In this work, we investigated the structure of the airborne bacterial community obtained by 16S rRNA gene sequencing performed on aerosol samples from different indoor and outdoor locations. The 48-h aerosol samples were collected in two laboratories, in the corridors, and on the roof of the Mathematics and Physics Department of the University of Salento (Italy). The investigation was carried out through the application of an innovative compositional data analysis approach, mainly based on a centered log-ratio transformation as a standardization procedure, the Aitchison distance for data ordination, and the principal component analysis via singular value decomposition for data clustering. This methodology allowed us to explore the main relationships among samples, identifying different results between indoor and outdoor samples both at the genus level and at the species level. Bacillus and Pseudomonas represented the most abundant genera identified in the analyzed samples. Out of the 21 identified bacterial species with the highest abundances in the collected aerosol samples, Acinetobacter lwoffii, Propionibacterium acnes, Diplorickettsia massiliensis, and Corynebacterium tuberculostearicum were the only four commonly classified as human opportunistic pathogens. Among the genera mostly associated with indoor environments, Hymenobacter and Arthrobacter could be noted as including many species that are unique in being radiation resistant.

This study aims to investigate the seasonal variation and source identification of fluorescent aerosol particles at the monitoring site of the University of Salento in Lecce, southeastern Italy. Utilizing a wideband integrated bioaerosol sensor (WIBS), this research work analyzes data from two specific monitoring days: one in winter (10 January 2024), marked by significant transport of anthropogenic particles from Eastern Europe, and another in early spring (6 March 2024), characterized by marine aerosol sources and occasional desert dust. This study focuses on the seven WIBS particle categories (A, B, C, AB, AC, BC, ABC), which exhibited distinct characteristics between the two days, indicating different aerosol compositions. Winter measurements revealed a predominance of fine-mode particles, particularly soot and bacteria. In contrast, spring measurements showed larger particles, including fungal spores, pollen fragments, and mineral dust. Fluorescence intensity data further emphasized an increase in biological and organic airborne material in early spring. These results highlight the dynamic nature of fluorescent aerosol sources in the Mediterranean region and the necessity of continuous monitoring for air quality assessments. By integrating WIBS measurements with air mass back-trajectories, this study effectively identifies fluorescent aerosol sources and their seasonal impacts, offering valuable insights into the environmental and health implications of aerosol variability in the investigated Mediterranean area.

This study investigates airborne bacterial and fungal communities in south-eastern Italy and Albania using advanced DNA-based techniques and compositional data analysis (CoDa). We assess the significance of airborne microbial communities, detailing our methodologies for site selection, sample collection, DNA extraction, and data analysis. Our results reveal distinct differences in microbial composition between the two regions, driven by local environmental factors. Specifically, Albanian samples showed higher abundances of bacterial species such as Rubellimicrobium roseum and Sphingomonas cynarae, while Italian samples were characterized by a prevalence of Truepera radiovictrix and Rubrobacter radiotolerans. In terms of fungi, Albanian sites exhibited greater abundance of Mycosphaerella tassiana, Aureobasidium pullulans, and Ascochyta herbicola. Aitchison distance-based dendrograms and principal component analysis (PCA) biplots, utilizing singular value decomposition, clearly delineated a geographical separation of microbial communities, underscoring the impact of regional atmospheric conditions on microbial composition. In the discussion, we interpret these findings in the context of regional environmental factors, highlighting their implications for understanding regional differences in airborne microbial communities. The conclusion emphasizes the effectiveness of advanced DNA techniques and CoDa in environmental microbiology, offering insights into how local environmental conditions shape microbial communities and suggesting directions for future research and public health considerations.

Atmospheric particulate-matter variability reflects the combined effects of emissions, meteorology, and regional transport, and its interpretation benefits from the integration of complementary observations. This study combines two years (2023–2024) of aerosol light absorption at 880 nm (Abs880), PM2.5, PM10, and meteorological observations from ACTRIS/EBAS and ARPA Puglia to characterize the urban-scale covariation in Lecce, southern Italy. Since the aerosol-absorption and ARPA measurements were obtained at sites separated by approximately 6.3 km, the resulting relationships are interpreted as urban-scale associations rather than as co-located source-attribution measurements. After quality assurance/quality control, UTC time normalization, daily aggregation (>75% completeness), temporal alignment, and z-score standardization for PCA, the integrated observations were examined using seasonal comparisons, linear regression, principal component analysis, and HYSPLIT backward trajectories. PM2.5/PM10 ratio showed seasonal variability in both study years, with higher mean values during the winter. The PM2.5 was positively associated with the Abs880 (R² = 0.68 in 2023 and 0.59 in 2024), and the season-stratified regressions remained positive in all the seasonal subsets, although their strength varied. The meteorology-adjusted sensitivity models also retained a positive Abs880 coefficient after an HAC correction for serial dependence. The wind speed showed a weaker inverse association with PM2.5, whereas the temperature and the relative humidity showed weak pairwise associations. A PCA identified a common covariance direction among PM2.5, PM10, and Abs880, with additional meteorological structure in the six-variable sensitivity analysis. Two representative episodes contrasted an enhanced Abs880 under low-wind conditions with a spring Saharan-dust intrusion. Overall, this study characterizes the relationships among particulate matter, aerosol light absorption, meteorology, and regional transport over the 2023–2024 observation period. FAIR-oriented data practices and interoperable monitoring infrastructures provide the methodological framework that enables the integration, traceability, and reproducibility of these analyses.