The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical Mediterranean climate.

Biological particles in the air such as pollen grains can cause environmental problems in the allergic population. Medical studies report that a prior knowledge of pollen season severity can be useful in the management of pollen-related diseases. The aim of this work was to forecast the severity of the Poaceae pollen season by using weather parameters prior to the pollen season. To carry out the study a historical database of 21 years of pollen and meteorological data was used. First, the years were grouped into classes by using cluster analysis. As a result of the grouping, the 21 years were divided into 3 classes according to their potential allergenic load. Pre-season meteorological variables were used, as well as a series of characteristics related to the pollen season. When considering pre-season meteorological variables, winter variables were separated from early spring variables due to the nature of the Mediterranean climate. Second, a neural network model as well as a discriminant linear analysis were built to forecast Poaceae pollen season severity, according to the three classes previously defined. The neural network yielded better results than linear models. In conclusion, neural network models could have a high applicability in the area of prevention, as the allergenic potential of a year can be determined with a high degree of reliability, based on a series of meteorological values accumulated prior to the pollen season.

Pollen allergy related to the area of residence in the city of Córdoba, south-west Spain.

The aim of this study was to analyse the relationship between the distribution of hay-fever patients in the city of Córdoba, south-western Iberian Peninsula, and the specific atmospheric biological content originating from local sources. Four different districts were established in the metropolitan area of the city, according to vegetational and urbanistic characteristics. Air samples were taken in each area using portable Hirst-type samplers (Lanzoni VPPS 1000) and the spectrum of biological content was defined. Patients attending the Allergy Unit at Córdoba Teaching Hospital in 2000 with allergic rhinitis and/or asthma, and displaying a positive reaction to aeroallergen extracts, were distributed within the areas as a function of their district of residence. Aerobiological results revealed differences in pollen content between areas, in terms of both quantity and number of pollen types recorded. These differences were largely due to proximity to rural areas, prevalence of pollen from typically urban species and the possible effect of urban architecture as a barrier to the dispersal/ concentration of particles and other pollutants. Patients were not uniformly distributed within the city. The majority lived in districts in which pollen from rural species was mixed with pollen from ornamentals. Patients living in typically urban districts displayed a higher prevalence of allergy to pollen from ornamentals. It is concluded that a high degree of exposure to the same environment may influence the development of sensitisation to the particular pollen load associated with that area.