Environmental DNA analysis of airborne poaceae (grass) pollen reveals taxonomic diversity across seasons and climate zones.

BACKGROUND: Grasses populate most biogeographical zones, and their diversity influences allergic sensitisation to pollen. Previously, the contribution of different Poaceae subfamilies to airborne pollen has mostly been inferred from historical herbarium records. We recently applied environmental (e)DNA metabarcoding at one subtropical site revealing that successive airborne grass pollen peaks were derived from repeated flowering of Chloridoid and Panicoid grasses over a season. This study aimed to compare spatiotemporal patterns in grass pollen exposure across seasons and climate zones. METHODS: Airborne pollen concentrations across two austral pollen seasons spanning 2017-2019 at subtropical (Mutdapilly and Rocklea, Queensland) and temperate (Macquarie Park and Richmond, New South Wales) sites, were determined with a routine volumetric impaction sampler and counting by light microscopy. Poaceae rbcL metabarcode sequences amplified from daily pollen samples collected once per week were assigned to subfamily and genus using a ribosomal classifier and compared with Atlas of Living Australia sighting records. RESULTS: eDNA analysis revealed distinct dominance patterns of grass pollen at various sites: Panicoid grasses prevailed in both subtropical Mutdapilly and temperate Macquarie Park, whilst Chloridoid grasses dominated the subtropical Rocklea site. Overall, subtropical sites showed significantly higher proportion of pollen from Chloridoid grasses than temperate sites, whereas the temperate sites showed a significantly higher proportion of pollen from Pooideae grasses than subtropical sites. Timing of airborne Pooid (spring), Panicoid and Chloridoid (late spring to autumn), and Arundinoid (autumn) pollen were significantly related to number of days from mid-winter. Proportions of eDNA for subfamilies correlated with distributions grass sighting records between climate zones. CONCLUSIONS: eDNA analysis enabled finer taxonomic discernment of Poaceae pollen records across seasons and climate zones with implications for understanding adaptation of grasslands to climate change, and the complexity of pollen exposure for patients with allergic respiratory diseases.

Decrypting seasonal patterns of key pollen taxa in cool temperate Australia: A multi-barcode metabarcoding analysis.

Pollen allergies pose a considerable global public health concern. Allergy risk can vary significantly within plant families, yet some key pollen allergens can only be identified to family level by current optical methods. Pollen information with greater taxonomic resolution is therefore required to best support allergy prevention and self-management. We used environmental DNA (eDNA) metabarcoding to deepen taxonomic insights into the seasonal composition of airborne pollen in cool temperate Australia, a region with high rates of allergic respiratory disease. In Hobart, Tasmania, we collected routine weekly air samples from December 2018 until October 2020 and sequenced the internal transcribed spacer 2 (ITS2) and chloroplastic tRNA-Leucine tRNA-Phenylalanine intergenic spacer (trnL-trnF) regions in order to address the following questions: a) What is the genus-level diversity of known and potential aeroallergens in Hobart, in particular, in the families Poaceae, Cupressaceae and Myrtaceae? b) How do the atmospheric concentrations of these taxa change over time, and c) Does trnL-trnF enhance resolution of biodiversity when used in addition to ITS2? Our results suggest that individuals in the region are exposed to temperate grasses including Poa and Bromus in the peak grass pollen season, however low levels of exposure to the subtropical grass Cynodon may occur in autumn and winter. Within Cupressaceae, both metabarcodes showed that exposure is predominantly to pollen from the introduced genera Cupressus and Juniperus. Only ITS2 detected the native genus, Callitris. Both metabarcodes detected Eucalyptus as the major Myrtaceae genus, with trnL-trnF exhibiting primer bias for this family. These findings help refine our understanding of allergy triggers in Tasmania and highlight the utility of multiple metabarcodes in aerobiome studies.

Diurnal pattern of Poaceae and Betula pollen flight in Central Europe.

In Central Europe the most common allergies are provoked by grass or birch pollen allergens. We determined the intra-daily behavior of airborne pollen grains of grasses (Poaceae) and birch (Betula ssp.) in Central Europe, based on data obtained from a network of automatic pollen monitors over Europe (www.pollenscience.eu). Our aim was to determine the time of day when the lowest concentrations occur, to provide allergic individuals the optimal time to ventilate their homes. The study was carried out in three Central European capitals, Berlin (Germany), Paris-Saclay (France), and Luxembourg (Luxembourg), as well as in eight stations in Germany (Altötting, Feucht, Garmisch-Partenkirchen, Hof, Marktheidenfeld, Mindelheim, Munich and Viechtach). The diurnal rhythm of these eleven locations was analyzed for either the complete, first week, peak week, peak day and last week of the pollen season. The data studied were reported as pollen/m3 measured in 3 h periods. Stations were classified as city, semi-populated or countryside areas using land-use and population density criteria. Grass pollen has a more pronounced diurnal rhythm than birch pollen concentrations. A significant difference was observed when comparing day (6-21 h) versus night (21-6 h) for all stations. No difference was detected between city and countryside for both pollen types, although for Poaceae a longer period of maximum concentrations was observed in big cities and higher day/night-time differences were registered in the countryside (6.4) than in cities (3.0). The highest pollen concentrations were observed between 9 and 18 h for grass, but the rhythm was less pronounced for birch pollen. For allergic individuals who want to bring in fresh air in their homes, we recommend opening windows after 21 h, but even better early in the morning between 6 and 9 h before pollinations (re)starts.

Volunteer-contributed observations of flowering often correlate with airborne pollen concentrations.

Characterizing airborne pollen concentrations is crucial for supporting allergy and asthma management; however, pollen monitoring is labor intensive and, in the USA, geographically limited. The USA National Phenology Network (USA-NPN) engages thousands of volunteer observers in regularly documenting the developmental and reproductive status of plants. The reports of flower and pollen cone status contributed to the USA-NPN's platform, Nature's Notebook, have the potential to help address gaps in pollen monitoring by providing real-time, spatially explicit information from across the country. In this study, we assessed whether observations of flower and pollen cone status contributed to Nature's Notebook can serve as effective proxies for airborne pollen concentrations. We compared daily pollen concentrations from 36 National Allergy Bureau (NAB) stations in the USA with flowering and pollen cone status observations collected within 200 km of each NAB station in each year, 2009-2021, for 15 common tree taxa using Spearman's correlations. Of 350 comparisons, 58% of correlations were significant (p < 0.05). Comparisons could be made at the largest numbers of sites for Acer and Quercus. Quercus demonstrated a comparatively high proportion of tests with significant agreement (median ρ = 0.49). Juglans demonstrated the strongest overall coherence between the two datasets (median ρ = 0.79), though comparisons were made at only a small number of sites. For particular taxa, volunteer-contributed flowering status observations demonstrate promise to indicate seasonal patterns in airborne pollen concentrations. The quantity of observations, and therefore, their utility for supporting pollen alerts, could be substantially increased through a formal observation campaign.

A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques.

The monitoring of airborne pollen has received much attention over the last decade, as the prevalence of pollen-induced allergies is constantly increasing. Today, the most common technique to identify airborne pollen species and to monitor their concentrations is based on manual analysis. Here, we present a new, low-cost, real-time optical pollen sensor, called Beenose, that automatically counts and identifies pollen grains by performing measurements at multiple scattering angles. We describe the data pre-processing steps and discuss the various statistical and machine learning methods that have been implemented to distinguish different pollen species. The analysis is based on a set of 12 pollen species, several of which were selected for their allergic potency. Our results show that Beenose can provide a consistent clustering of the pollen species based on their size properties, and that pollen particles can be separated from non-pollen ones. More importantly, 9 out of 12 pollen species were correctly identified with a prediction score exceeding 78%. Classification errors occur for species with similar optical behaviour, suggesting that other parameters should be considered to provide even more robust pollen identification.

Metabarcoding airborne pollen from subtropical and temperate eastern Australia over multiple years reveals pollen aerobiome diversity and complexity.

eDNA metabarcoding is an emergent tool to inform aerobiome complexity, but few studies have applied this technology with real-world environmental pollen monitoring samples. Here we apply eDNA metabarcoding to assess seasonal and regional differences in the composition of airborne pollen from routine samples collected across successive years. Airborne pollen concentrations over two sampling periods were determined using a continuous flow volumetric impaction air sampler in sub-tropical (Mutdapilly and Rocklea) and temperate (Macquarie Park and Richmond), sites of Australia. eDNA metabarcoding was applied to daily pollen samples collected once per week using the rbcL amplicon. Composition and redundancy analysis of the sequence read counts were examined. The dominant pollen families were mostly consistent between consecutive years but there was some heterogeneity between sites and years for month of peak pollen release. Many more families were detected by eDNA than counted by light microscopy with 211 to 399 operational taxonomic units assigned to family per site from October to May. There were 216 unique and 119 taxa shared between subtropics (27°S) and temperate (33°S) latitudes, with, for example, Poaceae, Myrtaceae and Causurinaceae being shared, and Manihot, Vigna and Aristida being in subtropical, and Ceratodon and Cerastium being in temperate sites. Certain genera were observed within the same location and season over the two years; Chloris at Rocklea in autumn of 2017-18 (0.625, p ≤ 0.004) and 2018-19 (0.55, p ≤ 0.001), and Pinus and Plantago at Macquarie Park in summer of 2017-18 (0.58, p ≤ 0.001 and 0.53, p ≤ 0.003, respectively), and 2018-19 (0.8, p ≤ 0.003 and 0.8, p ≤ 0.003, respectively). eDNA metabarcoding is a powerful tool to survey the complexity of pollen aerobiology and distinguish spatial and temporal profiles of local pollen to a far deeper level than traditional counting methods. However, further research is required to optimise the metabarcode target to enable reliable detection of pollen to genus and species level.

Field Evaluation of an Automated Pollen Sensor.

Background: Seasonal pollen is a common cause of allergic respiratory disease. In the United States, pollen monitoring occurs via manual counting, a method which is both labor-intensive and has a considerable time delay. In this paper, we report the field-testing results of a new, automated, real-time pollen imaging sensor in Atlanta, GA. Methods: We first compared the pollen concentrations measured by an automated real-time pollen sensor (APS-300, Pollen Sense LLC) collocated with a Rotorod M40 sampler in 2020 at an allergy clinic in northwest Atlanta. An internal consistency assessment was then conducted with two collocated APS-300 sensors in downtown Atlanta during the 2021 pollen season. We also investigated the spatial heterogeneity of pollen concentrations using the APS-300 measurements. Results: Overall, the daily pollen concentrations reported by the APS-300 and the Rotorod M40 sampler with manual counting were strongly correlated (r = 0.85) during the peak pollen season. The APS-300 reported fewer tree pollen taxa, resulting in a slight underestimation of total pollen counts. Both the APS-300 and Rotorod M40 reported Quercus (Oak) and Pinus (Pine) as dominant pollen taxa during the peak tree pollen season. Pollen concentrations reported by APS-300 in the summer and fall were less accurate. The daily total and speciated pollen concentrations reported by two collocated APS-300 sensors were highly correlated (r = 0.93-0.99). Pollen concentrations showed substantial spatial and temporal heterogeneity in terms of peak levels at three locations in Atlanta. Conclusions: The APS-300 sensor was able to provide internally consistent, real-time pollen concentrations that are strongly correlated with the current gold-standard measurements during the peak pollen season. When compared with manual counting approaches, the fully automated sensor has the significant advantage of being mobile with the ability to provide real-time pollen data. However, the sensor's weed and grass pollen identification algorithms require further improvement.

Detection and Recognition of Pollen Grains in Multilabel Microscopic Images.

Analysis of pollen material obtained from the Hirst-type apparatus, which is a tedious and labor-intensive process, is usually performed by hand under a microscope by specialists in palynology. This research evaluated the automatic analysis of pollen material performed based on digital microscopic photos. A deep neural network called YOLO was used to analyze microscopic images containing the reference grains of three taxa typical of Central and Eastern Europe. YOLO networks perform recognition and detection; hence, there is no need to segment the image before classification. The obtained results were compared to other deep learning object detection methods, i.e., Faster R-CNN and RetinaNet. YOLO outperformed the other methods, as it gave the mean average precision (mAP@.5:.95) between 86.8% and 92.4% for the test sets included in the study. Among the difficulties related to the correct classification of the research material, the following should be noted: significant similarities of the grains of the analyzed taxa, the possibility of their simultaneous occurrence in one image, and mutual overlapping of objects.

Detecting Airborne Pollen Using an Automatic, Real-Time Monitoring System: Evidence from Two Sites.

Airborne pollen monitoring has been an arduous task, making ecological applications and allergy management virtually disconnected from everyday practice. Over the last decade, intensive research has been conducted worldwide to automate this task and to obtain real-time measurements. The aim of this study was to evaluate such an automated biomonitoring system vs. the conventional 'gold-standard' Hirst-type technique, attempting to assess which may more accurately provide the genuine exposure to airborne pollen. Airborne pollen was monitored in Augsburg since 2015 with two different methods, a novel automatic Bio-Aerosol Analyser, and with the conventional 7-day recording Hirst-type volumetric trap, in two different sites. The reliability, performance, accuracy, and comparability of the BAA500 Pollen Monitor (PoMo) vs. the conventional device were investigated, by use of approximately 2.5 million particles sampled during the study period. The observations made by the automated PoMo showed an average accuracy of approximately 85%. However, it also exhibited reliability problems, with information gaps within the main pollen season of between 17 to 19 days. The PoMo automated algorithm had identification issues, mainly confusing the taxa of Populus, Salix and Tilia. Hirst-type measurements consistently exhibited lower pollen abundances (median of annual pollen integral: 2080), however, seasonal traits were more comparable, with the PoMo pollen season starting slightly later (median: 3 days), peaking later (median: 5 days) but also ending later (median: 14 days). Daily pollen concentrations reported by Hirst-type traps vs. PoMo were significantly, but not closely, correlated (r = 0.53-0.55), even after manual classification. Automatic pollen monitoring has already shown signs of efficiency and accuracy, despite its young age; here it is suggested that automatic pollen monitoring systems may be more effective in capturing a larger proportion of the airborne pollen diversity. Even though reliability issues still exist, we expect that this new generation of automated bioaerosol monitoring will eventually change the aerobiological era, as known for almost 70 years now.

Deep Learning Methods for Improving Pollen Monitoring.

The risk of pollen-induced allergies can be determined and predicted based on data derived from pollen monitoring. Hirst-type samplers are sensors that allow airborne pollen grains to be detected and their number to be determined. Airborne pollen grains are deposited on adhesive-coated tape, and slides are then prepared, which require further analysis by specialized personnel. Deep learning can be used to recognize pollen taxa based on microscopic images. This paper presents a method for recognizing a taxon based on microscopic images of pollen grains, allowing the pollen monitoring process to be automated. In this research, a deep CNN (convolutional neural network) model was built from scratch. Publicly available deep neural network models, pre-trained on image data (not including microscopic pictures), were also used. The results show that even a simple deep learning model produces quite good results when the classification of pollen grain taxa is performed directly from the images. The best deep learning model achieved 97.88% accuracy in the difficult task of recognizing three types of pollen grains (birch, alder, and hazel) with similar structures. The derived models can be used to build a system to support pollen monitoring experts in their work.

A new portable sampler to monitor pollen at street level in the environment of patients.

Allergic rhinitis caused by pollen exposure is one of the most common allergic diseases. Therefore monitoring pollen levels in ambient air is an important tool in research and health care. Most European monitoring stations collect airborne pollen at rooftop levels for measurements in the larger surrounding of the sampling station, and not in the direct environment of sensitized subjects. Here we present the development and evaluation of a portable pollen sampler, called "Pollensniffer", that was designed to collect pollen in the immediate environment of allergic subjects. Validation of the Pollensniffer against the standard volumetric pollen sampler showed for most pollen types high correlations between the number of pollen collected by those two devices (Spearman's Correlation Coefficient > 0.8); the Pollensniffer appeared to collect on average 5.8 times more pollen per hour than the static sampler. Pollen monitoring was performed using this Pollensniffer at street level at 3 different locations in the city of Leiden during 22 weeks in 2017 and 21 weeks in 2018, during three 15-min periods a day and at one day in the week. The results showed that the pollen levels for birch and grass pollen can significantly differ from location to location and per time of day. Furthermore, the Pollensniffer measurements at street level showed that birch and grass pollen grains were detected 1 1/2 and 2-3 weeks, respectively, before detection at rooftop level. The street measurements show that allergic subjects can encounter varying pollen levels throughout the city and that they can be exposed to grass and birch pollen and may experience hay fever symptoms, even before the sampler at rooftop level registers these pollen.

Tracking seasonal changes in diversity of pollen allergen exposure: Targeted metabarcoding of a subtropical aerobiome.

The importance of grass pollen to the global burden of allergic respiratory disease is well established but exposure to subtropical and temperate pollens is difficult to discern. Current monitoring of airborne pollen relies on light microscopy, limiting identification of taxa to family level. This informs seasonal fluctuations in pollen aerobiology but restricts analysis of aerobiological composition. We aimed to test the utility of DNA metabarcoding to identify specific taxa contributing to the aerobiome of environmental air samples, using routine pollen and spore monitoring equipment, as well as assess temporal variation of Poaceae pollen across an entire season. Airborne pollen concentrations were determined by light microscopy over two pollen seasons in the subtropical city of Brisbane (27°32'S, 153°00E), Australia. Thirty daily pollen samples were subjected to high throughput sequencing of the plastid rbcL amplicon. Amplicons corresponded to plants observed in the local biogeographical region with up to 3238 different operational taxonomic units (OTU) detected. The aerobiome sequencing data frequently identified pollen to genus levels with significant quantitative differences in aerobiome diversity between the months and seasons detected. Moreover, multiple peaks of Chloridoideae and Panicoideae pollen were evident over the collection period confirming these grasses as the dominant Poaceae pollen source across the season. Targeted high throughput sequencing of routinely collected airborne pollen samples appears to offer utility to track temporal changes in the aerobiome and shifts in pollen exposure. Precise identification of the composition and temporal distributions of airborne pollen is important for tracking biodiversity and for management of allergic respiratory disease.

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.

[Phenological characteristics of airborne pollen and its relationship with meteorological factors in Haidian District, Beijing, China during the period of 2012-2016]. / 2012­2016å¹´æµ·æ·€åŒºæ°”ä¼ èŠ±ç²‰ç‰©å€™ç‰¹å¾åŠå ¶ä¸Žæ°”è±¡è¦ç´ çš„å ³ç³».

We monitored the type and content of airborne pollen in Haidian District, Beijing City from 2012 to 2016 by the gravity precipitation method, and analyzed the variety of pollen, peak distribution features and changes of its content, and discontinuous variation of concentration. Multiple time scale analysis was carried out for pollen concentration by the ensemble empirical mode decomposition method (EEMD). The relationship between pollen concentration and meteorological factors was analyzed. The results indicated that during the research period, the main types of airborne pollen changed. Woody plants such as Cupressaceae and Salicaceae instead of herbaceous plants contributed the most content of pollen. There was no significant change of the yearly peak distribution of pollen concentration. The concentration in recent five years reduced, while the concentration of herbaceous plants (such as Scolopacjdae) increased. During the statistics period, pollen concentration showed discontinuous changes in early April, late May and early August. Pollen concentration had the change cycle of quasi 2 d, quasi 51 d and quasi 128 d. Among all meteorological factors, temperature played a dominant role in driving the concentration, which may significantly rise during 16 to 18 ℃. The impact of temperature changes on the daily concentration may be delayed and continuous; it is often highly related to the concentration 2-7 d later. Sunshine duration and wind speed had the most significant impact on daily pollen concentration.

Five-Year Data on Pollen Monitoring, Distribution and Health Impact of Allergenic Plants in Bucharest and the Southeastern Region of Romania.

Background and objectives: Respiratory allergies induced by allergenic pollen represent an important public health problem with increasing prevalence and severity in Europe. Romania has no aerobiology network and pollen measurements have been done for about ten years in the west region only. Materials and Methods: We established the first pollen monitoring center in the capital of Bucharest in 2013, based on collaboration with the Réseau National de Surveillance Aérobiologique (RNSA) from France. The aim of our paper is to present results from five years of pollen monitoring in the city center of Bucharest and preliminary data on distribution and health impact of some allergenic plants, mainly Ambrosia artemisiifolia, which is considered a real danger for the public health. Results: Our data show a significant atmospheric amount and a longer season than previously considered of grass (Gramineae) pollen and short period with a high level of Ambrosia pollen, while tree pollen looks less important in this area. The plant distribution data provided by specialists and information from affected persons showed the wide and increasing spread of Ambrosia in Bucharest and other cities from the south region. Preliminary health data from allergists confirmed that the number of patients with allergies to Ambrosia pollen is increasing from one year to another and almost all patients describe a high urban exposure from their living or working place. Conclusions: We consider that the recently implemented Law 62/2018 against Ambrosia may help reduce weed distribution and the atmospheric pollen load, but a more complex and coordinated strategy for controlling urban vegetation and reducing biologic pollution is needed.

The dynamics of pollen seasons of the most allergenic plants - 15-year observations in Warsaw.

INTRODUCTION: Allergic rhinitis concerns nearly 25% of the Polish population. Among pollen allergens, the most common reasons for allergic rhinitis are: grass, birch and mugwort. Knowledge of the characteristics of pollen seasons is necessary in diagnostics, monitoring of therapy and prevention of allergic rhinitis. P urpose: This work aims to analyze pollen seasons of the most allergenic plants in the Polish population; grass, birch and mugwort in the years 2003-2017 in Warsaw. M aterial and methods: Measurements of pollen concentration were carried out using Burkard volumetric spore trap operating in continuous mode. Analysis of pollen seasons was conducted based on the following characteristics: beginning, end, and length of season, the seasonal pollen index (SPI), defined as the sum of average daily pollen concentrations over the year, maximum daily concentration, number of days with maximum and threshold concentration. Linear regression together with the Pearson correlation coefficient were used in statistical analysis to study the relationship between variables; furthermore, descriptive characteristics of distributions studied were determined. R esults: The average beginning of the birch pollen season in the analyzed period is April 10th, and it belongs to seasons of average length (47 days on average). Birch pollen count above 75 grains/m3, when most allergic people develop symptoms, was recorded for an average of 18 days. The highest daily birch pollen count reaching 6321 grains/m3 (2012) exceeded the lowest value of the maximum concentration by almost 20 times (2015). Among the taxa analyzed, the highest values of daily counts and annual sums were recorded for birch pollen. The average date for the beginning of grass pollination season is on May 13th. It is the longest pollen season (on average 134 days), and the period when concentration exceeded 50 grains/m3 covered an average of 26 days. The highest daily grass pollen counts reaching 496 grains/m3 (2007) exceeded the lowest value of maximum concentration by 3.5 times (2016). The average date of the beginning of mugwort pollen season is July 16th. The season lasts 65 days on average, when concentration exceeding 30 grains /m3 was registered for an average of 12 days. The highest daily mugwort pollen count reaching 154 grains/m3 (2007) exceeded the lowest value of maximum concentration by 4 times (2013). For all analyzed taxa, the strongest correlated variables are the sum of average daily pollen concentrations over the year (SPI ) and daily maximum concentration (correlation for birch pollen = 0.92, for grass pollen = 0.88, and for mugwort pollen = 0.91). CONCLUSIONS: Periods of pollen in the air show certain variation in the analyzed 15-year period. The maximum concentration in the pollen season for the analyzed taxa and the the sum of average daily pollen concentrations over the year show the highest variability, particularly strongly expressed in the case of birch pollen. There is a linear relationship between the sum of average daily pollen concentrations over the year and the maximum concentration value as well as the number of days with the threshold concentration for all analyzed taxa. Variability of parameters describing the dynamics of pollen seasons indicates the need to monitor, both by patients with hay fever and physicians, the current information on the concentration of pollen in the air during the pollen season.

First allergenic pollen monitoring in Bucharest and results of three years collaboration with European aerobiology specialists.

INTRODUCTION: Respiratory allergies induced by allergenic plants pollen represent an important public health problem with increasing prevalence and severity. Aerobiologic study of allergenic pollens is performed in many countries on regular basis and correlated with health data from allergists in the frame of national aerobiology networks. Romania has no aerobiology network and pollen measurements have been done between 1999-2012 in West region only. In the frame of COST Action called Sustainable management of Ambrosia artemisiifolia in Europe (SMARTER FA 1203), three years collaboration with Reseau National de Surveillance Aerobiologique (RNSA) from France and the first pollen monitoring center in Bucharest were established.The aim of this paper is to present results of first pollen monitoring in Bucharest, activities of Romanian SMARTER group and collaboration with European aerobiology specialists. MATERIAL AND METHOD: We used a Hirst-type pollen trap placed on the roof of the Research Center from "Colentina" Clinical Hospital and the pollen monitoring method based on European Aeroallergen Network (EAN) standardized requirements. Monthly results during the pollen seasons 2014-2016 were sent to RNSA and EAN and posted on the European pollen information site. RESULTS: We found high amounts of allergenic pollen, mainly grasses from May to September and Ambrosia artemisiifolia during September. Conlcusions. We concluded that SMARTER offered access to aerobiology training, improved multidisciplinary collaboration and perspectives to further develop national and international projects. More coordinated efforts to develop national aerobiology network and to recuperate the gap comparing to other European countries in the field of aerobiology and respiratory allergology are needed.

Efficient and sensitive identification and quantification of airborne pollen using next-generation DNA sequencing.

Pollen monitoring is an important and widely used tool in allergy research and creation of awareness in pollen-allergic patients. Current pollen monitoring methods are microscope-based, labour intensive and cannot identify pollen to the genus level in some relevant allergenic plant groups. Therefore, a more efficient, cost-effective and sensitive method is needed. Here, we present a method for identification and quantification of airborne pollen using DNA sequencing. Pollen is collected from ambient air using standard techniques. DNA is extracted from the collected pollen, and a fragment of the chloroplast gene trnL is amplified using PCR. The PCR product is subsequently sequenced on a next-generation sequencing platform (Ion Torrent). Amplicon molecules are sequenced individually, allowing identification of different sequences from a mixed sample. We show that this method provides an accurate qualitative and quantitative view of the species composition of samples of airborne pollen grains. We also show that it correctly identifies the individual grass genera present in a mixed sample of grass pollen, which cannot be achieved using microscopic pollen identification. We conclude that our method is more efficient and sensitive than current pollen monitoring techniques and therefore has the potential to increase the throughput of pollen monitoring.

[Methods of studying airborne pollen and pollen calendars]. / Méthodes d'étude des pollens atmosphériques et calendriers polliniques.

INTRODUCTION: Pollen is a major cause of allergy and monitoring pollen in the air is relevant for diagnosis, treatment and prevention, as well as for biomedical and biological research. Many aero-biological studies have been conducted all over the world to ascertain aerial concentrations and seasonality of pollen grains. BACKGROUND: Monitoring of airborne biological particles is carried out by various gravimetric, impaction, and suction sampling devices. The Hirst trap, later modified to Burkard(®) or Lanzoni(®) traps, is the most widely used sampler. Counting and identifying pollen grains is then performed under optical microscopy. Based on differences in airborne pollen recorded over several years of observation, pollen calendars have been drawn up as aids to allergy diagnosis and management but they could be replaced advantageously by allergy-risk calendars. Pollen counts also provide valuable information about the geographical origin of pollen grains. VIEWPOINTS: Since the identifying and counting of pollen grains in ambient air samples is still a demanding and time-consuming task, there is an increasing interest in the automation of pollen monitoring. Furthermore, the divergence sometimes observed between clinical observations and pollen counts provides an incentive to collect aero-allergens directly. Lastly, pollen monitoring could be improved through the use of personal bioaerosol samplers. CONCLUSIONS: Great progress has been made in aerobiology for over a century, but much remains to be accomplished, particularly in relation with the standardization of methods.