Changes in the Pollen Seasons of the Early Flowering Trees Alnus spp. and Corylus spp. in Worcester United Kingdom 1996-2005

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-storey 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/m3) 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.

Constructing a 7-day Ahead Forecast Model for Grass Pollen at North London, United Kingdom

A number of media outlets now issue medium-range (~7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts. The objective of this study is to construct a medium-range (< 7 day) forecast model for grass pollen at north London. The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990-1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post peak periods of grass pollen release. The forecast consisted of five regression models. Two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods. Overall the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis. This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials.

Examining High Magnitude Grass Pollen Episodes at Worcester, United Kingdom, Using Back-Trajectory Analysis

Trajectory analysis is a valuable tool that has been used before in aerobiological studies, to investigate the movement of airborne pollen. This study has employed back-trajectories to examine the four highest grass pollen episodes at Worcester, during the 2001 grass pollen season. The results have shown that the highest grass pollen counts of the 2001 season were reached when air masses arrived from a westerly direction. Back-trajectory analysis has a limited value to forecasters because the method is retrospective and cannot be employed directly for forecasting. However, when used in conjunction with meteorological data this technique can be used to examine high magnitude events in order to identify conditions that lead to high pollen counts.

Application of WRF-Chem to Forecasting PM10 Concentration over Poland

The meteorological and chemical transport model WRF-Chem was implemented to forecast PM10 concentrations over Poland. WRF-Chem version 3.5 was configured with three one way nested domains using the GFS meteorological data and the TNO MACC II emissions. Forecasts, with 48h lead time, were run for a winter and summer period 2014. WRF-Chem in general captures the variability in observed PM10 concentrations, but underestimates some peak concentrations during winter-time. The peaks coincide with either stable atmospheric condition during nighttime in the lower part of the planetary boundary layer or on days with very low surface temperatures. Such episodes lead to increased combustion in residential heating, where hard coal is the main fuel in Poland. This suggests that a key to improvement in the model performance for the peak concentrations is to focus on the simulation of PBL processes and the distribution of emissions with high resolution in WRF-Chem.

Application of WRF-Chem to Forecasting PM10 Concentration Over Poland

The meteorological and chemical transport model WRF-Chem was implemented to forecast PM10 concentrations over Poland. WRF-Chem version 3.5 was configured with three one-way nested domains using the GFS meteorological data and the TNO MACC II emissions. The 48 hour forecasts were run for each day of the winter and summer period of 2014 and there is only a small decrease in model performance for winter with respect to forecast lead time. The model in general captures the variability in observed PM10 concentrations for most of the stations. However, for some locations and specific episodes, the model performance is poor and the results cannot yet be used by official authorities. We argue that a higher resolution sector-based emission data will be helpful for this analysis in connection with a focus on planetary boundary layer processes in WRF-Chem and their impact on the initial distribution of emissions on both time and space.

Alternaria Spores in the Air Across Europe: Abundance, Seasonality and Relationships with Climate, Meteorology and Local Environment

We explored the temporal and spatial variations in airborne Alternaria spore quantitative and phenological features in Europe using 23 sites with annual time series between 3 and 15 years. The study covers seven countries and four of the main biogeographical regions in Europe. The observations were obtained with Hirst-type spore traps providing time series with daily records. Site locations extend from Spain in the south to Denmark in the north and from England in the West to Poland in the East. The study is therefore the largest assessment ever carried out for Europe concerning Alternaria. Aerobiological data were investigated for temporal and spatial patterns in their start and peak season dates and their spore indices. Moreover, the effects of climate were checked using meteorological data for the same period, using a crop growth model. We found that local climate, vegetation patterns and management of landscape are governing parameters for the overall spore concentration, while the annual variations caused by weather are of secondary importance but should not be neglected. The start of the Alternaria spore season varies by several months in Europe, but the peak of the season is more synchronised in central northern Europe in the middle of the summer, while many southern sites have peak dates either earlier or later than northern Europe. The use of a crop growth model to explain the start and peak of season suggests that such methods could be useful to describe Alternaria seasonality in areas with no available observations.

Application of Redundancy Analysis for Aerobiological Data

An aerobiological survey was conducted through five consecutive years (2006–2010) at Worcester (England).The concentration of 20 allergenic fungal spore types was measured using a 7-day volumetric spore trap. The relationship between investigated fungal spore genera and selected meteorological parameters (maximum, minimum, mean and dew point temperatures, rainfall, relative humidity, air pressure,wind direction) was examined using an ordination method(redundancy analysis) to determine which environmental factors favoured their most abundance in the air and whether it would be possible to detect similarities between different genera in their distribution pattern. Redundancy analysis provided additional information about the biology of the studied fungi through the results of the Spearman’s rank correlation. Application of the variance inflation factor in canonical correspondence analysis indicated which explanatory variables were auto-correlated and needed to be excluded from further analyses. Obtained information will be consequently implemented in the selection of factors that will be a foundation for forecasting models for allergenic fungal spores in the future.