Source identification of the elemental fraction of particulate matter using size segregated, highly time-resolved data and an optimized source apportionment approach

Source emissions with high covariance degrade the performance of multivariate models, and often highly-time resolved data is needed to accurately extract contribution different emissions. Here, we use highly time-resolved size segregated elemental composition apportion sources fraction PM in Zürich (May 2019–May 2020). For collection, have used an ambient metals monitor, Xact 625i, equipped a sampling inlet alternating between PM2.5 PM10. By implementing interpolation newly proposed uncertainty estimation methodology, it was possible obtain PMF combined dataset PMcoarse (PM10-2.5) having from only one instrument. The combination switching system, instrument's time resolution, advanced source apportionment approaches yielded improved results terms number identified sources, as model, additionally diurnal seasonal variation dataset, also utilizes data. Thirteen elements were identified, i.e., sea salt (5.4%), biomass burning (7.2%), construction (4.3%), industrial (3.3%), light-duty vehicles Pb (0.7%), Zn dust (22.1%), transported (9.5%), sulfates (15.4%), heavy-duty (17%), railway (6.6%) fireworks (2.4%). Covid-19 lockdown effect area quantified. High-intensity events disproportionally affect solution, many cases, they are getting discarded before analysis, removing thus valuable information dataset. In this study, three-step approach get well-resolved unmixed solution when firework points included analysis. This can be for other and/or very contributions that distort Optimized techniques necessary effective air pollution monitoring.