RT Journal Article
T1 E-Science workflow: A semantic approach for airborne pollen prediction
A1 Hurtado-Requena, Sandro José
A1 Antequera-Gómez, María Luisa
A1 Barba-González, Cristóbal
A1 Picornell Rodríguez, Antonio
A1 Navas-Delgado, Ismael
K1 Polinosis
AB Allergic rhinitis has become a global health problem in recent decades because airborne pollen is a primary trigger of this respiratory disorder. Moreover, pollinosis can exacerbate the symptoms of asthma and favour respiratory infections. Seasonal pollen trends and climatic circumstances (such as temperature, precipitation, relative humidity, wind speed and direction, and other variables) can impact daily airborne pollen concentrations, influencing local pollen emission and dispersion. Because of that, pollen monitoring and prediction are becoming more relevant to the urban population and scientific interest is put into them. Due to such tasks’ high volume of data, scientists are starting to use computational tools like workflows to automate and speed up the process. Furthermore, using the expert scientific domain is critical for improving the analysis, allowing, among others, a better workflow configuration and data provenance. As semantic web technologies have been revealed as an essential means for knowledge representation, we implemented this workflow information as an ontology using formats like RDF(S) and OWL. Consequently, this paper provides a semantic-enhanced e-Science workflow based on the TITAN framework for pollen forecasting analysis using meteorological data. Furthermore, a catalogue of components is developed on the TITAN framework, which allows the creation of different workflow versions. Two case studies of pollen prediction were developed to test the implementation of the aforementioned methodologies. Both were elaborated with airborne pollen data obtained in the city of Málaga (Spain). Still, one was elaborated for Platanus pollen type (narrow annual main pollination period), while the other was done for Amaranthaceae pollen type (extensive annual main pollination period).
PB Elsevier
YR 2024
FD 2024-01
LK https://hdl.handle.net/10630/36267
UL https://hdl.handle.net/10630/36267
LA eng
NO Hurtado, S., Antequera-Gómez, M. L., Barba-González, C., Picornell, A., & Navas-Delgado, I. (2025). E-Science workflow: A semantic approach for airborne pollen prediction., 111230.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026