python #datascience #dataanalytics #environment
Air pollution is a major environmental health concern.
I wanted to understand how air quality differs between countries and how pollution levels changed over time.
This led me to build my Global Air Quality Analysis project.
Project Overview
The project explores annual PM2.5 exposure data for 87 countries between 2010 and 2019.
PM2.5 refers to fine particulate matter with a diameter of 2.5 micrometres or less.
The analysis uses data attributed to the World Health Organization's Global Health Observatory, accessed through Our World in Data.
My Analytical Process
The project follows a workflow covering data cleaning, validation, country comparisons, trend analysis and visualisation.
Using Python, Pandas, NumPy and Matplotlib, I explored questions such as:
- Which countries had the highest reported exposure?
- How did average exposure change between 2010 and 2019?
- Which countries showed improvements?
- How did regional trends differ?
Selected Results
The repository reports the following dataset findings:
| Indicator | Result |
|---|---|
| 2010 average PM2.5 | 35.3 µg/m³ |
| 2019 average PM2.5 | 31.7 µg/m³ |
| Highest 2019 value | Kuwait, 64.1 µg/m³ |
| Lowest 2019 value | Bahamas, 5.2 µg/m³ |
| Largest reported improvement | China, −9.0 µg/m³ |
These figures describe the analysed dataset and its averaging method. They should not automatically be interpreted as population-weighted global exposure estimates.
Understanding the Limitations
One important detail is that the project uses the historical WHO guideline of 10 µg/m³ for annual PM2.5 exposure.
The WHO updated its annual guideline to 5 µg/m³ in 2021.
The dataset also contains modelled national estimates rather than continuous direct measurements from every location.
National averages can hide substantial differences between cities, rural areas and seasons.
What I Learned
This project helped me practise translating environmental datasets into understandable visualisations.
It also highlighted why analytical conclusions need to explain their data sources, time periods and limitations.
Future Work
I would like to explore newer observations, uncertainty intervals and comparisons between urban and rural exposure.
GitHub: https://github.com/tasnem-tech/global-air-quality-analysis
How do you communicate uncertainty when presenting environmental data?
