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Understanding Air Pollution Modeling: Applications, Importance, and Best Practices

This workshop introduces the fundamentals of air pollution modeling and its significance in environmental assessments. Learn why modeling is essential for answering critical questions about pollution sources, trends, and impacts on air quality. Explore the types of models, including mathematical and computer models, and how they can aid in developing air pollution control strategies. Participants will gain insights on calibrating models, interpreting results, and utilizing empirical data to make informed environmental decisions. Ideal for researchers and professionals in environmental science.

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Understanding Air Pollution Modeling: Applications, Importance, and Best Practices

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Presentation Transcript

  1. Introduction to Modeling Part I Cristina Gonzalez-Maddux Cristina Gonzalez-Maddux ITEP, Research Specialist

  2. Why Model? • To answer questions • A source is emitting 250 tons/year of PM2.5 5 miles west of the reservation: effect on air we breathe? • What if same source was built on my reservation? • Or 10 miles north? • What if it emits 500 tons/year of PM2.5 ?

  3. Why Model? • To answer questions (cont.) • Where does pollution come from? • About pollution emitted by facility on or near my reservation • What kind and how much? • Once emitted, where does it go? • Where should I locate monitors? • Where is regional haze on my reservation coming from?

  4. Practical Applications

  5. Why Model? (cont.) • To Predict Future • Need to “calibrate” with reality • Check against data collected in field • To Interpret • Study system and/or organize field data • Does not require calibration, but “reality checks” always useful • Develop air pollution control plans • Assess environmental impacts • Project future AQ trends

  6. Why Model? (cont.) • Because EPA recommends it • New Source Review (NSR) Permits • PSD - estimate effects on increments • Non-attainment - Choose strategies to reduce pollution to attain NAAQS • Minor Sources • TIP Development • To understand a complex system • Weather • Air pollution • Ex: Trans-boundary (interstate) transport (CAA, Section 126 and/or 110(a)(2)(D)(i) – TAS?)

  7. What is a Model? • Any approximation of a field situation • A hypothesis! • Empirical model • Derived from information gained from observations or experiments • Mathematical (or numerical) model • Simulates field situation indirectly using equations • Workshop focuses on mathematical and empirical models

  8. What is a Model? (cont.) • Mathematical models have • Governing equation – represents physical processes occurring in system • Boundary equations (conditions) • Initial conditions (for time-dependent problems) X = Q * K * V * D * exp[-0.5 * (y/ Φ y)2 ] / (2 * Β * us * Φ y * Φ z)

  9. Mathematical Models – Iterative Process Schematic Courtesey: Dr. Gerda de Vries Assistant Professor Department of Mathematical Sciences -University of Alberta

  10. What is a Computer Model? • Set of commands used to solve mathematical or empirical model on computer • Computer programs are generic – written once • Model is designed each time you enter a set of boundary and initial conditions, and site- specific values, into computer program

  11. Computer Models • Commercial modeling programs • Make it easier for users to communicate with computer code and enter data • Often have graphical user interfaces (GUI) – What is that and how is it helpful?

  12. Graphical User Interface • Ease of data entry • Pre-processors and pathways • Easy visualization of modeling results • Alternative – developing code and manually building input files • AERMOD, CALUPUFF, WRPLOT, Emissions View

  13. Computer Models (cont.) • Graphics packages – Picture instead of number grid PM10 Concentrations

  14. Computer Model – Dangers • Modern modeling programs and graphics packages easy to use, produce impressive pictures and graphs • Model only as good as site-specific data, initial and boundary conditions you enter • Garbage IN = Garbage OUT

  15. What type of model should you use? • Step One: Establish your purpose! • Make predictions? Interpret and better understand what’s going on? • What do you want to learn? What questions do you want to answer? • Is modeling the best way to answer your questions? • Step Two: What type of model should you use?

  16. Models – Two Opinions • Models are worthless • Too expensive to run, require too much data • Real world too complex • Can never be proven “correct” • Models are essential for complex analyses • Combines human judgment with computer power • Provide framework for analyzing large data sets • Good way to make informed analysis or prediction

  17. Summary • Know why you want to use a model • Research: What kind of model will answer the questions you have? • Gather good information to use in your model • Use EPA preferred models if necessary

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