Jose María Maya Manzano

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Profesor Ayudante Doctor

Universidad de Extremadura

Spain

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About

Jose María Maya Manzano is an Assistant Professor at the Universidad de Extremadura in Spain. His research primarily focuses on the analysis of airborne pollen concentrations, bioaerosol detection, and the impacts of climate change on flowering patterns in urban environments. Recent publications include studies on pollen monitoring, meteorological influences on sampling devices, and machine learning applications for environmental analysis.

Recent Grants

Grant: Close

POllen Monitoring and ModELling (POMMEL)

Open Date: 2018-02-01

Close Date: 2020-03-01

Articles (18)

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Collaborators (22)

Claudia Lang

University Hospital of Zurich

SWITZERLAND

Jose María Maya Manzano

Research Interests

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About

Recent Grants

Articles (18)

Nuevas aportaciones florísticas para Extremadura (España)

Comparative Analysis of Real-Time Fluorescence-Based Spectroscopic Instruments: Bioaerosol Detection in the Urban Environment of Dublin City, Ireland

Airborne pollen concentrations overpass expectations in the tropical city of Medellín, Colombia

Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices

Effects of climate change on Platanus flowering in Western Mediterranean cities: Current trends and future projections

Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree

Perceptions of change in the environment caused by the COVID-19 pandemic: Implications for environmental policy

Correction to: Variability between Hirst-type pollen traps is reduced by resistance-free flow adjustment

Variability between Hirst-type pollen traps is reduced by resistance-free flow adjustment

Machine learning methods for low-cost pollen monitoring – Model optimisation and interpretability

Diurnal pattern of Poaceae and Betula pollen flight in Central Europe

Accuracy of a hand-held resistance-free flowmeters for flow adjustments of Hirst-Type pollen traps

Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests

Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps

Environmental exposure and sensitization patterns in a Swiss alpine pediatric cohort

Alternaria spore exposure in Bavaria, Germany, measured using artificial intelligence algorithms in a network of BAA500 automatic pollen monitors

A comprehensive aerobiological study of the airborne pollen in the Irish environment

A first evaluation of multiple automatic pollen monitors run in parallel

Collaborators (22)

Claudia Lang

Aoife Donnelly

Marianela Zanolla

David O'Connor

Tomas Rodríguez-Riaño

Sophie Anna Mills

Carsten Ambelas Skjøth

Sergio Reyes-Corredera

Ángel Enrique Salvo Tierra

Bernard Alain Clot

Ellen Renner

Juan Manuel Artés Vivancos

José Gonçalves

Ana Ortega-Olivencia

Eoin McGillicuddy

Łukasz Grewling

Jaime Francisco Pereña Ortiz

Eusebio López

Moises Martinez-Bracero

Celia Antunes

Arturo H. Ariño

Matt Smith

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