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.

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.

Potential Impact of Climate Change on Fungal Distributions: Analysis of 2 Years of Contrasting Weather in the UK

The impact of climate change on fungal growth and spore production is less well documented than for allergenic pollen grains, although similar implications for respiratory tract diseases in humans occur. Fungal spores are commonly described as either “dry” or “wet” according to the type of weather associated with their occurrence in the air. This study examined the distribution of selected fungal spores (Alternaria spp., Cladosporium spp., Didymella spp., Epicoccum spp., Leptosphaeria spp. and rusts) occurring in the West Midlands of UK during 2 years of contrasting weather. Spore specimens were collected using a 7-day volumetric air sampler and then analysed with the aid of light microscopy. Distributions of spores were then studied using normality tests and Mann–Whitney U test, while relationships with meteorological parameters were investigated using Spearman’s rank test and angular-linear correlation for wind direction analysis. Our results showed that so-called wet spores were more sensitive to the weather changes showing statistically significant differences between the 2 years of study, in contrast to “dry” spores. We predict that in following years we will observe accelerated levels in allergenic fungal spore production as well as changes in species diversity. This study could be a starting point to revise the grouping system of fungal spores as either “dry” or “wet” types and their response to climate change

An Analysis of Local Wind and Air Mass Directions and Their Impact on Cladosporium Distribution Using HYSPLIT and Circular Statistics

Ecological studies that examine species-environment relationships are often limited to several meteorological parameters, i.e. mean air temperature, relative humidity, precipitation, vapour pressure deficit and solar radiation. The impact of local wind, its speed and direction are less commonly investigated in aerobiological surveys mainly due to difficulties related to the employment of specific analytical tools and interpretation of their outputs. Identification of inoculum sources of economically important plant pathogens, as well as highly allergenic bioaerosols like Cladosporium species, has not been yet explored with remote sensing data and atmospheric models such as Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). We, therefore, performed an analysis of 24 h intra-diurnal cycle of Cladosporium spp. spores from an urban site in connection with both the local wind direction and overall air mass direction computed by HYSPLIT. The observational method was a volumetric air sampler of the Hirst design with 1 h time resolution and corresponding optical detection of fungal spores with light microscopy. The atmospheric modelling was done using the on-line data set from GDAS with 1° resolution and circular statistical methods. Our results showed stronger, statistically significant correlation (p ≤ 0.05) between high Cladosporium spp. spore concentration and air mass direction compared to the local wind direction. This suggested that a large fraction of the investigated fungal spores had a regional origin and must be located more than a few kilometers away from the sampling point.