- What exactly does it do?

- Observes the cumulative ranking of PM2.5 using each country’s data.

- How does it work?

- Runs Data_Processing.ipynb on Colab

- Data processing :

- Converts date string to datetime object

- Creates a cumulative dataframe

- Removes the underscores in country names

- Removes countries that never ranked in the top 20

- Makes a racing bar chart

- What do you hope to achieve?

- We hope to raise global awareness about PM2.5, which are invisible killers damaging our lungs.

- Through this project, we call on the United Nations to pay greater attention to the issue of air pollution.

- What tools, coding languages, hardware, or software did you use to develop your project?

- Tools:

- github

- colab

- jupyter notebook

- youtube

- Coding languages

- python : pandas, ipython-autotime, bar_chart_race

- What is the significance of NASA data?

- Using the MERRA PM2.5 data from NASA GES DISC, we take a data science approach to observe the emissions of each country, and hope to provide adaptable references for international standards, and finally a positive contribution to global environment.

What is PM2.5?

There are many polutions in the air, one of them is dust-like, granular substances known as particular matters (PM). PMs differ in diameters; for those smaller than 2.5 micrometers (μm) are known as PM2.5, which is measured in μg/m3.

PM2.5 is thinner than a hair grain, and can pass through the alveoli in our lungs and enter our blood flow. Therefore, the impact of PM2.5 on physical health and the ecology systems is nontrivial.

2023 NASA Space Apps Challenge

Alex is an alien.