Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices.

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency. The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm). Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables. Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

From MASK-air and SILAM to CATALYSE (Climate Action To Advance HeaLthY Societies in Europe).

Plant species vary under different climatic conditions and the distribution of pollen in the air. Trends in pollen distribution can be used to assess the impact of climate change on public health. In 2015, the Mobile Airways Sentinel networK for rhinitis and asthma (MASK-air®) was launched as a project of the European Innovation Partnership on Active and Healthy Ageing (EIP-on-AHA, DG Santé and DG CONNECT). This project aimed to develop a warning system to inform patients about the onset of the pollen season, namely, the System for Integrated modeLling of Atmospheric coMposition (SILAM). A global-to-meso-scale dispersion model was developed by the Finnish Meteorological Institute (FMI). It provides quantitative information on atmospheric pollution of anthropogenic and natural origins, particularly on allergenic pollens. Impact of Air Pollution on Asthma and Rhinitis (POLLAR, EIT Health) has combined MASK-air clinical data with SILAM forecasts. A new Horizon Europe grant (Climate Action to Advance HeaLthY Societies in Europe [CATALYSE]; grant agreement number 101057131), which came into force in September 2022, aims to improve our understanding of climate change and help us find ways to counteractit. One objective of this project is to develop early warning systems and predictive models to improve the effectiveness of strategies for adapting to climate change. One of the warning systems is focused on allergic rhinitis (CATALYSE Task 3.2), with a collaboration between the FMI (Finland), Porto University (Portugal), MASK-air SAS (France), ISGlobal (Spain), Hertie School (Germany), and the University of Zurich (Switzerland). It is to be implemented with the support of the European Academy of Allergy and Clinical Immunology. This paper reports the planning of CATALYSE Task 3.2.