Examining Ambrosia Pollen Episodes at Poznań (Poland) Using Back-trajectory Analysis

The pollen grains of Ambrosia spp. are considered to be important aeroallergens in parts of southern and central Europe. Back-trajectories have been analysed with the aim of finding the likely sources of Ambrosia pollen grains that arrived at Poznań (Poland). Temporal variations in Ambrosia pollen at Poznań from 1995–2005 were examined in order to identify Ambrosia pollen episodes suitable for further investigation using back-trajectory analysis. The trajectories were calculated using the transport model within the Lagrangian air pollution model, ACDEP (Atmospheric Chemistry and Deposition). Analysis identified two separate populations in Ambrosia pollen episodes, those that peaked in the early morning between 4 a.m. and 8 a.m., and those that peaked in the afternoon between 2 p.m. and 6 p.m.. Six Ambrosia pollen episodes between 2001 and 2005 were examined using backtrajectory analysis. The results showed that Ambrosia pollen episodes that peaked in the early morning usually arrived at Poznań from a southerly direction after passing over southern Poland, the Czech Republic, Slovakia and Hungary, whereas air masses that brought Ambrosia pollen to Poznań during the afternoon arrived from a more easterly direction and predominantly stayed within the borders of Poland. Back-trajectory analysis has shown that there is a possibility that long-range transport brings Ambrosia pollen to Poznań from southern Poland, the Czech Republic, Slovakia and Hungary. There is also a likelihood that Ambrosia is present in Poland, as shown by the arrival of pollen during the afternoon that originated primarily from within the country.

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.

Temporal and Georaphical Variations in Grass Pollen Seasons in Areas of Western Europe: an Analysis of Season dates at Sites of the European Pollen Information System.

Geographical and temporal variations in the start dates of grass pollen seasons are described for selected sites of the European Pollen Information Service. Daily average grass pollen counts are derived from Network sites in Finland, the Netherlands, Denmark, United Kingdom, Austria, Italy and Spain, giving a broad longitudinal transect over Western Europe. The study is part of a larger project that also examines annual and regional variations in the severity, timing of the peak and duration of the grass pollen seasons. For several sites, data are available for over twenty years enabling long term trends to be discerned. The analyses show notable contrasts in the progression of the seasons annually with differing lag times occurring between southern and northern sites in various years depending on the weather conditions. The patterns identified provide some insight into geographical differences and temporal trends in the incidence of pollinosis. The paper discusses the main difficulties involved in this type of analysis and notes possibilities for using data from the European Pollen Information service to construct pan European predictive models for pollen seasons.

Long-term and Short-term Forecast Models for Poaceae (Grass) Pollen in Poznan, Poland, Constructed Using Regression Analysis

Airborne concentrations of Poaceae pollen have been monitored in Poznań for more than ten years and the length of the dataset is now considered sufficient for statistical analysis. The objective of this paper is to produce long-range forecasts that predict certain characteristics of the grass pollen season (such as the start, peak and end dates of the grass pollen season) as well as short-term forecasts that predict daily variations in grass pollen counts for the next day or next few days throughout the main grass pollen season. The method of forecasting was regression analysis. Correlation analysis was used to examine the relationship between grass pollen counts and the factors that affect its production, release and dispersal. The models were constructed with data from 1994-2004 and tested on data from 2005 and 2006. The forecast models predicted the start of the grass pollen season to within 2 days and achieved 61% and 70% accuracy on a scale of 1-4 when forecasting variations in daily grass pollen counts in 2005 and 2006 respectively. This study has emphasised how important the weather during the few weeks or months preceding pollination is to grass pollen production, and draws attention to the importance of considering large-scale patterns of climate variability (indices of the North Atlantic Oscillation) when constructing forecast models for allergenic pollen.

Investigating Ambrosia Pollen Episodes in Poland Using Back-Trajectory Analysis

Background: The pollen grains of Ambrosia spp. are considered to be important aeroallergens. Previous studies have shown that the long-range transport of Ambrosia pollen to Poland is intermittent and mainly related to the passage of air masses over the Carpathian and Sudetes mountains from sources to the south, e.g. the Czech Republic, Slovakia and Hungary. In this study, Ambrosia pollen counts and back-trajectories from specific episodes in 1999 and 2002 have been analysed with the aim of identifying possible new sources of Ambrosia pollen arriving at three sites in Poland. Method: The combination of Ambrosia pollen measurements (daily average and bi-hourly concentrations) and air mass trajectory calculations were used to investigate two Ambrosia pollen episodes recorded at Rzeszow, Krakow and Poznań on the 4th and 5th September 1999 and 3rd September 2002. Ambrosia pollen counts were recorded by volumetric spore traps of the Hirst design. Trajectories were calculated using the transport model within the Lagrangian air pollution model, ACDEP (Atmospheric Chemistry and Deposition). Results: The collective results of pollen measurements and back-trajectory analysis indicate plumes of Ambrosia pollen travelling up through Poland from the southeast during the investigated episodes. In 1999, the plume was first recorded at Rzeszow in Southeastern Poland during the morning of the 4th September. Its route can be followed as it passed Krakow during the afternoon of the 4th, and later on the 4th and 5th September at Poznań. Similarly, back-trajectories calculated during the morning and afternoon from Krakow and Rzeszow on the 3rd September 2002 indicates that the air masses arrived at these sites from the East or Southeast. Conclusion: This study shows the progress of Ambrosia plumes into Poland from the southeast. Ambrosia pollen release occurs mainly during the day and so a midday peak in Ambrosia pollen concentrations may indicate a local source. However, if the plume of Ambrosia pollen tracked along its northwesterly path over Poland during investigated episodes did not originate from inside Poland, then it is likely that it came from the Ukraine. This identifies a possible new source of ragweed pollen for Poland. Trajectory analysis can only show the path along which an air mass travels, not the specific source area. Further investigation could therefore include source based transport models such as 3D Eulerian atmospheric transport models.

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.

A Community-Level, Mesoscale Analysis of Fish Assemblage Structure in Shoreline Habitats of a Large River Using Multivariate Regression Trees.

The mesoscale (100–102 m) of river habitats has been identified as the scale that simultaneously offers insights into ecological structure and falls within the practical bounds of river management. Mesoscale habitat (mesohabitat) classifications for relatively large rivers, however, are underdeveloped compared with those produced for smaller streams. Approaches to habitat modelling have traditionally focused on individual species or proceeded on a species-by-species basis. This is particularly problematic in larger rivers where the effects of biological interactions are more complex and intense. Community-level approaches can rapidly model many species simultaneously, thereby integrating the effects of biological interactions while providing information on the relative importance of environmental variables in structuring the community. One such community-level approach, multivariate regression trees, was applied in order to determine the relative influences of abiotic factors on fish assemblages within shoreline mesohabitats of San Pedro River, Chile, and to define reference communities prior to the planned construction of a hydroelectric power plant. Flow depth, bank materials and the availability of riparian and instream cover, including woody debris, were the main variables driving differences between the assemblages. Species strongly indicative of distinctive mesohabitat types included the endemic Galaxias platei. Among other outcomes, the results provide information on the impact of non-native salmonids on river-dwelling Galaxias platei, suggesting a degree of habitat segregation between these taxa based on flow depth. The results support the use of the mesohabitat concept in large, relatively pristine river systems, and they represent a basis for assessing the impact of any future hydroelectric power plant construction and operation. By combing community classifications with simple sets of environmental rules, the multivariate regression trees produced can be used to predict the community structure of any mesohabitat along the reach.