A0426
Title: Air quality data fusion using fixed rank Kriging with estimates at municipal level
Authors: Alessandro Fusta Moro - University of Bergamo (Italy) [presenting]
Jacopo Rodeschini - University of Bergamo (Italy)
Andrea Moricoli - University of Bergamo (Italy)
Alessandro Fasso - University of Bergamo (Italy)
Abstract: Within the ongoing Italian project "Growing resilient, inclusive, and sustainable" (GRINS), the need for a harmonised dataset containing all relevant variables at the municipal level has emerged. Implementing statistical models on this harmonised dataset about social, economic, and environmental data will allow researchers to gain meaningful insights from the data, providing a useful data-driven framework for policymakers. However, merging hundreds of different variables with different spatial and temporal supports and resolutions represents an important challenge. The purpose is to show how data fusion and the change of support problems are addressed within the statistical framework using the fixed rank Kriging model on air quality data. Air quality data come in different ways: chemical transport models (CTMs), national air quality monitoring network and European satellites (e.g. Sentinel 5P). Each source has its strengths and weaknesses and different spatial and temporal resolutions. It shows how to obtain harmonized data at the municipal level, respecting the peculiarities of each source and capitalizing on their strengths while mitigating their weaknesses. The method used further quantifies uncertainties along with predictions and provides intra-municipal information (e.g. population exposure curve within the same municipality).
