AI for Air Quality Monitoring: Predictive Models for Urban Health

To equip participants with the skills to build AI-driven models for air quality forecasting and develop pollution mitigation strategies to improve urban health outcomes.

• Understand the impact of key air pollutants (PM2.5, NOx, SO₂, CO) on urban health.
• Learn about various data collection sources for air quality monitoring (IoT sensors, monitoring stations, satellite data).
• Preprocess historical air quality data for analysis (handling missing data, time-series alignment, and spatial resolution).
• Build predictive models using AI techniques (ARIMA, LSTM, Random Forest) to forecast air quality levels.
• Apply machine learning model evaluation techniques (RMSE, MAE) to assess prediction accuracy.
• Develop pollution mitigation strategies and real-time air quality prediction dashboards for urban health improvement.
• Create geographic risk maps to identify areas with high pollution and health risks.

• Researchers, environmental engineers, urban planners, policy makers, and data scientists working in air quality, health, or urban sustainability.
• Basic knowledge of Python and machine learning concepts is helpful but not required.
• Participants should be interested in using AI to improve urban health and sustainability.

• Clean, preprocess, and prepare historical air quality data for analysis using Python (Pandas, NumPy).
• Build and evaluate machine learning models (ARIMA, LSTM, Random Forest) for predicting air quality.
• Apply feature engineering techniques to enhance prediction accuracy using environmental data.
• Create an interactive dashboard for real-time air quality predictions, mitigation strategies, and health risk alerts.
• Use geographic mapping tools to identify pollution hotspots and assess health risks in urban areas.