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Clinical & Experimental Allergy

Regional variations in grass pollen seasons in the UK, long-term trends and forecast models

Authors


Emberlin Pollen Research Unit, University College Worcester, Henwick Grove, Worcester WR2 6AJ, UK.

Abstract

Background

Three sites in the UK have daily records of pollen spanning several decades, giving the longest data sets worldwide. Previous research on London data revealed decreasing severity of grass pollen seasons. This is often taken as a model for the whole country but comparisons with Derby and Cardiff, in different regions of local climate and land-use, emphasize the need for regional studies.

Objective

The grass pollen seasons were analysed for three contrasting long-term sites to provide regional insight into the changing incidence of hay fever.

Methods

Pollen was monitored by volumetric instruments using standard techniques. Data have been taken from 1961 to 1993 to examine variation in grass pollen seasons in relation to land-use changes, cumulative temperatures and rainfall. Models were developed to predict total seasonal catches.

Results

At Cardiff the annual counts and severity increased in the 1960s, declined in the 1970s and rose again in the 1980s. At Derby and London the annual counts and severity declined but at different rates. Start dates have tended to become earlier at Cardiff and Derby, but later at London. Trends in annual counts and severity are similar to changes in grassland areas but they cannot be accounted for entirely by these. Weather in spring and early summer has tended to become warmer but there are no sustained patterns in June and July. No trends are apparent in the rainfall records for these months. The maximum explanation (r2≥ 95%) in forecast models was obtained using 10-day aggregates of weather.

Conclusion

The contrasting patterns both in the pollen records and land-use changes between the three sites emphasize the need for regional data. The predictive models achieved a high degree of explanation enabling pollen season severity to be forecast with high confidence shortly before the start date.

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