From real-time to long-term source apportionment of PM 10 using high-time-resolution measurements of aerosol physical properties: Methodology and example application at an urban background site (Aosta, Italy) Article Swipe
Henri Diémoz
,
Francesca Barnaba
,
Luca Ferrero
,
Ivan K. F. Tombolato
,
Caterina Mapelli
,
Annachiara Bellini
,
Claudia Desandre
,
Tiziana Magri
,
Manuela Zublena
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-5044
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2025-5044
Identifying aerosol sources is essential for designing effective air quality policies. This study introduces a novel PM10 source apportionment approach – RASPBERRY (Real-time Aerosol Source apportionment using Physics-Based Experimental data and multivaRiate factoR analYsis) – based on the analysis of aerosol physical properties, namely particle size distributions and spectrally resolved light absorption. The availability of such measurements at high temporal resolution enables aerosol source apportionment from real time to long-term scales. To demonstrate the implementation of RASPBERRY, we apply the method to a five-year hourly dataset (2020–2024) from an urban background site in the north-western Italian Alps, combining observations from a cost-effective optical particle counter (Palas Fidas 200) and an aethalometer (Magee Scientific AE33). RASPBERRY identifies six source factors contributing to PM10: traffic (9 %), biomass burning (10 %), two secondary aerosol modes (condensation, 23 %, and droplet, 16 %), desert dust (21 %), and local dust resuspension (21 %). Hourly resolved RASPBERRY estimates, averaged to daily values, show strong agreement with traditional chemical source apportionment techniques. Further validation is provided through comparisons with ground-based remote sensing (lidar-ceilometers, sun photometers) and modelling tools (Validated ReAnalysis ensemble from the Copernicus Atmosphere Monitoring Service). Selected real-time applications are also presented, including emergency surveillance during accidental events and the rapid identification of long-range transport of secondary particles, desert dust, and smoke (Canadian wildfires, 2023–2024). Although demonstrated at a single site, RASPBERRY is readily transferable to international air quality networks, as it relies on optical instruments commonly employed by regulatory authorities and environmental protection agencies.
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- https://doi.org/10.5194/egusphere-2025-5044
- OA Status
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- OpenAlex ID
- https://openalex.org/W7106316400
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106316400Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-2025-5044Digital Object Identifier
- Title
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From real-time to long-term source apportionment of PM 10 using high-time-resolution measurements of aerosol physical properties: Methodology and example application at an urban background site (Aosta, Italy)Work title
- Type
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articleOpenAlex work type
- Publication year
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2025Year of publication
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2025-11-21Full publication date if available
- Authors
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Henri Diémoz, Francesca Barnaba, Luca Ferrero, Ivan K. F. Tombolato, Caterina Mapelli, Annachiara Bellini, Claudia Desandre, Tiziana Magri, Manuela ZublenaList of authors in order
- Landing page
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https://doi.org/10.5194/egusphere-2025-5044Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/egusphere-2025-5044Direct OA link when available
- Concepts
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Apportionment, Aerosol, Environmental science, Air quality index, Aethalometer, Remote sensing, Meteorology, Biomass burning, AERONET, Angstrom exponent, Ceilometer, Atmospheric sciences, Air pollution, Data quality, Atmosphere (unit), Environmental monitoringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.source | 18, 64, 118, 165 |
| abstract_inverted_index.strong | 160 |
| abstract_inverted_index.Aerosol | 24 |
| abstract_inverted_index.Further | 168 |
| abstract_inverted_index.Italian | 96 |
| abstract_inverted_index.aerosol | 2, 41, 63, 132 |
| abstract_inverted_index.biomass | 126 |
| abstract_inverted_index.burning | 127 |
| abstract_inverted_index.counter | 105 |
| abstract_inverted_index.dataset | 86 |
| abstract_inverted_index.enables | 62 |
| abstract_inverted_index.factors | 119 |
| abstract_inverted_index.optical | 103, 241 |
| abstract_inverted_index.quality | 10, 235 |
| abstract_inverted_index.readily | 230 |
| abstract_inverted_index.scales. | 71 |
| abstract_inverted_index.sensing | 177 |
| abstract_inverted_index.sources | 3 |
| abstract_inverted_index.through | 172 |
| abstract_inverted_index.traffic | 123 |
| abstract_inverted_index.values, | 158 |
| abstract_inverted_index.Although | 222 |
| abstract_inverted_index.Selected | 193 |
| abstract_inverted_index.analysis | 39 |
| abstract_inverted_index.approach | 20 |
| abstract_inverted_index.averaged | 155 |
| abstract_inverted_index.chemical | 164 |
| abstract_inverted_index.commonly | 243 |
| abstract_inverted_index.droplet, | 138 |
| abstract_inverted_index.employed | 244 |
| abstract_inverted_index.ensemble | 186 |
| abstract_inverted_index.particle | 45, 104 |
| abstract_inverted_index.physical | 42 |
| abstract_inverted_index.provided | 171 |
| abstract_inverted_index.resolved | 50, 152 |
| abstract_inverted_index.temporal | 60 |
| abstract_inverted_index.(Canadian | 219 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.RASPBERRY | 22, 115, 153, 228 |
| abstract_inverted_index.Service). | 192 |
| abstract_inverted_index.agencies. | 251 |
| abstract_inverted_index.agreement | 161 |
| abstract_inverted_index.analYsis) | 34 |
| abstract_inverted_index.combining | 98 |
| abstract_inverted_index.designing | 7 |
| abstract_inverted_index.effective | 8 |
| abstract_inverted_index.emergency | 200 |
| abstract_inverted_index.essential | 5 |
| abstract_inverted_index.five-year | 84 |
| abstract_inverted_index.including | 199 |
| abstract_inverted_index.long-term | 70 |
| abstract_inverted_index.modelling | 182 |
| abstract_inverted_index.networks, | 236 |
| abstract_inverted_index.policies. | 11 |
| abstract_inverted_index.real-time | 194 |
| abstract_inverted_index.secondary | 131, 213 |
| abstract_inverted_index.transport | 211 |
| abstract_inverted_index.(Real-time | 23 |
| abstract_inverted_index.(Validated | 184 |
| abstract_inverted_index.Atmosphere | 190 |
| abstract_inverted_index.Copernicus | 189 |
| abstract_inverted_index.Monitoring | 191 |
| abstract_inverted_index.RASPBERRY, | 77 |
| abstract_inverted_index.ReAnalysis | 185 |
| abstract_inverted_index.Scientific | 113 |
| abstract_inverted_index.accidental | 203 |
| abstract_inverted_index.background | 91 |
| abstract_inverted_index.estimates, | 154 |
| abstract_inverted_index.identifies | 116 |
| abstract_inverted_index.introduces | 14 |
| abstract_inverted_index.long-range | 210 |
| abstract_inverted_index.particles, | 214 |
| abstract_inverted_index.presented, | 198 |
| abstract_inverted_index.protection | 250 |
| abstract_inverted_index.regulatory | 246 |
| abstract_inverted_index.resolution | 61 |
| abstract_inverted_index.spectrally | 49 |
| abstract_inverted_index.validation | 169 |
| abstract_inverted_index.wildfires, | 220 |
| abstract_inverted_index.Identifying | 1 |
| abstract_inverted_index.absorption. | 52 |
| abstract_inverted_index.authorities | 247 |
| abstract_inverted_index.comparisons | 173 |
| abstract_inverted_index.demonstrate | 73 |
| abstract_inverted_index.instruments | 242 |
| abstract_inverted_index.properties, | 43 |
| abstract_inverted_index.techniques. | 167 |
| abstract_inverted_index.traditional | 163 |
| abstract_inverted_index.Experimental | 29 |
| abstract_inverted_index.aethalometer | 111 |
| abstract_inverted_index.applications | 195 |
| abstract_inverted_index.availability | 54 |
| abstract_inverted_index.contributing | 120 |
| abstract_inverted_index.demonstrated | 223 |
| abstract_inverted_index.ground-based | 175 |
| abstract_inverted_index.measurements | 57 |
| abstract_inverted_index.multivaRiate | 32 |
| abstract_inverted_index.observations | 99 |
| abstract_inverted_index.photometers) | 180 |
| abstract_inverted_index.resuspension | 148 |
| abstract_inverted_index.surveillance | 201 |
| abstract_inverted_index.transferable | 231 |
| abstract_inverted_index.(2020–2024) | 87 |
| abstract_inverted_index.2023–2024). | 221 |
| abstract_inverted_index.Physics-Based | 28 |
| abstract_inverted_index.apportionment | 19, 26, 65, 166 |
| abstract_inverted_index.distributions | 47 |
| abstract_inverted_index.environmental | 249 |
| abstract_inverted_index.international | 233 |
| abstract_inverted_index.north-western | 95 |
| abstract_inverted_index.(condensation, | 134 |
| abstract_inverted_index.cost-effective | 102 |
| abstract_inverted_index.identification | 208 |
| abstract_inverted_index.implementation | 75 |
| abstract_inverted_index.(lidar-ceilometers, | 178 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.78134609 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |