Using meteorologic data to predict daily ragweed pollen levels

Stark, Paul C.; Ryan, Louise; McDonald, James L.; Burge, Harriet A.

doi:10.1007/bf02694505

Cited by 36 publications

(24 citation statements)

References 10 publications

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“…Other studies used meteorological data to predict not the seasonal quantities but the daily ragweed pollen concentrations (Raynor and Hayes 1970;Comtois and Sherknies 1992). Stark et al (1997) succeeded in predicting the ragweed pollen levels 1 day in advance, using a Poisson regression. Such predictions should also be interesting in the future and could complete our information for allergic patients.…”

Section: Discussionmentioning

confidence: 99%

Two statistical approaches to forecasting the start and duration of the pollen season of Ambrosia in the area of Lyon (France)

Laaidi

Thibaudon

Besancenot

2003

International Journal of Biometeorology

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The aim of the present study was to forecast the start and duration of the pollen season of Ambrosia from meteorological data, in order to provide early information to allergists and allergic people. We used the airborne pollen data from Lyon (France), sampled using a Hirst trap from 1987 to 1999, and the meteorological data for the same period: air temperature (minimal, maximal, and average), rainfall, relative humidity, sunshine duration and soil temperature. Two forecasting models were used, one summing the temperatures and the other making use of a multiple regression on 10-day or monthly meteorological parameters. The start of the pollen season was predicted with both methods, results being more accurate with the regression (the errors between the predicted and the observed SDP ranging from 0 to 3 days). The duration of the pollen season was predicted by a regression model, errors ranging from 0 to 7 days. The models were later tested with satisfactory results from 2 additional years (2000 and 2001). Such forecasting models are helpful for allergic people, who have to begin their anti-allergic treatment before the start of the pollen season and not when the symptoms have appeared, since a preventive treatment is more efficient than a curative one. The regression allows predictions to be made 3-5 weeks in advance and so it is of particular interest. The forecasts will be broadcast on the Internet.

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Section: Discussionmentioning

confidence: 99%

Two statistical approaches to forecasting the start and duration of the pollen season of Ambrosia in the area of Lyon (France)

Laaidi

Thibaudon

Besancenot

2003

International Journal of Biometeorology

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“…When nights are not hot enough as to favour anther dehiscence (i.e. >10-15°C) in mid-autumn, the pollen curve tails off in Ambrosia artemisiifolia (Bianchi 1959 ;Stark et al 1997 ) , although there may not have been any night frost. The effect of heating and of related meteorological variables can alter as the season progresses, especially as a result of changes in the amounts of remaining pollen to be dispersed or of pollen production (Smith and Emberlin 2005 ;Vázquez et al 2003 ) .…”

Section: Duration Of the Pollen Season And The Shape Of The Pollen Curvementioning

confidence: 96%

The Onset, Course and Intensity of the Pollen Season

et al. 2012

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The onset, duration and intensity of the period when pollen is present in the air varies from year to year. Amongst other things, there is an effect upon the quality of life of allergy sufferers. The production and emission of pollens are governed by interacting environmental factors. Any change in these factors may affect the phenology and intensity of the season. Readiness to fl ower in a plant, and the amount of pollen produced, is the result of conditions during an often long period foregoing fl owering. When a plant is ready to fl ower, temporary ambient circumstances e.g., irradiation and humidity, determine the timing of the actual

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“…Based on the work of different authors, Díaz de la Guardia et al [6]; Galán et al [7]; Sabariego et al [8]; Tortajada and Mateu [9], factors as temperature, humidity, and hours of sun and wind speed were incorporated in the model. This methodology explains approximately 75-80% of the variability in the airborne Cupressaceae pollen concentration.…”

Section: Methodologiesmentioning

confidence: 99%

Regression Models to Forecast Cupressaceae Pollen Concentration in the City of Granada (SE of Spain) Based on Climatic Variables

Fm¹

2016

J Biom Biostat

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Using meteorologic data to predict daily ragweed pollen levels

Cited by 36 publications

References 10 publications

Two statistical approaches to forecasting the start and duration of the pollen season of Ambrosia in the area of Lyon (France)

Two statistical approaches to forecasting the start and duration of the pollen season of Ambrosia in the area of Lyon (France)

The Onset, Course and Intensity of the Pollen Season

Regression Models to Forecast Cupressaceae Pollen Concentration in the City of Granada (SE of Spain) Based on Climatic Variables

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