Costa, Ana Cristina Marinho daMateu Mahiques, JorgeCabral, Pedro da Costa BritoNeto, João Maria Telo Abreu Jardine2024-03-272024-03-272024-03-01http://hdl.handle.net/10362/165510Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThis thesis conducts a spatiotemporal analysis of particulate matter (PM10 and PM2.5) in Lisbon, Portugal, through 2022, utilizing Empirical Bayesian Kriging 3D (EBK3D) and Space-Time Cube analysis to explore pollution dynamics. Focused on how Particulate Matter (PM) levels vary across Lisbon and identifying distinct patterns during different traffic periods on weekdays and weekends. It employs geostatistical methods to analyze pollution levels, offering insights into the spatial and temporal distribution of PM concentrations. Key findings highlight areas with persistent high pollution and temporal fluctuations throughout the city. This research helps in the understanding of Lisbon's PM related air pollution.engUrban Air QualityPM10PM2.5Empirical Bayesian Kriging 3DSpace-Time CubeEmerging Hot Spot AnalysisLocal Outlier AnalysisSDG 3 - Good health and well-beingSDG 11 - Sustainable cities and communitiesmaster thesis203561295