Air pollution has become one of the most serious environmental and public health challenges across Asia due to rapid industrialization, urbanization, increasing vehicle usage, and population growth. Many major Asian cities frequently experience poor air quality levels that can lead to respiratory diseases, cardiovascular problems, reduced life expectancy, and overall decline in quality of life. Monitoring and predicting air quality is therefore essential for governments, environmental agencies, and city planners to take timely preventive actions and develop effective pollution control policies. Air quality is typically measured using the Air Quality Index (AQI), which is calculated based on the concentration of several harmful pollutants. Among these, PM2.5 (fine particulate matter with a diameter less than 2.5 micrometers) is especially dangerous because it can penetrate deep into the lungs and bloodstream. PM10 refers to larger particulate matter (diameter less than 10 micrometers) that can cause respiratory irritation and health issues. Other important pollutants include NO₂ (Nitrogen Dioxide) produced mainly from vehicle emissions, SO₂ (Sulfur Dioxide) from industrial processes and fuel combustion, CO (Carbon Monoxide) from incomplete burning of fuels, and O₃ (Ground-level Ozone) formed through chemical reactions in the atmosphere. Weather conditions such as temperature, humidity, and wind speed also influence pollutant dispersion and concentration. The objective of this project is to analyze air quality patterns across multiple Asian countries and develop a machine learning model using the Decision Tree algorithm to predict the AQI category based on environmental, industrial, traffic, and meteorological factors. Decision Trees are particularly useful because they create clear and interpretable decision rules, allowing stakeholders to understand how different factors contribute to air pollution levels. By identifying the most influential pollutants and conditions, the model can assist environmental authorities in data-driven decision making, early warning systems, pollution control planning, and resource allocation. Ultimately, this project demonstrates how machine learning can support smarter environmental monitoring and policy decisions to address the growing air quality crisis in Asia.
https://colab.research.google.com/drive/1rZk8b7xHvoVCTBbxfs1VrnShnkwQi3aS?usp=sharing
Complete Analysis using Machine Learning Model – Decision Tree
By Ritika Deshmukh_Data Analyst_MacroEdtech
For Dataset
Contact : info@macroedtech.com
