Workshop Registration End Date :23 Apr 2026

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Virtual Workshop

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

StudentINR 2499/- OR USD 70
Ph.D. Scholar / ResearcherINR 3499/- OR USD 80
Academician / FacultyINR 4499/- OR USD 90
Industry ProfessionalINR 6499/- OR USD 105

Important Dates

Registration Ends
23 Apr 2026 Indian Standard Timing 7:00 PM IST
Workshop Dates
23 Apr 2026 to
25 Apr 2026  Indian Standard Timing 8:00 PM IST

Get an e-Certificate of Participation!

2024Certfiacte

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

  • Gain a clear understanding of air quality measurement and monitoring concepts.
  • Recognize sensor drift and evaluate its effect on data reliability.
  • Apply basic methods for calibration, drift correction, and anomaly detection.
  • Interpret air quality data to identify pollution trends and variations.
  • Build confidence in using analytics for environmental monitoring and informed decision-making.