scholarly journals Pollen monitoring in Perm Krai (Russia) – experience of 6 years

2015 ◽  
Vol 68 (4) ◽  
pp. 343-348 ◽  
Author(s):  
Larisa Viktorovna Novoselova ◽  
Nataliya Minaeva
Keyword(s):  
Airborne Pollen ◽  
Pollen Grains ◽  
Late Summer ◽  
Early Summer ◽  
Pollen Monitoring ◽  
Central Russia ◽  
Large Numbers ◽  
Geographical Regions ◽  
Poaceae Pollen ◽  
Perm Krai

Medical observations show that the level of pollinosis increased by 34.6% in central Russia in 2005–2012. This paper presents the results of 6-year pollen monitoring carried out with a Hirst-type pollen trap (Burkard Manufacturing Co. Ltd) between 2010 and 2015 in Perm Krai (Russia). Usually, sensitization of allergic people occurs in three periods: (i) spring due to the pollen of <em>Betula</em>, (ii) early summer due to Poaceae pollen, and (iii) late summer as a result of <em>Artemisia</em> pollen. <em>Betula</em> pollen, which is dominant (26.9–65.2% of total pollen counts), is recorded in large numbers in the period of flowering and occasionally during the entire period of pollination. Among herbaceous plants, the pollen of Poaceae, Urticaceae and <em>Artemisia</em> dominates in airborne pollen. The concentration of allergenic pollen grains in the air of Perm Krai is lower than in other European geographical regions.

scholarly journals Neural networks for increased accuracy of allergenic pollen monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcel Polling ◽  
Chen Li ◽  
Lu Cao ◽  
Fons Verbeek ◽  
Letty A. de Weger ◽  
...  
Keyword(s):  
Neural Networks ◽  
Airborne Pollen ◽  
Pollen Grains ◽  
Allergenic Pollen ◽  
Herbarium Specimens ◽  
Pollen Monitoring ◽  
Pollen Concentrations ◽  
Source Of Information ◽  
Urticaceae Pollen

AbstractMonitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

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

2015 ◽  
Vol 68 (4) ◽  
pp. 367-372
Author(s):  
Irene Câmara Camacho ◽  
Rita Câmara ◽  
Roberto Camacho
Keyword(s):  
Pollen Season ◽  
Airborne Pollen ◽  
Growing Season ◽  
Onset Date ◽  
Critical Levels ◽  
Pollen Monitoring ◽  
Madeira Island ◽  
Poaceae Pollen ◽  
The Mean ◽  
Low Levels

<p>The pollinic spectrum of the Madeira region is dominated by grass pollen, which also represents an important aeroallergen in Europe. The present work aims to analyze the main features of the Poaceae pollen season in the Madeira region to determine the allergic risk. The study took place in Funchal city, the capital of Madeira Island, over a period of 10 years (2003–2012). The airborne pollen monitoring was carried out with a Hirst type volumetric trap, following well-established guidelines.</p><p>In the atmosphere of Funchal, the mean annual Poaceae pollen index was 229. The mean Poaceae pollen season lasts 275 days, with an onset date in January/March and an end date in November/December. Poaceae counts showed a seasonal variation with 2 distinct peaks: a higher peak between March and June, and the second one in autumn. The peak values occurred mainly between April and June, and the highest peak was 93 grains/m<sup>3</sup>, detected on the 27th May of 2010. The Poaceae pollen remaining at low levels during the whole growing season, presenting a nil to low allergenic risk during most of the study period. Higher critical levels of allergens have been revealed after 2006. In general, the pollen risk from Poaceae lasted only a few days per year, despite the very long pollen season and the abundance of grasses in the landscape of Madeira Island.</p>

scholarly journals The late flowering of invasive species contributes to the increase of Artemisia allergenic pollen in autumn: an analysis of 25 years of aerobiological data (1995–2019) in Trentino-Alto Adige (Northern Italy)

Aerobiologia ◽  
2020 ◽  
Vol 36 (4) ◽  
pp. 669-682 ◽  
Author(s):  
Antonella Cristofori ◽  
Edith Bucher ◽  
Michele Rossi ◽  
Fabiana Cristofolini ◽  
Veronika Kofler ◽  
...  
Keyword(s):  
Invasive Species ◽  
Pollen Season ◽  
Airborne Pollen ◽  
Pollen Grains ◽  
Late Summer ◽  
Natura 2000 ◽  
Allergenic Pollen ◽  
Pollen Concentration ◽  
Pollen Concentrations ◽  
Negative Impacts

AbstractArtemisia pollen is an important aeroallergen in late summer, especially in central and eastern Europe where distinct anemophilous Artemisia spp. produce high amounts of pollen grains. The study aims at: (i) analyzing the temporal pattern of and changes in the Artemisia spp. pollen season; (ii) identifying the Artemisia species responsible for the local airborne pollen load.Daily pollen concentration of Artemisia spp. was analyzed at two sites (BZ and SM) in Trentino-Alto Adige, North Italy, from 1995 to 2019.The analysis of airborne Artemisia pollen concentrations evidences the presence of a bimodal curve, with two peaks, in August and September, respectively. The magnitude of peak concentrations varies across the studied time span for both sites: the maximum concentration at the September peak increases significantly for both the BZ (p < 0.05) and SM (p < 0.001) site. The first peak in the pollen calendar is attributable to native Artemisia species, with A. vulgaris as the most abundant; the second peak is mostly represented by the invasive species A. annua and A. verlotiorum (in constant proportion along the years), which are causing a considerable increase in pollen concentration in the late pollen season in recent years.. The spread of these species can affect human health, increasing the length and severity of allergenic pollen exposure in autumn, as well as plant biodiversity in both natural and cultivated areas, with negative impacts on, e.g., Natura 2000 protected sites and crops.

scholarly journals Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 717
Author(s):  
Ricardo Navares ◽  
José Luis Aznarte
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Airborne Pollen ◽  
Missing At Random ◽  
Spatial Relations ◽  
Computational Time ◽  
Pollen Monitoring ◽  
Distance Weighting ◽  
Poaceae Pollen ◽  
Data Points

Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper.

scholarly journals 50 Years of Pollen Monitoring in Basel (Switzerland) Demonstrate the Influence of Climate Change on Airborne Pollen

2021 ◽  
Vol 2 ◽  
Author(s):  
Regula Gehrig ◽  
Bernard Clot
Keyword(s):  
Climate Change ◽  
Pollen Season ◽  
Airborne Pollen ◽  
Linear Trend ◽  
Linear Increase ◽  
Pollen Monitoring ◽  
Pollen Species ◽  
Poaceae Pollen ◽  
Almost All ◽  
Start Dates

Climate change and human impact on vegetation modify the timing and the intensity of the pollen season. The 50 years of pollen monitoring in Basel, Switzerland provide a unique opportunity to study long-term changes in pollen data. Since 1969, pollen monitoring has been carried out in Basel with a Hirst-type pollen trap. Pollen season parameters for start dates, end dates and duration were calculated with different pollen season definitions, which are commonly used in aerobiology. Intensity was analyzed by the annual pollen integral (APIn), peak value and the number of days above specific thresholds. Linear trends were calculated with the non-parametric Mann Kendall method with a Theil-Sen linear trend slope. During the last 50 years, linear increase of the monthly mean temperatures in Basel was 0.95–1.95°C in the 3 winter months, 2–3.7°C in spring months and 2.75–3.85°C in summer months. Due to this temperature increase, the start dates of the pollen season for most of the spring pollen species have advanced, from 7 days for Poaceae to 29 days for Taxus/Cupressaceae. End dates of the pollen season depend on the chosen pollen season definition. Negative trends predominate, i.e., the pollen season mostly ends earlier. Trends in the length of the pollen season depend even more on the season definitions and results are contradictory and often not significant. The intensity of the pollen season of almost all tree pollen taxa increased significantly, while the Poaceae pollen season did not change and the pollen season of herbs decreased, except for Urticaceae pollen. Climate change has a particular impact on the pollen season, but the definitions used for the pollen season parameters are crucial for the calculation of the trends. The most stable results were achieved with threshold definitions that indicate regular occurrence above certain concentrations. Percentage definitions are not recommended for trend studies when the annual pollen integral changed significantly.

scholarly journals Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 145 ◽  
Author(s):  
Jesús Rojo ◽  
Jose Oteros ◽  
Antonio Picornell ◽  
Franziska Ruëff ◽  
Barbora Werchan ◽  
...  
Keyword(s):  
Airborne Pollen ◽  
Pollen Grains ◽  
Ground Level ◽  
Large Area ◽  
Dispersal Capacity ◽  
Pollen Types ◽  
Pollen Concentrations ◽  
Pollen Sources ◽  
Poaceae Pollen ◽  
The City

Airborne pollen concentrations vary depending on the location of the pollen trap with respect to the pollen sources. Two Hirst-type pollen traps were analyzed within the city of Munich (Germany): one trap was located 2 m above ground level (AGL) and the other one at rooftop (35 m AGL), 4.2 km apart. In general, 1.4 ± 0.5 times higher pollen amounts were measured by the trap located at ground level, but this effect was less than expected considering the height difference between the traps. Pollen from woody trees such as Alnus, Betula, Corylus, Fraxinus, Picea, Pinus and Quercus showed a good agreement between the traps in terms of timing and intensity. Similar amounts of pollen were recorded in the two traps when pollen sources were more abundant outside of the city. In contrast, pollen concentrations from Cupressaceae/Taxaceae, Carpinus and Tilia were influenced by nearby pollen sources. The representativeness of both traps for herbaceous pollen depended on the dispersal capacity of the pollen grains, and in the case of Poaceae pollen, nearby pollen sources may influence the pollen content in the air. The timing of the pollen season was similar for both sites; however, the season for some pollen types ended later at ground level probably due to resuspension processes that would favor recirculation of pollen closer to ground level. We believe measurements from the higher station provides a picture of background pollen levels representative of a large area, to which local sources add additional and more variable pollen amounts.

scholarly journals Automatic Image Classification Using Neural Networks Increases Accuracy for Allergenic Pollen Monitoring

2020 ◽  
Author(s):  
Marcel Polling ◽  
Chen Li ◽  
Lu Cao ◽  
Fons Verbeek ◽  
Letty de Weger ◽  
...  
Keyword(s):  
Airborne Pollen ◽  
Pollen Grains ◽  
Allergenic Pollen ◽  
Herbarium Specimens ◽  
Pollen Monitoring ◽  
Pollen Concentrations ◽  
Family Level ◽  
Source Of Information ◽  
Urticaceae Pollen

Abstract Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen are traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of a Convolutional Neural Network (CNN) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species have very low allergenic relevance, those from several species of Parietaria are severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these to train the CNN model VGG16. The model shows that Urticaceae pollen can be distinguished with 98.3% accuracy. We then apply our model on Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

Airborne pollen and spores in Melbourne

1966 ◽  
Vol 14 (1) ◽  
pp. 49 ◽  
Author(s):  
E Derrick
Keyword(s):  
Airborne Pollen ◽  
Seasonal Pattern ◽  
Fungal Spores ◽  
Pollen Grains ◽  
Weather Conditions ◽  
Early Summer ◽  
High Concentration ◽  
Long Period ◽  
High Concentrations ◽  
Airborne Pollen And Spores

The incidence, on exposed slides, of pollen grains and spores from the atmosphere of Melbourne is recorded. Observations over 4½ years show that 30 types of pollen appear regularly each year and few of these are in great abundance. The highest concentration of pollen is in the period August-December and the lowest concentration in the period Aprii-May. In eariy spring, pollen grains from conifers and deciduous trees are most numerous, and in later spring and summer those from grasses and plantains predominate. Variations in weather conditions produce variations in the duration and concentration of the pollen cloud, both annually and within the season. High concentrations of fungal spores, at times exceeding those of pollen grains, occur during late spring and early summer, but follow a less clearly defined seasonal pattern than the pollen cloud. Pollen normally transferred by insects may at times become airborne in significant concentration. Grass pollen, because of its presence in the air over a long period and its high concentration during the Melbourne pollinosis season, must be considered important in relation to seasonal allergy. Other types of pollen and fungal spores, which are in high concentration for a shorter period or in less quantity for a long period, may also contribute to allergic symptoms.

scholarly journals Pollen Production of Quercus in the North-Western Iberian Peninsula and Airborne Pollen Concentration Trends during the Last 27 Years

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 702
Author(s):  
María Fernández-González ◽  
Estefanía González-Fernández ◽  
Helena Ribeiro ◽  
Ilda Abreu ◽  
F. Javier Rodríguez-Rajo
Keyword(s):  
Climate Change ◽  
Airborne Pollen ◽  
Pollen Grains ◽  
Maximum Temperature ◽  
Pollen Production ◽  
Pollen Monitoring ◽  
Main Pollen Season ◽  
Pollen Concentrations ◽  
Oak Pollen ◽  
The Impact

Natural forests are considered a reservoir of great biological diversity constituting one of the most important ecosystems in Europe. Quercus study is essential to assess ecological conservation of forests, and also of economic importance for different industries. In addition, oak pollen can cause high sensitization rates of respiratory allergies in pollen-allergy sufferers. This study sought to know the pollen production of six oak species in the transitional area between the Eurosiberian and Mediterranean Bioclimatic Regions, and to assess the impact of climate change on airborne oak pollen concentrations. The study was conducted in Ourense (NW Spain) over the 1993–2019 period. A Lanzoni VPPS 2000 volumetric trap monitored airborne pollen. A pollen production study was carried out in ten trees randomly selected in several Quercus forest around the Ourense city. Oak pollen represented around 14% of annual total pollen registered in the atmosphere of Ourense, showing an increasing trend during the last decade. Pollen production of the six studied oak species follow the proportions 1:1:2:5:90:276 for Q. ilex, Q. faginea, Q. rubra, Q. suber, Q. pyrenaica, and Q. robur respectively. We detected a significant trend to the increase of the annual maximum temperature, whereas a decrease of the maximum and mean temperatures during three previous months to oak flowering. This could be related with the detected trend to a delay of the oak Main Pollen Season onset of 0.47 days per year. We also found significant trends to an increase of the annual pollen integral of 7.9% pollen grains per year, and the pollen peak concentration of 7.5% pollen grains per year. Quercus airborne pollen monitoring as well as the knowledge of the reproductive behavior of the main oak species, bring us an important support tool offering a promising bio-indicator to detect ecological variations induced by climate change.

scholarly journals Deep Learning Methods for Improving Pollen Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3526
Author(s):  
Elżbieta Kubera ◽  
Agnieszka Kubik-Komar ◽  
Krystyna Piotrowska-Weryszko ◽  
Magdalena Skrzypiec
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Airborne Pollen ◽  
Pollen Grains ◽  
Image Data ◽  
Learning Model ◽  
Neural Network Models ◽  
Pollen Monitoring ◽  
Microscopic Images ◽  
Deep Learning Model

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.

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