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In 2022, the concentration of airborne Amaranthaceae pollen was investigated in 10 Polish cities: Bialystok, Bydgoszcz, Cracow, Lublin, Olsztyn, Opole, Piotrkow Trybunalski, Szczecin, Warsaw, and Wroclaw. The volumetric method based on the use of Hirst pollen samplers was employed. The pollen season was determined with the 98% method. It was found that the Amaranthaceae pollen season started between June 17th and June 28th and ended between September 8th and September 25th. The maximum daily concentrations of the pollen of this taxon in the air of the examined cities were recorded between July 3rd and August 31st, with the highest values detected in Opole and Piotrkow Trybunalski and the lowest values recorded in Bialystok and Olsztyn. The annual Amaranthaceae pollen sum had highest value in Bydgoszcz and Opole and an over six-fold lower value in Bialystok.

Oak (Quercus sp.) is a common species in Poland. Que a 1, which triggers allergic reactions in spring, is the main oak allergen. The aim of the study was to compare the oak pollen season in 13 Polish cities in 2022. Pollen was monitored with the volumetric method. The analyses determined the annual sum of pollen grains, the length of the pollen season (using the 98% method), the highest peak pollen concentrations, the dates of maximum pollen concentrations, and the number of days with pollen concentrations above threshold values causing clinical allergy symptoms. The earliest onset of the oak pollen season was noted in Szczecin (April 11th), and the latest beginning was recorded in Lublin and Kielce (May 5th). The shortest and the longest pollen seasons were recorded in Cracow (22 days) and in Szczecin (48 days), respectively. The highest total daily concentration of pollen grains was recorded in Opole (5083) and the lowest value was noted in Bialystok (112) and Szczecin (328). The highest maximum concentration of oak pollen was recorded in Sosnowiec and Kielce (625 and 543 grains/m3, respectively), whereas the lowest values were noted in Bialystok and Szczecin (27 and 47 grains/m3, respectively). In most of the analyzed cities in Poland, the peak oak pollen release was recorded between May 7th and 12th. The highest risk of allergic reactions caused by oak pollen allergens was reported from Opole, Piotrkow Trybunalski, and Zielona Gora.

The study compares the yew and juniper pollen seasons in Bialystok, Bydgoszcz, Cracow, Lublin, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, and Wroclaw in 2020. The investigations were conducted using the volumetric method. The yew and juniper season started in all measurement sites between February 2nd (Szczecin) and March 2nd (Lublin). The peak values of seasonal pollen count occurred between February 23rd (in Szczecin) and March 28th. The highest daily pollen count was recorded in Lublin (867 grains/m3) and the lowest pollen count in Bialystok (45 grains/m3). The highest annual totals were recorded in Lublin and Wroclaw. Most days, with a concentration equal to or above 50 grains/m3, causing symptoms in allergic patients were recorded in Lublin and Warsaw.

In this work, the HYSPLIT model was used to reproduce birch pollen concentrations in Poland for the years 2015 and 2016, where there was significant variation in terms of pollen concentrations and start/end dates of the pollen season. The analysis of pollen observations showed that the seasonal pollen integral (SPIn) was low in 2015 with a shorter season compared to 2016. In 2016, SPIn was unusually high. The HYSPLIT model simulation, with a one-hour temporal resolution, was conducted during the birch pollen season (from March to May) for 2015 and 2016. Meteorological data were obtained from the WRF model. The birch coverage map of the European Forest Institute was used. The emission, introduced to the model, covered Central Europe with a resolution of 0.3° × 0.3°. The results were compared to data from 11 observation stations in Poland. The measured birch pollen concentrations for 2015 were overestimated by HYSPLIT at 8 of the 11 stations (normalized mean bias/NMB from 0.13 to 2.53) and underestimated for three stations (NMB from − 0.44 to − 0.15). For 2016, the model highly underestimated the pollen concentrations, with NMB ranging from − 0.45 to − 0.93. In general, the results show that the model can resolve the main peaks of pollen concentrations, which is a step forward in the application of the HYSPLIT model for birch pollen forecasting over Poland. We suggest the application of methods that can reduce the bias of temperature such as meteorological data assimilation or bias correction, which could improve calculation of the start of emissions and consequently the start of the pollen season as well as pollen concentrations.

The study aims to monitor the alder pollen season in selected Polish cities: Bialystok, Cracow, Lublin, Olsztyn, Opole, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw and Zielona Gora in 2021. Pollen concentrations were recorded by volumetric method using a Burkard-type sampler operating in a continuous volumetric mode. Alder pollen season, defined as the period with 98% of the annual total catch, started in 3rd decade of February in all monitoring sites. There was a marked variation in duration of the season between the sites. It lasted from 31 in Cracow to 54 days in Bialystok (38 days on average). The highest peak daily alder pollen concentrations were observed in Wroclaw (1879 grains/m3) on February 26th). The longest exposure to high concentrations of alder pollen, lasting 22–24 days, was detected in Zielona Gora, Piotrkow Trybunalski and Olsztyn. The alder pollen season in 2021, compared to the previous year, was longer, with higher average sum of daily concentrations over the season, higher maximum daily concentrations and longer exposure to high pollen concentrations at most monitoring sites.

Pollen exposure weakens the immunity against certain seasonal respiratory viruses by diminishing the antiviral interferon response. Here we investigate whether the same applies to the pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is sensitive to antiviral interferons, if infection waves coincide with high airborne pollen concentrations. Our original hypothesis was that more airborne pollen would lead to increases in infection rates. To examine this, we performed a cross-sectional and longitudinal data analysis on SARS-CoV-2 infection, airborne pollen, and meteorological factors. Our dataset is the most comprehensive, largest possible worldwide from 130 stations, across 31 countries and five continents. To explicitly investigate the effects of social contact, we additionally considered population density of each study area, as well as lockdown effects, in all possible combinations: without any lockdown, with mixed lockdown?no lockdown regime, and under complete lockdown. We found that airborne pollen, sometimes in synergy with humidity and temperature, explained, on average, 44% of the infection rate variability. Infection rates increased after higher pollen concentrations most frequently during the four previous days. Without lockdown, an increase of pollen abundance by 100 pollen/m3 resulted in a 4% average increase of infection rates. Lockdown halved infection rates under similar pollen concentrations. As there can be no preventive measures against airborne pollen exposure, we suggest wide dissemination of pollen?virus coexposure dire effect information to encourage high-risk individuals to wear particle filter masks during high springtime pollen concentrations. © 2021 National Academy of Sciences. All rights reserved., 1.1.1.2/VIAA/2/18/283; European Cooperation in Science and Technology, COST: CA18226; Academy of Finland, AKA: 318194, PS4A; Ministerio de Educación, Cultura y Deporte, MECD: FPU15/01668; Department of the Environment, Australian Government; Lietuvos Mokslo Taryba; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR: 451-03-68/2020-14/200358; European Social Fund, ESF: 09.3.3-LMT-K-712-01-0066; Helmholtz Association; Christine Kühne – Center for Allergy Research and Education, CK-CARE, ACKNOWLEDGMENTS. We thank Mr. Luis-Leopold Moelter and Mr. Mehmet Gökkaya for assistance in overall data curation. The study was partly implemented in the frame of the European Cooperation in Science and Technology (EU-COST) program, "New approaches in detection of pathogens and aeroallergens (ADOPT)," Grant CA18226 (EU Framework Program Horizon 2020). D.B. and C.T.-H. were supported by the Helmholtz Climate Initiative (HI-CAM), Mitigation and Adaptation. A.Ch. and D.V. were supported by the Municipality of Thessaloniki, Greece (Directorate for the Management of the Urban Environment, Department of Environment). This research has been partly supported by the European Social Fund (Project 09.3.3-LMT-K-712-01-0066) under grant agreement with the Research Council of Lithuania (LMTLT). The study was also partly conducted within the frame of the project of the European Community European Regional Development Fund (EC ERDF) and PostDoc Latvia (Grant 1.1.1.2/VIAA/2/18/283). M.S. acknowledges the Academy of Finland (Project PS4A, Grant 318194). We thank the Department of Health and Rehabilitation of Vinnytsia Regional Council, Ukraine, for providing the numbers of COVID-19 cases. A.H.A. acknowledges Angel Chaves and the Government of Navarra: Institute of Public and Labor Health of Navarra, within LIFE-IP NAdatpa-CC (LIFE16 IPC/ES/000001). A.P. was supported by a predoctoral grant financed by the Ministry of Education, Culture and Sports of Spain, in the Program for the Promotion of Talent and its Employability (Grant FPU15/01668). B.S. acknowledges the Ministry of Education, Science, and Technological Development of the Republic of Serbia (Grant 451-03-68/2020-14/200358). C.T.H. acknowledges the Christine Kühne–Center for Allergy Research and Education (CK-CARE), and The Initiative and Networking Fund of the Helmholtz Association (Immunology & Inflammation). We thank Jan Bumberger, Marcus Karsten, Paul Remmler, Jan C. Simon, and Regina Treudler for data provision and curation from Leipzig, Germany. We thank Claudia Langford Brown and Dana Flanders for statistical advice and scientific discussions. We thank Penelope Jones, Edith Bucher, Reyhan Gumusburun, Haydar Soydaner Karakus, Su Ozgur, and Asli Tetik Vardarli for data curation.

Various concentrations of Chenopodiaceae/Amaranthaceae pollen are detected in the air of many regions of Europe. The highest content of pollen produced by the taxon is reported in southern Europe and in other countries with a warm climate and low precipitation sums. The study was focused on characterization of the Chenopodiaceae/Amaranthaceae pollen season in 11 Polish cities: Bialystok, Bydgoszcz, Cracow, Lublin, Olsztyn, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw, and Zielona Gora in 2020. The volumetric method based on the use of the Lanzoni or Burkard pollen sampler was employed in the study. In 2020, the pollen season in the analyzed plant family began in the second half of June and ended during the first ten days of October. The earliest pollen season onset was recorded in Lublin (June 13th) and Szczecin (June 14th), whereas the latest beginning was noted in Wroclaw (July 5th). The earliest and latest end of the pollen season was recorded in Bialystok (September 6th) and in Olszyn (October 5th), respectively. In terms of length, the season was characterized by the shortest duration in Wroclaw (70 days) and the longest duration in Olsztyn (106 days). In most of the analyzed cities, maximum pollen concentrations were detected in the second half of August, and the highest values were recorded in Zielona Gora and Sosnowiec. Compared to 2019 and 2018, relatively low sums of the annual concentrations of Chenopodiaceae/Amaranthaceae pollen grains, i.e. in the range of 35–231, were recorded in Poland in 2020. The highest values of this parameter were reported in Olsztyn (231) and Lublin (230), whereas the lowest value was noted in Bialystok (35). The relatively low maximum concentrations of Chenopodiaceae/Amaranthaceae pollen recorded during the study year indicate a low risk of development of allergy symptoms induced by the presence of pollen of this taxon in the air.

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.

The study aims to compare the oak pollen season in selected Polish cities: Bialystok, Bydgoszcz, Cracow, Katowice, Piotrkow Trybunalski, Lublin, Olsztyn, Opole, Szczecin, Warsaw, and Wroclaw in 2020. Measurements were made using the volumetric method, with a Hirst-type sampler. Oak pollen season, defined as the period with 98% of the annual total catch, started between April 14th (in Opole) and April 25th (in Lublin). The season ended on June 1st at the latest; in Sosnowiec, Bydgoszcz, Olsztyn, and Bialystok. It lasted from 30 to 47 days (37 days on average). The maximum daily oak pollen concentrations were observed between April 24th and May 11th. The highest annual sum of oak pollen grains (SPI) was recorded in Lublin, while the lowest in Bialystok. The highest concentrations of 596 oak pollen grains/m3 were noted in Lublin on April 28th. The longest exposure to high concentrations of oak pollen (> 91 grains/m3), lasting 12–13 days, was recorded in Lublin, Opole, and Wroclaw.

We analyse the impact of ground-based data assimilation to the Weather Research and Forecasting (WRF) meteorological model on parameters relevant for birch pollen emission calculations. Then, we use two different emission databases (BASE - no data assimilation, OBSNUD - data assimilation for the meteorological model) in the chemical transport model and evaluate birch pollen concentrations. Finally, we apply a scaling factor for the emissions (BASE and OBSNUD), based on the ratio between simulated and observed seasonal pollen integral (SPIn) to analyse its impact on birch concentrations over Central Europe. Assimilation of observational data significantly reduces model overestimation of air temperature, which is the main parameter responsible for the start of pollen emission and amount of released pollen. The results also show that a relatively small bias in air temperature from the model can lead to significant differences in heating degree days (HDD) value. This may cause the HDD threshold to be attained several days earlier/later than indicated from observational data which has further impact on the start of pollen emission. Even though the bias for air temperature was reduced for OBSNUD, the model indicates a start for the birch pollen season that is too early compared to observations. The start date of the season was improved at two of the 11 stations in Poland. Data assimilation does not have a significant impact on the season's end or SPIn value. The application of the SPIn factor for the emissions results in a much closer birch pollen concentration level to observations even though the factor does not improve the start or end of the pollen season. The post-processing of modelled meteorological fields, such as the application of bias correction, can be considered as a way to further improve the pollen emission modelling.