Leveraging Data Science Techniques to Raise Awareness of Air Pollution in Nigeria

Challenge Background

“Pollution should never be the price of prosperity.” (Al Gore, 45th Vice President of the U.S.) Studies have shown that dirty air has caused more premature deaths, than other risk factors such as unsafe water, unsafe sanitation and malnutrition in Africa in 2013.

Periodically in Port Harcourt, a thick black haze rising to the skies is visible. Black soot settles on everything and is inhaled by everyone, as evidenced by black particles in nostrils, and throat soreness. This results in a double air pollution burden as the unresolved prevailing widespread pollution already exists and then the added emergence of particle pollution (said to be resulting from incomplete combustion of hydrocarbons). This poses a health risk for all individuals inhabiting the state, especially sensitive groups.

For relevant authorities to pay attention, more awareness should be raised on the issue and using insights generated from available data, solutions can be proffered.

The Problem

Raising awareness of this existing air pollution will call relevant authorities to act on the issue and will help individuals, especially sensitive groups, carry out safer practices.

Air quality data isn’t readily available or simply doesn’t exist in some cases. Port Harcourt-based climate-tech startup, Pyloop, has offered access to data sourced from one of their sensors. Using this data, engaging visualizations can be created, and insights generated from the data provided can be used to assess the quality of air in Port Harcourt.

Goal of the Project

• Access to air quality sensor data to build a data science project
• Give beginners the opportunity to carry out EDA using real-world data
• Make use of data visualizations and dashboards to present insights gathered from the data available
• Raise awareness on air pollution situation in Port Harcourt

Project Timeline

What you'll learn

1. Obtain sensor data provided by Pyloop

2. Carry out exploratory data analysis on the data provided

3. Visualize this data using Google Data Studio

4. Perform time series analysis on the data provided

5. Project documentation