Aerobiology

Airborne magnetic particles (AMPs) are associated with potentially toxic elements, and their size, mineralogy, and concentration can significantly impact both the environment and human health. However, their spatial analysis is often limited by small datasets, non-normality, and pronounced local variability. In this work, two sites with distinct demographic and geographic characteristics, the city of Mar del Plata (Argentina) and the Aburrá Valley region (Colombia), were analyzed using the fuzzy Magnetic Pollution Index (IMC) as an indicator of the concentration of AMPs. Moreover, an original methodological framework that explicitly incorporates measurement uncertainty through fuzzy numbers, combined with an approach modeling fuzzy semivariances via α-cuts, performs spatial prediction via ordinary kriging. This study produces maps that simultaneously reflect the magnitude of IMC and its associated uncertainty. Unlike classical geostatistics, the fuzzy-based model captures the inherent imprecision of magnetic measurements and reveals spatial patterns where uncertainty becomes informative about the type and origin of pollution. In particular, this approach demonstrates that areas with higher IMC levels are associated with high anthropic activity (near industrial zones, main avenues, slow traffic). In contrast, lower values were found in residential areas. Overall, the fuzzy-driven approach provides an additional layer of information not accessible through traditional methods, improving spatial interpretation and supporting the identification of priority areas for environmental monitoring.

The Danish asbestos executive order defines a range of situations and work processes that require no protective measures, due to an assumption of low dust generation and therefore negligible exposure to asbestos fibers. The purpose of the study was to investigate the exposure of workers performing tasks where low dust generation is assumed, as well as those in direct proximity. Five roof renovation projects, one facade removal project, and one removal project of whole pipes with intact asbestos insulation were included in the study. A total of 52 personal samples and 33 stationary samples were collected. The asbestos fiber concentrations measured in personal samples ranged from below the detection limit to 0.13 f/cm³ (fibers/cm³). Despite the large spread between projects, the measured concentrations of asbestos fibers in 45 of the 52 personal samples were above the Danish occupational exposure limit value (OEL) of 0.003 f/cm³. The concentration of asbestos fibers in 20 of 33 stationary samples was also above the Danish OEL. The results of personal and stationary measurements suggest that any work with asbestos-containing materials may be associated with a significant risk of exposure above the OEL and, thus, should not be considered a low-dust-generating process without measurements.

Co-exposure to airborne pollen and fungal spores is increasingly recognized as a contributor to asthma and allergic rhinitis, especially as climatic shifts since 2020 have intensified their seasonal overlap. We systematically searched PubMed and Google Scholar for studies published between 2020 and 2025 that assessed simultaneous pollen–fungi exposure and respiratory outcomes. Screening was performed independently by two reviewers, resulting in 12 eligible studies out of 320 records. Meta-analysis was not feasible due to substantial heterogeneity in exposure definitions, taxa, outcome measures, and analytical approaches. Overall, the studies indicate that short-term co-exposure tends to worsen respiratory symptoms and increase emergency visits or asthma exacerbations, with stronger effects in children, polysensitized individuals, and urban settings. However, effect sizes varied considerably across regions and methodologies. Environmental and climatological papers provided context for seasonal overlap but did not contribute clinical data. Current evidence suggests a potential synergistic effect, though more standardized exposure metrics are needed to refine risk estimates.