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Airborne pollen is important in relation to the social issues of pollinosis and of the environmental effects of genetically modified plants. Existing methods for pollen counting involve counting and classifying the grains that adhere to a sampling surface, requiring much time and skilled labor. We therefore have developed a method of automatically monitoring pollen, using a laser-optics instrument. In this instrument, the sideways and forward scattering of laser light by each particle is recorded in real time for computer processing. A field experiment was conducted in 2005, comparing our method with that of the older Hirst method. A scatter plot was made of the forward scattering vs. the sideways scattering for each particle. An algorithm was developed to find the optimum rectangular region of the plot for each type of pollen, and a count of points inside this region was taken as the count for that type of pollen. For the three most common types of pollen found in the field test (Urticaceae, Poaceae, and Ambrosia), the daily counts from this algorithm were compared with the daily counts from the Hirst-type (Burkard) sampler. There was a very high correlation (determination coefficient approximately 0.8) between the results of the two methods. [Copyright &y& Elsevier]

The EAACI Guidelines on the impact of short‐term exposure to outdoor pollutants on asthma‐related outcomes provide recommendations for prevention, patient care and mitigation in a framework supporting rational decisions for healthcare professionals and patients to individualize and improve asthma management and for policymakers and regulators as an evidence‐informed reference to help setting legally binding standards and goals for outdoor air quality at international, national and local levels. The Guideline was developed using the GRADE approach and evaluated outdoor pollutants referenced in the current Air Quality Guideline of the World Health Organization as single or mixed pollutants and outdoor pesticides. Short‐term exposure to all pollutants evaluated increases the risk of asthma‐related adverse outcomes, especially hospital admissions and emergency department visits (moderate certainty of evidence at specific lag days). There is limited evidence for the impact of traffic‐related air pollution and outdoor pesticides exposure as well as for the interventions to reduce emissions. Due to the quality of evidence, conditional recommendations were formulated for all pollutants and for the interventions reducing outdoor air pollution. Asthma management counselled by the current EAACI guidelines can improve asthma‐related outcomes but global measures for clean air are needed to achieve significant impact. [ABSTRACT FROM AUTHOR]

Over the past few years, a diverse range of automatic real-time instruments has been developed to respond to the needs of end users in terms of information about atmospheric bioaerosols. One of them, the SwisensPoleno Jupiter, is an airflow cytometer used for operational automatic bioaerosol monitoring. The instrument records holographic images and fluorescence information for single aerosol particles, which can be used for identification of several aerosol types, in particular different pollen taxa. To improve the pollen identification algorithm applied to the SwisensPoleno Jupiter and currently based only on the holography data, we explore the impact of merging fluorescence spectra measurements with holographic images. We demonstrate, using measurements of aerosolised pollen, that combining information from these two sources results in a considerable improvement in the classification performance compared to using only a single source (balanced accuracy of 0.992 vs. 0.968 and 0.878). This increase in performance can be ascribed to the fact that often classes which are difficult to resolve using holography alone can be well identified using fluorescence and vice versa. We also present a detailed statistical analysis of the features of the pollen grains that are measured and provide a robust, physically based insight into the algorithm's identification process. The results are expected to have a direct impact on operational pollen identification models, particularly improving the recognition of taxa responsible for respiratory allergies. [ABSTRACT FROM AUTHOR]

Over the past few years, a diverse range of automatic real-time instruments has been developed to respond to the needs of end users in terms of information about atmospheric bioaerosols. One of them, the SwisensPoleno Jupiter, is an airflow cytometer used for operational automatic bioaerosol monitoring. The instrument records holographic images and fluorescence information for single aerosol particles, which can be used for identification of several aerosol types, in particular different pollen taxa. To improve the pollen identification algorithm applied to the SwisensPoleno Jupiter and currently based only on the holography data, we explore the impact of merging fluorescence spectra measurements with holographic images. We demonstrate that combining information from these two sources results in a considerable improvement in the classification performance compared to using only a single source (balanced accuracy of 0.992 vs. 0.968 and 0.878). This increase in performance can be ascribed to the fact that often classes which are difficult to resolve using holography alone can be well identified using fluorescence and vice versa. We also present a detailed statistical analysis of the features of the pollen grains that are measured and provide a robust, physically-based insight into the algorithm's identification process. The results are expected to have a direct impact on operational pollen identification models, particularly improving the recognition of taxa responsible for respiratory allergies. [ABSTRACT FROM AUTHOR]

Limited number of studies have focused on the impact of pollen exposure on asthma. As a part of the EAACI Guidelines on Environment Science, this first systematic review on the relationship of pollen exposure to asthma exacerbations aimed to bridge this knowledge gap in view of implementing recommendations of prevention. We searched electronic iPubMed, Embase, and Web of Science databases using a set of MeSH terms and related synonyms and identified 73 eligible studies that were included for systemic review. When possible, meta‐analyses were conducted. Overall meta‐analysis suggests that outdoor pollen exposure may have an effect on asthma exacerbation, but caution is needed due to the low number of studies and their heterogeneity. The strongest associations were found between asthma attacks, asthma‐related ED admissions or hospitalizations, and an increase in grass pollen concentration in the previous 2‐day overall in children aged less than 18 years of age. Tree pollen may increase asthma‐related ED visits or admissions lagged up to 7‐day overall in individuals younger than 18 years. Rare data show that among subjects under 18 years of age, an exposure to grass pollen lagged up to 3 days may lower lung function. Further research considering effect modifiers of pollen sensitization, hay fever, asthma, air pollution, green spaces, and pre‐existing medications is urgently warranted to better evaluate the impacts of pollen on asthma exacerbation. Preventive measures in relation to pollen exposure should be integrated in asthma control as pollen increase continues due to climate change. [ABSTRACT FROM AUTHOR]

Abstract Pollen sampling has been performed using various sampling methods around the world. However, the sampling efficiencies of these methods remain unclear. In this study, we compared the sampling efficiency of a Sigma-2 two-layered aerosol sampling inlet and a Hirst-style wind vane inlet to reveal the relationship between sampling efficiency and samplers' physical characteristics. The initial results show a higher sampling efficiency for the former. To further explore this finding, we developed a theory based on 1) continuity equations for pollen grain concentrations and 2) impaction theory. Applying our developed theory to the experimental results revealed that impaction is a significant contributor to the relatively higher sampling efficiency of the two-layered sampling inlet, due to the larger cross-sectional area of the two-layered inlet and slower sampled pollen grain speed within the sampler. Additionally, applying a correction based on the theoretical impaction effects improved the experimental results. Our experiment demonstrated the possibility of a pollen sampler showing discrepancies in airborne pollen concentrations and explained the mechanism of this inaccuracy. The application of these results to pollen sampling will aid understanding of actual airborne pollen concentration. Graphical abstract Image 1 Highlights • The sampling efficiencies of two types of inlets were studied. • Changes in wind speed causes changes in the observed airborne pollen concentration. • Larger pollen grains tend to be less efficiently sampled in the calm air. • The impaction might cause the inaccuracy in the evaluation of the sampling results. [ABSTRACT FROM AUTHOR]

The sooty bark disease (SBD) is an emerging disease affecting sycamore maple trees (Acer pseudoplatanus) in Europe. Cryptostroma corticale, the causal agent, putatively native to eastern North America, can be also pathogenic for humans causing pneumonitis. It was first detected in 1945 in Europe, with markedly increasing reports since 2000. Pathogen development appears to be linked to heat waves and drought episodes. Here, we analyse the conditions of the SBD emergence in Europe based on a three-decadal timeseries data set. We also assess the suitability of aerobiological samples using a species-specific quantitative PCR assay to inform the epidemiology of C. corticale, through a regional study in France comparing twoyear aerobiological and epidemiological data, and a continental study including 12 air samplers from six countries (Czechia, France, Italy, Portugal, Sweden and Switzerland). We found that an accumulated water deficit in spring and summer lower than -132 mm correlates with SBD outbreaks. Our results suggest that C. corticale is an efficient airborne pathogen which can disperse its conidia as far as 310 km from the site of the closest disease outbreak. Aerobiology of C. corticale followed the SBD distribution in Europe. Pathogen detection was high in countries within the host native area and with longer disease presence, such as France, Switzerland and Czech Republic, and sporadic in Italy, where the pathogen was reported just once. The pathogen was absent in samples from Portugal and Sweden, where the disease has not been reported yet. We conclude that aerobiological surveillance can inform the spatial distribution of the SBD, and contribute to early detection in pathogen-free countries. [ABSTRACT FROM AUTHOR]

This study presents the first reference calibrations of three commercially available bioaerosol detectors. The Droplet Measurement Technologies WIBS-NEO (new version of the wideband integrated bioaerosol spectrometer), Plair Rapid-E, and Swisens Poleno were compared with a primary standard for particle number concentrations at the Federal Institute for Metrology (METAS). Polystyrene (PSL) spheres were used to assess absolute particle counts for diameters from 0.5 to 10 µm. For the three devices, counting efficiency was found to be strongly dependent on particle size. The results confirm the expected detection range for which the instruments were designed. While the WIBS-NEO achieves its highest efficiency with smaller particles, e.g. 90 % for 0.9 µm diameter, the Plair Rapid-E performs best for larger particles, with an efficiency of 58 % for particles with a diameter of 10 µm. The Swisens Poleno is also designed for larger particles but operates well from 2 µm. However, the exact counting efficiency of the Poleno could not be evaluated as the cut-off diameter range of the integrated concentrator unit was not completely covered. In further experiments, three different types of fluorescent particles were tested to investigate the fluorescent detection capabilities of the Plair Rapid-E and the Swisens Poleno. Both instruments showed good agreement with the reference data. While the challenge to produce known concentrations of larger particles above 10 µm or even fresh pollen particles remains, the approach presented in this paper provides a potential standardised validation method that can be used to assess counting efficiency and fluorescence measurements of automatic bioaerosol monitoring devices. [ABSTRACT FROM AUTHOR]

This study presents the first reference calibrations of three commercially available bioaerosol detectors. The Droplet Measurement Technologies WIBS-NEO, Plair Rapid-E, and Swisens Poleno were compared with a primary standard for particle number concentrations at the Federal Institute for Metrology METAS. Polystyrene (PSL) spheres were used to assess absolute particle counts for diameters from 0.5 μm to 10 μm. For the three devices, counting efficiency was found to be strongly dependent on particle size. The results confirm the expected detection range for which the instruments were designed. While the WIBS-NEO achieves its highest efficiency at smaller particles, e.g. 90 % for 0.9 μm diameter, the Plair Rapid-E performs best for larger particles, with an efficiency of 58 % for particles with a diameter of 10 μm. The Swisens Poleno is also designed for larger particles, but operates well from 2 μm. However, the exact counting efficiency of the Poleno could not be evaluated as the cut-off diameter range of the integrated concentrator unit was not completely covered. In further experiments, three different types of fluorescent particles were tested to investigate the fluorescent detection capabilities of the Plair Rapid-E and the Swisens Poleno. Both instruments showed good agreement with the reference data. While the challenge to produce known concentrations of larger particles above 10 μm or even fresh pollen particles remain, the approach presented in this paper provides a potential standardised validation method that can be used to assess counting efficiency and fluorescence measurements of automatic bioaerosol monitoring devices. [ABSTRACT FROM AUTHOR]

Background: Information about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3–9 days from the measurement). Recent technological developments have made possible automatic pollen measurements, which are available at high temporal resolution and in real time, although currently only scattered in a few locations across Europe. Materials & Methods: To promote the development of an extensive network across Europe and to ensure that this network will respond to end user needs, a stakeholder workshop was organised under the auspices of the EUMETNET AutoPollen Programme. Participants discussed requirements for the groups they represented, ranging from the need for information at various spatial scales, at high temporal resolution, and for targeted services to be developed. Results: The provision of real‐time information is likely to lead to a notable decrease in the direct and indirect health costs associated with allergy in Europe, currently estimated between €50–150 billion/year.1 Discussion & Conclusion: A European measurement network to meet end user requirements would thus more than pay for itself in terms of potential annual savings and provide significant impetus to research across a range of disciplines from climate science and public health to agriculture and environmental management. [ABSTRACT FROM AUTHOR]

Detection of bioaerosols, or primary biological aerosol particles (PBAPs), has become increasingly important for a wide variety of research communities and scientific questions. In particular, real-time (RT) techniques for autonomous, online detection and characterization of PBAP properties in both outdoor and indoor environments are becoming more commonplace and have opened avenues of research. With advances in technology, however, come challenges to standardize practices so that results are both reliable and comparable across technologies and users. Here, we present a critical review of major RT instrument classes that have been applied to PBAP research, especially with respect to environmental science, allergy monitoring, agriculture, public health, and national security. Eight major classes of RT techniques are covered, including the following: (i) fluorescence spectroscopy, (ii) elastic scattering, microscopy, and holography, (iii) Raman spectroscopy, (iv) mass spectrometry, (v) breakdown spectroscopy, (vi) remote sensing, (vii) microfluidic techniques, and (viii) paired aqueous techniques. For each class of technology we present technical limitations, misconceptions, and pitfalls, and also summarize best practices for operation, analysis, and reporting. The final section of the article presents pressing scientific questions and grand challenges for RT sensing of PBAP as well as recommendations for future work to encourage high-quality results and increased cross-community collaboration. [ABSTRACT FROM AUTHOR]

We present the first validation of the Swisens Poleno, currently the only operational automatic pollen monitoring system based on digital holography. The device provides in-flight images of all coarse aerosols, and here we develop a two-step classification algorithm that uses these images to identify a range of pollen taxa. Deterministic criteria based on the shape of the particle are applied to initially distinguish between intact pollen grains and other coarse particulate matter. This first level of discrimination identifies pollen with an accuracy of 96 %. Thereafter, individual pollen taxa are recognized using supervised learning techniques. The algorithm is trained using data obtained by inserting known pollen types into the device, and out of eight pollen taxa six can be identified with an accuracy of above 90 %. In addition to the ability to correctly identify aerosols, an automatic pollen monitoring system needs to be able to correctly determine particle concentrations. To further verify the device, controlled chamber experiments using polystyrene latex beads were performed. This provided reference aerosols with traceable particle size and number concentrations in order to ensure particle size and sampling volume were correctly characterized. [ABSTRACT FROM AUTHOR]

We present the first validation of the Swisens Poleno, currently the only operational automatic pollen monitoring system based on digital holography. The device provides in-flight images of all coarse aerosols and here we develop a two-step classification algorithm that uses these images to identify a range of pollen taxa. Deterministic criteria based on the shape of the particle are applied to initially distinguish between intact pollen grains and other coarse particulate matter. This first level of discrimination identifies pollen with an accuracy of 96 %. Thereafter, individual pollen taxa are recognised using supervised learning techniques. The algorithm is trained using data obtained by inserting known pollen types into the device and out of eight pollen taxa six can be identified with an accuracy of above 90 %. In addition to the ability to correctly identify aerosols, an automatic pollen monitoring system needs to be able to correctly determine particle concentrations. To further verify the device, controlled chamber experiments using polystyrene latex beads were performed. This provided reference aerosols with traceable particle size and number concentrations to ensure particle size and sampling volume were correctly characterised. [ABSTRACT FROM AUTHOR]

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. Unlabelled Image • The first automatic pollen monitoring network in the world was built in Bavaria, based on the pollen robot BAA500. • Collapsing a dense network by clustering analysis determined the number and position of monitoring stations. • Bavaria (Germany) can be clustered in 3 pollen zones and 8 sub-zones, based on airborne pollen concentrations. • In the studied network, the most abundant pollen types in Bavaria are: Pinus , Betula , Urticaceae and Poaceae. • In the studied network, the main pollination period in Bavaria ranges from February (Alnus) to October (Ambrosia). [ABSTRACT FROM AUTHOR]

Pollen-induced allergies are among the most prevalent non-contagious diseases, with about a quarter of the European population being sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive and requires narrow specialized abilities and substantial time, so that the pollen data are always delayed and subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct a first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANNs) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence-based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better, exceeding 80 % accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species, ending up with three well-resolved groups: (Alnus , Corylus , Betula and Quercus), (Salix and Populus) and (Festuca , Artemisia and Juniperus). Within these groups, pollen is practically indistinguishable for the first-level recognition procedure. Construction of multistep algorithms with sequential discrimination of pollen inside each group seems to be one of the possible ways forward. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and the issue of false positive pollen detections by Rapid-E is discussed. [ABSTRACT FROM AUTHOR]

Pollen-induced allergy is among the most-prevalent non-contagious diseases, with about a quarter of European population sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive, requires narrow specialization abilities and substantial time, so that the pollen data are always delayed, subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct the first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANN) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence- based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better exceeding 80% accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species ending up with three well-resolved groups: (Alnus, Corylus, Betula and Quercus), (Salix and Populus), and (Festuca, Artemisia, Juniperus). Within these groups, pollen is practically non-distinguishable for the first-level recognition procedure. Construction of multi-steps algorithms with sequential discrimination of pollen inside each group seems to be one of possible ways forwards. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and an issue of the false-positive pollen detections by Rapid-E is discussed. [ABSTRACT FROM AUTHOR]

Evaluating airborne pollen concentrations is important for the understanding of the spatiotemporal dispersion of pollen grains. Using two identical pollen monitors in parallel, we performed two experiments in order to study the influences of a) the physical characteristics (orientation) of the air inlet and b) the presence of obstacles in proximity to the monitors on airborne pollen concentration data. The first experiment consisted of an evaluation of airborne pollen concentrations using two different types of orifices; 1) a vertically oriented inlet and 2) a wind vane intake, both attached to the same type of automatic pollen sampler. The second experiment investigated the relationship between vertical wind speed and horizontal wind direction around an obstacle with the goal of studying the impact of micro-scale wind on pollen sampling efficiency. The results of the two experiments suggest that the wind path near an obstacle might be redirected in a vertical direction before or after the wind flows over the obstacle, which causes measurement errors of airborne pollen concentrations that are proportional to the vertical wind speed, especially when a vertically oriented inlet is used. [ABSTRACT FROM AUTHOR]

We present results from the development and validation campaign of an optical pollen monitoring method based on time-resolved scattering and fluorescence. Focus is first set on supervised learning algorithms for pollen-taxa identification and on the determination of aerosol properties (particle size and shape). The identification capability provides a basis for a pre-operational automatic pollen season monitoring performed in parallel to manual reference measurements (Hirst-type volumetric samplers). Airborne concentrations obtained from the automatic system are compatible with those from the manual method regarding total pollen and the automatic device provides real-time data reliably (one week interruption over five months). In addition, although the calibration dataset still needs to be completed, we are able to follow the grass pollen season. The high sampling from the automatic device allows to go beyond the commonly-presented daily values and we obtain statistically significant hourly concentrations. Finally, we discuss remaining challenges for obtaining an operational automatic monitoring system and how the generic validation environment developed for the present campaign could be used for further tests of automatic pollen monitoring devices. [ABSTRACT FROM AUTHOR]

Background: Most panel studies focusing on rhinitis and conjunctivitis symptoms and birch pollen have been performed on a linear basis and have not included air pollution and meteorological variables as potential confounders. The objective of this panel study was to assess the relationship between symptoms of seasonal allergic rhinoconjunctivitis and daily airborne birch pollen concentrations, checking with adequate statistical tools the shapes of concentration-response curves and controlling for confounders. Methods: The severity of rhinitis, conjunctivitis, and bronchial symptoms was recorded daily in March and April 2010 (3,311 person-days) in 61 seasonal allergic rhinoconjunctivitis patients sensitized to birch. Data were analyzed with generalized additive and generalized estimating equation models to quantify the effects of birch pollens. Results: The relationship between birch pollen and the percentage of patients with nasal, ocular and bronchial symptoms was linear until birch daily average concentrations of, respectively, 110, 70, and 70 grains/m3; it reached a plateau thereafter. For an increase of 10 grains/m3, the OR (95% CI) for nasal, ocular, and bronchial symptoms were, respectively, 1.07 (1.03-1.12), 1.17 (1.08-1.27), and 1.12 (1.03-1.21). At the beginning of the season, no nasal or ocular symptoms were observed below a threshold of 30 grains/m3. Conclusions: Adequate modeling regression shows that the clinical response to natural exposure to birch pollen in sensitized patients varies during Betula pollen season, with a threshold at the beginning of the season, while over the whole season the relationship is linear for nasal, ocular, and bronchial symptoms up to a saturation point, followed by a plateau. © 2013 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]