| 21 Nov 2025
From real-time to long-term source apportionment of PM10 using high-time-resolution measurements of aerosol physical properties: Methodology and example application at an urban background site (Aosta, Italy)
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.
Diémoz et al. presents a new PM10 source apportionment framework, RASPBERRY, based on aerosol physical properties derived from OPC and aethalometer measurements. By comparison with chemical PMF results, the authors showed the clear strengths of RASPBERRY. The manuscript is well organized and easy to follow. However, I still have some issues before the manuscript can be accepted.
Line 292, the authors mentioned “heuristic uncertainty” was optimized through trial-error methods or iterative approach. Given the strong influence of uncertainty definition of PMF outcomes, can authors provide more details how to derive this parameterization to ensure the reproductivity?
For traffic emissions between chemical and physical PMF results, their correlation is not that high, r2=0.45. The authors attributed to the detection limit of OPC. Is it possible to improve the physical PMF by introducing other factors, such as NOx?
The authors also mentioned RASPBERRY improves the efficiency of PMF. How much faster is RASPBERRY is compared to a normal PMF? Can we also apply RASPBEERY to the chemical PMF? If so, it would be helpful to discuss whether and how the results would differ from those obtained using a normal chemical PMF approach.