Ambient air quality monitoring is a governmental duty that is widely carried out in order to detect non-biological (“chemical”) components in ambient air, such as particles of < 10 µm (PM10, PM2.5), ozone, sulphur dioxide, and nitrogen oxides. These monitoring networks are publicly funded and air quality data are open to the public. The situation for biological particles that have detrimental effects on health, as is the case of pollen and fungal spores, is however very different. Most pollen and spore monitoring networks are not publicly funded and data are not freely available. The information regarding which biological particle is being monitored, where and by whom, is consequently often not known, even by aerobiologists themselves. This is a considerable problem, as local pollen data are an important tool for the prevention of allergic symptoms. The aim of this study was to review pollen monitoring stations throughout the world and to create an interactive visualization of their distribution. The method employed to collect information was based on: (a) a review of the recent and historical bibliography related to pollen and fungal spore monitoring, and (b) personal surveys of the managers of national and regional monitoring networks. The interactive application was developed using the R programming language. We have created an inventory of the active pollen and spore monitoring stations in the world. There are at least 879 active pollen monitoring stations in the world, most of which are in Europe (> 500). The prevalent monitoring method is based on the Hirst principle (> 600 stations). The inventory is visualised as an interactive and on-line map. It can be searched, its appearance can be adjusted to the users’ needs and it is updated regularly, as new stations or changes to those that already exist can be submitted online. The map shows the current situation of pollen and spore monitoring and facilitates collaboration among those individuals who are interested in pollen and spore counts. It might also help to improve the monitoring of biological particles up to the current level employed for non-biological components.

Research Highlights: Daily airborne Cupressaceae pollen disruption ranged from 20 to 90%; relative humidity (RH), rainfall and atmospheric pressure (AtP) were the major meteorological determinants of this phenomenon. Background and Objectives: Cupressaceae family includes several species that are widely used as ornamental plants pollinating in late winter-early spring and might be responsible for allergic outbreaks. Cupressaceae pollen disruption may favour allergen dissemination, potentiating its allergenicity. The aim of this work was to characterize the Cupressaceae pollen aerobiology in Évora, South of Portugal, in 2017 and 2018, particularly the pollen disruption, and to identify the meteorological parameters contributing to this phenomenon. Materials and Methods: Pollen was collected using a Hirst type 7-day pollen trap and was identified following the standard methodology. Temperature, RH, rainfall, global solar radiation (Global Srad), AtP, wind speed and direction were obtained from a weather station installed side-by-side to the Hirst platform. Back trajectories (12-h) of air masses arriving at Évora were calculated using the HYSPLIT model. Results: Cupressaceae pollen index was higher in 2017 compared to 2018 (>5994 and 3175 pollen/m³, respectively) and 36 ± 19% (2017) and 64 ± 17% (2018) of the pollen was disrupted. Higher levels of disrupted pollen coincided with RH > 60% and rainfall. Temperature, Global Srad and AtP correlated negatively with pollen disruption. Wind speed and wind direction did not significantly correlate with pollen disruption. Intra-diurnal pollen pattern peaked between 9:00 am–2:00 pm, suggesting local origin, confirmed by the back trajectory analysis. Intra-diurnal pollen disruption profile followed hourly pollen pattern and it negatively correlated with AtP, temperature and Global Srad but was uncorrelated with RH. Conclusions: The results suggest that RH, rainfall and AtP are the main factors affecting airborne Cupressaceae pollen integrity and in conjunction with daily pollen concentration may be used to predict the risk of allergy outbreaks to this pollen type.

The olive tree is one of the most common type of cultivation in the Mediterranean area, having high economic and social importance. The Alentejo region, Portugal, is an area with a high presence of olive groves, which in 2022 accounted for 201,474 hectares. The aim of this study was to assess the relationship between olive pollen, weather data, and olive tree production, between the years 2002 and 2022. Pollen data were obtained from an urban station located in Évora, in the Alentejo region, and were used to calculate several metrics, such as the Pollen Season Duration (PSD), Seasonal Pollen Index (SPIn), peak value, and weekly pollen accumulation values. Monthly minimum, maximum, and mean temperature and precipitation sums were obtained from the E-OBS observational dataset. Considering the relationship between pollen/weather and olive production, mutual information and correlation analyses were conducted. Subsequently, several machine learning algorithms were trained using pollen and weather datasets, and we obtained suitable forecast models for olive tree production after cross-validation. The results showed high variability in pollen concentrations in Évora over the years. Complex associations were found, with certain weeks of pollen accumulation showing significant mutual information with olive production, particularly during June. The analyzed linear correlation coefficients remained generally low, underscoring the challenge of predicting olive production based on linear relationships. Among the machine learning algorithms employed to predict olive production, Decision Trees, Extreme Gradient Boosting, and Gradient Boosting Regressor were the most robust performers (r² > 0.70), while linear models displayed a subpar performance (r² < 0.5), emphasizing the complexity of this approach. These models highlight the roles of maximum and minimum temperatures during March and May and pollen accumulation during the second half of June. The developed models may be used as decision-support tools by growers and stakeholders to further enhance the sustainability of the thriving olive sector in southern Portugal.

Prunus lusitanica L. is a paleotropical relic species with an Ibero-Maghrebian distribution, which is presently considered as an endangered species, recognized by the Natura 2000 Network (92/43/EEC) as a priority habitat for conservation in Europe. The mountains in the Portuguese mainland central region offer the best location for this species to occur. The main objective of this study is to measure the current conservation status of the communities of P. lusitanica, through the collection of field data, such as the number of existing individuals of each population and their location, which will then be comparatively analyzed based on the previous literature, published at least 15 years ago. Soil characterization analysis was carried out and the main threats to conservation were identified. As a result, a decline of approximately 40% was observed in the number of individuals and in the quality of their habitat. The main threats to their conservation were found to be the seasonal occurrence of rural fires and the expansion of invasive species, such as Acacia dealbata Link and Ailanthus altissima (Mill.) Swingle. Finally, we present the main management methodologies that should be considered for the valorization of this important vegetational relic in the central region of mainland Portugal.

Mediterranean forests dominated by Quercus species are of great ecological and economic value. The Quercus pollen season, peaking in April, varies in concentration due to geographical and climatic factors and has a remarkable allergenic potential. This study investigates Quercus trends in the Alentejo region of Portugal and examines the influence of meteorological parameters on DPC, PSD and SPIn, as well as the impact on allergic respiratory disease. The results show a progressive increase in Quercus Forest area from 1995 to 2018. Temperature and Precipitation are a key factor influencing pollen concentration, especially before peak of pollen season and prior to the pollen season. Particularly prior to the season, the precipitation of t-6 before influence, significantly, the pollen production. On the other hand, Global Srad and RH determine the beginning of the season. Using quartile-based categorization and multivariate statistical analysis, we identified years and scenarios within the IPCC projections where meteorological conditions influence may SPIn production. The study found a statistically significant correlation between high Quercus pollen concentrations in April and increased antihistamine sales. These findings are crucial for enhancing pollen forecast models and early warning systems.