The Air Quality Index (AQI) is a numerical scale used worldwide to assess and communicate the quality of air in a specific region. It measures how clean or polluted the air is and what associated health effects might be of concern for people. The AQI primarily tracks the levels of major pollutants in the atmosphere, such as particulate matter (PM2.5 and PM10), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and other harmful substances.
Each pollutant is assigned a score on the AQI scale, typically ranging from 0 to 500, where: 0-50: Good (Air quality is considered satisfactory, and air pollution poses little or no risk.) 51-100: Moderate (Air quality is acceptable, but some pollutants may be a concern for a very small number of people sensitive to air pollution.) 101-150: Unhealthy for Sensitive Groups (Members of sensitive groups may experience health effects, but the general public is less likely to be affected.) 151-200: Unhealthy (Everyone may begin to experience health effects; members of sensitive groups may experience more serious effects.) 201-300: Very Unhealthy (Health alert: everyone may experience more serious health effects.) 301-500: Hazardous (Health warning of emergency conditions: the entire population is more likely to be affected.)
In this project, we will analyze AQI data for various cities to understand pollution trends, examine seasonal variations, and visualize the impact of different pollutants. The analysis will help identify key factors affecting air quality and how it varies across different regions and times of the year
Technologies :
- Python Libraries : Pandas
- Data Visualization using Matplotlib