Prediction of the start of the grass pollen season for the western part of the Netherlands

Driessen, M. N. B. M.; Herpen, R.M.A. van; Moelands, R. P. M.; Spieksma, F. Th. M.

doi:10.1080/00173138909431010

Cited by 56 publications

(22 citation statements)

References 9 publications

(7 reference statements)

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“…The calculated SDP could then be used to adjust a forecasting model, which would allow prediction of the SDP in the following years. Many different methods of determining the main pollen season can be found in the literature, depending on the aim of the investigation (Mullenders et al 1972;Lejoly-Gabriel 1978;Nilsson and Persson 1981;Driessen et al 1989;Frenguelli et al 1991;El-Ghazali et al 1993;Spieksma and Nikkels 1998). Graphically, the pollination curve of a plant, plotted from accumulated daily percentages of pollen on the y-axis and the date of observation on the x-axis, looks like a sigmoid with two bends (Pathirane 1975;Lejoly-Gabriel 1978).…”

Section: Determination Of the Main Pollen Seasonmentioning

confidence: 98%

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: Determination Of the Main Pollen Seasonmentioning

confidence: 98%

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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“…pollen seasons. A threshold method, such as defining the start of the pollen season as the day when the cumulative daily count reached a certain figure (Σ75 or Σ100 method) (Dreissen et al 1989(Dreissen et al , 1990Adams-Groom et al 2002) or by defining the beginning and end of the season as the first and last days when the daily pollen count is greater than or equal to a certain threshold (Sanchez-Mesa et al 2003), was not used in this study because in some years very little pollen was recorded in the season and so mandatory thresholds may not have been reached. Instead, retrospective methods that define the season as the period in which 90% (Nilsson and Persson 1981), 95% (Goldberg et al 1988) and 98% (Emberlin et al 1993) of the total season's catch occurred were investigated.…”

Section: Analysis Of Pollen Seasonsmentioning

confidence: 99%

Changes in the pollen seasons of the early flowering trees Alnus spp. and Corylus spp. in Worcester, United Kingdom, 1996–2005

Emberlin

Smith

et al. 2006

Int J Biometeorol

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Previous work on Betula spp. (birch) in the UK and at five sites in Europe has shown that pollen seasons for this taxon have tended to become earlier by about 5-10 days per decade in most regions investigated over the last 30 years. This pattern has been linked to the trend to warmer winters and springs in recent years. However, little work has been done to investigate the changes in the pollen seasons for the early flowering trees. Several of these, such as Alnus spp. and Corylus spp., have allergens, which cross-react with those of Betula spp., and so have a priming effect on allergic people. This paper investigates pollen seasons for Alnus spp. and Corylus spp. for the years 1996-2005 at Worcester, in the West Midlands, United Kingdom. Pollen data for daily average counts were collected using a Burkard volumetric trap sited on the exposed roof of a three-story building. The climate is western maritime. Meteorological data for daily temperatures (maximum and minimum) and rainfall were obtained from the local monitoring sites. The local area up to approximately 10 km surrounding the site is mostly level terrain with some undulating hills and valleys. The local vegetation is mixed farmland and deciduous woodland. The pollen seasons for the two taxa investigated are typically late December or early January to late March. Various ways of defining the start and end of the pollen seasons were considered for these taxa, but the most useful was the 1% method whereby the season is deemed to have started when 1% of the total catch is achieved and to have ended when 99% is reached. The cumulative catches (in grains/m(3)) for Alnus spp. varied from 698 (2001) to 3,467 (2004). For Corylus spp., they varied from 65 (2001) to 4,933 (2004). The start dates for Alnus spp. showed 39 days difference in the 10 years (earliest 2000 day 21, latest 1996 day 60). The end dates differed by 26 days and the length of season differed by 15 days. The last 4 years in the set had notably higher cumulative counts than the first 2, but there was no trend towards earlier starts. For Corylus spp. start days also differed by 39 days (earliest 1999 day 5, latest 1996 day 44). The end date differed by 35 days and length of season by 26 days. Cumulative counts and lengths of season showed a distinct pattern of alternative high (long) and low (short) years. There is some evidence of a synchronous pattern for Alnus spp.. These patterns show some significant correlations with temperature and rainfall through the autumn, winter and early spring, and some relationships with growth degree 4s and chill units, but the series is too short to discern trends. The analysis has provided insight to the variation in the seasons for these early flowering trees and will form a basis for future work on building predictive models for these taxa.

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“…It is important to eliminate these tails so that the pollen season can be identified. In the current analysis two methods are investigated; the 98% method, whereby the season starts when I% of the total has been recorded and ends at the 99% level, and the sum75 method (Driessen et al 1989(Driessen et al , 1990 whereby the start of the season is defined as the first day when the cumulative daily count reaches 75 grains.…”

Section: Definition Of Pollen Seasonsmentioning

confidence: 99%

Variation in the start of the grass pollen season at selected sites in the united kingdom 1987–1992

Emberlin

Jones

Bailey

et al. 1994

Grana

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Prediction of the start of the grass pollen season for the western part of the Netherlands

Cited by 56 publications

References 9 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)

Changes in the pollen seasons of the early flowering trees Alnus spp. and Corylus spp. in Worcester, United Kingdom, 1996–2005

Variation in the start of the grass pollen season at selected sites in the united kingdom 1987–1992

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