Special issue AM216021 Latest Open for UCT
Section outline
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AI applications in air quality management and engineering Lesson
Key issues:
- Introduction to AI and relevance to air quality monitoring and management
- AI in air quality monitoring, sensor networks, real-time data collection and anomaly detection
- AI in predictive dispersion modeling, meteorological and source emission analysis
- AI for optimizing pollution control strategies, data visualization and interpretation for decisions
- Machine learning algorithms, regression models, neural networks, and clustering for air quality predictions
- Case studies of AI in urban air quality management, smart cities
- Applications of AI in early warning systems for air quality crises
- Challenges and limitations in data quality, availability, and processing
- Ethical considerations and AI transparency in decision-making
- Future trends in integration with climate change models
- AI for global and regional air quality collaborations
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Module 17. AI applications in air quality management and engineering File PDF
Supporting handouts for Lecture 17,
Printable pdf format, A4 size, landscape layout6.5 MB -
Shared media: Towards the next generation of air quality monitoring Page
Illustrative video clips:
- Copernicus missions and air quality measuring capabilities to the next level by European Space Agency, ESA
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Relevant link / literature URL
Applications of artificial intelligence in the field of air pollution
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Individual presentation 17 Workshop
Title 17: Artificial intelligence technologies for forecasting air pollution
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Practical exercise series / Topics 32 Assignment
Topic 32. Air quality monitoring based on big data-assisted artificial intelligence technique, 6pts
(a. 2pts / b. 2pts / c. 2pts)
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