Development and implementation of SOMA: A Secondary Organic Module for Aerosol integration in high-resolution air quality simulations Article Swipe
Giannis Ioannidis
,
Nikoletta Bouloti
,
Paul Tremper
,
Chaofan Li
,
Christos Boikos
,
Nikolaos Rapkos
,
Till Riedel
,
Miikka Dal Maso
,
Leónidas Ntziachristos
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-193
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-193
Secondary Organic Aerosols (SOAs) are formed following oxidation of Volatile Organic Compounds (VOCs) in the atmosphere and have a significant contribution to fine particulate matter concentrations. Understanding SOA formation is crucial, particularly in urban environments, where various emission sources contribute across different time scales. To decipher SOA formation dynamics, this study introduces SOMA (Secondary Organic Module for Aerosol) embedded in air quality modelling. SOMA considers VOC oxidation with OH using species concentrations, exposure duration, NOx levels and SOA yields as inputs, the latter obtained from the GECKO-A model. A total of 113 experiments are gathered from literature, involving four different VOC species (α-pinene, isoprene, limonene, and toluene), to produce correction factors depending on ozone (O3) levels, relative humidity (RH), and temperature (T). SOMA was linked to CFD modelling and was used to characterise the dispersion of toluene SOA emissions from traffic in a heavily trafficked area in Augsburg, Germany. The dispersion model was used to simulate pollutant recirculation in the examined area using a novel approach by combining both local road traffic emissions and background sources. SOA formation from toluene was examined over a 12-h period. The results indicated that background SOA constituted 21–53 % of the identified SOA mass. After 7 hours, the influence of background SOA on modelled concentrations became negligible due to precursor consumption and dilution. The combination of high-resolution pollution maps generated by CFD and atmospheric chemistry involving SOA formation enhances the air quality modelling capabilities and can provide valuable information to the scientific community.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-2025-193
- OA Status
- gold
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407581915
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407581915Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-2025-193Digital Object Identifier
- Title
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Development and implementation of SOMA: A Secondary Organic Module for Aerosol integration in high-resolution air quality simulationsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-14Full publication date if available
- Authors
-
Giannis Ioannidis, Nikoletta Bouloti, Paul Tremper, Chaofan Li, Christos Boikos, Nikolaos Rapkos, Till Riedel, Miikka Dal Maso, Leónidas NtziachristosList of authors in order
- Landing page
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https://doi.org/10.5194/egusphere-2025-193Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/egusphere-2025-193Direct OA link when available
- Concepts
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Soma, Aerosol, Air quality index, Resolution (logic), High resolution, Environmental science, Quality (philosophy), Computer science, Remote sensing, Meteorology, Physics, Artificial intelligence, Geology, Psychology, Neuroscience, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.crucial, | 31 |
| abstract_inverted_index.decipher | 46 |
| abstract_inverted_index.embedded | 59 |
| abstract_inverted_index.emission | 38 |
| abstract_inverted_index.enhances | 235 |
| abstract_inverted_index.examined | 161, 182 |
| abstract_inverted_index.exposure | 73 |
| abstract_inverted_index.gathered | 95 |
| abstract_inverted_index.humidity | 118 |
| abstract_inverted_index.modelled | 210 |
| abstract_inverted_index.obtained | 84 |
| abstract_inverted_index.relative | 117 |
| abstract_inverted_index.simulate | 156 |
| abstract_inverted_index.sources. | 176 |
| abstract_inverted_index.valuable | 244 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Augsburg, | 148 |
| abstract_inverted_index.Compounds | 12 |
| abstract_inverted_index.Secondary | 1 |
| abstract_inverted_index.chemistry | 231 |
| abstract_inverted_index.combining | 168 |
| abstract_inverted_index.considers | 65 |
| abstract_inverted_index.depending | 112 |
| abstract_inverted_index.different | 42, 100 |
| abstract_inverted_index.dilution. | 219 |
| abstract_inverted_index.duration, | 74 |
| abstract_inverted_index.dynamics, | 49 |
| abstract_inverted_index.emissions | 139, 173 |
| abstract_inverted_index.following | 7 |
| abstract_inverted_index.formation | 29, 48, 178, 234 |
| abstract_inverted_index.generated | 226 |
| abstract_inverted_index.indicated | 189 |
| abstract_inverted_index.influence | 205 |
| abstract_inverted_index.involving | 98, 232 |
| abstract_inverted_index.isoprene, | 104 |
| abstract_inverted_index.limonene, | 105 |
| abstract_inverted_index.modelling | 128, 239 |
| abstract_inverted_index.oxidation | 8, 67 |
| abstract_inverted_index.pollutant | 157 |
| abstract_inverted_index.pollution | 224 |
| abstract_inverted_index.precursor | 216 |
| abstract_inverted_index.toluene), | 107 |
| abstract_inverted_index.(Secondary | 54 |
| abstract_inverted_index.atmosphere | 16 |
| abstract_inverted_index.background | 175, 191, 207 |
| abstract_inverted_index.community. | 249 |
| abstract_inverted_index.contribute | 40 |
| abstract_inverted_index.correction | 110 |
| abstract_inverted_index.dispersion | 135, 151 |
| abstract_inverted_index.identified | 198 |
| abstract_inverted_index.introduces | 52 |
| abstract_inverted_index.modelling. | 63 |
| abstract_inverted_index.negligible | 213 |
| abstract_inverted_index.scientific | 248 |
| abstract_inverted_index.trafficked | 145 |
| abstract_inverted_index.(α-pinene, | 103 |
| abstract_inverted_index.atmospheric | 230 |
| abstract_inverted_index.combination | 221 |
| abstract_inverted_index.constituted | 193 |
| abstract_inverted_index.consumption | 217 |
| abstract_inverted_index.experiments | 93 |
| abstract_inverted_index.information | 245 |
| abstract_inverted_index.literature, | 97 |
| abstract_inverted_index.particulate | 24 |
| abstract_inverted_index.significant | 20 |
| abstract_inverted_index.temperature | 121 |
| abstract_inverted_index.capabilities | 240 |
| abstract_inverted_index.characterise | 133 |
| abstract_inverted_index.contribution | 21 |
| abstract_inverted_index.particularly | 32 |
| abstract_inverted_index.Understanding | 27 |
| abstract_inverted_index.environments, | 35 |
| abstract_inverted_index.recirculation | 158 |
| abstract_inverted_index.concentrations | 211 |
| abstract_inverted_index.concentrations, | 72 |
| abstract_inverted_index.concentrations. | 26 |
| abstract_inverted_index.high-resolution | 223 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.81330452 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |