Harnessing Artificial Intelligence for Real-Time Air Quality Assessment and Pollution Management: A Comprehensive Report

With a particular focus on enhancing environmental sustainability, this study investigates the integration of artificial intelligence (AI) with air quality assessment and anomaly detection systems. This study uses machine learning (ML) and deep learning (DL) models like Long Short-Term Memory (LSTM) and Random Forest (RF) to investigate real-time air quality monitoring and anomaly recognition. These AI methods help address important environmental issues by accurately identifying patterns of pollution and predicting future trends in air quality. The combination of real-time monitoring, predictive abilities, and decision support systems based on AI offers significant developments in environmental sustainability.

The validation process of high-capacity data frameworks, which are essential for real-time air quality assessment systems, is the subject of this paper.  By examining various stages such as data integrity authentication, performance benchmarking, schema consistency checks, and iterative feedback-driven refinement, the research provides a robust methodology for ensuring the scalability, accuracy, and efficiency of these frameworks.   The study emphasizes the importance of validation for managing large data volumes while conserving system performance and reliability in dynamic environments.  This study focuses on AI-powered frameworks to show how AI revolutionized air quality monitoring systems and made sure they could adapt to future technological developments.