Air Quality Prediction and Personalized Health Recommendations for Mexico City

Challenge Background

Mexico City, one of the world's largest metropolitan areas, has long struggled with air pollution due to its geography, high population density, and industrial activities. The city's air quality significantly impacts the health and quality of life of its over 21 million residents.

The Problem

Despite improvements in recent years, Mexico City still experiences frequent poor air quality episodes. Current systems provide general air quality information but lack personalized recommendations, especially for vulnerable populations.

Goal of the Project

1. Prediction Accuracy:

• Achieve 85% accuracy in 24-hour air quality forecasts for major pollutants (PM2.5, PM10, O3, NO2, SO2, CO).
• Provide accurate predictions for at least 90% of Mexico City's neighborhoods.

2. User Adoption:

• Attract users in Mexico City within the first year of launch.
• Maintain a 60% user retention rate after six months.

3. Personalization:

• Develop at least 10 distinct user profiles based on health conditions and sensitivity to air pollution.
• Provide personalized recommendations that are rated as "helpful" by 80% of users.

4. Data Integration:

• Successfully integrate real-time data from at least 30 air quality monitoring stations across Mexico City.
• Incorporate data from at least three additional sources (e.g., traffic, weather, satellite imagery) to improve predictions.

5. System Performance:

• Ensure the app can handle up to concurrent users without performance degradation.
• Provide air quality updates and alerts within 5 minutes of new data becoming available.

6. Stakeholder Engagement:

• Establish partnerships with at least five major hospitals or health clinics in Mexico City for data sharing and validation.
• Collaborate with at least three environmental NGOs for promotion and community outreach.

7. Policy Impact:

• Provide data-driven reports to city officials that influence at least two air quality improvement initiatives within the first two years.

8. Scalability:

• Design the system architecture to allow expansion to at least three other major Mexican cities within two years of launch.

10. Accessibility:

• Ensure the app is usable by people with disabilities, meeting WCAG 2.1 AA standards.
• Provide the app interface in both Spanish and English, with support for at least one indigenous language.

Project Timeline

What you'll learn

1. Data Science and Machine Learning:

• Gain proficiency in time series analysis and forecasting techniques.
• Develop skills in handling and preprocessing large-scale environmental data.
• Learn to build and optimize ensemble models for improved prediction accuracy.

2. Environmental Science:

• Understand the factors influencing urban air quality and their interrelationships.
• Learn about various air pollutants, their sources, and health impacts.

3. Health Informatics:

• Develop knowledge in integrating health data with environmental data for personalized recommendations.
• Understand privacy concerns and regulations related to health data in Mexico.

4. Software Engineering:

• Gain experience in developing scalable, real-time data processing systems.
• Learn best practices for building responsive mobile applications with high concurrent user loads.

5. User Experience Design:

• Develop skills in creating intuitive interfaces for displaying complex environmental data.
• Learn to design effective alert systems that prompt user action without causing alarm fatigue.

6. Data Visualization:

• Enhance ability to create clear, informative visualizations of air quality data and forecasts.
• Learn techniques for visualizing uncertainty in predictions.

7. Interdisciplinary Collaboration:

• Gain experience working with environmental scientists, health professionals, and policy makers.
• Develop skills in communicating technical concepts to non-technical stakeholders.

8. Localization and Cultural Adaptation:

• Learn to adapt AI systems to local cultural contexts and language requirements.
• Understand the importance of considering local environmental regulations and health guidelines.

9. Continuous Learning Systems:

• Develop skills in creating AI models that improve over time with new data.
• Learn techniques for monitoring model performance and detecting concept drift in environmental data.

10. Project Management:

• Gain experience in managing a complex, multi-stakeholder environmental health project.
• Learn to balance technical development with community engagement and policy impact.

11. Data Journalism:

• Develop skills in translating air quality data and predictions into compelling narratives for public awareness.
• Learn to create data-driven reports that can influence environmental policy.