This project analyzes satellite-recorded air pollution data (PM₂.₅, NO₂, CO, SO₂) alongside hospital-reported respiratory cases in Lagos, Nigeria (2018–2024). It explores pollution trends, seasonal patterns, and health impacts, and develops predictive models to forecast respiratory disease surges for better public health planning.
# Air Pollution and Respiratory Disease Analytics in Growing African Cities
**Case Study: Lagos, Nigeria**
## Table of Contents
1. Project Overview
2. Objectives
3. Data Dictionary
4. Research Questions
5. Methodology
6. Exploratory Data Analysis
7. Predictive Modelling
8. Key Takeaways and Recommendations
9. Limitations
10. Setup instructions
11. How to Run the Notebook
12. Acknowledgements
## 1. Project Overview
Rapid urbanization in African cities, such as Lagos (Nigeria), Nairobi (Kenya), Accra (Ghana), and Kinshasa (DR Congo), has led to worsening air quality due to vehicle emissions, industrial activities, and inadequate waste management. This project analyzes satellite-recorded air pollution data in conjunction with hospital-reported respiratory disease data in Lagos to investigate the relationship between pollution levels and health outcomes. The insights aim to support urban clean air initiatives and public health interventions across Africa.
### Why Lagos?
Lagos was chosen as the case study city because it represents a **typical example of rapid urban growth in Africa**.
- It is the **largest city in Nigeria** and one of the fastest-growing megacities in the world, with a population exceeding **20 million**.
- The city faces severe challenges from **traffic congestion, industrial activities, and open waste burning**, all of which contribute heavily to air pollution.
- Lagos also has **limited public health infrastructure** relative to its size, making respiratory diseases an urgent concern for its residents.
Insights gained from Lagos can serve as a **blueprint for other African cities** facing similar environmental and health challenges.
## 2. Objectives
This project set out to:
- Derive a pollution index combining all monitored pollutants.
- Monitor trends in air pollution levels over time.
- Analyze correlations between pollution spikes and hospital respiratory cases.
- Predict respiratory disease surges using pollution data.
- Identify high- …