Prediction of airborne pollen concentrations for the plane tree as a tool for evaluating allergy risk in urban green areas

• Published in: Landscape and urban planning Vol. 189; pp. 285 - 295
• Main Authors: Lara, Beatriz, Rojo, Jesús, Fernández-González, Federico, Pérez-Badia, Rosa
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2019
• Subjects: Allergy risk; Forecasting model; Meteorological variables; Plane tree pollen; Time series analysis; Plane tree pollen; Forecasting model; Allergy risk; Meteorological variables; Time series analysis
• ISSN: 0169-2046; 1872-6062
• DOI: 10.1016/j.landurbplan.2019.05.002

•Prediction models were constructed for airborne Platanus pollen concentrations.•Airborne Platanus concentrations were found to follow a three-year cyclical pattern.•Plane tree pollen coming from urban green zones can be predicted seven days ahead. The different species of the genus Platanus, commonly known as plane trees, are widely grown as an ornamental species in Mediterranean cities over recent years. The pollen of these species is a major source of allergens. However, surprisingly little published research has addressed methods for predicting the allergy risk prompted by this pollen. In this work, we developed models for predicting airborne Platanus pollen concentrations constructed using data from central Spain. Predictions are very useful to alert citizens and give allergy patients advanced warning of expected high airborne pollen concentrations. The prediction models indicate that airborne Platanus pollen concentrations can be forecasted up to seven days (one week) in advance, using a method which combines the analysis of long-term aerobiological data (in this case, over the eleven-year study period), in order to detect seasonal trends within time-series, with the modelling of short-term fluctuations in airborne pollen concentrations prompted by daily changes in meteorological variables and pollen concentrations over the previous days. The meteorological variables studied were maximum and minimum temperature, rainfall and relative humidity. The results of the validation of prediction models yielded a coefficient of correlation between observed and predicted values of R = 0.7, indicating that these models predict most of the pollen peaks in the airborne pollen curve.