From real-time to long-term source apportionment of PM <sub>10</sub> using high-time-resolution measurements of aerosol physical properties: methodology and example application at an urban background site (Aosta, Italy)
Bibliographic record
Abstract
Abstract. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".