The atmosphere represents an underexplored temporary habitat for airborne microbial communities such as eukaryotes, whose taxonomic structure changes across different locations and/or regions as a function of both survival conditions and sources. A preliminary dataset on the seasonal dependence of the airborne eukaryotic community biodiversity, detected in PM10 samples collected from July 2018 to June 2019 at a coastal site representative of the Central Mediterranean, is provided in this study. Viridiplantae and Fungi were the most abundant eukaryotic kingdoms. Streptophyta was the prevailing Viridiplantae phylum, whilst Ascomycota and Basidiomycota were the prevailing Fungi phyla. Brassica and Panicum were the most abundant Streptophyta genera in winter and summer, respectively, whereas Olea was the most abundant genus in spring and autumn. With regards to Fungi, Botrytis and Colletotrichum were the most abundant Ascomycota genera, reaching the highest abundance in spring and summer, respectively, while Cryptococcus and Ustilago were the most abundant Basidiomycota genera, and reached the highest abundance in winter and spring, respectively. The genus community structure in the PM10 samples varied day-by-day, and mainly along with the seasons. The impact of long-range transported air masses on the same structure was also proven. Nevertheless, rather few genera were significantly correlated with meteorological parameters and PM10 mass concentrations. The PCoA plots and non-parametric Spearman’s rank-order correlation coefficients showed that the strongest correlations generally occurred between parameters reaching high abundances/values in the same season or PM10 sample. Moreover, the screening of potential pathogenic fungi allowed us to detect seven potential pathogenic genera in our PM10 samples. We also found that, with the exception of Panicum and Physcomitrella, all of the most abundant and pervasive identified Streptophyta genera could serve as potential sources of aeroallergens in the studied area.

Environmental samples collected in Brindisi (Italy) by a Hirst-type trap and in Lecce (Italy) by a PM10 sampler were analysed by optical microscopy and DNA-metabarcoding, respectively, to identify airborne pollen and perform an exploratory study, highlighting the benefits and limits of both sampling/detection systems. The Hirst-type trap/optical-microscopy system allowed detecting pollen on average over the full bloom season, since whole pollen grains, whose diameter vary within 10–100 μm, are required for morphological detection with optical microscopy. Conversely, pollen fragments with an aerodynamic diameter ≤10 μm were collected in Lecce by the PM10 sampler. Pollen grains and fragments are spread worldwide by wind/atmospheric turbulences and can age in the atmosphere, but aerial dispersal, aging, and long-range transport of pollen fragments are favoured over those of whole pollen grains because of their smaller size. Twenty-four Streptophyta families were detected in Lecce throughout the sampling year, but only nine out of them were in common with the 21 pollen families identified in Brindisi. Meteorological parameters and advection patterns were rather similar at both study sites, being only 37 km apart in a beeline, but their impact on the sample taxonomic structure was different, likely for the different pollen sampling/detection systems used in the two monitoring areas.

The compositional analysis of 16S rRNA gene sequencing datasets is applied to characterize the bacterial structure of airborne samples collected in different locations of a hospital infection disease department hosting COVID-19 patients, as well as to investigate the relationships among bacterial taxa at the genus and species level. The exploration of the centered log-ratio transformed data by the principal component analysis via the singular value decomposition has shown that the collected samples segregated with an observable separation depending on the monitoring location. More specifically, two main sample clusters were identified with regards to bacterial genera (species), consisting of samples mostly collected in rooms with and without COVID-19 patients, respectively. Human pathogenic genera (species) associated with nosocomial infections were mostly found in samples from areas hosting patients, while non-pathogenic genera (species) mainly isolated from soil were detected in the other samples. Propionibacterium acnes, Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and jeikeium were the main pathogenic species detected in COVID-19 patients’ rooms. Samples from these locations were on average characterized by smaller richness/evenness and diversity than the other ones, both at the genus and species level. Finally, the ρ metrics revealed that pairwise positive associations occurred either between pathogenic or non-pathogenic taxa.

A preliminary local database of potential (opportunistic) airborne human and plant pathogenic and non-pathogenic species detected in PM10 samples collected in winter and spring is provided, in addition to their seasonal dependence and relationships with meteorological parameters and PM10 chemical species. The PM10 samples, collected at a Central Mediterranean coastal site, were analyzed by the 16S rRNA gene metabarcoding approach, and Spearman correlation coefficients and redundancy discriminant analysis tri-plots were used to investigate the main relationships. The screening of 1187 detected species allowed for the detection of 76 and 27 potential (opportunistic) human and plant pathogens, respectively. The bacterial structure of both pathogenic and non-pathogenic species varied from winter to spring and, consequently, the inter-species relationships among potential human pathogens, plant pathogens, and non-pathogenic species varied from winter to spring. Few non-pathogenic species and even fewer potential human pathogens were significantly correlated with meteorological parameters, according to the Spearman correlation coefficients. Conversely, several potential plant pathogens were strongly and positively correlated with temperature and wind speed and direction both in winter and in spring. The number of strong relationships between presumptive (human and plant) pathogens and non-pathogens, and meteorological parameters slightly increased from winter to spring. The sample chemical composition also varied from winter to spring. Some potential human and plant pathogens were correlated with chemicals mainly associated with marine aerosol and/or with soil dust, likely because terrestrial and aquatic environments were the main habitats of the detected bacterial species. The carrier role on the species seasonal variability was also investigated.

Both teflon and quartz PM2.5 filters collected from January to July 2021 at the monitoring site of the Department of Mathematics and Physics of the University of Salento in Lecce (Italy) were analyzed by integrating different characterization techniques (Particle Induced X-ray Emission PIXE, Isotope Ratio Mass Spectrometry IRMS, and Accelerator Mass Spectrometry AMS) at the CEDAD (Center of Applied Physics, Dating and Diagnostics) of the Department of Mathematics and Physics, University of Salento. The PM2.5 concentration analyses allowed to identify the variation of the main PM2.5 characteristics as a function of the season and the day of the week. This last characterization was integrated by the results from the PIXE, which allowed to identify the heavy elements and their concentrations. The main results showed the presence of different elements, such as S and Zn (considered as markers of anthropogenic sources for PM2.5) and Ca and Fe (as markers of natural sources). The concentrations of these elements showed a significant decrease during the weekend, mostly in the case of elements of anthropogenic origin, according to the data on the PM2.5 temporal evolution. Using the isotopic markers of carbon and nitrogen by means of the IRMS, we determined values of δ15N between 4.5 and 10.6‰, which are consistent with the origin of PM2.5 from anthropic combustion processes and a secondary contribution from vehicular traffic. Similarly, the values of δ13C obtained by IRMS were in the range between −24.4 and −26.7‰, generally associated with biomass combustion and with vehicular traffic. An analysis of the fossil and modern contribution was carried out on the PM2.5 filters by measuring radiocarbon using the integrated IRMS-EA system connected with the TANDETRON accelerator and AMS spectrometer. In more detail, we found a percentage of modern carbon in the range 71.6–92.4% that indicates a larger bio-derived contribution with respect to the contribution from fossil sources during the analyzed period. The parameters obtained from PIXE, IRMS, and AMS techniques were finally used as input for different ordination methods that allowed their deeper characterization.