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Modelling and Prediction of Aerosol Precursors in the CLOUD chamber

2025Dissertação de mestrado Acesso aberto
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dc.contributor.advisorBarbosa,António Joaquim Rosa Amorim
dc.contributor.advisorTomé,António Rodrigues
dc.contributor.authorMendeiros,Pedro Miguel Ferreira
dc.contributor.institutionFaculty of Sciences
dc.contributor.institutionDepartment of Physics
dc.date.accessioned2026-01-14T10:00:02Z
dc.date.available2026-01-14T10:00:02Z
dc.date.issued2025
dc.descriptionTese de Mestrado, Engenharia Física, 2025, Universidade de Lisboa, Faculdade de Ciências
dc.description.abstractThe CLOUD (Cosmics Leaving Outdoor Droplets) experiment at CERN addresses the largest source of uncertainty in global climate models: the effect of aerosols in cloud formation. This dissertation focused on optimizing data analysis routines on the CLOUD collaboration, aiming to reduce these climate uncertainties. The primary objective was twofold: to enhance the accuracy of the New Particle Formation rate calculation and to develop a robust predictive model for the rate of change of the key aerosol precursor, sulfuric acid. The methodological work involved the re-implementation and optimization of the Formation Rate calculation script in Python, which critically corrected a dimensional inconsistency in the coagulation sink term found in previous implementations, and incorporated advanced signal processing techniques for a more scientifically rigorous result. Furthermore, Machine Learning models were developed using CLOUD16 campaign data to predict the sulfuric acid concentration, incorporating key experimental parameters. After comparison, the non-linear XGBoost model was selected as the superior predictive tool, demonstrating a greater capacity to capture the complex, non-linear relationships driving sulfuric acid dynamics. In conclusion, this work provides a new, reliable analytical and predictive framework for the CLOUD collaboration, contributing directly to more precise data interpretation and supporting a deeper understanding of aerosol formation mechanisms.en
dc.formatapplication/pdf
dc.identifier.tid204176204
dc.identifier.urihttp://hdl.handle.net/10400.5/116591
dc.language.isoeng
dc.subjectCloud Formation
dc.subjectNew Particle Formation
dc.subjectMachine Learning
dc.subjectAerosols
dc.titleModelling and Prediction of Aerosol Precursors in the CLOUD chamberen
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsopenAccess

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