TOWARDS A METHOD FOR TAKING ACCOUNT OF UNCERTAINTIES THROUGH THE CALCULATION OF MASSES IN THE CONTEXT OF A FUSION OF HETEROGENEOUS MULTI-SENSOR DATA ON AIR POLLUTION

Air pollution has become a major challenge in the context of climate change. Detecting pollutants, especially fine particles, is crucial for combating pollution and mitigating its adverse effects on human health and the environment. However, the wide variety and variability of data sources introduce inherent uncertainty that is difficult to control. To address this challenge, the proposed method utilizes data fusion techniques to integrate diverse sources and heterogeneous information. By doing so, it aims to enhance the accuracy and reliability of pollutant detection. Advanced data analysis and modeling provide a comprehensive understanding of pollution patterns, sources, and impacts. By leveraging data fusion, the method enables a more effective assessment of pollution levels, identification of hotspots, and evaluation of pollutant dispersion. Additionally, it identifies potential correlations and conflicts between data sources, facilitating robust analysis and decision-making. In summary, this method acknowledges the urgency of addressing air pollution within the context of climate change. By incorporating data fusion and heterogeneous data, it improves monitoring and understanding of this pressing environmental challenge.