Reproducibility between counts of airborne allergenic pollen from two cities in the East Midlands,UK |
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Authors: | Catherine H Pashley Abbie Fairs Richard E Edwards John P Bailey Julie M Corden Andrew J Wardlaw |
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Institution: | (1) Aerobiology Unit, Institute for Lung Health, Department of Infection, Immunity and Inflammation, University of Leicester, Leicester, LE1 7RH, UK;(2) Department of Biology, University of Leicester, Leicester, LE1 7RH, UK;(3) Midlands Asthma and Allergy Research Association (MAARA), No. 1 Mill, The Wharf, Shardlow, Derby, DE72 2GH, UK |
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Abstract: | Historically in the East Midlands, UK, airborne pollen has been monitored in two cities, Derby and Leicester, situated 41 km
(25 miles) apart. The aim of the present study was to compare aerobiological data from both sites to determine if a forecast
based on data from one site would be sufficient for both, and to address the wider issue of reproducibility between geographically
separated sites. Pollen types recorded could be split into two groups according to annual abundance, maximum daily concentration
and the number of high count days. Six taxa made up the abundant group; ash, birch, grass, oak, nettle-type and yew-type,
representing 90 and 88% of the total air spora for Derby and Leicester, respectively. Three consecutive years of grass and
nettle pollen data are presented, supported by one year of abundant tree pollen data. There were highly significant positive
correlations between the counts obtained. Line charts showing the average number of pollen grains m−3 air day−1 show similar trends, and Bland–Altman plots show little discrepancy between the amounts of pollen counted on any given day.
Each day was classified according to the UK accepted threshold levels for grass. Weighted kappa statistics showed substantial
or almost perfect agreement between the forecast classifications. With the caveat that this would not apply in a region with
restrictions to air flow such as a mountain range or with extreme fluctuations such as a coastline site, this study suggests
that data from a single site is suitable for forecasting a distance of up to 41 km. |
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