ML Models for Air Quality Prediction and Health Impact

About This Course

Air pollution isn’t just an environmental issue—it’s a daily public-health risk. Cities need faster ways to predict when air quality will worsen and understand who will be impacted most, so responses can be planned early (alerts, traffic controls, hospital readiness, school advisories, etc.).

This 3-week course walks you through the end-to-end pipeline: sourcing real air-quality data, building and validating forecasting models, and then converting those predictions into health-impact indicators such as exposure risk and vulnerability insights. You’ll work with real-world datasets from sources like OpenAQ, EPA, CPCB, WHO, and Sentinel-5P, and learn how to communicate outputs through dashboards and visual tools.

Aim

To train participants to design and apply machine learning models for air quality prediction and health impact estimation, supporting policy, urban planning, and early intervention strategies.

Course Objectives

By the end of this course, participants will be able to:

• Use ML forecasting to support data-driven air quality management

• Turn model predictions into actionable public-health insights

• Work confidently across environment + health datasets (interdisciplinary approach)

• Build awareness tools using predictive intelligence and visual storytelling

• Integrate real-time sensing data with analytics pipelines for monitoring and alerts

Course Structure

Module 1: Foundations of Air Quality and Data Acquisition

• Understanding the core pollutants and indices:

  • AQI, PM2.5, PM10, NO₂, O₃ and what they mean in daily life

• Health guidelines and why thresholds matter (short-term vs long-term exposure)

• Where your data comes from (and what each source is good for):

  • OpenAQ, EPA, CPCB, WHO, Sentinel-5P

• Exploratory analysis:

  • spotting seasonal patterns, spikes, missing values, and spatial–temporal trends

Module 2: Machine Learning for Air Quality Forecasting

• Forecasting methods you can actually deploy:

  • Linear Regression, ARIMA, XGBoost, LSTM

• Feature engineering that improves predictions:

  • lag features, rolling stats, weather co-variables (where available), location signals

• Model evaluation (so you trust your results):

  • RMSE, MAE, R², and classification-style checks when relevant (Confusion Matrix)

• Tools covered:

  • Python, Scikit-learn, Prophet, TensorFlow

Module 3: Health Impact Estimation and Deployment

• Turning pollution forecasts into health signals:

  • estimating short-term vs long-term risk

• Exposure mapping:

  • who is exposed, where the hotspots are, and how vulnerability changes by region

• Linking ML outputs to public-health metrics:

  • practical framing for DALY, mortality risk (concept + application approach)

• Communicating outcomes clearly:

  • dashboards and reporting using Streamlit, Power BI, Dash

Who Should Enrol?

• Environmental engineers, sustainability teams, and urban data analysts

• Health data scientists, epidemiologists, and public-health researchers

• AI/ML professionals working in climate, environment, or smart cities

• Government air-quality officers and public-health planners

• Graduate students and researchers in environmental health or data science