Correlation between pollen counts and symptoms in two different areas of the Iberian Peninsula: Cordoba (Spain) and Evora (Portugal).

This work was designed to analyse the symptoms of pollen allergy in two different cities of the Iberian Peninsula, Cordoba and Evora. Fifteen patients with pollinosis in Cordoba and twenty-seven in Evora were included in this study. Rhinitis symptoms scores were recorded in both cities in order to correlate these data with those of pollen counts. Linear regression analyses were performed in order to find out the contribution of different taxa to the sign of allergy symptoms in the allergenic population. Skin prick tests were also considered. A high and significant correlation was found for Poaceae (0.89), Olea (0.73) and Plantago (0.56) in Cordoba and Olea (0.53) in Evora. Results also suggest that the incidence of the different pollen types in allergy patients is higher in Cordoba than in Evora. A rural lifestyle in Evora that confers a better protection from environmental agents in addition to a less contaminated atmosphere could be the cause.

Pollen allergy in Cordoba city: frequency of sensitization and relation with antihistamine sales.

The occurrence of symptoms in pollen allergy patients in urban areas may be affected by local environmental factors such as sources of pollution, natural and ornamental vegetation, local architecture impeding dispersion, etc. The aim of this study was to analyse the frequency of sensitization in pollen allergy patients and the relationship with antihistamine sales. For this study, a large number of clinical records, together with pharmaceutical and pollen data, were collected between 1999 and 2001 in the city of Córdoba, in the south of the Iberian Peninsula. Differences were observed in the symptoms suffered by pollen allergy patients in different areas of the city due to varying local emission of both biological and non-biological particles. Temporal distribution of symptoms over the three study years was influenced by meteorological factors, especially rainfall patterns; higher water supply to plants was associated with increased airborne pollen concentrations. Air pollution might be one of the main factors affecting the distribution of pollen allergy patients within the city. Recent years have seen a worsening of symptoms and increased sensitization to urban species such as plane-trees.

The use of a neural network to forecast daily grass pollen concentration in a Mediterranean region: the southern part of the Iberian Peninsula.

BACKGROUND: Pollen allergy is a common disease causing hayfever in 15% of the population in Europe. Medical studies report that a prior knowledge of pollen content in the air can be useful in the management of pollen-related diseases. OBJECTIVES: The aim of this work was to forecast daily Poaceae pollen concentrations in the air by using meteorological data and pollen counts from previous days as independent variables. METHODS: Linear regression models and co-evolutive neural network models were used for this study. Pollen was monitored by a Hirst-type spore trap using standard techniques. The data were obtained from the Spanish Aerobiology Network database, University of Cordoba Monitoring Unit. The set of data includes a series of 20 years, from 1982 to 2001. A classification of the years according to their allergenic potential was made using a K-mean cluster analysis with pollen and meteorological parameters. Statistical analysis was applied to all the years of each class with the exception of the most recent year, which was used for model validation. RESULTS: It was observed that cumulative variables and pollen values from previous days are the most important factors in the models. In general, neural network equations produce better results than linear regression equations. CONCLUSION: Co-evolutive neural network models, which obtain the best forecasts (an almost 90% "good" classification), make it possible to predict daily airborne Poaceae pollen concentrations. This new system based on neural network models is a step toward the automation of the pollen forecast process.