Leveraging Satellite Data and Machine Learning for Air Quality Prediction in Africa

AirQo, a research initiative at Makerere University and 2024 Mozilla Technology Fund Awardee, is sharing results from their recent AirQo African Air Quality Prediction Challenge. They write:

From seasoned professionals to eager students, a diverse group of machine learning, data science, engineering, and statistics enthusiasts came together to share their experiences in the AirQo African Air Quality Prediction Challenge. What unites them? A passion for applying their skills to real-world challenges and a desire to make a positive impact on environmental justice in Africa.

With over 857 data enthusiasts enrolled, the AirQo African Air Quality Prediction Challenge is a testament to the fight for clean air. The challenge is in collaboration with Zindi and Mozilla.

While the competition is still ongoing, we’re excited to share some of the participants’ insights and experiences during the challenge.

The motivations for joining the challenge are as varied as the participants themselves, who come from Africa, South America, and Europe. Some are driven by the desire to develop solutions that can be implemented and make a tangible difference in people’s lives. Others are drawn to the intellectual challenge of using satellite data and machine learning for air quality prediction in Africa. There’s also a strong sense of purpose, with participants wanting to contribute to environmental justice and help communities most affected by air pollution.

The participants see immense potential for AI to play a positive role in environmental justice. By analyzing data and identifying patterns, AI can help us understand how environmental burdens are distributed and empower communities to advocate for clean air. There’s a shared belief that AI can revolutionize environmental justice by not only informing policy decisions but also bridging the gap between communities and fostering sustainable development.

Using satellite data for air quality prediction in Africa comes with its own set of challenges. Participants mentioned issues like limited ground-based monitoring infrastructure, noisy and uncorrelated data, and the complex atmospheric dynamics in certain regions. Ethical considerations are also paramount, with participants emphasizing the importance of transparency, data bias mitigation, and community involvement in the development process.

With the competition almost coming to an end, participants shared their strategies for staying focused and productive. These include setting clear goals, creating schedules, taking breaks, and minimizing distractions. There’s a strong sense of dedication and a shared drive to push forward and deliver the best solutions.

There’s a lot of optimism about the potential impact of this challenge. Participants hope it will spark innovation, collaboration, and the development of accurate and accessible air quality monitoring solutions for Africa. They believe that by leveraging AI and satellite data effectively, we can improve air quality monitoring, empower communities, and inform policies that promote environmental justice and public health across the continent.

The participants’ passion for environmental issues extends beyond this competition. Many expressed interest in pursuing other data science projects related to the environment, such as climate prediction, renewable energy optimization, deforestation monitoring, and water quality analysis. This highlights the vast potential of data science to address environmental challenges and create a more sustainable future.