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.

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.

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.

Grass Gazers: Using citizen science as a tool to facilitate practical and online science learning for secondary school students during the COVID-19 lockdown

The coronavirus disease of 2019 (COVID-19) pandemic has impacted educational systems worldwide during 2020, including primary and secondary schooling. To enable students of a local secondary school in Brisbane, Queensland, to continue with their practical agricultural science learning and facilitate online learning, a "Grass Gazers" citizen science scoping project was designed and rapidly implemented as a collaboration between the school and a multidisciplinary university research group focused on pollen allergy. Here, we reflect on the process of developing and implementing this project from the perspective of the school and the university. A learning package including modules on pollen identification, tracking grass species, measuring field greenness, using a citizen science data entry platform, forensic palynology, as well as video guides, risk assessment and feedback forms were generated. Junior agriculture science students participated in the learning via online lessons and independent data collection in their own local neighborhood and/or school grounds situated within urban environments. The university research group and school coordinator, operating in their own distributed work environments, had to develop, source, adopt, and/or adapt material rapidly to meet the unique requirements of the project. The experience allowed two-way knowledge exchange between the secondary and tertiary education sectors. Participating students were introduced to real-world research and were able to engage in outdoor learning during a time when online, indoor, desk-based learning dominated their studies. The unique context of restrictions imposed by the social isolation policies, as well as government Public Health and Department of Education directives, allowed the team to respond by adapting teaching and research activity to develop and trial learning modules and citizen science tools. The project provided a focus to motivate and connect teachers, academic staff, and school students during a difficult circumstance. Extension of this citizen project for the purposes of research and secondary school learning has the potential to offer ongoing benefits for grassland ecology data acquisition and student exposure to real-world science.

[Phenological characteristics of airborne pollen and its relationship with meteorological factors in Haidian District, Beijing, China during the period of 2012-2016]

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.

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.

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.