Correlation of Carbon Monoxide Levels Between Ground Measurements and Sentinel-5P Satellite Data Using Artificial Intelligence Models

In recent years, we have observed a growing interest in using satellite technology to monitor air quality on Earth. This article presents an attempt to correlate carbon monoxide (CO) levels between ground-based data and data obtained from the Sentinel-5P satellite mission. Using modern artificial intelligence models, such as deep neural networks, a multidimensional data analysis was conducted to identify patterns and dependencies. This study represents an important step towards the integration of ground and satellite data for more accurate monitoring and forecasting of global air quality, as well as an attempt to provide information on the carbon monoxide levels on Earth in the absence of ground-based sensors. It highlights the potential for using advanced technology in environmental science to tackle global challenges. The article further delves into the methodology used for data processing and analysis, emphasizing the robustness and accuracy of AI techniques in handling large datasets. This research also opens avenues for future studies focusing on other harmful pollutants, offering a comprehensive view of the environmental health of our planet. This innovative approach is not only critical for environmental monitoring but also holds significant implications for public health, policy-making, and sustainable development.