OluAyo06/Satellite-Driven-PM2.5-Estimation-for-African-Cities
Domain:
environment and energygeospatialRecord type:
project
Creator:
Olu
Host:
Air pollution is a major global health threat, with sub-Saharan Africa experiencing the most severe impacts due to limited monitoring systems and rapid urbanization. This project aims to close these data gaps by using satellite-derived information and machine learning to estimate PM2.5 pollution levels across eight key African cities
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# **Air Quality Prediction Using Satellite AOD and Machine Learning**
This project aims to estimate **PM2.5 concentrations** across major African cities using **satellite-derived Aerosol Optical Depth (AOD)** and machine learning techniques. It supports ongoing efforts by **AirQo** and **Mozilla Foundation** to bridge air-quality data gaps in sub-Saharan Africa and empower communities with accurate environmental information.
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## **Project Summary**
Air pollution remains one of the world’s most serious environmental health risks, responsible for roughly **seven million premature deaths annually**. Sub-Saharan Africa is severely affected due to limited monitoring infrastructure and rising urban exposure.
This project leverages:
* **Satellite AOD measurements**
* **Ground-based PM2.5 observations**
* **Machine learning models**
to estimate pollution levels in eight African cities:
* **Lagos**
* **Accra**
* **Nairobi**
* **Yaoundé**
* **Bujumbura**
* **Kisumu**
* **Kampala**
* **Gulu**
The models developed here are intended to support deployment on the **AirQo digital platform**, enabling users to access real-time, hyperlocal air-quality information.
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## **Project Structure**
```
📂 Air-Quality-Prediction
│
├── 📓 Air Quality Prediction.ipynb
├── 📁 data/
│ ├── train.csv
│ ├── test.csv
│ └── additional datasets…
│
├── 📁 scripts/
│ └── preprocessing.py
│
├── README.md
└── requirements.txt
```
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## **Notebook Workflow**
### **1. Cleaning of Dataset**
* Handling missing values
* Removing duplicates
* Column formatting
* Data validation
### **2. Exploration of Data**
* Summary statistics
* Variable distributions
* Outlier detection
* Time-series trends
### **3. Exploration of Test Dataset**
* Checking structure and consistency
* Matching columns with training dataset
* Identifying missing or mismatched features
### **4. Encoding**
* Converting categorical variables
* Label and one-hot encoding
* Preparing data for modeling
## …