Aerosol source apportionment uncertainty linked to the choice of input chemical components.

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.

Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain.

Ambient particulate matter (PM) is a major contributor to air pollution, leading to adverse health effects on the human population. It has been suggested that the oxidative potential (OP, as a tracer of oxidative stress) of PM is a possible determinant of its health impact. In this study, samples of PM10, PM2.5, and PM1 were collected roughly every four days from January 2018 until March 2019 at a Barcelona urban background site and Montseny rural background site in northeastern Spain. We determined the chemical composition of samples, allowing us to perform source apportionment using positive matrix factorization. The OP of PM was determined by measuring reactive oxygen species using dithiothreitol and ascorbic acid assays. Finally, to link the sources with the measured OP, both a Pearson's correlation and a multiple linear regression model were applied to the dataset. The results showed that in Barcelona, the OP of PM10 was much higher than those of PM2.5 and PM1, whereas in Montseny results for all PM sizes were in the same range, but significantly lower than in Barcelona. In Barcelona, several anthropogenic sources were the main drivers of OP in PM10 (Combustion + Road Dust + Heavy Oil + OC-rich) and PM2.5 (Road Dust + Combustion). In contrast, PM1 -associated OP was driven by Industry, with a much lower contribution to PM10 and PM2.5 mass. Meanwhile, Montseny exhibited no clear drivers for OP evolution, likely explaining the lack of a significant difference in OP between PM10, PM2.5, and PM1. Overall, this study indicates that size fraction matters for OP, as a function of the environment typology. In an urban context, OP is driven by the PM10 and PM1 size fractions, whereas only the PM1 fraction is involved in rural environments.