Alnus Airborne Pollen Trends during the Last 26 Years for Improving Machine Learning-Based Forecasting Methods
Author:
Novo-Lourés María12, Fernández-González María3ORCID, Pavón Reyes12ORCID, Espinosa Kenia C. Sánchez3ORCID, Laza Rosalía12ORCID, Guada Guillermo3ORCID, Méndez José R.12ORCID, Fdez-Riverola Florentino12ORCID, Rodríguez-Rajo Francisco Javier3ORCID
Affiliation:
1. CINBIO, Department of Computer Science, ESEI—Escuela Superior de Ingeniería Informática, University of Vigo, 32004 Ourense, Spain 2. SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain 3. Department of Plant Biology and Soil Sciences, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
Abstract
Black alder (Alnus glutinosa (L.) Gaertn.) is a species of tree widespread along Europe and belongs to mixed hardwood forests. In urban environments, the tree is usually located along watercourses, as is the case in the city of Ourense. This taxon belongs to the betulaceae family, so it has a high allergenic potential in sensitive people. Due to the high allergenic capacity of this pollen type and the increase in global temperature produced by climate change, which induces a greater allergenicity, the present study proposes the implementation of a Machine Learning (ML) model capable of accurately predicting high-risk periods for allergies among sensitive people. The study was carried out in the city of Ourense for 28 years and pollen data were collected by means of the Hirst trap model Lanzoni VPPS-2000. During the same period, meteorological data were obtained from the meteorological station of METEOGALICIA in Ourense. We observed that Alnus airborne pollen was present in the study area during winter months, mainly in January and February. We found statistically significant trends for the end of the main pollen season with a lag trend of 0.68 days per year, and an increase in the annual pollen integral of 112 pollen grains per year and approximately 12 pollen grains/m3 per year during the pollen peak. A Spearman correlation test was carried out in order to select the variables for the ML model. The best ML model was Random Forest, which was able to detect those days with medium and high labels.
Funder
Consellería de Educación, Universidades e Formación Profesional, Xunta de Galicia Consellería de Sanidade, Xunta de Galicia
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