![]() ![]() Due to non‐linearity and non‐normality of the data set, Cladosporium spore concentrations were predicted using artificial neural networks (ANN). The original factors, as well as with lags (up to 3 days), were used as the explaining variables. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation, maximum and average wind speed, relative humidity, maximum, minimum, average and dew point temperature. Aerobiological sampling was conducted over 2004–2007, using a Lanzoni trap. Monthly forecasting models were developed for the airborne spore concentrations of Cladosporium, the most abundant fungal taxa in the area. Aiming to reduce the risk for allergic individuals, we constructed predictive models for the fungal spore circulation in Szczecin, Poland. Moulds are common aeroallergens and Cladosporium is considered to be one of the more prevalent examples of these. ![]()
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March 2023
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