Influence of environmental drivers on allergy to pollen grains in a case study in Spain (Madrid): meteorological factors, pollutants, and airborne concentration of aeroallergens

2021 ◽  
Author(s):  
Martha Cabrera ◽  
Javier Subiza ◽  
Enrique Fernández-Caldas ◽  
Belén Garzón García ◽  
Stella Moreno-Grau ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Pollen Grains ◽  
Environmental Drivers ◽  
Airborne Concentration

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 Status of invasive plants on local pollination networks: A case study of Tagetes minuta in Tibet based on pollen grains from pollinators

2019 ◽  
Vol 27 (3) ◽  
pp. 306-313
Author(s):  
Tu Yanli ◽  
◽  
Wang Liping ◽  
Wang Xilong ◽  
Wang Linlin ◽  
...  
Keyword(s):  
Invasive Plants ◽  
Pollen Grains ◽  
Tagetes Minuta

scholarly journals Mugwort pollen season in the air of Poland in 2019

Alergoprofil ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 23-28
Author(s):  
Małgorzata Puc ◽  
Piotr Rapiejko ◽  
Agnieszka Lipiec ◽  
Małgorzata Malkiewicz ◽  
Katarzyna Dąbrowska-Zapart ◽  
...  
Keyword(s):  
Pollen Season ◽  
Pollen Grains ◽  
Start Date ◽  
Airborne Concentration ◽  
Pollen Concentrations ◽  
Artemisia Species ◽  
Pollen Seasons ◽  
June July ◽  
Artemisia Pollen ◽  
Seasonal Pollen

  The Asteraceae family is one of the largest families, comprising 67 genera and 264 species in Poland. However, only a few genera, including Artemisia, are potential allergenic sources. The aim of the study was to compare the mugwort pollen seasons in Bialystok, Bydgoszcz, Sosnowiec, Lublin, Piotrkow Trybunalski, Opole, Olsztyn, Szczecin, Warsaw and Wroclaw in 2019. The investigations were carried out using the volumetric method. Seasonal Pollen Index was estimated as the sum of daily average pollen concentrations in the given season. The mugwort pollen season is mainly observed in June, July and at the beginning of September. In 2019 the pollen season of mugwort started first in Opole, on the June 26th. At the latest, a pollen season ended in Bydgoszcz and Warsaw, at the end of September. The differences of pollen seasons duration were extremely considerable, from 35 to 83 days. The highest airborne concentration of 97 pollen grains/m3 was noted in Lublin on the July 31st. The maximum values of seasonal pollen count in Polish cities occurred between July 28th and August 12th, most often between in late July and early August. The highest mugwort pollen allergen hazard occurred in 2019 in Lublin, Warsaw, Opole and Wroclaw, and was 2–3 times higher than in other cities. The highest variability in the analysed seasons was found in start date, while the lowest in the peak value and SPI value. In the pollen season in 2019, 2 peaks of Artemisia pollen concentrations were observed as a result of the order of flowering of A. vulgaris and A. campestris. Information on the pollination of various Artemisia species will be used to avoid excessive exposure to allergens of these pollen grains.

Environmental flow requirements in arid zone rivers – a case study from the Lake Eyre Basin, central Australia

2003 ◽  
Vol 48 (7) ◽  
pp. 65-72 ◽  
Author(s):  
J.F. Costelloe ◽  
J.T. Puckridge ◽  
J.R.W. Reid ◽  
J. Pritchard ◽  
P. Hudson ◽  
...  
Keyword(s):  
Arid Zone ◽  
Environmental Flow ◽  
Environmental Drivers ◽  
Ecological Processes ◽  
Discharge Data ◽  
Spatial And Temporal Scales ◽  
Biological Responses ◽  
Key Drivers ◽  
Real Flow

The ARIDFLO project takes a multi-disciplinary approach to the collection and analysis of data required to formulate appropriate environmental flow requirements for rivers in the Lake Eyre Basin. The key drivers of the ecological processes underpinning the health of these rivers are identified by modelling whole-of-ecosystem biological responses to hydrological events over a range of spatial and temporal scales. First, the hydrology of these poorly gauged (often ungauged) rivers needs to be modelled and validated to mimic real flow and inundation patterns at the catchment, reach and waterbody scale. Modelled and actual discharge data are then used to provide a suite of hydrological predictor variables which, in conjunction with other environmental variables, are used to model observed biotic responses. The key hydrologic and environmental drivers identified by the statistical models need to be taken into account when determining environmental flow requirements for these river systems. Further work is required to assess the predictive power of the models in the highly variable, complex systems of the Lake Eyre Basin rivers.

Investigating the influence of meteorological factors on particulate matters: A case study based on path analysis

2019 ◽  
Vol 31 (3) ◽  
pp. 479-491
Author(s):  
Ya-Gao Qin ◽  
Chen Yi ◽  
Guo-Liu Dong ◽  
Jian-Zhang Min
Keyword(s):  
Path Analysis ◽  
Meteorological Factors ◽  
Atmospheric Temperature ◽  
Particulate Matters ◽  
Analysis Method ◽  
Absolute Value ◽  
Pm2.5 And Pm10 ◽  
The Impact ◽  
Main Influence

The meteorological factors play an important role to influence the concentration of the particulate matters. The path analysis method is employed to investigate the influence of meteorological factors (including atmospheric temperature ( AT), relative humidity ( RH), and wind speed ( WS)) on particulate matters (including PM2.5 and PM10) in Dazhou city. The following results are obtained: (1) The direct path coefficients of AT, RH, and WS to PM2.5 and PM10 are all negative, which means that the concentration of particulate matters would be declined following with the increasing of AT, RH, and WS. (2) The meteorological factors would explain about 17.43 and 16.52% variance of PM2.5 and PM10, respectively. However, 82.57% variance of PM2.5 and 83.48% variance of PM10 would be determined by non-meteorological factors. (3) AT and WS are the most important meteorological factors to modulate the concentration of particulate matters. AT would explain 12.73 and 8.78% variance of PM2.5 and PM10, respectively. WS would explain 6.54 and 8.69% variance of PM2.5 and PM10, respectively. (4) According to the absolute value of the determination coefficients, the main influence on the concentration of PM2.5 is the direct influence by AT and the impact on the concentration of PM10 is by the combined contribution of meteorological factors.

Identifying environmental drivers of insect phenology across space and time: Culicoides in Scotland as a case study

2012 ◽  
Vol 103 (2) ◽  
pp. 155-170 ◽  
Author(s):  
K.R. Searle ◽  
A. Blackwell ◽  
D. Falconer ◽  
M. Sullivan ◽  
A. Butler ◽  
...  
Keyword(s):  
Seasonal Abundance ◽  
Environmental Drivers ◽  
Spatial And Temporal Patterns ◽  
Biting Midges ◽  
Vector Borne Diseases ◽  
Temperature And Precipitation ◽  
Vector Borne ◽  
Analytical Tools ◽  
Insect Phenology

AbstractInterpreting spatial patterns in the abundance of species over time is a fundamental cornerstone of ecological research. For many species, this type of analysis is hampered by datasets that contain a large proportion of zeros, and data that are overdispersed and spatially autocorrelated. This is particularly true for insects, for which abundance data can fluctuate from zero to many thousands in the space of weeks. Increasingly, an understanding of the ways in which environmental variation drives spatial and temporal patterns in the distribution, abundance and phenology of insects is required for management of pests and vector-borne diseases. In this study, we combine the use of smoothing techniques and generalised linear mixed models to relate environmental drivers to key phenological patterns of two species of biting midges, Culicoides pulicaris and C. impunctatus, of which C. pulicaris has been implicated in transmission of bluetongue in Europe. In so doing, we demonstrate analytical tools for linking the phenology of species with key environmental drivers, despite using a relatively small dataset containing overdispersed and zero-inflated data. We demonstrate the importance of landcover and climatic variables in determining the seasonal abundance of these two vector species, and highlight the need for more empirical data on the effects of temperature and precipitation on the life history traits of palearctic Culicoides spp. in Europe.

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