Smart Automation for Air Quality Monitoring using ML Techniques

Ongoing industrial expansion presents persistent challenges in tracking and evaluating atmospheric quality in metropolitan regions. Assessing environmental air conditions is crucial for establishing contamination levels within specific areas. Atmospheric contamination continues to represent a major worldwide concern affecting both citizens and regulatory authorities. It results in substantial ecological harm, including acidic precipitation and climate change, while creating severe health hazards such as heart-related illnesses and dermatological malignancies. The primary goal of this framework is to utilize artificial intelligence techniques for computing the Air Quality Index (AQI). To guarantee reliable forecasting, the framework implements robust categorization methodologies. This suggested approach can assist metropolitan regions in controlling and minimizing contamination, providing advantages to both inhabitants and administrative organizations. It predicts the AQI employing parameters such as PM₁₀, PM₂.₅, NO₂, O₃, CO, and SO₂. Functioning as a live monitoring application, it aims to assist individuals in minimizing their contact with atmospheric pollutants.