United States Environmental Pro tection Agency Office of Research and Development Washington, DC 20460 EPA/600/R-99/030 March 1999

* On assignment from the National O ceanic and Atmospheric Administrati on, U.S. Department of Commerce 

## S CIENCE 

## A LGORITHMS OF THE 

## EPA M ODELS 

## -3 C OMMUNITY 

## M ULTISCALE 

## A IR 

## Q UALITY 

## (CMAQ) M ODELING 

## S YSTEM Edited by: D. W. B YUN *

### and J. K. S. C HING * Atmospheric Modeling Division National Exposure Research Laboratory U.S. Environmental Pro tection Agency

Research Triangle Park, NC 27711EPA/600/R-99/030 DISCLAIMER The information in this document has been funded wholly or in part by the United S tates Environmental Pro tection Agency. It has been subjected to the Agency’s peer and administrative review, and has been approved for publi cation as an EPA document. Mention of trade names or commercial products does not constitute endorsement nor recommendation for use.EPA/600/R-99/030

iv CONTENTS FOREWORD
............................................................... iii ACRONYMS
.............................................................. xiii ACKNOWLEDGMENTS
.................................................... xvi EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-1 1.0 INTRO DUCTION TO THE MODELS-3 FRAMEWORK AND THE C OMMUNITY MULTISCALE AIR QUALITY MODEL (CMAQ)
........................ 1-1 (J. Ching and D. Byun) 1.1 The Models-3 Emissions, Meteorology, and the C MAQ Modeling Systems . . . 1-3 1.2 CMAQ Interf ace Processors
....................................... 1-5 1.3 The CMAQ Chemical Trans port Model (CCTM)
....................... 1-7 1.4 Analysis of CMAQ Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-9 1.4.1 Process Analysis (Chapter 16)
................................ 1-9 1.4.2 Aggregation (Chapter 17)
................................... 1-10 1.5 Management of CMAQ Science Information Obj ects and Codes in Models-3 1-10 1.5.1 Program Control Processors (Chapter 15)
...................... 1-10 1.5.2 C MAQ Code Integration (Chapter 18)
........................ 1-11 1.6 Post Release Studies and Near-Future Plans . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11 1.6.1 C MAQ Evaluation Study
................................... 1-11 1.6.2 Testing Operational Configurations
........................... 1-11 1.6.3 Extensions and Science Additions
............................ 1-12 1.7 Opportunities and Encouragement for Long Term Extensions and Science Community Involvement
......................................... 1-12 1.8 References
.................................................... 1-14 2.0 MODELS-3 ARCHITECTURE: A UNIFYING FRAMEWORK FOR ENVIRONMENTAL MODELING AND ASSESSMENT . . . . . . . . . . . . . . . . . . . . 2-1 (J. Novak and S. Leduc) 2.1 Introduction
.................................................... 2-1 2.2 Overview of the Models-3 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.2.1 D ataset Manager
.......................................... 2-1 2.2.2 Program Manager
.......................................... 2-2 2.2.3 Study Planner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2 2.2.4 Str ategy Manager
.......................................... 2-3 2.2.5 Tool Manager
............................................. 2-3 2.2.6 Science Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5 2.2.7 Model Builder
............................................ 2-5 2.2.8 Source Code Manager
...................................... 2-6 2.2.9 Framework Administrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6EPA/600/R-99/030

v 2.3 Models-3 System Architecture
...................................... 2-6 2.4. Schedule and Future Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9 2.5 References
..................................................... 2-9 3.0 DEVELOPING METEOROLOGICAL FIELDS
........................... 3-1 (T. Otte) 3.1 Credits and Disclaimers for Use of MM5
.............................. 3-1 3.2 Meteorology Model Pre-Processing
.................................. 3-1 3.2.1 Defining the Simulation Domain ( TERRAIN ) . . . . . . . . . . . . . . . . . . . . 3-1 3.2.2 Processing the M eteorological Background Fields (DATAGRID )
..... 3-2 3.2.3 Obj ective Analysis (RAWINS)
................................ 3-2 3.2.4 S etting the Initial and Boundary Conditions (INTERP ) . . . . . . . . . . . . . 3-3 3.3 The Meteorology Model (MM5)
.................................... 3-3 3.3.1 Brief History
.............................................. 3-3 3.3.2 Horizontal and Vertical Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4 3.3.3 Prognostic Equations
....................................... 3-4 3.3.4 Model Physics
............................................ 3-5 3.3.5 Nesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9 3.3.6 Four-Dimensional D ata Assimilation
........................... 3-9 3.4 Meteorology Model Post-Processing
................................ 3-10 3.5 Changes to the MM5 System's Software for Models-3
.................. 3-10 3.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11 4.0 EMISSION SUBSYSTEM
.............................................. 4-1 (B. Benjey, J. Godow itch, and G. Gipson) 4.1 Emission Inventory Processors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1.1 Discussion
............................................... 4-1 4.1.2 General MEPPS Structure
.................................. 4-10 4.2 The MEPPS Emission Processing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11 4.2.1 The Inventory D ata Analyzer (IDA)
.......................... 4-13 4.2.2 The MEPPS Input Processor (INPRO)
........................ 4-15 4.2.3 Processing Procedure
...................................... 4-16 4.2.4 Modeled Emission Data
.................................... 4-32 4.2.5 Chemical Speciation of Emission Data
........................ 4-77 4.2.6 Output Processor (OUTPRO)
............................... 4-91 4.3 Models-3 Emission Projection Processor (MEPRO)
.................... 4-90 4.4 Emission Processing Interface
..................................... 4-93 4.4.1 Overview of Key Features of ECIP . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-93 4.4.2 Plume Rise of Point Source Emissions
......................... 4-95 4.4.3 Method for the Treatment of Initial Vertical Plume Spread
........ 4-98 4.4.4 Vertical Allo cation of Plume Emissions
........................ 4-99 4.4.5 Generation of 3-D Emissions
................................ 4-99 4.5 Data Requirements
.............................................. 4-99 4.6 Plans for Improvement
......................................... 4-101EPA/600/R-99/030

vi 4.7 References
................................................... 4-102 5.0 FUNDAMENTALS OF ONE-ATMOSPHERE DYNAMICS FOR MULTISCALE AIR QUALITY MODELING
...................................................................................... 5-1 (D. Byun) 5.1 Governing Equations and Approximations for the Atmosphere ........................ 5-1 5.1.1 Governing Equations in a Generalized Curv ilinear Coordin ate System 5-2 5.1.2 Assumptions of Atmospheric Dynamics
................................................ 5-5 5.2 Choice of Vertical Coordin ate System for Air Quality Modeling .................... 5-10 5.3 Coupling of M eteorology and Air Quality
....................................................... 5-17 5.3.1 M eteorological D ata for Air Quality Modeling
.................................... 5-17 5.3.2 Off-line and On-line Modeling Paradigms ........................................... 5-17 5.4 Mass Conservation
........................................................................................... 5-21 5.4.1 Mass Consistency in M eteorological Data
........................................... 5-21 5.4.2 Techniques for Mass Conservation in Air Quality Models .................. 5-22 5.4.3 Temporal Interpolation of M eteorological Data
.................................. 5-24 5.5 Conclusion
........................................................................................................ 5-25 5.6 References ........................................................................................................ 5-26 Appendix 5A. Tensor Primer and Derivation of the Continuity Equation in a Generalized Curv ilinear Coordin ate System ........................................ 5-29 6.0 GOVERNING EQUATIONS AND C OMPUTATIONAL STRUCTURE OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) CHEMICAL TRANSPORT MODEL
............................................................................................... 6-1 (D. Byun, J. Young, and M.T. Odman) 6.1 Derivation of the Atmospheric Diffusion Equation ........................................... 6-2 6.2 Representation of Science Processes in C MAQ Modeling System
.................... 6-8 6.2.1 Supporting Models and Interf ace Processors
......................................... 6-8 6.2.2 Modularity Concept of CMAQ .............................................................. 6-9 6.2.3 Description of Science Processes
......................................................... 6-14 6.3 Equivalent Model Formulations for Different Vertical Coordin ates ................ 6-25 6.4 Nesting Techniques .......................................................................................... 6-27 6.5 Summary
.......................................................................................................... 6-30 6.6 References
........................................................................................................ 6-30 Appendix 6A. Concentration Units Used for Air Quality Studies
............................... 6-33EPA/600/R-99/030

vii 7.0 NUMERICAL TRANSPORT ALGORITHMS FOR THE C OMMUNITY MULTISCALE AIR QUALITY (CMAQ) CHEMICAL TRANSPORT MODEL IN GENERALIZED COORDINATES
....................................................................... 7-1 (D. Byun, J. Young, J. Pleim, M.T. Odman, and K. Alapaty) 7.1 Numerical Advection Algorithms
....................................................................... 7-2 7.1.1 Conservation Form Equation for Adv ection
.......................................... 7-3 7.1.2 Classification of Advection Schemes ..................................................... 7-5 7.1.3 Description of Adv ection Schemes in CCTM
........................................ 7-5 7.1.4 Treatment of Boundary Conditions
...................................................... 7-10 7.1.5 Test of Algorithms with Idealized Linear Horizontal Flow Fields ....... 7-11 7.1.6 Vertical Adv ection
............................................................................... 7-14 7.1.7 Adjustment of Mass Conservation Error
.............................................. 7-15 7.2 Vertical Mixing Algorithms .............................................................................. 7-17 7.2.1 Closure Problem
................................................................................... 7-18 7.2.2 Computing Vertical Mixing with the Eddy Diffusion Formulation: K-Theory .............................................................................................. 7-20 7.2.3 Flux Form Represen tation of Vertical Mixing
..................................... 7-30 7.3 Horizontal Mixing Algorithms
.......................................................................... 7-42 7.4 Conclusions ...................................................................................................... 7-44 7.5 References
........................................................................................................ 7-45 Appendix 7A Numerical Solvers for Diffusion Equations .......................................... 7-50 8.0 GAS-PHASE CHEMISTRY
........................................................................................ 8-1 (G. Gipson and J. Young) 8.1 Background ........................................................................................................ 8-2 8.2 Chemical Mechanisms in the CMAQ System
.................................................... 8-3 8.2.1 CB4 Mechanism
..................................................................................... 8-4 8.2.2 RADM2 Mechanism .............................................................................. 8-5 8.2.3 SAPRC-97 Mechanism
.......................................................................... 8-6 8.2.4 Extended Mechanisms
........................................................................... 8-7 8.2.5 Changing or Adding Mechanisms in CMAQ ........................................ 8-10 8.3 Reaction Kinetics
............................................................................................. 8-11 8.3.1 R eaction Rates
..................................................................................... 8-11 8.3.2 R ate Constant Expressions ................................................................... 8-12 8.4 Mathematical Modeling
................................................................................... 8-14 8.4.1 Governing Equations
............................................................................ 8-15 8.4.2 SMVGEAR ........................................................................................... 8-17 8.4.3 QSSA Solver
......................................................................................... 8-21 8.4.4 Summary
.............................................................................................. 8-24 8.5 References ........................................................................................................ 8-24 Appendix 8A Chemical Mechanisms Included in the CMAQ System
....................... 8-28EPA/600/R-99/030

viii 9.0 PLUME-IN-GRID TREATMENT OF MAJOR POINT SOURCE EMISSIONS
.................................................................................................................. 9-1 (N. G illani and J. Godow itch) 9.1 Introduction
........................................................................................................ 9-1 9.2 Overview of the Conceptual Framework of the Plume-in-Grid Tr eatment .. 9-3 9.3 Formulation of the Plume-in-Grid Modeling Components ................................ 9-4 9.3.1 Description of the Plume Dynamics Model
........................................... 9-4 9.3.2 Formulation of the Plume-in-Grid Module
.......................................... 9-11 9.4 Summary .......................................................................................................... 9-27 9.5 References
........................................................................................................ 9-27 10.0 THE AEROSOL PORTION OF MODELS-3 CMAQ ........................................... 10-1 (F. Binkowski) 10.1 Aerosol Dynamics
............................................................................................ 10-3 10.1.1 Modal Definitions ................................................................................. 10-3 10.1.2 New Particle Production by Nucleation
............................................... 10-4 10.1.3 Primary Emissions
................................................................................ 10-5 10.1.4 Numerical Solvers ................................................................................ 10-6 10.1.5 Mode Merging by Renaming
................................................................ 10-9 10.2 Aerosol Dry Deposition
.................................................................................. 10-10 10.3 Cloud Processing of Aerosols ........................................................................ 10-10 10.4 Aerosol Chemistry .......................................................................................... 10-13 10.5 Visib ility
......................................................................................................... 10-14 10.6 Summary
........................................................................................................ 10-16 10.7 References ...................................................................................................... 10-16 11.0 CLOUD DYNAMICS AND CHEMISTRY
.............................................................. 11-1 (S. Ros elle and F. Binkowski) 11.1 Background
...................................................................................................... 11-1 11.2 Model Description
............................................................................................ 11-1 11.2.1 Subgrid Conv ective Cloud Scheme ...................................................... 11-2 11.2.2 Resolved Cloud Scheme
....................................................................... 11-6 11.3 Conclusions
...................................................................................................... 11-7 11.4 References ........................................................................................................ 11-8 12.0 METEOROLOGY-CHEMISTRY INTERFACE PROCESSOR (MCIP) FOR MODELS-3 COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM
.............................................................................................. 12-1 (D. Byun, J. Pleim, R. T ang, and A. Bourgeois) 12.1 Introduction
...................................................................................................... 12-1 12.1.1 MCIP Functions
................................................................................... 12-3 12.1.2 MCIP’s D ata Dependency ................................................................... 12-5 12.1.3 Compu tational Structure
...................................................................... 12-7 12.2 D ata Types, Coordin ates, and Grids
................................................................ 12-9EPA/600/R-99/030

ix 12.2.1 M eteorological D ata Types
.................................................................. 12-9 12.2.2 Coordin ates ........................................................................................ 12-13 12.2.3 Modification of Grid Structure
........................................................... 12-17 12.3 Estimation of Physical Parameters ................................................................. 12-22 12.3.1 PBL Parameters ................................................................................. 12-22 12.3.2 Dry Deposition Velocities
.................................................................. 12-36 12.3.3 Cloud Parameters and Solar Radiation
.............................................. 12-48 12.4 M eteorological D ata for CCTM with Generalized Coordin ate System ......... 12-51 12.4.1 Thermodynamic Variables: Pressure, Density and Entropy
............... 12-51 12.4.2 Vertical Jacobian and Layer Height
................................................... 12-53 12.4.3 Contravariant Velocity Components .................................................. 12-56 12.4.4 Mass Consistent Temporal Interpolation of M eteorological Parameters
.......................................................................................... 12-59 12.4.5 Optional Conversion of Nonhydros tatic Data to Hydros tatic Meteorological D ata for MM5
........................................................... 12-60 12.5 Operation of MCIP
........................................................................................ 12-62 12.5.1 MCIP Modules ................................................................................... 12-62 12.5.2 Building MCIP
................................................................................... 12-62 12.5.3 Executing MCIP
................................................................................. 12-64 12.5.4 Defining Grid and Domain for MCIP ................................................. 12-67 12.5.5 Extension of MCIP for Other M eteorological Models ....................... 12-71 12.6 Concluding Remarks
...................................................................................... 12-72 12.7 References
...................................................................................................... 12-73 Appendix 12A MCIP Output Data
............................................................................ 12-79 Appendix 12B Examples of Nest Domain Definitions for C MAQ system
............... 12-82 Appendix 12C Sample MCIP Configuration File ...................................................... 12-84 Appendix 12D Sample MCIP Run Script
.................................................................. 12-86 13.0 THE INITIAL CONCENTRATION AND BOUNDARY CONDITION PROCESSORS ........................................................................................................... 13-1 (G. Gipson) 13.1 Introduction
...................................................................................................... 13-1 13.2 Overview of the ICON and BCON Processors ................................................ 13-1 13.3 Input Sources
.................................................................................................... 13-2 13.3.1 Time Invariant Concentration Profiles
................................................. 13-2 13.3.2 CCTM Concentration files ................................................................... 13-4 13.3.3 Tr acer Species
...................................................................................... 13-4 13.4 Spatial Interpolation
......................................................................................... 13-6 13.4.1 Horizontal Interpolation ....................................................................... 13-6 13.4.2 Vertical Interpolation
........................................................................... 13-7 13.5 ICON/BCON Species Processing
.................................................................... 13-8 13.6 Mechanism Conversions ................................................................................ 13-10 13.7 ICON/BCON Applications
............................................................................. 13-11EPA/600/R-99/030

x 13.8 References
...................................................................................................... 13-12 14.0 PHOTOL YSIS RATES FOR CMAQ
....................................................................... 14-1 (S. Ros elle, K. Schere, J. Pleim, A. H anna) 14.1 Background ...................................................................................................... 14-1 14.2 Preprocessor JPROC: Calcul ate Clear-sky Photolysis Rate Table
.................. 14-2 14.3 Subroutine PHOT: Table Interpolation and Cloud Attenuation
....................... 14-3 14.4 Conclusions ...................................................................................................... 14-4 14.5 References
........................................................................................................ 14-5 15.0 PROGRAM CONTROL PROCE SSING IN MODELS-3 ...................................... 15-1 (J. Young) 15.1 Domain Configuration
...................................................................................... 15-2 15.2 Input/Output Applications Progra mming Interface .......................................... 15-3 15.3 Other CCTM Configuration Control
................................................................ 15-3 15.3.1 CCTM Process Analysis
...................................................................... 15-3 15.3.2 CCTM Fixed Data ................................................................................ 15-4 15.4 Generalized Chemistry
..................................................................................... 15-5 15.4.1 Design
................................................................................................... 15-5 15.4.2 Operation .............................................................................................. 15-6 15.4.3 Supported R eaction Types ................................................................... 15-8 15.4.4 Mechanism Parsing Rules
.................................................................... 15-9 15.4.5 Chemical Species Include Files
............................................................ 15-9 15.6 Conclusion ...................................................................................................... 15-15 15.7 References
...................................................................................................... 15-15 16.0 PROCESS ANALYSIS .............................................................................................. 16-1 (G. Gipson) 16.1 Integr ated Process Rate Analysis
.................................................................. 16-2 16.1.1 Compu tation of Integrated Process Rates ............................................ 16-2 16.1.2 Example IPR Analyses
......................................................................... 16-4 16.1.3 Implemen tation of IPR Analysis in the C MAQ System
....................... 16-5 16.1.4 Use of the PACP to set up an IPR Analysis ......................................... 16-6 16.2 Integr ated Reaction Rate Analysis
................................................................... 16-8 16.2.1 Compu tation of Integrated Reaction Rates
.......................................... 16-9 16.2.2 Example IRR Analyses ...................................................................... 16-10 16.2.3 Implemen tation of IRR Analysis the C MAQ system
........................ 16-12 16.2.4 Use of the PACP to set up an IRR Analysis
...................................... 16-13 16.3 Conclusion ...................................................................................................... 16-18 16.4 References
..................................................................................................... 16-19EPA/600/R-99/030

xi 17.0 AN AGGREGATION AND EPISODE S ELECTION SCHEME DESIGNED TO SUPPORT MODELS-3 CMAQ
................................................................................ 17-1 (R. Cohn, B. Eder, and S. Leduc) 17.1 Introduction
...................................................................................................... 17-1 17.1.1 Background
.......................................................................................... 17-1 17.1.2 Obj ectives ............................................................................................. 17-2 17.2 Summary of the Approach
............................................................................... 17-3 17.2.1 Basic Elements of the Methodology
.................................................... 17-3 17.2.2 Rationale, Scope, and L imitations ........................................................ 17-4 17.2.3 Str ategy ................................................................................................ 17-5 17.3 Cluster Analysis of Wind Fields
....................................................................... 17-7 17.3.1 Description of Wind Data .................................................................... 17-7 17.3.2 Basic Cluster Analysis Technique
........................................................ 17-7 17.3.3 Illustration of Cluster Analysis Results
................................................ 17-8 17.4 Evaluation of Alternative Aggregation Approaches ...................................... 17-10 17.4.1 Description of Alternative Approaches
.............................................. 17-10 17.4.2 Description of M eteorological Data
................................................... 17-11 17.4.3 Analysis Methods ............................................................................... 17-11 17.4.4 Results
................................................................................................ 17-12 17.5 Refinement of the Sampling Approach
.......................................................... 17-20 17.5.1 D etermination of Appropri ate Numbers of Strata and Events ........... 17-21 17.5.2 Selection of Stratified Sample of Events ............................................ 17-26 17.6 Application and Evaluation
............................................................................ 17-28 17.6.1 Application of the Aggregation Proce dure
......................................... 17-28 17.6.2 Evaluation .......................................................................................... 17-30 17.7 Summary and Discussion
............................................................................... 17-31 17.8 References
...................................................................................................... 17-32 18.0 INTEGRATION OF SCIENCE CODES INTO MODELS-3
................................. 18-1 (J. Young) 18.1 Introduction ...................................................................................................... 18-1 18.2 Classes and Modules
........................................................................................ 18-3 18.2.1 Operational Design
............................................................................... 18-3 18.2.2 CGRID ................................................................................................. 18-4 18.2.3 Class Driver
.......................................................................................... 18-5 18.2.4 Synchronization Time Step
................................................................... 18-6 18.3 Input/Output Applications Progra mming Interface .......................................... 18-7 18.4 Code Configuration Management
.................................................................... 18-9 18.4.1 The Need
.............................................................................................. 18-9 18.4.2 The Tool ............................................................................................. 18-10 18.4.3 The Repository
................................................................................... 18-11 18.5 How a Model is Constru cted
.......................................................................... 18-13 18.5.1 Obj ect Oriented Concepts .................................................................. 18-13 18.5.2 Global Name Table Data
.................................................................... 18-13EPA/600/R-99/030

xii 18.5.3 Build Template
................................................................................... 18-14 18.6 How a Model is Executed .............................................................................. 18-17 18.7 Using the Models-3 Framework
..................................................................... 18-17 18.8 Conformant Code
........................................................................................... 18-18 18.8.1 Thin Interface ..................................................................................... 18-18 18.8.2 Coding Rules
...................................................................................... 18-19 18.8.3 Science Process Code Template
........................................................ 18-20 18.8.4 Robustness and Compu tational Efficiency ........................................ 18-23 18.9 Conclusion
...................................................................................................... 18-24 18.10 References
...................................................................................................... 18-24EPA/600/R-99/030

iii FOREWORD The Models-3 Community Multiscale Air Quality (CMAQ) modeling system has been developed under the leadership of the Atmospheric Modeling Division of the EPA National Exposure Research Laboratory in Research Triangle Park, NC. This new generation of modeling software was under development for seven years and was made available in June 1998 without charge for use by air quality regulators, policy makers, industry, and scientists to address multiscale, multi-pollutant air quality concerns. Models-3/CMAQ has a unique framew ork and science design that enables scientists and regulators to build their own modeling system to suit their needs. Users can access pre-installed modeling systems provided by the EPA or can incorpor ate their own modeling systems to work within the existing framework software. This direct user involvement is key to the concept of a community modeling and analysis system. This approach to model development, appli cati on, and analysis leverages the community's complementary talents and resources to set new standards for rapid incorporation of better science into air quality model applications. The resulting comprehensive system forms the foundation upon which the community, including governments, industry, academia, and other stakeholders, can collaborate in the examination of issues and the subsequent development of strategies that meet society challenges of environmental pro tection. The release of Models-3/CMAQ is one of the many steps which we hope w ill unite the community under the common goal of advancing our knowledge and ab ilities to tackle critical problems of the future in far more eff ective ways than have been attempted in the past. Scientifically sound modeling systems, developed and supported by the community, are one method of achieving this goal. The June 1998 release of the Models-3/C MAQ computer code was accompanied by a User Manual [EPA-600/R-98/069(b)] to serve as a reference on how to use the software system. This Science Document is the counterpart to the User Manual in that it presents the peer reviewed scientific bases for the Models-3/C MAQ modeling systems. This document also includes components such as interf ace processors, process analysis routines, and the present and planned evaluation program. F. A. Schiermeier March 1999EPA/600/R-99/030

xvi ACKNOWLEDGMENTS The development of the science components of the Models-3 Community Multiscale Air Quality (CMAQ) system represents a major undertaking by a large team of dedicated atmospheric scientists and a relatively long term effort beginning in the early 1990s, and covers a wide area of subject material as em bodied in each of the contributing chapters. Most of the EPA Models-3 CMAQ Science Team are from the Atmospheric Modeling Division ( AMD) in the EPA Office of Research And Development’s National Exposure Research Laboratory (NERL). With several exceptions the contributing authors from this Division are on d etail from the Air Resources Laboratory of the National O ceanic and Atmospheric Administration ( NOAA). The NOAAAMD e ffort is supported through Interagency Agreement (DW13937252) with the EPA. The authors of each of the chapters of this document would like to collectively e xpress their gratitude to the other members of the NOAA Divisi on. In particular we acknowledge the encouragement and support of the Director of the Atmospheric Modeling Division, Francis Schiermeier. We are indebted to Dr. Robin Dennis for his vision and role in ensuring the implemen tation of holisticone-atmosphere approach into C MAQ. We also recognize the technical help from our colleagues Tom Pierce, Steven Howard, Alfreida Torian, and Gary Walter. We also want to recognize the contributions, helpful discussions, and assistance of many members of the science community, either through formal agreements or through peer interest. For example, the work was supported by several cooperative agreements including: • Atmospheric Modeling Research-Scientific and Computational (CR-822066: MCNC), Principal Investigator (PI): Kenneth Galluppi Ed B ilicki, Steve Fine, Alison Eyth, and Rohit Mathur; • Research on Computational Framew ork in Generic Grids, Adaptive Grids, and Subgrid Treatment of Air Quality Simulation (CR-822053: MCNC), PI: Talat Odman; with R.K. Srivastava and D.S. McRae, North Carolina State University); • Transport Algorithms for Air Quality Simulation Models (CR-822059, MCNC), PI: Talat Odman; • Emissions Modeling Research with High-Performance Computing (CR-822074: MCNC), PI: Carlie Coats; • Advanced Modeling of Meteorology in Support of Air Quality Models (CR-822628: (MCNC), PI: Aijun Xiu, Kiran Alapaty, John N. McHenry, and Adel F. Hanna; with Nelson L. Seaman and Aijun Deng, Pennsylvania State University; and J ohn S. Kain, National Severe Storms Laboratory); • A Flexible and Efficient Methodology for Modeling Aerosol for Air Quality Models (CR-823634: MCNC), PI: Uma Shankar, Mark Read and Atanas Trayanov; with Anthony Wexler, University of Delaware; and John H. Seinfeld, California Institute of Technology; • Develop Methods for Technology Transfer of Advanced Regional/Urban Air Quality Models (CR-822080: NCSU), PI: Alan Schula; and • Plume-in-Grid development for a Multiscale Air Quality Modeling System (IAG DW64937190, Tennessee Valley Authority (TVA)), PI’s: Robert E. Imhoff; with Noor Gillani, Arastoo Biazar (now at Monash University, Australia), and Yu-Ling Lu, University of Alabama, Huntsville).EPA/600/R-99/030

xvii The following scientists also contributed under the formal contr acts listed: Ruen Tang, Chris Maxwell, and Hao Jin of the Technical Support Group, Dyntel Corp. (General Services Administration IAG DW47937823); Tod Plessel, and Yan Ching Zhang of the Visualization Laboratory, Lockheed Martin (EPA Contr act 68-W7-0055); and Nick Moghari, Joe Susick, and Dave Tivel of Science Applications International Corporation (SAIC) (EPA Contr act 68-W1- 0055). Significant contributions were made by: visiting scientists, including Sang-Mi Lee of the Seoul National University, Korea, Chong Bum Lee of the KangWeon University, Korea, and Seiji Sugata of the National Institute for Environmental Studies, Japan; UCAR’s post doctoral fellows Yonghong Li, Qingyuan Song and Shoba Kondragun ta; and Dr. Ingmar Akermann of Ford Research in Aachen, Germany. It is also a pleasure to thank the colleagues who engaged us in many seminal technical discussions on various aspects of the C MAQ system. We recognize P rofessors Harvey Jeffries, University of North Carolina at Chapel Hill; Ted Russell, Georgia Institute of Tec hnology; Nelson Seaman and Jack Kain, Pennsylvania State University; Dick McNider, University of Alabama, Sonia Kreidenweis, Colorado State University, Panos George opolus of Rutgers University, and Itsushi Uno of Kyushu University, Japan. Many of our EPA colleagues provided stimulating discussion or reviews of our effort including Gail Tonnesen, Deborah Luecken, Carey Jang, John S. Irwin ( NOAA) and Ed Edney. We are indebted to the following peer reviewers, who gave unselfishly of their time and whose comments and suggestions were extremely valuable in improving the scientific asp ects of the report. Listed in alphabetical order, the reviewers are followed by the chapter(s) they reviewed: Jeff Brook, Atmospheric Environment Service, Canada (Ch. 17); David Chock, Ford Research Laboratory (Ch. 2 and 7); Henry Hogo, Southern California Air Quality Modeling Division (Ch. 8 and 16); Sasha Madronich, National Center for Atmospheric Research (Ch. 14); Paul ette Middleton, RAND Environmental Science & Policy Center (Ch. 10); Ted Russell, Georgia Institute of Technology (Ch. 18); Nelson Seaman, Pennsylvania S tate University (Ch. 3); Christian Seigneur, Atmospheric and Environmental Research, Inc. (Ch. 10 and 11); Trevor Sholtz, ORTECH Corporation (Ch. 4); Saffet Tanrikulu, California Environmental Pro tection Agency (Ch. 9 and 12); Robert Yarmartino, Earth Tech (Ch. 5 and 6); and Zion Wang (Ch. 13, 15, and 16). Finally, the editors note with great appreciation, the efforts of Brian Eder, who fac ilitated many of the activities and tasks involved in the preparation and completion of this document. We thank the Raleigh, North Carolina, staff of SAIC, especially Alice Gilliland (now with NOAAAMD) and Andrea Verykoukis for their critical contributions in the area of technical editing, assuring a high level of consistency and quality in all the chapters of this manuscript. We are also grateful to Alice Gilliland for her invaluable work on the Executive Summary.EPA/600/R-99/030 

## E XECUTIVE 

## S UMMARY


### T HIS SCIENCE DOCUMENT PRESENTS THE PROCESSORS AND ALGORITHMS THAT EMBODY THE INITIAL RELEASE OF THE 

### M ODELS 

### -3 C OMMUNITY 

### MULTISCALE 

### A IR 

### Q UALITY 

### (CMAQ) MODELING SYSTEM 

### . CMAQ IS A MULTIPLE POLLUTANT MODEL THAT CONTAINS NEW SCIENTIFIC APPROACHES TO AIR QUALITY MODELING 

### , WHICH REPRESENT THE CURRENT STATE OF SCIENCE 

### . This CMAQ Science Document is a living document that will be updated as the state of the science progresses. The CMAQ Science Document provides a basis and point of reference for the s tate of the science captured in the June1998 initial release of Models-3. Current and future efforts to improve the Models-3 modeling system(s) w ill depart from the scientific reference points presented in this document. Models-3 is a flexible software system that provides a user-interf ace framew ork for C MAQ air quality modeling applications and tools for analysis, management of model input/output, and visualization of data. The Models-3 framew ork relies on two modeling systems to provide the meteorological and emissions d ata needed for air quality modeling. With this d ata, the Models-3 CMAQ modeling system can be used for urban and regional scale air quality simulation of tropospheric ozone, acid deposition, visib ility, and particul ate matter (PM 2.5 and PM 10 ). The meteorological and emissions modeling systems that are provided with the current release of Models-3 will be described in this document. However, C MAQ is designed as an open system where alternative models can be used to generate the data. This CMAQ Science Document contains chapters that a ddress specific scientific and technical issues involved in the development and application of the Models-3 C MAQ modeling system. The principal researchers for each model com ponent or function authored the coinciding chapter in this document. They serve as the points of contact for scientific questions regarding their CMAQ air quality model com ponents. For instructions on using the Models-3 framework and using the MM5, MEPPS, and CMAQ modeling systems, refer to the Models-3 User Manual (EPA/600/R-98/069b, National Exposure Research Laboratory, Research Triangle Park, NC) andTutorial (EPA/600/R-98/069c, National Exposure Research Laboratory, Research Triangle Park, NC). An overview of the MEPPS emissions, MM5 meteorology, and C MAQ air quality modeling systems is provided in Chapter 1. Chapter 2 then introduces the Models-3 framework and structure and explains how the framework’s user-interf ace is used with the MEPPS and CMAQ modeling systems. More detailed discussions on the modeling systems are then discussed separately in the following chapters. The amount of d etail and the length of these discussions vary depending on whether this information has already been provided elsewhere. Some chapters provide a synopsis of the scientific components and refer to previously published material on the subject, while other chapters provide extensive d etail on new scientific techniques that are not currently described in other publi cations.EPA/600/R-99/030

ES-2 MM5 Meteorological d ata are essential for many processes simul ated in the C MAQ chemical transport model including transport, chemistry, and cloud processes. The Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) is the only mete orology model compatible with the initial release of Models-3. MM5 is a complex, s tate -of-the-science community model, and it is maintained by NCAR. MM5 is well documented by its primary developers in technical notes and refereed journal articles. Chapter 3 briefly describes the scientific asp ects of MM5, including grid definitions, model physics, nesting, and four dimensional d ata ass imilati on. These descriptions generally direct the user to more complete documentation a bout particular asp ects of MM5. To promote the flexib ility of C MAQ, additional meteorology models w ill be compatible with Models-3 future releases. MEPPS Chapter 4 provides a description of the Models-3 Emission Processing and Projection System (MEPPS) structure, its scientific a pproach, and the assumptions used in modeling and processing emission data in the Models-3 framew ork. The chapter also discusses data flow and quality control used with emission invent ory and meteorological input d ata for MEPPS. The description of the main Emission Processor addresses the basis of spatial and temporal allo cation procedures, and the methods and assumptions used in modeling mobile and biogenic emissions and in the “lumping” of individual chemical species are also presented. This chapter also explains the procedures used by the Models-3 Emission Proj ection Processor to estimate emission data for use in modeling future air quality scenarios. THE CMAQ CHEMICAL TRANSPORT MODEL (CCTM) Fundamentals of One-Atmosph ere Dynamics for Multiscale Air Quality Modeling Chapter 5 provides information essential to the proper use of meteorological d ata in air quality modeling systems. The chapter introduces a robust and fully compressible set of governing equations for the atmosphere, which provides an integral view of atmospheric modeling. The limitations of several simplifying assumptions on atmospheric dynamics are presented, as are concepts of on-line and off-line coupling of meteorological and air quality models. In addition, this chapter describes a procedure for conserving the mixing ratio of trace species even in the case of mete orological d ata that are not mass consistent. In summary, Chapter 5 attempts to bridge the information gap between dynamic meteorologists and air quality modelers by highlighting the impli cation of using different meteorological coordin ates and dynamic assumptions for air quality simulations. Governing Equations and Computational Structure In Chapter 6, the governing diffusion equation is derived in a generalized coordin ate system, which is suitable for multiscale atmospheric applications. C MAQ’s use of generalized c oordin ates for its governing equations provides the flexib ility to span multiple scales and to inc orporate meteorological d ata on different c oordin ates. The C MAQ system’s modularity concepts and fractional time-step formulation, and CCTM’s key science processes are described. Chapter 6 also presents the dynamic formulations of several popular Eulerian air quality models as emulated by the governing diffusion equations in the generalized coordinate system.EPA/600/R-99/030

ES-3 Numerical Tra nsport Algorithms The transport processes in the atmosphere primarily consist of advection and diffusion. In Chapter 7, C MAQ’s numerical algorithms for advection and vertical and horizontal diffusion are discussed. To provide the CMAQ system with multiscale capability, the trans port processes, both adv ection and diffusion, are formul ated in conservation (i.e., flux) forms for the generalized coordin ate system. Therefore, C MAQ’s numerical trans port algorithms w ill function under a wide variety of dynamical situations and concentration distribution char acteristics. Users are enc ouraged to experiment with their own algorithms to test different numerical schemes for air quality simulations. Gas Phase Chemistry Chapter 8 examines the way gas-phase chemistry is tr eated in CMAQ. The CMAQ system c urrently includes two base chemical mechanisms, RADM2 and CB4, while the incorporation of a third, the SAPRC97 mechanism, is planned for the future. Chapter 8 describes each of these chemical mechanisms as well as the manner in which the first two are linked to the aqueous chemistry and aerosol formation processes. The chapter also discusses procedures for entering new chemical mechanisms in the CMAQ system, the represen tation of reaction kinetics, the numerical modeling of gas-phase chemistry, and the two numerical solvers included in C MAQ, SMVGEAR and a variant of the QSSA method. Plume-in-Grid Chapter 9 introduces the plume-in-grid (PinG) technique developed for C MAQ. PinG is designed to tr eat more realistically the dynamic and chemical processes impacting selected major point s ource pollutant plumes in CMAQ. The Plume Dynamics Model (PDM) simulates plume rise, horizontal and vertical plume growth, and transport of each plume section during the subgrid scale phase. The PinG module simul ates the relevant physical and chemical processes during a subgrid scale phase. This technique is in contrast to the traditional Eulerian grid modeling method of instantly mixing the point source emissions into an entire grid cell volume. Chapter 9 describes the technical approach and model formulation of the relevant processes, and discusses the capabilities and limitations of the initial version of the PinG approach. The Aerosol Module One of CMAQ’s key strengths is that it is a multi -pollutant model that fully addresses the criteria pollutants PM and ozone. Chapter 10 discusses the aerosol module of C MAQ, which is designed to be an efficient and ec onomical depiction of aerosol dynamics in the atmosphere. This chapter discusses the techniques for distributing particulates in three modes: coagulati on, particle growth by the addition of new mass, and particle formation. The aerosol module considers both PM 2.5 and PM 10 and includes estimates of the primary emissions of elemental and organic ca rbon, dust, and other species not further specified. Secondary species considered are sulf ate, nitrate, ammonium, water and organic from precursors of anthropogenic and biogenic origin. Cloud Chemistry and Dynamics Chapter 11 discusses the role and functions of clouds in C MAQ. Clouds are involved in aqueous chemical r eactions, vertical mixing of pollutants, and removal of aerosols by wet deposition, all of which aff ect theEPA/600/R-99/030

ES-4 concentration of air pollutants. CMAQ’s cl oud module performs several functions related to cloud physics and chemistry, and it models three types of clouds: sub-grid convective precipitating clouds, sub-grid non-precipitating clouds, and grid-resolved clouds. The cloud module vertically redistributes pollutants for the sub-grid clouds, calculates in-cloud and precipitation scavenging, pe rforms aqueous chemistry, and accumulates wet deposition amounts. 

### CMAQ INTERFACE PROCESSORS PREPARE INPUT DATA FROM SOURCES INCLUDING THE EMISSION AND METEOROLOGICAL MODELING SYSTEMS FOR USE IN THE 

### CMAQ CTM. E ACH OF THESE PROCESSORS 

### , EACH OF WHICH HAVE SPECIFIC FUNCTIONS 

### , ARE DESCRIBED IN THE CHAPTERS MENTIONED BELOW 

### . The interface processors that handle input d ata from the emissions and meteorological models are essential because C MAQ is an open system in which meteorological and emissions d ata are calculated separately (i.e., “ off-line”), rather than during the chemical transport model simulation. These interf ace processors also add extra quality control, so that inconsistencies between input d ata and the CCTM are min imized. ECIP In addition to describing the Models-3 MEPPS emission modeling system, Chapter 4 discusses the Emission-Chemistry Interf ace Processor (ECIP). ECIP serves as the key link between the MEPPS system and CCTM. ECIP’s primary function is to generate hourly 3-D emission d ata files for CCTM from the individual emission file types produced by the MEPPS. The key inputs for ECIP are the area emissions file, the s tack parameter and emission files for the point sources gener ated in MEPPS, and a set of meteorological d ata files generated by the Mete orology-Chemistry Interf ace Processor (MCIP) for the CCTM domain. All major point sources are subj ect to plume-rise and initial vertical dispersion processes before being allo cated to a particular vertical model layer. MCIP Chapter 12 describes MCIP, which links meteorological models, such as MM5, with the CCTM system to provide the complete set of mete orological d ata needed for air quality simulation. To support CCTM’s multiscale generalized coordinate implementati on, MCIP provides appropri ate dynamic meteorological parameters to allow mass-consistent air quality computations. MCIP deals with issues related to data format translation, conversion of parameter units, diagnostic estimations of parameters not provided, extr action of data for appropri ate window domains, and reconstruction of meteorological d ata on different grid and layer structures. MCIP also relies on the Landuse Processor (LUPROC) to provide landuse and vegetation information to define surface characteristics to compute dry deposition and other PBL parameters. LUPROC extracts information about landuse in the C MAQ domain from a landuse d atabase and converts it into the fr actional la nduse d ata used in MCIP. Initial and Boundar yConditions Initial conditions provide a simulation’s starting point, while boundary conditions define influences from outside the domain.EPA/600/R-99/030

ES-5 Chapter 13 describes the two interface processors that generate the concentration fields for the initial and boundary conditions for CCTM. The chapter describes how the initial condition (ICON) and boundary condition (BCON) processors can be used to generate the concentration fields from either predefined default vertical profiles or from other CMAQ simulation results when model nesting is being pe rformed. This chapter also discusses generating initial and boundary concentrations for special tr acer species and procedures for horizontal and vertical interpolation and conversions between chemical mechanisms. Photolysis Rate Processor Many chemical reactions in the atmosphere are initiated by the photodissociation of numerous tr ace gases, including NO 2 , O 3 , and HCHO. Chapter 14 describes the photolysis r ate processor (JPROC) that produces the photolysis r ates used in the CMAQ chemical trans port simulation. JPROC predicts photolysis r ates for various altitudes, latitudes, and zenith angles. Currently, the radiative transfer algorithm assumes clear-sky conditions (i.e., no clouds present), and CCTM then attenuates for cloudiness. As described above, each of the C MAQ interf ace processors incorpor ate raw data for CMAQ and perform functions such as calculating parameters and inte rpolating or converting d ata. The functions of the interface processors also include capabilities to handle raw d ata with various resolutions or measurement units. Raw input d ata is c urrently specified in the source code for JPROC, LUPROC, ICON, and BCON; however, the interface processors in future releases of CMAQ w ill be modified to handle a more generalized set of input d ata. 

### P ROGRAM CONTROL PROCESSORS 

### , A SET OF PROGRAMS EMBEDDED IN THE 

### M ODELS 

### -3 FRAMEWORK 

### , HANDLE SCIENCE INFORMATION OBJECTS SUCH AS GRID AND LAYER SPECIFICATIONS 

### , CHEMICAL MECHANISMS 

### , AND MODEL CONFIGURATIONS FOR REPEATED USE ACROSS SEVERAL PROCESS COMPONENTS OF 

### CMAQ. Program Control Processing (PCP) Chapter 15 explains how PCP is used within Models-3 to set up internal arrays, map species names, define global parameters, and establish linkages among processors in the Models-3 CMAQ system. Specifi cations needed for the CCTM simulation (e.g., grid and coordin ate c onditions and chemical species names) are entered into the Models-3 system once by the graphical user interfaces, and an object -oriented d atabase accessible by all model com ponents is established. PCP utilizes this information in the obj ect database and automatically generates the required global FORTRAN include files. As a part of PCP, Models-3 CMAQ system employs a generalized chemistry mechanism processor (MP), also calledthe “mechanism reader.” It greatly simplifies the task of implementing or altering gasphase chemistry mechanisms and provides the capab ility of easily and safely using different mechanisms in the CMAQ system.EPA/600/R-99/030

ES-6 Integration of Science Codes into Models-3 One of the major objectives of the Models-3 proj ect was to develop a flexible, comprehensive air quality modeling system with a modular coding structure that allows easy repl acement of science process components. Chapter 18 describes the modularity concepts, code management method, and integration schemes of CMAQ science code with the Models-3 framew ork. The CMAQ FORTRAN code was integr ated into the Models-3 framew ork with the following set of design, coding, and implementation standards: (1) modularity to allow easy exchange of science process solvers, (2) a standard subroutine interf ace at the module level, (3) restriction of coding pr actices, (4) the Models-3 I/O API (http://www.iceis.mcnc .org/EDSS/ioapi/index.html/), which contains standardized file I/O functions and a modeler-friendly interf ace built on top of self-describing netCDF (http://www.unid ata.ucar.edu/packages/netcdf/) files that are portable across most Unix platforms. 

### M ODELS 

### -3 ALSO PROVIDES ANALYSIS ROUTINES FOR USE WITH 

### CMAQ OUTPUT

### , WHICH CAN BE USED TO PROVIDE PROCESS ANALYSIS RESULTS AND STATISTICAL AGGREGATION TECHNIQUES 

### . Process Analysis Chapter 16 describes the implementation of process analysis techniques in the CMAQ modeling system. These techniques can be used in CMAQ to provide insights into how model predictions are obtained, which is particularly useful when modeling nonlinear systems like atmospheric photochemistry. Two techniques are available in the CMAQ system, integr ated process rate (IPR) analysis and integrated reaction rate (IRR) analysis. IPR analysis can be used to d etermine the relative contributions of individual physical and chemical processes, and IRR analysis can help elucidate im portant chemical pathways and identify key chemical characteristics. Aggregation Chapter 17 discusses a statistical procedure called aggregation that is applied to CMAQ’s outputs in order to derive the seasonal and annual estimates required by assessment studies. Assessment studies require CMAQ-based distributional estimates of ozone, acidic deposition, and PM 2.5 , as well as visibility, on seasonal and a nnual time frames. Unfortun ately, it is not financially feasible to execute C MAQ over such extended time periods. Therefore, in pr actice C MAQ must be executed for a finite number of episodes or “events,” which are selected to represent a variety of mete orological classes. The aggregation technique is used to incorpor ate these episode simulations into a nnual and seasonal estimates. 

### T HE 

### M ODEL 

### -3 CMAQ MODELING SYSTEM IS BEING FORMALLY EVALUATED TO ASSESS THE PERFORMANCE OF 

### CMAQ’ S NEW DEVELOPMENTS IN AIR QUALITY MODELING 

### . T HE EVALUATION WILL PROVIDE THE BASIS FOR UNDERSTANDING THE STRENGTHS OR WEAKNESSES OF THE CURRENT STATE 

### - OF 

### - SCIENCE IN 

### CMAQ. With an evaluation of CMAQ simulations of 36, 12, and 4 km grid resolution, CMAQ’s pe rformance can be evalu ated on both the regional and urban scales. This evaluationEPA/600/R-99/030

ES-7 will include an initial comparison of relative pe rformance against the R ADM model and diagnostic evaluation against databases from regional studies such as the 1995 Southern Oxidant Study condu cted in the vicinity of Nashv ille, TN and the 1995 NARSTO-NE study. CMAQ can be c onfigured for a wide range of appli cations, from scientific studies to regulatory applications. While the scientific community can take advantage of C MAQ’s ab ility to create alternative applications for research and development purposes, regulatory appli cations depend upon a standardized, evalu ated form of C MAQ for regulatory applications. The CMAQ evaluation program w ill provide the scientific benchmark needed for this. 

### F UTURE EXTENSIONS OF 

### CMAQ INCLUDE NEAR 

### - TERM EFFORTS TO PROVIDE A NEW CHEMICAL MECHANISM AND EMISSION MODELING SYSTEM 

### . The SAPRC-97 g as p hase mechanism will s oon be incorpor ated into C MAQ, in addition to the current CB-IV and RADM2 mechanisms available. The SAPRC mechanism will be incorpor ated with a fixed subset of the approximately 100 organic species contained in the semi-explicit version of the SAPRC mechanism. The S p arse Matrix O p erator Kernel Emissions modelin gs y stem ( SMOKE ) (http://envpro.ncsc.org/products/) w ill also be inc orpor ated into C MAQ in the near-term. The SMOKE model formul ates emissions modeling in terms of sparse matrix operations which require considerably less time to perform than current systems. 

### W E ENCOURAGE THE FULL PARTICIPATION AND INVOLVEMENT OF THE SCIENTIFIC AND MODELING COMMUNITIES IN THE GROWTH AND USE OF 

### M ODELS 

### 3 CMAQ. T HERE ARE MANY IDEAS AND PLANS FOR FUTURE DEVELOPMENTS OF CMAQ 

### , INCLUDING TOXIC POLLUTANT MODELING AND LINKAGES TO OTHER MODELS 

### . Modelin gatmos p heric toxic p ollutantsA key opportunity for C MAQ is developing the capability to model toxic pollutants. Models of airborne toxic pollutants are essential for human exposure and risk assessments. They can also be used to assess the exchange of toxic compounds between the atmosphere and sensitive ecosystems. With the ab ility to simulate toxic pollutant processes in addition to the current photochemical oxidants and particulates, it is planned to trans port the C MAQ model to a finer than urban scale to link with human exposure models. New linka g es with g lobal models It is hoped that information from the urban and regional CMAQ applications and from global modeling applications can be bridged. CMAQ output, produced using s tate of the science techniques, can be used to benchmark or examine the parametric basis of process formulations in global models. In addition, global model output can be used to improve or enhance the initial and boundary conditions for regional and urban scale C MAQ simulations.EPA/600/R-99/030

ES-8 Modeling ecosystems Efforts to combine environmental modeling techniques to encompass an entire ecosystem is needed to address issues including (a) nutrient cycling through the atmosphere, w ater bodies, and soil and (b) acidic wet and dry deposition into sensitive ecosystems, including critical load analyses. With this ecosystem modeling approach, air quality issues can be studied in combination with other asp ects of environmental health.
This is the Executive Summary of Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , edited by D. W. Byun and J. K. S. Ching, 1999.