© 2019, ISB. Alternaria and Cladosporium are the most common airborne fungal spores responsible for health problems, as well as for crop pathologies. The study of their behavior in the air is a necessary step for establishing control and prevention measures. The aim of this paper is to develop a logistic regression model for predicting the daily concentrations of airborne Alternaria and Cladosporium fungal spores from meteorological variables. To perform the logistic regression analysis, the concentration levels are binarized using concentration thresholds. The fungal spore data have been obtained at eight aerobiological monitoring stations of the Aerobiological Network of Catalonia (NE Spain). The meteorological data used were the maximum and minimum daily temperatures and daily rainfall provided by the meteorological services. The relationship between the meteorological variables and the fungal spore levels has been modeled by means of logistic regression equations, using data from the period 1995–2012. Values from years 2013–2014 were used for validation. In the case of Alternaria, three equations for predicting the presence and the exceedance of the thresholds 10 and 30 spores/m3 have been established. For Cladosporium, four equations for the thresholds 200, 500, 1000, and 1500 spores/m3 have been established. The temperature and cumulative rainfall in the last 3 days showed a positive correlation with airborne fungal spore levels, while the rain on the same day had a negative correlation. Sensitivity and specificity were calculated to measure the predictive power of the model, showing a reasonable percentage of correct predictions (ranging from 48 to 99%). The simple equations proposed allow us to forecast the levels of fungal spores that will be in the air the next day, using only the maximum and minimum temperatures and rainfall values provided by weather forecasting services.
|Journal||International Journal of Biometeorology|
|Publication status||Published - 1 Jan 2019|
- Fungal spore daily concentration levels
- Fungal spore dispersal patterns
- Prediction model