An Intelligent Machine Learning Model to Identify Green Zones

Green zones are locations or regions where the air quality is deemed to be good or healthy for the general public in the context of air quality. The Air Quality Index (AQI), a numerical scale intended to convey how contaminated the air is and what potential health impacts may be of concern, is commonly used to identify these zones. This work proposes a thorough and reliable model that focuses on important variables like nitrogen dioxide (NO2), sulfur dioxide (SO2), and respirable suspended particulate matter (RSPM) in order to find green areas with great air quality. The model reliably measures the Air Quality Index (AQI) and creates a clear threshold for designating places with good air quality through the use of historical air quality data and sophisticated machine learning algorithms. The results of this study have important ramifications for environmental, public health, and urban planning policy, offering vital insights to promote sustainable development and produce healthier living conditions for locals.