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Average daily contributions (90% CI black bar) to PM 10 in relative (%, left pie charts), OPAA and OPDTT per source's mass in absolute (OP m in nmol/min m−3, middle bar plot) and OPAA and OPDTT per volume in relative (OP v in %, right bar plot) values grouped according to sources common activities. Arrows indicate (additional) associations between the sources. [Display omitted] • Concentrations of PM 10 at the site are comparable to other Alpine valleys, however, OP levels are higher compared to European sites. • Extensive tests with PMF were performed including organic tracers. • The most important sources of PM 10 , OP per µg of source as well as OP per m3 at the site are biomass burning and activities related to cement production. • Sources with important contributions to PM 10 do not necessarily have high OP. • An unusual chloride-rich source was identified with high OP per µg of source. Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM 10 sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM 10 filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM 10 were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM 10 , OP AA and OP DDT , respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM 10 mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM 10 and OP in complex environments and are thus useful for PM control strategies and actions. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of lacking specific sources' tracers on source apportionment is evaluated. • Levoglucosan was, in most cases, crucial to identify biomass burning contributions. • Vehicle exhaust source resulted to be less sensitive to the choice of analytes. • Major and trace elements were crucial to identify vehicle non-exhaust, shipping, mineral and industrial sources. • At least EC-OC, major ions, trace element, and biomass burning tracers should be mandatory. For a Positive Matrix Factorization (PMF) aerosol source apportionment (SA) studies there is no standard procedure to select the most appropriate chemical components to be included in the input dataset for a given site typology, nor specific recommendations in this direction. However, these choices are crucial for the final SA outputs not only in terms of number of sources identified but also, and consequently, in the source contributions estimates. In fact, PMF tends to reproduce most of PM mass measured independently and introduced as a total variable in the input data, regardless of the percentage of PM mass which has been chemically characterized, so that the lack of some specific source tracers (e.g. levoglucosan) can potentially affect the results of the whole source apportionment study. The present study elaborates further on the same concept, evaluating quantitatively the impact of lacking specific sources' tracers on the whole source apportionment, both in terms of identified sources and source contributions. This work aims to provide first recommendations on the most suitable and critical components to be included in PMF analyses in order to reduce PMF output uncertainty as much as possible, and better represent the most commons PM sources observed in many sites in Western countries. To this aim, we performed three sensitivity analyses on three different datasets across EU, including extended sets of organic tracers, in order to cover different types of urban conditions (Mediterranean, Continental, and Alpine), source types, and PM fractions. Our findings reveal that the vehicle exhaust source resulted to be less sensitive to the choice of analytes, although source contributions estimates can deviate significantly up to 44 %. On the other hand, for the detection of the non-exhaust one is clearly necessary to analyze specific inorganic elements. The choice of not analysing non-polar organics likely causes the loss of separation of exhaust and non-exhaust factors, thus obtaining a unique road traffic source, which provokes a significant bias of total contribution. Levoglucosan was, in most cases, crucial to identify biomass burning contributions in Milan and in Barcelona, in spite of the presence of PAHs in Barcelona, while for the case of Grenoble, even discarding levoglucosan, the presence of PAHs allowed identifying the BB factor. Modifying the rest of analytes provoke a systematic underestimation of biomass burning source contributions. SIA factors resulted to be generally overestimated with respect to the base case analysis, also in the case that ions were not included in the PMF analysis. Trace elements were crucial to identify shipping emissions (V and Ni) and industrial sources (Pb, Ni, Br, Zn, Mn, Cd and As). When changing the rest of input variables, the uncertainty was narrow for shipping but large for industrial processes. Major and trace elements were also crucial to identify the mineral/soil factor at all cities. Biogenic SOA and Anthropogenic SOA factors were sensitive to the presence of their molecular tracers, since the availability of OC alone is unable to separate a SOA factor. Arabitol and sorbitol were crucial to detecting fungal spores while odd number of higher alkanes (C27 to C31) for plant debris. [ABSTRACT FROM AUTHOR]