
Air Quality AI: Spatiotemporal Fusion, Concept Drift & Forecasting
Skills you will gain:
About Workshop:
Designed for researchers, professionals, and learners, this workshop focuses on measuring air quality parameters, identifying sensor drift, and detecting environmental anomalies using data-driven methods.
Aim: To provide participants with a strong understanding of air quality analytics by focusing on accurate measurement techniques, sensor drift identification, and intelligent detection methods for reliable environmental monitoring and data-driven decision-making.
Workshop Objectives:
- Understand key air quality parameters and monitoring methods.
- Learn the basics of sensor drift and its impact on data accuracy.
- Explore methods for drift analysis, calibration, and correction.
- Identify pollution patterns and anomalies using analytics.
- Apply air quality data for effective environmental monitoring and decision-making.
What you will learn?
Day 1 Air Quality Components and Common Pollutants
- Air Quality Components, measurements and WHO Standards ( PM2.5 –PM10 )
- Data Sources: Ground-based sensors, satellite imagery, and meteorological data,
- Analytics Techniques time-series analysis for daily/seasonal trends, regression analysis, and spatial mapping of pollution.
- Monitoring and Visualization Methods: Ambient Air Monitoring Method, maps and dashboards (e.g., Air Gradient) that visualize pollution hotspots.
- Measure Air Quality Index and AI tools for Air Quality measure ,WHO Standards
- Hands-on: Google AI Air View, IQAir Air Visual, Computer Vision and Geospatial AI
Day 2 : Real-time monitoring systems and Drift Systems
- Components, Data Flow, Sensors (PM2.5, PM10 ,CO, VOCs), Microcontrollers (Node MCU, ESP8266, Arduino), and communication modules (GPRS/Wi-Fi)
- Data Flow: Sensors detect pollutants, send data to a microcontroller for preprocessing, then transmit to cloud platforms (e.g., Blink) for visualization on mobile/web applications.
- Features: Automated alerts, 24-hour monitoring, and data storage for historical analysis.
- Monitoring Devices : Portable/Low-Cost Sensors: MQ-series (MQ135 for air quality, MQ7 for CO), particulate matter (PM) sensors.
- Commercial Monitors: IQAir AirVisual Pro (measures PM2.5, temperature, humidity).
- Hands –on: Climate Trace: An AI-powered platform that uses satellite data to map and monitor emissions from, major industrial sources like power plants.
- IoT-Enabled AI Sensors: Low-cost, AI-integrated sensors (like the Bosch BME 690) deployed in urban environments or schools to provide real-time data analysis, often using algorithms such as Artificial Neural Networks (ANN) or Random Forest for enhanced accuracy.
Day 3: Air Quality Analytics: Drift & Detect
- Sensor Data Drift & Calibration,
- Pollution Drift and Forecasting Systems
- Air Quality Early Warning System (AQ-EWS)
- Drift Detection Methods, AI/ML for Forecasting and Detection:
- Impact Assessment: Particulate Matter, gaseous,
- Health impact assessments and Specific software for air quality modeling
- Hands-on: Commercial Monitors: IQAir AirVisual Pro (measures PM2.5, , temperature, humidity).EPA REal TIme Geospatial Data Viewer (RETIGO): sensortoolkit (Python Library): IQAir AirVisual Platform: OpenAQ API: AQI.in API Open-Meteo Air Quality API: Computer Vision for Air Quality
Mentor Profile
Fee Plan
Important Dates
23 Apr 2026 Indian Standard Timing 7:00 PM IST
23 Apr 2026 to 25 Apr 2026 Indian Standard Timing 8:00 PM IST
Get an e-Certificate of Participation!

Intended For :
- Students and early researchers in environmental science or data analytics
- Ph.D. scholars, researchers, and academicians
- Environmental engineers and air quality professionals
- AI/ML and data science practitioners working with sensor data
- IoT professionals involved in environmental monitoring
- Industry professionals in sustainability, smart cities, and pollution control
Career Supporting Skills
Workshop Outcomes
