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Article

Data Mining Methods to Detect Airborne Pollen of Spring Flowering Arboreal Taxa

Department of Plant Biology and Soil Sciences, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
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Author to whom correspondence should be addressed.
Academic Editor: David F. Greene
Forests 2021, 12(12), 1801; https://doi.org/10.3390/f12121801
Received: 8 October 2021 / Revised: 9 December 2021 / Accepted: 16 December 2021 / Published: 18 December 2021
(This article belongs to the Special Issue Tree Pollen and Floral Biology)
Variations in the airborne pollen load are among the current and expected impacts on plant pollination driven by climate change. Due to the potential risk for pollen-allergy sufferers, this study aimed to analyze the trends of the three most abundant spring-tree pollen types, Pinus, Platanus and Quercus, and to evaluate the possible influence of meteorological conditions. An aerobiological study was performed during the 1993–2020 period in the Ourense city (NW Spain) by means of a Hirst-type volumetric sampler. Meteorological data were obtained from the ‘Ourense’ meteorological station of METEOGALICIA. We found statistically significant trends for the Total Pollen in all cases. The positive slope values indicated an increase in pollen grains over the pollen season along the studied years, ranging from an increase of 107 to 442 pollen grains. The resulting C5.0 Decision Trees and Rule-Based Models coincided with the Spearman’s correlations since both statistical analyses showed a strong and positive influence of temperature and sunlight on pollen release and dispersal, as well as a negative influence of rainfall due to washout processes. Specifically, we found that slight rainfall and moderate temperatures promote the presence of Pinus pollen in the atmosphere and a marked effect of the daily thermal amplitude on the presence of high Platanus pollen levels. The percentage of successful predictions of the C5.0 models ranged between 62.23–74.28%. The analysis of long-term datasets of pollen and meteorological information provides valuable models that can be used as an indicator of potential allergy risk in the short term by feeding the obtained models with weather prognostics. View Full-Text
Keywords: data mining; C5.0 decision trees algorithm; pollen trends; urban environment; meteorology data mining; C5.0 decision trees algorithm; pollen trends; urban environment; meteorology
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MDPI and ACS Style

González-Fernández, E.; Álvarez-López, S.; Piña-Rey, A.; Fernández-González, M.; Rodríguez-Rajo, F.J. Data Mining Methods to Detect Airborne Pollen of Spring Flowering Arboreal Taxa. Forests 2021, 12, 1801. https://doi.org/10.3390/f12121801

AMA Style

González-Fernández E, Álvarez-López S, Piña-Rey A, Fernández-González M, Rodríguez-Rajo FJ. Data Mining Methods to Detect Airborne Pollen of Spring Flowering Arboreal Taxa. Forests. 2021; 12(12):1801. https://doi.org/10.3390/f12121801

Chicago/Turabian Style

González-Fernández, Estefanía, Sabela Álvarez-López, Alba Piña-Rey, María Fernández-González, and Francisco J. Rodríguez-Rajo. 2021. "Data Mining Methods to Detect Airborne Pollen of Spring Flowering Arboreal Taxa" Forests 12, no. 12: 1801. https://doi.org/10.3390/f12121801

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