Characterization of aerosol sources in León (Spain) using Positive Matrix Factorization and weather types

Highlights: • A one-year aerosol sampling period was conducted in León (Spain) to label the sources. • The PMF found 6 main aerosol contributors, from traffic (29%) to biomass burning (3%). • A linear model estimates each source supply to PM₁₀ from tracer elements and airflows. • PMF analysis along with circulation weather types provide a good estimation for PM₁₀. • MLR models built offer a simplification for determining source apportionment. A one-year aerosol sampling campaign, between 2016 and 2017, was conducted in a suburban area of León city, Spain. An association between the Positive Matrix Factorization (PMF) results and air masses through circulation weather types was carried out, through the construction of linear models from the PM₁₀ concentrations and its chemical composition. The aerosol sources, identified by PMF six-factor solution, were: traffic (29%), aged sea salt (26%), secondary aerosols (16%), dust (13%), marine aerosol (7%) and biomass burning (3%). Traffic and secondary factors showed the highest PM₁₀ contribution in the hybrid cyclonic types with wind component from the first and second quadrant. Anticyclonic types with wind component from the first quadrant exhibited high values of secondary, aged sea salt and dust factors. The highest contributions of the dust factor were also associated with northerly types. The linear models built for estimating the source apportionment of PM₁₀, from aerosol chemical composition and geostrophic flow, showed positive coefficients for: westerly flows (WF) in marine factor, southerly flows (SF) in secondary and traffic factors, and shear southerly vorticities (ZS) in dust factor. Negative dependences were observed for ZS in aged sea salt factor and for SF in dust factor. The PM₁₀ mass concentration calculated by the linear models and by the PMF model were strongly correlated. This can be very useful to determine the contribution of a specific source to PM₁₀ in León, only by knowing some meteorological and chemical variables.