scholarly journals Estimates of common ragweed pollen emission and dispersion over Europe using RegCM-pollen model

2015 ◽  
Vol 12 (21) ◽  
pp. 17595-17641 ◽  
Author(s):  
L. Liu ◽  
F. Solmon ◽  
R. Vautard ◽  
L. Hamaoui-Laguel ◽  
Cs. Zs. Torma ◽  
...  
Keyword(s):  
Exposure Time ◽  
Invasive Plant ◽  
Airborne Pollen ◽  
Ambrosia Artemisiifolia ◽  
Plant Distribution ◽  
Ragweed Pollen ◽  
Pollen Emission ◽  
Common Ragweed ◽  
Community Land Model ◽  
Pollen Concentrations

Abstract. Common ragweed (Ambrosia artemisiifolia L.) is a highly allergenic and invasive plant in Europe. Its pollen can be transported over large distances and has been recognized as a significant cause of hayfever and asthma (D'Amato et al., 2007; Burbach et al., 2009). To simulate production and dispersion of common ragweed pollen, we implement a pollen emission and transport module in the Regional Climate Model (RegCM) version 4 using the framework of the Community Land Model (CLM) version 4.5. In the online model environment where climate is integrated with dispersion and vegetation production, pollen emissions are calculated based on the modelling of plant distribution, pollen production, species-specific phenology, flowering probability, and flux response to meteorological conditions. A pollen tracer model is used to describe pollen advective transport, turbulent mixing, dry and wet deposition. The model is then applied and evaluated on a European domain for the period 2000–2010. To reduce the large uncertainties notably due to ragweed density distribution on pollen emission, a calibration based on airborne pollen observations is used. Resulting simulations show that the model captures the gross features of the pollen concentrations found in Europe, and reproduce reasonably both the spatial and temporal patterns of flowering season and associated pollen concentrations measured over Europe. The model can explain 68.6, 39.2, and 34.3 % of the observed variance in starting, central, and ending dates of the pollen season with associated root mean square error (RMSE) equal to 4.7, 3.9, and 7.0 days, respectively. The correlation between simulated and observed daily concentrations time series reaches 0.69. Statistical scores show that the model performs better over the central Europe source region where pollen loads are larger. From these simulations health risks associated common ragweed pollen spread are then evaluated through calculation of exposure time above health-relevant threshold levels. The total risk area with concentration above 5 grains m−3 takes up 29.5 % of domain. The longest exposure time occurs on Pannonian Plain, where the number of days per year with the daily concentration above 20 grains m−3 exceeds 30.

scholarly journals Ragweed pollen production and dispersion modelling within a regional climate system, calibration and application over Europe

2016 ◽  
Vol 13 (9) ◽  
pp. 2769-2786 ◽  
Author(s):  
Li Liu ◽  
Fabien Solmon ◽  
Robert Vautard ◽  
Lynda Hamaoui-Laguel ◽  
Csaba Zsolt Torma ◽  
...  
Keyword(s):  
Exposure Time ◽  
Airborne Pollen ◽  
Regional Climate ◽  
Ambrosia Artemisiifolia ◽  
Plant Distribution ◽  
Ragweed Pollen ◽  
Pollen Production ◽  
Common Ragweed ◽  
Community Land Model ◽  
Pollen Concentrations

Abstract. Common ragweed (Ambrosia artemisiifolia L.) is a highly allergenic and invasive plant in Europe. Its pollen can be transported over large distances and has been recognized as a significant cause of hay fever and asthma (D'Amato et al., 2007; Burbach et al., 2009). To simulate production and dispersion of common ragweed pollen, we implement a pollen emission and transport module in the Regional Climate Model (RegCM) version 4 using the framework of the Community Land Model (CLM) version 4.5. In this online approach pollen emissions are calculated based on the modelling of plant distribution, pollen production, species-specific phenology, flowering probability, and flux response to meteorological conditions. A pollen tracer model is used to describe pollen advective transport, turbulent mixing, dry and wet deposition. The model is then applied and evaluated on a European domain for the period 2000–2010. To reduce the large uncertainties notably due to the lack of information on ragweed density distribution, a calibration based on airborne pollen observations is used. Accordingly a cross validation is conducted and shows reasonable error and sensitivity of the calibration. Resulting simulations show that the model captures the gross features of the pollen concentrations found in Europe, and reproduce reasonably both the spatial and temporal patterns of flowering season and associated pollen concentrations measured over Europe. The model can explain 68.6, 39.2, and 34.3 % of the observed variance in starting, central, and ending dates of the pollen season with associated root mean square error (RMSE) equal to 4.7, 3.9, and 7.0 days, respectively. The correlation between simulated and observed daily concentrations time series reaches 0.69. Statistical scores show that the model performs better over the central Europe source region where pollen loads are larger and the model is better constrained. From these simulations health risks associated to common ragweed pollen spread are evaluated through calculation of exposure time above health-relevant threshold levels. The total risk area with concentration above 5 grains m−3 takes up 29.5 % of domain. The longest exposure time occurs on Pannonian Plain, where the number of days per year with the daily concentration above 20 grains m−3 exceeds 30.

scholarly journals 2350 Creating a comprehensive municipal inventory of common ragweed (Ambrosia artemisiifolia) to predict allergenic pollen exposures

2018 ◽  
Vol 2 (S1) ◽  
pp. 7-7
Author(s):  
Daniel S. W. Katz ◽  
Stuart Batterman
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Airborne Pollen ◽  
Ambrosia Artemisiifolia ◽  
Allergenic Pollen ◽  
Pollen Production ◽  
Common Ragweed ◽  
Lack Of Information ◽  
Pollen Concentrations ◽  
The City

OBJECTIVES/SPECIFIC AIMS: One of the key difficulties in predicting allergenic pollen exposures has been a lack of information on source plant location and abundance. However, the increasing availability of spatially explicit data from remote sensing offers new opportunities to create comprehensive inventories of allergenic pollen producing plants. METHODS/STUDY POPULATION: In this study, we use a spatially oriented field survey to map common ragweed (Ambrosia artemisiifolia) in Detroit, MI, USA. We then combine this with remote sensing imagery and LiDAR to predict ragweed presence and potential pollen production across 344 km2 of Detroit. Finally, we compare this with measurements of airborne pollen concentrations collected throughout the city. RESULTS/ANTICIPATED RESULTS: Our initial results show that ragweed is present in ~2% of the city, and its presence and abundance are strongly associated with demolished building (p<0.001). The uneven distribution of ragweed plants across the city leads to substantially higher pollen concentrations in neighborhoods where more buildings have been recently demolished. DISCUSSION/SIGNIFICANCE OF IMPACT: Our approach offers an effective way to quantify allergenic pollen production, airborne concentrations, and exposures across a large metropolitan area. This in turn provides insight on how to best reduce airborne pollen concentrations: in this case, by changing post-demolition land management practices.

scholarly journals Heat wave event facilitates defensive responses in invasive C3 plant Ambrosia artemisiifolia L. under elevated CO2 concentration to the detriment of insect herbivores

2021 ◽  
Author(s):  
Zhenya Tian ◽  
Chao Ma ◽  
Chenchen Zhao ◽  
Yan Zhang ◽  
Xuyuan Gao ◽  
...  
Keyword(s):  
Heat Wave ◽  
Species Interactions ◽  
Invasive Plant ◽  
Poor Performance ◽  
Ambrosia Artemisiifolia ◽  
Plant Distribution ◽  
C3 Plant ◽  
Common Ragweed ◽  
Heat Wave Event ◽  
Wave Event

AbstractTo predict and mitigate the effects of climate change on communities and ecosystems, the joint effects of extreme climatic events on species interactions need to be understood. Using the common ragweed (Ambrosia artemisiifolia L.)—leaf beetle (Ophraella communa) system, we investigated the effects of heat wave and elevated CO2 on common ragweed growth, secondary metabolism, and the consequent impacts on the beetle. The results showed that elevated CO2 and heat wave facilitated A. Artemisiifolia growth; further, A. artemisiifolia accumulated large amounts of defensive secondary metabolites. Being fed on A. artemisiifolia grown under elevated CO2 and heat wave conditions resulted in the poor performance of O. communa (high mortality, long development period, and low reproduction). Overall, under elevated CO2, heat wave improved the defensive ability of A. artemisiifolia against herbivores. This implies that heat wave event will relieve harm of A. artemisiifolia to human under elevated CO2. On the other hand, super adaptability to climatic changes may aggravate invasive plant distribution, posing a challenge to the control of invasive plants in the future.

scholarly journals EMPOL 1.0: a new parameterization of pollen emission in numerical weather prediction models

2013 ◽  
Vol 6 (6) ◽  
pp. 1961-1975 ◽  
Author(s):  
K. Zink ◽  
A. Pauling ◽  
M. W. Rotach ◽  
H. Vogel ◽  
P. Kaufmann ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Birch Pollen ◽  
Small Scale ◽  
Pollen Emission ◽  
Numerical Weather ◽  
Pollen Concentrations ◽  
Scale Modelling ◽  
Reactive Trace Gases

Abstract. Simulating pollen concentrations with numerical weather prediction (NWP) systems requires a parameterization for pollen emission. We have developed a parameterization that is adaptable for different plant species. Both biological and physical processes of pollen emission are taken into account by parameterizing emission as a two-step process: (1) the release of the pollen from the flowers, and (2) their entrainment into the atmosphere. Key factors influencing emission are temperature, relative humidity, the turbulent kinetic energy and precipitation. We have simulated the birch pollen season of 2012 using the NWP system COSMO-ART (Consortium for Small-scale Modelling – Aerosols and Reactive Trace Gases), both with a parameterization already present in the model and with our new parameterization EMPOL. The statistical results show that the performance of the model can be enhanced by using EMPOL.

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

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0123077 ◽  
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

scholarly journals Chemical composition and phytotoxicity of essential oil from invasive plant, Ambrosia artemisiifolia L.

2021 ◽  
Vol 211 ◽  
pp. 111879
Author(s):  
Caixia Han ◽  
Hua Shao ◽  
Shixing Zhou ◽  
Yu Mei ◽  
Zhenrui Cheng ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Essential Oil ◽  
Invasive Plant ◽  
Ambrosia Artemisiifolia

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.

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