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Norris-Hill, Jane (1992) Aeropalynology in North London. Doctoral thesis, University of North London.
This study investigates the abundance and dispersal of pollen in an urban area with a view to making accurate predictions of daily pollen counts. Two-hourly pollen counts of more than 60 different pollen types have been recorded over four complete growing seasons in the heavily urbanized area of North London and this is interpreted in relation to meteorological conditions, local pollen source areas, topography and the urban morphology. The analysis and forecasting of airborne pollen concentrations has relevance within three subject areas. Hayfever sufferers are able to use the forecasts to avoid times of high pollen counts; and this is of particular importance as the incidence of allergic respiratory diseases is higher in urban than in rural areas, and the incidence is believed to be increasing. The research has relevance also for Quaternary palynologists u an increased understanding of modem day pollen dispersal can aid in the interpretation of fossil pollen stratigraphies, as well as to the dispersal of particulate pollutants in urban areas.
An initial investigation of pollen abundance illuminates seasonal, daily and two-hourly variations in concentration which are examined in detail in relation to both past and present meteorological conditions. Three pollen taxa (Gramineae, Betula and Platanus are selected for further analysis to develop various models which are able to predict average daily pollen concentrations of these taxa two or three days in advance. The forecasting models are based upon a multiple regressional analysis of pollen counts and twelve meteorological variables and attain levels of explanation approaching S6%. An attempt is made also to predict the severity of the Gramineae pollen season by examining the average daily temperatures in the months preceding the start of the season.
This research is novel in the level of detail of the analysis of pollen concentrations as well as in attempting to predict pollen counts using a variety of methods, especially in the use of accumulated values of maximum daily temperature and sunshine hours.
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