Wind Influence on the Grass Airborne Pollen in Bahía Blanca, Argentina

In the present study, we analyzed the main parameters related with the potential fertilization ability of two grapevine varieties, Godello and Mencía, during the years 2017 and 2018. The research was carried out in two vineyards of the Galician winegrowing Designation of Origin areas of Ribeiro and Ribeira Sacra. Ten vines of each variety were selected for bunch and flower counting, pollen calculations, pollen viability studies by means of aceto-carmine (AC) stain and 2, 3, 5-triphenyl tetrazolium chloride (TTC) methods, and the determination of their germination rate. In all vineyards the 50% fruitset was reached, except for Godello in Cenlle during 2017. The mean coulure value was higher for Godello (40.5%) than for Mencía (31%). Analyzing the pollen production per plant and airborne pollen levels, we observed important discordances between them, which can be due to the influence of weather conditions and be related with self-pollination processes. We found important differences on pollen viability depending on the applied method and variety, with higher values for the AC method than the TTC for both varieties in all study plots, and higher values for Mencía variety than Godello. Regarding germination rates, we observed a marked reduction in 2017 with respect to 2018, in all study sites and for both varieties. The analyzed parameters were useful to explain the different productive abilities of Godello and Mencía varieties in the two studied bioclimatic regions of Ribeiro and Ribeira Sacra.

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A 3-year airborne pollen and fungal spores record in San Carlos de Bariloche, Patagonia, Argentina

Aerobiologia ◽

10.1007/s10453-006-9037-8 ◽

2006 ◽

Vol 22 (4) ◽

pp. 247-257 ◽

Cited By ~ 17

Author(s):

Maria Martha Bianchi ◽

Susana Ester Olabuenaga

Keyword(s):

Airborne Pollen ◽

Fungal Spores ◽

San Carlos

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The Relationship between Symptom Flare of Atopic Dermatitis and Airborne Japanese Cedar and Cypress Pollen Counts: A Self-Scoring Diary Study

ISRN Dermatology ◽

10.5402/2012/218538 ◽

2012 ◽

Vol 2012 ◽

pp. 1-5 ◽

Cited By ~ 1

Author(s):

Haruko Nishie ◽

Mariko Kato ◽

Shiori Kato ◽

Hiroshi Odajima ◽

Rumiko Shibata ◽

...

Keyword(s):

Atopic Dermatitis ◽

Real Time ◽

Pollen Season ◽

Airborne Pollen ◽

Japanese Cedar ◽

Diary Study ◽

Pollen Counts ◽

High Level ◽

The Relationship

Background. With an increase in Japanese cedar and cypress (JC) pollinosis, the relationship between JC pollen and atopic dermatitis (AD) has been studied. Some reports suggest that JC pollen can be one exacerbating factor for AD, but there has been no report that discusses JC pollen counts relating to AD symptom flare although actual airborne JC pollen counts can widely fluctuate throughout the pollen season. Objective. The relationship between symptom flare of AD and airborne JC pollen counts was examined. Methods. We monitored JC pollen counts in real time and divided the counts into low and high level. We then analyzed self-scored “itch intensity” recorded by 14 AD patients through a self-scoring diary. Results. Among the 14 patients, 7 had significantly higher itch intensity while the pollen counts were high. Conclusion. Even during the pollen season, actual airborne pollen counts can widely fluctuate. Our study suggested that symptom flare of AD could be influenced by the actual pollen counts.

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A contribution to the knowledge of Cupressaceae airborne pollen in the middle west of Spain

Aerobiologia ◽

10.1007/s10453-015-9376-4 ◽

2015 ◽

Vol 31 (4) ◽

pp. 435-444 ◽

Cited By ~ 3

Author(s):

David Rodríguez de la Cruz ◽

Estefanía Sánchez-Reyes ◽

José Sánchez-Sánchez

Keyword(s):

Airborne Pollen ◽

Middle West

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Olive crop-yield forecasting based on airborne pollen in a region where the olive groves acreage and crop system changed drastically

Aerobiologia ◽

10.1007/s10453-017-9483-5 ◽

2017 ◽

Vol 33 (4) ◽

pp. 473-480 ◽

Cited By ~ 6

Author(s):

Helena Ribeiro ◽

Ilda Abreu ◽

Mário Cunha

Keyword(s):

Crop Yield ◽

Airborne Pollen ◽

Olive Groves ◽

Yield Forecasting ◽

Crop Yield Forecasting ◽

Crop System

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Trends and threshold exceedances analysis of airborne pollen concentrations in Metropolitan Santiago Chile

PLoS ONE ◽

10.1371/journal.pone.0123077 ◽

2015 ◽

Vol 10 (5) ◽

pp. e0123077 ◽

Cited By ~ 2

Author(s):

Richard Toro A. ◽

Alicia Córdova J. ◽

Mauricio Canales ◽

Raul G. E. Morales S. ◽

Pedro Mardones P. ◽

...

Keyword(s):

Airborne Pollen ◽

Pollen Concentrations ◽

Threshold Exceedances

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Neural networks for increased accuracy of allergenic pollen monitoring

Scientific Reports ◽

10.1038/s41598-021-90433-x ◽

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.

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