Quantitative trends in annual totals of five common airborne pollen types (Betula, Quercus, Poaceae, Urtica, and Artemisia), at five pollen-monitoring stations in western Europe

Aerobiologia ◽  
2003 ◽  
Vol 19 (3/4) ◽  
pp. 171-184 ◽  
Author(s):  
F.Th.M. Spieksma ◽  
J.M. Corden ◽  
M. Detandt ◽  
W.M. Millington ◽  
H. Nikkels ◽  
...  
2013 ◽  
Vol 4 (1) ◽  
pp. 3-9
Author(s):  
V. V. Rodinkova

Hay fever is important allergenic complain with number of patients rising year by year. Ukraine holds the leading positions in Europe in accordance with pollinosis morbidity. Therefore, it’s important to determine regional pollen spectrum for all five climatic and geographical zones of the country having certain variety of plants’ allergens. There are just a few cities with a constant pollen monitoring carried out in Ukraine. They are Vinnytsia, Kyiv, Odessa and Lviv. Palynological range of other Ukrainian cities remains unknown or poorly studied. Dnipropetrovsk – Ukrainian city with location in the Central part of the country in the Steppe zone – isn’t exception as well. Thus, the aim of our study was to determine the pattern of airborne pollen distribution and pollen calendar creation for the city of Dnipropetrovsk. Pollen count obtained at Vinnytsia National Pirogov Memorial Medical University (VNMU) by Aerobiology Research Group. Study was held in 2010 from the 17th of March till the 20th of October on daily basis employed volumetric methods using the Burkard trap. It stands on the roof of the Dnipropetrovsk Municipal hospital at 20 meters of a relative height above ground. The air samples were sent by currier mail on weekly basis from Dnipropetrovsk to Vinnytsia for the research term. 51 pollen types were determined during the study period. The aeropalinological research was done for the Dnipropetrovsk at first. Study was conducted in association with the European Aeroallergen Network (EAN). The EAN tools and the software package “Statistica 5.5” were used for data statistical analysis. The study showed prevalence of the airborne herbal pollen types in Dnipropetrovsk. The “weeds : trees” pollen ratio was «88 : 12». Most abundant pollen rain (59% of total annual pollen count) was produced by Ambrosia. The second position with 6% was held by Amaranthus / Chenopodiaceae pollen group and Urtica dioica pollen. Artemisia and other representatives of Asteraceae constituted of 5% each. The most abundant tree pollen rain (4% from total annual count) was produced by the Populus species. Betula pollen was the next having up 2% of annual pollen rain in Dnipropetrovsk. As can be seen, the first arboreal spring-summer pollination wave was not massive in Dnipropetrovsk. It was represented by Populus, Betula, Acer, Fraxinus, Quercus, Ulmus, Pinus, Juglans pollen spread in the end of March, whole April and the first weeks of May mostly. However, the second wave was intensively seen from the mid of July till the mid of October. Important airborne pollen producing taxa were Artemisisia, Ambrosia, Asteraceae, Chenopodiaceae, Urtica, Plantago, Polygonaceae pollen at that time. Important airborne pollen allergens of Poaceae family (grasses) held the 7th position in the total annual pollen rain and were recorded between two pollination waves from the mid of May till the end of June mostly. The worst period for the patients was associated with the Betula, Acer and Quercus pollination from 13th of April  till the mid of May and with Ambrosia and Artemisia pollination from 28th of July, till September, 30. The present airborne pollen calendar should be considered while diagnosing the hay fever symptoms in sensitive patients. It’s important to continue the pollen count and control in Dnіpropetrovsk due to constant changing of climatic and anthropogenic conditions impacting the pollen production and release.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcel Polling ◽  
Chen Li ◽  
Lu Cao ◽  
Fons Verbeek ◽  
Letty A. de Weger ◽  
...  

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.


2020 ◽  
Author(s):  
Maryam Al-Nesf ◽  
Dorra Gharbi ◽  
Hassan M. Mobayed ◽  
Blessing Reena Dason ◽  
Ramzy Mohammed Ali ◽  
...  

2017 ◽  
Vol 11 (2) ◽  
pp. 937-948 ◽  
Author(s):  
Daniela Festi ◽  
Luca Carturan ◽  
Werner Kofler ◽  
Giancarlo dalla Fontana ◽  
Fabrizio de Blasi ◽  
...  

Abstract. Dating of ice cores from temperate non-polar glaciers is challenging and often problematic. However, a proper timescale is essential for a correct interpretation of the proxies measured in the cores. Here, we introduce a new method developed to obtain a sub-seasonal timescale relying on statistically measured similarities between pollen spectra obtained from core samples and daily airborne pollen monitoring samples collected in the same area. This approach was developed on a 10 m core retrieved from the temperate-firn portion of Alto dell'Ortles glacier (Eastern Italian Alps), for which a 5-year annual/seasonal timescale already exists. The aim was to considerably improve this timescale, reaching the highest possible temporal resolution and testing the efficiency and limits of pollen as a chronological tool. A test of the new timescale was performed by comparing our results to the output (date of layer formation) of the mass balance model EISModel, during the period encompassed by the timescale. The correspondence of the results supports the new sub-seasonal timescale based on pollen analysis. This comparison also allows us to draw important conclusions on the post-depositional effects of meltwater percolation on the pollen content of the firn core as well as on the climatic interpretation of the pollen signal.


2020 ◽  
Author(s):  
Xiaoxia Shang ◽  
Elina Giannakaki ◽  
Stephanie Bohlmann ◽  
Maria Filioglou ◽  
Annika Saarto ◽  
...  

Abstract. We present a novel algorithm for characterizing the optical properties of pure pollen particles, based on the depolarization values obtained in lidar measurements. The algorithm was first tested and validated through a simulator, and then applied to the lidar observations during a four-month pollen campaign from May to August 2016 at the European Aerosol Research Lidar Network (EARLINET) station in Kuopio (62°44′ N, 27°33′ E), in Eastern Finland. Twenty types of pollen were observed and identified from concurrent measurements with Burkard sampler; Birch (Betula), pine (Pinus), spruce (Picea) and nettle (Urtica) pollen were most abundant, contributing more than 90 % of total pollen load, regarding number concentrations. Mean values of lidar-derived optical properties in the pollen layer were retrieved for four intense pollination periods (IPPs). Lidar ratios at both 355 and 532 nm ranged from 55 to 70 sr for all pollen types, without significant wavelength-dependence. Enhanced depolarization ratio was found when there were pollen grains in the atmosphere, and even higher depolarization ratio (with mean values of 25 % or 14 %) was observed with presence of the more non-spherical spruce or pine pollen. The depolarization ratio at 532 nm of pure pollen particles was assessed, resulting to 24 ± 3 % and 36 ± 5 % for birch and pine pollen, respectively. Pollen optical properties at 1064 nm and 355 nm were also estimated. The backscatter-related Ångström exponent between 532 and 1064 nm was assessed as ~ 0.8 (~ 0.5) for pure birch (pine) pollen, thus the longer wavelength would be better choice to trace pollen in the air. The pollen depolarization ratio at 355 nm of 17 % and 30 % were found for birch and pine pollen, respectively. The depolarization values show a wavelength dependence for pollen. This can be the key parameter for pollen detection and characterization.


2015 ◽  
Vol 68 (4) ◽  
pp. 367-372
Author(s):  
Irene Câmara Camacho ◽  
Rita Câmara ◽  
Roberto Camacho

<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>


Aerobiologia ◽  
1995 ◽  
Vol 11 (3) ◽  
pp. 189-194 ◽  
Author(s):  
Manuel Munuer Giner ◽  
José Sebastián Carrión García ◽  
Juan Guerra Montes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Maryam A. Al-Nesf ◽  
Dorra Gharbi ◽  
Hassan M. Mobayed ◽  
Blessing Reena Dason ◽  
Ramzy Mohammed Ali ◽  
...  

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 717
Author(s):  
Ricardo Navares ◽  
José Luis Aznarte

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.


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