Classroom course calendar

424 Introduction to Receptor Modeling

4-day lecture course
3 CEUs

Student Materials:
Student Classroom Materials 1 (PDF 234 PP, 4.67 MB)
Student Classroom Materials 2 (PDF 156 PP, 2.91 MB)
Student Classroom Materials 3 (PDF 95 PP,1.0 MB)
Student Classroom Materials 4 (PDF 62 PP,1.9 MB)

Course Description
Receptor models are a group of measurement-based air quality models for identifying the concentration of specific source categories at air measurement (receptor) sites. Students successfully completing this course will learn the major components and criteria for a successful CMB study, methods for ambient sampling and analysis, the requirements for source profiles, and principles of receptor modeling evaluation and validation. Students will be able to apply EPA's CMB software program to appropriate particle and gas monitoring databases. CMB model applications for particulate matter and volatile organic compounds (VOCs) will be reviewed. While this course focuses on Chemical Mass Balance (CMB) receptor modeling, the student will learn the fundamentals of other source apportionment techniques, including optical and scanning electron microscopy, radiocarbon analysis and multiple linear regression. Homework assignments will require students to apply CMB model software (provided) to test data sets using their PCs.

Major topics

• Mathematics and assumptions of receptor modeling
• Analytical methods for ambient and source measurements
• Application of the EPA receptor modeling software
• New applications (air toxics and volatile organic gases)
• Development of receptor models
• Source composition
• Microscopy and receptor modeling
• Regulatory case studies

Desired Background
Engineering/scientific degree, skill in using a PC for scientific applications, and 6 months of equivalent work experience in emission inventories, atmospheric dispersion modeling, or completion of course SI: 410, and analytical methods for particles and gas samples, or completion of course 435 or SI:434. Students will also find it helpful to have completed a course in statistics, (e.g., SI:473A and B).