Investigation of relationships and interconnections between Pollen and Air Quality data with the aid of Computational Intelligence Methods

The impact of airborne pollen to human health has been recognized as being important, while several studies are suggesting that the synergy between high pollen concentrations and air pollutants may induce and exacerbate allergic reactions, thus affecting overall quality of life of citizens. In this paper, the detailed pollen, air quality and meteorological data for the years 2006 and 2007, for the city of Kuopio (Finland) were analyzed using Self Organizing Maps (SOM) and k-Means clustering, in order to identify complex, non-linear relations and interconnections within the data. The results obtained indicate that there are strong correlations between pollen concentrations of particular species (Birch, Alder and Spruce) with air pollutant concentration, such as Particulate Matter (PM10) and Ozone (O3). Furthermore, pollen concentrations were identified to be strongly dependent on meteorological parameters, indicating negative correlation with humidity and positive correlation with temperature, solar radiation, atmospheric pressure and wind speed for some pollen species. The methodology applied was capable of identifying and quantifying the interconnections between certain pollen and air quality parameters, resulting to crucial information that could be facilitated in the area of synergy investigations between pollen and air pollution.