Source regions of ragweed pollen arriving in south-western Poland and the influence of meteorological data on the HYSPLIT model results.

We have investigated the relationship between the inflow of air masses and the ragweed pollen concentration in SW Poland (Wroclaw) for a 10-year period of 2005-2014. The HYSPLIT trajectory model was used to verify whether episodes of high concentrations can be related to regions outside of the main known ragweed centres in Europe, like Pannonian Plain, northern Italy and Ukraine. Furthermore, we used two different meteorological data sets (the global GDAS data set and from the WRF mesoscale model; the meteorological parameters were: U and V wind components, temperature and relative humidity) into HYSPLIT to evaluate the influence of meteorological input on calculated trajectories for high concentration ragweed episodes. The results show that the episodes of high pollen concentration (above 20 pm-3) represent a great part of total recorded ragweed pollen in Wroclaw, but occur rarely and not in all years. High pollen episodes are connected with air masses coming from south and south-west Europe, which confirms the existence of expected ragweed centres but showed that other centres near Wroclaw are not present. The HYSPLIT simulations with two different meteorological inputs indicated that footprint studies on ragweed benefit from a higher resolution meteorological data sets.

The dynamics of the Corylus, Alnus, and Betula pollen seasons in the context of climate change (SW Poland).

The changes in the main features of early spring tree or shrub pollen seasons are important due to the significant impact on the occurrence of pollen-related allergy symptoms. This study shows the results of pollen monitoring for a period of eleven years (2003-2013) using a Burkard volumetric spore trap. The main characteristics of the hazel, alder, and birch pollination season were studied in Wroclaw (SW Poland). The statistical analyses do not show a significant trend of annual total pollen count or shift in timing of the pollen season in the period of analysis. The research confirms a great impact (at the statistically significant level of 0.05) of the heat resources on pollination season (the value of the correlation coefficient ranges from -0.63 up to -0.87). Meteorological variables (e.g. sum of temperature for selected period) were compiled to 5-year running means to examine trends. Changes in the pollination period features due to climate change including both timing and intensity of pollen productivity, would have important consequences for allergy sufferers.

Types of Artemisia pollen season depending on the weather conditions in Wroclaw (Poland), 2002-2011.

The aim of the study was to characterise Artemisia pollen season types according to weather conditions in Wroclaw (south-western Poland) in the years 2002-2011. Over the period analysed, the start date of the pollen season (determined by the 95 % method) ranged from 10 July 2002 to 28 July 2010. The start date of the pollen season can be determined by using Crop Heat Units (CHUs). During the period 2002-2011, the Artemisia pollen season started after the cumulative value of CHUs had reached 2,000-2,100 °C. The three distinguished types of Artemisia pollen season are best described by the frequency of weather types defined by the type of circulation, mean daily air temperature, and the occurrence of rain. The variation in these factors affected the dynamics of the pollen season. The noteworthy frequency of days with rain and high seasonal sum of precipitation totals as well as the dominance of cyclonic weather from the westerly direction had an impact on the extension of the pollen season. The meteorological factors that directly affect pollen release and transport primarily include air humidity, expressed as vapour pressure (r > 0.3, p < 0.01), temperature(r from 0.2 to 0.4, p < 0.01). The relationships between averaged meteorological data and daily pollen concentration were stronger (r > 0.5, p < 0.01). Based on the correlation analysis, the meteorological variables were selected and regression equations were established using stepwise backward regression analysis.