Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree.

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan. The first model is based on linear regression and the second one is based on phenological modelling. We tested our models on an original and comprehensive dataset from Islamabad. The mean absolute errors (MAEs) for the start day are 2.3 and 3.7 days for the linear and phenological models, respectively, while for the peak day, the MAEs are 3.3 and 4.0 days, respectively. These encouraging results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season. Timely action could reduce the burden of symptoms, mitigate the risk of acute attacks and potentially prevent deaths due to acute pollen-induced allergy.

Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps.

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.

Drivers of the release of the allergens Bet v 1 and Phl p 5 from birch and grass pollen.

The drivers affecting the Pollen Allergen Potency (PAP, amount of allergen released per pollen) are sparsely known. Betula and Poaceae airborne pollen are the two main allergenic pollen in the World. Airborne pollen and their allergens Bet v 1 and Phl p 5 were simultaneously measured from 2010 to 2015 in Davos (Switzerland) and Munich (Germany) by using volumetric traps and ChemVol cascade impactors. Daily variations in PAP were analysed in PM>10 and PM2.5-10 air fractions and generalized additive models were created to explain which factors determine PAP, including meteorological parameters and inorganic pollutants. 87.1 ± 13.9% of Bet v 1 and 88.8 ± 15.5% of Phl p 5 was detected in the fraction PM>10 where most pollen grains were collected. Significantly higher PAP for grasses (3.5 ± 1.9 pg Phl p 5/pollen grain) were observed in Munich than in Davos (2.4 ± 1.5 pg/pollen grain, p < 0.001), but not for Betula (2.5 ± 1.6 pg Bet v 1/pollen grain in Munich and 2.3 ± 1.7 in Davos, N.S.). PAP varied between days, years and location, and increased along the pollen season for Poaceae, but remaining constant for Betula. Free allergens (allergens observed in the fraction with limited pollen, PM2.5- 10) were recorded mostly at the beginning or at the end of the pollen season, being linked to higher humidity and rainy days. Also, PAP was higher when the airborne pollen concentrations increased rapidly after one day of low/moderate levels. Our findings show that pollen exposure explains allergen exposure only to a limited extend, and that day in the season, geographic location and some weather conditions need to be considered also to explain symptoms of allergic individuals.

How to select the optimal monitoring locations for an aerobiological network: A case of study in central northwest of Spain.

Airborne pollen concentration varies depending on several factors, such as local plant biodiversity, geography and climatology. These particles are involved in triggering pollinosis in a share of worldwide human population, and adequate monitoring is, therefore, important. However, the pollen traps in aerobiological monitoring networks are usually installed in cities, and the features of the whole territory are not taken into account. The aim of this study was to analyze what environmental parameters are more suitable as regards setting up monitoring stations throughout a territory in order to obtain an aerobiological network that can represent environmental diversity. The analysis was carried out in 13 locations in Castilla y León over an 8 year period. This is a favorable territory in which to conduct this type of study owing to its climatic features, orography and biodiversity. The ten most abundant pollen types in the region were analyzed, and a clustering analysis was calculated with different distances so as to obtain homogeneous groups of stations. Moreover, the clusters obtained were analyzed in combination with altitudinal and different bioclimatic parameters, which derived from temperature and precipitation. The result here shows that the Castilla y León aerobiological network RACYL represents most of the environmental variability of the territory. Furthermore, it can be divided into two clusters and five sub-clusters for which the start of the main pollen season is different. This corresponds with the division of the territory as regards bioclimatic conditions. The most important bioclimatic parameters were the seasonality of the precipitation and the maximum temperature of the warmest month, although orography must also be taken into account. All of these help discover the optimal places in which to install traps and could reduce the number of monitoring stations. This study additionally provides data for unmonitored areas with similar bioclimatic conditions to those monitored.

Effects of future climate change on birch abundance and their pollen load.

Climate change impacts on the structure and function of ecosystems will worsen public health issues like allergic diseases. Birch trees (Betula spp.) are important sources of aeroallergens in Central and Northern Europe. Birches are vulnerable to climate change as these trees are sensitive to increased temperatures and summer droughts. This study aims to examine the effect of climate change on airborne birch pollen concentrations in Central Europe using Bavaria in Southern Germany as a case study. Pollen data from 28 monitoring stations in Bavaria were used in this study, with time series of up 30 years long. An integrative approach was used to model airborne birch pollen concentrations taking into account drivers influencing birch tree abundance and birch pollen production and projections made according to different climate change and socioeconomic scenarios. Birch tree abundance is projected to decrease in parts of Bavaria at different rates, depending on the climate scenario, particularly in current centres of the species distribution. Climate change is expected to result in initial increases in pollen load but, due to the reduction in birch trees, the amount of airborne birch pollen will decrease at lower altitudes. Conversely, higher altitude areas will experience expansions in birch tree distribution and subsequent increases in airborne birch pollen in the future. Even considering restrictions for migration rates, increases in pollen load are likely in Southwestern areas, where positive trends have already been detected during the last three decades. Integrating models for the distribution and abundance of pollen sources and the drivers that control birch pollen production allowed us to model airborne birch pollen concentrations in the future. The magnitude of changes depends on location and climate change scenario.

An operational robotic pollen monitoring network based on automatic image recognition.

There is high demand for online, real-time and high-quality pollen data. To the moment pollen monitoring has been done manually by highly specialized experts. Here we evaluate the electronic Pollen Information Network (ePIN) comprising 8 automatic BAA500 pollen monitors in Bavaria, Germany. Automatic BAA500 and manual Hirst-type pollen traps were run simultaneously at the same locations for one pollen season. Classifications by BAA500 were checked by experts in pollen identification, which is traditionally considered to be the "gold standard" for pollen monitoring. BAA500 had a multiclass accuracy of over 90%. Correct identification of any individual pollen taxa was always >85%, except for Populus (73%) and Alnus (64%). The BAA500 was more precise than the manual method, with less discrepancies between determinations by pairs of automatic pollen monitors than between pairs of humans. The BAA500 was online for 97% of the time. There was a significant correlation of 0.84 between airborne pollen concentrations from the BAA500 and Hirst-type pollen traps. Due to the lack of calibration samples it is unknown which instrument gives the true concentration. The automatic BAA500 network delivered pollen data rapidly (3 h delay with real-time), reliably and online. We consider the ability to retrospectively check the accuracy of the reported classification essential for any automatic system.

Understanding hourly patterns of Olea pollen concentrations as tool for the environmental impact assessment.

Bioinformatics clustering application for mining of a large set of olive pollen aerobiological data to describe the daily distribution of Olea pollen concentration. The study was performed with hourly pollen concentrations measured during 8 years (2011-2018) in Extremadura (Spain). Olea pollen season by quartiles of the pollen integral in preseason (Q1: 0%-25%), in-season (Q2 and Q3: 25%-75%) and postseason (Q4: 75%-100%). Days with pollen concentrations above 100 grains/m3 were clustered according to the daily distribution of the concentrations. The factors affecting the prevalence of the different clusters were analyzed: distance to olive groves and the moment during the pollen season and the meteorology. During the season, the highest hourly concentrations during the day where between 12:00 and 14:00, while during the preseason the highest hourly concentrations were detected in the afternoon and evening hours. In the postseason the pollen concentrations were more homogeneously distributed during 9-16 h. The representation shows a well-defined hourly pattern during the season, but a more heterogeneous distribution during the preseason and postseason. The cluster dendrogram shows that all the days could be clustered in 6 groups: most of the clusters shows the daily peaks between 11:00 and 15:00 with a smooth curve (Cluster 1 and 3) or with a strong peak (2 and 5). Days included in cluster 9 shows an earlier peak in the morning (before 9:00). On the other hand, cluster 6 shows a peak in the afternoon, after 15:00. Hourly concentrations show a sharper pattern during the season, with the peak during the hours close to the emission. Out of the season, when pollen is expected to come from farther distances, the hourly peak is located later from the emission time of the trees. Significant factors for predicting the hourly pattern were wind speed and direction and the distance to the olive groves.

Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations.

Airborne pollen is a recognized biological indicator and its monitoring has multiple uses such as providing a tool for allergy diagnosis and prevention. There is a knowledge gap related to the distribution of pollen traps needed to achieve representative biomonitoring in a region. The aim of this manuscript is to suggest a method for setting up a pollen network (monitoring method, monitoring conditions, number and location of samplers etc.). As a case study, we describe the distribution of pollen across Bavaria and the design of the Bavarian pollen monitoring network (ePIN), the first operational automatic pollen network worldwide. We established and ran a dense pollen monitoring network of 27 manual Hirst-type pollen traps across Bavaria, Germany, during 2015. Hierarchical cluster analysis of the data was then performed to select the locations for the sites of the final pollen monitoring network. According to our method, Bavaria can be clustered into three large pollen regions with eight zones. Within each zone, pollen diversity and distribution among different locations does not vary significantly. Based on the pollen zones, we opted to place one automatic monitoring station per zone resulting in the ePIN network, serving 13 million inhabitants. The described method defines stations representative for a homogeneous aeropalynologically region, which reduces redundancy within the network and subsequent costs (in the study case from 27 to 8 locations). Following this method, resources in pollen monitoring networks can be optimized and allergic citizens can then be informed in a timely and effective way, even in larger geographical areas.

Near-ground effect of height on pollen exposure.

The effect of height on pollen concentration is not well documented and little is known about the near-ground vertical profile of airborne pollen. This is important as most measuring stations are on roofs, but patient exposure is at ground level. Our study used a big data approach to estimate the near-ground vertical profile of pollen concentrations based on a global study of paired stations located at different heights. We analyzed paired sampling stations located at different heights between 1.5 and 50 m above ground level (AGL). This provided pollen data from 59 Hirst-type volumetric traps from 25 different areas, mainly in Europe, but also covering North America and Australia, resulting in about 2,000,000 daily pollen concentrations analyzed. The daily ratio of the amounts of pollen from different heights per location was used, and the values of the lower station were divided by the higher station. The lower station of paired traps recorded more pollen than the higher trap. However, while the effect of height on pollen concentration was clear, it was also limited (average ratio 1.3, range 0.7-2.2). The standard deviation of the pollen ratio was highly variable when the lower station was located close to the ground level (below 10 m AGL). We show that pollen concentrations measured at >10 m are representative for background near-ground levels.

Artemisia pollen is the main vector for airborne endotoxin.

BACKGROUND: Endotoxin (LPS) released from gram-negative bacteria causes strong immunologic and inflammatory effects and, when airborne, can contribute to respiratory conditions, such as allergic asthma. OBJECTIVES: We sought to identify the source of airborne endotoxin and the effect of this endotoxin on allergic sensitization. METHODS: We determined LPS levels in outdoor air on a daily basis for 4 consecutive years in Munich (Germany) and Davos (Switzerland). Air was sampled as particulate matter (PM) greater than 10 µm (PM > 10) and PM between 2.5 and 10 µm. LPS levels were determined by using the recombinant Factor C assay. RESULTS: More than 60% of the annual endotoxin exposure was detected in the PM > 10 fraction, showing that bacteria do not aerosolize as independent units or aggregates but adhered to large particles. In Munich 70% of annual exposure was detected between June 12th and August 28th. Multivariate modeling showed that endotoxin levels could be explained by phenological parameters (ie, plant growth). Indeed, days with high airborne endotoxin levels correlated well with the amount of Artemisia pollen in the air. Pollen collected from plants across Europe (100 locations) showed that the highest levels of endotoxin were detected on Artemisia vulgaris (mugwort) pollen, with little on other pollen. Microbiome analysis showed that LPS concentrations on mugwort pollen were related to the presence of Pseudomonas species and Pantoea species communities. In a mouse model of allergic disease, the presence of LPS on mugwort pollen was needed for allergic sensitization. CONCLUSIONS: The majority of airborne endotoxin stems from bacteria dispersed with pollen of only one plant: mugwort. This LPS was essential for inducing inflammation of the lung and allergic sensitization.

Impact of land cover changes and climate on the main airborne pollen types in Southern Spain.

Airborne pollen concentrations strongly correlate with flowering intensity of wind-pollinated species growing at and around monitoring sites. The pollen spectrum, and the variations in its composition and concentrations, is influenced by climatic features and by available nutritional resources but it is also determined by land use and its changes. The first factor influence is well known on aerobiological researches but the impact of land cover changes has been scarcely studied until now. This paper reports on a study carried out in Southern Spain (Córdoba city) examining airborne pollen trends over a 15-year period and it explores the possible links both to changes in land use and to climate variations. The Seasonal-Trend Decomposition procedure based on Loess (STL) which decomposes long-term data series into smaller seasonal component patterns was applied. Trends were compared with recorded changes in land use at varying distances from the city in order to determine their possible influence on pollen-count variations. The influence of climate-related factors was determined by means of non-parametric correlation analysis. The STL method proved highly effective for extracting trend components from pollen time series, because their features vary widely and can change quickly in a short term. Results revealed mixed trends depending on the taxa and reflecting fluctuations in land cover and/or climate. A significant rising trend in Olea pollen counts was observed, attributable both to the increasing olive-growing area but also to changes in temperature and rainfall. Poaceae pollen concentrations also increased, due largely to an expansion of heterogeneous agricultural areas and to an increase in pollen season length positively influenced by rainfall and temperature. By contrast, the significant declining trend observed for pollen from ruderal taxa, such as Amaranthaceae, Rumex, Plantago and Urticaceae, may be linked to changes in urban planning strategies with a higher building pressure.

Phenological changes in olive (Ola europaea L.) reproductive cycle in southern Spain due to climate change.

INTRODUCTION: Modifications of crop species phenology due to a changing environment are of interest because of their impact on fruit set and final harvest. Pre-flowering and flowering phenophases in olive groves at different sites of southern Spain were examined, in order to chart potential trends and determine major correlations with weather-related parameters, especially temperature and water availability. The high prevalence of olive pollen allergy in the Mediterranean population makes this study highly relevant. MATERIALS AND METHODS: Ten sites in Cordoba province (Spain) during a 17-year period (1996-2012). BBCH phenology scale. Meteorological data from 1960 were analyzed; data from 1996 included on modeling analysis. Linear Mixed Models (LMMs) were developed, combining phenological and meteorological data. RESULTS: Since 1960, local spring temperatures have increased 1.5ºC, the number of spring rainfall days has fallen 11 days, total rainfall has declined 150 mm. Despite phenological differences between sites, attributable to altitude, phenological development during the season followed a similar pattern. Flowering dates advanced 2 days, while inflorescence emergence was delayed 24 days. Trend slopes revealed differences, an earlier period (1996-2002) with a sharp flowering advance of 15 days, and a later period (2003-2012) characterized by a gradual advance and a high bud emergence delay of 22 days. CONCLUSIONS: LMMs was revealed as an appropriate technique for phenology behaviour analysis displaying both fixed and random interactions. Cultivars grown in the study province are adapted to climate with a synchronized response, although climate change is affecting theolive reproductive cycle in southern Spain; therefore, the timing of pollen release, with subsequent consequences on allergic population as phenological changes, could have impacts on flowering period and pollen production. Further investigation is required of the implications for crop production in Mediterranean ecosystems.

Google trends, una herramienta útil para detectar la presencia de polen atmosférico / Google trends, useful tool in airborne pollen detection

La polinosis es una de las alergias más comunes en el mundo, condicionando de manera directa la calidad de vida de la población, siendo la polinosis al olivo una de las más frecuentes en zonas de clima Mediterráneo. La previsión de los niveles de polen en la atmósfera se ha convertido en un objetivo fundamental para alergólogos y aerobiólogos, tratando de prevenir los síntomas de la polinosis. Google trends es una herramienta desarrollada por Google, que nos facilita información acerca de la dinámica de búsquedas de los usuarios a través de internet. Como objetivo principal de este trabajo se pretende realizar un valoración preliminar de la utilidad de esta herramienta para detectar la presencia de polen en la atmósfera y para explorar el impacto de polinosis entre la población.Para alcanzar dichos objetivos se han empleado datos aerobiológicos sobre concentración de polen de olivo en la atmósfera de Córdoba (España) y datos sobre tendencias de búsqueda en el buscador Google de las palabras ôAlergiaõ, ôPolenõ y ôOlivoõ. Se ha observado una elevada correlación y relación geográfica entre la tendencia de búsquedas de palabras relacionadas con polinosis y las concentraciones reales de polen atmosférico. Esta herramienta posee una gran potencial en la lucha contra la polinosis, ya que permite conocer información instantánea acerca de los síntomas en la problación, a diferencia de los métodos tradicionales que presentan un desfase de la información. De este modo, el presente trabajo ofrece evidencias acerca del potencial de Google trends para detectar la presencia de aeroalérgenos y para estudiar el impacto de la polinosis...

Pollinosis is one of the most extended allergies in the world, determining directly the quality of life of the population. Airborne pollen forecasting has become a key objective for allergists and aerobiologists trying to prevent the symptoms of hay fever. Google trends is a tool developed by Google, which provides information about the dynamics of user searches through internet. The main aim of this work is to conduct a preliminary assessment of the usefulness of this tool to detect the presence of large concentrations of pollen in the atmosphere and to explore the impact of pollinosis among the population.To achieve these objectives, the analysis is based on data for olive pollen concentrations in the atmosphere of Cordoba (Spain) and search counts in Google of the words "Allergy", "Pollen" and "Olive". We observed a high correlation and a high geographic relationship between the number of searches for words related to pollen allergy, and the actual concentrations of atmospheric pollen. This tool possesses a great potential in the fight against allergy because it can provide instant and on-line information about symptoms in population, unlike traditional methods that have a mismatch of information. Thus, the present study provides evidence about the potential of Google trends to detect the presence of aeroallergens and to study the impact of pollinosis...

Automatic and Online Pollen Monitoring.

BACKGROUND: Pollen are monitored in Europe by a network of about 400 pollen traps, all operated manually. To date, automated pollen monitoring has only been feasible in areas with limited variability in pollen species. There is a need for rapid reporting of airborne pollen as well as for alleviating the workload of manual operation. We report our experience with a fully automated, image recognition-based pollen monitoring system, BAA500. METHODS: The BAA500 sampled ambient air intermittently with a 3-stage virtual impactor at 60 m3/h in Munich, Germany. Pollen is deposited on a sticky surface that was regularly moved to a microscope equipped with a CCD camera. Images of the pollen were constructed and compared with a library of known samples. A Hirst-type pollen trap was operated simultaneously. RESULTS: Over 480,000 particles sampled with the BAA500 were both manually and automatically identified, of which about 46,000 were pollen. Of the automatically reported pollen, 93.3% were correctly recognized. However, compared with manual identification, 27.8% of the captured pollen were missing in the automatic report, with most reported as unknown pollen. Salix pollen grains were not identified satisfactorily. The daily pollen concentrations reported by a Hirst-type pollen trap and the BAA500 were highly correlated (r = 0.98). CONCLUSIONS: The BAA500 is a functional automated pollen counter. Its software can be upgraded, and so we expected its performance to improve upon training. Automated pollen counting has great potential for workload reduction and rapid online pollen reporting.

Design of a downscaling method to estimate continuous data from discrete pollen monitoring in Tunisia.

The study of microorganisms and biological particulate matter that transport passively through air is very important for an understanding of the real quality of air. Such monitoring is essential in several specific areas, such as public health, allergy studies, agronomy, indoor and outdoor conservation, and climate-change impact studies. Choosing the suitable monitoring method is an important step in aerobiological studies, so as to obtain reliable airborne data. In this study, we compare olive pollen data from two of the main air traps used in aerobiology, the Hirst and Cour air samplers, at three Tunisian sampling points, for 2009 to 2011. Moreover, a downscaling method to perform daily Cour air sampler data estimates is designed. While Hirst air samplers can offer daily, and even bi-hourly data, Cour air samplers provide data for longer discrete sampling periods, which limits their usefulness for daily monitoring. Higher quantities of olive pollen capture were generally detected for the Hirst air sampler, and a downscaling method that is developed in this study is used to model these differences. The effectiveness of this downscaling method is demonstrated, which allows the potential use of Cour air sampler data series. These results improve the information that new Cour data and, importantly, historical Cour databases can provide for the understanding of phenological dates, airborne pollination curves, and allergenicity levels of air.