Spatial and Temporal Variations in the Annual Pollen Index Recorded by Sites Belonging to the Portuguese Aerobiology Network

This study presents the findings of a 10-year survey carried out by the Portuguese Aerobiology Network (RPA) at seven pollen-monitoring stations: five mainland stations (Oporto, Coimbra, Lisbon, Évora and Portimão) and two insular stations [Funchal (Madeira archipelago) and Ponta Delgada (Azores archipelago)]. The main aim of the study was to examine spatial and temporal variations in the Annual Pollen Index (API) with particular focus on the most frequently recorded pollen types. Pollen monitoring (2003–2012) was carried out using Hirst-type volumetric spore traps, following the minimum recommendations proposed by the European Aerobiology Society Working Group on Quality Control. Daily pollen data were examined for similarities using the Kruskal–Wallis nonparametric test and multivariate regression trees. Simple linear regression analysis was used to describe trends in API. The airborne pollen spectrum at RPA stations is dominated by important allergenic pollen types such as Poaceae, Olea and Urticaceae. Statistically significant differences were witnessed in the API recorded at the seven stations. Mean API is higher in the southern mainland cities, e.g. Évora, Lisbon and Portimão, and lower in insular and littoral cities. There were also a number of significant trends in API during the 10-year study. This report identifies spatial and temporal variations in the amount of airborne pollen recorded annually in the Portuguese territory. There were also a number of significant changes in API, but no general increases in the amount of airborne pollen

Main features of Poaceae pollen season in Madeira region (Portugal)

The pollinic spectrum of the Madeira region is dominated by grass pollen, which also represents an important aeroallergen in Europe. The present work aims to analyze the main features of the Poaceae pollen season in the Madeira region to determine the allergic risk. The study took place in Funchal city, the capital of Madeira Island, over a period of 10 years (2003–2012). The airborne pollen monitoring was carried out with a Hirst type volumetric trap, following well-established guidelines. In the atmosphere of Funchal, the mean annual Poaceae pollen index was 229. The mean Poaceae pollen season lasts 275 days, with an onset date in January/March and an end date in November/December. Poaceae counts showed a seasonal variation with 2 distinct peaks: a higher peak between March and June, and the second one in autumn. The peak values occurred mainly between April and June, and the highest peak was 93 grains/m3 , detected on the 27th May of 2010. The Poaceae pollen remaining at low levels during the whole growing season, presenting a nil to low allergenic risk during most of the study period. Higher critical levels of allergens have been revealed after 2006. In general, the pollen risk from Poaceae lasted only a few days per year, despite the very long pollen season and the abundance of grasses in the landscape of Madeira Island.info:eu-repo/semantics/publishedVersio

Recent developments in monitoring and modelling airborne pollen, a review

Public awareness of the rising importance of allergies and other respiratory diseases has led to increased scientific effort to accurately and rapidly monitor and predict pollen, fungal spores and other bioaerosols in our atmosphere. An important driving force for the increased social and scientific concern is the realisation that climate change will increasingly have an impact on worldwide bioaerosol distributions and subsequent human health. In this review we examine new developments in monitoring of atmospheric pollen as well as observation and source-orientated modelling techniques. The results of a Scopus® search for scientific publications conducted with the terms ‘Pollen allergy’ and ‘Pollen forecast’ included in the title, abstract or keywords show that the number of such articles published has increased year on year. The 12 most important allergenic pollen taxa in Europe as defined by COST Action ES0603 were ranked in terms of the most ‘popular’ for model-based forecasting and for forecasting method used. Betula, Poaceae and Ambrosia are the most forecast taxa. Traditional regression and phenological models (including temperature sum and chilling models) are the most used modelling methods, but it is notable that there are a large number of new modelling techniques being explored. In particular, it appears that Machine Learning techniques have become more popular and led to better results than more traditional observation-orientated models such as regression and time-series analyses

Monitoring Pollen Counts and Pollen Allergy Index Using Satellite Observations in East Coast of the United States

Allergic diseases have become increasingly common over the world during the last four decades, and they are affecting millions of people. Pollination is an important process in the life cycle of plants. However, pollen exposure is associated with allergic diseases such as asthma and seasonal allergic rhinitis (hay fever). As a result, the total annual expenditure for asthma-associated morbidity is about $56 billion in the United States, and the overall cost of allergic diseases is over$18 billion annually. For allergic rhinitis, the annual medical cost is approximately $3.4 billion. The intensity and frequency of the pollen exposures can be easily affected by many factors such as climate, vegetation, and topography, which are difficult to predict in large scales. Vegetation is very important as a pollen source, and the amount and time of pollinations depend on the flowering and growth of plants. With optimal water and temperature, vegetation can reach a maximum growth and flowering during a growing season, which means that maximum amount of pollen can be released from the plants. However, if the requirements of water and temperature cannot be met in the specific times within the growing season, pollen dispersal will be affected negatively. It is an urgent need to develop models or systems for predicting pollen events at large scales and providing early warning to prevent pollen effects on people. Unlike manual pollen counting at local sites, remote sensing facilitates the pollen estimates at large scales with temporally and spatially distributed observations, which significantly reduces the time and labor costs. With remotely sensed observations, Artificial Neural Network (ANN) helps us fill the gaps in understanding of the relationships between environmental variables and pollen concentration. At this point, I investigated pollen estimates from satellite observations in the states of East Coast United States with short and long-term data. This region is highly populated with a population of 104 million. In addition, this region has a great variety of temperature, precipitation, and vegetation. The final goal of this project is to investigate the relationships between satellite-derived variables (precipitation, land surface temperature (LST), and enhance vegetation index (EVI2)) and pollen count and further to generate a model for the prediction of pollen counts at high temporal and spatial resolutions. For this purpose, to predict pollen concentration using environmental variables, a Neural Network Analysis was performed. The results showed that strong correlations existed between pollen counts and environmental variables, except for precipitation in most locations. The validation analysis using regression models revealed strongly significant relationships between the observed and predicted pollen concentrations obtained for short and long-term data. The R squares (R2) for long term pollen counts were mostly higher than 0.5, ranging from 0.5542 for Olean, NY to 0.8589 for Savannah, GA. For short term predictions of pollen allergy index, R2 ranged from 0.53 to 0.966 except for a few sites, especially in southern Florida. The pollen distribution was mostly affected by precipitation in the southern part, whereas it was influenced by temperature in the northern part. Moreover, results demonstrated that ANN is a suitable tool for complicated statistical analysis and EVI2 combining with LST and precipitation is a reliable predictor of pollen variation. Overall the results provide a better understanding of pollen variation with vegetation seasonality and climate variables, which could assist an approach towards the establishment of an early warning system for allergy patients

Alnus airborne pollen trends during the last 26 years for improving machine learning-based forecasting methods

Black alder (Alnus glutinosa (L.) Gaertn.) is a species of tree widespread along Europe and belongs to mixed hardwood forests. In urban environments, the tree is usually located along watercourses, as is the case in the city of Ourense. This taxon belongs to the betulaceae family, so it has a high allergenic potential in sensitive people. Due to the high allergenic capacity of this pollen type and the increase in global temperature produced by climate change, which induces a greater allergenicity, the present study proposes the implementation of a Machine Learning (ML) model capable of accurately predicting high-risk periods for allergies among sensitive people. The study was carried out in the city of Ourense for 28 years and pollen data were collected by means of the Hirst trap model Lanzoni VPPS-2000. During the same period, meteorological data were obtained from the meteorological station of METEOGALICIA in Ourense. We observed that Alnus airborne pollen was present in the study area during winter months, mainly in January and February. We found statistically significant trends for the end of the main pollen season with a lag trend of 0.68 days per year, and an increase in the annual pollen integral of 112 pollen grains per year and approximately 12 pollen grains/m3 per year during the pollen peak. A Spearman correlation test was carried out in order to select the variables for the ML model. The best ML model was Random Forest, which was able to detect those days with medium and high labels.Xunta de Galicia | Ref. ED431C 2022/03-GRCXunta de Galicia | Ref. CO-0034-2021 00V

Madeira-a tourist destination for asthma sufferers

Madeira Island is a famous tourist destination due to its natural and climatic values. Taking into account optimal weather conditions, flora richness and access to various substrates facilitating fungal growth, we hypothesised a very high risk of elevated fungal spore and pollen grain concentrations in the air of Funchal, the capital of Madeira. Concentration levels of the most allergenic taxa were measured from 2003 to 2009, using a 7-day volumetric air sampler, followed by microscopy analysis. Dependence of bioaerosols on the weather conditions and land use were assessed using spatial and statistical tools. Obtained results were re-visited by a comparison with hospital admission data recorded at the Dr. Nélio Mendonça Hospital in Funchal. Our results showed that despite propitious climatic conditions, overall pollen grain and fungal spore concentrations in the air were very low and did not exceed any clinically established threshold values. Pollen and spore peak concentrations also did not match with asthma outbreaks in the winter. Identification of places that are "free" from biological air pollution over the summer, such as Madeira Island, is very important from the allergic point of view.info:eu-repo/semantics/publishedVersio