oa Monitoring of Atmospheric Air Quality and the Impact of Renewable Energy Sources using AI Data Analysis

Air pollution remains a pressing global issue, contributing to millions of premature deaths annually. The combustion of fossil fuels in power generation, industry, and transportation releases harmful pollutants such as NO2, SO2, and PM2-5. The transition to renewable energy sources particularly wind, solar, and bioenergy offers significant air quality improvements by reducing these emissions. This paper explores the role of renewable energy in mitigating air pollution and the integration of artificial intelligence (AI) to monitor and predict air quality changes.

AI-driven approaches, including machine learning models and remote sensing analytics, enhance the ability to assess pollution trends and forecast the impact of energy transitions. Predictive modeling using AI techniques such as support vector machines, random forests, and deep learning (LSTM, GRU) enables accurate forecasting of pollutant levels based on various environmental and energy-related parameters.

Empirical evidence demonstrates that regions with high renewable energy adoption have experienced substantial reductions in air pollution. Case studies from the U.S., Europe, and China confirm that integrating renewables into the energy grid lowers NO2, SO2, and PM2-5 concentrations, leading to significant public health benefits. AI-powered environmental monitoring further strengthens the ability to optimize pollution reduction strategies, ensuring that clean energy transitions yield sustained air quality improvements.