inadequate representation of secondary organic aerosol (SOA) emitted from wood combustion. The study highlights the impact of anthropogenic volatile organic compounds (AVOC) on SOA formation, which are currently underrepresented in air quality models. Sensitivity tests adjusting SOA yields and AVOC emissions increase OM concentrations of up to 40 % at the measurement sites. These ﬁndings emphasize the need for accurate parameterization of AVOC derived SOA formation and residential heating emissions to better tackle wintertime air quality challenges in Central Europe.

Received: 14 Mar 2025 – Discussion started: 30 Apr 2025

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Status: closed

RC1:
'Comment on egusphere-2025-1225', Anonymous Referee #1, 02 Jun 2025
Manuscript egusphere-2025-1225 by H. Wiedenhaus et al. reports model results of the COSMO-MUSCAT chemistry transport model on particulate matter concentrations during winter months, with a focus on emissions from residential heating. Modeled organic matter concentrations are compared against measurements at three observation sites with specific aerosol instrumentation. The employed source apportionment by tagging method is robust and carefully applied (only for primary components). The study investigates the impact of SOA formation from anthropogenic VOCs related to wood burning emissions. A weak component of the model system is the emission inventory for residential heating. The study should try to better identify and isolate wood burning as the missing source of primary/secondary OM. The sensitivity tests are well performed but it is difficult to evaluate the impact on anthropogenic SOA concentrations and their spatial distribution. I strongly recommend the addition of one more sensitivity test including more detailed wood burning emissions. The conclusions are based on the findings of the model study and future directions are well formulated.
Specific Comments:
1.) Introduction (P2, line 28-34): Suggest rewriting the paragraph on transboundary transport of pollution to Germany. Expand on the influence of long-range transport from eastern Europe, also including reports from EMEP. The sentence in line 28 (“the inflow of air masses from the east”) is not logical and should be deleted.
2.) Introduction (P2, line 54-58): Add a brief description of the TRACE project, its objectives and how this study addresses the project’s objectives.
3.) Model description: how frequent is the exchange of variables between the meteorological and the chemistry-transport component? What is the expected advantage of the online coupling specifically for this study compared to using an offline coupled CTM?
4.) Model description: it is stated that the GRETA emission database was provided with resolution of 0.5 km x 1 km. The usual GRETA grid has a resolution of 1 km x 1km. Why did you choose this resolution and was any reprojection on the COSMO grid required? It would be good to mention the specific temporal profile for other combustion (i.e., for residential heating).
5.) Emissions: Different years of the emissions were used as emissions for 2021. Was something done to adjust for year-to-year changes in emissions? How is the expected variability between the years for the different source types?
6.) SOA formation (P 6, line 160-170): A table should be added with a list of the different model surrogates of SOA precursors from the different parent VOCs. In particular, the precursors of anthropogenic SOA should be detailed. If possible, supplement the relevant reactions and stoichiometric yields of the two pseudo-products.
7.) Comparison model-measurement: clearly state that the statistics of the model-observation comparison are given in Table A1. The evaluation should be expanded by calculation of the normalized mean bias (NMB) and FAC2 (fraction of modeled values within factor 2 of measured values). When discussing model underestimation always include the relative bias as NMB (RMSE represents the model error in terms of bias and correlation). The reference to Stern et al. (2008) is not adequate as it refers to PM10 which is much more determined by dust resuspension and Saharan dust events than PM2.5. There are several AQME intercomparison studies which could be cited for discrepancies among models and between modeled and measured concentrations. For PM2.5, different treatment of the formation of secondary aerosols is certainly the most important reason for discrepancies between models. On P10, line 257-259, it is discussed that increased heating and limited mobility caused underestimation of “total pollutants”. I would expect that the two activities have opposite effects on certain pollutants, for example NO2 concentration might decrease due to limited mobility whereas PM2.5 concentrations might increase due to more heating in households. The sentence needs to be revised.
8.) Organic Matter (P 13): Figure 4 shows good agreement among Sunset offline and Sunset online. It should be discussed why OM from Sunset agrees with AMS at Kosetice but not at the other sites. Further it should be discussed which of the measurement methods should serve as the guideline for comparison of the modeled OM (OM in PM2.5 plus total SOA plus OM from outside the domain). In the text, the terms AMS and ACMS are used interchangeably. It is unclear whether ACMS is an additional instrument or combined with AMS. If it is a separate instrument, why are OM measurements of ACMS not included in Figure 4? At least, it should be made clearer in the text.
9.) The paragraph on P 14 (line 316-320) should be rephrased. “The discrepancy in modelled PM2.5 concentrations” probably means discrepancy between modeled and measured PM2.5 concentrations. Background PM2.5 is hardly ever driven by elemental carbon since EC concentration are usually rather low (except near sources). Sulfate and nitrate are more likely candidates for mismatch with observed PM2.5, thus the agreement for these components should be stated as well.
10.) The numbering and headers of the sections after 3.3 (“Source attribution …”) appear to be random and are not well motivated. I suggest bracketing the sections that follow under a “Discussions” chapter (section 4). The second part of section 3.3 could be split off as a discussion section on biomass burning / wood combustion. Together with section 3.4 (“Effects of COVID-19”) and section 4 (“anthropogenic secondary organic aerosol”) this would form the new discussion chapter.
11.) Anthropogenic secondary organic aerosol (P 20): The study of Bergström et al. (2012) was first and should appear first in this section. It would be good to structure the discussion related to publications on SOA modeling in chronological order, as there have been drastic developments in the treatment of anthropogenic SOA in the last two decades. Can the IVOC emissions be implemented in your model such that IVOC condense to pre-existing OM depending on their volatility or undergo atmospheric aging?
12.) Sensitivity study results: as in my previous point 8, I wonder which observed metrics / measurement instrument should be used to evaluate changes in mean OM concentrations from the sensitivity tests? On P 22, line 502-510: (a) give absolute OM increment for Melpitz, (b) refer to Table 4 again, (c) discuss that OM from AMS is overestimated with S3 at Frydlant. In Figure 9, denote error bars for Sunset offline OM which considers instrument uncertainty and OC-factor uncertainty and denote error bars for AMS PM1 data.
13.) Sensitivity study results: the discussion of the sensitivity results for CSL emissions and phenol SOA leaves some open questions. Which of the scenarios (base, S1-S3) is now best in reproducing SOA spatial distribution? Figure 10 shows that S3 increases modelled OM in other areas but not around the three study sites. This probably reflects that absorption of SOA to existing PM happens in places where the emissions of PM are already high. This would indicate missing primary OM emissions given the underestimation of measured OM at the sites.
14.) To test this hypothesis, I suggest to conduct an additional sensitivity test with more detailed residential combustion emission data for the Czech Republic as used in Bartik et al. (2024).
Technical corrections:
P3, line 57: replace “identify PM sources” by “identify primary PM sources”.

P3, line 61: give long name of TRACE and provide web site.

P5, line 125: resolution should be given in km x km. Same holds for the resolution of CAMS-REG-v5 stated in the next line.

P12, line 267: replace “below 3 km” by “below 3 km height”.

Figures: captions of Figures 5-10 denote “elemental carbon < PM2.5” or “organic matter < PM2.5”. This is not common terminology. Please replace by “in PM2.5” if that is the meaning of “<”.
Citation: https://doi.org/10.5194/egusphere-2025-1225-RC1
- AC1: 'Reply on RC1', Hanna Wiedenhaus, 06 Aug 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1225/egusphere-2025-1225-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-1225-AC1
RC2:
'Comment on egusphere-2025-1225', Anonymous Referee #2, 03 Jun 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1225/egusphere-2025-1225-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-1225-RC2
- AC2: 'Reply on RC2', Hanna Wiedenhaus, 06 Aug 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1225/egusphere-2025-1225-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-1225-AC2

Status: closed

RC1:
'Comment on egusphere-2025-1225', Anonymous Referee #1, 02 Jun 2025
Manuscript egusphere-2025-1225 by H. Wiedenhaus et al. reports model results of the COSMO-MUSCAT chemistry transport model on particulate matter concentrations during winter months, with a focus on emissions from residential heating. Modeled organic matter concentrations are compared against measurements at three observation sites with specific aerosol instrumentation. The employed source apportionment by tagging method is robust and carefully applied (only for primary components). The study investigates the impact of SOA formation from anthropogenic VOCs related to wood burning emissions. A weak component of the model system is the emission inventory for residential heating. The study should try to better identify and isolate wood burning as the missing source of primary/secondary OM. The sensitivity tests are well performed but it is difficult to evaluate the impact on anthropogenic SOA concentrations and their spatial distribution. I strongly recommend the addition of one more sensitivity test including more detailed wood burning emissions. The conclusions are based on the findings of the model study and future directions are well formulated.
Specific Comments:
1.) Introduction (P2, line 28-34): Suggest rewriting the paragraph on transboundary transport of pollution to Germany. Expand on the influence of long-range transport from eastern Europe, also including reports from EMEP. The sentence in line 28 (“the inflow of air masses from the east”) is not logical and should be deleted.
2.) Introduction (P2, line 54-58): Add a brief description of the TRACE project, its objectives and how this study addresses the project’s objectives.
3.) Model description: how frequent is the exchange of variables between the meteorological and the chemistry-transport component? What is the expected advantage of the online coupling specifically for this study compared to using an offline coupled CTM?
4.) Model description: it is stated that the GRETA emission database was provided with resolution of 0.5 km x 1 km. The usual GRETA grid has a resolution of 1 km x 1km. Why did you choose this resolution and was any reprojection on the COSMO grid required? It would be good to mention the specific temporal profile for other combustion (i.e., for residential heating).
5.) Emissions: Different years of the emissions were used as emissions for 2021. Was something done to adjust for year-to-year changes in emissions? How is the expected variability between the years for the different source types?
6.) SOA formation (P 6, line 160-170): A table should be added with a list of the different model surrogates of SOA precursors from the different parent VOCs. In particular, the precursors of anthropogenic SOA should be detailed. If possible, supplement the relevant reactions and stoichiometric yields of the two pseudo-products.
7.) Comparison model-measurement: clearly state that the statistics of the model-observation comparison are given in Table A1. The evaluation should be expanded by calculation of the normalized mean bias (NMB) and FAC2 (fraction of modeled values within factor 2 of measured values). When discussing model underestimation always include the relative bias as NMB (RMSE represents the model error in terms of bias and correlation). The reference to Stern et al. (2008) is not adequate as it refers to PM10 which is much more determined by dust resuspension and Saharan dust events than PM2.5. There are several AQME intercomparison studies which could be cited for discrepancies among models and between modeled and measured concentrations. For PM2.5, different treatment of the formation of secondary aerosols is certainly the most important reason for discrepancies between models. On P10, line 257-259, it is discussed that increased heating and limited mobility caused underestimation of “total pollutants”. I would expect that the two activities have opposite effects on certain pollutants, for example NO2 concentration might decrease due to limited mobility whereas PM2.5 concentrations might increase due to more heating in households. The sentence needs to be revised.
8.) Organic Matter (P 13): Figure 4 shows good agreement among Sunset offline and Sunset online. It should be discussed why OM from Sunset agrees with AMS at Kosetice but not at the other sites. Further it should be discussed which of the measurement methods should serve as the guideline for comparison of the modeled OM (OM in PM2.5 plus total SOA plus OM from outside the domain). In the text, the terms AMS and ACMS are used interchangeably. It is unclear whether ACMS is an additional instrument or combined with AMS. If it is a separate instrument, why are OM measurements of ACMS not included in Figure 4? At least, it should be made clearer in the text.
9.) The paragraph on P 14 (line 316-320) should be rephrased. “The discrepancy in modelled PM2.5 concentrations” probably means discrepancy between modeled and measured PM2.5 concentrations. Background PM2.5 is hardly ever driven by elemental carbon since EC concentration are usually rather low (except near sources). Sulfate and nitrate are more likely candidates for mismatch with observed PM2.5, thus the agreement for these components should be stated as well.
10.) The numbering and headers of the sections after 3.3 (“Source attribution …”) appear to be random and are not well motivated. I suggest bracketing the sections that follow under a “Discussions” chapter (section 4). The second part of section 3.3 could be split off as a discussion section on biomass burning / wood combustion. Together with section 3.4 (“Effects of COVID-19”) and section 4 (“anthropogenic secondary organic aerosol”) this would form the new discussion chapter.
11.) Anthropogenic secondary organic aerosol (P 20): The study of Bergström et al. (2012) was first and should appear first in this section. It would be good to structure the discussion related to publications on SOA modeling in chronological order, as there have been drastic developments in the treatment of anthropogenic SOA in the last two decades. Can the IVOC emissions be implemented in your model such that IVOC condense to pre-existing OM depending on their volatility or undergo atmospheric aging?
12.) Sensitivity study results: as in my previous point 8, I wonder which observed metrics / measurement instrument should be used to evaluate changes in mean OM concentrations from the sensitivity tests? On P 22, line 502-510: (a) give absolute OM increment for Melpitz, (b) refer to Table 4 again, (c) discuss that OM from AMS is overestimated with S3 at Frydlant. In Figure 9, denote error bars for Sunset offline OM which considers instrument uncertainty and OC-factor uncertainty and denote error bars for AMS PM1 data.
13.) Sensitivity study results: the discussion of the sensitivity results for CSL emissions and phenol SOA leaves some open questions. Which of the scenarios (base, S1-S3) is now best in reproducing SOA spatial distribution? Figure 10 shows that S3 increases modelled OM in other areas but not around the three study sites. This probably reflects that absorption of SOA to existing PM happens in places where the emissions of PM are already high. This would indicate missing primary OM emissions given the underestimation of measured OM at the sites.
14.) To test this hypothesis, I suggest to conduct an additional sensitivity test with more detailed residential combustion emission data for the Czech Republic as used in Bartik et al. (2024).
Technical corrections:
P3, line 57: replace “identify PM sources” by “identify primary PM sources”.

P3, line 61: give long name of TRACE and provide web site.

P5, line 125: resolution should be given in km x km. Same holds for the resolution of CAMS-REG-v5 stated in the next line.

P12, line 267: replace “below 3 km” by “below 3 km height”.

Figures: captions of Figures 5-10 denote “elemental carbon < PM2.5” or “organic matter < PM2.5”. This is not common terminology. Please replace by “in PM2.5” if that is the meaning of “<”.
Citation: https://doi.org/10.5194/egusphere-2025-1225-RC1
- AC1: 'Reply on RC1', Hanna Wiedenhaus, 06 Aug 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1225/egusphere-2025-1225-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-1225-AC1
RC2:
'Comment on egusphere-2025-1225', Anonymous Referee #2, 03 Jun 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1225/egusphere-2025-1225-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-1225-RC2
- AC2: 'Reply on RC2', Hanna Wiedenhaus, 06 Aug 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1225/egusphere-2025-1225-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-1225-AC2

Viewed

Total article views: 441 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
356	69	16	441	16	33

HTML: 356
PDF: 69
XML: 16
Total: 441
BibTeX: 16
EndNote: 33

Views and downloads (calculated since 30 Apr 2025)

Month	HTML	PDF	XML	Total
Apr 2025	26	0	26
May 2025	132	32	6	170
Jun 2025	77	16	6	99
Jul 2025	37	10	1	48
Aug 2025	84	11	3	98

Cumulative views and downloads (calculated since 30 Apr 2025)

Month	HTML	PDF	XML	Total
Apr 2025	26	0	26
May 2025	132	32	6	170
Jun 2025	77	16	6	99
Jul 2025	37	10	1	48
Aug 2025	84	11	3	98

Viewed (geographical distribution)

Total article views: 453 (including HTML, PDF, and XML) Thereof 453 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 30 Aug 2025

Short summary

This study examines winter air quality in Central Europe, focusing on the impact of domestic heating. Using a chemical transport model and measurements, it was found that the model underestimated organic particle concentrations. This was due to an underestimation of gases from domestic heating that form secondary organic particles. Improving the model by increasing these emissions and the particle formation led to better results, demonstrating the important role of heating emissions in winter.


Total:	0
HTML:	0
PDF:	0
XML:	0