Association of air pollution with airborne pollen concentrations: A meta-analysis

In the context of increased respiratory allergy associated with pollen grains, interest in establishing the interaction between airborne pollen concentrations and air pollutants has intensified. This meta-analysis evaluates the impacts of air pollutants on seasonal index parameters across different pollen types. The comprehensive search, conducted on December 31, 2024, encompassed PubMed, Google Scholar, Web of Science, and SCOPUS with filters of keyword combination from 2000 to 2024. Our analysis covered several pollen types with a focus on long-term trends in the relationship between air pollutants and main pollen season parameters, i.e., Seasonal Pollen Index (SPIn) and Annual Pollen Integral (APIn). Latent class analysis (LCA) was applied for the first time to assess the correlation between pollen concentration indices and major air pollutants. The study included 27 articles encompassing 863 observations/counts between 2000 and 2024. The tree pollen taxa showed the highest correlation of APIn with air pollutants, while they showed moderate to weak pollutant correlations with SPIn. The weed taxa showed consistent results in APIn and SPIn with high correlation, especially with PM₂.₅ and NO_x. In both the APIn and SPIn time periods, there is a high correlation between the concentrations of grass pollen and PM and NO₂ pollutants. According to this correlation, grass pollen concentrations tend to be high when NO₂ and PM levels are high. The significant differences between SPIn and APIn responses highlight the significance of temporal scale, with longer series reflecting cumulative negative impacts that might be masked at shorter timescales. LCA showed that NO₂ emerged as the most influential pollutant, affecting the largest number of taxa across multiple classes. These findings underscore the potential associative effects of air pollutants and airborne pollen concentration, highlighting the necessity of incorporating pollution indicators into allergenic pollen forecasting and modeling studies on pollutant-pollen interactions across different regions.