The Association Between Airborne Pollen Monitoring and Sensitization in the Hot Desert Climate

2020 ◽  
Author(s):  
Maryam Al-Nesf ◽  
Dorra Gharbi ◽  
Hassan M. Mobayed ◽  
Blessing Reena Dason ◽  
Ramzy Mohammed Ali ◽  
...  
Keyword(s):  
Airborne Pollen ◽  
Pollen Monitoring ◽  
Desert Climate

scholarly journals The association between airborne pollen monitoring and sensitization in the hot desert climate

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

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 Linking pollen deposition and snow accumulation on the Alto dell'Ortles glacier (South Tyrol, Italy) for sub-seasonal dating of a firn temperate core

2017 ◽  
Vol 11 (2) ◽  
pp. 937-948 ◽  
Author(s):  
Daniela Festi ◽  
Luca Carturan ◽  
Werner Kofler ◽  
Giancarlo dalla Fontana ◽  
Fabrizio de Blasi ◽  
...  
Keyword(s):  
Airborne Pollen ◽  
Ice Cores ◽  
Snow Accumulation ◽  
Balance Model ◽  
Pollen Deposition ◽  
Layer Formation ◽  
Italian Alps ◽  
Pollen Monitoring ◽  
Pollen Content ◽  
Firn Core

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.

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>

Aprpaoches to airborne pollen in SE spain. First survey in murcia: one year of pollen monitoring (1993–94)

Aerobiologia ◽  
1995 ◽  
Vol 11 (3) ◽  
pp. 189-194 ◽  
Author(s):  
Manuel Munuer Giner ◽  
José Sebastián Carrión García ◽  
Juan Guerra Montes
Keyword(s):  
Airborne Pollen ◽  
Se Spain ◽  
Pollen Monitoring ◽  
One Year

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.

Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen

Aerobiologia ◽  
2017 ◽  
Vol 33 (3) ◽  
pp. 351-362 ◽  
Author(s):  
Shigeto Kawashima ◽  
Michel Thibaudon ◽  
Soken Matsuda ◽  
Toshio Fujita ◽  
Natalie Lemonis ◽  
...  
Keyword(s):  
Monitoring System ◽  
Seasonal Changes ◽  
Airborne Pollen ◽  
Pollen Monitoring ◽  
Laser Optics

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 Personalized Pollen Monitoring and Symptom Scores: A Feasibility Study in Grass Pollen Allergic Patients

2021 ◽  
Vol 2 ◽  
Author(s):  
Letty A. de Weger ◽  
Peter Th. W. van Hal ◽  
Bernadette Bos ◽  
Frank Molster ◽  
Marijke Mostert ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Grass Pollen ◽  
Airborne Pollen ◽  
Mobile App ◽  
Pollen Monitoring ◽  
Pollen Counts ◽  
Pollen Concentrations ◽  
Symptom Scores ◽  
Pollen Exposure ◽  
Grass Pollen Counts

Background: Pollen is a major trigger for allergic symptoms in sensitized individuals. Airborne pollen is usually monitored by Hirst type pollen samplers located at rooftop level, providing a general overview of the pollen distribution in the larger surroundings. In this feasibility study, grass pollen-sensitized subjects monitored the pollen in their direct environment using a portable pollen sampler (Pollensniffer) and scored their symptoms, to study the relation between symptom severity and personal grass pollen exposure. For comparison the symptoms were also correlated with pollen collected by the rooftop sampler.Methods: After recruitment 18 participants were screened for grass pollen specific (GP-sIgE) of which 12 were eligible. Nine participants completed the study (May, 2018). They were asked to monitor personal pollen exposure using a Pollensniffer on their way to school, work or other destination, and to score their symptoms via a mobile app on a scale from 0 to 10. Daily pollen concentrations were collected by a Hirst type sampler at rooftop level. Pollen grains were analyzed using a microscope.Results: Three of the four participants with high GP-sIgE (≥9.6 kU/l) reported high symptom scores (&gt;4) and an analysis showed a significant correlation (CC) between eye, nose, and lung symptoms and the grass pollen counts collected by the Pollensniffer, as well as the daily grass pollen concentrations monitored by the rooftop sampler (CC≥0.54). In contrast, the participants with low GP-sIgE levels (&lt;9.6 kU/l) reported low symptom scores (≤4) and often other sensitizations were present. For these subjects, no significant positive correlations (CC&lt;0.3) of symptoms with either grass pollen collected by the personal or the rooftop sampler were found.Conclusion: The results of this feasibility study suggest that correlations between the severity of clinical symptoms of grass pollen allergic patients, and grass pollen counts as determined by the Pollensniffer or a rooftop sampler, is restricted to patients with high GP-sIgE levels, high symptom scores, and no relevant other sensitizations. Based on the low numbers of subjects with severe symptoms included in this feasibility study, no conclusions can be drawn on the performance of the Pollensniffer in relating symptoms and pollen exposure in comparison with the rooftop sampler.Trial Registration: The study was approved by the Committee Medical Ethics of the LUMC (approval numbers: NL63953.058.17/ P17.304).

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