Environmental exposure assessments lack integrated indicators capable of accounting for the simultaneous impact of biological and chemical stressors. To address this, we developed the Pollen Resilience Index (PRI), an integrative indicator designed to measure the combined burden of allergenic pollen, air pollutants, and heat stress. The index incorporates hourly concentrations of four pollen taxa (Alnus, Betula, Poaceae, and Artemisia), five pollutants (PM10, NO2, O3, SO2, and CO), and a Humidex index calculated from forecasted temperature and relative humidity. Each input was quantised into five levels; an hourly PRI was then calculated using a maxpooling rule, and hourly values were aggregated into daily classes to facilitate comparison with health-related records. PRI was assessed using anonymised symptom diaries from Patient Hay Fever Diary users. Across moderate conditions (PRI 2-4), symptom reports appeared broadly stable, whereas more pronounced changes were observed under environmental extremes. At PRI 5, 53.0% of users reported nose symptoms (compared with 38.3% at PRI 4), and 32.1% reported eye symptoms (compared with 23.4%). Under more favourable conditions (PRI 1), symptom prevalence was lower (eye symptoms 14.2% vs. 24.5% at PRI 2). Results suggest that the PRI is a compact and scalable tool that effectively captures both symptom escalation and relief, providing a practical means to complement traditional air-quality indicators and enhance health communication. The PRI model is open access and can be downscaled and tailored to selected territories and specific user needs.

The impact of meteorological factors and air pollution on the dynamics of allergenic pollen dispersal was investigated in this study using automated monitoring data. In 2022-2024, pollen concentration data for Alnus, Betula, Corylus, and Poaceae collected in Vilnius (Lithuania) using an automated bioaerosol measurement device were applied to analyse not only pollen seasonality (start, end, and length) but also correlations with meteorological variables and air pollution. A 40-day pre-season window was used to investigate the effects of pre-season meteorological conditions on changes in pollen concentration and seasonality. The study revealed that the Betulaceae family (including Alnus, Betula, Corylus) pollen season started earlier than the Poaceae family, but was shorter (82 days on average), while the Poaceae family had the longest season (93 days on average). Pollen concentrations increased between 2022 and 2024. The study found statistically significant positive correlations linking air temperature and hourly pollen concentrations (Betula: r = 0.39 to 0.62, p < 0.01; weaker with other pollen types) and negative correlations with relative air humidity (Betula: r = −0.47 to −0.67, p < 0.01; weaker with other pollen types), while the effect of precipitation, wind direction and wind speed was mostly statistically weak. With respect to Betula and Corylus, pre-season meteorological conditions with higher air temperatures and lower relative humidity were associated with higher pollen concentrations, and for Corylus and Poaceae, they were associated with a longer season. Air pollution analysis revealed that pollen concentrations were more often positively correlated with PM, NOx and CO, but these relationships were not unidirectional and depended on plant taxa. This study enables automated pollen monitoring data to reveal relevant aspects of the statistical analysis of meteorological parameters and air pollution, as well as the dynamics of airborne pollen concentration.

Citizens have been increasingly involved in decision-making and management related to urban trees, forests, and green spaces in a variety of ways and for various reasons. Professionals responsible for planning or managing these resources are often involved in public participation. Their positive attitudes and perceptions are considered essential preconditions for successful public participation. However, knowledge of these attitudes and perceptions is still scarce in many European countries. Hence, a survey was distributed in 10 European countries, yielding a total of 582 responses. The purpose of the study was to explore professionals’ experiences with public participation, their attitudes towards involving the public in various planning and management activities, and perceived advantages and challenges of public participation. It also examined whether and how their sociodemographic and professional characteristics were related to their attitudes and perceptions. Professionals were supportive of participation in many activities, but not in tree assessments. They perceived many advantages but also challenges to public participation. Professionals’ sociodemographic characteristics had a somewhat limited effect on their attitudes and perceptions, while professional characteristics held almost no significance. Given the professionals' mostly positive attitudes towards public participation, addressing perceived challenges, particularly those related to a lack of capacities (such as internal or external support, staff, time, and money), would be beneficial for better promoting public participation in these countries. In contrast to the literature on public professionals' attitudes towards public participation, our results show that professionals were positive regardless of their previous experience with public participation and despite a perceived lack of capacities.

Climate change is a key factor determining changes in plant phenology. The start and end dates of the pollen season, as well as its duration, are closely linked to shifting meteorological conditions. Rising air temperatures have a particularly strong impact, but changes in other meteorological factors, such as precipitation, are also important. This study analyses two genera within the Betulaceae family, alder (Alnus) and birch (Betula) pollen seasons from 2005 to 2023 in Lithuania, focusing on season dates, duration, pollen concentration, and their relationships with meteorological parameters. The dates and duration of the pollen season were evaluated using two definitions. The analysis is based on daily aerobiological observation data from Vilnius, Siauliai, and Klaipeda.The alder pollen season typically begins in March and lasts, on average, from 24 to 36 days in different study sites. Over the past two decades, a significant trend toward an earlier start of the season has been observed, with the beginning date moving up by 13–34 days, depending on the location and calculation method. The duration of the season varied slightly, and the end dates did not show statistically significant differences. The increase in air temperature during February and March was the primary factor driving the season's earlier start.The birch pollen season in Lithuania usually begins in mid-April and lasts about 30 days. Changes are statistically insignificant, despite minor shifts in the start and end dates. A weak but significant correlation exists between February–March temperatures and the beginning of the birch pollen season, while a weak negative correlation was observed between April–May temperatures and the season-end dates.

The advent of automatic pollen and fungal spore monitoring over the past few years has brought about a paradigm change. The provision of real-time information at high temporal resolution opens the door to a wide range of improvements in terms of the products and services made available to a widening range of end-users and stakeholders. As technology and methods mature, it is essential to properly quantify the impact automatic monitoring has on the different end-user domains to better understand the real long-term benefits to society. In this paper, we focus the main domains where such impacts are expected, using Europe as a basis to provide qualitative estimates and to describe research needs to better quantify impacts in future. This will, in part, also serve to justify further investment and help to expand monitoring networks.

The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980-2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.

Aeroallergens or inhalant allergens, are proteins dispersed through the air and have the potential to induce allergic conditions such as rhinitis, conjunctivitis, and asthma. Outdoor aeroallergens are found predominantly in pollen grains and fungal spores, which are allergen carriers. Aeroallergens from pollen and fungi have seasonal emission patterns that correlate with plant pollination and fungal sporulation and are strongly associated with atmospheric weather conditions. They are released when allergen carriers come in contact with the respiratory system, e.g. the nasal mucosa. In addition, due to the rupture of allergen carriers, airborne allergen molecules may be released directly into the air in the form of micronic and submicronic particles (cytoplasmic debris, cell wall fragments, droplets etc.) or adhered onto other airborne particulate matter. Therefore, aeroallergen detection strategies must consider, in addition to the allergen carriers, the allergen molecules themselves. This review article aims to present the current knowledge on inhalant allergens in the outdoor environment, their structure, localization, and factors affecting their production, transformation, release or degradation. In addition, methods for collecting and quantifying aeroallergens are listed and thoroughly discussed. Finally, the knowledge gaps, challenges and implications associated with aeroallergen analysis are described.

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant’s reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating annual total pollen concentrations than those of the GM approach. These are the first published modeled maps using altitude correction and phenology to recover missing pollen information. We created a web page (http://euragweedpollen.gmf.u-szeged.hu/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions.

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we have developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant's reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating seasonal pollen integrals than those of the GM approach. These are the first published modelled maps using altitude correction and flowering phenology to recover missing pollen information. We created a web page (http://euragweedpollen.gmf.u-szeged.hu/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions.

The number of children suffering from respiratory allergies and asthma has been increasing worldwide and, hence, it is crucial to understand the burden of inhalant biological particles present in school facilities, where children spend one third of their life. From the perspective of indoor air quality, while there are numerous studies on outdoor bioaerosol exposure, there are still uncertainties regarding the diversity and deposition of airborne pollen and fungi indoors. When it comes to schools, there is limited research as to the potential bioaerosol exposure. Here we studied the indoor environment of public schools aiming to reveal whether primary schools of different sizes and at localities of different levels of urbanization may exhibit a variability in the biodiversity and abundance of particles of biological origin, which could pose a risk to child health. To achieve this, 11 schools were selected, located in a variety of environments, from downtown, to city centre-periphery, and to the suburbs. Fungal and pollen samples were collected from various surfaces in school classrooms and corridors, using passive air sampling and swab sampling. We demonstrated that fungi and pollen are detected in school premises during and after the vegetation season. The highest diversity of bioaerosols was found on the top of cabinets and windowsills, with Penicillium, Cladosporium and Acremonium being the most abundant indoors. The levels of fungi were higher in schools with more students. The diversity and amount of pollen in the spring were significantly higher than in samples collected in autumn. Our findings complemented existing evidence that bioaerosol measurements in schools (including kindergartens or informal education facilities) are vital. Hence, we here suggest that, in addition to monitoring air quality and bacterial levels indoors, fungi and pollen measurements have to be integrated in the existing regular biomonitoring campaigns so as to prevent exposure, increase awareness and manage efficiently allergic symptomatology.

To benefit allergy patients and the medical practitioners, pollen information should be available in both a reliable and timely manner; the latter is only recently possible due to automatic monitoring. To evaluate the performance of all currently available automatic instruments, an international intercomparison campaign was jointly organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich, Germany (March-July 2021). The automatic systems (hardware plus identification algorithms) were compared with manual Hirst-type traps. Measurements were aggregated into 3-hourly or daily values to allow comparison across all devices. We report results for total pollen as well as for Betula, Fraxinus, Poaceae, and Quercus, for all instruments that provided these data. The results for daily averages compared better with Hirst observations than the 3-hourly values. For total pollen, there was a considerable spread among systems, with some reaching R2 > 0.6 (3 h) and R2 > 0.75 (daily) compared with Hirst-type traps, whilst other systems were not suitable to sample total pollen efficiently (R2 < 0.3). For individual pollen types, results similar to the Hirst were frequently shown by a small group of systems. For Betula, almost all systems performed well (R2 > 0.75 for 9 systems for 3-hourly data). Results for Fraxinus and Quercus were not as good for most systems, while for Poaceae (with some exceptions), the performance was weakest. For all pollen types and for most measurement systems, false positive classifications were observed outside of the main pollen season. Different algorithms applied to the same device also showed different results, highlighting the importance of this aspect of the measurement system. Overall, given the 30 % error on daily concentrations that is currently accepted for Hirst-type traps, several automatic systems are currently capable of being used operationally to provide real-time observations at high temporal resolutions. They provide distinct advantages compared to the manual Hirst-type measurements.

Betula and Pinus pollen, which are dispersed in natural surface waters, release biologically active compounds into the water bodies. This study aims to evaluate variations in the distribution and composition of phenolic compounds in suspended particles in natural water bodies during pollen spreading. Samples taken from water bodies of different trophic levels were analyzed by microscopy, UV/VIS spectroscopy, HPTLC, and HPLC/DAD. The study revealed that the total phenolic content in water-suspended particles varied from 3.0 mg/g to 11.0 mg/g during Betula and Pinus pollen spreading. It was also observed that the surface water of dystrophic natural lakes had a higher content of phenolic compounds than the eutrophic, hypereutrophic, and mesotrophic water bodies. Chlorogenic, trans-ferulic, vanillin, and 3,4-dihydroxybenzoic acids were frequently detected in the surface water samples. Experimental measurements have shown variations in the release of phenolic compounds from Betula pollen into water (p < 0.05). After the exhibition of pollen, the distilled water predominantly contained bioactive chlorogenic acid. Further in situ investigations are necessary to gain a more comprehensive understanding of the function of phenolic compounds in aquatic ecosystems. The exploration of the release of bioactive compounds from pollen could provide valuable insights into the potential nutritional value of pollen as a nutrient source for aquaculture.

The school environment is crucial for the child’s health and wellbeing. On the other hand, the data about the role of school’s aerosol pollution on the etiology of chronic non-communicable diseases remain scarce. Objectives: To evaluate the level of indoor aerosol pollution in primary schools and its relation to the incidence of doctor’s diagnosed asthma among younger school-age children. Methods: The cross-sectional study was carried out in 11 primary schools of Vilnius during one year of education from autumn 2017 to spring 2018. Particle number (PNC) and mass (PMC) concentrations in the size range of 0.3-10 µm were measured using an Optical Particle Sizer (OPS, TSI model 3330). The annual incidence of doctor’s diagnosed asthma in each school was calculated retrospectively from the data of medical records. Results: The total number of 6-11 years old children participated in the study was 3638. The incidence of asthma per school ranged from 1.8 to 6.0%. Mean indoor air pollution based on measurements in classrooms during the lessons was calculated for each school. Levels of PNC and PMC in schools ranged between 33.0-168.0 part/cm 3 and 1.7-6.8 µg/m 3 , respectively. There was a statistically significant correlation between the incidence of asthma and PNC as well as asthma and PMC in the particle size range of 0.3-1 µm (r=0.66, p=0.028) and (r=0.71, p=0.017) respectively. No significant correlation was found between asthma incidence and indoor air pollution in the particle size range of 0.3-2.5 and 0.3-10 µm. Conclusions: We concluded that the number and mass concentrations of indoor air aerosol pollution in primary schools in the particle size range of 0.3-1 μm are primarily associated with the incidence of doctor’s diagnosed asthma among younger school age-children.

Standards for manual pollen and fungal spore monitoring have been established based on several decades of experience, tests, and research. New technological and methodological advancements have led to the development of a range of different automatic instruments for which no standard yet exist. This paper aims to provide an overview of aspects that need to be considered for automatic pollen and fungal spore monitoring, including a set of guidelines and recommendations. It covers issues relevant to developing an automatic monitoring network, from the instrument design and calibration through algorithm development to site selection criteria. Despite no official standard yet existing, it is essential that all aspects of the measurement chain are carried out in a manner that is as standardised as possible to ensure high-quality data and information can be provided to end-users.

The potential benefits of public urban green spaces (UGS) are widely recognized and well documented, but the actual realization of these benefits depends on appropriate design and ongoing maintenance. To properly consider the needs and preferences of users, the professionals who plan and manage UGS should ideally be guided by the same perceptions that motivate the people who benefit from them. This exploratory international study was aimed at assessing the perceptions of urban residents and their level of satisfaction with specific aspects of UGS quality, and the extent to which these perceptions align with those of the professionals responsible for providing UGS-related services. The data collection was conducted in five European countries (Croatia, Israel, Italy, Lithuania, and Spain) in 2020-2021. The results show that UGS professionals generally underestimate the fears that are experienced by users at night, especially women, but correctly prioritize the importance of tangible solutions such as adequate lighting and cleanliness. Users in all countries emphasized "nature" and "quiet" as factors that improve their general sense of wellbeing in UGS, whereas these two aspects were largely overlooked by professionals in almost all countries. In addition, user satisfaction with specific UGS characteristics ranging from accessibility to park furniture was overestimated by professionals. These findings reinforce the concern that the benefits and services of green spaces can only be maximized if UGS professionals recognize the actual needs and desires of UGS users, from the phase of planning and landscape design to the everyday management and maintenance of these shared amenities.

Pollen monitoring has traditionally been carried out using manual methods first developed in the early 1950s. Although this technique has been recently standardised, it suffers from several drawbacks, notably data usually only being available with a delay of 3–9 days and usually delivered at a daily resolution. Several automatic instruments have come on to the market over the past few years, with more new devices also under development. This paper provides a comprehensive overview of all available and developing automatic instruments, how they measure, how they identify airborne pollen, what impacts measurement quality, as well as what potential there is for further advancement in the field of bioaerosol monitoring.

This study investigates the use of pollen elastically scattered light images for species identification. The aim was to identify the best recognition algorithms for pollen classification based on the scattering images. A series of laboratory experiments with a Rapid-E device of Plair S.A. was conducted collecting scattering images and fluorescence spectra from pollen of 15 plant genera. The collected scattering data were supplied to 32 different setups of 8 computer vision models based on deep neural networks. The models were trained to classify the pollen types, and their performance was compared for the test sub-samples withheld from the training. Evaluation showed that most of the tested computer vision models convincingly outperform the basic convolutional neural network used in our previous studies: the accuracy gain was approaching 10% for best setups. The models of the Weakly Supervised Object Detection approach turned out to be the most accurate, but also slow. However, even the best setups still did not provide sufficient recognition accuracy barely reaching 65%–70% in the repeated tests. They also showed many false positives when applied to real-life time series collected by Rapid-E. Similar to the previous studies, fusion of the new scattering models with the fluorescence-based identification demonstrated almost 15% higher skills than either of the approaches alone reaching 77–83% of the overall classification accuracy.

A coordinated observational and modelling campaign targeting biogenic aerosols in the air was performed during spring 2021 at two locations in Northern Europe: Helsinki (Finland) and Siauliai (Lithuania), approximately 500 km from each other in north-south direction. The campaign started on March 1, 2021 in Siauliai (12 March in Helsinki) and continued till mid-May in Siauliai (end of May in Helsinki), thus recording the transition of the atmospheric biogenic aerosols profile from winter to summer. The observations included a variety of samplers working on different principles. The core of the program was based on 2-and 2.4-hourly sampling in Helsinki and Siauliai, respectively, with sticky slides (Hirst 24-h trap in Helsinki, Rapid-E slides in Siauliai). The slides were subsequently processed extracting the DNA from the collected aerosols, which was further sequenced using the 3-rd generation sequencing technology. The core sampling was accompanied with daily and daytime sampling using standard filter collectors. The hourly aerosol concentrations at the Helsinki monitoring site were obtained with a Poleno flow cytometer, which could recognize some of the aerosol types. The sampling campaign was supported by numerical modelling. For every sample, SILAM model was applied to calculate its footprint and to predict anthropogenic and natural aerosol concentrations, at both observation sites. The first results confirmed the feasibility of the DNA collection by the applied techniques: all but one delivered sufficient amount of DNA for the following analysis, in over 40% of the cases sufficient for direct DNA sequencing without the PCR step. A substantial variability of the DNA yield has been noticed, generally not following the diurnal variations of the total-aerosol concentrations, which themselves showed variability not related to day-time. An expected upward trend of the biological material amount towards summer was observed but the day-to-day variability was large. The campaign DNA analysis produced the first high-resolution dataset of bioaerosol composition in the North -European spring. It also highlighted the deficiency of generic DNA databases in applications to atmospheric biota: about 40% of samples were not identified with standard bioinformatic methods.

In 2016, the highest birch ( Betula spp.) pollen concentrations were recorded in Kraków (Poland) since the beginning of pollen observations in 1991. The aim of this study was to ascertain the reason for this phenomenon, taking the local sources of pollen in Poland and long-range transport (LRT) episodes associated with the pollen influx from other European countries into account. Three periods of higher pollen concentrations in Kraków in 2016 were investigated with the use of pollen data, phenological data, meteorological data and the HYSPLIT numerical model to calculate trajectories up to 4 days back (96 h) at the selected Polish sites. From 5 to 8 April, the birch pollen concentrations increased in Kraków up to 4000 Pollen/m 3 , although no full flowering of birch trees in the city was observed. The synoptic situation with air masses advection from the South as well as backward trajectories and the general birch pollen occurrence in Europe confirm that pollen was transported mainly from Serbia, Hungary, Austria, the Czech Republic, Slovakia, into Poland. The second analyzed period (13–14 April) was related largely to the local flowering of birches, while the third one in May (6–7 May) mostly resulted from the birch pollen transport from Fennoscandia and the Baltic countries. Unusual high pollen concentrations at the beginning of the pollen season can augment the symptomatic burden of birch pollen allergy sufferers and should be considered during therapy. Such incidents also affect the estimation of pollen seasons timing and severity. Graphical Abstract

Automatically operating particle detection devices generate valuable data, but their use in routine aerobiology needs to be harmonized. The growing network of researchers using automatic pollen detectors has the challenge to develop new data processing systems, best suited for identification of pollen or spore from bioaerosol data obtained near-real-time. It is challenging to recognise all the particles in the atmospheric bioaerosol due to their diversity. In this study, we aimed to find the natural groupings of pollen data by using cluster analysis, with the intent to use these groupings for further interpretation of real-time bioaerosol measurements. The scattering and fluorescence data belonging to 29 types of pollen and spores were first acquired in the laboratory using Rapid-E automatic particle detector. Neural networks were used for primary data processing, and the resulting feature vectors were clustered for scattering and fluorescence modality. Scattering clusters results showed that pollen of the same plant taxa associates with the different clusters corresponding to particle shape and size properties. According to fluorescence clusters, pollen grouping highlighted the possibility to differentiate Dactylis and Secale genera in the Poaceae family. Fluorescent clusters played a more important role than scattering for separating unidentified fluorescent particles from tested pollen. The proposed clustering method aids in reducing the number of false-positive errors.