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Introduction to Modeling Part I

Introduction to Modeling Part I. Cristina Gonzalez-Maddux Cristina Gonzalez-Maddux ITEP, Research Specialist. Why Model?. To answer questions A source is emitting 250 tons/year of PM 2.5 5 miles west of the reservation : effect on air we breathe?

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Introduction to Modeling Part I

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  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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