AI for Air Quality Monitoring: Predictive Models for Urban Health
Harness AI to Monitor, Predict, and Mitigate Urban Air Pollution for Healthier Cities.
About This Course
This 3-day hands-on workshop focuses on using AI and machine learning techniques to monitor and predict air quality in urban environments. Participants will explore the impact of pollutants on health, use various data sources (IoT sensors, satellite data, etc.), and build predictive models to forecast air quality. Additionally, participants will learn how to design pollution mitigation strategies and visualize air quality data through dashboards to inform urban health improvements.
Aim
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.
Workshop Objectives
- 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.
Workshop Structure
📅 Day 1 — Understanding Air Quality and Data Collection
- Air quality and health: Impact of pollutants (PM2.5, NOx, SO₂, CO) on urban health outcomes
- Data collection sources: IoT sensors, monitoring stations, satellite data, and environmental variables
- Data preprocessing: Handling missing data, time-series alignment, and spatial resolution adjustments
- Hands-on: Import, clean, and preprocess historical air quality data (temperature, pollutants) using Python (Pandas, NumPy)
📅 Day 2 — Building Predictive Models for Real-Time Air Quality Forecasting
- Modeling air quality prediction: Regression and time-series forecasting (ARIMA, LSTM, Random Forest)
- Feature engineering: Using environmental data (weather, traffic, pollution history) to enhance prediction models
- Model evaluation: Using RMSE, MAE, and other metrics to evaluate model performance
- Hands-on: Build and train a machine learning model to predict air quality levels using environmental data. Evaluate model accuracy
📅 Day 3 — AI Tools for Pollution Mitigation and Urban Health Improvement
- Pollution mitigation strategies: Using AI to design action plans for air quality improvement
- Visualization and decision support: Real-time air quality prediction, pollution mitigation, and health risk alerts
- Geographic risk mapping: Identifying areas with high pollution and health risks
- Hands-on: Build an interactive dashboard to visualize real-time air quality predictions and suggest mitigation strategies. Implement geographic risk mapping for pollution hotspots
Who Should Enrol?
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Researchers, environmental engineers, urban planners, policy makers, and data scientists working in air quality, health, or urban sustainability.
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Basic knowledge of Python and machine learning concepts is helpful but not required.
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Participants should be interested in using AI to improve urban health and sustainability.
Important Dates
Registration Ends
02/10/2026
IST 4 : 30 PM
Workshop Dates
02/10/2026 – 02/12/2026
IST 5 : 30 PM
Workshop Outcomes
- 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.
Fee Structure
Student
₹2499 | $65
Ph.D. Scholar / Researcher
₹3499 | $75
Academician / Faculty
₹4499 | $85
Industry Professional
₹6499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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