A STUDY ON AI IN AIR QUALITY METRICS

Abstract: Air pollution poses a significant threat to the ecosystems. Accurate prediction of pollutant levels is essential for informed decision-making and effective conservation policies. This paper explores the application of linear regression as a predictive tool to estimate air pollutant levels, emphasizing its utility in environmental management. By leveraging historical data and identifying key influencing factors, linear regression can provide transparent, interpretable predictions that aid in setting realistic norms and conservation goals. The study utilizes the "Air Quality Index - New Delhi" dataset to illustrate the application of linear regression in forecasting air quality, demonstrating its potential in proactive environmental monitoring and policy-making.

Keywords: Air Pollution, Linear Regression, Predictive Analysis, Air Quality Index (AQI).