The ClimAIr project will expand the evidence-based understanding of climate change, air pollution, and non-communicable respiratory diseases by using Artificial Intelligence (AI) tools. It will gather data on greenhouse gases levels and disaster risks, information on serious air pollutants and respiratory diseases' prevalence. The AI powered tools will be employed to generate better intervention methods and improve public health outcomes. Federated Learning (FL) will be used to develop AI models to protect patients' privacy. By raising public awareness and delivering the ClimAIr tool - specifically designed to health workers, urban planners and policy makers - the project aims to influence policy decisions, promote healthier environments, and reduce respiratory diseases in Europe, which will be tested and validated the ClimAIr tool in specific municipalities that are part of the project. ClimAIr draws on a consortium of 21 partners from 15 European countries, including carefully selected health centres across Europe - in Spain, Luxembourg, Ukraine, Italy, France, Germany, Greece, Romania and Poland - focused on respiratory diseases, which will provide disease data and explore metabolic routes of the studied contaminants/diseases. ClimAIr is composed of an interdisciplinary team formed by research centres, ethical AI and modelling experts, SSH specialists, municipal governance, and a Communication \& Dissemination (C\&D) expert team dedicated to achieving and spread the results of the project.
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Mean Symptom Score of Allergic Rhinitis Patients by Environmental Exposure
Timeframe: Retrospective data collected over a 3-year period prior to study enrollment.
Proteomic Biomarker Levels (NPX) by Environmental Exposure
Timeframe: Samples collected prospectively between months 10 and 15 (October-March, out-of-pollen season).