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Int J Biometeorol ; 59(3): 339-46, 2015 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-24844880

RESUMEN

Alternaria is frequently found as airborne fungal spores and is recognized as an important cause of respiratory allergies. The aerobiological monitoring of fungal spores was performed using a Burkard volumetric spore traps. To establish predicting variables for daily and weakly spore counts, a stepwise multiple regression between spore concentrations and independent variables (meteorological parameters and lagged values from the series of spore concentrations: previous day or week concentration (Alt t - 1) and mean concentration of the same day or week in other years (C mean)) was made with data obtained during 2009-2011. Alternaria conidia are present throughout the year in the atmosphere of Tetouan, although they show important seasonal fluctuations. The highest levels of Alternaria spores were recorded during the spring and summer or autumn. Alternaria showed maximum daily values in April, May or October depending on year. When the spore variables of Alternaria, namely C mean and Alt t - 1, and meteorological parameters were included in the equation, the resulting R (2) satisfactorily predict future concentrations for 55.5 to 81.6 % during the main spore season and the pre-peak 2. In the predictive model using weekly values, the adjusted R (2) varied from 0.655 to 0.676. The Wilcoxon test was used to compare the results from the expected values and the pre-peak spore data or weekly values for 2012, indicating that there were no significant differences between series compared. This test showed the C mean, Alt t - 1, frequency of the wind third quadrant, maximum wind speed and minimum relative humidity as the most efficient independent variables to forecast the overall trend of this spore in the air.


Asunto(s)
Contaminantes Atmosféricos/aislamiento & purificación , Alternaria , Modelos Teóricos , Esporas Fúngicas/aislamiento & purificación , Microbiología del Aire , Recuento de Colonia Microbiana , Monitoreo del Ambiente/estadística & datos numéricos , Predicción , Marruecos , Análisis de Regresión , Estaciones del Año , Tiempo (Meteorología)
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