CivE 729D

Air Quality Modeling

Description

This course provides a comprehensive introduction to air pollution modeling techniques, covering basic concepts and advanced technologies. Students will explore various models used to predict the dispersion of pollutants, assess their impact on air quality, and understand the dynamics of atmospheric pollutants. Furthermore, the course integrates machine learning to enhance traditional modeling approaches, offering insights into cutting-edge research and future directions in air pollution management. Through a blend of theoretical knowledge and hands-on exercises, participants will gain the modeling skills in order to address comprehensive environmental challenges and contribute to the development of sustainable solutions.

Learning Outcomes

By the end of this course students will have:
A comprehensive understanding of air quality fundamentals and modeling approaches. This includes knowledge of key air pollutants, criteria for air quality assessment, and the emission, transport, removal, and life cycle of atmospheric pollutants
Proficiency in identifying various air quality models for application and evaluation purposes
Practical skills in employing Python to develop personalized models for simulating transport processes
The capability to apply established air quality models for addressing real-world problems
The skills to create predictive models for atmospheric pollutants using machine learning techniques to analyze sources and components using Python

• Details
• Pre-Requisites
• Co-Requisites
• GA - Graduate Attributes
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Undergraduate Program(s)

Sections & Respective Instructors

B1 - 2024/2025 - Winter - Haoran Yu