Open Access

Maria P. Plaza, Jose Oteros, Vivien Leier-Wirtz, Franziska Kolek, Annette Menzel, Jeroen T. M. Buters, Claudia Traidl-Hoffmann, Athanasios Damialis

• Alternaria is a fungal phytopathogen affecting over 4,000 species and causing 20% of agricultural production losses. About 9% of people in Europe are sensitized to its allergens. Understanding spore concentration variability under different environmental conditions can optimize fungicide use and improve allergy diagnosis and treatment. This study examines the spatio-temporal abundance of airborne Alternaria spores across varying climate and pollution regimes, hypothesizing that regional land cover is the main predictor of spore concentrations. In 2015, airborne Alternaria spores were monitored at 23 sites in Bavaria using Hirst-type spore traps. Concentrations were assessed on a bihourly scale and differences between bioclimatic zones were analysed using regression (GLM, GLZ), variance (ANOVA, ANCOVA) and cluster analyses, controlling for meteorology, air quality and land use. Machine learning techniques, including random forest, regression tree and XGBoost, were also implemented toAlternaria is a fungal phytopathogen affecting over 4,000 species and causing 20% of agricultural production losses. About 9% of people in Europe are sensitized to its allergens. Understanding spore concentration variability under different environmental conditions can optimize fungicide use and improve allergy diagnosis and treatment. This study examines the spatio-temporal abundance of airborne Alternaria spores across varying climate and pollution regimes, hypothesizing that regional land cover is the main predictor of spore concentrations. In 2015, airborne Alternaria spores were monitored at 23 sites in Bavaria using Hirst-type spore traps. Concentrations were assessed on a bihourly scale and differences between bioclimatic zones were analysed using regression (GLM, GLZ), variance (ANOVA, ANCOVA) and cluster analyses, controlling for meteorology, air quality and land use. Machine learning techniques, including random forest, regression tree and XGBoost, were also implemented to detect complex, non-linear patterns, while stepwise regression was used to identify the most influential predictors. The seasonal fungal index (SFI) of Alternaria spores varied considerably between locations. Cluster analysis identified five main groups based on the maximum concentration and monthly distribution. The highest SFI values were in the north, including Bayreuth, Bamberg and Hof, but with shorter season. SFI decreased toward the south with lower temperatures, but seasons lengthened. One-third of spores appeared after 6 pm, with half of daily peaks post-8 pm. At higher altitudes, spore circulation was more variable, with peaks mostly at night. NO₂ and air temperature had a greater impact on spore levels than land use. Our results indicate that in a world with warmer nights and higher pollution fungal spores may enhance growth and sporulation, increasing the risk of exposure to both human health and agricultural productivity, highlighting the need for monitoring and potential mitigation of fungal pathogens.…