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AI For Air Quality Monitoring

COURSE NO: C01-041
PDH CREDIT: 1
COURSE PROVIDER: Brian Lisiewski, P.E.
AI For Air Quality Monitoring
Course Highlights

This online engineering CPD course examines the application of artificial intelligence and machine learning to air quality monitoring and emissions management.
 

AI and machine learning provide data-driven tools for analyzing air quality and emissions information to improve pollution detection, forecasting, and environmental decision-making while maintaining regulatory compliance.
 

Air quality management involves complex sensor networks, meteorological conditions, emissions sources, and regulatory requirements, requiring reliable monitoring and engineering oversight to protect public health and ensure that AI-supported decisions remain within established safety and compliance limits.
 

This course covers AI/ML integration with air monitoring systems, sensor networks, meteorological data, emissions monitoring, pollution forecasting, emission control support, model validation, uncertainty analysis, configuration control, documentation, and audit trails. It also emphasizes ISO 9001-style risk management, regulatory compliance, and the continued responsibility of Professional Engineers when using AI as a decision-support tool.
 

This 1 CPD online course is applicable to environmental and chemical engineers, as well as other technical professionals who are interested in learning more about AI applications in air quality monitoring and emissions management.

Learning Objectives

This PE continuing education course is intended to provide you with the following specific knowledge and skills:

  • Understanding air quality monitoring, emissions control, sensor technologies, and AI/ML applications
  • Familiarizing with anomaly detection, pollution forecasting, and advisory control for emissions management
  • Understanding AI as a decision-support tool and the Professional Engineer’s responsibility for AI-informed decisions
  • Learning how Verification & Validation (V&V), uncertainty margins, and safety factors support reliable AI models
  • Understanding model governance, configuration control, PE sign-off, and audit trails for AI applications
  • Familiarizing with AI integration risks and FMEA-based approaches for maintaining safety and reliability
  • Understanding how AI, IoT sensors, and big data improve air quality monitoring while addressing implementation challenges
Course Document
In this professional engineering CEU course, you need to review the course document titled, “AI For Air Quality Monitoring”, prepared by Brian Lisiewski, P.E.
To view, print and study the course document, please click on the following link(s):
AI FOR AIR QUALITY MONITORING (572 KB)
Course Quiz
Once you complete your course review, you need to take a multiple-choice quiz consisting of ten (10) questions to earn 1 CPD credits. The quiz will be based on the entire document.
The minimum passing score is 70%. There is no time limit on the quiz, and you can take it multiple times until you pass at no additional cost.
Certificate of Completion

Upon successful completion of the quiz, print your Certificate of Completion instantly. (Note: if you are paying by check or money order, you will be able to print it after we receive your payment.) For your convenience, we will also email it to you. Please note that you can log in to your account at any time to access and print your Certificate of Completion.

To buy the course and take the quiz, please click on: