Localized Detection, Control and Combating of Greenhouse Gas Emissions using IoT and Data Science

This research presents a pioneering dual-strategy framework.Sophisticated mathematical modeling, artificial intelligence (AI), and machine learning (ML) techniques are leveraged in order to address critical urban environmental challenges. The system functions in real-time, computing the Air Quality Index (AQI) by assimilating data from diverse sources. In parallel, an AI-driven model employs a comprehensive dataset to recommend tailored strategies for optimal green cover expansion, accounting for variables such as plant species selection, soil attributes, and carbon sequestration rates. The initiative is aligned with established environmental policies and facilitates community engagement through a robust feedback loop. Expert collaboration guides this dynamic project, promising a paradigm shift in urban sustainability practices. Rigorous evaluation and refinement mechanisms guarantee the adaptability and effectiveness of the framework. This research not only advances the field of environmental engineering but also provides actionable insights for policymakers and urban planners seeking for greener, healthier urban environments.