title: Forecasting ragweed pollen characteristics with nonparametric regression methods over the most polluted areas in Europe.
abstract: Nonparametric time-varying regression methods were developed to forecast daily ragweed pollen concentration, and the probability of the exceedance of a given concentration threshold 1 day ahead. Five-day and 10-day predictions of the start and end of the pollen season were also addressed with a nonparametric regression technique combining regression analysis with the method of temperature sum. Our methods were applied to three of the most polluted regions in Europe, namely Lyon (Rhône Valley, France), Legnano (Po River Plain, Italy) and Szeged (Great Plain, Hungary). For a 1-day prediction of both the daily pollen concentration and daily threshold exceedance, the order of these cities from the smallest to largest prediction errors was Legnano, Lyon, Szeged and Legnano, Szeged, Lyon, respectively. The most important predictor for each location was the pollen concentration of previous days. The second main predictor was precipitation for Lyon, and temperature for Legnano and Szeged. Wind speed should be considered for daily concentration at Legnano, and for daily pollen threshold exceedances at Lyon and Szeged. Prediction capabilities compared to the annual cycles for the start and end of the pollen season decreased from west to east. The order of the cities from the lowest to largest errors for the end of the pollen season was Lyon, Legnano, Szeged for both the 5- and 10-day predictions, while for the start of the pollen season the order was Legnano, Lyon, Szeged for 5-day predictions, and Legnano, Szeged, Lyon for 10-day predictions.
authors.affiliation: Department of Climatology and Landscape Ecology, University of Szeged, Szeged, Hungary. makra@geo.u-szeged.hu
authors.forename: László
authors.initials: L
authors.lastname: Makra
authors.forename: István
authors.initials: I
authors.lastname: Matyasovszky
authors.forename: Michel
authors.initials: M
authors.lastname: Thibaudon
authors.forename: Maira
authors.initials: M
authors.lastname: Bonini
mesh_terms: D031164:Ambrosia / Q000254:growth & development / Q000378:metabolism*; D002947:Cities / Q000453:epidemiology; D004784:Environmental Monitoring / Q000706:statistics & numerical data; D062665:Epidemiological Monitoring; D005060:Europe / Q000453:epidemiology; D005544:Forecasting; D006801:Humans; D011058:Pollen / Q000254:growth & development / Q000276:immunology / Q000378:metabolism*; D012044:Regression Analysis; D012621:Seasons; D013997:Time Factors