Correlation Network of WHO Air Quality Variables

Introduction

• Air quality is a global health concern

• Pollutants are often related to each other

• This project builds a correlation network to visualize relationships

Objective

• Analyze correlations among PM2.5, PM10, NO2, and Ozone

• Construct a network graph using R

• Highlight both positive and negative correlations

Data Source

• WHO Air Quality Database (Excel file)

• Contains city‑level pollutant measurements

• Columns renamed for clarity (PM25, PM10, NO2, Ozone)

Data Preparation

• Loaded dataset using read_excel()

• Cleaned column names for consistency

• Selected relevant variables for analysis

Insights

• PM2.5 and PM10 strongly correlated

• NO2 and Ozone moderately correlated

• Negative correlations highlighted in orange

• Network graph shows pollutant interactions clearly

Visualization

Conclusion

• Correlation network helps identify key pollutant relationships

• Supports better understanding of air quality data

• Useful for research and policy decisions