Types of Models Marti Blad Northern Arizona University College of Engineering & Technology

2 Models Meteorological Diagnostic Prognostic Emissions Type of chemicals Rates of release Sources Building impacts Surface Terrain complexity Air turbulence Viewing GUI to see pictures Receptor Human Ecological impact

3 EPA MODELS—Screening

4 EPA MODELS—Regulatory

5 EPA Models—Other

6 Models = Representations Simplified representation of complex system Used to study & understand the complex Numerical Set of equations Describe = quantify

7 Box Model Concept Time= t t, x t, x, y t, x, y, z

8 1-D and 2-D Models

9 3-Dimensional Models

10 Types of Air Quality Models Dispersion models Solves turbulent dispersion of unreactive species based on Gaussian distributions Chemical Tracer Models (CTMs) Lagrangian (trajectory) models Eulerian (grid) models

11 Lagrangian Air Quality Models From “INTERNATIONAL AIR QUALITY ADVISORY BOARD PRIORITIES REPORT, the HYSPLIT Model” (

12 Lagrangian Model Strengths Easy to code, run and analyze Explicit mechanisms easily modified Evaluate chemical effects Isolate from the meteorology Facilitates evaluation of source-receptor Numerically efficient

13 Eulerian Air Quality Models Figure from

14 Eulerian Models (cont.) Plume in Grid (P in G) Simulates atmospheric chemistry Gas phase & reactions photolysis Transport Advection & diffusion Deposition Particle modeling & visibility

15 Eulerian Model Strengths Contain detailed 4-D descriptions Meteorological and transport processes Predicts species concentrations Defined geographical and temporal domain Simulates multi-day scenarios

16 What is a dispersion model? Repetitious solution of dispersion equations Based on principles of transport, diffusion Computer-aided simulation of atmospheric dispersion from emission Allows assessment of air quality problem in spatial, temporal terms (i.e., space & time)

17 Gaussian-Based Dispersion Models Plume dispersion in lateral & horizontal planes characterized by a Gaussian distribution See picture next slide Pollutant concentrations predicted are estimations Uncertainty of input data values approximations used in the mathematics intrinsic variability of dispersion process

18 C (x,y,z) Downwind at (x,y,z) ? Gaussian Dispersion hh h H z x y   h = plume rise h = stack height H = effective stack height H = h +  h

19 Gaussian Dispersion Concentration

20 Simple Gaussian Model Assumptions Continuous pollutant emissions Conservation of mass in atmosphere Steady-state meteorological conditions Concentration profiles represented by Gaussian distribution – bell curve shape

21 Model Considerations Actual pattern of dispersion depends on atmospheric conditions prevailing during release Major meteorological factors that influence dispersion of pollutants Atmospheric stability (& temperature) Mixing height Wind speed & direction

22 Maximum Mixing Depth

23 Review Atmospheric Effects

24 Computer Model Input Appropriate meteorological conditions Appropriate for the location Appropriate for the averaging time period Stack or source emission data Pollutant emission data Stack or source specific data Receptor data

25 Model Considerations (cont.) Height of plume rise calculated Momentum and buoyancy Can significantly alter dispersion & location of downwind maximum ground-level concentration Effects of nearby buildings estimated Downwash wake effects Can significantly alter dispersion & location of downwind max. ground-level concentration

26 Computer Model Input (cont.) Plume data Source type Velocity of release Temperature of release BPIP recommended Models downwash Multiple stacks and buildings

27 Maximum Mixing Height (MMD)

28 Coastal or Large Water Bodies

29 Coastal Complexity

30 Complex Terrain Different math for flat or elevated terrain

31 Types of Dispersion Models Gaussian Plume Analytical approximation of dispersion Numerical or CFDs Transport & diffusional flow fields Statistical & Empirical Based on experimental or field data Physical Flow visualization in wind tunnels, etc.

32 Models Useful tools: right model for your needs Allows assessment of air quality problem Space – different distances Time – different times of day Situations – change weather Understand limitations Assumptions in science speak