A study on the spatial and temporal variability in airborne Betula pollen concentration in five cities in Poland using multivariate analyses

Co-exposure to highly allergenic airborne pollen and fungal spores in Europe

The study is aimed at determining the potential spatiotemporal risk of the co-occurrence of airborne pollen and fungal spores high concentrations in different bio-climatic zones in Europe. Birch, grass, mugwort, ragweed, olive pollen and Alternaria and Cladosporium fungal spores were investigated at 16 sites in Europe, in 2005-2019. In Central and northern Europe, pollen and fungal spore seasons mainly overlap in June and July, while in South Europe, the highest pollen concentrations occur frequently outside of the spore seasons. In the coldest climate, no allergy thresholds were exceeded simultaneously by two spore or pollen taxa, while in the warmest climate most of the days with at least two pollen taxa exceeding threshold values were observed. The annual air temperature amplitude seems to be the main bioclimatic factor influencing the accumulation of days in which Alternaria and Cladosporium spores simultaneously exceed allergy thresholds. The phenomenon of co-occurrence of airborne allergen concentrations gets increasingly common in Europe and is proposed to be present on other continents, especially in temperate climate.

Impact of meteorological parameters and air pollutants on airborne concentration of Betula pollen and Bet v 1 allergen

The intensity of birch pollen season is expressed by seasonal pollen integral (SPIn, the sum of the mean daily pollen concentration during the birch pollination period) and the amount of Bet v 1 allergen released per birch pollen grain expressed by pollen allergen potency (PAP). Betula pollen and Bet v 1 allergen were simultaneously measured in the air of Bratislava from 2019 to 2022 by using two Burkard traps (Hirst-type and cyclone) in order to evaluate the causes of the seasonal variation in the SPIn and PAP levels. The highest SPIn (19,975 pollen/m3) was observed in 2022 and the lowest one (1484 pollen/m3) in 2021. The average daily PAP level (4.0 pg Bet v 1/pollen) was highest in 2019 and lowest (2.5 pg Bet v 1/pollen) in 2020. We found that seasonal variation in SPIn was associated mainly with the changes in environmental conditions during the pre-season period, whereas the year-to-year variation in PAP levels was attributed to environmental conditions during both pre- and in-season periods. Our results indicate that rainy weather in June 2020 and cold overcast weather in January‒February 2021 resulted in low SPIn in 2021. On the other hand, dry weather in June 2021 and warm weather in January‒February 2022 resulted in high SPIn in 2022. The low average daily PAP level in 2020 was associated with (1) low levels of gaseous air pollutants in March, when the ripening of pollen takes place; (2) an earlier start of the birch main pollen season (MPS); and (3) dry weather during the MPS. On the other hand, high PAP level in 2019 was associated with higher levels of air pollutants in March and during the MPS.

Analysis of changes in Betula pollen season start including the cycle of pollen concentration in atmospheric air

Birch belongs to the most important allergenic taxa in Europe, therefore information on the start dates of the pollen season is very important for allergists and their patients as well as for climatologists. The study examined changes in the start of the birch pollen season as well as determined the trend of these changes. Pollen monitoring was performed in Lublin (eastern Poland) in the period 2001-2019 using the volumetric method. The Makra-test was used to detect periods with significantly higher or lower average of the onset than the average for the whole dataset. Two significant falls in the average of the pollen season start were found in 2007 and 2014. Besides, taking into account the 2-3-year rhythm of high and low concentrations of birch pollen in the atmospheric air, linear trends were fitted for the subsets of high and low abundance seasons. Significant changes in Betula pollen season start dates were only determined for the highly abundance seasons, while the results for seasons with a low concentration did not allow rejecting the hypothesis about the lack of a linear trend in the changes in the studied parameter. Moreover, a significant polynomial relationship was found between the beginning of a pollen season and the average values of monthly temperatures preceded a season. These analyses show that the start dates of the Betula pollen season are getting significantly earlier. The dynamics of changes differ between seasons with high and low concentrations of pollen.

Changes in qualitative and quantitative traits of birch (Betula pendula) pollen allergenic proteins in relation to the pollution contamination

Birch (Betula pendula) pollen causes inhalant allergy in about 20% of human population in Europe, most of which is sensitive to the main birch allergen, Bet v1. The aim of the study was to find out (i) whether and how the analysed birch individuals differ in regard to composition of individual subunits of pollen proteins and to protein content in these subunits; (ii) whether the level of particulate matter relates to concentration of Bet v1 allergen. Study was performed in Southern Poland, in 2017-2019. Pollen material was collected at 20 sites, of highly or less polluted areas. Protein composition was analysed by SDS-PAGE, while the concentration of Bet v1 was evaluated by ELISA. The obtained results were estimated at the background of the particulate matter (PM10) level and the birch pollen seasons in Kraków. The electrophoregrams of pollen samples collected at different sites showed huge differences in staining intensities of individual protein subunits, also among important birch allergens: Bet v1, Bet v2, Bet v6 and Bet v7. The level of Bet v1 was significantly higher in the pollen samples collected at the more polluted sites. While the birch pollen allergenic potential is determined, the both pollen exposure and the content of the main allergenic components should be considered, as factors causing immunological response and clinical symptoms manifestation in sensitive individuals.

Extension of WRF-Chem for birch pollen modelling-a case study for Poland

In recent years, allergies due to airborne pollen allergens have shown an increasing trend, along with the severity of allergic symptoms in most industrialized countries, while synergism with other common atmospheric pollutants has also been identified as affecting the overall quality of citizenly life. In this study, we propose the state-of-the-art WRF-Chem model, which is a complex Eulerian meteorological model integrated on-line with atmospheric chemistry. We used a combination of the WRF-Chem extended towards birch pollen, and the emission module based on heating degree days, which has not been tested before. The simulations were run for the moderate season in terms of birch pollen concentrations (year 2015) and high season (year 2016) over Central Europe, which were validated against 11 observational stations located in Poland. The results show that there is a big difference in the model's performance for the two modelled years. In general, the model overestimates birch pollen concentrations for the moderate season and highly underestimates birch pollen concentrations for the year 2016. The model was able to predict birch pollen concentrations for first allergy symptoms (above 20 pollen m^-3) as well as for severe symptoms (above 90 pollen m^-3) with probability of detection at 0.78 and 0.68 and success ratio at 0.75 and 0.57, respectively for the year 2015. However, the model failed to reproduce these parameters for the year 2016. The results indicate the potential role of correcting the total seasonal pollen emission in improving the model's performance, especially for specific years in terms of pollen productivity. The application of chemical transport models such as WRF-Chem for pollen modelling provides a great opportunity for simultaneous simulations of chemical air pollution and allergic pollen with one goal, which is a step forward for studying and understanding the co-exposure of these particles in the air.

Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping

Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions.