Evaluating the impact of the aerosol sampling time interval on CWT and PSCF source-receptor models: A critical...

Multi-day sampling is not uncommon in ambient air studies. This work examines how the sampling time interval affects the effectiveness of source-receptor trajectory statistical methods, specifically the Concentration Weighted Trajectory (CWT) and the Potential Source Contribution Function (PSCF), in identifying potential aerosol sources. By analyzing long-term series of beryllium-7 radioactive aerosols measured in Helsinki (26 m asl), PM₁₀ concentrations at Viznar (1260 m asl) in southeastern Spain, and several controlled synthetic sources, we compared the outcomes of CWT and PSCF analyses from daily to weekly resolutions. Our findings indicate that sampling time intervals ranging from one day to one week do not substantially influence source identification. The application of Kernel Density Estimation filters out less significant sources or misleading air mass trajectories, enhancing the accuracy of the models and the way accuracy is measured. Notably, using CWT and PSCF with 1500 m backward trajectories on PM₁₀ data in southeastern Spain did not reveal significant source signals. It did so at 3000 m, as North African dust layers are most commonly transported to the southern Iberian Peninsula above 2500 m. This underscores the necessity of prior knowledge about specific transport patterns to interpret the model output accurately. In scenarios involving several synthetic sources, the PSCF method more accurately pinpointed source locations, while the CWT model identified broader areas reflecting air mass patterns over the sources. However, both models exhibited limitations in detecting continuous, low-intensity emission sources. To overcome these challenges, we introduced a sectoral approach that divides the study area and applies CWT and PSCF analyses to each sector individually. Integrating this sectoral approach into source-receptor methods could notably improve the precision of these methods in identifying low-emission sources.