| Autor: |
Sofia G; Department of Agricultural, Food and Environmental Sciences, Research Unit of Applied Botany, University of Perugia, Perugia, Italy. sofia.ghitarrini@studenti.unipg.it., Emma T; Department of Agricultural, Food and Environmental Sciences, Research Unit of Applied Botany, University of Perugia, Perugia, Italy., Veronica T; Department of Agricultural, Food and Environmental Sciences, Research Unit of Applied Botany, University of Perugia, Perugia, Italy., Giuseppe F; Department of Agricultural, Food and Environmental Sciences, Research Unit of Applied Botany, University of Perugia, Perugia, Italy. |
| Abstrakt: |
Many works carried out in the last decades have shown that the pollen season for taxa flowering in winter and spring, in temperate regions, has tended to be earlier, probably due to the continuous rise in temperature. The mean annual temperature in Perugia, Central Italy, was about 0.5 °C higher in the last three decades compared with that registered from 1952 to 1981. The increase of temperature took place mainly in winter and spring, while no significant variation was recorded during the summer and autumn. This scenario shows variations in the timing and behavior of flowering of many spontaneous plants such as grasses, whose phenology is strongly influenced by air temperature. This work reports fluctuations in the airborne grass pollen presence in Perugia over a 33-year period (1982-2014), in order to study the influence of the warming registered in recent years on the behavior of pollen release of this taxon. The grass pollen season in Perugia typically lasts from the beginning of May to late July. The start dates showed a marked trend to an earlier beginning of the season (-0.4 day/year), as well as a strong correlation with the average temperatures of March and April. The peak is reached around 30th May, but the annual pollen index (API) is following a decreasing trend. The correlation between starting dates and spring temperatures could be interesting for the constitution of a forecasting model capable of predicting the presence of airborne grass pollen, helping to plan therapies for allergic people. |
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