ENVIRON International Corporation



## USER’S GUIDE

C OMPREHENSIVE 

## A IR QUALITY 

## M ODEL

WITH EX TENSIONS



## V ERSION 

## 5.30

**ENVIRON International Corporation 773 San Marin Drive, Suite 2115**  **Novato, California 94998**
**415-899-0700**

**www.camx.com www.environcorp.com** 

**December 2010**

773 San Marin Drive, Suite 2115, Novato, CA 94998 415.899.0700 

# CAMx Ozone
Particulates
ToxicsCopyright: ENVIRON International Corporation,
1997 – 2010

This publication may be reproduced for non-commercial purposes with appropriate attributio n.ACKNOWLEDGMENTS

ENVIRON acknowledges the following groups for their contributions to the development of
CAMx:

• The Texas Commission on Environmental Quality (TCEQ ), for sponsoring the development, testing, and review of numerous compon ents of the model;

• The Lake Michigan Air Directors Consortium (LADCo), for sponsoring the development, testing and review of numerous components of the mo del;

• The Coordinating Research Council (CRC), for sponso ring the development, testing, and review of numerous components of the model;

• The Carnegie-Mellon University, Department of Chemi cal Engineering, for providing full-science PM algorithms, assistance in incorpora ting them into CAMx, and testing the implementation;

• The University of Texas, Center for Energy and Envi ronmental Resources, for assistance in developing and testing the Open-MP multi-process or capability;

• Atmospheric, Meteorological, and Environmental Tech nologies (ATMET), for providing libraries and implementation support for the HDF5 o utput format and the MPI parallel processing capability;

• The Midwest Ozone Group (MOG), for sponsoring the d evelopment and testing of the MPI parallel processing capability;

• The U.S. Environmental Protection Agency (EPA), for sponsoring the development, testing, and review of the MPI parallel processing capability. Special thanks to Dr. Jon Pleim for assistance with our implementation of ACM 2;

• The San Francisco Bay Area Air Quality Management D istrict (BAAQMD), for supporting and testing the coupling of SAPRC99 gas- phase chemistry to the PM chemistry algorithm;

• The American Petroleum Institute (API), for sponsor ing the development and testing of improvements to the vertical advection algorithm;

• Atmospheric and Environmental Research (AER), for d eveloping the mercury chemistry algorithm.CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx i TABLE OF CONTENTS

Page

1. OVERVIEW ................................................................................................................... ...... 1-1

1.1 CAMx Features ............................................................................................................ ... 1-2
1.2 CAMx Extensions .......................................................................................................... . 1-4 1.3 Updates in CAMx Version 5.30...................................................................................... 1-5

2. CORE MODEL FORMULATION ..................................................................................... 2-1

2.1 Numerical Approach ....................................................................................................... 2-1 2.2 CAMx Grid Configuration .............................................................................................. 2-3
2.3 Treatment of Emissions .................................................................................................. 2-7 2.4 Transport Fundamentals ................................................................................................ 2- 11 2.5 Transport Algorithms .................................................................................................... 2-14 2.6 Wet Deposition ........................................................................................................... .. 2-17 2.7 Dry Deposition ........................................................................................................... ... 2-24

3. CHEMISTRY MECHANISMS ........................................................................................... 3-1

3.1 Gas-Phase Chemistry ...................................................................................................... 3-2 3.2 Aerosol Chemistry ........................................................................................................ 3-23 3.3 Mercury Chemistry ....................................................................................................... 3-28 3.4 Simple Chemistry via Mechanism 10 ........................................................................... 3-32

4. PLUME-IN-GRID SUBMODEL ......................................................................................... 4-1

4.1 CAMx PiG Formulation ................................................................................................. 4-2
4.2 IRON PiG ................................................................................................................. ...... 4-7
4.3 GREASD PiG ............................................................................................................... 4-12
4.4 Particulate Matter in PiG .............................................................................................. 4 -13 4.5 PiG Configuration ........................................................................................................ . 4-15

5. CORE MODEL INPUT/OU TPUT STRUCTURES .......................................................... 5-1

5.1 CAMx Run Control File ................................................................................................. 5- 2 5.2 CAMx Chemistry Parameters File ................................................................................ 5-14 5.3 Photolysis Rates File .................................................................................................... . 5-23
5.4 Albedo/Haze/Ozone File ............................................................................................... 5-2 4 5.5 FORTRAN Binary Input Files ...................................................................................... 5-27 5.6 FORTRAN Binary Output Files ................................................................................... 5-41 5.7 HDF5 Output Files ........................................................................................................ 5-46

6. OZONE SOURCE APPORTIO NMENT TECHNOLOGY ............................................. 6-1

6.1 Introduction ............................................................................................................. ........ 6-1
6.2 OSAT Formulation ......................................................................................................... 6-5 6.3 Running CAMx with OSAT ......................................................................................... 6-21CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx ii 6.4 Input File Formats ....................................................................................................... .. 6-28 6.5 Output File Formats ...................................................................................................... 6-32 6.6 Postprocessing............................................................................................................ ... 6-33 6.7 Steps in Developing I nputs and Running OSAT .......................................................... 6-35

7. PARTICULATE SOURCE APPO RTIONMENT TECHNOLOGY ............................... 7-1

7.1 Approaches to PM Source Attr ibution in Grid Models .................................................. 7-1 7.2 PSAT Methodology ........................................................................................................ 7-4 7.3 Testing PSAT ............................................................................................................. ..... 7-7 7.4 Running CAMx with PSAT .......................................................................................... 7-12 7.5 Steps in Developing Inputs and Running PSAT ............................................................ 7-15

8. DECOUPLED DIRECT METHOD FO R SENSITIVITY ANALYSIS .......................... 8-1

8.1 Implementation ........................................................................................................... .... 8-3 8.2 Running CAMx with DDM and HDDM ........................................................................ 8-6 8.3 Approaches to Define IC, BC and Emissions Sensitivities .......................................... 8-11 8.4 DDM Output Files ........................................................................................................ 8-11 8.5 DDM Sensitivity Coefficient Names ............................................................................ 8-12 8.6 Steps in Developing Inputs and Running DDM ........................................................... 8-16

9. PROCESS ANALYSIS ......................................................................................................... 9-1

9.1 Process Analysis in CAMx ............................................................................................. 9-1 9.2 Running Process Analysis............................................................................................... 9- 6 9.3 Postprocessing............................................................................................................ ..... 9-9 9.4 Chemical Process Analys is and Grid Nesting .............................................................. 9-14
9.5 CPA Demons tration ...................................................................................................... 9 -17

10. REACTIVE TRACERS ................................................................................................... 10-1

10.1 Description of RTRAC ............................................................................................... 10-1 10.2 Description of RTCMC............................................................................................... 10-7
10.3 Reactive Tracers in IRON PiG ................................................................................. 10-13 10.4 Running CAMx with Reactive Tracers ..................................................................... 10-16

11. THE CAMx MODELING SYSTEM .............................................................................. 11-1

11.1 CAMx Program Structure ........................................................................................... 11-2 11.2 Compiling CAMx ....................................................................................................... 11 -4 11.3 Running CAMx ........................................................................................................... 11-7
11.4 CAMx Pre- and Post-Processors ............................................................................... 11-14

12. REFERENCES ............................................................................................................... ... 12-1CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx iii TABLES

Table 2-1. Summary of the CAMx modul es for key physical processes. ............................ 2-1 Table 2-2. Relationships between season and month/latitude used in the CAMx Wesely/Slinn dry deposition model. Exception: seasons for the area
within 50N-75N and 15W-15E are inte rnally set to those of latitude band 35-50 to account for regions of Europe in which the climate is influenced by the Gulf Stream. ......................................................................... 2-28

Table 3-1. Gas-phase chemical mechanis ms currently implemented in CAMx. ................. 3-1
Table 3-2. Reactions and ra te constants for the CB4 mechanism (Mechanism 3). .............. 3-4 Table 3-3. Species in the CB4 mechanism (Mechanism 3). ................................................ 3-6 Table 3-4. Properties of VOC species in the CB4 mechanism: average carbon numbers, k OH values (ppm -1 min -1 ) and maximum incremental reactivity (MIR) values (mole O 3 /mole VOC). ................................................... 3-6
Table 3-5. Reactions and rate c onstants for the SAPRC99 mechanism (Mechanism 5). ................................................................................................... 3-7 Table 3-6. Species in the SAP RC99 mechanism (Mechanism 5). ..................................... 3-11 Table 3-7. Properties of VOC species in the SAPRC99 mechanism: average carbon numbers, molecular weights, k OH values (ppm -1 min -1 ) and maximum incremental reactivity (MIR) values (mole O 3 /mole VOC) ............ 3-13
Table 3-8. Reactions and ra te constants for the CB05 m echanism (Mechanism 6) ........... 3-14 Table 3-9. Species in the CB05 mechanism (Mechanism 6). ............................................ 3-17 Table 3-10. Properties of VOC species in the CB05 mechanism: average carbon numbers, molecular weights, k OH values (ppm -1 min -1 ) and maximum incremental reactivity (MIR) values (mole O 3 /mole VOC). ............................. 3-18 Table 3-11. SOA precursor reac tions included in CAMx. ................................................... 3-24 Table 3-12. Properties of CG/SOA pairs.............................................................................. 3-25 Table 3-13. List of PM species fo r the CAMx CF aerosol option. ...................................... 3-27

Table 5-1. Data requir ements of CAMx. .............................................................................. 5-1 Table 5-2. CAMx output file suffixes a nd their corresponding file types. .......................... 5-9 Table 5-3. Description of the CAMx ch emistry parameters file. The record labels exist in columns 1-20, and wh ere given, the input data for that
record start in column 21. The form at denoted “list” indicates a freeformat list of numbers (com ma or space-delimited). ........................................ 5-15 Table 5-4a. Rate constant expression types supported in CAMx and order of expression parameters for the chemistry parameters file. ................................. 5-22 Table 5-4b. List of parameters that mu st be provided in the CAMx chemistry parameter file for each of the seven types of rate constant expressions. Use ppm/minute units for A and Kelvin for Ea and TR. The variable O is the order of the reaction (1 to 3).. .............................................................. 5-23CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx iv Table 5-5. The 11 CAMx landuse categ ories and the defa ult surface roughness values (m) assigned to each category by season for the WESELY89 dry deposition option. Winter is de fined for conditions where there is snow present; winter months with no snow are assigned to the Fall category. Roughness for water is taken as the maximum of the baseline value given in the table, and the function 5

. 2 6
0 10 2 w z − × = , where w is surface wind speed (m/s). The UV albedos are used in the albedo/haze/ozone (AHO) file. ......................................................................... 5-30 Table 5-6. The 26 CAMx landuse categ ories and the default annual minimum and maximum LAI and surface roughness (m) ranges assigned to each category for the ZHANG03 dry deposition option. Roughness for

water is calculated from the function 5

. 2 6
0 10 2 w z − × = , where w is surface wind speed (m/s). The UV albedos are used in the albedo/haze/ozone (AHO) file. ......................................................................... 5-32
Table 5-7. List of stead y-state radical species names used in the CB4, CB05, and SAPRC99 gas-phase chemical mechanisms. .................................................... 5-42

Table 6-1. Numbers of emission file sets (i.e., low level files and point source file) needed for different model configurations. ............................................... 6-23 Table 6-2. Format for the receptor definition file. ............................................................. 6-31

Table 8-1. DDM output f ile suffix names. ......................................................................... 8-12

Table 9-1. Process information reported by the IPR option. ................................................ 9-3 Table 9-2. Chemical Process Analysis (CPA) variables calcul ated in CAMx for the CB4, CB05 and SAPRC99 mechanisms. ...................................................... 9-4 Table 9-3. Process analysis keywords and associated CAMx output files. ......................... 9-6

Table 10-1. Keywords, options and default values for the Cont rol section of the
IMC file. ............................................................................................................ 10-9
Table 10-2a. Recommended SCICHEM rate constant expression types for use in
CAMx. ............................................................................................................ 10-14 Table 10-2b. Parameters required by SCICHEM rate constant expression types. ............... 10-15 Table 10-3. Determining the reaction orde r and consequent unit dimensions for
rate constants. .................................................................................................. 10-15 Table 10-4. RTCMC parameters default settings in the Includes/rtcmc.com
include file. ..................................................................................................... 10-19

Table 11-1. Parameters and their defaults in camx.prm used to statically dimension local arrays in low-level subroutines. .............................................. 11-3 Table 11-2. Example of computer re quirements for several non-MPI CAMx configurations on a dual-pro cessor Athlon 2800+ (2.1Ghz) PC; compiled using the Portland Group compiler. ................................................ 11-13 Table 11-3. CAMx speed performance with MPI and OMP for the distribution test case available at www.camx.com (processed on 2.83 Ghz Intel Xeon chipsets). ......................................................................................................... 11-14 Table 11-4. CAMx speed performance with MPI and OMP for the EPA/OAQPS test case (processed on 2.83 Ghz Intel Xeon chipsets). ................................. 11-14CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx v FIGURES

Figure 2-1. A horizontal representation of the Arakawa C variable configuration
used in CAMx. .................................................................................................... 2-4 Figure 2-2. An example of horizontal grid nesting, showing two telescoping nested grids within a 10× 10 cell master grid. The outer nest contains 10×12 cells, and the inner nest contains 6×10 cells. ........................................... 2-5 Figure 2-3. A schematic illustration of the CAMx vertical grid indexing convention and vertical nesting. Th e lowest 2 master (coarse) grid layers are split into 4 layers in nest ed Fine Grid 1, and the lowest 5 layers of Fine Grid 1 are split into 9 layers in nested Fine Grid 2. Note that the nested grids possess a matching layer interface for every parent grid layer interface. .................................................................................. 2-6 Figure 2-4. Schematic representation of the turbulent ex change among layers within a vertical grid column dur ing convective adjustment in the ACM2 (taken from Pleim [2007]). ................................................................... 2-16 Figure 2-5. Comparison of monthly LAI data embedded in the Zhang dry deposition scheme against episod e-specific LAI for June 2005. ...................... 2-30 Figure 2-6. Comparison of particle dry deposition velo cities as a function of size and wind speeds (m/s) for three models: black – Zhang et al. (2001); blue – Slinn and Slinn (1980); or ange – AERMOD (EPA, 1998).
Results are shown for a forest la nduse category during daytime neutral stability. Particle de nsity was set at 1.5 g/cm 3 . ................................................ 2-32 Figure 2-7 Comparison of particle dry deposition ve locities determined by the Zhang et al. (2001) and Slinn and Slinn (1980) models, as a function of landuse and wind speed during day time neutrally stable conditions. Particle density was set at 1.5 g/cm 3 ................................................................. 2-32

Figure 3-1. Impact of chemical decay on the concentration of the donor species
PMT. ................................................................................................................. 3-33 Figure 3-2. Impact of chemical producti on on the concentrati on of the recipient species PM2. ..................................................................................................... 3-33

Figure 4-1. Schematic representation of CAMx PiG puff shape in the horizontal. Directional orientation of the puff is arbitrary, and evolves during the age of the puff according to wind direction, shears and diffusive growth along its trajectory. ................................................................................. 4-3 Figure 4-2. Plan-view schematic represen tation of a chain of PiG puffs emitted from a point source into an evolving gridded wind field. The red line connected by dots represents puff centerlines, with dots representing leading and trailing points of each puff. The CAMx computational grid is denoted by the blue lines. ........................................................................ 4-6 Figure 4-3. Example of a single point s ource PiG plume as depicted by a sampling grid with 200 m resolution (shown by the extent of the plot). This sampling grid was set within a CAMx computational grid with 4-km
resolution. The source location is arbitrary and is emitting an inert
tracer. ................................................................................................................ 4-12CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx vi Figure 5-1. A sample CAMx job script th at generates a “CAMx.in” file and runs
the model. .......................................................................................................... 5-11 Figure 5-2a. Example CAMx chemistry parameters file for Mechanism 6 (CF scheme), including the mercury species HG0, HG2, and HGP. ....................... 5-17 Figure 5-2b. Example inert chemistry paramete rs file (requires chemistry flag to be set false-see the description of the CAMx control file). ................................... 5-21 Figure 5-3. Example of th e first several panels of lookup data in the photolysis rates input file. .................................................................................................. 5-25 Figure 5-4. Example of the structure of a 2-grid abedo/haze /ozone input file showing panels for all mandato ry and optional fields. ..................................... 5-28

Figure 6-1. Example of the sub-division of a CAMx domain into separate areas for geographic source apportionment. ................................................................ 6-2 Figure 6-2. Example display of ozone source apportionment information provided by OSAT for a run with 17 source areas and 1 emission group. ........................ 6-4 Figure 6-3. Daytime reactions of ozone with HOx (OH and HO 2 ) showing potential for reformation of ozone or ozone destruction via peroxide
formation. .......................................................................................................... 6-12 Figure 6-4. Box model simulation to evaluate P H202 /P HNO3 indicator for VOC vs. NOx limited ozone formation (see text ). Ozone is plotted against the left axis, P H202 /P HNO3 is plotted against the right axis. For P H202 /P HN03 below 0.35 ozone formation is VOC limited. Thus, the transition form
VOC to NOx limited ozone formation is predicted to occur about 12
noon. Initial VOC/NOx = 10:1. ....................................................................... 6-15 Figure 6-5. Effect on ozone of contro lling VOC and NOx by 30 percent for the base scenario shown in Figure 6-4. The vertical dashed line shows the
transition form VOC to NOx limite d ozone formation for the base case derived in Figure 6-4. Initia l base case VOC/NOx = 10:1. ..................... 6-15 Figure 6-6. Change in ozone (control-base) for the VOC and NOx control scenarios shown in Figure 6-5. Th e vertical dashed line shows the
transition from VOC to NOx limited ozone formation for the base case derived in Figure 6-4. Initia l base case VOC/NOx = 10:1. ..................... 6-16 Figure 6-7a. As Figure 6-4, but for an Initial VOC/NOx = 20:1. ......................................... 6-16 Figure 6-7b. As in Figure 6-6, but fo r an Initial VOC/NOx = 20:1. ..................................... 6-17 Figure 6-8a. As Figure 6-4, but for an Initial VOC/NOx = 5:1. ........................................... 6-17 Figure 6-8b. As Figure 6-6, but for an Initial VOC/NOx = 5:1 ............................................ 6-18 Figure 6-9a. An example of OSAT input r ecords in the CAMx run control file. The options for this run are as follow s: this is a two-grid run, master and nested grid surface concentratio ns are written to file, a single tracer type is to be used for all boundaries, 19 source regions, one emission group (i.e., zero additiona l emission files and no leftover group), and two timing tracer releases pe r day. This is the first day of the simulation (i.e., restart is fals e), so no OSAT restart files are supplied. ............................................................................................................ 6-24 Figure 6-9b. As in Figure 6-9(a), but in th is case the run is a continuation, and so the restart flag is set to TRUE and a “Tracer Restart” files are supplied. ............................................................................................................ 6-25CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx vii Figure 6-9c. As in Figure 6-9(a), but in this case the run is a continuation day of a run with three emission groups. The three emission groups are defined by supplying two pairs of ex tra emission files for each grid (AREA group 1, AREA group 2, and POINT group 2), and setting the “Use_Leftover_Group” flag to TRUE for the model to calculate the third group internally. The POINT group 1 filename is blank because group 1 is a category with no point s ource emissions (e.g., biogenics). ........... 6-26 Figure 6-9d. As in Figure 6- 9(c) (i.e. a continuation day of a run with three emission groups), but in this case all three emission groups are defined explicitly by supplying extra em ission files (AREA group 1, AREA group 2, POINT group 2, AREA group 3, and POINT group 3). Therefore, the “Use_Leftover_Group ” flag is set to FALSE. The POINT group 1 filename is blank because group 1 is a category with no point source emissions (e.g., biogenics). ..................................................... 6-27 Figure 6-10. Example source area mapping file for the domain and source areas shown in Figure 6-1. ......................................................................................... 6-30 Figure 6-11. Example Receptor Concentrati on File. Lines ending with “..” are truncated to fit the page, and the f ile would continue with data for additional receptors and hours in the same format. .......................................... 6-34

Figure 7-1. The CAMx modeling domain for PSAT testing showing sub-division to geographic areas and locations of four hypothetical point sources (+ symbols). ............................................................................................................. 7-9 Figure 7-2. Comparison of sulfate contributions ( μ g/m 3 ) from the hypothetical MRPO point source on 28 June 2001 at hour 15: (a) PSAT result; (b)
Zero-out result. .................................................................................................... 7-9 Figure 7-3. Comparison of episode aver age (June 18 to July 21, 2001) sulfate contributions ( μ g/m 3 ) from the hypothetical MR PO point source: (a) PSAT result; (b) Zero -out result. ...................................................................... 7-11 Figure 7-4. PSAT apportionment of reactiv e nitrogen (NOy) to initial conditions and emissions during a 24-hour box model simulation. ................................... 7-11 Figure 7-5. Source-Oriented External Mixture (SOEM) appor tionment of reactive nitrogen (NOy) to initial conditi ons and emissions during a 24-hour
box model simulation. ....................................................................................... 7-12 Figure 7-6. This figure follows from Figur e 6-8(d): it is a c ontinuation day of a 2- grid run with three emission groups , and all three emission groups are defined explicitly by supplying extra emission files (AREA group 1, AREA group 2, POINT group 2, AREA group 3, and POINT group 3). Therefore, the “Use_Leftover_Gr oup” flag is set to FALSE. The POINT group 1 filename is blank because group 1 is a category with no point source emissions (e.g., biogenics). PSAT will trace PM
sulfate and nitrate species only. ........................................................................ 7-14CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx viii Figure 8-1. Example input of DDM opti ons and filenames via the CAMx control file. In this case, CAMx is run with two grids, and DDM is configured to track emissions from four source regions and two source groups (biogenic and anthropogenic). Initial and boundary conditions are tracked as a single sensitivity called “ALL”, while emissions are
tracked with three sensitivities called “NOx”, “VOC”, and “ALL”. Additionally, second-orde r sensitivities to biogenic VOC emissions from the first source region will be computed. No source region map is provided for the nested grid (the region assignments on the nest are defined by the master grid). Initial condition sensitivity is not being tracked (no file provided), and only the group 2 point sources are tracked (no biogenic point s ources are available). ............................................ 8-10 Figure 8-2. Example concordance of long and short sensitivity coefficient names from the CAMx diagnostic output file. ............................................................. 8-13

Figure 9-1. Example section of a CAMx control file specifying options for Process Analysis. ................................................................................................ 9-7 Figure 9-2. Time-averaged IPR analys is for PSO4 and PNO3 on June 13 and 14, 2002; all of the processes are plotted individually. ........................................... 9-11 Figure 9-3. IPR time series anal ysis for PSO4 and PNO3; Lateral boundary/Chemistry terms are not aggregated. ................................................ 9-12 Figure 9-4. IPR time series anal ysis for PSO4 and PNO3; Lateral boundary/Chemistry terms are aggregated to a single net term, in contrast to Figure 9-3. ....................................................................................... 9-13 Figure 9-5. Surface OH concentrations fo r the OTAG master grid area with (a) and without (b) vertical nesting in the nested grid area. ................................... 9-16 Figure 9-6. Surface ozone concentrations for the OTAG master grid area with (a) and without (b) vertical nesting in the nested grid area. ................................... 9-16 Figure 9-7. The CAMx Los Angeles mode ling domain (Yarwood et al., 2003). ................ 9-18 Figure 9-8. PBL depth (m) diagnosed by VRTAVG for 14:00 on August 5, 1997. ........... 9-19 Figure 9-9. PBL average ozone (ppb) at 14:00 on August 5, 1997. .................................... 9-19 Figure 9-10. Relative response of ozone to a 50% NOx emission reduction at 14:00 on August 5, 1997. ............................................................................................ 9-20 Figure 9-11. Ozone production rate (ppb/ hr) at 14:00 on August 5, 1997. ........................... 9-21 Figure 9-12. Difference (CB4-SAPRC99) in ozone production rate (ppb/hr) at 14:00 on August 5, 1997. .................................................................................. 9-21 Figure 9-13. Ratio of instantaneous producti on rates for hydrogen peroxide to nitric acid (H 2 O 2 produced / HNO 3 produced) at 14:00 on August 5, 1997. ............. 9-22 Figure 9-14. Ozone production rate in ar eas defined as NOx-sensitive by the

P H2O2 /P HNO3 indicator ratio at 14:00 on August 5, 1997. .................................. 9-22 Figure 9-15. Ozone production rate in ar eas defined as VOC-sensitive by the

P H2O2 /P HNO3 indicator ratio at 14:00 on August 5, 1997. .................................. 9-23 Figure 9-16. HOx radical initiation rate (ppb/hour) at 14:00 on August 5, 1997. ................. 9-24 Figure 9-17. HOx radical reaction rate (ppb/hour) at 14:00 on August 5, 1997. .................. 9-25 Figure 9-18. HOx chain length at 14:00 on August 5, 1997.................................................. 9-26 Figure 9-19. Ratio of the HOx chain le ngth for CB4 to SAPRC99 at 14:00 on August 5, 1997. ................................................................................................. 9-26 Figure 9-20. RO 2 organic peroxy radical reacti on rate (ppb/hour) at 14:00 on August 5, 1997. ................................................................................................. 9-27CAMx v5.30 User’s Guide, December 2010 Table of Contents

Y:\CAMx\UsersGuide\v5.30\TOC.docx ix Figure 9-21. Conversion rate (ppb/hr) for NO 2 to HNO 3 by reaction with OH at 14:00 on August 5, 1997. .................................................................................. 9-28 Figure 9-22. Rate of NOx recycling (ppb/ hr) from NOz at 14:00 on August 5, 1997 .......... 9-29 Figure 9-23. Ozone destruction rate by chemical reactions (ppb/hr) at 14:00 on August 5, 1997. ................................................................................................. 9-30 Figure 9-24. OH + isoprene as a fracti on of OH + VOC reactions at 14:00 on August 5, 1997. ................................................................................................. 9-31

Figure 10-1. Example RTRAC chemistry i nput file for modeling the MATES toxic species using CB4 as the host chemical mechanism. ....................................... 10-2 Figure 10-2. Example RTRAC chemistry inpu t file for modeling toxic species with
SAPRC99 as the host chemical mechanism. .................................................... 10-3 Figure 10-3. Example RTRAC receptor input file identifying the grid cells with locations of point source complexe s where hourly decay rates will be output for subgrid-scale point source modeling (see format for OSAT receptor file in Table 6-2). ................................................................................ 10-6 Figure 10-4. Example RTCMC ch emistry input file. ............................................................ 10-8 Figure 10-5. Example RTRAC tracer plume emanating from a large point source and displayed on a surface sampling grid with 1-km grid spacing. The figure illustrates the entire exte nt of the sampling grid. ................................. 10-16 Figure 10-6. Example input of RTRAC op tions and filenames within the CAMx
control file. ...................................................................................................... 10-18

Figure 11-1. Schematic diagram of the CA Mx modeling system. See Table 5-1 for a detailed list of specifi c model input requirements for the five major data classes shown at the top of th e figure. Certain pre- and postprocessor programs shown in the figure are described in this section. Third-party models, processors, a nd visualization software are not
described in this User’s Guide and are not distributed with CAMx. ................ 11-1 Figure 11-2. An example of global ozone column from the Ozone Monitoring Instrument (OMI) platform. White areas denote missing data. From http://jwocky.gsfc.nasa.gov/ . .......................................................................... 11-17CAMx v5.30 User’s Guide, December 2010

Overview

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_01_overview .docx 1-1 1. OVERVIEW

The C omprehensive A ir quality M odel with e x tensions (CAMx) is an Eulerian photochemical dispersion model that allows for an integrated “one -atmosphere” assessment of gaseous and particulate air pollution (ozone, PM 2.5 , PM 10 , air toxics, mercury) over many scales ranging fro m sub-urban to continental. It is designed to unify all of the technical features required of “state-of

the-science” air quality models into a single syste m that is computationally efficient, easy to use, and publicly available. The model code has a highl y modular and well-documented structure which eases the insertion of new or alternate algor ithms and features. The input/output file formats are compatible with the Urban Airshed Model genre and are compatible with many existing pre- and post-processing tools.

CAMx simulates the emission, dispersion, chemical r eaction, and removal of pollutants in the troposphere by solving the pollutant continuity equ ation for each chemical species ( l ) on a system of nested three-dimensional grids. The Eule rian continuity equation describes the time dependency of the average species concentration ( c l ) within each grid cell volume as a sum of all of the physical and chemical processes operating on that volume. This equation is expressed mathematically in terrain-following height ( z ) coordinates as follows:

where V

H is the horizontal wind vector, η is the net vertical transport rate, h is the layer interface height, ρ is atmospheric density, and K is the turbulent exchange (or diffusion) coefficie nt. The first term on the right-hand side represents horizo ntal advection, the second term represents net resolved vertical transport across an arbitrary spa ce- and time-varying height grid, and the third term represents sub-grid scale turbulent diffusion. Chemistry is treated by simultaneously solving a set of reaction equations defined from sp ecific chemical mechanisms. Pollutant removal includes both dry surface uptake (depositio n) and wet scavenging by precipitation.

CAMx can perform simulations on three types of cart esian map projections: Universal Transverse Mercator, Rotated Polar Stereographic, a nd Lambert Conic Conformal. CAMx also offers the option of operating on a curvi-linear ge odetic latitude/longitude grid system as well. Furthermore, the vertical grid structure is defined externally, so layer interface heights may be specified as any arbitrary function of space and/or time. This flexibility in defining the horizontal and vertical grid structures allows CAMx to be configured to match the grid of any meteorological model that is used to provide enviro nmental input fields. 

## ( ) ( )

Removal l

Chemistry l

Emission l l l l l H H l

t c

t c

t c c K t z h

c

z c c V t c

∂ ∂
+ ∂ ∂
+ ∂ ∂
+ ∇

⋅ ∇ +    

   

∂

∂ ∂

− ∂ ∂
+

⋅ ∇

− = ∂ ∂ ρ ρ η /

2CAMx v5.30 User’s Guide, December 2010

Overview

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_01_overview .docx 1-2 1.1 CAMx FEATURES

In addition to the attributes it shares with most p hotochemical grid models, some of the most
notable features of CAMx are:

Two-Way Nested Grid Structure : This feature allows CAMx to be run with coarse g rid spacing over a wide regional domain in which high spatial r esolution is not particularly needed, while within the same run, applying fine grid nests in sp ecific areas where high resolution is needed.

Flexi-Nesting : CAMx offers the ability for users to arbitrarily introduce and/or remove various nested grids at any point during the course of a si mulation. Upon model restart, CAMx automatically diagnoses any changes to the grid sys tem. User’s can supply complete information for new grids (emissions, landuse, meteorology) or allow CAMx to interpolate any or all of these inputs from parent grids. Example applications of flexi-nesting include running spin-up days with a single coarse master grid and introducing ne sts just for episode days, or evaluating sensitivity to grid configuration when designing a model application.

Multiple Photochemical and Gas Phase Chemistry Mech anism Options : Users can select among two versions of Carbon Bond IV (CB4) chemistry (CAM x Mechanisms 3 and 4), the 2005 version of Carbon Bond (CB05) chemistry (CAMx Mecha nism 6) or the 1999 version of SAPRC chemistry (CAMx Mechanism 5).

Treatment of Particulate Matter : CAMx features a “one-atmosphere” treatment for o zone and particulate matter (PM) with detailed algorithms fo r the relevant science processes, including aqueous chemistry (RADM-AQ), inorganic aerosol ther modynamics/partitioning (ISORROPIA), and secondary organic aerosol formation/partitionin g (SOAP). Nitrate uptake by calcium on fine
crustal PM is calculated external to ISORROPIA. Th e particulate chemistry mechanism utilizes products from the gas-phase photochemistry for prod uction of sulfate, nitrate, condensable organic gases, and chloride. The one-atmosphere oz one/PM treatment is linked to CB4 gasphase chemistry via CAMx “Mechanism 4”, to CB05 gas -phase chemistry via CAMx “Mechanism 6”, and SAPRC99 gas-phase chemistry via CAMx “Mechanism 5.” CAMx provides two options for the representation of the particle size distribution: a static two-mode coarse/fine (CF) scheme, and the multi-sectional CM U scheme, which treats the size evolution of each aerosol constituent among a number of fixed si ze sections.

Mercury Chemistry : CAMx may be used to model the chemistry of three mercury species, two gaseous (HG0 and HG2) and one particulate (HGP), vi a gas and aqueous pathways. The mercury chemistry module requires total PM concentr ations, so mercury can only be modeled with Mechanisms 4, 5 or 6 (CF) by including mercury species among the list of modeled species. All of the rate and equilibrium constants for the m ercury mechanism are hard-coded within chemistry module.

User-Defined Chemistry Mechanism : The model recognizes “Mechanism 10” as a simple way to define your own chemistry mechanism. This optio n is intended for users to define simple chemical transformations between gas and/or aerosol species. You must develop your own Mechanism 10 subroutine and chemistry parameters fi le. An example might be a case where the user has only SO 2 emissions from a few sources and wishes to estimat e first-order decay to sulfate without the need to employ full photochemic al oxidant chemistry.CAMx v5.30 User’s Guide, December 2010

Overview

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_01_overview .docx 1-3 Chemical Kinetics Solver Options : Three options are available to solve gas-phase c hemistry. The Euler-Backward Iterative (EBI) solver has repla ced the original Chemistry Mechanism Compiler (CMC) solver employed in past versions of CAMx. EBI provides improved accuracy with similar speed compared to CMC. Alternatively, users may select the Implicit-Explicit Hybrid (IEH) chemical solver of Sun, Chock and Wink ler (1994). The IEH solver accuracy is comparable to reference methods such as LSODE but i s several times slower than the EBI solver. The model also includes the fully explicit LSODE solver. LSODE can be used to "benchmark" a simulation to evaluate the performanc e of EBI or IEH. We do not recommend use of LSODE for typical applications as the model will run much more slowly.

Plume-in-Grid (PiG) Module : CAMx features a PiG sub-model to treat the chemis try and dispersion of point source emission plumes at sub-g rid scales; individual plume segments are tracked by the Lagrangian module while undergoing d ispersion and chemical evolution, until such time as their pollutant mass should be represe nted within the grid model framework. Plume dispersion is determined using a second-order closu re puff spread calculation. The full photochemical chemistry option is called Incrementa l Reactions for Organics and NOx (IRON) PiG, and may be applied to point sources of any kin d to simulate full CB4, CB05 or SAPRC99 gas-phase photochemistry. Gas-phase chemistry in P iG is solved using LSODE. The GREASD PiG option is designed to treat the early chemical evolution of large NOx point source plumes before oxidant production becomes important to faci litate ozone and PM source apportionment Probing Tools. PM chemistry is only treated by the GREASD PiG option. Both PiG options include a “sampling grid” capability to passively s ample plume surface concentrations at any resolution, which is particularly useful to visuali ze near-source sub-grid scale impacts.

Horizontal Advection Solver Options : The Area Preserving Flux-Form advection solver of Bott (1989) and the Piecewise Parabolic Method (PPM) of Colella and Woodward (1984) are available in CAMx. These schemes possess high-orde r accuracy, little numerical diffusion, and are sufficiently quick for applications on very lar ge grids. Either of these solvers may be
selected via the CAMx run control file.

Vertical Diffusion (Mixing) Options : By default, CAMx employs a standard “K-theory” approach for vertical diffusion to account for sub- grid scale mixing layer-to-layer, where threedimensional diffusivity fields values are provided via input file. Version 2 of the Asymmetric Convective Model (ACM2; Pleim, 2007) is available a s an alternative to the K-theory approach. ACM2 includes mixing between adjacent layers using K-theory and includes mixing between non-adjacent layers only for transfer from the surf ace to layers aloft during convective conditions. Thus, ACM2 includes the basic features of both local and the most important component of non-local exchange. Note that ACM2 in creases CAMx runtime considerably relative to the default K-theory, so use of paralle lization with ACM2 is encouraged.

Dry Deposition Options : CAMx offers two dry deposition options: the orig inal approach based on the work of Wesely (1989) and Slinn and Slinn (1 980); and an updated approach based on the work of Zhang et al. (2001; 2003). The Zhang schem e is a state-of-the-science algorithm that incorporates vegetation density effects via leaf ar ea index (LAI), possesses an updated representation of non-stomatal deposition pathways including a better snow cover treatment, and has been tested extensively through its use in dail y air quality forecasting. It has been shown to reproduce observed fluxes of ozone and SO 2 with reasonable accuracy. The originalCAMx v5.30 User’s Guide, December 2010

Overview

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_01_overview .docx 1-4 Wesely/Slinn model is formulated for 11 landuse cat egories, while the Zhang model uses 26 landuse categories.

Advanced Photolysis Model : The TUV radiative transfer and photolysis model ( Madronich, 2002), developed at the National Center of Atmosphe ric Research (NCAR), is used as a CAMx preprocessor to provide the air quality model with a multi-dimensional lookup table of clear-sky photolysis rates by surface albedo, total ozone col umn, haze turbidity, altitude, and zenith angle. CAMx includes two optional schemes to internally ad just clear-sky photolysis rates for the presence of clouds. The default approach uses a fa st in-line version of TUV (Emery et al., 2010) to calculate photolysis adjustment profiles through each cloudy grid column. The original cloud adjustment approach has been retained as an option, which is based on the parameterization developed for the Regional Acid Deposition Model (R ADM; Chang, et al., 1987).

Parallel Processing: Multi-processor support is included in CAMx in two ways: (1) use of OpenMP (OMP) compiler directives within the CAMx co de, which allows parallel processing on shared-memory (e.g., multi-core) computers; and (2) use of the Message Passing Interface (MPI) protocol, which allows for parallel processing on d istributed memory (networked) computer cluster environments. Significant speed improvemen ts can be realized using parallel processing, depending on the size of the application and number /speed of CPUs to which the CAMx
simulation is distributed. Both MPI and OMP can be used in combination to maximize speed performance. To use MPI you must have the MPICH ut ility installed on your system. To use OMP, your fortran compiler must include libraries t o support its in-code directives.

1.2 CAMx EXTENSIONS

The CAMx “extensions” include:

Ozone Source Apportionment Technology (OSAT) : The OSAT and its derivatives (e.g., APCA) allow CAMx to track source region and/or source cat egory contributions to predicted grid cell ozone concentration; thus, for any selected recepto r point and time, the model gives a clear picture of the likely distribution of ozone and ozo ne precursors by source category and/or source region, as well as an indication as to whether the ozone at the selected time and location would more likely respond to upwind NO
x or VOC controls.

Particulate Source Apportionment Technology (PSAT) : PSAT provides source-category and geographic source apportionment of modeled PM by in dividual species or by several species aggregation methods. This Probing Tool works simil arly to OSAT methods for ozone. It works in conjunction with the Mechanism 4, 5 or 6 (CF) ch emistry only.

Decoupled Direct Method (DDM) and Higher-Order Deco upled Direct Method (HDDM) for Source Sensitivity of Ozone and Other Gas Species : The CAMx implementation calculates firstorder (DDM) sensitivity coefficients, and both firs t and second order (HDDM) sensitivity coefficients, of all gas concentrations to changes in emissions, initial conditions and boundary conditions. The primary purpose of (H)DDM is to es timate the impact of region- and categoryspecific emission changes on pollutant concentratio ns. DDM provides very accurate sensitivity estimates for relative small changes (10-20%). HDD M runs faster than DDM and allows for more accurate sensitivity estimates for larger chan ges (up to 70%), but its first-order estimates
are less accurate than DDM.CAMx v5.30 User’s Guide, December 2010

Overview

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_01_overview .docx 1-5 Process Analysis (PA): The PA tool is designed to provide in-depth analy ses of the physical and chemical processes in an air quality model. Throug h PA, one can more fully understand the complex interactions of the different processes, ex plain simulation results within the context of model formulation, and improve the design of contro l strategies. Three components of PA are implemented in CAMx: (1) Integrated Processes Rate (IPR) analysis, which provides detailed process rate information for each bulk process in C AMx (i.e., advection, diffusion, deposition, emissions, chemistry, etc.) for selected grid cells and selected species; (2) Integrated Reaction Rate (IRR) analysis, which provides detailed reacti on rate information for all reactions in the chemical mechanism for selected grid cells; and (3) Chemical Process Analysis (CPA), which is closely related to the IRR method but is designed t o be more user friendly and accessible. PA is available for use in CB4, CB05, and SAPRC99 chemica l mechanisms, and for EBI, IEH, and LSODE chemistry solvers.

Reactive Tracer (RTRAC) Source Apportionment : The RTRAC method provides a flexible approach for tracking the emission, dispersion, che mistry, and deposition of multiple gas and particle tracers. The tracers operate in parallel to the CAMx host model. Gas-phase chemistry can be defined as a simple decay rate for each spec ies (photolysis and reactions with radicals), or as a complex non-linear system defined by the user through the RTRAC Chemical Mechanism Compiler (RTCMC). RTRAC can also output locally sp ecific tracer decay rates for input to a separate Gaussian or puff dispersion model for sub- grid scale or “fence line” dispersion calculations. Potential RTRAC applications include simulating gaseous and particle air toxic compounds from specific source sectors. RTRAC spec ies can also be carried within IRON PIG puffs. An RTRAC/PiG “sampling grid” capability can be used to passively sample surface concentrations of RTRAC species within reactive plu mes at very small spatial scales.

1.3 UPDATES IN CAMx VERSION 5.30

CAMx version 5.30 includes the following major upda tes and additional capabilities over
version 5.20:

New Dry Deposition Option : CAMx offers two dry deposition options: the orig inal approach based on the work of Wesely (1989) and Slinn and Sl inn (1980); and an updated approach based on the work of Zhang et al. (2001; 2003). The new scheme is a state-of-the-science algorithm that incorporates vegetation density effects via le af area index (LAI), possesses an updated representation of non-stomatal deposition pathways including a better snow cover treatment, and has been tested extensively through its use in dail y air quality forecasting. It has been shown to reproduce observed fluxes of ozone and SO 2 with reasonable accuracy. The original Wesely/Slinn model is formulated for 11 landuse cat egories, while the Zhang model uses 26 landuse categories. A new landuse input file forma t is introduced that supports both landuse categorizations as well as an optional LAI data fie ld.

Cloud Photolysis Adjustments Using an In-Line Versi on of TUV : CAMx includes two optional schemes to adjust clear-sky photolysis rates for th e presence of clouds. The new default approach uses a fast in-line version of TUV (Emery et al., 2010) to calculate photolysis adjustment profiles through each cloudy grid column . The original CAMx cloud adjustment approach has been retained as an option, which is b ased on the parameterization developed for the Regional Acid Deposition Model (RADM; Chang, et al., 1987). While the new option isCAMx v5.30 User’s Guide, December 2010

Overview

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_01_overview .docx 1-6 very fast, the scheme maintains accuracy in the cal culation of clear-sky photolysis rates, while allowing clouds to be directly involved in radiativ e transfer calculations through each grid column. The definition of cloud optical depth in t he cloud/rain file has changed; thus, cloud/rain files generated for CAMx v5.30 and later will not w ork correctly in earlier versions of CAMx.

Plume Rise Update : The plume rise algorithm was updated to calculat e plume depth at final rise and to distribute point source emissions into all m odel layers spanned by plume depth. In prior versions, point emissions were emitted into the sin gle layer containing the altitude of final rise.CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-1 2. CORE MODEL FORMULATION

This section introduces the basic numerical approac h employed in CAMx, and describes the technical formulation of the transport and removal processes. The specific chemical mechanisms
and their numerical solvers are discussed in detail in Section 3.

2.1 NUMERICAL APPROACH

The physical representations and the numerical meth ods used for each term of the pollutant continuity equation (described in Section 1) are su mmarized in Table 2-1. CAMx includes peeraccepted algorithms and component formulations, and its modular framework permits easy substitution of additional and/or updated algorithm s in the future.

Table 2-1 **. Summary of the CAMx models and methods for key p hysical processes.** 

Process Physical Model s Numerical Method s

**Horizontal advection Eulerian continuity equation** • **Bott (1989)**

• **PPM (Colella and Woodward,1984)**

**Horizontal diffusion K-theory 1 st**  **order closure Explicit simultaneous 2-D solver**

**Vertical advection Eulerian continuity equation Implicit backward-Euler (time) hybrid**  **centered/upstream (space) solver**

**Vertical diffusion** • **K-theory 1 st**  **order closure**

• **Non-local mixing** • **Implicit backwards-Euler (time) centered (space) solver** 

• **Explicit ACM2 non-local convection/diffusion (Pleim, 2007)** 

**Gas-Phase Chemistry** • **Carbon Bond IV (Gery et al., 1989)**

• **Carbon Bond 2005 (Yarwood et al., 2005b)**

• **SAPRC99 (Carter, 2000)**

• **Inorganic/organic aerosol precursors** • **EBI (Hertel et al., 1993)**

• **IEH (Sun et al., 1994)**

• **LSODE (Hindmarsh, 1983)**

**Aerosol Chemistry** • **Aqueous inorganic chemistry**

• **Inorganic thermodynamics/partitioning**

• **Organic thermodynamics/partitioning**

• **Static 2-mode or multi-section size models** • **RADM-AQ (Chang et al., 1987)**

• **ISORROPIA (Nenes et al., 1998)**

• **SOAP (Strader et al., 1999)**

• **CMU (Pandis et al., 1993)**

**Dry deposition** • **Resistance model for gases (Wesely, 1989) and aerosols (Slinn and Slinn, 1980)** 

• **Resistance model for gases (Zhang et al., 2003) and aerosols (Zhang et al., 2001)**  **Deposition velocity as surface boundary condition in vertical**  **diffusion solver**

**Wet deposition Scavenging model for gases and aerosols**  **(Seinfeld and Pandis, 1998) Exponential decay as a function of**  **scavenging coefficient**CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-2 The continuity equation is numerically marched forw ard in time over a series of time steps. At each step, the continuity equation is replaced by a n operator-splitting approach that calculates the separate contribution of each major process (emissi on, advection, diffusion, chemistry, and removal) to concentration change within each grid c ell. The specific equations that are solved individually in the operator-splitting process are shown in order below:

where c l is species concentration ( μ mol/m 3 for gasses, μ g/m 3 for aerosols), E l is the local species emission rate ( μ mol/s for gasses, μ g/s for aerosols), ∆#t is timestep length (s), u and v are the respective east-west (

x ) and north-south (

y ) horizontal wind components (m/s), A yz and A
xz are cell cross-sectional areas (m 2 ) in the y-z and x-z planes, respectively, m is the ratio of the transformed distance on the various map projections to true distance ( m =1 for curvi-linear latitude/longitude coordinates), and Λ l is the wet scavenging coefficient (s -1 ).

A master driving time step for the model is interna lly determined during the simulation for the largest and coarsest (master) grid. Time steps typ ically range from 5-15 minutes for grid cell sizes of 10-50 km, to a minute or less for small ce ll sizes of 1-2 km. As a result, nested grids require multiple driving time steps per master step depending on their grid sizes relative to the master grid spacing. Furthermore, multiple transpo rt and chemistry time steps per driving step are used as necessary to ensure accurate solutions for these processes on all grids. 

## ( )

( )

( ) ( )

equations eaction specific r Mechanism t c c t c y ρ/ c K m y x ρ/ c K m
x m t c z ρ/ c ρK

z t c t z h

c

z c

t c m c vA

y A m

t c m c uA

x A m

t c z y x E

m t c

Chemistry l l l Scavenging Wet l l Y l X diffusion XY l l

v diffusion l l l

transport Z l l xz

xz advection Y l l yz

yz advection X l l

Emission l

−

= ∂ ∂ Λ

− = ∂ ∂   

        

∂ ∂

∂ ∂
+      

∂ ∂

∂ ∂

= ∂ ∂      

∂ ∂

∂ ∂

= ∂ ∂ ∂ ∂ ∂

− ∂ ∂

= ∂ ∂      

∂ ∂

− = ∂ ∂        

∂ ∂

− = ∂ ∂ ∂ ∂ ∂ = ∂ ∂

ρ ρ Z 2 2 2 2

ηCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-3 The first process in each time step for a given gri d is the injection of emissions from all sources. CAMx then performs horizontal advection and vertica l advection, followed by vertical diffusion, horizontal diffusion, chemistry, and finally wet sc avenging.

Although advection is performed separately in the x (east-west), y (north-south), and z (vertical) directions, the numerical linkage between these com ponents has been developed in a mass consistent fashion to preserve the density field at each time step. This maintains the flexibility to allow many types of meteorological models, and mode ling grid resolutions, projections, and layer structures, to characterize transport in CAMx

.

Trace gases and particles are removed from the atmo sphere via deposition to the ground. Dry deposition refers to the direct sedimentation and/o r diffusion of material to various terrestrial surfaces and uptake into biota. Wet deposition ref ers to the uptake of material into cloud water and precipitation, and its subsequent transfer to t he surface. The efficiency with which wet and dry deposition processes can remove pollutants from the air depends upon the physical and chemical properties of the pollutants, local meteor ological conditions, the type of surface on which they are being deposited, and on the frequenc y, duration, and intensity of precipitation events. Dry deposition is an important removal mec hanism, but it is not explicitly treated as a separate process in the time-splitting approach. I nstead, deposition velocities for each species are calculated based on species chemical properties and local meteorological/surface conditions, and used as the lower boundary condition for vertic al diffusion. This appropriately couples the surface removal of pollutants through each column o f cells via the vertical mixing process.

2.2 CAMx GRID CONFIGURATION

Grid Cell Arrangement

CAMx carries pollutant concentrations at the center of each grid cell volume, representing the average concentration over the entire cell. Meteor ological fields are supplied to the model to quantify the state of the atmosphere in each grid c ell for the purposes of calculating transport and chemistry. CAMx internally carries these variables in an arrangement known as an “Arakawa C” grid configuration (Figure 2-1). State variable s such as temperature, pressure, water vapor, and cloud water are located at cell center along wi th pollutant concentration, and represent grid cell average conditions. Wind components and diffu sion coefficients are carried at cell
interfaces to describe the transfer of mass in and out of each cell face. Note in Figure 2-1, for example, that horizontal wind components u and v are staggered from each other. This facilitates the solving of the transport equations in “flux for m”.

Depending upon the source of meteorological data, i t is recommended that the user directly provide the gridded horizontal wind fields in the s taggered Arakawa C configuration. However, this is not always feasible, and so CAMx offers the option for the user to supply all meteorological variables, including horizontal wind components, at cell center; in this case CAMx internally interpolates the winds to cell inte rfaces. Note that this leads to a slight smoothing effect on the horizontal wind fields.

Figure 2-1 also describes the horizontal cell index ing convention used in CAMx. Each cell is defined by the index pair ( i,j ), where i ranges from 1 to nx (the number of cells in the east-west direction), and j ranges from 1 to ny (the number of cells in the north-south direction) . TheCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-4 Figure 2-1. **A horizontal representation of the Arakawa C vari able configuration used in CAMx.** 

eastern and northern faces of the cell are indexed ( i,j ), while the western and southern faces are indexed ( i -1,

j ) and ( i,j -1), respectively.

In the vertical, most variables are carried at each layer midpoint (defined as exactly half way between layer interfaces). Again the exceptions ar e those variables that describe the rate of mass transport across the layer interfaces, which includ e the vertical diffusion coefficient K V and the vertical transport rate η

. These variables are carried in the center of eac h cell horizontally, but are located at the top of the layer (i.e., the inte rface) vertically.

Grid Nesting

CAMx incorporates two-way grid nesting, which means that pollutant concentration information propagates into and out of all grid nests during mo del integration. Any number of grid nests can be specified in a single run, while grid spacings a nd vertical layer structures can vary from one grid nest to another. The nested grid capability o f CAMx allows cost-effective application to large regions in which regional transport occurs, y et at the same time providing fine resolution to address small-scale impacts in selected areas.

Each grid nest is defined over a subset of master ( coarsest) grid cells. The range of master grid row and column indices that define the coverage of each nested grid must be specified in the run control file. An integer number of nested grid cel ls must span one master grid cell; this number is referred to as a “meshing factor”. “Buffer” cel ls are added around the perimeter of each nested grid to hold boundary conditions. Buffer cells are added automatically within CAMx and should not be specified by the user in the run control fil e. All nested grid output files contain data for the entire array of computational and buffer cells; however, buffer cell concentrations are considered invalid and should be ignored. Addition ally, all nested grid input files must contain data for the entire array of computational and buff er cells. An example of a horizontal nesting arrangement is shown in Figure 2-2. Here, two tele scoping fine grid nests are defined: one with **(i, j) T, p, q, Kv**  **(i, j) (i, j)** 

**(i, j-1) (i-1, j)**  **u, Kx u, Kx**  **v, Ky**

**v, Ky**CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-5 Figure 2-2. **An example of horizontal grid nesting, showing tw o telescoping nested grids within**  **a 10×10 cell master grid. The outer nest contains 10×12 cells (including buffer cells to hold**  **internal boundary conditions), and the inner nest c ontains 6×10 cells (including buffer cells).** 

a meshing factor of 2 spanning master grid cells (5 ,4) to (8,8), and one with a meshing factor of 4 spanning master grid cells (6,6) to (6,7).

Nesting in the vertical is allowed, but only by sub -dividing parent grid layers into a series of finer layers. To maximize flexibility in the verti cal grid structure, each parent grid layer may be individually split into a unique set of fine layers , or not split at all. The vertical layer division is defined by the input height/pressure file for each grid. Figure 2-3 displays an example of how layers may be defined for a master grid and two fin e grid nests. Vertical nesting is not recommended because it can lead to mass consistency errors (discussed shortly). The indexing convention in the vertical is also shown in Figure 2-3, where the top of layer k is referenced as
interface k . Index k=0 is at the ground. **Coarse Grid Boundary Cells**

**Coarse Grid Boundary Cells Coarse Grid Boundary Cells** **Coarse Grid Boundary Cells**

**Fine Buffer Cells**

**Fine Buffer Cells Fine Buffer Cells** **Fine Buffer Cells**

**Boundary of Fine Grid 1**

**Boundary of Fine Grid 2**

**1 2 3 4 5 6 7**  **8 9 10 10** 

**9**

**8**

**7**

**6**

**5**

**4**

**3**

**2**

**1**CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-6 Figure 2-3. **A schematic illustration of the CAMx vertical gri d indexing convention and vertical**  **nesting. The lowest 2 master (coarse) grid layers are split into 4 layers in nested Fine Grid 1,**  **and the lowest 5 layers of Fine Grid 1 are split in to 9 layers in nested Fine Grid 2. Note that the**  **nested grids possess a matching layer interface for every parent grid layer interface.** 

Restrictions on specifying the size and resolution of all grid nests include the following:

1) The ratio of master grid cell size to nested grid c ell size must be an integer (e.g., a “meshing factor” of 3 means that 3 nested cells spa n the distance of 1 master cell, resulting in an area of 9 nested cells per master c ell); 2) For telescoping grids (a nested grid containing an even finer grid), the cell size of the finest grid must be a common denominator for all pa rent grids above it (e.g., a 36-12-4 km or 36-12-2 km arrangement is allowed, but a 36-1 2-9 km is not); 3) The restriction in (2) above does not apply to para llel nested grids of the same generation (e.g., 4 km and 5 km grids can be located in differ ent areas of a master grid provided that the master cell size is some multiple of 20 km); 4) Nested grids cannot overlap, although they may shar e a common boundary or edge; 5) Nested grids cannot extend into a boundary, or non- modeled, area of the master grid; 6) CAMx is currently configured to allow four “generat ions” of nests (e.g., four levels of telescoping grids); this can be extended in the cod e easily if more than four levels of nests are required; 7) The total vertical depth of each nested grid must e xactly match the depth of the master grid; 8) Nested grid vertical layers may exactly match the p arent grid vertical structure, or the parent grid layers may be individually split into s everal finer grid layers (defined via the nested grid height/pressure files); 9) A parent grid layer may be unevenly split into any number of finer layers, as long as there is a matching nested grid layer interface at each p arent grid layer interface;

**Coarse Grid Fine Grid 1 Fine Grid 2** 

**h(1) h(2)**  **h(3) h(4)**  **h(nc)**

**h(2) h(4)**  **h(6) h(7)**  **h(8) h(9)**  **h(10) h(nf2)** CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-7 10) Nested grids that share a common horizontal boundar y must possess equivalent layer

structures.

The following Fortran binary I/O files must be prov ided for the master grid, and optionally provided for each nested grid:

• gridded surface emissions;

• fractional landuse distribution;

• height/pressure (defines the layer interface struct ure);

• horizontal wind components;

• temperatures;

• vertical diffusivities;

• water vapor;

• clouds and precipitation

Any of these input files may be supplied for each n ested grid, or none at all. If any of these files are not supplied for a particular nested grid, the Flexi-Nest algorithm within CAMx interpolates the missing fields from the parent grid. Clearly i t is desirable to provide nested grid data whenever possible. However, the ability to interpo late data is useful for testing sensitivity to grid configurations or for situations when it is no t possible to run a meteorological model for all grid nests.

The Flexi-Nest option also allows users to redefine the nested grid configuration at any point in a simulation. Nested grids can be introduced or remo ved only at the time of a model restart since a
new CAMx user control file must be used to redefine the grid configuration. For example, the user may wish to “spin-up” the model using a region al-scale master grid and a single nest over an area of interest for two days. Starting at 6 AM on the third day, the user might introduce one or more nests within the original nested grid for m ore detailed analysis. This would require that the model be restarted at 6 AM of the third day wit h a new control file that defines the position of the two new nests and (optionally) provides any additional input fields for these grids. CAMx will internally reconcile the differences in grid s tructure between the nested grid restart files and the new user control file, and then interpolate any data fields not supplied to CAMx for the new nests from the parent grid(s).

2.3 TREATMENT OF EMISSIONS

Pollutant emissions are treated in two basic ways w ithin CAMx: low-level (gridded) emissions that are released into the lowest (surface) layer o f the model; and elevated stack-specific (point) emissions with buoyant plume rise that can be emitt ed into any model layer. Emissions are injected into each cell of every grid at every time step during the simulation. Gridded and point emissions are provided to CAMx in separate input fi les. External emission processing systems are used to develop gridded and point, time- and sp ace-resolved, chemically-speciated input files for CAMx. These external programs are not discusse d in this User’s Guide; see Section 11 for more information on emission systems that are used to support CAMx applications.CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-8 Gridded Emissions

Two-dimensional gridded low-level emissions are def ined by space- and time-varying rates for each individual gas and PM species to be modeled. Gridded emissions represent sources that emit near the surface and that are not sufficiently buoyant to reach into the upper model layers. Such emission categories include:

• Low-level stack (point) emissions that are too smal l to result in plume rise above the model surface layer;

• Other non-point industrial sources (fugitive leaks, tanks, etc.);

• Mobile sources (cars, trucks, non-road vehicles, ra ilroad, marine, aircraft, etc.);

• Residential sources (heating, cooking, consumer pro ducts);

• Commercial sources (bakeries, refueling stations, d ry cleaners);

• Biogenic sources;

• Natural sources (small fires, wind-blown dust).

The spatial distribution of each individual source within these categories is defined by the modeling grid. Information such as population dist ribution, housing density, roadway networks, vegetative cover, etc. is typically used as a surro gate to distribute regional emission estimates for each source to the grid system. Processing tools a re used to combine emissions from all sources into a single input file for each grid (see Section s 5 and 11).

Elevated Point Emissions

Similarly to gridded emissions, elevated point emis sions are defined by space- and time-varying rates for each individual gas and PM species to be modeled. The only difference is that these sources emit from individual stacks with buoyant ri se that may take them into upper model layers. These types of sources are almost always a ssociated with large industrial processes, such as electric generators, smelters, refineries, large factories, etc. The spatial distribution of these points is specifically given by the coordinates of the stacks themselves (grid locations are determined within CAMx). Plume rise is determined within CAMx as a function of stack parameters (height, diameter, exit velocity and tem perature) and ambient meteorological conditions, so the point source file provides speci ated time-resolved emission rates and stack parameters for each individual source. A single po int source file provides the definition of all stacks and their emissions over the entire modeling domain (see Sections 5 and 11).

Plume rise is calculated using the multi-layer stab ility-dependent algorithm of Turner et al. (1986). This approach calculates the momentum and buoyant plume rise energy from the stack, takes the larger of these two values, and determine s the dissipation of that energy via mixing with ambient air according to the meteorological co nditions through the host model layer. If sufficient energy remains to reach into the next mo del layer, the calculation for buoyant rise repeats for the meteorological conditions of that l ayer, and so on, until a layer is found where the plume cannot rise any farther. All emissions from this source are then injected into the grid cell directly above the stack at this layer height. Thi s algorithm was adopted for CAMx because it provides a more realistic handling of stable layers aloft that can trap plume rise, whereas this effect would not be realized based on meteorologica l conditions at stack top alone.CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-9 Under neutral/unstable conditions, momentum rise at the stack top is calculated from

while buoyancy rise is the lesser of

and

In these expressions d
s is stack diameter (m), v s is stack exit velocity (m/s), h
s is stack height (m),

w is ambient wind speed (m/s), and z b is the distance between the stack top and the base of the current model layer. A minimum wind speed of 1 m/s is specified to avoid unrealistically large plume rise. Buoyancy flux f is initially calculated from stack parameters, but is set to residual flux entering the bottom of any higher layer. The initial buoyancy flux at stack top is given by

where g is gravitational acceleration (9.8 m 2 /s), T
s is stack exit temperature (K), and T is ambient temperature (K). The residual flux calculation int o the next higher layer depends on which neutral/unstable buoyancy rise equation was origina lly used in the current layer:

or

where z t is the distance between stack top and the top of t he current model layer.

Under stable conditions, momentum rise at the stack top is calculated from the lesser of H
mu and

while buoyancy rise is the lesser of

and w v d H s

s

mu 3

=



## ( )

b bu z

w f H + = 5/3 / 30



# ( ) ( ) 

# [ ]

b s bu z w f h w f H + + = 5/2 3 5/3 3 /

200 / 24



###        

−

=

s

s

s s 

### T T T d v g f 4

2
0

3/5

30      

−

= t

bu z H

w f



## ( ) 3/2

3 3 

### 1 10 5.5 −

− 

###        

− +

− × =

t bu s
t bu 

### z

H h

z H w f

6/1 3/1 2 2 2/1 

### 646 .0 − 

###      

∂ ∂        

=
z

w T d v T H

s s s

ms 

### θ

3/1

3 1 

### 8.1 

  

   

+

     

∂ ∂

= −

b bs 

### z

z w T f H θCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-10 In these expressions, ∂θ

/dz is the potential temperature gradient (a measure o f atmospheric stability). Here also, buoyancy flux is initially c alculated from stack parameters, but is set to residual flux if the plume extends into the next hi gher layer. The residual flux calculation depends on which stable buoyancy rise equation was or iginally used:

or

When final plume rise is reached using stable buoya ncy rise, it is adjusted downward to twothirds of the rise through the stable depth. After final plume rise is determined, the rise is further adjusted downward by stack tip downwash according to a critical Froude number and ambient wind speed. The stack Froude number is given by

For F <3, no downwash adjustment is made to final plume ris e. Above that value, the following downwash factors (

D ) are applied depending upon the ambient wind speed at stack top:

CAMx injects point source emissions into all model l ayers spanned by the plume depth at final (adjusted) rise. Plume depth is determined as a fu nction of stack diameter, plume temperature, plume velocity, time of plume rise, and ambient win d and temperature conditions. A uniform mass distribution through plume depth is assumed. If this depth is wholly contained within a single layer, that layer receives all emission mass . If this depth spans several layers, then fractions of the emissions are injected into these layers according to the fraction of plume depth spanning those layers. We apply the “rule-of-thumb ” that plume depth equals plume rise (Turner and Schulze, 2007) as a maximum limit for plume dep th.

The following equations are used to define the plume depth after reaching final rise. These are based on the approach used in the SCIPUFF model (EPR I, 2000) and were developed for use in the CAMx plume-in-grid (PiG) submodel. The plume dep th D p at final rise is given by 8/3

3/8 1

3/1
0 

### 1.4 

  

   

+

     

∂ ∂

= −

b bs 

### z

z f T f H θ



# ( ) 3 3 

### 56 .0 b

t 

### z z T w

z f f

−

∂ ∂

− = θ



# ( ) 3/8 3/8 3/1
0 24 .0 b t z z T f

z f f

−

∂ ∂

− = θ



## ( ) T T d g v T F

s

s s



### − = 2



## ( )

s s s

s s s

v w D v w v
v w v D rise plume no w v D

3

2 ,1 3

2

, 3 ,0

< = <

≤ −

= < =CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-11



# ( )

2/1 2 2 2 3 t

K D D s p + =

where D
s is stack diameter, K is plume diffusivity during rise, and t is the tim e of rise. The time of rise is determined by dividing final plume rise by the mean plume rise speed V p ; the latter is set to half the stack exit velocity. A lower limit of 1 m/s is applied to the exit velocity, so the
minimum value of V p is 0.5 m/s. The plume diffusivity is determined b y scaling initial plume width (according to stack diameter) by the turbulen t flux moment q p 2 : 

# ( ) 2 2 15 .0 p

s q D K =

where

 
 

 
 




 
 

 

+ + = 2

2 2 2 2 3

4.0

p p p p V

v v V f q

The turbulent flux moment is a function of the mean plume rise speed V p , the ambient wind speed v taken at the level of final rise, and a plume entr ainment coefficient f p :


# ( ) 



 
 

 

− + = 2



### 2 4 1

p

p

s p 

### V

T T T g D f

where g is the gravitational constant (9.8 m 2 /s), T is ambient temperature at the level of final rise,
and T p is the mean plume temperature, taken as the mean o f the stack exit temperature and the ambient temperature at final rise.

2.4 TRANSPORT FUNDAMENTALS

The CAMx transport algorithm is both mass conservat ive and mass consistent. Mass conservation refers to the ability to accurately ac count for all sources and sinks of mass in the model, with no spurious loss or gain of mass during model integration. To be mass conservative, CAMx internally carries concentrations of each spec ies as a density (μmol/m 3 for gases, μg/m 3 for aerosols), and solves the advection/diffusion e quations in flux form. This also serves to simplify mass budget accounting, which is used by t he various source apportionment and process analysis options. Gas concentrations are internall y converted to volumetric mixing ratio (parts per million, or ppm) for the chemistry step, and wh en they are written to the average output files.

Mass consistency refers to the model’s ability to t ransport pollutant mass exactly equivalent to the input atmospheric momentum field. For example, a model that is perfectly mass consistent will preserve a unity pollutant mixing ratio field given constant unity boundary and initial
conditions and zero sources and sinks. Sources of poor mass consistency in air quality models are typically related to the following (in order of importance):CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-12 1) supplying meteorology that is inherently inconsiste nt, e.g., from an interpolative objective analysis or diagnostic wind model in whic h wind, temperature, moisture, and pressure fields are all independently developed; 2) spatially interpolating or averaging meteorological model fields, especially threedimensional

wind vector fields, to an air quality model grid o f different resolution and/or different mapping projection; 3) relying on a nested grid model to internally interp olate coarse grid meteorological parameters to finer grids, especially in cases of v arying vertical resolution among the grids; 4) employing numerical techniques within the air quali ty model that are mass inconsistent, e.g., holding input wind and density fields constan t between meteorological update times, assuming non-divergent three-dimensional winds, etc .; and 5) employing different numerical and/or physical metho ds in the air quality and meteorological models.

It is expected that CAMx users will prepare high qu ality, mass consistent meteorological fields using advanced prognostic models so as to minimize inconsistencies in the inputs themselves. CAMx operates on the map projections and grid syste ms employed in several widely used meteorological models (e.g., MM5, RAMS, and WRF) so that translation of meteorological data to CAMx requires as little manipulation as possible . The practice of developing meteorological input fields using objective analysis or “diagnosti c” approaches is highly discouraged.

However, CAMx provides a very important flexibility that allows the air quality grid to differ from the meteorological grid in terms of projection and/or resolution when necessary. This, of course, leads to a potentially large source of mass consistency error as noted in point (2) above, and if nested grids are specified where higher reso lution meteorological fields are unavailable, additional error may occur as in point (3). Theref ore, adjustments must be made to correct for the external inconsistencies coming into the model. Such adjustments could be applied to the meteorological fields during the interpolation/aver aging procedure, but this approach would not account for internal sources of inconsistency withi n the air quality model itself (points 3-5 above). To account for all sources of error while maintaining flexibility on input requirements and grid configuration, CAMx internally minimizes t he sources of mass inconsistency as the model integrates forward in time. As discussed bel ow, the model does this in several ways.

First, the transport equations are written and solv ed in mass “flux” form. Gridded meteorological inputs are carried in an “Arakawa C” arrangement (Figure 2-1), which optimizes the calculation of mass flux divergences while ensu ring mass conservation. These points are important for the calculations described further be low. Solutions based on the alternative “advective” form cannot guarantee mass conservation or consistency.

Second, CAMx can accept input meteorological fields for each individual nested grid, if available from a meteorological model. This reduce s errors stemming from internal interpolations from parent to nested grids. Input gridded fields of layer heights, temperature, pressure, horizontal winds, water vapor, and vertic al diffusivity are then interpolated in time to the unique time steps for each modeling grid. Sinc e the vertical grid structure is defined via external inputs, layer interface heights may be spe cified as any arbitrary function of space and/or time. This allows the CAMx vertical grid system to exactly match all or a subset of any meteorological model layer structures, whether they are defined in terms of physical height above ground, normalized height coordinates (i.e., “sigma-z”), or normalized pressureCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-13 coordinates (i.e., “sigma-p”). Therefore, total ve rtical transport is the combination of resolved vertical advection and mass exchange across undulat ing layer interfaces.

Third, and most importantly, the grid- and timestep -specific horizontal momentum fields are
used to determine an instantaneous vertical velocit y field that balances the local atmospheric continuity equation for the specific grid configura tion employed. To calculate a vertical velocity profile for a given grid column, the atmospheric co ntinuity equation is locally integrated through the depth of the column. Normally this integration would be performed by assuming a nondivergent wind field ( ∇⋅ V = 0 ) or a non-divergent momentum field ( ∇⋅ ρ V = 0 ). Both of these approaches presume that the three dimensional diver gence components exactly balance to result in a zero net change in local atmospheric density. The non-divergent wind assumption is not valid for model applications spanning a significant fraction of the atmospheric scale height (8 or 9 km). In either case, the presumption of zero den sity tendency is usually invalid within air quality models since the externally supplied wind a nd density fields are not balanced in the first place (see points 1-3 above). In CAMx, such a dens ity-momentum balance obviously does not exist since a linear rate of temperature, pressure and horizontal wind change is imposed at each time step between meteorological update times.

In CAMx, the vertical velocity profile at each time step is determined from vertical integration of the divergent continuity equation:

which is

where the local time-rate change of atmospheric den sity ∂ ρ / ∂ t in each grid cell is known from the input meteorological fields. In this approach, a v ertical velocity profile w(z) is constructed that provides a balance between the imposed density tend ency and the resolved horizontal momentum divergence in each grid cell at each time step. The total vertical transport rate η at a particular layer interface is defined as the combin ation of resolved vertical velocity and the local time-rate of change of the layer interface height:

The total vertical transport rate is used for subse quent vertical transport calculations for all pollutants. A zero vertical velocity is specified at the ground, while a column- and pollutantspecific zero-gradient mixing ratio condition is ap plied at the top of the model.

The fourth way CAMx ensures mass consistency is to calculate the horizontal flux divergence of atmospheric density in a manner that is numerically consistent with the procedure used to horizontally transport pollutants. Atmospheric den sity is advected in and out of each cell using the same equation as for pollutants: ρV t ρ

⋅ ∇

− = ∂ ∂



## ( ) dz ρV t ρ z ρw z

H H 

## ∫ 

    

⋅ ∇ + ∂ ∂

− =

0

     

∂ ∂
+        

∂ ∂

= ⋅ ∇

m vA

y A m

m uA

x A m ρV xz

xz yz

yz H H ρ ρ 2 2 w t h

− ∂ ∂

= ηCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-14 and this equation is solved using either the Bott ( 1989) or PPM (Colella and Woodward, 1984) advection solvers, as selected by the user (see Sec tion 2.5 below). The resulting horizontal advective fluxes include resolved momentum converge nce/divergence rates as well as any artificial divergences caused by the horizontal and vertical grid specifications (e.g., spatially varying vertical grid structure, or systematic dist ortions associated with the map projections). In simple tests in which a uniform pollutant field of unity mixing ratio is transported throughout a single regional grid over several days using actual episodic meteorological inputs, this approach has been shown to provide nearly exact (to 10 -3 -10 -4 %) consistency between the density and pollutant fields.

2.5 TRANSPORT ALGORITHMS

Horizontal advection is performed using the area pr eserving flux-form advection solver of Bott (1989) or the Piecewise Parabolic Method (PPM) of C olella and Woodward (1984) as implemented by Odman and Ingram (1993). These two finite difference schemes were incorporated into CAMx because they provide higher order accuracy with minimal numerical diffusion, yet are equivalent in execution speed co mpared to other simpler advection algorithms when operating on equivalent time steps. In CAMx, the Bott scheme is allowed to take larger time steps than PPM because Bott remains stable for Courant-Friedrichs-Levy (CFL) numbers up to 1 (i.e., the ratio of wind speed to grid spacing per timestep). Time steps are determined for Bott using a CFL number of 0.9, while time steps fo r PPM are restrained by a CFL number of 0.5. Therefore, the Bott option results in a faste r simulation than the PPM option, perhaps at the price of some accuracy. We recommend testing both for your specific application.

The Bott and PPM algorithms have been shown to prod uce significant differences in concentration predictions compared to previously us ed flux-corrective schemes such as the Smolarkiewicz (1983) algorithm. This is a result o f the considerably lower amount of numerical
diffusion associated with Bott and PPM. As discuss ed by Yamartino (2000) the latest advection solvers reduce numerical diffusion to the point whe re modelers need to be concerned about including appropriate levels of explicit horizontal diffusion. Currently, there is very little information on the appropriate level of horizontal diffusion for Eulerian grid models. We see this issue becoming increasingly important as more accurate advection solvers are utilized in grid
models. This issue is not limited to CAMx.

Explicit horizontal diffusion coefficients are dete rmined within CAMx using a deformation approach based on the methods of Smagorinsky (1963) :

Separate diffusivity components are generated for f luxes in the x - and y -directions since K X and

K Y are calculated for separate cell faces in the Arak awa C grid arrangement. The value of K 0 is specified according to the approach in MM5 (Anthes and Warner, 1978): 2 /1 2 2

/ 

### 2

4    

           

∂ ∂

− ∂ ∂
+        

∂ ∂
+ ∂ ∂ ∆ ∆ + = y v

x u

x v

y u y x K K

o Y X

t y x K ∆

∆ ∆

× = − 3
0 10 3CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-15 A maximum value of K X/Y is set to maintain numerical stability for the giv en grid-specific timestep. A minimum value is set to 1 m 2 /s. Horizontal diffusion is applied using an expli cit simultaneous two-dimensional flux-divergence calcul ation.

Vertical transport and K-theory diffusion are both solved using implicit backward-Euler integration schemes. Since implicit schemes are ab solutely stable, only one solution step is necessary per driving time step. Explicit approach es require potentially many sub-steps (on the order of 10-100) to maintain a stable solution, whi ch introduces the potential for excessive
numerical diffusion. Whereas the vertical advectio n step is implicitly solved in a single step, Ktheory vertical diffusion is implicitly solved over potentially several sub-steps, depending upon the magnitude of the vertical diffusivity relative to the vertical layer depths (CAMx uses the smaller of the current grid time step or a maximum 5 minute sub-step). This ensures non-local diffusive coupling among all layers that exhibit hi gh diffusivity, especially for long driving time steps on coarser grids that would otherwise only ex perience mixing between adjacent layers during a single implicit step. Gridded vertical di ffusion coefficients must be supplied to the model for the master grid via input file; the user may optionally supply vertical diffusion coefficients for any or all nested grids.

ACM2 Vertical Diffusion

The representation of sub-grid turbulent diffusion (or “mixing”) in photochemical grid models has not significantly advanced for several decades. Simple eddy viscosity (or “K-theory”) firstorder closure approaches have been used in most mod els. As a “local” closure technique, Ktheory only treats mass transfer cell-by-cell (hori zontal) or layer-by-layer (vertical), analogously to the diffusion of heat through a solid medium. W hereas K-theory adequately characterizes horizontal diffusion and weak vertical mixing durin g neutral and stable conditions, the shortcomings of K-theory are related to its inadequ ate treatment of deep vertical convective boundary layer mixing. In convective situations, b uoyant plumes deriving energy in the surface layer are quickly and efficiently mixed deep into t he atmosphere within eddies that are usually much larger than the individual model layers. K-th eory therefore mixes the convective boundary layer much less efficiently than commonly observed. This has been shown to have very important ramifications for chemistry, especially d uring transition periods between stable/neutral and convective conditions. Modelers have commonly adjusted for the lack of adequate vertical mixing by arbitrarily increasing the magnitude of t he input vertical diffusivity fields.

A promising non-local mixing scheme was developed i n the early 1990’s called transilient theory (Raymond and Stull, 1990). This approach calculate s the elements of a transilient matrix, where each element specifies the transfer rate from a giv en layer to any other layer, either adjacent or distant. In fact, K-theory is just a special case of transilient theory, in which transfer can only occur between adjacent layers above and below, lead ing to the trivially-solved “tri-diagonal” matrix. However, a fully populated transilient mat rix is difficult to determine and until recently the approach has not been widely used in chemistry models.

The U.S. Environmental Protection Agency (EPA) has recently developed a new asymmetric convective model (ACM2) for the Models-3 Community Scale Air Quality (CMAQ) model (Pleim, 2007). This is considered a truncated, or simplified form of the general transilient mixing scheme in that it includes: (1) mixing among adjacent layers using K-theory (i.e, symmetrically along the diagonal of the transilient matrix); (2) rapid upward non-local mixingCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-16 from the surface layer to all layers through the bo undary layer (the convective aspect); and (3) slower compensating downward mixing layer-by-layer from the top of the boundary layer to the surface (the asymmetric aspect). All other matrix elements outside of these three transport paths are ignored. Figure 2-4 shows this approach schema tically. During non-convective conditions, ACM2 reverts back to the local K-theory component. Thus, ACM2 includes the basic features of both local and the most important component of n on-local exchange. Tests comparing ACM2 against standard K-theory result in more realistic profiles of ozone precursors, but usually minor impacts on surface ozone.

Figure 2-4. **Schematic representation of the turbulent exchang e among layers within a vertical**  **grid column during convective adjustment in the ACM 2 (taken from Pleim [2007]).** 

The ACM2 parameterization has been implemented in C AMx as an alternative to the K-theory approach. All variables needed to calculate the tr ansfer rates are available from the existing input files, or are already calculated within CAMx for other purposes. The CAMx ACM2 option does not require that ACM2 be run within the meteor ological model used to derive inputs for CAMx. However, EPA states that using ACM2 in both meteorological and chemistry models does lead to consistent boundary layer characteriza tion. Meteorological interface programs (e.g., MM5CAMx, WRFCAMx, and RAMSCAMx) have been updated t o include an option to generate vertical diffusivities in the same manner as the approach used within the CMAQ ACM2 algorithm, which performs its diffusivity calculati ons internally. The CMAQ ACM2 diffusivity calculation is a hybrid of two methodologies: (1) b oundary layer scaling based on Hostlag and Boville (1993); and (2) local scaling according to Richardson number and vertical shear similar to Liu and Carroll (1996). The former is used with in the stable boundary layer or if it is larger than the latter. The CAMx implementation of ACM2 s upports OMP and MPI parallel processing, and all probing tools except the Integr ated Process Rates algorithm of Process Analysis.

Note that ACM2 increases CAMx runtime considerably. Whereas the original CAMx K-theory solver is implicit and does not need to use small t ime steps, ACM2 and its K-theory counterpart both use explicit solvers, which must divide the mo del time step into many very small sub-steps to generate a stable solution. The slow-down can b e exacerbated by using the much larger ACM2 vertical diffusivity values instead of the ori ginal diffusivity options available in the CAMx meteorological preprocessors. The larger ACM2 diffusivity values further increase the number of integration sub-steps.CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-17 2.6 WET DEPOSITION

Wet deposition is the predominant removal process f or particles. Particles act as cloud condensation nuclei and subsequently the cloud drop lets grow and collect into sufficiently large sizes to fall as precipitation. Additionally, part icles that are entrained into the cloud, and that exist below precipitating clouds, can be directly s cavenged by precipitation via accretion and impaction processes. The rates of nucleation and i mpaction depend upon cloud type (e.g., prolonged stratiform vs. vigorous convective develo pment), precipitation rate, and particle size
distribution.

Wet deposition can also be an important removal pro cess for relatively soluble gaseous pollutants and this occurs through the following se ries of steps:

• Mixing of trace gas and condensed water in common a ir space;

• Absorption of gas molecules by water droplets;

• Possible aqueous-phase reactions of the pollutant w ithin water droplets;

• Precipitation of droplets to the earth’s surface;

• Diffusion of ambient gases into falling precipitati on.

It is important to note that each of the above step s may be reversible, so that the overall wet deposition rate for gases depends on the net result s of the forward and backward processes at each step.

The rate at which the first and fourth steps procee d depends on the frequency and nature of cloud formation and precipitation events. For example, m id-latitude cyclonic storm events (e.g., winter storms) will result in different deposition rates t han convective precipitation events such as
summertime thunderstorms. The rates at which the s econd, third, and fifth steps proceed depend on the extent to which the pollutant in question di ssolves in water and its overall reaction rate once in solution. Cloud water droplets can absorb gases from the air up the limit of their solubility in water. For many pollutants this solu bility far exceeds the amount of pollutant present in the air so that the distribution of the pollutant between the air and water droplets is determined by the Henry’s Law constant, which is de fined as the ratio of pollutant concentrations in the liquid-phase to the gas-phase at equilibrium. High values for the Henry’s law constant (greater than about 10,000 M/atm) indi cate a strong tendency to dissolve in water droplets, whereas low values (less than about 100 M /atm) indicate a tendency to remain in the air (Seinfeld and Pandis, 1998). Equilibrium between c oncentrations of pollutants in the air and in water droplets is usually established on time scale s of minutes, so equilibrium conditions can generally be assumed to exist in the atmosphere.

If a pollutant partitions significantly into water droplets it can be removed by precipitation. Averaged globally, highly soluble gases that are re moved efficiently by wet deposition (e.g., nitric acid) are estimated to have lifetimes due to rainout of about 5 days (Warneck, 1989). Clearly, on regional or local scales, lifetimes aga inst wet deposition will depend upon the frequency and intensity of rainout events. Gases t hat are removed less efficiently by wet deposition will have longer lifetimes. For example , the corresponding global average lifetime of formaldehyde against rainout is estimated to be 14 days (Warneck, 1989).CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-18 The basic model implemented in CAMx is a scavenging approach in which the local rate of concentration change ∂c/∂t within or below a precipitating cloud depends on a scavenging
coefficient Λ

:

The scavenging coefficient is determined differentl y for gases and particles, based upon relationships described by Seinfeld and Pandis (199 8). Two components are calculated for gases: (1) direct diffusive uptake of ambient gases into falling precipitation; and (2) accretion of cloud droplets that contain dissolved gases. Two c omponents are also determined for particles: (1) impaction of ambient particles into falling pre cipitation with an efficiency that is dependent upon particle size; and (2) accretion of cloud drop lets that contain particle mass. Each of these processes are described below.

The external environmental inputs to the CAMx wet d eposition algorithm include the threedimensional gridded distribution of cloud and preci pitation water contents, with the precipitation contents broken down into liquid, snow, and ice (“g raupel”). Scavenging rate equations were derived in terms of equivalent liquid precipitation rates, so the input precipitation water contents are internally translated into this metric.

The following general assumptions are made in the C AMx scavenging model:

1) Rain drops, snow flakes, and graupel particles are each separately represented by a single mean size, mass, and fall speed, which are determin ed from equivalent liquid precipitation rate; 2) There is no mixed-phased precipitation within a giv en grid cell – the dividing line between liquid rainfall and the two frozen forms is 273 K; 3) Snow is only associated with stratiform precipitati on, and graupel only with convective precipitation; 4) Liquid cloud water is allowed to exist below 273 K – a linear ramp function is applied to apportion total cloud water into liquid form betwee n 243-273 K (all cloud water is assumed to be in ice crystal form below 243 K); 5) All gasses can directly diffuse into or from liquid rainfall (only strong acids can diffuse into frozen precipitation) at a rate according to t he precipitation’s state of saturation, pollutant diffusivity, and aerodynamic consideratio ns; 6) All gases can dissolve into liquid cloud water, whi ch can be scavenged by all precipitation forms – dissolved gasses are in equil ibrium with ambient concentrations according to Henry’s Law; 7) PM is irreversibly scavenged directly by all precip itation forms via impaction, and by uptake into cloud water (liquid and ice) as condens ation nuclei that is itself scavenged by all precipitation forms; 8) All in-cloud PM mass exists in cloud water (i.e., n o “dry” aerosols exist in the interstitial air between cloud droplets) – all PM species and si zes are hygroscopic and internally
mixed. c Λ t c

− = ∂ ∂CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-19 Precipitation Parameters

The mean raindrop diameter d d (m) and fall speed v d

(m/s) are taken from the empirical estimates of Scott (1978). The drop diameter is related to r ainfall rate P (mm/hr), and the fall speed relationship has been modified to better agree with data provided by Seinfeld and Pandis (1998):

The precipitation water content L p (g/m 3 ) is related to rainfall rate by

where ρ

w is liquid water density (g/m 3 ). This equation can be simplified to a dependency on just rainfall rate when the above relations for drop vel ocity and size are substituted. The resulting expression for rainfall rate as a function of preci pitation water content is

Locatelli and Hobbs (1974) developed power law equa tions relating average diameters, fall speeds, and masses for 15 precipitating ice “habits ”. We consolidated these relationships into two forms, termed “snow” and “graupel”, by fitting new power law curves to the Locatelli and Hobbs data. For snow,

And for graupel,

Note that in these equations for ice, d d is in mm and crystal mass m d is in mg. By assuming that the number density and mass of snow/graupel crystal s are equal to those for rain drops given equal precipitation water contents, we can relate t he magnitudes of ice size and fall speed to equivalent liquid precipitation rate.

Gas Scavenging

Wet scavenging of gases by precipitation occurs wit hin and below precipitating clouds. Below the cloud, the total gas concentration in a given g rid cell is available for scavenging. Within a cloudy cell the total gas concentration must first be partitioned into an aqueous fraction c aq within cloud water and the remaining gaseous fracti on c g within the interstitial air. Both aqueous and interstitial gasses within a cloudy cell are av ailable for scavenging, but are removed at
different rates as described below. 21 .0 4 10 0.9 P d d −

× =

d d d v 3100 =

6.2

61 .0

059 .0 1.1

d d d d

d m d v

= = 8.1

20 .0

035 .0 83 .0

d d d d
d m d v

= = d

w
p v

P L 6

10 6.3 × = ρ

27

.1 7 10 1 

       ×

=

w p L P ρCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-20 Scavenging of Gases in Cloud Water

The aqueous concentration in liquid cloud water (or cloud ice in the case of strong acids) is determined by Henry’s Law thereby assuming the solu tion exists in steady-state equilibrium. The Henry’s Law constant for a given gas species k 0 (M/atm) specifies the ratio of pollutant mass in aqueous solution (M or mol/l h2o ) to its equilibrium partial pressure (atm) at stan dard pressure and temperature. This constant is adjusted for tem perature T and converted to a dimensionless
molar ratio:

where R is the ideal gas constant (8.206 × 10 -2 l atm/mol K), and A is the temperature dependence factor. Dissociation of ammonia, nitric acid, and sulfur dioxide as a function of cloud and rainwater pH is also considered in the solubility c alculation. The effective Henry’s Law constant
H thus expresses the equilibrium ratio of the aqueou s concentration c aq (mass per volume of water) to the gas concentration c g (mass per volume of air),

which are related to total concentration c by

where L
c is cloud liquid water content (g/m 3 ).

The fraction of gases present in cloud water can be removed by precipitation via accretion of cloud water onto the falling hydrometeor (liquid or ice). As the hydrometeor falls, it sweeps a cylindrical volume per unit time equal to

where d

c is the size of cloud droplets. This implies that the ambient motion of cloud droplets is insignificant compared to the hydrometeor fall spee d. Due to aerodynamic perturbations of air flow around the falling hydrometeor, a collection e fficiency is applied, i.e., the fraction of cloud droplets within the collection volume that are scav enged by precipitation. For large hydrometeors with sizes greater than 0.5 mm and dro plets 10-20 μm, we take this efficiency to be 0.9 (Seinfeld and Pandis, 1998). Also, we furth er assert that (d d + d
c ) 2 ~ d d 2

. Assuming then that a mono-disperse distribution of hydrometeors a re falling through a mono-disperse distribution of cloud water droplets, the scavengin g coefficient for precipitation collecting cloud
water is

where E is the collection efficiency (0.9) and N d is the hydrometeor number density. The number density can be expressed in terms of equival ent rainfall rate, fall velocity, and drop size: 

## ( )

d

c d v d d V 2 4 + = π

d

d d c N

E v d Λ 2 4 π

= 

     

    

− = T

A T R k H 1

298 1 exp 0

g

aq

c c H =

w

c
aq g L

c c c ρ

+ =CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-21 Substituting the relationship between N d and rainfall parameters, then

The cloud water scavenging coefficient is scaled by the ratio of aqueous pollutant concentration to total grid cell concentration to achieve the aqu eous-phase scavenging coefficient:

Scavenging of Ambient Gases

Given the relatively short residence times of falli ng precipitation through a given grid cell, aqueous equilibrium between ambient gas and precipi tation cannot be assumed and so the transfer of ambient gas into liquid rainfall (or ic e in the case of strong acids) is explicitly
calculated. The maximum rate of transfer W of a gas to a falling hydrometeor containing no pr eexisting pollutant mass is

The mass transfer coefficient K
c can be determined for a falling hydrometeor with s peed v d and
diameter d

d by

where D g and ν are the molecular diffusivity of the gas species a nd air, respectively.

Following the methodology of Seinfeld and Pandis (1 998), the rate of c aq increase can be represented by a mass balance with the rate of tran sport to the hydrometeor:

The expression for W

is substituted into the equation above, rearranged , and expressed in terms of rainfall velocity. We then assume that through a given model layer the ambient gas concentration and hydrometeor pH and size is consta nt. Multiplying by the number density of falling hydrometeors N d described above yields the gas-phase concentration scavenged by all drops falling through the layer: g c c H K W =

 
 

 
 
             

+ = 3/1 2 /1 6.0 2

g

d d

d g

c D

d v
d D K ν

ν

W d dt dc d d aq d 2 3 6 1

π π =

d

d c g v

d K P Λ 6

10 67 .1 − × = d c d P

E Λ 7

10 2.4

− × =

w c

aq

c a c L

c Λ Λ ρ = 6/ 10 8.2

3 7

d d d v

d P N

π −

×

=CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-22 Within cloudy layers, the gas-phase and aqueous-pha se scavenging coefficients are added to provide the total in-cloud scavenging rate for gase s, Λ = Λ g + Λ
a . For below-cloud scavenging,

Λ = Λ g .

Solubility Limits on Gas Scavenging

There is a chance for rainfall to become super-satu rated for sparingly soluble gasses as it falls through a grid column. The net scavenging coeffici ent for gases described above provides for the maximum potential uptake rate into clean rain w ater, so careful consideration must be given to appropriately determine the sign and magnitude o f ambient grid cell concentration change according to the degree of rainfall saturation. Th e change in gas concentration is relaxed toward the difference between the maximum possible gas in solution for the given conditions c eq , and the amount of pre-existing gas in solution from lay ers above c 0 ,

Here c eq is determined from the total liquid water in the c ell (rain plus cloud water) and from Henry’s law equilibrium according to the total gas concentration in the cell (ambient grid concentrations plus total gas in pre-existing solut ion). If the concentration change is positive, mass is added to the rain water ( c 0 is augmented) and removed from the grid cell; if n egative, mass is removed from rain water ( c 0 is decremented) and added to the grid cell.

Aerosol Scavenging

Scavenging of Aqueous Aerosols

All aerosols within cloudy layers are assumed to ex ist within cloud water. Therefore, the scavenging coefficient for aqueous aerosols is exac tly the same as for the scavenging of cloud droplets: Λ

= Λ
c .

Scavenging of Dry Particles

Wet scavenging of dry particles only occurs below p recipitating clouds. We use the same scavenging coefficient as derived for the collectio n of cloud droplets:

For rain or graupel, the collection efficiency E is a function of particle size d p , and is given by Seinfeld and Pandis (1998) as: d c d P

E Λ 7

10 2.4

− × = 

## ( ) 

## [ ]

t c c c eq ∆ Λ

− − − = ∆ exp

1 ) ( 0CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-23 where μ and μ w are the kinematic viscosity of air (1.8×10 -5 kg/ms) and water (10 -3 kg/ms), respectively, φ

= d p /d d is the ratio of particle size to hydrometeor size, R
e is the Reynolds number for the hydrometeor, S
c is the Schmidt number for the collected particle, and S t is the Stokes number of the collected particle. The Reyno lds number is given by

while the Schmidt number is

where D

p is the particle Brownian diffusivity:

Here, k is the Boltzman constant (1.38×10 -23 J/K) and C is the Cunningham correction factor for small particles:

and where λ is the mean free path of air (6.5×10 -8 m). The Stokes number is given by

where ρ

p is the particle density. The S * parameter is given by

Note that particle size d p and density ρ

p are affected by aerosol water content, which is determined from local humidity and PM deliquescence properties according to the ISORROPIA model (see Section 3). 

# ( ) 

# ( )

( )

2/3

* * 2/1 2/1 2/1 3/1 2/1

3/2 2 1 4 16

.0 4.0 1 4



      

+

− − +      

+ + + + + =

S S S S R S

R S R S R d E

t t e

w c e c e

c e p

φ

μ μ φ



### ν

2 d d

e 

### v D R =

p c 

### D S ν



### =

p p 

### d

C T k D πμ 

### 3 =



            

− + + = λ λ p

p d

d C 55 .0 exp 4.0 257 .1 2 1

d p p d

t 

### d C d v S 

### μ ρ

9 2



### =



## ( ) ( )

e e 

### R R S

+

+ + +

= 1 ln 1 12

/ 1 ln 2.1

*CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-24 Snow presents a complication for the efficiency calcula tion, since it is not a single solid mass but rather a collection of crystals arranged in a myriad of po ssible shapes that can lead to significant aerodynamic drag. This drag affects particle collection efficiency. To simplify the calculation, we use the value for E determined for rain and graupel, but set a lower limit for E of 1 × 10 -3 based on the work of Sauter and Wang (1989).

2.7 DRY DEPOSITION

For many compounds, dry deposition can be as importan t as wet deposition as a removal process. Due to the difficulty of making direct measure ments of dry deposition and the need for a suitable model parameterization, dry deposition is often treated as a first-order removal mechanism, where the flux of a pollutant to the surfac e is the product of a characteristic deposition velocity and its concentration in the “s urface layer” (i.e., the lowest model layer). Deposition velocities are derived from models that acco unt for the reactivity, solubility, and diffusivity of gases, the sizes of particles, local mete orological conditions, and season-dependent surface characteristics. The factors affecting deposition are discussed in more detail below.

For a given species, particle size, and grid cell, CAM x determines a deposition velocity for each landuse type in that cell and then linearly combines them according to the fractional distribution of landuse. The deposition flux is used as the lower boundary condition in the vertical diffusion algorithm. Aerosol size spectra and species-dependent properties needed for the deposition velocity calculations are externally supplied to CAM x for all pollutant species via the chemistry parameters file; gridded landuse is supplied to the mas ter grid and optionally any nested fine grids; the season is determined by the simulation da te and location on the globe.

Movement of material along a path from the atmosphere, through any plant canopy, and onto the various plant and ground surfaces within and below the canopy is typically modeled by analogy to an electrical circuit. Resistances in serial and p arallel arrangements are used to represent the relative ease with which material moves through differen t portions of the deposition pathway. Each branch of the circuit represents a different path by which material may be deposited. For example, gaseous pollutants may transfer through the l owest layers of the atmosphere partially into a plant canopy, through the stomatal openings o n plant leaves and into the plant mesophyll tissue. Alternatively, the material may travel all th e way through the plant canopy and deposit on the ground surface.

CAMx offers two dry deposition options: the original ap proach based on the work of Wesely (1989) and Slinn and Slinn (1980); and an updated appro ach based on the work of Zhang et al. (2001; 2003). Both of these options are briefly described below.

The Wesely/Slinn Model

Dry Deposition of Gases

Wesely (1989) developed a resistance model that incorp orates the major elements described above. Deposition velocity v d is calculated from three primary resistances r (s/m) in series:CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-25 s b a d r

r r V

+

+ = 1

The aerodynamic resistance r a represents bulk transport through the lowest model laye r by turbulent diffusion, and operates equivalently for all g ases and small particles. The magnitude of this resistance depends on the intensity of turbulent motion, which in turn depends on solar insolation, wind speed, surface roughness, and near-sur face temperature lapse rate. In CAMx it
is calculated from

where u * is friction velocity (m/s), k is von Karman’s constant, z is the lowest model layer midpoint height (m), z 0 is the surface roughness length (m), and φ h is a stability correction term. The surface layer parameterization of Louis (1979) is used to supply friction velocity and stability correction as a function of input surface met eorology and roughness length. By default, roughness length is internally assigned according to s eason and the input gridded distribution of 11 landuse types based on values used in AERMOD (EPA , 1998). However, the user may override this default by explicitly providing gridded ro ughness fields via the albedo/haze/ozone file as described in Section 5. In general, aerodynam ic resistance is at a minimum on warm, sunny days with strong mixing due to surface heating a nd mechanical turbulence, and at a maximum on cool, calm nights when turbulent mixing is suppressed.

The quasi-laminar sublayer (or boundary) resistance r b represents molecular diffusion through the thin layer of air directly in contact with the part icular surface to which material is being deposited. It is usually assumed to depend only on the molecular diffusivity of each pollutant species, and is given by

where S

c is the Schmidt number, or the ratio of air viscosity t o species molecular diffusivity.

Over land, surface resistance r s is expressed as several more serial and parallel resista nces that depend upon the physical and chemical properties of th e surface in question:

where the first set of parallel resistances represents the pathway into the stomatal ( r st ) and mesophyll ( r

m ) portions of active plants, the second is the pathway into the upper canopy ( r uc ), the third is the pathway into the lower canopy ( r dc and r cl ), and the fourth is the pathway to the ground surface ( r ac and r gs ). Many of these resistances are season- and landuse-de pendent, and are built into Wesely’s model; some in turn are adjuste d within CAMx for solar insolation, moisture stress, and surface wetness (by default, vege tation is assumed to be unstressed, but several degrees of moisture stress can be introduced to t he model through the gridded albedo/haze/ozone input file as described in Section 5). A few other resistances have been * 3/2 2

u k S

r c b =

gs ac cl dc uc m st s



### r r r r r r r r

+ + + + + + = 1

1 1 1 1 

    

−        

= h

o a z u k r φ

1 ln 1

*CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-26 developed by Wesely for SO 2 and ozone, and so are scaled for each gaseous species based on the following pollutant properties:

• Molecular diffusivity;

• Henry’s law solubility;

• Chemical reactivity toward oxidation of biological sub stances.

This allows the resistance approach to be used to est imate deposition velocities for a wide range of gaseous pollutants.

Over water, the surface resistance is based on some im provements adopted by Kumar et al. (1996) in UAM-AERO, following Sehmel (1980):

where T

s is surface temperature (K).

The surface resistances for strong acids (e.g., nitric, sulfuric, and hydrochloric acid, peroxides) are set to zero given their strong rates of update by b iota and other surfaces (Huebert and Robert, 1995; Wesely and Hicks, 2000). The species for which surface resistance is set to zero are defined in the CAMx chemistry parameters file.

Dry Deposition of Aerosols

Surface deposition of particles occurs via diffusion, im paction, and/or gravitational settling. Particle size is the dominant variable controlling the se processes. The resistance approach of Slinn and Slinn (1980), as implemented in UAM-AERO (Kum ar et al., 1996), has been adopted in CAMx. Particle deposition velocity for a given aero sol size is calculated using the following resistance equation:

where v

sed is the gravitational settling (or sedimentation) veloc ity. This velocity is dependent on aerosol size and density:

where D is the log-mean particle diameter (m) of a given size se ction, ρ p is particle density (g/m 3 ), g is gravitational acceleration, and ν is the viscosity of air. The factor C is the Cunningham correction for small particles, as described earlier for wet scavenging of particles. Note that particle size and density are affected by aero sol water content, which is determined from local humidity and PM deliquescence properties ac cording to the ISORROPIA model (see Section 3). * S s u

T H r 5

10 3.9 1

− × =

sed

b a b a sed d v r r r r v v +

+ + = 1



### ν ρ

18 2
p sed 

### C

g D

v =CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-27 Aerodynamic resistance r a is identical to the value used for gaseous dry dep osition. Resistance to diffusion through the quasi-laminar sub-layer la yer depends on aerosol Brownian diffusion and inertial impaction. Particles are assumed to r emain on a surface once they impact, so resuspension effects are ignored. Boundary resista nce r b is given by

The stokes number S t is calculated from

Specification of Season

The Wesely (1989) deposition algorithm specifies th e various surface resistances by land cover type for five seasons:

• Spring

• Summer

• Fall

• Winter

• Winter with snow-cover

CAMx internally defines a season map to determine f our of these five seasons by month and latitude (Table 2-2). Five latitude bands exist in each hemisphere:

• Tropical < 20 °

• Sub-tropical 20 ° to 35 °

• Temperate 35 ° to 50 °

• Cool 50 ° to 75 °

• Polar > 75 °

The seasons in the Northern and Southern hemisphere s are offset by six months. This offset does not cause any discontinuity at the equator bec ause all 12 months are defined as summer in the tropical band at the equator. This season map is generalized and may not be ideal for all locations. The season map is coded into data state ments in the “ CAMx/chmdat.f ” subroutine and could be changed to better suit a specific regi on. 

# ( )

t S

c b S u r /3

3/2

* 10 1

− − + =

g

u v S sed

t ν 2

*

=CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-28 Table 2-2. **Relationships between season and month/latitude u sed in the CAMx Wesely/Slinn**  **dry deposition model. Exception: seasons for the a rea within 50N-75N and 15W-15E are**  **internally set to those of latitude band 35-50 to a ccount for regions of Europe in which the**  **climate is influenced by the Gulf Stream.**

Month Latitude Band

Northern Hemisphere Southern

Hemisphere < 20 ° 20 ° - 35 ° 35 ° - 50 ° 50 ° - 75 ° > 75 °

Tropical Sub

- tropical Temperate Cool Polar
**Jan Jul Summer Winter Winter Winter Winter Feb Aug Summer Spring Winter Winter Winter**  **Mar Sep Summer Spring Spring Winter Winter Apr Oct Summer Spring Spring Spring Winter**  **May Nov Summer Summer Spring Spring Winter Jun Dec Summer Summer Summer Summer Spring** 
**Jul Jan Summer Summer Summer Summer Summer Aug Feb Summer Summer Summer Summer Fall**  **Sep Mar Summer Summer Fall Fall Winter Oct Apr Summer Fall Fall Fall Winter**  **Nov May Summer Fall Fall Winter Winter**
**Dec Jun Summer Fall Winter Winter Winter**

Specifying Snow-Cover

The season map shown in Table 2-2 does not specify any snow-cover because snow-cover is quite variable in space and time. Instead, CAMx pr ovides a way of explicitly specifying which grid cells are snow-covered during the simulation. An optional snow-cover data field may be added to the albedo/haze/ozone column (AHO) file to specify the spatial and temporal distribution of snow cover during a simulation (see Section 5). The snow-cover input is a binary value where 1 signifies that the grid cell is snow- covered, while 0 is snow-free. Snow-covered grid cells are assigned the Wesely (1989) surface r esistances for the category “winter with snowcover”, regardless of the season. Snow cover also impacts photolysis rates (see Section 3).

Snow-cover data can be developed in several ways:

• Spatially interpolated snow-cover observations;

• Analyses of snow-cover data included in existing me trological data sets, such as the NCEP Eta Data Assimilation System (EDAS);

• Analyses of climatological snow-cover based on surf ace reflectivity data from the
satellite-based instruments.

The Zhang Model

The Wesely/Slinn model was the most comprehensive a lgorithm at the time for regional-scale air quality modeling, but no longer represents the curr ent state of the science. Furthermore, it is defined for a limited number of land cover types th at are in turn characterized by typical eastern U.S. vegetation types, density, and seasonal condit ions. The deposition velocities calculated with the Wesely/Slinn model may not be sufficiently accurate outside the eastern U.S. or for atypical conditions (e.g., seasonal transitions, dr ought stress). It was desirable to implement a dry deposition algorithm in CAMx that incorporates updates in theory as well as recent measurements. One of the most important aspects of newer dry deposition schemes is the use ofCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-29 “leaf area index” (LAI) to scale pollutant uptake i nto biota. The LAI is defined as the ratio of the one-sided green leaf area to a unit area of the gro und. This index characterizes the amount of vegetation present, and can be measured by satellit es instruments at fairly high spatial resolution.

A recent LAI-based algorithm is used in Environment Canada’s AURAMS air quality model (Zhang et al. 2003; Zhang et al. 2008). This schem e is a state-of-the-science algorithm that possesses an updated representation of non-stomatal deposition pathways and has been tested extensively through its use in daily air quality fo recasting. It has been shown to reproduce
observed fluxes of ozone and SO 2 with reasonable accuracy.

Dry Deposition of Gases

The gas resistance model of Zhang et al. (2003) inv okes the same 3-resistance equation for deposition velocity as the Wesely (1989) model. Th e equations for aerodynamic resistance ( r a ) and boundary resistance ( r b ) are very similar to the Wesely (1989) formulation s. However, Zhang et al. (2003) replace the surface resistance ( r s ) equation with a new relationship for the overall canopy resistance:

gs ac cut m st st c



### r r r r r W r

+

+ + + − = 1

1 1 1

where W

st is the fraction of stomatal blocking under wet con ditions, r cut is the cuticle resistance, and all other resistances have similar meaning to t he Wesely model. Stomatal resistance ( r st ) is calculated using a sunlit/shade (so-called “two-big -leaf”) stomatal resistance sub-model. Following Wesely (1989), values for r g and r cut are calculated for SO 2 and O 3 and then scaled for other gaseous species. Cuticle resistance is sligh tly different from that defined in traditional bigleaf models in that it also considers the aerodynam ic and quasi-laminar resistances of individual leaves. This is done by parameterizing r cut as a function of friction velocity, similar to the concept of overall cuticle uptake considered in a m ulti-layer model framework.

LAI is used in functions for r ac , and r cut , where the LAI for any given day is linearly inter polated from monthly default LAI as a function of landuse t ype. To account for LAI effects on surface roughness (

z 0 ), a similar daily LAI interpolation is applied to that parameter. Hence, the Zhang model does not require the specification of season, as all resistance equations are continuous over each month (note that CAMx automatically appli es the 6-month offset for applications in the southern hemisphere).

For snow on the ground and leaves, both r gs and r cut are adjusted by a snow cover fraction, which is assumed to be 0.8 when snow cover is flagged for a grid cell in CAMx. Snow cover is defined through an optional record in the input albedo/haze /ozone file, as described in Section 5. For surfaces without canopies, r gs is defined as the resistance to any surface (e.g. soil, ice, snow and water), r ac is set to zero, and very large values are used for r st , r m and r cut .

The Zhang scheme uses 26 land use categories, where as the Wesely scheme uses 11. Therefore, the CAMx land use file was expanded to handle up to 26 categories (see Section 5). The Zhang
model includes a set of embedded annual surface rou ghness ranges and monthly LAI specific toCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-30 each of the 26 landuse categories. The capabilitie s of the scheme were extended by adding the option to use episode-specific (i.e., satellite-der ived) LAI data. Satellite-based LAI data from MODIS (MODerate-resolution Imaging Spectroradiomete r) 1 can be processed into gridded LAI fields that are passed to CAMx as an optional recor d in the landuse input file (see Section 5). The optional gridded LAI fields are used to scale t he default landuse-specific LAI values. For each grid cell, a landuse-weighted default LAI is d etermined according to the landuse fractions present. An LAI scaling factor is then determined as the ratio of the input LAI to the landuseweighted default LAI. This factor is used to scale the individual default LAI values for each landuse type in the grid cell, subject to the annua l maximum to minimum range among the monthly default values. Figure 2-5 illustrates how the use of satellite LAI data introduces additional episode-specific variation into the LAI field.

Figure 2

- 5

. **Comparison of monthly LAI data embedded in the Zh ang dry deposition scheme**  **against episode-specific LAI for June 2005.**

In developmental tests, the Zhang dry deposition mo del has tended to generate lower ozone deposition rates relative to the Wesely model, whic h leads to higher ozone predictions overall. This effect is seasonally dependent and will vary w ith the definition of LAI. Ozone is less sensitive to the source of LAI (whether Zhang defau lts or satellite-enhanced) than to the choice of deposition model.

1 MODIS provides LAI at 250 meter spatial resolution and 16 day temporal resolution.



### LAI from Satellite **Zhang Embedded LAI Data**

**Modified LAI using Satellite Data**CAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-31 Dry Deposition of Aerosols

Many previous particle dry deposition models were e valuated against wind tunnel measurements, which may not be representative of field measuremen ts. Ruijgrok et al. (1995) showed that estimates from these early models differ greatly, e specially in the range of 0.1-1.0 μ m (by 2 to 3 orders of magnitude). Theoretically, particles in this range deposit at rates much less than 0.01 cm/s, a value comparable only to laboratory (wind t unnel) studies. According to Zhang et al. (2001), much higher values have been obtained in ma ny field studies, including for sub-micron sulfate in which deposition velocities of one to tw o orders of magnitude higher have been measured. Gallagher et al. (1997) state that much higher deposition velocity values, typically 1 cm/s or more for sub-micron aerosol deposition to a forest, are consistent across the aerosol size spectrum. The Zhang et al. (2001) study devel oped a simple parameterization of particle dry deposition as a function of aerosol size and landus e that predicts higher deposition velocities for sub-micron aerosols, especially over rough vegetate d surfaces.

The deposition of aerosols is based on the model of Slinn (1982), but using simplified empirical parameterizations to replace detailed canopy inform ation. The aerosol deposition velocity is
defined as:

s a sed d r

r v v +

+ = 1

where v

sed and r a have identical meanings as the Slinn and Slinn (19 80) definition described earlier for the Wesely/Slinn deposition model. Not e that in this case, the virtual serial resistance

r a ⋅ r s ⋅ v sed has been removed, which results in higher values o f deposition velocity. The surface resistance is given by



## ( ) 1 * 0 1

R

E E E u r
IN IM B s +

+ = ε

where the variable E

includes the collection efficiencies for Brownian diffusion, impaction, and interception, respectively, R 1 is a factor representing the fraction of particles that stick to the surface, and ε

0 is an empirical constant that is set to a value of 3. The Brownian collection efficiency depends on the Schmidt number, while the impaction efficiency and R 1 depend on the
Stokes number.

The collection efficiency by interception also exis ts if the particle passes an obstacle at a distance shorter than its physical dimensions (e.g., large p articles passing near hairy leaves). Zhang et al. (2001) adopted a simple equation for this term that is a function of particle diameter and a characteristic radius, for which default values are given for different land use and seasonal categories.

Figures 2-6 and 2-7 compare estimated particle depo sition velocities from the Zhang model, the Slinn and Slinn (1980) model, and the AERMOD model (EPA, 1998). Calculations were made for daytime, neutrally stable conditions for a rang e of wind speeds and land use categories. Figure 2-6 shows that the Zhang model increases dep osition velocities over forest by roughly an order of magnitude for the 0.1-1 μ m range, yet reduces deposition velocities above 1 μ m. FigureCAMx v5.30 User’s Guide, December 2010

Core Model Formulation

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_02_formulat ion.docx 2-32 2-7 clearly shows the Zhang model generates sub-mic ron deposition velocities that are ten times higher than the Slinn and Slinn (1980) model over t he five landuse types tested.

Figure 2-6. **Comparison of particle dry deposition velocities as a function of size and wind**  **speeds (m/s) for three models: black – Zhang et al. (2001); blue – Slinn and Slinn (1980);**  **orange – AERMOD (EPA, 1998). Results are shown for a forest landuse category during**  **daytime neutral stability. Particle density was se t at 1.5 g/cm**  **3**

**.**

Figure 2-7 **Comparison of particle dry deposition velocities determined by the Zhang et al.**  **(2001) and Slinn and Slinn (1980) models, as a func tion of landuse and wind speed during**  **daytime neutrally stable conditions. Particle dens ity was set at 1.5 g/cm**  **3** Forest

**0.01 0.1**  **1 10**  **100**

**0.001 0.01 0.1 1 10 100**

Particle diameter ( μ m)Vd(cm s-1) **wind = 2**

**wind = 5**

**wind = 10**

**wind = 15**

**wind = 2**

**wind = 5**

**wind = 10**

**wind = 15**

**wind = 2**

**wind = 5**

**wind = 10**

**wind = 15**

**0.0 0.2**  **0.4 0.6**  **0.8**

**0 0.2 0.4 0.6 0.8**Zhang [cm/s]

Slinn 80 [cm/s]

Particle (D p = 0.35 μ m) deposition in day time

**water, 0.8m/s forest, 0.8m/s**  **crops, 0.8m/s urban, 0.8m/s**  **swamp, 0.8m/s water, 2m/s**  **forest, 2m/s crops, 2m/s**  **urban, 2m/s swamp, 2m/s**  **water, 4 m/s forest, 4m/s**  **crops, 4m/s urban, 4m/s**  **swamp, 4m/s water, 6m/s**  **forest, 6m/s crops, 6m/s**  **urban, 6m/s swamp, 6m/s**  **water, 8m/s forest, 8m/s**  **crops, 8m/s urban, 8m/s**  **swamp, 8m/s water, 10m/s**  **forest, 10m/s crops, 10m/s**  **urban, 10m/s swamp, 10m/s** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-1 3. CHEMISTRY MECHANISMS

The CAMx gas-phase chemical mechanisms include the 2005 version of Carbon Bond (CB05; Yarwood et al., 2005b), SAPRC99 (Carter, 2000) and several versions of Carbon Bond IV (CB4; Gery et al., 1989). There is also an interface tha t allows a simple user-defined chemical mechanism to be employed by coding the mechanism in a user-supplied subroutine. The gasphase chemical mechanisms currently supported are l isted in Table 3-1.

Table 3-1. **Gas-phase chemical mechanisms currently implemente d in CAMx.** 

Mechanism ID Description

**6 CB05, the 2005 version of the Carbon Bond mechanism**  **developed for**

**use in EPA atmospheric modeling studies (Yarwood et al., 2005b). This**  **mechanism optionally includes the same aerosol and mercury chemistry**  **as Mechanism 4. 156 reactions and up to 89 species (54 state gases, up**  **to 22 state particulates, and 13 radicals).**

**5 The fixed parameter version of the SAPRC99 gas-phas**  **e mechanism**

**(Carter, 2000). This mechanism optionally includes the same aerosol and**  **mercury chemistry as Mechanism 4. 217 reactions an d up to 114 species**  **(76 state gases, up to 22 state particulates, and 1 6 radicals).** 

**3 CB4 (Gery et al., 1989) gas-phase chemistry with re**  **vised PAN chemistry,**

**additional radical-radical termination reactions an d updated isoprene**  **chemistry (Carter 1996; Whitten et al.,1996). 96 r eactions and 36 species**  **(26 state gases and 10 radicals).**

**4 Mechanism 3 with additional inorganic gas-phase rea**  **ctions important for**

**regional modeling (Yarwood et al., 2005a). This me chanism optionally**  **includes aerosol and mercury chemistry including: s econdary organic**  **aerosol formation from condensable gases, aqueous P M chemistry,**  **inorganic PM thermodynamics, and aerosol size evolu tion. 113 reactions**  **and up to 76 species (up to 44 state gases, up to 2 2 state particulates,**  **and 10 radicals).**

**10 A user-defined simple chemistry mechanism can be de**  **veloped for any**

**gas and/or particulate species, which is defined by a “Mechanism 10”**  **parameters file and solved within a user-supplied s ubroutine called** 

**“** chem10.f **.”**CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-2 The selection of which mechanism to employ in a giv en CAMx application is determined by the “chemistry parameter” input file. This file define s the mechanism number, the number of gas and aerosol species, and the number of reactions fo r the mechanism, lists the species by name with associated physico-chemical properties, lists the reaction rate constants and temperature dependencies for each reaction, and defines which r eactions are photolytic. Chemistry parameter input files for the CB05, SAPRC99, and CB4 mechanis ms listed in Table 3-1 are provided with CAMx and are not normally modified by users. See S ection 5 for additional information on the format and usage of these files. Chemistry paramet ers files are specific to versions of CAMx. Always use chemistry parameters files with the righ t CAMx version number, do not attempt to
use files for another CAMx version.

3.1 GAS-PHASE CHEMISTRY

Two versions of the CB4 mechanism are included in C AMx, labeled Mechanisms 3 and 4.
Mechanism 3 was established in the mid-1990’s as th e “default” mechanism for simulations without aerosols; it comprises CB4 (Gery et al., 19 89) with revised PAN chemistry, additional radical-radical termination reactions and updated i soprene chemistry (Carter 1996; Whitten et al., 1996). Mechanism 4 is intended for aerosol modelin g with CB4 and has the following updates
to Mechanism 3:

• Seventeen additional inorganic chemistry reactions are added that are most important for regional/annual ozone, PM, mercury and toxics model ing (Yarwood et al., 2005a).

• Rate expressions for gas-phase reactions 26, 36, an d 82 are updated to explicitly include pressure dependence.

• The rate for reaction 83 is set to zero. This react ion was used in the UAM to represent heterogeneous SO 2 to sulfate conversion but now should be set to zer o because aqueous chemistry is explicitly handled.

• The rate for reaction 18 (N 2 O 5 + H 2 O –> 2 HNO 3 ) is reduced to a value that is consistent with just the known gas-phase processes (Mentel, Bl eilebens and Wahner, 1996). Heterogeneous conversion of N 2 O 5 to HNO 3 is treated in the aerosol chemistry module.

Mechanism 5 is the SAPRC99 chemistry adapted for ph otochemical grid modeling by selecting a specific “fixed parameter” lumping scheme (Carter, 2000). The SAPRC99 mechanism was
added as an alternate mechanism because it is chemi cally up-to-date, has been tested extensively against environmental chamber data, and uses a diff erent approach for VOC lumping than the
Carbon Bond mechanisms.

Mechanism 6 is the 2005 version of the Carbon Bond mechanism (CB05) developed for EPA atmospheric modeling studies. Updates in CB05 comp ared to the earlier CB4 mechanisms are:

• Updated rate constants based on recent (2003 – 2005 ) IUPAC and NASA evaluations.

• An extended inorganic reaction set for urban to rem ote tropospheric conditions.

• NOx recycling reactions to represent the fate of NO x over multiple days.

• Explicit organic chemistry for methane and ethane.

• Explicit methylperoxy radical, methyl hydroperoxide and formic acid.

• Lumped higher organic peroxides, organic acids and peracids.

• Internal olefin (R-HC=CH-R) species called IOLE.CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-3 • Higher aldehyde species ALDX making ALD2 explicitly acetaldehyde.

• Higher peroxyacyl nitrate species from ALDX called PANX.

• Lumped terpene species called TERP.

The CB05 was evaluated against smog chamber data fr om the Universities of North Carolina and California at Riverside (Yarwood et al., 2005b). T he new higher aldehyde and internal olefin species improve mechanism performance for these spe cies and produce oxidants more rapidly at low VOC/NOx ratios. The new terpene species improv es simulation of oxidants and PM from biogenic emissions. Several new organic peroxide s pecies improve the simulation of oxidants
that are involved in PM sulfate formation. The add ition of explicit methylperoxy radical improves the simulation of hydrogen peroxide under low NOx conditions.

All mechanisms described above are all balanced for nitrogen conservation so that predicted NOy can be calculated as the sum of nitrogen contai ning species. A listing of the species, reactions, and VOC species properties in Mechanism 3 (CB4), Mechanism 5 (SAPRC99), Mechanism 6 (CB05) are provided in Tables 3-2 throu gh 3-10, respectively.CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-4 Table 3-2. **Reactions and rate constants for the CB4 mechanis m (Mechanism 3).** 

Reaction Number Reactants Products k 298 1 (ppm –n min –1 )

**1 NO2 NO + O Photolysis** 

**2 O O3 4.323E+06** 

**3 O3 + NO NO2 2.664E+01** 

**4 O + NO2 NO 1.375E+04** 

**5 O + NO2 NO3 2.309E+03** 

**6 O + NO NO2 2.438E+03** 

**7 NO2 + O3 NO3 4.731E-02** 

**8 O3 O Photolysis** 

**9 O3 O1D Photolysis** 

**10 O1D O 4.250E+10** 

**11 O1D + H2O 2 OH 3.260E+05** 

**12 O3 + OH HO2 1.000E+02** 

**13 O3 + HO2 OH 2.999E+00** 

**14 NO3 0.89 NO2 + 0.89 O + 0.11 NO Photolysis** 

**15 NO3 + NO 2 NO2 4.417E+04** 

**16 NO3 + NO2 NO + NO2 5.901E-01** 

**17 NO3 + NO2 N2O5 1.853E+03** 

**18 N2O5 + H2O 2 HNO3 1.900E-06** 

**19 N2O5 NO3 + NO2 2.776E+00** 

**20 NO + NO 2 NO2 1.539E-04** 

**21 NO + NO2 + H2O 2 HONO 1.600E-11** 

**22 NO + OH HONO 9.799E+03** 

**23 HONO NO + OH Photolysis** 

**24 OH + HONO NO2 9.770E+03** 

**25 HONO + HONO NO + NO2 1.500E-05** 

**26 NO2 + OH HNO3 1.682E+04** 

**27 OH + HNO3 NO3 2.179E+02** 

**28 HO2 + NO OH + NO2 1.227E+04** 

**29 HO2 + NO2 PNA 0.000E+00** 

**30 PNA HO2 + NO2 0.000E+00** 

**31 OH + PNA NO2 0.000E+00** 

**32 HO2 + HO2 H2O2 4.144E+03** 

**33 HO2 + HO2 + H2O H2O2 2.181E-01** 

**34 H2O2 2 OH Photolysis** 

**35 OH + H2O2 HO2 2.520E+03** 

**36 OH + CO HO2 3.220E+02** 

**37 FORM + OH HO2 + CO 1.500E+04** 

**38 FORM 2 HO2 + CO Photolysis** 

**39 FORM CO Photolysis** 

**40 FORM + O OH + HO2 + CO 2.370E+02** 

**41 FORM + NO3 HNO3 + HO2 + CO 9.300E-01** 

**42 ALD2 + O C2O3 + OH 6.360E+02** 

**43 ALD2 + OH C2O3 2.400E+04** 

**44 ALD2 + NO3 C2O3 + HNO3 3.700E+00** 

**45 ALD2 FORM + 2 HO2 + CO + XO2 Photolysis** 

**46 C2O3 + NO FORM + NO2 + HO2 + XO2 2.820E+04** 

**47 C2O3 + NO2 PAN 1.370E+04** 

**48 PAN C2O3 + NO2 2.540E-02** 

**49 C2O3 + C2O3 2 FORM + 2 XO2 + 2 HO2 3.700E+03** 

**50 C2O3 + HO2 0.79 FORM + 0.79 XO2 + 0.79 HO2 + 0.7 9 OH 9.600E+03** 

**51 2**  **OH FORM + XO2 + HO2 2.100E+01** 

**52 PAR + OH 0.87 XO2 + 0.13 XO2N + 0.11 HO2 + 0.11 ALD2 - 0.11 PAR + 0.76 ROR 1.203E+03** 

**53 ROR 0.96 XO2 + 1.1 ALD2 + 0.94 HO2 - 2.1 PAR + 0 .04 XO2N 1.371E+05** 

**54 ROR HO2 9.545E+04** 

**55 ROR + NO2 NTR 2.200E+04** 

**56 O + OLE 0.63 ALD2 + 0.38 HO2 + 0.28 XO2 + 0.3 CO + 0.2 FORM**  **+ 0.02 XO2N + 0.22 PAR + 0.2 OH**  **5.920E+03**CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-5 Reaction
Number Reactants Products k 298 1 (ppm –n min –1 )

**57 OH + OLE FORM + ALD2 - PAR + XO2 + HO2 4.200E+04** 

**58 O3 + OLE 0.5 ALD2 + 0.74 FORM + 0.22 XO2 + 0.1 O H + 0.33 CO + 0.44 HO2 - PAR 1.800E-02** 

**59 NO3 + OLE 0.91 XO2 + FORM + 0.09 XO2N + ALD2 + N O2 - PAR 1.135E+01** 

**60 O + ETH FORM + 1.7 HO2 + CO + 0.7 XO2 + 0.3 OH 1 .080E+03** 

**61 OH + ETH XO2 + 1.56 FORM + 0.22 ALD2 + HO2 1.192E+04** 

**62 O3 + ETH FORM + 0.42 CO + 0.12 HO2 2.700E-03** 

**63 TOL + OH 0.44 HO2 + 0.08 XO2 + 0.36 CRES + 0.56 TO2 9.150E+03** 

**64 TO2 + NO 0.9 NO2 + 0.9 HO2 + 0.9 OPEN + 0.1 NTR 1.200E+04** 

**65 TO2 CRES + HO2 2.500E+02** 

**66 OH + CRES 0.4 CRO + 0.6 XO2 + 0.6 HO2 + 0.3 OPEN 6.100E+04** 

**67 CRES + NO3 CRO + HNO3 3.250E+04** 

**68 CRO + NO2 NTR 2.000E+04** 

**69 OPEN C2O3 + HO2 + CO Photolysis** 

**70 OPEN + OH XO2 + 2 CO + 2 HO2 + C2O3 + FORM 4.400E+04** 

**71 OPEN + O3 0.03 ALD2 + 0.62 C2O3 + 0.7 FORM + 0.03 XO2 + 0.69**  **CO + 0.08 OH + 0.76 HO2 + 0.2 MGLY**  **1.500E-02**

**72 OH + XYL 0.7 HO2 + 0.5 XO2 + 0.2 CRES + 0.8 MGLY + 1.1 PAR + 0.3 TO2 3.620E+04** 

**73 OH + MGLY XO2 + C2O3 2.600E+04** 

**74 MGLY C2O3 + HO2 + CO Photolysis** 

**75 O + ISOP 0.75 ISPD + 0.5 FORM + 0.25 XO2 + 0.25 HO2 + 0.25 C2O3 + 0.25 PAR 5.320E+04** 

**76 OH + ISOP 0.912 ISPD + 0.629 FORM + 0.991 XO2 + 0.912 HO2 + 0.088 XO2N 1.472E+05** 

**77 O3 + ISOP 0.65 ISPD + 0.6 FORM + 0.2 XO2 + 0.066 HO2 + 0.266**  **OH + 0.2 C2O3 + 0.15 ALD2 + 0.35 PAR + 0.066 CO**  **1.897E-02**

**78 NO3 + ISOP 0.2 ISPD + 0.8 NTR + XO2 + 0.8 HO2 + 0.2 NO2 + 0.8 ALD2 + 2.4 PAR 9.963E+02** 

**79 XO2 + NO NO2 1.200E+04** 

**80 XO2 + XO2 2.000E+03** 

**81 XO2N + NO NTR 1.200E+04** 

**82 SO2 + OH SULF + HO2 1.110E+03** 

**83 SO2 SULF 8.167E-05** 

**84 MEOH + OH FORM + HO2 1.349E+03** 

**85 ETOH + OH HO2 + ALD2 4.714E+03** 

**86 XO2 + HO2 8.900E+03** 

**87 XO2N + HO2 8.900E+03** 

**88 XO2N + XO2N 2.000E+03** 

**89 XO2 + XO2N 4.000E+03** 

**90 OH + HO2 1.626E+05** 

**91 CRO 2.778E-04** 

**92 OH + ISPD 1.565 PAR + 0.167 FORM + 0.713 XO2 + 0.503 HO2 + 0.**  **334 CO + 0.168 MGLY + 0.273 ALD2 + 0.498 C2O3**  **4.967E+04**

**93 O3 + ISPD 0.114 C2O3 + 0.15 FORM + 0.85 MGLY + 0.154 HO2 + 0.**  **268 OH + 0.064 XO2 + 0.02 ALD2 + 0.36 PAR + 0.225 CO**  **1.050E-02**

**94 NO3 + ISPD 0.357 ALD2 + 0.282 FORM + 1.282 PAR + 0.925 HO2 + 0**  **.643 CO + 0.85 NTR + 0.075 C2O3 + 0.075 XO2 + 0.15 HNO3**  **1.478E+00**

**95 ISPD 0.333 CO + 0.067 ALD2 + 0.9 FORM + 0.832 PAR + 1.03**  **3 HO2 + 0.7 XO2 + 0.967 C2O3**  **Photolysis**

**96 NO2 + ISOP 0.2 ISPD + 0.8 NTR + XO2 + 0.8 HO2 + 0.2 NO + 0.8 ALD2 + 2.4 PAR 2.200E-04**  **Notes:**

**1 Rate constants are shown for 298 K and 1 atmospher**  **e in units of ppm and minutes. See the CAMx chemistry parameters file (Section 5) for**  **the temperature and pressure dependencies.** 

**2 Reaction 51 implicitly assumes 1.85 ppm of methane**  **in the rate constant.**CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-6

Table 3-3. **Species in the CB4 mechanism (Mechanism 3).** Species Name Description **NO2 Nitrogen dioxide**
**NO Nitric oxide O Oxygen atom in the O**  **3 (P) electronic state** 
**O3 Ozone**
**NO3 Nitrate radical O1D Oxygen atom in the O**  **1 (D) electronic state**  **OH Hydroxyl radical HO2 Hydroperoxy radical**  **N2O5 Dinitrogen pentoxide**
**HNO3 Nitric acid**
**HONO Nitrous acid PNA Peroxynitric acid (HNO**  **4 )**  **H2O2 Hydrogen peroxide**
**CO Carbon monoxide FORM Formaldehyde**  **ALD2 Higher aldehyde (based on acetaldehyde) C2O3 Acylperoxy radical (based on acetylperoxy)**  **XO2 NO to NO2 conversion from alkylperoxy (RO 2**  **) radical PAN Peroxyacyl nitrate (based on peroxyacetyl nitra**  **te) PAR Paraffin carbon bond (C-C)**  **XO2N NO to organic nitrate conversion from alkylper oxy (RO**  **2 ) radical**  **ROR Secondary alkoxy radical NTR Organic nitrate (RNO**  **3 )**  **OLE Olefin carbon bond (C=C)**
**ETH Ethene TOL Toluene and other monoalkyl aromatics**  **CRES Cresol and higher molecular weight phenols TO2 Toluene-hydroxyl radical adduct**  **OPEN Aromatic ring opening product CRO Methylphenoxy radical**  **MGLY Methylglyoxal and other aromatic products XYL Xylene and other polyalkyl aromatics**  **ISOP Isoprene ISPD Isoprene product (lumped methacrolein, methyl**  **vinyl ketone, etc.)**
**SO2 Sulfur dioxide SULF Sulfuric acid (gaseous)** 
**MEOH Methanol**
**ETOH Ethanol**

Table 3-4 **. Properties of VOC species in the CB4 mechanism: average carbon numbers,**  k **OH values (ppm**  **-1 min**  **-1 ) and maximum incremental reactivity (MIR) values (**  **mole O 3**  **/mole VOC).** Species Carbon Number Average k OH MIR
**PAR 1 1.20E+03 0.3458**
**OLE 2 4.20E+04 4.831**
**ETH 2 1.19E+04 2.328**
**TOL 7 9.15E+03 0.5139**
**XYL 8 3.62E+04 2.383**
**FORM 1 1.50E+04 5.771**
**ALD2 2 2.40E+04 3.007**
**ISOP 5 1.47E+05 4.375**
**MEOH 1 1.35E+03 0.4101**
**ETOH 2 4.71E+03 0.6650**CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-7 Table 3-5. **Reactions and rate constants for the SAPRC99 mech anism (Mechanism 5).** 

Reaction Number Reactants Products k 298 1 (ppm –n min –1 )
**1 NO2 NO + O Photolysis** 
**2 O + O2 + M O3 + M 2.105E-05** 
**3 O + O3 2O2 1.175E+01** 
**4 O + NO + M NO2 + M 3.676E-03** 
**5 O + NO2 NO + O2 1.435E+04** 
**6 O + NO2 NO3 2.690E+03** 
**7 O3 + NO NO2 + O2 2.682E+01** 
**8 O3 + NO2 O2 + NO3 5.202E-02** 
**9 NO + NO3 2NO2 3.849E+04** 
**10 NO + NO + O2 2NO2 7.104E-10** 
**11 NO2 + NO3 N2O5 2.268E+03** 
**12 N2O5 NO2 + NO3 3.169E+00** 
**13 N2O5 + H2O 2HNO3 3.841E-07** 
**14 NO2 + NO3 NO + NO2 + O2 9.689E-01** 
**15 NO3 NO + O2 Photolysis** 
**16 NO3 NO2 + O Photolysis** 
**17 O3 O + O2 Photolysis** 
**18 O3 O1D + O2 Photolysis** 
**19 O1D + H2O 2OH 3.250E+05** 
**20 O1D + M O + M 4.248E+04** 
**21 OH + NO HONO 1.094E+04** 
**22 HONO OH + NO Photolysis** 
**23 HONO HO2 + NO2 Photolysis** 
**24 OH + HONO NO2 9.549E+03** 
**25 OH + NO2 HNO3 1.326E+04** 
**26 OH + NO3 HO2 + NO2 2.954E+04** 
**27 OH + HNO3 NO3 + H2O 2.176E+02** 
**28 HNO3 OH + NO2 Photolysis** 
**29 OH + CO HO2 3.081E+02** 
**30 OH + O3 HO2 9.794E+01** 
**31 HO2 + NO OH + NO2 1.244E+04** 
**32 HO2 + NO2 HNO4 0.000E+00** 
**33 HNO4 HO2 + NO2 0.000E+00** 
**34 HNO4 0.61HO2 + 0.61NO2 + 0.39OH + 0.39NO3 Photol ysis** 
**35 HNO4 + OH NO2 0.000E+00** 
**36 HO2 + O3 OH 2.763E+00** 
**37 HO2 + HO2 HO2H + O2 4.235E+03** 
**38 HO2 + HO2 + H2O HO2H + O2 + H2O 2.348E-01** 
**39 NO3 + HO2 0.8OH + 0.8NO2 + 0.2HNO3 5.909E+03** 
**40 NO3 + NO3 2NO2 + O2 3.373E-01** 
**41 HO2H 2OH Photolysis** 
**42 HO2H + OH HO2 2.504E+03** 
**43 OH + HO2 1.641E+05** 
**44 OH + SO2 HO2 + SULF 1.444E+03** 
**45 OH + H2 HO2 9.898E+00** 
**46 CXO2 + NO NO2 + HCHO + HO2 1.076E+04** 
**47 CXO2 + HO2 COOH + O2 7.691E+03** 
**48 CXO2 + NO3 HCHO + HO2 + NO2 1.920E+03** 
**49 CXO2 + CXO2 MEOH + HCHO + O2 3.914E+02** 
**50 CXO2 + CXO2 2HCHO + 2HO2 1.581E+02** 
**51 RO2R + NO NO2 + HO2 1.334E+04** 
**52 RO2R + HO2 ROOH 2.200E+04** 
**53 RO2R + NO3 NO2 + O2 + HO2 3.398E+03** 
**54 RO2R + CXO2 HO2 + 0.75HCHO + 0.25MEOH 2.954E+02** 
**55 RO2R + RO2R HO2 5.170E+01** 
**56 R2O2 + NO NO2 1.334E+04** 
**57 R2O2 + HO2 HO2 2.200E+04** 
**58 R2O2 + NO3 NO2 3.398E+03** 
**59 R2O2 + CXO2 CXO2 2.954E+02** 
**60 R2O2 + RO2R RO2R 5.170E+01** 
**61 R2O2 + R2O2 5.170E+01** 
**62 RO2N + NO RNO3 1.334E+04** 
**63 RO2N + HO2 ROOH 2.200E+04** 
**64 RO2N + CXO2 HO2 + 0.25MEOH + 0.5MEK + 0.5PROD + 0.75HCHO 2.954E+02** 
**65 RO2N + NO3 NO2 + O2 + HO2 + MEK 3.398E+03** 
**66 RO2N + RO2R HO2 + 0.5MEK + 0.5PROD 5.170E+01** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-8 Reaction
Number Reactants Products k 298 1 (ppm –n min –1 )
**67 RO2N + R2O2 RO2N 5.170E+01** 
**68 RO2N + RO2N MEK + HO2 + PROD 5.170E+01** 
**69 CCO3 + NO2 PAN 1.548E+04** 
**70 PAN CCO3 + NO2 3.123E-02** 
**71 CCO3 + NO CXO2 + NO2 3.152E+04** 
**72 CCO3 + HO2 0.75CO3H + 0.25CO2H + 0.25O3 2.084E+0 4** 
**73 CCO3 + NO3 CXO2 + NO2 5.909E+03** 
**74 CCO3 + CXO2 CO2H + HCHO 1.425E+04** 
**75 CCO3 + RO2R CO2H 1.108E+04** 
**76 CCO3 + R2O2 CCO3 1.108E+04** 
**77 CCO3 + RO2N CO2H + PROD 1.108E+04** 
**78 CCO3 + CCO3 2CXO2 2.295E+04** 
**79 RCO3 + NO2 PAN2 1.783E+04** 
**80 PAN2 RCO3 + NO2 2.661E-02** 
**81 RCO3 + NO NO2 + CCHO + RO2R 4.132E+04** 
**82 RCO3 + HO2 0.75RC3H + 0.25RC2H + 0.25O3 2.084E+0 4** 
**83 RCO3 + NO3 NO2 + CCHO + RO2R 5.909E+03** 
**84 RCO3 + CXO2 RC2H + HCHO 1.425E+04** 
**85 RCO3 + RO2R RC2H 1.108E+04** 
**86 RCO3 + R2O2 RCO3 1.108E+04** 
**87 RCO3 + RO2N RC2H + PROD 1.108E+04** 
**88 RCO3 + CCO3 CXO2 + CCHO + RO2R 2.295E+04** 
**89 RCO3 + RCO3 2CCHO + 2RO2R 2.295E+04** 
**90 BZCO + NO2 PBZN 2.024E+04** 
**91 PBZN BZCO + NO2 1.873E-02** 
**92 BZCO + NO NO2 + BZO + R2O2 4.132E+04** 
**93 BZCO + HO2 0.75RC3H + 0.25RC2H + 0.25O3 2.084E+0 4** 
**94 BZCO + NO3 NO2 + BZO + R2O2 5.909E+03** 
**95 BZCO + CXO2 RC2H + HCHO 1.425E+04** 
**96 BZCO + RO2R RC2H 1.108E+04** 
**97 BZCO + R2O2 BZCO 1.108E+04** 
**98 BZCO + RO2N RC2H + PROD 1.108E+04** 
**99 BZCO + CCO3 CXO2 + BZO + R2O2 2.295E+04** 
**100 BZCO + RCO3 CCHO + RO2R + BZO + R2O2 2.295E+04** 
**101 BZCO + BZCO 2BZO + 2R2O2 2.295E+04** 
**102 MCO3 + NO2 MPAN 1.783E+04** 
**103 MPAN MCO3 + NO2 2.127E-02** 
**104 MCO3 + NO NO2 + HCHO + CCO3 4.132E+04** 
**105 MCO3 + HO2 0.75RC3H + 0.25RC2H + 0.25O3 2.084E+ 04** 
**106 MCO3 + NO3 NO2 + HCHO + CCO3 5.909E+03** 
**107 MCO3 + CXO2 RC2H + HCHO 1.425E+04** 
**108 MCO3 + RO2R RC2H 1.108E+04** 
**109 MCO3 + R2O2 MCO3 1.108E+04** 
**110 MCO3 + RO2N 2RC2H 1.108E+04** 
**111 MCO3 + CCO3 CXO2 + HCHO + CCO3 2.295E+04** 
**112 MCO3 + RCO3 HCHO + CCO3 + CCHO + RO2R 2.295E+04**
**113 MCO3 + BZCO HCHO + CCO3 + BZO + R2O2 2.295E+04** 
**114 MCO3 + MCO3 2HCHO + 2CCO3 2.295E+04** 
**115 TBUO + NO2 RNO3 3.545E+04** 
**116 TBUO ACET + CXO2 5.923E+04** 
**117 BZO + NO2 NPHE 5.620E+04** 
**118 BZO + HO2 PHEN 2.200E+04** 
**119 BZO PHEN 6.000E-02** 
**120 BZNO + NO2 2XN 5.620E+04** 
**121 BZNO + HO2 NPHE 2.200E+04** 
**122 BZNO NPHE 6.000E-02** 
**123 HCHO 2HO2 + CO Photolysis** 
**124 HCHO CO Photolysis** 
**125 HCHO + OH HO2 + CO 1.359E+04** 
**126 HCHO + HO2 HCO3 1.167E+02** 
**127 HCO3 HO2 + HCHO 9.060E+03** 
**128 HCO3 + NO HC2H + NO2 + HO2 1.076E+04** 
**129 HCHO + NO3 HNO3 + HO2 + CO 8.477E-01** 
**130 CCHO + OH CCO3 2.341E+04** 
**131 CCHO CO + HO2 + CXO2 Photolysis** 
**132 CCHO + NO3 HNO3 + CCO3 4.027E+00** 
**133 RCHO + OH 0.034RO2R + 0.001RO2N + 0.965RCO3 + 0 .034CO + 0.034CCHO 2.954E+04** 
**134 RCHO CCHO + RO2R + CO + HO2 Photolysis** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-9 Reaction
Number Reactants Products k 298 1 (ppm –n min –1 )
**135 RCHO + NO3 HNO3 + RCO3 5.421E+00** 
**136 ACET + OH HCHO + CCO3 + R2O2 2.840E+02** 
**137 ACET CCO3 + CXO2 Photolysis** 

**138 MEK + OH 0.37RO2R + 0.042RO2N + 0.616R2O2 + 0.492CCO3 +**  **0.096RCO3 + 0.115HCHO + 0.482CCHO + 0.37RCHO 1.741E+03** 

**139 MEK CCO3 + CCHO + RO2R Photolysis** 
**140 MEOH + OH HCHO + HO2 1.351E+03** 
**141 COOH + OH 0.35HCHO + 0.35OH + 0.65CXO2 8.111E+0 3** 
**142 COOH HCHO + HO2 + OH Photolysis** 
**143 ROOH + OH RCHO + 0.34RO2R + 0.66OH 1.625E+04** 
**144 ROOH RCHO + HO2 + OH Photolysis** 
**145 GLY 2CO + 2HO2 Photolysis** 
**146 GLY HCHO + CO Photolysis** 
**147 GLY + OH 0.63HO2 + 1.26CO + 0.37RCO3 1.625E+04** 
**148 GLY + NO3 HNO3 + 0.63HO2 + 1.26CO + 0.37RCO3 1. 424E+00** 
**149 MGLY HO2 + CO + CCO3 Photolysis** 
**150 MGLY + OH CO + CCO3 2.216E+04** 
**151 MGLY + NO3 HNO3 + CO + CCO3 3.584E+00** 
**152 BACL 2CCO3 Photolysis** 
**153 PHEN + OH 0.24BZO + 0.76RO2R + 0.23GLY 3.885E+0 4** 
**154 PHEN + NO3 HNO3 + BZO 5.584E+03** 
**155 CRES + OH 0.24BZO + 0.76RO2R + 0.23MGLY 6.204E+ 04** 
**156 CRES + NO3 HNO3 + BZO 2.024E+04** 
**157 NPHE + NO3 HNO3 + BZNO 5.584E+03** 
**158 BALD + OH BZCO 1.906E+04** 
**159 BALD Photolysis** 
**160 BALD + NO3 HNO3 + BZCO 3.867E+00** 

**161 METH + OH 0.5RO2R + 0.416CO + 0.084HCHO + 0.416MEK + 0.084MGL**  **Y + 0.5MCO3**  **4.962E+04**

**162 METH + O3 0.008HO2 + 0.1RO2R + 0.208OH + 0.1RCO3 + 0.45CO +**  **0.2HCHO + 0.9MGLY + 0.333HC2H 1.670E-03** 

**163 METH + NO3 0.5HNO3 + 0.5RO2R + 0.5CO + 0.5MCO3 + 0.5XN 6.762E+00** 
**164 METH + O RCHO 9.365E+03** 

**165 METH 0.34HO2 + 0.33RO2R + 0.33OH + 0.67CCO3 + 0.67CO +**  **0.67HCHO + 0.33MCO3 Photolysis** 

**166 MVK + OH 0.3RO2R + 0.025RO2N + 0.675R2O2 + 0.675CCO3 + 0.3HC**  **HO + 0.675RCHO + 0.3MGLY**  **2.796E+04**

**167 MVK + O3 0.064HO2 + 0.05RO2R + 0.164OH + 0.05RCO3 + 0.475CO**  **+ 0.1HCHO + 0.95MGLY + 0.351HC2H**  **6.765E-03**

**168 MVK + O 0.45RCHO + 0.55MEK 6.382E+03**  **169 MVK 0.3CXO2 + 0.7CO + 0.7PROD + 0.3MCO3 Photoly sis** 

**170 ISPD + OH 0.67RO2R + 0.041RO2N + 0.289MCO3 + 0.336CO + 0.055H**  **CHO + 0.129CCHO + 0.013RCHO + 0.15MEK + 0.332PROD + 0.1**  **5GLY + 0.174MGLY**  **9.144E+04**

**171 ISPD + O3 0.4HO2 + 0.048RO2R + 0.048RCO3 + 0.285OH + 0.498CO**  **+ 0.125HCHO + 0.047CCHO + 0.21MEK + 0.023GLY + 0.742M**  **GLY + 0.1HC2H + 0.372RC2H**  **6.175E-03**

**172 ISPD + NO3 0.799RO2R + 0.051RO2N + 0.15MCO3 + 0.572CO + 0.15HN**  **O3 + 0.227HCHO + 0.218RCHO + 0.008MGLY + 0.572RNO3 +**  **0.278XN 1.477E+02** 

**173 ISPD 1.233HO2 + 0.467CCO3 + 0.3RCO3 + 1.233CO + 0.3HCHO**  **+ 0.467CCHO + 0.233MEK**  **Photolysis**

**174 PROD + OH 0.379HO2 + 0.473RO2R + 0.07RO2N + 0.029CCO3 + 0.049**  **RCO3 + 0.213HCHO + 0.084CCHO + 0.558RCHO + 0.115MEK +**  **0.329PROD 2.216E+04** 

**175 PROD 0.96RO2R + 0.04RO2N + 0.515R2O2 + 0.667CCO3 + 0.333**  **RCO3 + 0.506HCHO + 0.246CCHO + 0.71RCHO**  **Photolysis**

**176 RNO3 + OH 0.338NO2 + 0.113HO2 + 0.376RO2R + 0.173RO2N + 0.596**  **R2O2 + 0.01HCHO + 0.439CCHO + 0.213RCHO + 0.006ACET +**  **0.177MEK + 0.048PROD + 0.31RNO3 + 0.352XN 1.152E+04** 

**177 RNO3 NO2 + 0.341HO2 + 0.564RO2R + 0.095RO2N + 0.152R2O2**  **+ 0.134HCHO + 0.431CCHO + 0.147RCHO + 0.02ACET +**  **0.243MEK + 0.435PROD Photolysis** 

**178 DCB1 + OH RCHO + RO2R + CO 7.386E+04** 
**179 DCB1 + O3 1.5HO2 + 0.5OH + 1.5CO + GLY 2.954E-0 3** 
**180 DCB2 + OH R2O2 + RCHO + CCO3 7.386E+04** 
**181 DCB2 RO2R + 0.5CCO3 + 0.5HO2 + CO + R2O2 + 0.5G LY + 0.5MGLY Photolysis** 
**182 DCB3 + OH R2O2 + RCHO + CCO3 7.386E+04** 
**183 DCB3 RO2R + 0.5CCO3 + 0.5HO2 + CO + R2O2 + 0.5G LY + 0.5MGLY Photolysis** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-10 Reaction
Number Reactants Products k 298 1 (ppm –n min –1 )
**184 CH4 + OH CXO2 9.402E+00** 
**185 ETHE + OH RO2R + 1.61HCHO + 0.195CCHO 1.258E+04**
**186 ETHE + O3 0.12OH + 0.12HO2 + 0.5CO + HCHO + 0.3 7HC2H 2.346E-03** 
**187 ETHE + NO3 RO2R + RCHO + XN 3.022E-01** 

**188 ETHE + O 0.5HO2 + 0.2RO2R + 0.3CXO2 + 0.491CO + 0.191HCHO +**  **0.25CCHO + 0.009GLY 1.077E+03** 

**189 ISOP + OH 0.907RO2R + 0.093RO2N + 0.079R2O2 + 0.624HCHO +**  **0.23METH + 0.32MVK + 0.357ISPD 1.450E+05** 

**190 ISOP + O3 0.266OH + 0.066RO2R + 0.008RO2N + 0.126R2O2 + 0.192**  **MCO3 + 0.275CO + 0.592HCHO + 0.1PROD + 0.39METH + 0.16MV**  **K + 0.204HC2H + 0.15RC2H**  **1.897E-02**

**191 ISOP + NO3 0.187NO2 + 0.749RO2R + 0.064RO2N + 0.187R2O2 + 0.93**  **6ISPD + 0.813RNO3**  **9.958E+02**

**192 ISOP + O 0.01RO2N + 0.24R2O2 + 0.25CXO2 + 0.24MCO3 + 0.24HCH**  **O + 0.75PROD**  **5.318E+04**

**193 TERP + OH 0.75RO2R + 0.25RO2N + 0.5R2O2 + 0.276HCHO + 0.474RC**  **HO + 0.276PROD**  **1.221E+05**

**194 TERP + O3 0.567OH + 0.033HO2 + 0.031RO2R + 0.18RO2N + 0.729R2**  **O2 + 0.123CCO3 + 0.201RCO3 + 0.157CO + 0.235HCHO +**  **0.205RCHO + 0.13ACET + 0.276PROD + 0.001GLY + 0.031 BACL**  **+ 0.103HC2H + 0.189RC2H 1.014E-01** 

**195 TERP + NO3 0.474NO2 + 0.276RO2R + 0.25RO2N + 0.75R2O2 + 0.474R**  **CHO + 0.276RNO3 + 0.25XN**  **9.714E+03**

**196 TERP + O 0.147RCHO + 0.853PROD 4.830E+04** 
**197 ALK1 + OH RO2R + CCHO 3.752E+02** 

**198 ALK2 + OH 0.246OH + 0.121HO2 + 0.612RO2R + 0.021RO2N + 0.16CO**  **+ 0.039HCHO + 0.155RCHO + 0.417ACET + 0.248GLY +**  **0.121HC2H 1.533E+03** 

**199 ALK3 + OH 0.695RO2R + 0.07RO2N + 0.559R2O2 + 0.236TBUO +**  **0.026HCHO + 0.445CCHO + 0.122RCHO + 0.024ACET +**
**0.332MEK 3.515E+03** 

**200 ALK4 + OH 0.835RO2R + 0.143RO2N + 0.936R2O2 + 0.011CXO2 +**  **0.011CCO3 + 0.002CO + 0.024HCHO + 0.455CCHO + 0.244RCHO + 0.452ACET + 0.11MEK + 0.125PROD**  **6.486E+03**

**201 ALK5 + OH 0.653RO2R + 0.347RO2N + 0.948R2O2 + 0.026HCHO +**  **0.099CCHO + 0.204RCHO + 0.072ACET + 0.089MEK + 0.417PROD**  **1.378E+04**

**202 ARO1 + OH 0.224HO2 + 0.765RO2R + 0.011RO2N + 0.055PROD + 0.11**  **8GLY + 0.119MGLY + 0.017PHEN + 0.207CRES + 0.059BALD +**  **0.491DCB1 + 0.108DCB2 + 0.051DCB3 8.793E+03** 

**203 ARO2 + OH 0.187HO2 + 0.804RO2R + 0.009RO2N + 0.097GLY + 0.287**  **MGLY + 0.087BACL + 0.187CRES + 0.05BALD + 0.561DCB1 +**  **0.099DCB2 + 0.093DCB3 3.900E+04** 

**204 OLE1 + OH 0.91RO2R + 0.09RO2N + 0.205R2O2 + 0.732HCHO +**  **0.294CCHO + 0.497RCHO + 0.005ACET + 0.119PROD 4.770E+04** 

**205 OLE1 + O3 0.155OH + 0.056HO2 + 0.022RO2R + 0.001RO2N + 0.076C**  **XO2 + 0.345CO + 0.5HCHO + 0.154CCHO + 0.363RCHO + 0.001AC**  **ET + 0.215PROD + 0.185HC2H + 0.05CO2H + 0.119RC2H**  **1.574E-02**

**206 OLE1 + NO3 0.824RO2R + 0.176RO2N + 0.488R2O2 + 0.009CCHO +**  **0.037RCHO + 0.024ACET + 0.511RNO3 + 0.489XN 1.862E+01** 

**207 OLE1 + O 0.45RCHO + 0.437MEK + 0.113PROD 7.208E +03** 

**208 OLE2 + OH 0.918RO2R + 0.082RO2N + 0.001R2O2 + 0.244HCHO +**  **0.732CCHO + 0.511RCHO + 0.127ACET + 0.072MEK + 0.061BALD + 0.025METH + 0.025ISPD**  **9.339E+04**

**209 OLE2 + O3 0.378OH + 0.003HO2 + 0.033RO2R + 0.002RO2N + 0.137R**  **2O2 + 0.197CXO2 + 0.137CCO3 + 0.006RCO3 + 0.265CO + 0.269**  **HCHO + 0.456CCHO + 0.305RCHO + 0.045ACET + 0.026MEK +**  **0.006PROD + 0.042BALD + 0.026METH + 0.073HC2H + 0.129CO2H + 0.303RC2H**  **1.576E-01**

**210 OLE2 + NO3 0.391NO2 + 0.442RO2R + 0.136RO2N + 0.711R2O2 + 0.03**  **CXO2 + 0.079HCHO + 0.507CCHO + 0.151RCHO + 0.102ACET +**  **0.001MEK + 0.015BALD + 0.048MVK + 0.321RNO3 + 0.288 XN**  **1.072E+03**

**211 OLE2 + O 0.013HO2 + 0.012RO2R + 0.001RO2N + 0.012CO + 0.069R**  **CHO + 0.659MEK + 0.259PROD + 0.012METH**  **3.087E+04**

**212 ETOH + OH 0.05RO2R + 0.95HO2 + 0.081HCHO + 0.96 CCHO 4.919E+03** 

**213 MTBE + OH 0.743RO2R + 0.078RO2N + 0.381R2O2 + 0.162CXO2 +**  **0.016TBUO + 0.234HCHO + 0.024ACET + 0.719MEK + 0.007PROD**  **4.343E+03**

**214 MBUT + OH 0.935RO2R + 0.065RO2N + 0.311HCHO + 0.624CCHO +**  **0.311RCHO + 0.624ACET 9.346E+04** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-11 Reaction
Number Reactants Products k 298 1 (ppm –n min –1 )

**215 MBUT + O3 0.099OH + 0.099HO2 + 0.365CO + 0.3HCHO + 0.7RCHO +**  **0.015ACET + 0.259HC2H + 0.285RC2H 1.374E-02** 

**216 MBUT + NO3 0.935RO2R + 0.065RO2N + 0.935RCHO + 0.934ACET + RNO3 1.760E+01** 
**217 MBUT + O 0.45RCHO + 0.55MEK 2.969E+04** 
**Notes:**

**1 Rate constants are shown for 298 K and 1 atmospher**  **e in units of ppm and minutes. See the CAMx chemis try**  **parameters file for the temperature and pressure de pendencies (Section 5).** 

Table 3-6. **Species in the SAPRC99 mechanism (Mechanism 5).** Species Name Description **NO2 Nitrogen dioxide**
**NO Nitric oxide**
**O3 Ozone O Oxygen atom in the O**  **3 (P) electronic state** 
**NO3 Nitrate radical N2O5 Dinitrogen pentoxide** 
**HNO3 Nitric acid O1D Oxygen atom in the O**  **1 (D) electronic state**  **OH Hydroxyl radical**
**HONO Nitrous acid HO2 Hydroperoxy radical** 
**CO Carbon monoxide HNO4 Peroxynitric acid**  **HO2H Hydrogen peroxide**
**SO2 Sulfur dioxide SULF Sulfuric acid (gaseous)**  **CXO2 Methylperoxy radical HCHO Formaldehyde**  **COOH Methyl hydroperoxide**
**MEOH Methanol RO2R Organic peroxy radical converting NO to NO2 wi**  **th HO2 production ROOH Organic hydroperoxide**  **R2O2 Organic peroxy radical converting NO to NO2 RO2N Organic peroxy radical converting NO to organi**  **c nitrate RNO3 Organic nitrate**  **MEK Methylethyl ketone PROD Organic product**  **CCO3 Acetylperoxy radical PAN Peroxyacetyl nitrate**  **CO3H Peroxyacetic acid**
**CO2H Acetic acid RCO3 Higher acylperoxy radical (based on propylpero**  **xy) PAN2 Higher peroxyacetyl nitrate (based on peroxypr**  **opyl nitrate) CCHO Acetaldehyde**  **RC3H Higher percarboxylic acid RC2H Higher carboxylic acid**  **BZCO Peroxybenxoyl radical PBZN Peroxybenzoyl nitrate**  **BZO Phenoxy radical MCO3 Peroxyacyl radical from methacrolein**  **MPAN PAN compound from methacrolein TBUO Tertiary-butoxy radical** 
**ACET Acetone NPHE Nitrophenol** 
**PHEN Phenol**CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-12 Species Name Description **BZNO Nitrophenol reaction product XN Other nitrogen containing product**  **HCO3 Adduct from HO2 plus formaldehyde**
**HC2H Formic acid RCHO Higher aldehyde (based on propionaldehyde)**  **GLY Glyoxal MGLY Methylglyoxal**  **BACL Biacetyl**
**CRES Cresol BALD Benzaldehyde** 
**METH Methacrolein MVK Methylvinyl ketone**  **ISPD Isoprene product DCB1 Aromatic ring opening dicarbonyl product**  **DCB2 Aromatic ring opening dicarbonyl product DCB3 Aromatic ring opening dicarbonyl product** 
**ETHE Ethene ISOP Isoprene**  **TERP Terpene ALK1 Lumped alkane 1**  **ALK2 Lumped alkane 2 ALK3 Lumped alkane 3**  **ALK4 Lumped alkane 4 ALK5 Lumped alkane 5**  **ARO1 Lumped aromatic 1 ARO2 Lumped aromatic 2**  **OLE1 Lumped olefin 1**
**OLE2 Lumped olefin 2** 
**ETOH Ethanol MTBE Methyl tertiary butyl ether**  **MBUT Methylbutenol**CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-13 Table 3-7 **. Properties of VOC species in the SAPRC99 mechani sm: average carbon numbers,**  **molecular weights,** k **OH values (ppm**  **-1 min**  **-1 ) and maximum incremental reactivity (MIR) values**  **(mole O 3**  **/mole VOC).** Species Molecular Weight Carbon Number Average k OH 1 MIR
**ALK1 46.81 1.88 3.75E+02 0.39**
**ALK2 47.02 2.65 1.53E+03 0.80**
**ALK3 57.72 2.92 3.51E+03 2.19**
**ALK4 73.23 4.36 6.49E+03 3.11**
**ALK5 106.97 6.42 1.38E+04 4.95**
**ETHE 28.05 2 1.26E+04 5.44**
**OLE1 61.68 3.99 4.77E+04 9.73**
**OLE2 79.05 5.67 9.34E+04 10.98**
**ISOP 68.12 5 1.45E+05 12.62**
**TERP 136.24 10 1.22E+05 8.54**
**ARO1 100.47 7.27 8.79E+03 6.72**
**ARO2 113.93 8.58 3.90E+04 15.36**
**HCHO 30.03 1 1.36E+04 6.49**
**CCHO 44.05 2 2.34E+04 5.79**
**RCHO 67.32 3.66 2.95E+04 9.45**
**BALD 108.21 7.15**
**ACET 58.08 3**
**MEK 72.82 4.03**
**PROD 106.44 6.48**
**MEOH 32.04 1 1.35E+03 1.34**
**GLY 58.04 2**
**MGLY 72.06 3**
**BACL 101.31 6.37**
**PHEN 94.11 6**
**CRES 115.38 7.52**
**METH 56.06 3**
**MVK 112.17 7**
**ISPD 70.45 4.03**
**ETOH 46.07 2 4.92E+03 1.62**
**MTBE 88.15 5 4.34E+03 1.43**
**MBUT 86.13 5 9.35E+04 9.12**
**Notes:**

**1** k **OH values are given to assist in assigning VOC specie**  **s to SAPRC99 lumped species**

**.**CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-14 Table 3-8. **Reactions and rate constants for the CB05 mechani sm (Mechanism 6).** 

Reaction Number Reactants Products k 298 1 (ppm –n min –1 )
**1 NO2 NO + O Photolysis** 
**2 O + O2 + M O3 + M 2.220E-05** 
**3 O3 + NO NO2 2.888E+01** 
**4 O + NO2 NO 1.513E+04** 
**5 O + NO2 NO3 4.852E+03** 
**6 O + NO NO2 2.459E+03** 
**7 NO2 + O3 NO3 4.765E-02** 
**8 O3 O Photolysis** 
**9 O3 O1D Photolysis** 
**10 O1D + M O + M 4.368E+04** 
**11 O1D + H2O 2 OH 3.250E+05** 
**12 O3 + OH HO2 1.071E+02** 
**13 O3 + HO2 OH 2.853E+00** 
**14 NO3 NO2 + O Photolysis** 
**15 NO3 NO Photolysis** 
**16 NO3 + NO 2 NO2 3.920E+04** 
**17 NO3 + NO2 NO + NO2 9.691E-01** 
**18 NO3 + NO2 N2O5 1.742E+03** 
**19 N2O5 + H2O 2 HNO3 3.693E-07** 
**20 N2O5 + H2O + H2O 2 HNO3 6.554E-11** 
**21 N2O5 NO3 + NO2 3.168E+00** 
**22 NO + NO + O2 2 NO2 7.114E-10** 
**23 NO + NO2 + H2O 2 HONO 1.820E-11** 
**24 NO + OH HONO 1.094E+04** 
**25 HONO NO + OH Photolysis** 
**26 OH + HONO NO2 7.184E+03** 
**27 HONO + HONO NO + NO2 1.477E-05** 
**28 NO2 + OH HNO3 1.545E+04** 
**29 OH + HNO3 NO3 2.280E+02** 
**30 HO2 + NO OH + NO2 1.196E+04** 
**31 HO2 + NO2 PNA 0.000E+00** 
**32 PNA HO2 + NO2 0.000E+00** 
**33 OH + PNA NO2 0.000E+00** 
**34 HO2 + HO2 H2O2 4.319E+03** 
**35 HO2 + HO2 + H2O H2O2 2.397E-01** 
**36 H2O2 2 OH Photolysis** 
**37 OH + H2O2 HO2 2.504E+03** 
**38 O1D + H2 OH + HO2 1.625E+05** 
**39 OH + H2 HO2 9.887E+00** 
**40 OH + O HO2 4.861E+04** 
**41 OH + OH O 2.773E+03** 
**42 OH + OH H2O2 9.298E+03** 
**43 OH + HO2 1.641E+05** 
**44 HO2 + O OH 8.670E+04** 
**45 H2O2 + O OH + HO2 2.517E+00** 
**46 NO3 + O NO2 1.477E+04** 
**47 NO3 + OH HO2 + NO2 3.250E+04** 
**48 NO3 + HO2 HNO3 5.170E+03** 
**49 NO3 + O3 NO2 1.477E-02** 
**50 NO3 + NO3 2 NO2 3.375E-01** 
**51 PNA 0.61 HO2 + 0.61 NO2 + 0.39 OH + 0.39 NO3 Pho tolysis** 
**52 HNO3 OH + NO2 Photolysis** 
**53 N2O5 NO2 + NO3 Photolysis** 
**54 XO2 + NO NO2 1.307E+04** 
**55 XO2N + NO NTR 1.307E+04** 
**56 XO2 + HO2 ROOH 1.160E+04** 
**57 XO2N + HO2 ROOH 1.160E+04** 
**58 XO2 + XO2 1.005E+02** 
**59 XO2N + XO2N 1.005E+02** 
**60 XO2 + XO2N 1.005E+02** 
**61 NTR + OH HNO3 + HO2 + 0.33 FORM + 0.33 ALD2 + 0. 33 ALDX - 0.66 PAR 2.604E+02** 
**62 NTR NO2 + HO2 + 0.33 FORM + 0.33 ALD2 + 0.33 ALD X - 0.66 PAR Photolysis** 
**63 SO2 + OH SULF + HO2 1.313E+03** 
**64 ROOH + OH XO2 + 0.5 ALD2 + 0.5 ALDX 8.412E+03**  **65 ROOH OH + HO2 + 0.5 ALD2 + 0.5 ALDX Photolysis**
**66 OH + CO HO2 3.376E+02** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-15 Reaction
Number Reactants Products k 298 1 (ppm –n min –1 )
**67 OH + CH4 MEO2 9.371E+00** 
**68 MEO2 + NO FORM + HO2 + NO2 1.132E+04** 
**69 MEO2 + HO2 MEPX 7.503E+03** 
**70 MEO2 + MEO2 1.37 FORM + 0.74 HO2 + 0.63 MEOH 5.1 94E+02** 
**71 MEPX + OH 0.7 MEO2 + 0.3 XO2 + 0.3 HO2 1.098E+04**
**72 MEPX FORM + HO2 + OH Photolysis** 
**73 MEOH + OH FORM + HO2 1.346E+03** 
**74 FORM + OH HO2 + CO 1.330E+04** 
**75 FORM 2 HO2 + CO Photolysis** 
**76 FORM CO Photolysis** 
**77 FORM + O OH + HO2 + CO 2.340E+02** 
**78 FORM + NO3 HNO3 + HO2 + CO 8.568E-01** 
**79 FORM + HO2 HCO3 1.167E+02** 
**80 HCO3 FORM + HO2 9.054E+03** 
**81 HCO3 + NO FACD + NO2 + HO2 8.272E+03** 
**82 HCO3 + HO2 MEPX 1.860E+04** 
**83 FACD + OH HO2 5.909E+02** 
**84 ALD2 + O C2O3 + OH 6.631E+02** 
**85 ALD2 + OH C2O3 2.047E+04** 
**86 ALD2 + NO3 C2O3 + HNO3 3.520E+00** 
**87 ALD2 MEO2 + CO + HO2 Photolysis** 
**88 C2O3 + NO MEO2 + NO2 2.961E+04** 
**89 C2O3 + NO2 PAN 1.548E+04** 
**90 PAN C2O3 + NO2 1.986E-02** 
**91 PAN C2O3 + NO2 Photolysis** 
**92 C2O3 + HO2 0.8 PACD + 0.2 AACD + 0.2 O3 2.082E+0 4** 
**93 C2O3 + MEO2 0.9 MEO2 + 0.9 HO2 + FORM + 0.1 AACD 1.582E+04** 
**94 C2O3 + XO2 0.9 MEO2 + 0.1 AACD 2.356E+04** 
**95 C2O3 + C2O3 2 MEO2 2.294E+04** 
**96 PACD + OH C2O3 1.156E+03** 
**97 PACD MEO2 + OH Photolysis** 
**98 AACD + OH MEO2 1.156E+03** 
**99 ALDX + O CXO3 + OH 1.036E+03** 
**100 ALDX + OH CXO3 2.932E+04** 
**101 ALDX + NO3 CXO3 + HNO3 9.602E+00** 
**102 ALDX MEO2 + CO + HO2 Photolysis** 
**103 CXO3 + NO ALD2 + NO2 + HO2 + XO2 3.098E+04** 
**104 CXO3 + NO2 PANX 1.548E+04** 
**105 PANX CXO3 + NO2 1.986E-02** 
**106 PANX CXO3 + NO2 Photolysis** 
**107 PANX + OH ALD2 + NO2 4.432E+02** 
**108 CXO3 + HO2 0.8 PACD + 0.2 AACD + 0.2 O3 2.082E+ 04** 
**109 CXO3 + MEO2 0.9 ALD2 + 0.9 XO2 + HO2 + 0.1 AACD + 0.1 FORM 1.582E+04** 
**110 CXO3 + XO2 0.9 ALD2 + 0.1 AACD 2.356E+04** 
**111 CXO3 + CXO3 2 ALD2 + 2 XO2 + 2 HO2 2.294E+04** 
**112 CXO3 + C2O3 MEO2 + XO2 + HO2 + ALD2 2.294E+04** 
**113 OH + ETHA 0.991 ALD2 + 0.991 XO2 + 0.009 XO2N + HO2 3.545E+02** 
**114 OH + ETOH HO2 + 0.9 ALD2 + 0.05 ALDX + 0.1 FORM + 0.1 XO2 4.711E+03** 

**115 PAR + OH 0.87 XO2 + 0.13 XO2N + 0.11 HO2 + 0.06 ALD2 - 0.11**  **PAR + 0.76 ROR + 0.05 ALDX**  **1.196E+03**

**116 ROR 0.96 XO2 + 0.6 ALD2 + 0.94 HO2 - 2.1 PAR + 0.04 XO2**  **N + 0.02 ROR + 0.5 ALDX**  **1.316E+05**

**117 ROR HO2 9.600E+04** 
**118 ROR + NO2 NTR 2.216E+04** 

**119 O + OLE 0.2 ALD2 + 0.3 ALDX + 0.3 HO2 + 0.2 XO2 + 0.2 CO +**  **0.2 FORM + 0.01 XO2N + 0.2 PAR + 0.1 OH**  **5.773E+03**

**120 OH + OLE 0.8 FORM + 0.33 ALD2 + 0.62 ALDX + 0.8 XO2 + 0.95 H**  **O2 - 0.7**
**PAR 4.727E+04** 

**121 O3 + OLE 0.18 ALD2 + 0.74 FORM + 0.32 ALDX + 0.22 XO2 + 0.1**  **OH + 0.33 CO + 0.44 HO2 - PAR**  **1.634E-02**

**122 NO3 + OLE NO2 + FORM + 0.91 XO2 + 0.09 XO2N + 0.56 ALDX + 0.3**  **5 ALD2 - PAR**  **7.356E-01**

**123 O + ETH FORM + 1.7 HO2 + CO + 0.7 XO2 + 0.3 OH 1.077E+03** 
**124 OH + ETH XO2 + 1.56 FORM + 0.22 ALDX + HO2 1.20 4E+04** 
**125 O3 + ETH FORM + 0.63 CO + 0.13 HO2 + 0.13 OH + 0.37 FACD 2.605E-03** 
**126 NO3 + ETH NO2 + XO2 + 2 FORM 3.096E-01** 

**127 IOLE + O 1.24 ALD2 + 0.66 ALDX + 0.1 HO2 + 0.1 XO2 + 0.1 CO**  **+ 0.1**
**PAR 3.398E+04** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-16 Reaction
Number Reactants Products k 298 1 (ppm –n min –1 )
**128 IOLE + OH 1.3 ALD2 + 0.7 ALDX + HO2 + XO2 9.354 E+04** 

**129 IOLE + O3 0.65 ALD2 + 0.35 ALDX + 0.25 FORM + 0.25 CO + 0.5 O**  **+ 0.5 OH + 0.5 HO2**  **3.095E-01**

**130 IOLE + NO3 1.18 ALD2 + 0.64 ALDX + HO2 + NO2 5. 731E+02** 
**131 TOL + OH 0.44 HO2 + 0.08 XO2 + 0.36 CRES + 0.56 TO2 8.751E+03** 
**132 TO2 + NO 0.9 NO2 + 0.9 HO2 + 0.9 OPEN + 0.1 NTR 1.196E+04** 
**133 TO2 CRES + HO2 2.520E+02** 
**134 OH + CRES 0.4 CRO + 0.6 XO2 + 0.6 HO2 + 0.3 OPE N 6.056E+04** 
**135 CRES + NO3 CRO + HNO3 3.250E+04** 
**136 CRO + NO2 NTR 2.068E+04** 
**137 CRO + HO2 CRES 8.125E+03** 
**138 OPEN C2O3 + HO2 + CO Photolysis** 
**139 OPEN + OH XO2 + 2 CO + 2 HO2 + C2O3 + FORM 4.43 2E+04** 

**140 OPEN + O3 0.03 ALDX + 0.62 C2O3 + 0.7 FORM + 0.03 XO2 + 0.69**  **CO + 0.08 OH + 0.76 HO2 + 0.2 MGLY**  **1.490E-02**

**141 OH + XYL 0.7 HO2 + 0.5 XO2 + 0.2 CRES + 0.8 MGL Y + 1.1 PAR + 0.3 TO2 3.706E+04** 
**142 OH + MGLY XO2 + C2O3 2.511E+04** 
**143 MGLY C2O3 + HO2 + CO Photolysis** 

**144 O + ISOP 0.75 ISPD + 0.5 FORM + 0.25 XO2 + 0.25 HO2 + 0.25 C**  **XO3 +**
**0.25 PAR 5.318E+04** 

**145 OH + ISOP 0.912 ISPD + 0.629 FORM + 0.991 XO2 + 0.912 HO2 + 0**  **.088 XO2N**  **1.473E+05**

**146 O3 + ISOP 0.65 ISPD + 0.6 FORM + 0.2 XO2 + 0.066 HO2 + 0.266**  **OH + 0.2 CXO3 + 0.15 ALDX + 0.35 PAR + 0.066 CO**  **1.898E-02**

**147 NO3 + ISOP 0.2 ISPD + 0.8 NTR + XO2 + 0.8 HO2 + 0.2 NO2 + 0.8**  **ALDX + 2.4 PAR**  **9.954E+02**

**148 NO2 + ISOP 0.2 ISPD + 0.8 NTR + XO2 + 0.8 HO2 + 0.2 NO + 0.8 A**  **LDX + 2.4**
**PAR 2.216E-04** 

**149 OH + ISPD 1.565 PAR + 0.167 FORM + 0.713 XO2 + 0.503 HO2 + 0.**  **334 CO + 0.168 MGLY + 0.252 ALD2 + 0.21 C2O3 + 0.25 CXO3 +**  **0.12**
**ALDX 4.963E+04** 

**150 O3 + ISPD 0.114 C2O3 + 0.15 FORM + 0.85 MGLY + 0.154 HO2 + 0.**  **268 OH + 0.064 XO2 + 0.02 ALD2 + 0.36 PAR + 0.225 CO**  **1.049E-02**

**151 NO3 + ISPD 0.357 ALDX + 0.282 FORM + 1.282 PAR + 0.925 HO2 + 0**  **.643 CO + 0.85 NTR + 0.075 CXO3 + 0.075 XO2 + 0.15 HNO3**  **1.477E+00**

**152 ISPD 0.333 CO + 0.067 ALD2 + 0.9 FORM + 0.832 PAR + 1.03**  **3 HO2 + 0.7 XO2 + 0.967 C2O3**  **Photolysis**

**153 TERP + O 0.15 ALDX + 5.12 PAR 5.318E+04** 

**154 TERP + OH 0.75 HO2 + 1.25 XO2 + 0.25 XO2N + 0.28 FORM + 1.66**  **PAR + 0.47 ALDX**  **9.997E+04**

**155 TERP + O3 0.57 OH + 0.07 HO2 + 0.76 XO2 + 0.18 XO2N + 0.24 FO**  **RM + 0.001 CO + 7 PAR + 0.21 ALDX + 0.39 CXO3**  **1.128E-01**

**156 TERP + NO3 0.47 NO2 + 0.28 HO2 + 1.03 XO2 + 0.25 XO2N + 0.47 A**  **LDX + 0.53 NTR**  **9.833E+03**

**Notes:**

**1 Rate constants are shown for 298 K and 1 atmospher**  **e in units of ppm and minutes. See the CAMx chemis try parameters**  **file for the temperature and pressure dependencies (Section 5).** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-17

Table 3-9. **Species in the CB05 mechanism (Mechanism 6).** Species Name Description
**NO Nitric oxide NO2 Nitrogen dioxide** 
**O3 Ozone O Oxygen atom in the O**  **3 (P) electronic state**  **O1D Oxygen atom in the O 1**  **(D) electronic state OH Hydroxyl radical**  **HO2 Hydroperoxy radical H2O2 Hydrogen peroxide** 
**NO3 Nitrate radical N2O5 Dinitrogen pentoxide** 
**HONO Nitrous acid**
**HNO3 Nitric acid PNA Peroxynitric acid (HNO**  **4 )** 
**CO Carbon monoxide FORM Formaldehyde**  **ALD2 Acetaldehyde C2O3 Acetylperoxy radical**  **HCO3 Adduct from HO2 plus formaldehyde PAN Peroxyacetyl nitrate**  **ALDX Propionaldehyde and higher aldehydes CXO3 C3 and higher acylperoxy radicals**  **PANX C3 and higher peroxyacyl nitrates XO2 NO to NO2 conversion from alkylperoxy (RO**  **2 ) radical**  **XO2N NO to organic nitrate conversion from alkylper oxy (RO**  **2 ) radical**  **NTR Organic nitrate (RNO 3**  **)**
**ETOH Ethanol MEO2 Methylperoxy radical** 
**MEOH Methanol MEPX Methylhydroperoxide** 
**FACD Formic acid**
**ETHA Ethane ROOH Higher organic peroxide**  **AACD Acetic and higher carboxylic acids PACD Peroxyacetic and higher peroxycarboxylic acids**  **PAR Paraffin carbon bond (C-C) ROR Secondary alkoxy radical** 
**ETH Ethene OLE Terminal olefin carbon bond (R-C=C)**  **IOLE Internal olefin carbon bond (R-C=C-R) ISOP Isoprene**  **ISPD Isoprene product (lumped methacrolein, methyl vinyl ketone, etc.)**  **TERP Terpene TOL Toluene and other monoalkyl aromatics**  **XYL Xylene and other polyalkyl aromatics CRES Cresol and higher molecular weight phenols**  **TO2 Toluene-hydroxyl radical adduct OPEN Aromatic ring opening product**  **CRO Methylphenoxy radical MGLY Methylglyoxal and other aromatic products** 
**SO2 Sulfur dioxide SULF Sulfuric acid (gaseous)** CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-18 Table 3-10 **. Properties of VOC species in the CB05 mechanism: average carbon numbers,**  **molecular weights,** k **OH values (ppm**  **-1 min**  **-1 ) and maximum incremental reactivity (MIR) values**  **(mole O 3**  **/mole VOC).**

Species Carbon Number Average k OH MIR
**PAR 1 1.20E+03 0.319**
**ETHA 2 3.54E+02 0.109**
**MEOH 1 1.35E+03 0.361**
**ETOH 2 4.71E+03 1.042**
**ETH 2 1.20E+04 4.370**
**OLE 2 4.73E+04 8.235**
**IOLE 4 9.35E+04 13.109**
**ISOP 5 1.47E+05 11.555**
**TERP 10 1.00E+05 8.824**
**FORM 1 1.33E+04 4.496**
**ALD2 2 2.05E+04 4.454**
**ALDX 2 2.93E+04 6.807**
**TOL 7 8.75E+03 2.941**
**XYL 8 3.71E+04 14.790**

Photolysis Rates

The rates of atmospheric photolysis reactions depen d upon solar irradiance and therefore are
sensitive to the amount of solar radiation transmit ted through the atmosphere as well as reflected from the earth’s surface (albedo). Photolysis rate s are externally derived assuming clear sky conditions as a function of five parameters: solar zenith angle, altitude, total ozone column, surface albedo, and atmospheric turbidity. The rat es are provided to CAMx as a large lookup table that spans the range of conditions for each o f the five dimensions. This table may be developed using the CAMx photolysis rate pre-proces sor, which incorporates the Tropospheric Ultraviolet and Visible (TUV) radiative transfer mo del (Madronich; 1993, 2002). The CAMx version of TUV is modified to output photolysis rat e information in a format directly compatible with CAMx for the CB4, SAPRC99, or CB05 chemical me chanism. See Sections 5 and 11 for more information on developing photolysis inputs.

Effects of Clouds

Photochemical production of ozone is strongly influ enced by the presence of clouds, which can
both attenuate and enhance the actinic flux of ultr aviolet (UV) and visible radiation responsible for photolysis. The specific radiative impact depe nds on many factors, including cloud height and depth, water content and phase (i.e., liquid dr oplets or ice crystals), and cloud form (i.e., widespread stratus or isolated cumulus). CAMx incl udes two optional schemes to adjust clearsky photolysis rates for the presence of clouds. T he default approach uses a fast in-line version of TUV (Emery et al., 2010) to calculate photolysis adjustment profiles through each cloudy grid column. The original CAMx cloud adjustment approac h has been retained as an option, which is based on the parameterization developed for the Reg ional Acid Deposition Model (RADM; Chang, et al., 1987). Both options are further des cribed below.

Both the default TUV and original RADM cloud adjust ments utilize cloud optical depth fields provided by the CAMx cloud/rain file. Note that si nce CAMx v5.30, the definition of cloud optical depth in the cloud/rain file has changed to accommodate the TUV cloud adjustmentCAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-19 scheme. Therefore, cloud/rain files specifically generated for CAMx v5.30 and later will not work correctly in earlier versions of CAMx.

The CAMx meteorological interface pre-processors ge nerate cloud water and optical depth fields from the variable fields present in the raw meteoro logical output files. Cloud optical depth τ is calculated in each model grid cell according to the approach of DelGenio et al. (1996) and Voulgarkakis et al. (2009), which satisfactorily ap proximates the effects of random cloud overlap according to



## ( ) 2/3


### 2 3

c d w 

### F

r z L

ρ τ 

### ∆

=

where L is cloud liquid water content (g/m 3 ), ∆ z is the cell depth containing cloud water, ρ w is the density of liquid water (10 6 g/m 3 ), and F
c is fractional cloud cover. The mean cloud droplet
radius r

d is not dependent on water phase, and is assumed t o be a typical tropospheric value of

1.5 × 10 -5 m for liquid cloud water.

In-Line TUV Adjustment

The default cloud adjustment employs a streamlined version of TUV for the single purpose of calculating cloud adjustment factors to scale prede termined clear-sky photolysis rates. The inline TUV is run twice for each cloudy grid column: once for clear conditions and again with clouds to derive a vertical profile of the cloudy:c lear actinic flux ratio. This ratio is then applie d as a multiplicative factor to the clear-sky value i n each grid cell. This approach maintains accuracy in the calculation of clear-sky photolysis rates, while allowing clouds to be directly involved in radiative transfer calculations through each grid column.

The in-line TUV solver calculates and passes back p hotolysis adjustment factor profiles through each grid column containing at least one cloudy lay er. TUV is not called for completely clear grid columns, and the adjustment profile in such ca ses is set to a uniform value of 1. TUV includes a calculation of integrated atmospheric de nsity above the top CAMx layer, based on the U.S. standard atmosphere, so that atmospheric atten uation of the UV stream is properly calculated entering the model top. TUV assumes con stant Mie scattering parameters for clouds: a single scattering albedo of 0.99, and an asymmetr y factor of 0.85.

Other aspects of the in-line TUV model were substan tially streamlined to minimize runtimes. First, radiative calculations are performed for onl y a single representative wavelength (350 nm). Second, since absorption by gases occurs in rather narrow UV bands relative to the broad-band influence of clouds, the absorption from oxygen, oz one, nitrogen dioxide and sulfur dioxide were removed. Third, the extraterrestrial flux was not needed as it cancels out in the calculation of the cloudy:clear ratio. Fourth, the plane-parallel ver sion of the delta-Eddington approach was used in lieu of the more complex and expensive pseudo-sp herical geometry. Finally, TUV is provided a zero profile of aerosol optical depth, but this c apability is maintained as a place-holder for future updates. Currently haze is included in the clear-sky calculations performed in the fullscience TUV pre-processor. Preliminary tests again st the full-science TUV showed that the
streamlined version resulted in less than 1% differ ences in actinic flux ratio (Emery et al., 2010).CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-20 RADM Adjustment

The original RADM adjustment to clear-sky photolysi s rates J clear is given by

A
c is the vertical cloud attenuation factor, which is calculated separately for above clouds and within/below clouds according to:



## ( )

layer cloud below within t A layer cloud above t A

r c r c 

### /

, cos 6.1 ,

cos 1 1

φ φ



### = − + =

where φ is solar zenith angle ( ≤ 60 ° ) and t r is the energy transmission coefficient for normall y incident light. For zenith angles greater than 60 ° , values for A
c at φ = 60 ° are used. The expression for the energy transmission coefficient is derived from delta-Eddington theory and is given by

where f is the scattering phase function asymmetry factor, given by Chang et al. (1987) as 0.86,
and Τ is total vertically integrated cloud optical depth ( = ∑ τ [z] ) above the current grid cell to the top of the troposphere. For optically thin clouds ( Τ < 5), no adjustment is made to the photolysis

rates.

Effects of Surface Albedo including Snow Cover

Photolysis rates also depend the amount of solar ra diation reflected from the earth’s surface (albedo). The CAMx albedo/haze/ozone column (AHO) input file specifies the UV albedo for each grid cell. The UV albedo generally is calcula ted from land cover data using characteristic values by land cover type as shown in Tables 5-5 an d 5-6. This approach results in UV albedos that fall in the range 0.04 to 0.08 and are constan t in time, because the land cover data are constant in time. Analyses of reflected UV radiati on recorded in satellite data (Herman and Celarier, 1997) report similar UV albedo values in the range 0.02-0.08 for surfaces not covered by snow.

CAMx can optionally treat the impact of snow cover on photolysis rates (and deposition). Snow is much more reflective than other types of surface s and so snow cover will enhance the rates of photolysis reactions. Since most photolysis reacti ons depend upon ultra-violet radiation it is important to characterize the effect of snow cover on UV albedo. Measurements of the UV albedo for Antarctic snow report values of 0.96 to 0.98 over a wide range of snow grain sizes and solar zenith angles. However, these high values sh ould be considered an upper limit to the reflectivity of snow-covered surfaces at the scale of grid cells because snow may not cover all surface features (e.g., in forests and urban areas) and because the snow may be dirty. For areas with snow cover, satellite climatology shows surfac e albedos spanning a wide range of values from 0.2 to nearly 1.0. Potential reasons for this wide range of values include coarse resolution (1 ° latitude by 1.25 ° longitude) such that snow-covered and snow-free su rfaces are combined, 

## ( ) f e t r

−

Τ + −

= Τ

−

1 3 4 5 

## ( ) 

## [ ]

clear

c c J A F J 1 1

− + =CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-21 differences between rough and smooth land cover typ es (e.g., forest vs. open range land), and differences between clean and dirty snow.

The CAMx AHO and photolysis rate input files classi fy grid cells to one of five albedo ranges. If snow cover data are provided in the AHO file, CAMx assigns the highest albedo value included in the AHO and photolysis rate input files for snow covered grid cells. Therefore, when providing snow-cover data to CAMx, the fifth (highe st) albedo range should be assigned a value representative of snow and the lower four albedo ra nges should represent surfaces without snowcover. We recommend a UV albedo of 0.5 for snow co vered surfaces because this is in the middle of the range reported by Herman and Celarier (1997) and because grid cells should be
defined as snow-covered if the fractional cover is greater than 0.5.

Gas-Phase Chemistry Solvers

Solving the time evolution of gas-phase chemistry r equires numerically integrating a set of ordinary differential equations (ODEs) and is among the most computationally expensive operations performed in a photochemical grid model. One reason for this is that the ODEs describing tropospheric chemistry are “stiff” – mea ning that the chemical species involved have widely varying production and/or decay times. The computational efficiency of the gas-phase chemistry solver strongly influences the overall ef ficiency of a grid model. CAMx includes three chemistry solvers that offer differing trade- offs between accuracy and efficiency.

The most accurate solution methods available for st iff ODEs are “Gear” type implicit solvers (Gear, 1971) such as LSODE (Hindmarsh, 1983). Gear solvers are stable when applied to “stiff” problems, such as gas-phase chemistry, but are gene rally too slow for routine use in grid model applications. Sun et al. (1994) developed an impli cit-explicit hybrid (IEH) method based on
LSODE that is accurate and much faster than LSODE. Hertel et al. (1993) developed an implementation of the Euler Backward Iterative (EBI ) method that is very efficient and also accurate because it utilizes explicit algebraic for mulae to solve several important groups of species (HOx, NOx, etc.) Hertel’s EBI solver is th e most efficient solver available in CAMx and has replaced the CMC solver used in CAMx versions p rior to 5.10.

LSODE

CAMx includes the double precision version of the L ivermore Solver for Ordinary Differential Equations (LSODE; Hindmarsh, 1983) distributed by t he Netlib repository of numerical algorithms (http://www.netlib.org/). LSODE is too slow for everyday use but valuable as a
reference method within CAMx. LSODE is based on Ge ar’s method with numerical refinements to improve efficiency and ease of use (Radhakrishna n and Hindmarsh, 1993). Gear methods (Gear, 1971) are implicit and employ backwards-diff erentiation formulae to step forward in time by taking multiple steps. The converged solutions at each step are saved in a history matrix and used to predict the next solution. Thus, LSODE mus t initially take short time steps to build the history matrix and may then take progressively long er steps. LSODE is most efficient for long integration times (and inefficient for short integr ation times) and therefore least burdensome for coarse grid model applications that have relatively long coupling times between gas-phase chemistry and other processes, e.g., advection.CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-22 User-supplied information required by LSODE is esse ntially the error control parameters and the functions defining the system of ODEs, f (y,t), where y is the vector of species concentrati ons and t is time. Supplying a subroutine to evaluate the time derivatives of species concentrations ( f = dy/dt) is mandatory. Supplying a function to evalu ate the Jacobian matrix ( J = d f /dy) is optional since, if not supplied, LSODE can derive a numerica l Jacobian by finite difference between repeated evaluations of f . Supplying an algebraic Jacobian ensures accuracy , although a numerical Jacobian may be equally accurate if adequ ate precision (e.g., double precision) is employed. Supplying an algebraic Jacobian is more efficient when J is sparse, but for condensed
mechanisms such as CB05 J is not sparse and the numerical Jacobian method is faster. CAMx
uses the numerical Jacobian method with a relative error tolerance of 10 -7 and an absolute error
tolerance of 10 -10

.

IEH Solver

A stiffly-stable solver, such as LSODE, is only nee ded to solve the chemical species that have time constants comparable to or shorter than the in tegration time and which interact strongly with each other. Slowly changing species may be tr eated as constant for the duration of an LSODE solution and then solved outside LSODE using a simple explicit method. Sun et al. (1993) called this approach an implicit-explicit hy brid method and Chock et al. (1994) showed that IEH methods offer useful computational efficie ncy and accuracy. The IEH method is faster than LSODE because fewer species are solved using t he expensive Gear method, but less accurate than LSODE because of inaccuracy in the ex plicit method. The speed and accuracy of an IEH method is influenced by the designation of “ fast” species to be solved implicitly and “slow” species to be solved explicitly.

The CAMx IEH solver designates species as fast, slo w or steady-state (this designation is performed when mechanisms are implemented for the I EH solver and can’t be changed by the user). Steady-state species are solved by assuming that they are in a quasi-stationary state (dy/dt = 0) with respect to other species and, therefore, that y = P/L, where P and L are respectively the production and loss rates for species y. Oxygen at oms, O( 1 D) and O( 3 P), are the most rapidly changing species in tropospheric chemistry mechanis ms. Solving O-atoms with a steady-state assumption reduces stiffness and permits the single -precision version of LSODE to be used by the IEH solver. To ensure IEH solver accuracy with single precision, the Jacobian matrix is supplied algebraically to LSODE (see discussion, ab ove). The relative error tolerance for
LSODE within IEH is 5 x10 -5 and the absolute error tolerance is 10 -7 . The O-atom concentrations are recalculated every time the deri vatives ( f ) or Jacobian ( J ) are evaluated for LSODE. Slow species concentrations are calculated using rates evaluated at the middle of each IEH step. The IEH step size is limited to a few mi nutes for the slow species integration and multiple IEH steps are taken sequentially if needed

.

EBI Solver

The backward Euler method solves concentrations (y) as y(t+h) = y(t) + h f , where f is the time derivative of species concentrations ( f = dy/dt) evaluated at t+h. The method must be iter ated to convergence in y(t+h) because species concentration s are interdependent. The basic EBI method is not efficient for stiff problems such as troposp heric chemistry because convergence is slow and the step size (h) must be short. Hertel et al. (1993) greatly improved the efficiency andCAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-23 accuracy of the method by developing analytical sol utions to the EBI equation for groups of species that are strongly coupled (e.g. HOx and NOx /O 3 ). Time steps of up to 3 minutes are taken by the Hertel EBI solver in CAMx.

Chemical Mechanism Compiler

Each of the CAMx chemistry solvers relies upon equa tions that define the species time derivatives ( f

= dy/dt) and, in some cases, the Jacobian matrix ( J = d f /dy) for each chemical mechanism. The preprocessor portion of the CAMx ch emical mechanism compiler (CMC) reads a listing of the mechanism reactions and generates the Fortran statements for the rate equations and Jacobian matrix. This approach avoids potentia l errors resulting from coding equations by
hand.

For the IEH and EBI chemistry solvers, the CMC prep rocessor requires user input that determines how each species is solved. The CMC pre processor could be used to generate many different solver implementations for a given mechan ism, only some of which will be robust. The CMC preprocessor is not distributed with CAMx to av oid potential problems with User’s generating invalid chemistry solvers that produce u nreliable results.

3.2 AEROSOL CHEMISTRY

Aerosol chemistry processes are currently linked to the CB4 (Mechanism 4), CB05 (Mechanism 6), and SAPRC99 (Mechanism 5) gas-phase chemistry m echanisms (Table 3-1). CAMx provides two options for treating aerosol size dist ributions: the CF scheme and CMU scheme.
The CF scheme divides the size distribution into tw o static modes (coarse and fine). Primary species are modeled as fine and/or coarse particles , while all secondary species are modeled as fine particles only. The CMU scheme employs a sect ional approach that dynamically models the
size evolution of each aerosol constituent across a number of fixed size sections. The user
defines the number of size sections and their size ranges in the chemistry parameters file (Section 5). Aerosol water is explicitly treated in both CF and CMU options; it affects aerosol size and density. The gas-phase chemistry of Mechanisms 4, 5 and 6 may be run alone (no aerosols) by simply setting the aerosol option keyword to “NONE” (rather than “CF” or “CMU”) and omitting the entire list of aerosol species in the chemistry parameters file (see Section 5).

Additional Gas-Phase Species

When either the CF or CMU aerosol option is selecte d, the following gas-phase species are added to model gas-aerosol interactions:

1) Ammonia (NH3) as a precursor for inorganic aerosol. 2) Gaseous sulfuric acid (SULF) as a precursor to sulf ate aerosol. 3) Hydrogen chloride (HCL) as a product of acidified s ea salt aerosol and aerosol precursor. 4) Condensable organic gases (CG1-CG7) as precursors t o secondary organic aerosol (SOA)
formation. 5) Organic gases that form CG species via oxidation re actions, namely toluene (TOLA), xylene (XYLA), monoterpenes (TRP), sesquiterpenes ( SQT) and isoprene (ISP).CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-24 SOA Precursors

Secondary organic aerosol (SOA) species exist in eq uilibrium with condensable gasses (CG) that can be produced by VOC oxidation:

VOC + oxidant → CG ↔ SOA

CG formation from VOC oxidation reactions (Table 3- 11) is handled within the SOA module rather than the main gas-phase chemistry, as descri bed below. This approach has the following advantages: (1) separates the VOC precursors and lu mping schemes for oxidant chemistry and SOA formation, e.g., for aromatics, different lumpi ng schemes may be appropriate for oxidant and SOA formation: (2) allows the same SOA mechani sm to be used with different oxidant mechanisms: (3) allows inclusion of SOA precursors without explicitly defining oxidant reactions, e.g., sesquiterpenes are explicit in the SOA module but their oxidant formation may be represented by surrogate species.

Table 3-11. **SOA precursor reactions included in CAMx.** 

Precursor Reaction CG Products 1 k 298 2 (ppm – 1 min – 1 ) Anthropogenic
**Toluenes TOLA + OH 0.044 CG1 + 0.085 CG2 8.75E+03 Xylenes XYLA + OH 0.027 CG1 + 0.118 CG2 3.71E+04**  Biogenic **Isoprene ISP + O none 5.32E+04** 

**ISP + OH 0.015 CG3 + 0.12 CG4 1.47E+05**
**ISP + O3 none 1.90E-02** 
**ISP + NO3 none 9.96E+02**  **Terpenes TRP + O 0.065 CG5 + 0.29 CG6 4.12E+04**

**TRP + OH 0.065 CG5 + 0.29 CG6 7.76E+04**
**TRP + O3 0.065 CG5 + 0.29 CG6 1.33E-01**
**TRP + NO3 0.065 CG5 + 0.29 CG6 9.18E+03 Sesquiterpenes SQT + OH 0.85 CG7 2.91E+05** 

**SQT + O3 0.85 CG7 1.71E+01 SQT + NO3 0.85 CG7 2.81E+04** 
**Notes:**

**1 Yield values are in ppm/ppm.** 

**2 Rate constants are shown for 298 K and 1 atmospher**  **e in ppm -1**  **min -1** 

**.**

Emissions of SOA precursors must be provided separa tely from the emissions of oxidant precursors, e.g., isoprene emissions must be specia ted both as ISOP for oxidant chemistry and ISP for SOA chemistry. This “double counting” of e missions is correct because the species (e.g., ISOP and ISP) serve different purposes. Ideally, e missions processors will develop VOC speciation schemes that suit both oxidant and SOA m odeling. In the absence of more refined information, emissions of SOA precursors may be set equal to emissions of oxidant precursors as follows: ISP = ISOP; TRP = TERP; TOLA = TOL or ARO1 ; XYLA = XYL or ARO2.

Some emitted VOCs are semi-volatile (SVOCs) and can condense directly to SOA, e.g., biogenic emissions of oxygenated VOCs. SOA formation from SVOC emissions may be accounted for by including the SVOC in the CAMx emissions as one of the CGs listed in Table 3-12. Choose aCAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-25 CG that has appropriate volatility properties, and account for any molecular weight difference between the SVOC and surrogate CG (moles CG emitted = moles SVOC x MW SVOC /MW CG ).
Table 3-12. **Properties of CG/SOA pairs.**

Species Molecular

Weight (g mole -1 ) Saturation

Concentration (μg m -3 at 298 K) Heat of vaporization (kJ mole -1 )

**CG1/SOA1 150 7.82 66.8**

**CG2/SOA2 150 227 66.8**

**CG3/SOA3 130 0.726 42**

**CG4/SOA4 130 136 42**

**CG5/SOA5 180 3.92 75.5**

**CG6/SOA6 180 55.8 75.5**

**CG7/SOA7 210 0 –**

**SOPA 220 – –**

**SOPB 220 – –**

Aerosol Processes

Aerosol processes are modeled using either the CF o r CMU algorithm, depending on how the aerosol option keyword is set in the chemistry para meters file. The CF and CMU aerosol chemistry algorithms both perform the following thr ee processes:

1) Aqueous sulfate and nitrate formation in resolved c loud water using the RADM aqueous chemistry algorithm (Chang et al., 1987). 2) Partitioning of condensable organic gases (CG1-CG7) to secondary organic aerosols (SOA1-SOA7) to form a condensed “organic solution p hase” using a semi-volatile, equilibrium scheme called SOAP (Strader et al., 199 9). 3) Partitioning of inorganic aerosol constituents (sul fate, nitrate, ammonium, sodium, and chloride) between the gas and aerosol phases using the ISORROPIA thermodynamic module (Nenes et al., 1998, 1999). 4) Uptake of nitric acid by calcium in soil dust parti cles is calculated external to
ISORROPIA.

The aqueous chemistry does not treat sub-grid scale clouds; clouds are assumed to either occupy the entirety of a grid cell volume, or be completel y absent from it. Cloudy grid cells are determined by cloud liquid water contents above a t hreshold of 0.05 g/m 3 as defined by input gridded cloud water fields. Aqueous chemistry is c alculated for each cloud grid cell at each model time step. The SOAP and ISORRIOPA equilibriu m algorithms are called on a unique aerosol “coupling” time step that is defined within the chemistry parameters file. By default, the aerosol coupling time step is 15 minutes, and this is used for all grids in a simulation regardless of the grid-specific driving time step.CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-26 The CAMx SOA module consists of two parts: gas-phas e oxidation chemistry that forms condensable gases (CGs), and equilibrium partitioni ng between gas and aerosol phases for each CG/SOA pair. Table 3-11 lists the oxidation reacti ons and CG products. The CG yields are expressed as ppm of CG formed per ppm of VOC reacte d so that CG concentrations follow the CAMx convention for gases and are in ppm. The phys ical properties of CG/SOA pairs are
shown in Table 3-12. The SOAs formed from the CGs a re in units of μg/m 3 as are all other aerosol species.

Studies are starting to elucidate mechanisms for po lymerization reactions in organic aerosol phases. Polymerization increases the molecular wei ght of the condensed aerosol and reduces the volatility. Detailed descriptions of polymerizatio n will likely depend upon the chemical composition of the organic and inorganic aerosol ph ases (e.g., aerosol acidity). Based on limited available data we assumed that the SOA polymerized to a non-volatile form (i.e., moved permanently to the aerosol phase) with a lifetime o f about 1 day (Kalberer et al., 2004). Polymerization slowly forms organic aerosol polymer s called SOPA (anthropogenic) and SOPB (biogenic).

Uptake of nitric acid on mineral dust particles is one of the pathways of particle nitrate formation. For example, calcium in soil dust parti cles reacts with nitric acid to form calcium nitrate. Based on IMPROVE observations, we estimat e 3.5% mass fraction of calcium in fine dust particles (FCRS), which is assumed to be fully neutralized by nitrate. Since the current ISORROPIA implementation in CAMx does not consider mineral cations other than sodium, nitrate uptake by calcium in soil dust is calculate d external to ISORROPIA. CAMx outputs total particulate nitrate, i.e., the sum of particle nitr ate determined by ISORROPIA and calcium
nitrate.

Table 3-13 shows the aerosol species that can be in cluded with the CF scheme. Some species must be present for this scheme (“Mandatory Species ”) to establish linkages between gas and aerosol phase chemistry. Other species are optiona l (meaning that they can be removed from the chemistry parameters input file), except that sodiu m and chloride must always be present or absent together (i.e., one cannot be present withou t the other). If sodium and chloride are not modeled then default background values are used wit hin CAMx.

In the CMU scheme, CRST is used to identify all pri mary inert material, which replaces the CF species of FPRM, FCRS, CPRM, and CCRS in Table 3-13 . Individual aerosol species names specify both the constituent and the size section u sing a set naming convention, e.g., PSO4_1 refers to particle sulfate in the section 1. The C MU scheme requires that the complete list of all aerosol species be present in the chemistry paramet ers file (i.e., no aerosol species are optional).CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-27

Table 3-13. **List of PM species for the CAMx CF aerosol option**

**.** Internal Label Name Mandatory Species
**PSO4 Sulfate X** 
**PNO3 Particulate Nitrate X** 
**PNH4 Particulate Ammonium X**
**PH2O Aerosol Water Content X SOA1-2 Anthropogenic SOA (see Table 3-12) X**  **SOA3-7 Biogenic SOA (see Table 3-12) X SOPA Polymerized anthropogenic SOA X**  **SOPB Polymerized biogenic SOA X**
**NA Sodium**
**PCL Particulate Chloride POA Primary Organic Aerosol**  **PEC Primary Elemental Carbon**
**FPRM Fine Other Primary (diameter**  ≤ **2.5 μm)**
**FCRS Fine Crustal (diameter**  ≤ **2.5 μm) CPRM Coarse Other Primary** 
**CCRS Coarse Crustal**

Aerosol Sectional Approach

Unlike the CF scheme, where each species is represe nted by a single particle size, the CMU scheme institutes an additional step to distribute the bulk aerosol concentrations from the aqueous/aerosol chemistry modules into each size bi n. For inorganic aerosol species, ISORROPIA yields the bulk aerosol composition at eq uilibrium. The aerosol size distribution is then determined by distributing the change in aeros ol mass during the time step into each size bin using a weighting factor (Pandis et al., 1993). Th e fraction f i,k of total flux of species i between gas and aerosol phases that condenses onto or evapo rates from an aerosol size section k is given by, 

# ( ) ( ) 

# ( ) ( ) 

# ∑ +

− +

−

=

k k eq i i i k k k

eq i i i k k
k i c

c D d N c c D d N f 1 2 1 2

, β π β π

,

where N

k and d k are the number and mean diameter of particles in t he section k , respectively, D i ,

c i , and c i eq are the diffusivity, bulk gas-phase concentration, and equilibrium concentration at the particle surface of species i , respectively, β k =2 λ / ad k , λ is the mean free path of air, and a is the accommodation coefficient (Pandis et al., 1993). A ssuming that c i eq is independent of particle size, the fraction is reduced to,



## ( ) ( ) k

k k

k k k k k
k i f

d N d N f =

+ +

= 

# ∑ 1

1

, β

β

.

The above weighting factor then depends on the surf ace area only.CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-28 For organic aerosols, SOAP calculates the bulk equi librium composition. Using the pseudo-ideal solution assumption (Strader et al., 1999), the eff ect of chemical composition of the particle can be incorporated into the weighting factor: 

# ( ) ( ) 

# ( ) ( ) 

# ∑ +

− +

−

=

k k i k i i k k k

i k i i k k

k i c

x c d N c x c d N f 1 1

*

, *

,

, β

β

.

where x i,k is the mole fraction of species i in the section k and c i * is the effective saturation concentration of species i . Since the fraction determines the composition of each size section, the above equation should be solved iteratively at each time step. Assuming that the chemical composition changes slowly during a time step, howe ver, the mole fractions can be approximated with those from the previous time step (Koo et al., 2003).

For cloud/fog droplets, RADM is used to calculate s ulfate and nitrate formation in the bulk aqueous phase. The added mass is then distributed into each size bin by a weighting factor which is based on the size-resolved aqueous chemist ry model simulation results (Fahey and Pandis, 2001).

In addition, mathematical descriptions of nucleatio n and coagulation have been added to the CMU scheme. The nucleation model employs the nucle ation rate parameterization proposed by Russell et al. (1994). The model assumes a linear sulfuric acid vapor concentration variation for the given time step of the aerosol module based on the initially available sulfuric acid and assigns all the nucleated mass to the first section of the distribution. The coagulation rate of the aerosol particles is modeled according to Seinfeld and Pandis (1998). A high-resolution distribution is used for the coagulation calculatio ns by subdividing each section of the original
distribution into 3 additional sections.

3.3 MERCURY CHEMISTRY

Mercury exists in the atmosphere as elemental mercu ry, Hg(0), and oxidized mercury, Hg(II) (Schroeder and Munthe, 1998). Hg(II) can be inorga nic (e.g., mercuric chloride, HgCl 2 ) or organic (e.g., methyl mercury, MeHg). It can also be present as particulate mercury (e.g., mercuric oxide, HgO, or mercury sulfide, HgS). In the global atmosphere, Hg(0) is the dominant form. Hg(II) typically constitutes a few percent o f total mercury and is predominantly in the gas phase. MeHg concentrations in the atmosphere are n egligible, about a factor of 10 to 30 lower than Hg(II) concentrations, based on analysis of pr ecipitation samples conducted by Frontier Geosciences, Inc. (e.g., Seigneur et al., 1998). H owever, Hg(II) becomes methylated in water bodies, where it can bioaccumulate in the food chai n. Hg(0) is sparingly soluble and is not removed significantly by wet deposition; its dry de position velocity is also believed to be low. As a result, Hg(0) has a long atmospheric lifetime, on the order of several months, that is governed by its oxidation to Hg(II). On the other hand, Hg(II) is quite soluble; it is consequently removed rapidly by wet and dry deposition processes . Particulate mercury, Hg(p), is mostly present in the fine fraction of particulate matter (PM 2.5 ), although some Hg(p) may be present in coarse PM (e.g., Landis and Keeler, 2002).CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-29 Known transformations among inorganic mercury speci es include the gas-phase oxidation of Hg(0) to Hg(II), the aqueous-phase oxidation of Hg( 0) to Hg(II), the aqueous-phase reduction of Hg(II) to Hg(0), various aqueous-phase equilibria o f Hg(II) species and the aqueous-phase adsorption of Hg(II) to PM. The inorganic mercury chemistry modules implemented in CAMx are based on our current knowledge of these transfo rmations. However, it should be noted that our knowledge of mercury chemistry continues to evo lve as new laboratory data become available, and the Hg chemical kinetic mechanisms i n CAMx and other models that treat the atmospheric fate of mercury will need to be revised accordingly.

Below, we provide additional details on the gas- an d aqueous-phase mercury chemistry mechanisms implemented in CAMx, and the implementat ion approach.

Gas-Phase Chemistry

The gas-phase transformations include the oxidation of Hg(0) to Hg(II) by ozone (O 3 ) (Hall, 1995), hydrogen chloride (HCl) (Hall and Bloom, 199 3), hydrogen peroxide (H 2 O 2 ) (Tokos et al., 1998), molecular chlorine (Cl 2 ) (Ariya et al., 2002), and hydroxyl radicals (OH) (Sommar et al., 2001):



### Hg(0) (g) + O 3 

### (g) → Hg(II) (g), k = 3 x 10 -20 

### cm 3 

### molec -1 

### s -1



### Hg(0) (g) + HCl (g) → HgCl 2 

### (g), k = 1 x 10 -19 

### cm 3 

### molec -1 

### s -1



### Hg(0) (g) + H 2 

### O 2 

### (g) → Hg(OH) 2 

### (g), k = 8.5 x 10 -19 

### cm 3 

### molec

-1 

### s -1



### Hg(0) (g) + Cl 2 

### (g) → HgCl 2 

### (g), k = 2.6 x 10 -18 

### cm 3 

### molec -1 

### s -1



### Hg(0) (g) + OH (g) → Hg(OH) 2 

### (g), k = 8 x 10 -14 

### cm 3 

### molec -1 

### s -1

The reaction rate constants provided above are for temperatures in the range of 20 to 25 o C; no temperature dependence information is available.

Aqueous-Phase Chemistry

The aqueous-phase chemistry includes the reduction of Hg(II) to Hg(0) via reaction with hydroperoxy radicals (HO 2 ) and by the formation of the sulfite complexes (at low HCl concentrations), HgSO 3 and Hg(SO 3 ) 2 2- , as well as the oxidation of Hg(0) to Hg(II) by di ssolved
O 3 , OH, and Cl 2 . Adsorption of Hg(II) species on atmospheric part iculate matter (PM) is simulated using an adsorption coefficient (K = 34 L g -1 ) recommended by Seigneur et al. (1998).
The relevant reactions are listed below. Note that the gas-liquid equilibria and ionic equilibria of the non-mercury species (e.g., SO 2 , O 3 ) involved in the mercury aqueous-phase chemistry a re not shown here, since they are identical to those in th e other CAMx mechanisms.

Gas-liquid Equilibria



### Hg(0) (g) ⇔ Hg(0) (aq), K = 0.11 M atm -1 (Sanemasa, 1975; Clever et al., 1985) 

### HgCl 2 

### (g) ⇔ HgCl 2 

### (aq), K = 1.4 x 10 6 

### M atm -1 (Lindqvist and Rodhe, 1985) 

### Hg(OH) 2 

### (g) ⇔ Hg(OH) 2 

### (aq), K = 1.2 x 10 4 

### M atm -1 (Lindqvist and Rodhe, 1985)CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-30 The Henry's Law constants listed above are for temp eratures in the range of 20 to 25 o C. Temperature dependence information is available for the Hg(0) Henry's Law constant but the validity of this information for temperatures below 0 o C is not established.

Aqueous-phase Equilibria



### HgCl 2 

### (aq) ⇔ Hg 2 + 

### + 2Cl - 

### , K = 10 -14 

### M 2 (Sillen and Martel, 1964) 

### Hg(OH) 2 

### (aq) ⇔ Hg 2 + 

### + 2OH

- 

### , K = 10 -22 

### M 2 (Sillen and Martel, 1964)


### Hg 2 + 

### + SO 3

2- 

### ⇔ HgSO 3 

### , K = 2.1 x 10 13 

### M -1 (van Loon et al., 2001)


### HgSO 3 

### + SO 3

2- 

### ⇔ Hg(SO 3 

### ) 2 2- 

### , K = 1 x 10 10 

### M -1 (van Loon et al., 2001)

Adsorption of Hg(II) on PM



### Hg(II) (aq) ⇔ H(II) (p), K = 34 L g -1 (Seigneur et al., 1998)

Aqueous-phase Kinetics



### Hg(0) (aq) + O 3 

### (aq) → Hg 2 + 

### , k = 4.7 x 10 7 

### M -1 

### s -1 (Munthe, 1992) 

### Hg(0) (aq) + OH (aq) → Hg 2 + 

### , k = 2 x 10 9 

### M -1 

### s -1 (Lin and Pehkonen, 1997) 

### HgSO 3 

### (aq) → Hg(0) (aq), k = 0.0106 s -1 (van Loon et al., 2000) 

### Hg(II) (aq) + HO 2 

### (aq) → Hg(0) (aq), k = 1.7 x 10 4 

### M -1 

### s -1 (Pehkonen and Lin, 1998) 

### Hg(0) (aq) + HOCl (aq) → Hg 2 + 

### , k = 2.09 x 10 6 

### M -1 

### s -1 (Lin and Pehkonen, 1998) 

### Hg(0) (aq) + OCl - 

### → Hg 2 + 

### , k = 1.99 x 10 6 

### M -1 

### s -1 (Lin and Pehkonen, 1998)

In the last two reactions listed above, HOCl and OC l - come from the dissolution and subsequent dissociation of molecular chlorine (Cl 2 ). Note that Hg(II) (aq) refers to all divalent Hg species in solution (i.e., Hg 2+ + HgCl 2 (aq) + Hg(OH) 2 (aq) + HgSO 3 + Hg(SO 3 ) 2 2- ).

The rate constants listed for the aqueous-phase kin etics are for temperatures in the range of 20 to
25 o C. Temperature dependence information is available for the HgSO 3 reduction reaction.

As mentioned previously, the gas- and aqueous-phase Hg transformations presented above represent the current state of the science (Ryabosh apko et al., 2002; Seigneur et al., 2001a, 2003a), but our knowledge of mercury chemistry cont inues to evolve. For example, Gardfeldt and Johnson (2003) have recently challenged the aqu eous-phase reduction of Hg(II) to Hg(0) by
dissolved HO 2 , suggesting that this pathway is unimportant. The re also seems to be some circumstantial evidence of reduction of Hg(II) to H g(0) in power plant plumes from various experimental studies that is not accounted for in c urrent treatments of Hg chemistry (e.g., Edgerton et al., 2001; Seigneur et al., 2001b). Ad ditional details are provided in a recent scoping study for mercury deposition conducted for the Midw est Regional Planning Organization by Seigneur et al. (2003b).CAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-31 Implementation Approach

The approach used to implement the mercury transfor mation pathways, discussed above, into CAMx is based on the assumption that the mercury sp ecies concentrations are much smaller than those of the species with which they react. Thus, the concentrations of the non-mercury species can be assumed to be constant during the mercury ch emistry calculations and analytical solutions are available for both the gas-phase and aqueous-ph ase conversions.

The mercury chemistry discussed in the previous sec tions requires the concentrations of the following non-mercury species: O 3 , H 2 O 2 , OH, SO 2 , HO 2 , Cl 2 , HCl, and atmospheric particulate matter (PM). The concentrations of most of these s pecies are available from CAMx. However,
Cl 2 and HCl are not explicitly simulated because the e missions data required to model these species are generally inadequate or unavailable. T hus, we specify typical vertical profiles of HCl
and Cl 2 concentrations. The Cl 2 concentrations are prescribed to be non-zero over oceans and zero elsewhere. Also, daytime Cl 2 concentrations are lower than nighttime values to account for
the fact that Cl 2 is photolyzed during the day. The zenith angle is used for the determination of night/day. A 2-D array of integer values (1 if oce an, 0 if not) is used to determine if the grid column is predominantly over ocean. This array is initialized at the beginning of the simulation from an input file and is specific for the modeling domain and grid.

The mercury aqueous-phase chemistry module also req uires the specification of cloud liquid water content (LWC) and cloudwater pH. Both these variables are available from CAMx – the mercury aqueous-phase chemistry module is invoked a fter the CAMx PM aqueous-phase chemistry calculations are performed, so the cloudw ater pH has already been calculated. Note that the PM aqueous-phase module (based on the RADM aqueous-phase chemistry module that is also used in Models-3/CMAQ) does not explicitly simulate the cloud chemistry of OH and
HO 2 radicals. The concentrations of these radicals ca n be reduced by their heterogeneous chemistry within clouds (e.g., Jacob, 2000; Jaegle et al., 2001). In the CAMx implementation, we account for this by reducing the concentrations of OH and HO 2 radicals by factors of 2 and 10, respectively.

Chemistry Parameters for Mercury

The mercury chemistry module requires total PM conc entrations, so mercury can only be modeled in conjunction with PM chemistry. In CAMx, PM chemistry is modeled using Mechanisms 4, 5 and 6. Mercury chemistry is select ed by invoking Mechanism 4, 5 or 6 and including mercury species among the list of modeled species. The CAMx mercury species

names are:

HG0 – elemental gaseous mercury, or Hg(0) HG2 – reactive gaseous mercury, or Hg(II) HGP – primary particulate mercury, or Hg(P)

CAMx requires that all three or none of these speci es be included in a simulation. Therefore, mercury chemistry is not required for PM modeling, but if mercury chemistry is selected then all three mercury species must be modeled. All of the rate constants and equilibrium constants for the mercury chemistry module are hard-coded and so no mercury reaction rate data are includedCAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-32 in the chemistry parameters input file (see Section 5). This is similar to the RADM aqueous chemistry and ISORROPIA inorganic aerosol equilibri um modules.

Several physical properties of the mercury species must be specified on the chemistry parameters file (see Figure 5-2a). The physical properties sp ecified for the gas species (Henry’s Law, molecular diffusivity, surface reactivity) influenc e the deposition characteristics. The Henry
constant for HG2 is assumed to be similar to that o f HNO 3 because these two gases have similar solubility. The HG2 species represents HgCl 2 and Hg(OH) 2 . The Henry constant for the former
is 1.4E+06 M atm-1 and for the latter it is 1.2E+04 M atm -1 . No temperature dependence is assumed for the HG2 Henry constant because there is no information available to determine this. The molecular diffusivity ratio for HG2 was calcula ted as the average for HgCl 2 and Hg(OH) 2

. The surface reactivity parameter is set to zero for strong acids, such as HNO 3 , that have a strong tendency to stick to surfaces. Setting the surface reactivity to zero overrides the surface resistance calculated in the Wesely (1989) depositi on algorithm and forces the surface resistance to zero. The reactivity parameter for HG2 is set t o 1 so that surface resistance will be calculated based on the Henry constant using Wesely’s algorith m.

The dry deposition of HG0 is set to zero by choosin g a very low Henry constant (similar to CO). This is based on the assumption that background emi ssions and dry deposition of HG0 balance each other over the modeling domain. This assumpti on is justified by the fact that the atmospheric lifetime of HG0 (about 1 year) greatly exceeds its residence time (days to weeks) within a regional modeling domain. Since backgroun d emissions of HG0 are not included in the mercury emissions inventory, this means that the dr y deposition of HG0 should be ignored. In CAMx, setting a Henry constant of smaller the 1.0E- 08 M atm -1 results in zero dry and wet deposition for the species. The HG0 Henry constant is set to 1.0E-10 M atm -1

.

3.4 SIMPLE CHEMISTRY VIA MECHANISM 10

CAMx offers 5 different chemical mechanisms for ozo ne and particulate matter modeling. However, the minimum level of chemistry is the CB4 mechanism for ozone with 37 species and 96 reactions. Using CB4 requires significant effor t to prepare all of the input data, particularly with regard to emissions. There are many cases whe n air pollution problems could be investigated with a much simpler chemistry scheme. An example of this would include modeling SO 2 from a few specific sources over a relatively smal l region, and treating conversion to sulfate by assuming a representative decay rate. An approach was necessary to provide a simple and flexible scheme that can be used for mod eling chemical reactions other than the ozone or secondary PM reactions treated by the full -science mechanisms.

The new simple chemistry scheme is selected by spec ifying a mechanism ID of 10 in the chemistry parameters input file. The specific chem ical reactions to be modeled must be set in
the subroutine chem10.f , which resides in the main source code directory. The user must modify this subroutine to implement whatever reacti ons are desired. Follow the guidelines for implementing reactions that are included in the sub routine. This approach requires some knowledge from the user, but also provides complete flexibility in implementing reactions.

The simple chemistry has been tested using a modeli ng database for primary PM species in Cairo, Egypt (personal communication from Mr. Usama Faramawy, Egyptian Environmental Affairs Agency). Results of the tests are shown in Figures 3-1 and 3-2. A simple chemicalCAMx v5.30 User’s Guide, December 2010

Chemistry Mechanisms

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_03_chemistr y.docx 3-33 reaction was implemented where the species PMT deca yed to PM2 with a first-order rate constant of 10% per hour. These species were selec ted because PMT has high emissions and PM2 has low emissions. This modeling is not intend ed to represent any actual chemical reactions for PM species in Cairo, but rather is ju st a hypothetical testing scenario. The expected
result is that the chemical reaction will lower the concentration of PMT and raise the concentration of PM2. Figure 3-1 shows the expecte d result, that the PMT concentration is lowered by the reaction. The reduction in PMT beco mes proportionately greater at longer
downwind distances because there has been more time for reaction to take place. Figure 3-2 shows the complementary increase in PM2 due to chem ical production.

No Chemical Decay Chemical Decay

Figure 3-1. **Impact of chemical decay on the concentration of the donor species PMT.** 

No Chemical Production Chemical Production

Figure 3-2. **Impact of chemical production on the concentratio n of the recipient species PM2.** CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-1 4. PLUME-IN-GRID SUBMODEL

Photochemistry is a highly non-linear problem becau se chemical reaction rates among most compounds depend upon their ambient concentrations. In Eulerian air quality models, ambient concentrations depend on how well the modeling grid resolves emissions, transport, and chemical history. Thus, grid resolution plays a vi tal role in the ability of the model to properly characterize photochemical conditions. Increasing resolution should in theory lead to a better model as the time/space discretization tends toward a continuum. However, practical and theoretical considerations suggest that the lower l imit on horizontal grid spacing is about 1000 meters for Eulerian air quality models such as CAMx . Nevertheless, even higher resolution is often necessary to adequately simulate chemistry wi thin concentrated point source plumes.

As a result, many modern Eulerian models contain a Plume-in-Grid sub-model that tracks individual plume segments (or puffs) in a Lagrangia n sense, accounting for plume-scale dispersion and chemical evolution, until such time as puff mass can be adequately represented within the larger grid model framework. At this po int the puff mass is added to the grid system as a virtual source, and that mass is then carried by the core grid model processes. It is important to understand that the inclusion of a Lagrangian pu ff model within an Eulerian grid model is a
forced construct. The formulations of the two mode ling approaches are fundamentally different and there is no theoretically “correct” methodology . This explains the variety of Plume-in-Grid methodologies that are in use today.

There are two schools of thought regarding the appr opriate times at which to transfer puff mass into the host model grid system. The first primari ly focuses on the physical size of puffs relative to grid cells. It is assumed that once puffs are l arge enough to be adequately resolved by a grid,
their mass should be added to the host cell. The t wo principle problems associated with this approach are deciding what the appropriate size cri teria are to be, and how to distribute puff mass into the grid system. The second school focus es on the chemical maturity of the puffs (Gillani and Pleim, 1996), and the potential perfor mance effects of dumping immature plume mixtures into a grid system. The main problem seen with this approach is identifying chemically mature plume mixtures within an ever-evolving puff relative to ever-evolving grid chemistry. Puffs may reach a stage of chemical maturity after very long simulation periods (say 24 hours), however it is unrealistic to assume that puffs will maintain physical coherence over such a long period given the cumulative effects of wind shears and mixing gradients. In summary, neither of the above approaches is completely satisfactory and we strongly believe that further research is needed to develop new approaches to PiG modeling th at adequately address both chemical maturity and physical size considerations.

Two CAMx Plume-in-Grid (PiG) approaches are availab le that provide different levels of chemical complexity. Both approaches share common design features for puff initialization, puff structure, transport, and growth. They deviate in how they treat chemistry and when they transfer mass from puffs to grid cells. This secti on details the structure and functionality of both options.

The first PiG approach is aimed at treating the ear ly chemical evolution of large NO
x plumes when mostly inorganic gas-phase reactions are opera tive. The method is called the G reatly

R educed E xecution a nd S implified D ynamics (GREASD) PiG. The streamlining employed in this version was based upon our experiences in deve loping, utilizing, and studying the responsesCAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-2 of more detailed PiG algorithms for large NOx plume s. The GREASD PiG model works with the Ozone and Particulate Source Apportionment Tech nology (OSAT/PSAT). This is possible because of the simplified approach used in GREASD P iG and because compatibility with the source apportionment Probing Tools was an explicit design objective.

The second approach treats the full suite of gas-ph ase photochemistry for all types of point sources and so by necessity incorporates much more chemical complexity than the first. This CAMx plume treatment is called the I ncremental R eactions for O rganics and N Ox (IRON) PiG. Chemical processes are simulated within each plume segment using an “incremental chemistry” approach, whereby puffs carry the incremental contr ibutions of the puff relative to the grid concentrations. Incremental puff concentrations ca n be positive or negative, depending upon the species and stage of plume evolution. A similar ch emistry approach is used in the SCICHEM Lagrangian model (EPRI, 2000). IRON PiG supports t he Reactive Tracer (RTRAC) Probing Tool. However, the incremental chemistry technique in IRON PiG is sufficiently complex as to prohibit the implementation of source apportionment Probing Tools. Furthermore, IRON PiG
does not treat PM.

4.1 CAMx PiG FORMULATION

Basic Puff Structure and Diffusive Growth

Both GREASD and IRON PiG sub-models share a common physical structure and growth algorithm. A stream of plume segments (puffs) is r eleased from a designated point source. Each puff possesses a longitudinal length and directiona l orientation defined by the separation of a leading and a trailing point. Initial separation o f these points is determined by the wind vector at final plume rise. Each point is then subsequently and independently transported through the gridded wind fields, which directly accounts for pu ff stretching and changes to centerline orientation due to deforming shears. The official "position" of each puff is defined by the center point of each puff between the endpoints. This pos ition defines the grid cell in which the puff
resides for the calculation of diffusion and chemis try.

Like other puff models, the shape of each puff is c haracterized by a spread tensor, which is
defined from a set of Gaussian standard deviations (so-called “sigmas”) along the three spatial axes ( σ

x , σ y , σ z ). Diffusive growth is defined by the growth in th ese sigma values. The total
cross-sectional width extends ±1.5 σ y from puff centerline. The limits of ±1.5 σ y result in an average concentration across the Gaussian distribut ion that nearly equals a uniformly mixed concentration across that distance. Similarly, the total cross-sectional depth extends ±1.5 σ z from puff centerline (with limits placed on depth by the ground and by capping stable layers aloft). The total longitudinal length is the distance betwe en the puff endpoints ±1.5 σ x . Horizontal area and total volume are calculated using the formulae for an ellipse. Figure 4-1 presents a schematic representation of each puff in horizontal cross-section.

We have developed an explicit solution approach for puff growth that shares SCICHEM theory and concepts (EPRI, 2000), but includes some simpli fications. SCICHEM solves predictive spatial moment equations with second-order closure that relate the evolution of the puff spread tensor ( σ

ij = σ i ×σ j ) to resolved mean shears and turbulent velocity st atistics.CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-3 Figure 4-1. Schematic representation of CAMx PiG puff shape i n the horizontal. Directional orientation of the puff is arbitrary, and evolves d uring the age of the puff according to wind direction, shears and diffusive growth along its tr ajectory.

The Reynolds-averaged second-moment transport equat ion is given as

where 

### u is the mean wind vector component, the primed valu es represent turbulent fluctuations from the mean, and the angle brackets denote integr als over space. The Reynolds averaging process always introduces higher-order fluctuation correlations, and this is given by the turbulent
flux moments c

u x ′ ′ ′ , where c

u ′ ′ represents the turbulent flux of concentration. I t is these last

two diffusion terms that SCICHEM solves in its seco nd-order closer scheme.

For sub-puff scale turbulence, SCICHEM employs the restriction that the only off-diagonal component of c

u x ′ ′ ′ to be considered is the symmetric horizontal term ( i = x , j = y ), and then

only for the large-scale (meso to synoptic) contrib ution to puff deformation when puff scale reaches such dimensions. In CAMx, we ignore this o ff-diagonal flux moment term altogether since puff mass is ultimately introduced to the gri d when puff size is at the grid scale (1-50 km in practically all applications), and thus puffs never reach spatial scales at which this term becomes important. SCICHEM also makes the assumption that the horizontal turbulence is isotropic,

c v y c u x ′′′
= ′′ ′ . This results in a single diffusivity equation fo r both x and y directions, and a

single diffusivity for the z direction:

Q c v y K K y x ′′′

= =

Q c w z K z ′′ ′

= P (

l e a d )

P ( t r a i l ) y 1

. 5 x

1

. 5

Q c u x
Q c u x

x u

x u

dt d i j j i

k i jk

k j

ik ij ′′ ′

+ ′ ′ ′

+ ∂ ∂
+ ∂ ∂

= σ σ σCAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-4 In our approach for CAMx, we have adopted the SCICH EM second-order tendency equations to model the time-evolution of puff turbulent flux mom ents (represented by diffusivities K
x =K y and

K
z ) and their contribution to the evolution of puff s pread (represented by the diagonal components of the puff spread tensor, σ x 2

, σ y 2 and σ

z 2 ). Puff spread is defined for puff depth ( σ z ), puff width ( σ

y ), and puff length ( σ x ). We account for the effects of wind shears in th e evolution of lateral and longitudinal spread, but a ssume that the evolution of vertical spread is solely the result of turbulent fluxes.

The resulting two Reynolds-averaged second-moment t ransport equations for CAMx PiG are:

where D is total horizontal deformation of horizontal wind (see Section 2.5). S

is the vertical shear of horizontal wind, which is broken down into puff-relative lateral and longitudinal components:

The difference between θ and φ represents the relative angle between the puff’s l ongitudinal orientation and the direction of vertical shear; a cross-puff shear results in more lateral spread while along-puff shear results in more longitudinal spread.

The SCICHEM tendency equation for the horizontal tu rbulent flux moment is

where A = 0.75, q 2 = v

v ′′ , and Λ is the horizontal turbulent length scale. Separat e equations are given for two different boundary layer turbulence s cales (shear- and buoyancy-produced), such
that

Within the surface-based boundary layer, the horizo ntal velocity variance is given by x y z y y y K S D dt d 2

2 2 2 2

+

+ = σ

σ σ σ z z K dt d 2 2

= σ

c

v y q A Qq c v y dt d

′′′ Λ − = ′′′ 2

buoyancy

shear c

v y c v y c v y ′′′ + ′′′
= ′′′



## ( )

i shear z

z u q /

1 5.2 2

* 2

− = 

## ( ) 

## [ ]

i buoyancy z z w q / exp 5.1 1 13 .0 2

* 2 + = 

## ( ) 2/1 2 2

cos 

      

+

− = dz

dv

dz du S y φ

θ x

x z x x x K S D dt d 2

2 2 2 2

+

+ = σ

σ σ σ



## ( ) 2/1 2 2 sin 

      

+

− = dz

dv

dz du S y φ

θCAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-5 where u * is the friction velocity, w * is the convective velocity scale, z is height above the surface,
and z i is the height of the surface-based boundary layer. The horizontal turbulent length scale is given by

In the stable boundary layer, only the shear compon ents of q 2 and Λ are applied. Above the boundary layer, SCICHEM applies rough approximation s for the velocity variance and turbulent length scale: q 2 = 0.25 m 2 /s 2 , and Λ = 1000 m.

The SCICHEM tendency equation for the vertical turb ulent flux moment is

where q v 2 = w

w ′ ′

, Λ
v is the vertical turbulent length scale, and K
z eq is the equilibrium diffusivity. Whereas a specific equation for K
z eq is formulated for SCICHEM, we have chosen to speci fy the value of this parameter from the gridded fields of vertical diffusivity in CAMx. Again SCICHEM gives separate equations for shear- and buo yancy-produced turbulence scales.

Within the surface-based boundary layer, the vertic al velocity variance is given by

The vertical turbulent length scale for both shear and buoyancy is equal to Λ shear given above for horizontal length scale. Above the boundary layer, SCICHEM again applies rough approximations for the velocity variance and turbul ent length scale: q v 2 = 0.01 m 2 /s 2 , and Λ
v = 10 m.

The external variables needed by PiG to complete th e dispersion calculations include z i , u * and

w * . All of these are available from an internal modu le in CAMx that calculates these boundary layer similarity theory parameters. Thus, no addit ional parameters are needed to be input to the
model.

Puff Transport

A fresh set of new puffs are released from all PiG point sources within the modeling domain for the duration of the smallest time step among the ma ster and all nested grids. The length of each puff is determined by the combination of the mean t otal wind speed at the height of final plume rise and time step. Limits are placed on maximum e xtruded length based on half the finest resolution in the given simulation. If winds and t ime-steps are such that the maximum allowed 

## ( ) ( ) 2

2 2 65

.0 1

3.0 1 1

z

z i shear + = Λ

i

buoyancy z

3.0 = Λ



# ( )

c w z QK q A c w z dt d
eq

z

v v ′′

′

− Λ = ′′ ′



## ( )

i shear v z

z u q /

1 5.1 2

* 2

− =


## ( ) ( )

i i buoyancy v z z z z w q / 05 .1 / 1.1 3/2 2

* 2

− =CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-6 length is violated, then several puffs are automati cally extruded from a given stack per time step.
The user can also set a maximum time interval of re lease if more puffs (better plume resolution)
are desired over the default automation. The orien tation of the puff length is along the total wind vector. Total puff volume is determined by stack v olumetric flow rate in conjunction with growth due to turbulent entrainment following the S CICHEM approach. Initially, σ x = σ y and σ

z values are explicitly calculated from this entrainm ent calculation.

The effects of resolved wind shear on plume deforma tion (i.e., at plume scales larger than individual puff scales) are treated using a “chaine d puff” approach (Figure 4-2). Points at the leading and trailing edges of the puff centerline a re individually transported through the gridded wind fields, which directly accounts for puff stret ching and changes to centerline orientation due to deforming shears. Since puffs can extend over m ultiple layers, layer density-weighted average wind components are determined for each end point based on the vertical coverage of the puff.

Figure 4-2. Plan-view schematic representation of a chain of PiG puffs emitted from a point source into an evolving gridded wind field. The re d line connected by dots represents puff centerlines, with dots representing leading and tra iling points of each puff. The CAMx computational grid is denoted by the blue lines.

The “chain” aspect means that at least initially (a s puffs are extruded from the stack) the trailing point of a puff emitted at time t will be the leading point of a puff emitted at tim e t+dt

. However, as the puffs are advected downstream, the leading point of one puff will deviate from the trailing point the puff behind it due to evolvi ng puff depth and wind fields. Puff volume is conserved in convergent/divergent wind fields. Puf f endpoints may move closer together or further apart, in wind fields that are slowing or a ccelerating downstream. We compute puff endpoint separation changes and then adjust puff width s and depths to maintain constant puff StackCAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-7 volume. The change in computed puff endpoint spaci ng defines puff length tendencies, then puff depth tendencies are computed from grid-resolved ve rtical wind shear ( dw/dz ), and finally we determine the puff width tendencies required to con serve puff volume.

The official “position” of each puff is defined by the center point of each puff between the endpoints. This position defines which grid domain and grid cell the puff resides for the calculation of diffusion and chemistry. This defin ition holds even if the puff is sufficiently long that the endpoints are in different grid cells (or even different grid domains if near a domain boundary). With our definition for termination whe n horizontal area approaches grid cell area, the extents of the puff length should not extend ac ross more than two grid cells.

4.2 IRON PiG

The IRON PiG model incorporates a complete treatmen t of gas-phase chemistry in point source pollutant plumes, while secondarily adding addition al features central for treating toxic pollutants not normally carried by the standard CAM x chemical mechanisms. Several approaches have been developed to treat photochemis try within point source plume models. One of the more elegant methodologies is the incrementa l chemistry idea embodied in the SCICHEM model (EPRI, 2000). However, we found that the imp lementation of incremental chemistry in SCICHEM is very complex, especially in its handling of the chemistry of overlapping puffs, and not sufficiently flexible to allow for the adaptati on of numerous chemistry mechanisms and/or solvers. In adopting this innovative approach for the IRON PiG, it was necessary to utilize an accurate numerical solution approach based on the L SODE chemical solver.

The Concept of Incremental Chemistry

For a second-order reaction between puff species A and B , the total reaction rate is given by:

(1)

where P

A and P

B are the total puff concentrations of each species. The total puff concentrations can be expressed as the sum of the background and p uff perturbation concentrations:

where C is the ambient concentration and c is the puff increment concentration (which can be positive or negative). Thus the reaction rate is f ound to be:

or

If we subtract the rate of change of the background

,

(2) 

## ( ) ( )

B B A A T 

### C

c C c k R +

+ =



## ( )

B A B A B A B A T 

### C C C c c C c c k R +

+ + =

B A Ambient C C R = 

## ( ) ( )

B A T 

### P P k R =



## ( )

A A A 

### C c P + = 

## ( )

B B B 

### C c P + =CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-8 by assuming that it is explicitly and separately tr eated by the grid model, we obtain the reaction rate for the puff increments:

(3)

Equation 3 is the basis of the SCICHEM incremental chemical kinetic solver. One problem with this approach is the mixed terms, C A c B and c

A C B . Most chemical solver packages are designed to solve rate equations for total concentration, as in Equation 1. Thus, for the IRON PiG we developed an alternative numerical solution scheme for puff perturbation chemistry. We note that the CAMx chemical solver can be independently applied to the rate equation for total puff concentrations, Equation 1, and to the rate equatio n for ambient concentrations, Equation 2. By subtraction of the two solutions, we obtain the sol ution to rate Equation 3. This requires no modification to, and is obviously completely self-c onsistent with, the CAMx chemical solvers. Once the incremental puff reaction rates are obtain ed they are applied to the incremental puff mass to calculate the new (adjusted for chemistry) incremental concentrations. These new puff increments are subsequently advected and dispersed by the transport portions of the PiG code.

Puff Constructs For Incremental Chemistry

The PiG sub-model includes three constructs designe d specifically to facilitate the incremental chemistry approach:

• Treatments to handle puff-grid information exchange for puffs spanning multiple model layers;

• The concept of “virtual dumping” to handle the chem ical impacts of large puffs that can overlap other puffs within a given grid column;

• The concept of multiple puff “reactor” cells to acc ount for the chemical effects of concentration distributions within each puff.

Each of these is described below.

Puff Layer Spanning

The PiG is designed to chemically process point sou rce plume mass within streams of puffs until such time that each puff can be adequately resolved on the horizontal grid. Puffs are allowed to vertically span multiple grid model layers before t hey reach horizontal grid scales. This introduces technical implications for defining “bac kground” concentrations and ambient conditions for puff chemistry, as well as for trans ferring plume incremental mass to the grid. The solution employed is to:

1) Assume that the vertical distribution of puff conce ntration is always uniform; 2) Distribute puff mass transfer (via “leaking” and “d umping”) to the grid according to the puff fractional coverage across each model layer an d by density-weighting; and 3) Determine mean background concentrations and other ambient conditions (e.g., temperature, humidity, etc.) over the puff vertical span via similar fractional layer-density weighting. 

## ( )

B A B A B A P 

### C

c c C c c k R +

+ =CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-9 Horizontally, the mean background concentration and ambient conditions are taken from the single host grid column containing each puff center point, even if the puff is large and/or spans a horizontal cell interface. As described earlier, p uffs are considered to be elliptical in the horizontal, with the minor axis spanning the cross- wind puff width (defined as ± 1.5 σ y ), and the major axis spanning the along-wind puff length (def ined as length ± 1.5 σ y

on each end). However, given the complications associated with mu ltiple layer spanning and mass-weighting of ambient inputs and dumped mass, puffs are rectan gular and uniform in the vertical, with total puff depth defined as ± 1.5 σ z .

Puff Overlap and the Idea of Virtual Dumping

The chemical effects of puff overlap were considere d to be an important attribute of PiG. However, we wished to maintain a relatively simple approach, while appropriately accounting for the largest probable effects. We assume that t he largest effect is the condition in which older expansive puffs span significant fractions of their host grid cell, thereby potentially overlapping all other puffs contained within the same cell. In stead of geometrically determining fractional overlap puff-by-puff, we instead introduce a proces s that we refer to as “virtual dumping.”

For a given grid column, the mass from all puffs de termined to be “sufficiently” large are temporarily dumped to the column, distributed accor ding to each puff’s vertical layer span, and added together along with the pre-existing grid con centrations. This process is referred to as a “virtual dump” of puff mass to the grid column. Th e criteria to determine a “sufficiently” large puff is the same that initiates puff mass leaking t o the grid (as described below). In this approach, all large puffs contribute to the backgro und chemistry step for other puffs occupying the same grid column. Double-counting of chemistry is avoided by not including a puff’s contribution to the background while it’s specific background and incremental chemical calculations are performed.

Multiple Puff Reactors

Accounting for full chemistry potentially introduce s significant non-linear effects that are highly dependent upon the distribution of pollutant mass w ithin each puff. Especially for ozone, aircraft flights through power plant plumes have often recor ded wide concentration variations relative to ambient conditions: within the plume core where NOx remains concentrated, ozone is often depressed and follows NO-NO 2 -ozone equilibrium, whereas on plume fringes where NOx is dilute and mixes with ambient VOC, ozone concentrat ions can exhibit concentration maxima. Past models have accounted for cross-plume chemistr y variations through the use of reactors, with approaches ranging from multiple rectangular s labs to concentric shells.

The user may select multiple reactors as well to su b-divide the puff. Any number of reactors may be chosen (the default is 1). Multiple reactor s simply divide the total puff volume evenly, and the initial mass assignments for newly emitted puffs are made using the standard error
function that results in an initial Gaussian-like m ass/concentration distribution among the reactors. This provides a mechanism for simulating the differing chemical processing that takes place in various concentration regimes. As the pur pose of the reactors is merely to represent the range of photochemical conditions that are likely t o occur at various locations within the puff asCAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-10 it undergoes differential shearing and mixing, ther e is no particular physical orientation assigned to these reactors with respect to each other or to the puff as a whole. Thus, there is no communication (i.e., diffusional mass exchange) bet ween the reactors. The same background concentration chemistry applies to all reactors of a given puff. When puff mass is leaked or dumped, all reactors shed the same relative fractio n of mass.

In summary, chemistry is solved for each puff “reac tor” in three steps:

1) The layer-mean background (grid + overlapping puff) concentrations and environmental conditions over the volume occupied by the puff are stored and then chemically updated via the LSODE gas-phase chemistry mechanism; 2) The pre-updated mean background concentrations are added to the puff increments and the total concentrations are chemically updated; an d 3) The updated results from step 1 are subtracted from the updated results of step 2 to provide the updated incremental concentrations.

An important consequence of this approach is that t he incremental puff mass may be positive or negative. For example, a high-NOx puff that is des troying ambient ozone will have negative ozone increments. The puff increments are subseque ntly advected and dispersed by the transport portions of the PiG code. Note that the updated ba ckground concentrations, which include “virtual dumps” of mass from large puffs, are used for reference only in the puff incremental chemistry algorithm; the actual grid concentrations are updated in the grid chemistry routine.

Puff Dumping and PiG Rendering

Mass transfer from puff to grid can happen in two w ays: slowly, termed “leaking”, or suddenly, termed “dumping.” As described earlier, all mass i s transferred to the vertical grid structure in a density-weighted fashion according to each puff’s f ractional layer coverage. The process of leaking ensures that puff mass is transferred to th e grid continuously, rather than in discrete lumps of pollutants with very different concentrati ons than those in the grid. Sudden dumping can cause unphysical numerical shocks in the grid a nd can lead to unrealistic gridded concentration patterns that appear as “bullseyes”. The idea behind puff leakage is to account for turbulent shearing of mass from the main plume and its subsequent dispersion to the grid scale. This rate of transfer should be directly proportion al to the puff size relative to the grid scale.

Puff leakage is controlled by comparing the horizon tal area of a puff to a specified leakage parameter, defined as a fraction of horizontal grid cell area. When a puff is first emitted there is no leakage. As the puff grows in volume the concen trations within the reactors are reduced accordingly by dilution. When the puff area exceed s the leakage onset parameter, a fraction of the mass in each puff reactor is transferred to the grid. This fraction is determined by the relative exceedance of the leakage parameter; initial leakag e is slow as the exceedance is relatively small, but leakage rates grow as the puff continues to gro w beyond the leakage parameter.

The reduced mass from leakage is compensated by a r educed effective volume, so that concentrations are not artificially diluted by leak age (an essential chemical imperative). Thus,
two distinct volumes are tracked: the actual volume (defined by the puff spread sigmas) and the
effective volume. While these are identical before leakage, they obviously deviate after leakageCAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-11 is initiated, and thereafter the relative deformati on of the actual puff volume (via diffusion, shearing, etc.) is used to scale the deformation of effective puff volume.

Eventually the horizontal span of the puff will exc eed the grid cell area, and the remaining mass is then dumped all at once to the grid. However, b ecause of the combination of photochemical processing and leakage, by the time a puff dumps th e potential for producing numerical shocks is much reduced. Furthermore, if the puff exceeds a u ser-defined maximum age, puff mass is transferred to the grid at the rate of 10% per time step.

While the mass confined to the puffs at any given t ime has not yet affected the grid concentrations, it will eventually, so it can be so mewhat misleading to sequester this mass from visualizations of a model simulation. The puff mas s can be optionally incorporated into the model average output files for visualization purpos es (referred to as “PiG rendering”). Rendering employs a “virtual dump” of the puff mass es into the average concentration array each time step. As described for chemistry, virtua l puff mass is added as an increment over the entire grid column according to fractional layer-de nsity weighting over puff depth, thus diluting it’s concentrations relative to that within the puf f. The actual puff mass remains within the puffs
over the course of their lifetimes. It should be n oted that this visualization is available for 3-D average output files, and can produce some rather s tartling effects in output displays, including very narrow virtual plumes, or streaks, representin g mass moving through the grid in sub-grid puffs, but not subject to grid-scale eddy diffusion

.

High Resolution Puff Sampling

PiG optionally employs surface-layer puff sampling of CB4, CB05, or SAPRC99 species on a user-defined grid of arbitrary horizontal resolutio n, similarly to the way nested grids are defined. Sampling grids are entirely passive, and intended t o provide a display of the plume concentrations at scales much smaller than typicall y used for the finest computational grids (i.e., <1 km), primarily around and downwind of a source c omplex. Sampled PiG concentrations are time-averaged like the output concentrations provid ed on the computational grids, and are written to files with similar format so that they m ay be readily viewed and manipulated with CAMx post-processing software. Additional informat ion on configuring and using PiG sampling grids is provided in Section 5.

Given that the puffs constantly evolve via diffusiv e growth and reshaping due to deforming shears, the sampling procedure includes trigonometr ic calculations to define which sampling points are influenced by each puff. This influence is determined according to the puffs’ twodimensional horizontal Guassian shape shown in Figu re 4-1. To include a sufficiently large percentage of mass across each puff for sampling, l imits of ±3 σ x/y in both horizontal dimensions are used to define the puffs’ total elliptical area coverage. Puffs are only sampled if they extend vertically into the model’s surface layer.

Sampling grids are defined in the CAMx control file (see Section 5), and array dimensions must be set sufficiently large in the CAMx Fortran param eters file in ./Includes/camx.prm (see Section 11). An example of the type of plume detai l that can be visualized using a sampling grid is provided in Figure 4-3. In this case, a very fi ne 200 m sampling grid is set within a 4-km computational grid.CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-12 Figure 4-3. Example of a single point source PiG plume as dep icted by a sampling grid with 200 m resolution (shown by the extent of the plot). This sampling grid was set within a CAMx computational grid with 4-km resolution. The sourc e location is arbitrary and is emitting an inert
tracer.

4.3 GREASD PiG

The GREASD PiG is designed for large NOx point sour ces, where inorganic chemistry is applicable only for early plume evolution with the intention of aging the NOx to mitigate the chemical effects of emitting fresh point source NO directly from the stack to the modeling grid. Therefore, this option should not be used for VOC s ources. The chemical limitations applied within GREASD PiG force the puffs to transfer their mass to the grid before full photochemical processing can develop, and usually before puffs re ach size criteria relative to the grid spacing.

Concentrations and chemistry in GREASD PiG are trea ted with the same incremental techniques
as described in Section 4.2 for IRON PiG. In fact they are the same model, but with a distinct difference in the criteria that control when the pu ffs dump their incremental mass to the grid. The objective for GREASD PiG is to perform this dum ping at about the time when non-linear organic chemistry starts to become important. This was a specific design constraint that allowed source apportionment Probing Tools to be including into the PiG algorithm. Kumar and Russell (1996) found that a PiG model with simplified inorg anic chemistry produced results that were very similar to full chemistry.

EPRI (2000) describes the chemical evolution of lar ge point source plumes in terms of three stages, as described below:CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-13 • Stage 1 : Early plume conditions where NOx concentrations are very high and radical concentrations are negligible – simple NO/NO 2 /O 3 photostationary state applies along with the NO-NO self reaction;

• Stage 2 : Mid-range plume conditions where radical concent rations are sufficiently large to generate secondary inorganic acids like nitrate and sulfate;

• Stage 3 : Long-range plume conditions where sufficient mix ing with the ambient air leads to the full range of gas-phase reactions involving VOC oxidation and ozone formation.

The GREASD PiG treats plume chemistry during Stages 1 and 2. We define the onset of Stage 3 chemistry when the following criterion is met:

This conservative threshold ensures that GREASD puf fs transfer all of their mass to the grid before the onset of any non-linear oxidant producti on, which is compatible with the source apportionment requirements.

4.4 PARTICULATE MATTER IN PiG

Particulate chemistry in the CAMx PiG closely paral lels the approach for gas-phase chemistry. The ISORROPIA, SOAP and RADM-AQ chemistry modules a re called for each puff in the same manner and sequence as they are for the grid c hemistry, but using the PiG incremental chemistry approach; i.e., by separate integrations for background and puff+background in order to determine the evolution of puff incremental PM c oncentrations. Note that the PM capability in PiG was designed specifically for the static 2-m ode PM chemistry only (CF), and not for the multi-section PM chemistry (CMU). The PM capabilit y is linked to the simper GREASD PiG option to limit the amount of non-linear chemistry in the PM treatment. The addition of PM, and the PSAT Probing Tool, necessitated the addition of dry and wet deposition processes to the
CAMx PiG. These are described below.

PM Chemistry

The PiG performs gas and particulate chemical calcu lations for a given puff over each time step of the host grid where the puff resides. This is d one by calling a chemistry driver routine that is practically identical to the grid chemistry driver, except that layer-averaged ambient conditions over puff depth and PiG increments are passed to th e chemistry algorithms rather than cellspecific conditions. The driver routine contains t he specific calls to all supported CAMx chemistry mechanism solvers, including the 2-mode ( CF) PM algorithms.

Non-linear effects in gas-phase chemistry are trans mitted to the PM and potentially amplified through the aqueous and equilibrium PM chemistry al gorithms. The implementation of PSAT in PiG prompted the need to limit the impact of such n on-linearity in the plume chemistry similarly to the constraints imposed by OSAT for gas-phase ch emistry. Therefore, PM and PSAT can only be run using the GREASD PiG option. Other lim itations associated with running PM in
PiG include: 

## [ ] [ ] [ ] [ ] [ ] [ ] [ ][ ] 1.0

2 2 > + +

NO OH k VOC

OH k SO OH k CO OH kCAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-14 • No puff leaking is allowed;

• No virtual dumping is allowed;

• No overlap chemistry is performed.

To maintain consistency with the grid chemistry, th e PiG calls the aqueous PM chemistry algorithm at every time step for each puff that res ides in a cloudy grid cell. The remainder of the PM chemistry (ISORROPIA and SOAP) continues to be c alled on the PM “coupling” time step (default is 15 minutes), as these routines are not based on integrating conversion rates, but rather are equilibrium calculations independent of time st ep.

Deposition

The CAMx PiG treats the removal of gas and PM speci es from each puff via deposition processes. Both dry and wet deposition calculation s presented unique implementation issues for puffs. The most difficult issue for both forms of deposition was how to manage deposition exchange between puffs and the ground in the case o f negative puff concentration increments.

Dry Deposition

Dry deposition needs to consider the following: (1) the point at which puffs begin to deposit to the surface; (2) how to handle deposition through p otentially deep puffs that may straddle several layers of varying stability since the puffs do not themselves resolve these stratifications or vertical concentration distributions; (3) managing deposition fluxes of negative concentration increments. Our solution to issue (1) was to ignor e dry deposition within puffs until they diffusively grow to the ground, although in reality deposition occurs on roughness elements that extend some distance above the nominal surface (tre es, buildings, etc.). We implemented a criterion that the bottom of the puff must extend t o or below the midpoint of the surface layer, or below 10 m (whichever is larger), in order for dry deposition to be active.

Issue (2) can be handled in a variety of ways and c omplexity. The current implementation institutes a simpler solution and we will consider more complicated improvements for future developments if evidence suggests that they would b e necessary. PiG utilizes pre-computed species-dependent deposition velocities derived for the grids. Each puff in a particular grid cell is provided the host cell’s deposition velocities f or each species, and these are used to determine the flux of mass through the fraction of puff depth occupying the model’s surface layer.

Issue (3) is unique to the incremental chemistry co ncept introduced with IRON PiG. The flux of material depositing to the ground is given by d 

### v c F

⋅ = , where by the normal definition a positive deposition velocity v d

leads to a positive deposition flux to the ground. If the puff increment c is negative, then a negative flux is calculated (flux from ground to puff). This is appropriate if we consider the following argument. Dry deposition ap plied to a grid cell removes some pollutant mass from the entire volume. If there is a puff ex isting in that cell with a negative concentration increment, then the amount of mass removed from the cell was over estimated if we consider the puff’s contribution to total cell mass. The negati ve deposition flux calculated for this puff leads to the addition of mass to the puff increment. Add ing mass to a negative increment reduces theCAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-15 magnitude of the increment, as expected for a depos ition process. This mass is taken from the grid cell’s accumulated deposited mass to maintain accurate mass accounting within the model.

Wet Deposition

Wet deposition needs to consider the following: (1) how to handle scavenging of pollutants through potentially deep puffs that may straddle se veral layers of varying cloud and rain water
contents but that do not themselves resolve vertica l concentration distributions; (2) managing deposition fluxes of negative concentration increme nts in combination with the potential for mass to move in and out of rainwater as it falls (e .g., for slightly soluble gasses); (3) accounting for the initial pollutant concentrations in rainwat er as they enter the top of each puff.

It was important to maintain consistency between th e treatment of wet deposition and the approach for puff chemistry. The chemistry relies on the assumption of vertically well-mixed puff reactors that can span multiple layers, and th is is why layer-density weighted average ambient conditions are passed to the chemistry rout ines. To maintain this assumption for wet deposition, a single scavenging rate is applied thr ough the entire puff depth as effectively a single layer of pollutant. This was found to be th e simplest implementation approach. This single scavenging rate is calculated according to l ayer-density weighted average ambient cloud
and rainwater contents.

Wet scavenging is performed throughout the entire d epth of the puff to determine the amount of
flux in or out of rainwater. Total concentrations (puff + background) are used to determine species-dependent scavenging rates using the identi cal algorithm as for grid removal. The rates are used to derive removal fractions, and these fra ctions are then applied directly to the puff incremental mass for each species. Removal fractio ns are considered positive for the standard case of mass moving from puff to rain. Note that n egative puff mass increments in combination with a positive removal fraction lead to a reversal of the flux direction (rain to puff), and that is acceptable by an argument similar to that presented for dry deposition. We account for impacts on the mass budget appropriately by adding or subtr acting from the wet deposition mass array according to the net fluxes into and out of rainwat er, respectively.

We further assume that the top boundary condition f or rainwater entering the top of each puff is
zero. This means that the removal fraction is alwa ys positive (from puff to rain) in the singlelayer puff. In contrast, for gridded concentration s the layer-by-layer buildup of slightly soluble species can lead to a reversal of fluxes (from rain to grid) if super saturation is diagnosed in a particular layer.

4.5 PiG CONFIGURATION

Selecting the individual elevated point sources to receive the PiG treatment is accomplished by setting their stack diameters negative within the h eader (time-invariant point list) section of the CAMx input point source file. CAMx will run correc tly with these negative diameters even if the PiG algorithm is not invoked. CAMx preprocesso rs exist to ease the procedure of ranking elevated point sources by emission rate and flaggin g the sources that the user wishes to treat.CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-16 Invoking the CAMx PiG sub-model is controlled by ke ywords in the CAMx control file ( CAMx.in ), as described in Section 5. The choices are:

PiG_Submodel = 'NONE', PiG_Submodel = 'GREASD', PiG_Submodel = 'IRON',

Note that the single choice between GREASD and IRON applies to all flagged point sources. It is not possible to make a single CAMx run with IRON PiG applied to a certain group of sources, and GREASD PiG applied to another group. Also note that GREASD must be selected to run PiG with OSAT, PM and PSAT; IRON must be selected t o run PiG with the RTRAC Probing Tool. Several additional parameters are used to co nfigure the PiG. It is important to note that all PiG configuration parameters exist in the CAMx Fort ran parameters file ( ./Includes/camx.prm ), as described in Section 11. PiG parameters are grouped together and briefly described at the end of that file. By effectively configuring the PiG submodel in the code, the default PiG configuration (as recommended by the model developers) is preset within
the model distribution and alleviates the need for users to select settings on their own.

The default values are shown below:

parameter ( MXPIG = 10000 ) parameter ( MXRECTR = 1 ) parameter ( FLEAK = .25 ) parameter ( LEAKON = .FALSE. ) parameter ( LVISPIG = .FALSE. ) parameter ( OVERLAP = .FALSE. ) parameter ( DXYMAX = 0. ) parameter ( AGEMAX = 18.*3600. ) parameter ( PIGMXDT = 300. )

Users should exercise thoughtful consideration when altering these default values. A description of each of the remaining parameters is provided bel ow, along with guidance in setting values.

Guidance on the Use of CAMx PiG

PiG Keyword

The PiG keyword controls whether the PiG option is to be invoked in a CAMx simulation, and
whether the emissions are treated with the GREASD o r IRON options. This keyword can be
switched from NONE to GREASD or IRON on a model res tart to invoke the PiG treatment at any point during a multi-day simulation. To allow for this, it is not mandatory to provide CAMx with a pre-existing PiG output file upon a model re start – CAMx will not stop if this file is missing. It is recommended that this file be provi ded on all subsequent restarts since the PiG output file is needed to reinitialize the PiG modul e, otherwise all mass contained in puffs at the end of the previous run will be lost. If the PiG k eyword is switched to NONE on a model restart, CAMx will continue the simulation without PiG, but all mass contained in puffs at the end of the previous run will be lost.CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-17 • Guidance:

- Invoke GREASD or IRON PiG at any point during a mul ti-day simulation, or none at all. Once PiG is started, provide CAMx with the PiG output file from the previous run for all subsequent model restarts so that no po int source mass is lost.

- GREASD PiG should be invoked for large NOx point so urces only, since it does not provide any organic chemistry. GREASD PiG does sup port PM chemistry (CF). It can be run in conjunction with the OSAT and PSAT Pr obing Tools. It does not support DDM, PA, or RTRAC.

- IRON PiG can be invoked for any point sources to tr eat gas-phase chemical evolution using CB4, CB05, or SAPRC99 mechanisms. IRON PiG does not treat particulate chemistry. It can be run in conjunctio n with the RTRAC Probing Tool. It does not support OSAT, PSAT, DDM, or PA.

Both GREASD and IRON options use the expensive LSOD E chemistry solver exclusively, so users will notice a significant impact on run time, particularly if many (~thousands) puffs are to be carried and/or puffs are configured with many pu ff reactor cells (see below). Since the lifetime of GREASD puffs are much shorter than thei r IRON counterparts, GREASD PiG will
run faster than IRON PiG for the same number of fla gged sources. PiG chemistry is internally parallelized using OMP to maximize PiG speed perfor mance.

Number of PiG Puffs

MXPIG sets the maximum number of PiG puffs to be ex pected during a simulation. It is used to statically allocate memory arrays for the PiG sub-m odel. A value of 10,000 is usually sufficient for most applications in which PiG is used; set thi s parameter to 1 if PiG is not used to conserve memory. If this parameter is exceeded during a sim ulation, the model will halt. If this happens, simply increase MXPIG , recompile the model executable, and restart the s imulation.

• Guidance: Use the default value for most simulation s, or set to 1 if PiG is not to be used. If the model stops because MXPIG is exceeded, incre ase its value, recompile, and restart
the model.

Number of PiG Reactors

MXRECTR sets the number of puff reactors; when grea ter than 1, each puff is separated into that number of reactor cells and primary emissions are a pportioned among them using a Gaussian distribution. Since chemistry is performed for eac h individual reactor cell (both background and puff+background), this parameter can affect the spe ed of chemical computations in the PiG. We have not seen a significant sensitivity to values g reater than 1, but testing for each application is
warranted.

• Guidance: Use the default of 1 for initial simulati ons, but test the sensitivity to this parameter for each unique application.CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-18 Leakage Parameters

FLEAK, LEAKON, and DXYMAX together control puff lea kage to the computational grid and ultimately puff termination. When LEAKON is true, FLEAK and DXYMAX control when puffs begin to leak portions of reactor mass to the grid along their trajectory. When LEAKON is false, no leaking is performed and puffs maintain a ll of their mass until they reach sizes for termination, at which point all mass is directly in troduced to the grid at that point. DXYMAX sets the maximum dimension that puff size will be c ompared to for leaking and termination; when it is zero, puff size will be compared to grid area, but when it is non-zero, puff size will be compared to the value of DXYMAX. FLEAK is the rel ative fraction of horizontal puff area to cell area, or DXYMAX, above which leaking will begi n and continue until sufficient mass is shed and the puff is terminated. In the example ab ove, puffs will begin to leak mass when they reach 25% of the host grid cell’s area.

• Guidance: If LEAKON is set to true, maintain FLEAK at the default value of 0.25. Then test model sensitivity to different values of FLEAK and/or DXYMAX.

• Leaking is not allowed when PiG is run with PM.

Overlap Flag

OVERLAP controls whether puff overlap is to be trea ted in the background chemistry step. As stated earlier, puffs only overlap if they meet the size criteria for leaking; all puffs smaller than this size do not overlap any other puffs in the sam e grid cell.

• Guidance: We recommend that the OVERLAP flag remai n set to the default value of
“false”.

• Overlap is not allowed when PiG is run with PM.

Virtual Puff Rendering

LVISPIG is a flag that turns on puff “rendering” to the computational grid average concentrations. When it is false, the chemical eff ects of puff mass are not seen on the output average files until they either begin to leak mass to the grid and/or they are terminated and their mass is entirely introduced to the computation grid . However, when the flag is true, all puff mass that resides in each grid column is summed, ap portioned vertically to each grid cell according to puff vertical extent (via density and layer-depth weighting), converted to concentrations, and added to the average gridded co ncentrations for output. This process is referred to as rendering since the effects of all p uff mass can be readily visualized in the CAMx output.

• Guidance: This option has no impact on the actual C AMx chemical solution. However, output average concentration files will be affected by puff rendering, and therefore could impact graphics of CAMx results and model performan ce measures.

• Virtual dumps are not allowed when PiG is run with PM.CAMx v5.30 User’s Guide, December 2010

Plume-in-Grid Submodel

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_04_PiG.docx 4-19 Maximum Puff Age

AGEMAX is the age limit for all PiG puffs (IRON and GREASD). When puffs reach this age limit, they are terminated and all of their mass is transferred to the host grid.

The assumption of a stream of coherent puffs become s less valid with time as horizontal and vertical wind shears increase plume spread. At som e point the plume mass is better resolved on the grid than within puffs. In CAMx the rate of pu ff expansion most often determines this transfer of mass, and the maximum puff age provides a safety check to ensure that puffs do not persist for unrealistic times in stable environment s. The maximum puff age should be set long enough to allow puffs to persist overnight, but a l ifetime of longer than a day is probably not
realistic.

• Guidance: limit puff age to 12-24 hours – we find t hat 18 hours works best since it will allow puffs emitted in the late afternoon to last t hrough the night and into the following morning. The purpose is to restrict growth-limited puffs to terminate over night simply as a result of age constraints. Twelve hours is se en to be too short in this regard; puffs usually do not reach 24 hours of age before being t erminated by grid constraints.

Maximum Puff Release Interval

PIGMXDT sets the maximum frequency of release and b y default is set to 300 seconds (5 minutes). This value should be adequate for most a pplications. However, if the user wishes to improve plume resolution by increasing the number o f puffs, the frequency of release can be increased by reducing the value of PIGMXDT. This v alue supersedes the automated puff release rate that is determined by wind speed and grid size

.

• Guidance: Maintain the default value of 300 s and a llow PiG to use the automated PiG release frequency. Set to a lower value if better plume resolution is desired; note that more puffs will be released and this could slow the model markedly.CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-1

5. CORE MODEL INPUT/OUTPUT STRUCTURES

CAMx requires input files that configure each simul ation, define the chemical mechanism, and describe the photochemical conditions, surface char acteristics, initial/boundary conditions, emission rates, and various meteorological fields o ver the entire modeling domain. Table 5-1 summarizes the input data requirements of CAMx. Pr eparing this information requires several preprocessing steps to translate “raw” emissions, m eteorological, air quality and other data into final input files for CAMx. We expect that most CA Mx applications will utilize output from a prognostic meteorological model that can generate a ll of the required time varying threedimensional meteorological fields. Most CAMx I/O f ile formats are compatible with those of the UAM genre, which allows the user to employ wide ly available software designed specifically for these formats to develop the input files or to post-process and visualize the output files.

Table 5-1. **Data requirements of CAMx.**

Data Types Data Fields

**Meteorology** Supplied by a Meteorological Model • **3-Dimensional Gridded Fields: - Vertical Grid Structure**  **- Horizontal Wind Components - Temperature**  **- Pressure - Water Vapor**  **- Vertical Diffusivity - Clouds/Precipitation**  **Air Quality** Obtained from Measured Ambient Data • **Gridded Initial Concentrations**

• **Gridded Boundary Concentrations**

**Emissions** Supplied by an Emissions Model • **Elevated Point Sources**

• **Combined Gridded Sources**
**- Low-level Point - Mobile**  **- Area/Non-road Mobile - Biogenic** 

**Geographic** Developed from Terrain and Landuse/Landcover Maps,Drought Index Maps, Modeled or Satellitederived Snow Cover, Satellite-derived LAI • **Gridded Surface Characteristics - Land Use Category** 
**- UV Albedo - Snow Cover**  **- Leaf area index (LAI) - Land/Water Mask**  **- Roughness Length - Drought Stress** 
**- Terrain Elevation**

**Photolysis** Derived from Satellite Measurements and Radiative Transfer Models • **Atmospheric Radiative Properties - Gridded Haze Opacity Codes**  **- Gridded Ozone Column Codes - Photolysis Rates Lookup Table** CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-2 CAMx produces gridded time-averaged concentration o utput files; the user selects the time interval (usually hourly), the species to be output (either “state” species defined in the chemistry parameters file or steady-state “radical” species), and whether the output contains just twodimensional surface layer fields or entire three-di mensional fields. A separate average output file is written for each grid employed in the simul ation in UAM format. Surface deposition fields for the same user-selected species are also written to output files with the same structure as the average concentration files. Gridded three-dim ensional instantaneous concentrations of all species on all grids are written at the end of the simulation to allow for a model restart. Commonly available graphical visualization and anim ation software can be used to view CAMx average and deposition output files. The CAMx Prob ing Tool options provide their own information in separate output files in UAM format that can be viewed directly or further postprocessed for detailed analysis. Diagnostic output files include three files that track computer resources, echo input selections, provide mass budg et and diagnostic summaries, and provide error/warning messages.

5.1 CAMx RUN CONTROL FILE

CAMx reads a text (ASCII) run control file named “ CAMx.in ” that must exist locally in the directory from which the model is run. This file i s in the Fortran “namelist” format, and contains all user-specified control parameters for a given s imulation, including model configuration, option-specific inputs, and I/O filenames. The run control file must contain the primary namelist
module labeled “ &CAMx_Control ”, which provides all of the information to configu re and run the core model. Additional namelist modules may be provided in the run control file to configure and run the various CAMx Probing Tools. These optional namelist modules are ignored if none of the Probing Tools are selected i n the primary namelist.

Each record in the CAMx control file contains a var iable name that is explicitly set to a numerical, logical, or character value. The variab le names are used by the program directly, and therefore cannot be changed without source code mod ifications. Character strings must be enclosed by single quotes, and all variable assignm ents must be delimited with commas. The order of the records may be arranged in any fashion that the user prefers. Any number of comment statements may be included anywhere within the namelists, provided that they do not interrupt “ variable_name = value, ” assignments. The “ ! ” character is the Fortran
namelist comment delimiter.

Certain variables are multi-dimension arrays; the u ser may provide a comma-delimited list of values to fill the array or assign values to specif ic array elements. Certain other variables are optional or associated with option flags; these do not need to appear in the namelist if their associated options are not invoked, and they will b e ignored if they remain in the file. If the user does not provide necessary inputs, the model will s top with a descriptive error message. We suggest that users start with the CAMx.in template that is provided with the source code.

A detailed description of the run control file vari ables necessary to run the core model are provided below.CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-3 Description of the CAMx Run Control File

&CAMx_Control Label for the primary namelist module that confi gures the core model; it must begin in column 2

& Flag ending a namelist module; it must be in c olumn 2

Run_Message 60-character simulation message, written to outp ut files to label
the run

Model Clock Control

Time_Zone Integer time zone (0=UTC, 5=EST, 6=CST, 7=MST, 8=PST)

Restart Logical model restart flag (TRUE=read restart f ile, FALSE=read initial conditions file)

Start_Date_Hour Integer array start time (YYYY, MM, DD, HHmm)

End_Date_Hour Integer array end time (YYYY, MM, DD, HHmm)

Maximum_Timestep Real maximum allowable timestep (minutes)

Met_Input_Frequency Real input frequency of environmental fields (min utes)

Ems_Input_Frequency Real input frequency of emissions (minutes)

Output_Frequency Real output frequency (minutes)

Map Projection Parameters

Map_Projection Character mapping projection keyword (LAMBERT, P OLAR, UTM, LATLON)

UTM_Zone Integer UTM zone

POLAR_Longitude_Pole Real longitude of projection pole (degrees, west< 0)

POLAR_Latitude_Pole Real latitude of projection pole (degrees, south< 0)

LAMBERT_Center_Longitude Real central longitude of modeling domain (degrees , west<0)

LAMBERT_Center_Latitude Real central latitude of modeling domain (degrees , south<0)

LAMBERT_True_Latitude1 Real first true latitude of projection (degrees, south<0)

LAMBERT_True_Latitude2 Real second true latitude of projection (degrees, south<0)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-4 Parameters For The Master (First) Grid

Number_of_Grids Integer number of grids in simulation

Master_Origin_XCoord Real x-coordinate of domain southwest corner (km, or degrees for LATLON)

Master_Origin_YCoord Real y-coordinate of domain southwest corner (km, or degrees for LATLON)

Master_Cell_XSize Real cell size in x (km, or degrees for LATLON)

Master_Cell_Ysize Real cell size in y (km, or degrees for LATLON)

Master_Grid_Columns Integer number of master grid columns (E-W grid c ells)

Master_Grid_Rows Integer number of master grid rows (N-S grid cel ls)

Number_of_Layers Integer array (by grid) number of grid layers

Parameters For The Nested Grids

Nest_Meshing_Factor Integer array (by grid) nested grid cell size rel ative to master grid

Nest_Beg_I_Index Integer array (by grid) master grid column conta ining western edge of nest

Nest_End_I_Index Integer array (by grid) master grid column conta ining eastern edge of nest

Nest_Beg_J_Index Integer array (by grid) master grid row containi ng southern edge
of nest

Nest_End_J_Index Integer array (by grid) master grid row containi ng northern edge
of nest

Model Options

Diagnostic_Error_Check Logical model startup diagnostic flag (TRUE=stops before first timestep indicating successful model initialization

, FALSE=continues with simulation after model initial ization)

Advection_Solver Character horizontal advection solver keyword (P PM, BOTT)

Chemistry_Solver Character chemistry solver keyword (EBI, IEH, LS ODE)

PiG_Submodel Character PiG submodel keyword (NONE, GREASD, IR ON)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-5 Probing_Tool Character Probing Tool keyword (NONE, OSAT, GOAT

, APCA, PSAT, DDM, HDDM, PA, IPR, IRR, RTRAC, RTCMC)

Chemistry Logical chemistry flag (TRUE=chemistry on, FALS E=chemistry off)

Drydep_Model Character dry deposition model keyword (NONE, WE SELY89, ZHANG03)

Wet_Deposition Logical wet deposition flag (TRUE=deposition on, FALSE=deposition off)

ACM2_Diffusion Logical ACM2 vertical diffusion flag (TRUE=ACM2 on, FALSE=standard K-theory diffusion)

TUV_Cloud_Adjust Logical TUV cloud photolysis adjustment flag (TRUE=use inline TUV model [default], FALSE=use original RADM parameterization)

Staggered_Winds Logical staggered input wind field flag (TRUE=wi nds are input at cell interfaces, FALSE=winds are input at cell m idpoints) – ignored if this flag is provided on input wind file s.

Super_Stepping Logical super-stepping flag (TRUE=use super-step ping for horizontal advection to maximize model speed, FALSE =do not use super-stepping).

Gridded_Emissions Logical gridded emissions flag (TRUE=gridded emis sions will be used, FALSE=gridded emissions will be ignored)

Point_Emissions Logical elevated point source flag (TRUE=point e missions will be used, FALSE=point emissions will be ignored)

Ignore_Emission_Dates Logical date-insensitive emission flag (TRUE=date s on emission files will be ignored, FALSE=dates on emission file s will be checked against simulation date)

Output Specifications

Root_Output_Name Character root output path/filename (see Table 5 -2 for description of file suffixes)

Average_Output_3D Logical 3-D average output file flag (TRUE=output full 3-D concentration fields, FALSE=output surface layer co ncentration fields)

Output_3D_Grid Logical array (by grid) 3-D average output file flag (TRUE=output full 3-D concentration fields for spec ified grid, FALSE=output surface layer concentration fields)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-6 HDF_Format_Output Logical HDF average output format flag (TRUE=outp ut additional HDF format files, FALSE=do not output HD F format files)

HDF_File_Root Character HDF root output path/filename

Number_of_Output_Species Integer number of species to be written to average and deposition output files

Output_Species_Names Character array (by output species) species names to be written to average and deposition output files

PiG_Sampling_Grid Logical sampling grid flag for IRON PiG output (TRUE=sampling grids are specified, FALSE=sampling grids will not be generated)

Sample_Background Logical flag to include background concentrations (TRUE=background concentrations from the host compu tational grid will be added to puff increments, FALSE=only p uff increments will be shown)

Number_of_Sampling_Grids Integer number of sampling grids

SG_Beg_I_Index Integer array (by sampling grid) master grid colum n containing western edge of sampling grid

SG_End_I_Index Integer array (by sampling grid) master grid colum n containing eastern edge of sampling grid

SG_Beg_J_Index Integer array (by sampling grid) master grid row c ontaining southern edge of sampling grid

SG_End_J_Index Integer array (by sampling grid) master grid row c ontaining northern edge of sampling grid

SG_Mesh_Factor Integer array (by sampling grid) cell size relativ e to master grid

Input Files

Chemistry_Parameters Character input chemistry parameters path/filenam e

Photolyis_Rates Character input photolysis rates path/filename ( optional according to Chemistry flag and Chemistry_Parameter s file)

Initial_Conditions Character input master grid initial conditions pa th/filename (ignored if Restart=TRUE)

Boundary_Conditions Character input master grid boundary conditions p ath/filename

Albedo_Haze_Ozone Character input albedo/haze/ozone column path/fil ename (ignored if Chemistry=FALSE and Dry_Deposition=FALS E)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-7 Point_Sources Character input elevated point source emissions path/filename (ignored if Point_Emissions=FALSE)

Master_Grid_Restart Character input master grid restart path/filename (ignored if Restart=FALSE)

Nested_Grid_Restart Character input nested grid restart path/filename (ignored if Restart=FALSE or Number_of_Grids=1)

PiG_Restart Character input PiG restart path/filename (ignor ed if Restart=FALSE or PiG_Submodel=FALSE)

Landuse_Grid Character array (by grid) input landuse path/fil ename (ignored if Dry_Deposition=FALSE and PiG_Submodel=NONE, optiona l for nested grids)

ZP_Grid Character array (by grid) input height/pressure path/filename (optional for nested grids)

Wind_Grid Character array (by grid) input wind path/filen ame (optional for nested grids)

Temp_Grid Character array (by grid) input temperature pat h/filename (optional for nested grids)

Vapor_Grid Character array (by grid) input water vapor pat h/filename (ignored if Chemistry=FALSE and Dry_Deposition=FALS E and Wet_Deposition=FALSE, optional for nested grids)

Cloud_Grid Character array (by grid) input cloud/rain path /filename (required if Wet_Deposition=TRUE, otherwise optiona l, optional for nested grids)

Kv_Grid Character array (by grid) input vertical diffus ivity path/filename (optional for nested grids)

Emiss_Grid Character array (by grid) input gridded emissio ns path/filename (ignored if Gridded_Emissions=FALSE, optional for n ested grids)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-8 The order of the variables follows the template ava ilable with the source code. The Probing Tools require separate additional namelist modules in the run control file, and their contents vary among the various options. The specific run contro l information needed for each of these options is provided in Sections 6 through 10.

The short simulation “run message” is written to al l output files to describe and label the run. The user specifies the simulation start/end year, m onth, day, and hour; the model uses julian dates internally. All times must be given in milit ary format (e.g., 1:30 PM must be given as 1330). The simulation time zone must match the tim e zone in which the emission and environmental inputs are developed, usually the mos t central time zone in the domain.

The grid projection may be selected as Cartesian (f ixed physical distance coordinates on a flat plane) or curvi-linear geodetic (following the curv ed surface of the Earth). The Cartesian options include Universal Transverse Mercator (UTM) , Lambert Conic Conformal (LAMBERT), and Rotated Polar Stereographic (POLAR). The geodetic option performs the simulation on a latitude/longitude grid (LATLON). All gridded input files must be defined on the grid projection specified for the CAMx simulati on. The definition of nested grids is specified
in the CAMx.in file in terms of the range of master grid cells th at each nested grid spans (see Section 2).

The user has the option of selecting among the Bott or Piecewise Parabolic Method horizontal advection solvers by specifying “BOTT” or “PPM” as keywords in the run control file. The user also has the option to use the EBI, IEH, or LSODE c hemistry solvers for gas-phase chemistry by specifying these respective keywords. Probing Tool s are selected by specifying one of the allowed keywords; no Probing Tool will be run if th is keyword is set to “None”.

Super stepping maximizes the model’s speed performa nce by setting the largest grid-specific driving time steps possible. This results in the n eed for potentially many sub-steps to be applied in horizontal advection on a layer-by-layer basis t o maintain a stable solution. Prior to version 5.00, super stepping was performed automatically (t ransparent to the user). While super stepping has little impact on surface concentrations in non- MPI mode, larger differences are seen using MPI. A “super-stepping” flag was added to the cont rol namelist that allows users to specifically turn off super stepping when they wish to compare c oncentrations between MPI and non-MPI runs in the most consistent manner possible. Super stepping can reduce the accuracy of the vertical transport solution, especially in high win d conditions over complex terrain.

The user specifies a “root” path and filename that will be used for all standard CAMx core model output files. The model appends suffixes to these root names according to the file type generated. Alternatively, the user may select HDF5 output files. In this case, a separate HDF root path and filename is required, and CAMx will automatically generate grid- and hourspecific average concentration and sampling grid ou tput files in HDF5 format in lieu of the standard UAM file formats. The relationship betwee n CAMx file types is shown in Table 5-2. A subset of species and steady-state radicals may b e output to the average concentration output files; see the description of output file formats l ater in this section. There are two flags that control whether 3-D average output files are genera ted. The first (original) flag will toggle 3-D output for all grids in the run. The second is the “ Output_3D_Grid ” flag array, which allows 3-D average output to be set for specific grids. T he original flag supersedes the grid-specific flag.CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-9

Table 5-2. **CAMx output file suffixes and their corresponding f ile types.** 

Suffix CAMx File Type

**.out Text simulation tracking file (CPU, input files rea**  **d, error/warning**

**messages)**

**.diag Text simulation diagnostic file (repeat of run cont**  **rol inputs, PiG**

**diagnostics, miscellaneous diagnostic output)**

**.mass Text mass budget file for subsequent postproc essing** 

**.inst Binary master grid instantaneous concentration file**  **at the end of the**

**simulation (used for restarts)**

**.finst Binary nested grid instantaneous concentration file**  **at the end of the**

**simulation (used for restarts)**

**.PiG Binary PiG sub-model file (used for restarts)**

Standard CAMx Output Option

**.avrg.grd** nn **Binary (UAM format) average concentration file for grid**  nn **; optionally**

**contains 2-D layer 1 concentration field or full 3- D concentration field** 

**.depn.grd** nn **Binary (UAM format) surface deposition file for gr id**  nn

**.smp** nn **Binary (UAM format) average surface concentration f ile for PiG** 

**sampling grid** nn **(optional)**

HDF5 Output Option

**-CX-YYYY-MM-DD-HHMMSS-head.txt Text header file for all grids at specific date/t**  **ime**

**-CX-YYYY-MM-DD-HHMMSS-g** nn **.h5 HDF average concentration file for grid**  nn **at specific date/time; optionally contains 2-D layer 1 concentration fie**  **ld or full 3-D concentration field** 

**-CS-YYYY-MM-DD-HHMMSS-head.txt (optional) Text header file for all sampling grids at specif**  **ic date/time**

**-CS-YYYY-MM-DD-HHMMSS-g** nn **.h5 (optional) HDF average surface concentration file for sampli**  **ng grid** nn **at specific date/time** CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-10 The description of PiG submodel is provided in Sect ion 4. PiG sampling grids are set identically to the way nested grids are specified for the host model, with one exception: there are no vertical levels to define (sampling grids are currently only 2-D surface fields). The same rules that apply for the specification of nested grids holds for the specification of all sampling grids (see Section 2 of the User’s Guide). The “mesh factor” sets the resolution or cell size of the sampling grid relative to the master grid. The CAMx diagnostic o utput file provides information on the location and size of each sampling grid to help ens ure proper setup.

If CAMx cannot find or open a non-blank input filen ame provided in the run control file, the model will stop with an error. CAMx will accept bl ank input filenames for only those files that are optional.

On computer platforms running Unix or Linux the gen eration of the run control file is most easily accomplished in the job script that actually runs the model; Figure 5-1 shows an example of a CAMx job script that builds a “CAMx.in” file a nd runs the model for each day to be simulated. Alternatively, the run control file cou ld be written separately with a name specific to a given simulation, then linked or copied to the st andard “CAMx.in” filename before the model is executed at a command line or in a job script.CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-11 #!/bin/csh
# # CAMx 5.30 # setenv OMP_NUM_THREADS 4
setenv MPSTKZ 128M
limit stacksize unlimited # set EXEC = "../../src/CAMx.v5.30.noMPI.pg_linu xomp" #

set RUN = "v5.30.midwest.36.12.noMPI" set INPUT = "../inputs" set MET = "../inputs/met" set EMIS = "../emiss" set PTSRCE = "../ptsrce" set OUTPUT = "../outputs"
# mkdir -p $OUTPUT
# # --- set the dates and times ---- #

set RESTART = "NO" foreach today (03.154 04.155) set JUL = $today:e set CAL = $today:r set YESTERDAY = `echo ${CAL} | awk '{printf("%2.2d" ,$1-1)}'`
# if( ${RESTART} == "NO" ) then

set RESTART = "false"

else

set RESTART = "true"
endif # # --- Create the input file (always called CAMx.in ) #

cat << ieof > CAMx.in

&CAMx_Control

Run_Message = 'CAMx 5.30 Test Problem -- Mech 6 CF CB05 $RUN',

!--- Model clock control ---

Time_Zone = 0, ! (0=UTC,5=E ST,6=CST,7=MST,8=PST) Restart = .${RESTART}., Start_Date_Hour = 2002,06,${CAL},0000, ! (YYYY, MM,DD,HHmm) End_Date_Hour = 2002,06,${CAL},2400, ! (YYYY, MM,DD,HHmm)

Maximum_Timestep = 15., ! minutes Met_Input_Frequency = 60., ! minutes Ems_Input_Frequency = 60., ! minutes Output_Frequency = 60., ! minutes

!--- Map projection parameters ---

Map_Projection = 'LAMBERT', ! (LAMBERT, POLAR,UTM,LATLON) LAMBERT_Central_Meridian = -97., ! deg (west< 0,south<0) LAMBERT_Center_Longitude = -97., ! deg (west< 0,south<0) LAMBERT_Center_Latitude = 40., ! deg (west< 0,south<0) LAMBERT_True_Latitude1 = 45., ! deg (west< 0,south<0) LAMBERT_True_Latitude2 = 33., ! deg (west< 0,south<0)

Figure 5-1. **A sample CAMx job script that generates a “CAMx.i n” file and runs the model.** CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-12 !--- Parameters for the master (first) grid ---

Number_of_Grids = 2, Master_Origin_XCoord = -792., ! km or deg, SW corner of cell(1,1) Master_Origin_YCoord = -1656., ! km or deg, SW corner of cell (1,1) Master_Cell_XSize = 36., ! km or deg Master_Cell_YSize = 36., ! km or deg Master_Grid_Columns = 68, Master_Grid_Rows = 68, Number_of_Layers(1) = 16,

!--- Parameters for the second grid ---

Nest_Meshing_Factor(2) = 3, ! Cell size relative to master grid Nest_Beg_I_Index(2) = 22, ! Relative t o master grid Nest_End_I_Index(2) = 51, ! Relative t o master grid Nest_Beg_J_Index(2) = 22, ! Relative t o master grid Nest_End_J_Index(2) = 58, ! Relative t o master grid Number_of_Layers(2) = 16,

!--- Model options ---

Diagnostic_Error_Check = .false., ! True = wi ll stop after 1st timestep Advection_Solver = 'PPM', ! (PPM,BOTT ) Chemistry_Solver = 'EBI', ! (EBI,IEH, LSODE) PiG_Submodel = 'None', ! (None,GRE ASD,IRON) Probing_Tool = 'None', ! (None,OSA T,GOAT,APCA,PSAT,DDM, HDDM,PA,IPR,IRR,RTRAC,RTCMC) Chemistry = .true., Drydep_Model = ‘WESELY89’, ! (NONE, WE SELY89, ZHANG03) Wet_Deposition = .true., ACM2_Diffusion = .false., TUV_Cloud_Adjust = .true., Staggered_Winds = .true., Super_Stepping = .true., Gridded_Emissions = .true., Point_Emissions = .true., Ignore_Emission_Dates = .true.,

!--- Output specifications ---

Root_Output_Name = '$OUTPUT/CAMx.$RUN.2002 06${CAL}', Average_Output_3D = .false., HDF_Format_Output = .false., Number_of_Output_Species = 21, Output_Species_Names(1) = 'NO', Output_Species_Names(2) = 'NO2', Output_Species_Names(3) = 'O3', Output_Species_Names(4) = 'SO2', Output_Species_Names(5) = 'H2O2', Output_Species_Names(6) = 'HNO3', Output_Species_Names(7) = 'NH3', Output_Species_Names(8) = 'PNO3', Output_Species_Names(9) = 'PSO4', Output_Species_Names(10) = 'PNH4', Output_Species_Names(11) = 'POA', Output_Species_Names(12) = 'PEC', Output_Species_Names(13) = 'FPRM', Output_Species_Names(14) = 'CPRM', Output_Species_Names(15) = 'CCRS', Output_Species_Names(16) = 'FCRS', Output_Species_Names(17) = 'SOA1', Output_Species_Names(18) = 'SOA2', Output_Species_Names(19) = 'SOA3',

Figure 5-1. **(continued).**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-13 Output_Species_Names(20) = 'SOA4', Output_Species_Names(21) = 'SOA5',

!--- Input files ---

Chemistry_Parameters = '$INPUT/CAMx5.2.chemparam.6 _CF', Photolyis_Rates = '$INPUT/tuv.200206.STL.txt'

, Initial_Conditions = '$INPUT/IC.vistas_2002gt2a_ STL_36_68X68_16L.2002081', Boundary_Conditions = '$INPUT/BC.vistas_2002gt2a_ STL_36_68X68_16L.2002${JUL}', Albedo_Haze_Ozone = '$INPUT/ahomap.200206.STL_3 6_68X68_12_92X113.txt', Point_Sources = '$PTSRCE/ptsrce.stl.36km.20 02${JUL}.a0.bin', Master_Grid_Restart = '$OUTPUT/CAMx.$RUN.200206${ YESTERDAY}.inst', Nested_Grid_Restart = '$OUTPUT/CAMx.$RUN.200206${ YESTERDAY}.finst', PiG_Restart = ' ',

Emiss_Grid(1) = '$EMIS/emiss.stl.36km.200206${CA L}.a1.bin', Landuse_Grid(1) = '$INPUT/lu.STL_36_68X68.bin', ZP_Grid(1) = '$INPUT/met/camx.zp.200206${CAL} .36k.bin', Wind_Grid(1) = '$INPUT/met/camx.uv.200206${CAL} .36k.bin', Temp_Grid(1) = '$INPUT/met/camx.tp.200206${CAL} .36k.bin', Vapor_Grid(1) = '$INPUT/met/camx.qa.200206${CAL} .36k.bin', Cloud_Grid(1) = '$INPUT/met/camx.cr.200206${CAL} .36k.bin', Kv_Grid(1) = '$INPUT/met/camx.kv.200206${CAL} .36k.bin', Emiss_Grid(2) = '$EMIS/emiss.stl.12kmsmall.20020 6${CAL}.a1.bin', Landuse_Grid(2) = '$INPUT/lu.STL_12_92X113.bin', ZP_Grid(2) = '$INPUT/met/camx.zp.200206${CAL} .12ksmall.bin', Wind_Grid(2) = '$INPUT/met/camx.uv.200206${CAL} .12ksmall.bin', Temp_Grid(2) = '$INPUT/met/camx.tp.200206${CAL} .12ksmall.bin', Vapor_Grid(2) = '$INPUT/met/camx.qa.200206${CAL} .12ksmall.bin', Cloud_Grid(2) = '$INPUT/met/camx.cr.200206${CAL} .12ksmall.bin', Kv_Grid(2) = '$INPUT/met/camx.kv.200206${CAL} .12ksmall.bin',

/

!-------------------------------------------------- -----------------------------

ieof # # --- Execute the model --- # if( ! { $EXEC } ) then

exit
endif

end

Figure 5-1. **(concluded).**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-14 5.2 CAMx CHEMISTRY PARAMETERS FILE

Chemistry parameters are provided in a text (ASCII) file that provides chemical information for
the simulation and selects which mechanism is to be used. The chemistry parameters file format is unique to CAMx; the formats are defined in Table 5-3, and samples are given in Figure 5-2. Some records in this file are labeled (columns 1-20 ) to indicate the type of information to supply on that line (starting in column 21). For records that are not labeled, data start in column 1. Some chemistry parameter records are optional, depe nding upon the logical flags indicated for such records, and are shown in Table 5-3 by a check in the third column; if the indicated option is not invoked these records should not appear in t he file. The first record of the chemistry parameters file must contain the string “ VERSION5.3 ”, which indicates that this file is compatible with CAMx version 5.20.

If the chemistry flag is set “true” on the CAMx.in file, CAMx checks that certain properties of the selected mechanism are consistent with paramete rs supplied on the input file (e.g., number of reactions, photolysis reactions and species). If a ny discrepancies are found, they are reported in the output message file and the simulation is halte d. The user may also specify an inert simulation by setting the chemistry flag to “false” . In this case, any number of arbitrarily named species may be listed, and chemistry reaction param eters are read but not used. The lower bound values specified in the chemistry parameter file ar e used to specify initial and/or boundary conditions for species that are omitted from the in itial and/or boundary condition files. The Henry’s Law, diffusion, reactivity, and surface res istance scaling parameters for gases are used for dry deposition calculations (Wesley, 1989). Ae rosol density and diameters are used for both wet and dry deposition calculations.

The chemistry parameters file controls how photolys is rates are calculated in CAMx. So-called “primary” photolysis rates are input to CAMx via th e photolysis rates file. The primary photolysis reactions are identified by number in th e chemistry parameters file and the photolysis
rates file must match this declaration. So-called “secondary” photolysis rates are set by scaling factors to one of the primary reactions. Clearly, this approach requires at least one primary photolysis reaction.

CAMx supports several equations for specifying rate constants, as shown in Table 5-4a. The type of equation used for each reaction is identifi ed by the second parameter specified for each reaction – a number between 1 and 7 (Table 5-3). T he number of additional parameters required depends upon the expression type and varies between 2 and 13, as shown in Table 5-4b. Currently, the CB4 mechanisms implemented in CAMx ( Table 3-1) use expression types 1, 2, 4 and 7, whereas CB05 uses types 1, 3, 4, and 7, and SAPRC99 uses types 1, 3, 4, 6 and 7. Expression type 4 (Troe expression) allows for a co mplete description of dependencies on temperature and pressure; background information on Troe expressions may be found in the NASA and IUPAC rate constant compilations (NASA, 19 97; IUPAC, 1992).

Rate constants can be specified in molecular units (e.g., cm 3 molecule -1 s -1 ) or ppm units (e.g.,

ppm -1 min -1 ). All the rate constants must be in a single unit s system; CAMx will determine which units system is being used from the magnitude of the rate constants. Diagnostic information on the rate constants and units system is output by CAMx at run-time.CAMx v5.30 User’s Guide, December 2010Core Model Input/Output StructuresY:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx5-15Table 5-3.**Description of the CAMx chemistry parameters file. The record labels exist in columns 1-20, and where** **given, the input data for that record start in column 21. The forma** **t denoted “list” indicates a free-format list of nu mbers (comma or space-delimited).** Record Label(columns 1-20)RecordOptionalFormatDescriptionCAMx version**A Model version keyword (VERSION5.3)**Mechanism ID**list ID number of chemical mechanism (see Table 3- 1)** Aerosol Option**A Keyword for aerosol scheme (CF, CMU, or NONE) 1** Description**A Message record to describe this file**No of gas species**list Number of gas species (NGAS ≥ 1)**No of aero species**list Number of aerosol species (NAERO ≥ 0)** **Aerosol chemistry timestep (min) (**if NAERO > 0**) Number of size bins (NBIN ≥ 1) (** if NAERO > 0**) 2** **Aerosol diameter (**μ**m) for NBIN+1 cut points (**if NAERO > 0**)**No of reactions**list Number of reactions (NREACT ≥ 0)**Prim photo rxns**list Number of primary photolysis reactions (NPHOT1 ≥ 0)** **List of primary photolysis reaction ID numbers (mus t match the photolysis rates input file)** No of sec photo rxn**list Number of secondary photolysis reactions (NPH OT2 ≥ 0)** ID, prim ID, scale√**list**If NPHOT2 > 0, repeat this record for each secondary photolysisreaction**ID number of the secondary photolysis reaction ID number of the primary photolysis reaction used f** **or scaling Secondary reaction scale factor** Species Records**Heading**Gas Spec ...**Heading 5X,** **A10, E10.0,** **E10.0, F10.0,** **F10.0, F10.0** **F10.0 Repeat this record for each gas species (start in c** **olumn 1) Gas species name** **Lower bound concentration (ppm) Henry’s law constant (M/atm)** **Henry’s law temperature dependence (K) Molecular diffusivity ratio** **Wesley’s reactivity parameter Surface resistance scaling factor for strong acids** **(0-1)**CAMx v5.30 User’s Guide, December 2010Core Model Input/Output StructuresY:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx5-16Table 5-3.**(Continued) Description of the CAMx chemistry param eters file. The record labels exist in columns 1-2** **0, and where given, the input data for that record start in colu** **mn 21. The format denoted “list” indicates a free- format list of numbers (comma or** **space-delimited).**Record Label(columns 1-20)RecordOptionalFormatDescriptionAero Spec ...√**Heading (**if NAERO > 0**)**√**5X, A10,** **E10.0, F10.0** If NAERO > 0, repeat this record for each aerosol species**(start in column 1) Aerosol species name** **Lower bound concentration (**μ**g/m 3** **) Species density (g/cm** **3 )** Reaction Records√**Heading (**if NREACT > 0**)**Rxn Typ Param ...√**Heading (**if NREACT > 0**)**√**list**If NREACT > 0, repeat this record for each gas-phase reaction**(start in column 1) Reaction ID number** **Rate constant expression ID number (1-7, as shown i n Table 5-4)** **Rate constant parameters (depending on expression t ype in Table 5-4). For reactions** **identified as photolysis reactions above, the rate constant is not used and is customarily** **set to zero. 1** **Used only for Mechanisms 4, 5 or 6 (otherwise can be left blank): CF = Coarse/Fine scheme; CMU = mult** **i-sectional scheme; NONE = gas-phase chemistry only.** **2 For the CF scheme, NBIN** must be set to 2**, and the user specifies the coarse/fine size range s.** CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-17 CAMx Version |VERSION5.3 Mechanism ID |6 Aerosol Option |CF Description |CB05 plus PM (CF) with Hg: PNA rates set to 0; molecular units No of gas species |54 #aero, dt, bins |22 15.0 2 0.039 2.5 10.0 No of reactions |156 Prim photo rxns |19 1 8 9 14 15 25 36 51 52 53 6 2 65 75 76 87 91 102 143 152 No of sec photo rxn|4 ID, prim ID, scale |72 65 1.0 |97 65 0.0 |106 91 1.0 |138 75 9.0 Species Records Gas Spec lower bnd H-law T-fact Dif frat Reactvty Rscale
1 NO 1.00E-15 1.90e-03 -1480. 1.29 0.0 1.
2 NO2 1.00E-09 1.00e-02 -2516. 1.60 0.8 1.
3 O3 1.00E-09 1.10e-02 -2415. 1.63 1.0 1.
4 PAN 1.00E-09 3.60e+00 -5910. 2.59 0.6 1.
5 NO3 1.00E-12 1.00e+05 -4000. 1.86 0.1 0.
6 N2O5 1.00E-12 1.00e+05 -4000. 2.45 0.1 0.
7 OLE 1.00E-09 5.00e-03 -4000. 1.80 0.0 1.
8 PAR 1.00E-04 1.00e-03 -4000. 2.00 0.0 1.
9 TOL 1.00E-09 1.20e+00 -4000. 2.26 0.0 1.
10 XYL 1.00E-09 1.40e+00 -4000. 2.43 0.0 1.
11 FORM 1.00E-09 6.30e+03 -6492. 1.29 0.0 1.
12 ALD2 1.00E-09 6.30e+03 -6492. 1.56 0.0 1.
13 ETH 1.00E-09 1.00e-02 -4000. 1.25 0.0 1.
14 CRES 1.00E-09 2.70e+03 -6492. 2.45 0.0 1.
15 MGLY 1.00E-09 2.70e+03 -6492. 2.00 0.0 1.
16 OPEN 1.00E-12 2.70e+03 -6492. 2.47 0.0 1.
17 PNA 1.00E-09 2.00e+04 -5910. 2.09 1.0 1.
18 CO 1.00E-04 1.00e-10 0. 1.25 0.0 1.
19 HONO 1.00E-09 5.90e+01 -4781. 1.62 1.0 1.
20 H2O2 1.00E-09 7.40e+04 -6643. 1.37 1.0 0.
21 HNO3 1.00E-09 2.10e+05 -8707. 1.87 0.0 0.
22 ISOP 1.00E-09 1.00e-02 -4000. 1.94 0.0 1.
23 MEOH 1.00E-09 2.20e+02 -4932. 1.33 0.0 1.
24 ETOH 1.00E-09 2.20e+02 -4932. 1.60 0.0 1.
25 ISPD 1.00E-09 6.30e+03 -6492. 1.97 0.0 1.
26 NTR 1.00E-09 9.40e+03 -8706. 2.72 0.0 1.
27 SO2 1.00E-09 1.22e+00 -3156. 1.89 0.0 1.
28 SULF 1.00E-12 1.00e+10 0. 2.33 0.0 0.
29 ROOH 1.00E-12 1.00E+02 -4000. 2.06 0.8 0.
30 ALDX 1.00E-12 6.30E+03 -6492. 1.80 0.0 1.
31 MEPX 1.00E-12 3.05E+02 -5250. 1.63 0.8 0.
32 FACD 1.00E-12 5.68E+03 -6060. 1.83 0.0 1.
33 PACD 1.00E-12 5.00E+03 -4000. 2.24 0.0 1.
34 AACD 1.00E-12 5.00E+03 -4000. 2.03 0.0 1.
35 PANX 1.00E-09 3.60E+00 -5910. 2.74 0.6 1.
36 ETHA 1.00E-04 1.73E-03 -4000. 1.43 0.0 1.
37 IOLE 1.00E-12 5.00E-03 -4000. 2.05 0.0 1.
38 TERP 1.00E-12 4.90E-02 -4000. 2.75 0.0 1.
39 NH3 1.00E-09 5.76e+01 -4100. 0.97 0.0 0.
40 HCL 1.00E-12 1.00e+05 -4000. 1.42 0.0 0.
41 TOLA 1.00E-09 1.20e+00 -4000. 2.26 0.0 1.
42 XYLA 1.00E-09 1.40e+00 -4000. 2.43 0.0 1.
43 ISP 1.00E-09 1.00e-02 -4000. 1.94 0.0 1.
44 TRP 1.00E-12 4.90e-02 -4000. 2.75 0.0 1. 45 SQT 1.00E-12 4.90e-02 -4000. 3.37 0.0 1.
46 CG1 1.00E-12 1.00e+05 -4000. 2.89 0.0 1.
47 CG2 1.00E-12 1.00e+05 -4000. 2.89 0.0 1.
48 CG3 1.00E-12 1.00e+05 -4000. 2.69 0.0 1.

Figure 5-2a. **Example CAMx chemistry parameters file for Mechan ism 6 (CF scheme),**  **including the mercury species HG0, HG2, and HGP.**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-18 49 CG4 1.00E-12 1.00e+05 -4000. 2.69 0.0 1.
50 CG5 1.00E-12 1.00e+05 -4000. 3.16 0.0 1.
51 CG6 1.00E-12 1.00e+05 -4000. 3.16 0.0 1.
52 CG7 1.00E-12 1.00e+05 -4000. 3.42 0.0 1.
53 HG0 1.00E-12 1.00e-10 0. 3.34 0.0 1.
54 HG2 1.00E-12 2.00e+05 -4000. 3.76 0.0 1. Aero Spec lower bnd Density

1 PNO3 1.00E-09 1.5
2 PSO4 1.00E-09 1.5
3 PNH4 1.00E-09 1.5
4 POA 1.00E-09 1.0
5 SOA1 1.00E-09 1.0
6 SOA2 1.00E-09 1.0
7 SOA3 1.00E-09 1.0
8 SOA4 1.00E-09 1.0
9 SOA5 1.00E-09 1.0
10 SOA6 1.00E-09 1.0
11 SOA7 1.00E-09 1.0
12 SOPA 1.00E-09 1.0
13 SOPB 1.00E-09 1.0
14 PEC 1.00E-09 2.0
15 FPRM 1.00E-09 3.0
16 FCRS 1.00E-09 3.0
17 CPRM 1.00E-09 3.0
18 CCRS 1.00E-09 3.0
19 NA 1.00E-09 2.0
20 PCL 1.00E-09 2.0
21 HGP 1.00E-12 2.0
22 PH2O 1.00E-09 1.0
Reaction Records Rxn Typ Order Parameters (1 to 12, depending upon T yp)
1 1 1 0.000E+00
2 3 3 6.000E-34 0.0 -2.40 300.0
3 3 2 3.000E-12 1500.0 0.00 300.0
4 3 2 5.600E-12 -180.0 0.00 300.0
5 4 2 2.500E-31 0.0 -1.80 300.0 2.200E-11 0.0 -
0.70 300.0 0.60 1.00
6 4 2 9.000E-32 0.0 -1.50 300.0 3.000E-11 0.0 0.00
300.0 0.60 1.00
7 3 2 1.200E-13 2450.0 0.00 300.0
8 1 1 0.000E+00
9 1 1 0.000E+00
10 3 2 2.100E-11 -102.0 0.00 300.0
11 1 2 2.200E-10
12 3 2 1.700E-12 940.0 0.00 300.0
13 3 2 1.000E-14 490.0 0.00 300.0
14 1 1 0.000E+00
15 1 1 0.000E+00
16 3 2 1.500E-11 -170.0 0.00 300.0
17 3 2 4.500E-14 1260.0 0.00 300.0
18 4 2 2.000E-30 0.0 -4.40 300.0 1.400E-12 0.0 -
0.70 300.0 0.60 1.00
19 1 2 2.500E-22
20 1 3 1.800E-39
21 4 1 1.000E-03 11000.0 -3.50 300.0 9.700E+14 11080.0 0.10
300.0 0.45 1.00
22 3 3 3.300E-39 -530.0 0.00 300.0
23 1 3 5.000E-40
24 4 2 7.000E-31 0.0 -2.60 300.0 3.600E-11 0.0 -
0.10 300.0 0.60 1.00
25 1 1 0.000E+00
26 3 2 1.800E-11 390.0 0.00 300.0
27 1 2 1.000E-20

Figure 5-2a. **(continued).**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-19 28 4 2 2.000E-30 0.0 -3.00 300.0 2.500E-11 0.0 0.00
300.0 0.60 1.00
29 6 2 2.400E-14 -460.0 0.00 300.0 2.700E-17 -2199.0 0.00
300.0 6.500E-34 -1335.0 0.00 300 .0
30 3 2 3.500E-12 -250.0 0.00 300.0
31 1 2 0.000E+00
32 1 1 0.000E+00
33 1 2 0.000E+00
34 7 2 2.300E-13 -600.0 0.00 300.0 1.700E-33 -1000.0 0.00
300.0
35 7 3 3.220E-34 -2800.0 0.00 300.0 2.380E-54 -3200.0 0.00
300.0
36 1 1 0.000E+00
37 3 2 2.900E-12 160.0 0.00 300.0
38 1 2 1.100E-10
39 3 2 5.500E-12 2000.0 0.00 300.0
40 3 2 2.200E-11 -120.0 0.00 300.0
41 3 2 4.200E-12 240.0 0.00 300.0
42 4 2 6.900E-31 0.0 -1.00 300.0 2.600E-11 0.0 0.00
300.0 0.60 1.00
43 3 2 4.800E-11 -250.0 0.00 300.0
44 3 2 3.000E-11 -200.0 0.00 300.0
45 3 2 1.400E-12 2000.0 0.00 300.0
46 1 2 1.000E-11
47 1 2 2.200E-11
48 1 2 3.500E-12
49 1 2 1.000E-17
50 3 2 8.500E-13 2450.0 0.00 300.0
51 1 1 0.000E+00
52 1 1 0.000E+00
53 1 1 0.000E+00
54 3 2 2.600E-12 -365.0 0.00 300.0
55 3 2 2.600E-12 -365.0 0.00 300.0
56 3 2 7.500E-13 -700.0 0.00 300.0
57 3 2 7.500E-13 -700.0 0.00 300.0
58 1 2 6.800E-14
59 1 2 6.800E-14
60 1 2 6.800E-14
61 3 2 5.900E-13 360.0 0.00 300.0
62 1 1 0.000E+00
63 4 2 3.000E-31 0.0 -3.30 300.0 1.500E-12 0.0 0.00
300.0 0.60 1.00
64 3 2 3.010E-12 -190.0 0.00 300.0
65 1 1 0.000E+00
66 7 2 1.440E-13 0.0 0.00 300.0 3.430E-33 0.0 0.00
300.0
67 3 2 2.450E-12 1775.0 0.00 300.0
68 3 2 2.800E-12 -300.0 0.00 300.0
69 3 2 4.100E-13 -750.0 0.00 300.0
70 3 2 9.500E-14 -390.0 0.00 300.0
71 3 2 3.800E-12 -200.0 0.00 300.0
72 1 1 0.000E+00
73 3 2 7.300E-12 620.0 0.00 300.0
74 1 2 9.000E-12
75 1 1 0.000E+00
76 1 1 0.000E+00
77 3 2 3.400E-11 1600.0 0.00 300.0
78 1 2 5.800E-16
79 3 2 9.700E-15 -625.0 0.00 300.0
80 3 1 2.400E+12 7000.0 0.00 300.0
81 1 2 5.600E-12
82 3 2 5.600E-15 -2300.0 0.00 300.0
83 1 2 4.000E-13

Figure 5-2a. **(continued).**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-20 84 3 2 1.800E-11 1100.0 0.00 300.0
85 3 2 5.600E-12 -270.0 0.00 300.0
86 3 2 1.400E-12 1900.0 0.00 300.0
87 1 1 0.000E+00
88 3 2 8.100E-12 -270.0 0.00 300.0
89 4 2 2.700E-28 0.0 -7.10 300.0 1.200E-11 0.0 -
0.90 300.0 0.30 1.00
90 4 1 4.900E-03 12100.0 0.00 300.0 5.400E+16 13830.0 0.00
300.0 0.30 1.00
91 1 1 0.000E+00
92 3 2 4.300E-13 -1040.0 0.00 300.0
93 3 2 2.000E-12 -500.0 0.00 300.0
94 3 2 4.400E-13 -1070.0 0.00 300.0
95 3 2 2.900E-12 -500.0 0.00 300.0
96 3 2 4.000E-13 -200.0 0.00 300.0
97 1 1 0.000E+00
98 3 2 4.000E-13 -200.0 0.00 300.0
99 3 2 1.300E-11 870.0 0.00 300.0
100 3 2 5.100E-12 -405.0 0.00 300.0
101 1 2 6.500E-15
102 1 1 0.000E+00
103 3 2 6.700E-12 -340.0 0.00 300.0
104 4 2 2.700E-28 0.0 -7.10 300.0 1.200E-11 0.0 -
0.90 300.0 0.30 1.00
105 4 1 4.900E-03 12100.0 0.00 300.0 5.400E+16 13830.0 0.00
300.0 0.30 1.00
106 1 1 0.000E+00
107 1 2 3.000E-13
108 3 2 4.300E-13 -1040.0 0.00 300.0
109 3 2 2.000E-12 -500.0 0.00 300.0
110 3 2 4.400E-13 -1070.0 0.00 300.0
111 3 2 2.900E-12 -500.0 0.00 300.0
112 3 2 2.900E-12 -500.0 0.00 300.0
113 3 2 8.700E-12 1070.0 0.00 300.0
114 3 2 6.900E-12 230.0 0.00 300.0
115 1 2 8.100E-13
116 3 1 1.000E+15 8000.0 0.00 300.0
117 1 1 1.600E+03
118 1 2 1.500E-11
119 3 2 1.000E-11 280.0 0.00 300.0
120 1 2 3.200E-11
121 3 2 6.500E-15 1900.0 0.00 300.0
122 3 2 7.000E-13 2160.0 0.00 300.0
123 3 2 1.040E-11 792.0 0.00 300.0
124 4 2 1.000E-28 0.0 -0.80 300.0 8.800E-12 0.0 0.00
300.0 0.60 1.00
125 3 2 1.200E-14 2630.0 0.00 300.0
126 3 2 3.300E-12 2880.0 0.00 300.0
127 1 2 2.300E-11
128 3 2 1.000E-11 -550.0 0.00 300.0
129 3 2 8.400E-15 1100.0 0.00 300.0
130 3 2 9.600E-13 270.0 0.00 300.0
131 3 2 1.800E-12 -355.0 0.00 300.0
132 1 2 8.100E-12
133 1 1 4.200E+00
134 1 2 4.100E-11
135 1 2 2.200E-11
136 1 2 1.400E-11
137 1 2 5.500E-12
138 1 1 0.000E+00
139 1 2 3.000E-11
140 3 2 5.400E-17 500.0 0.00 300.0
141 3 2 1.700E-11 -116.0 0.00 300.0

Figure 5-2a. **(continued).**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-21 142 1 2 1.700E-11
143 1 1 0.000E+00
144 1 2 3.600E-11
145 3 2 2.540E-11 -407.6 0.00 300.0
146 3 2 7.860E-15 1912.0 0.00 300.0
147 3 2 3.030E-12 448.0 0.00 300.0
148 1 2 1.500E-19
149 1 2 3.360E-11
150 1 2 7.100E-18
151 1 2 1.000E-15
152 1 1 0.000E+00
153 1 2 3.600E-11
154 3 2 1.500E-11 -449.0 0.00 300.0
155 3 2 1.200E-15 821.0 0.00 300.0
156 3 2 3.700E-12 -175.0 0.00 300.0

Figure 5-2a. **(concluded).**

CAMx Version |VERSION5.3 Mechanism ID |0 Aerosol Option | Description |inert test No of gas species |1 No of aero species |0 No of reactions |0 Prim photo rxns |0 No of sec photo rxn|0 Species Records Gas Spec lower bnd H-law T-fact Diff rat Reactvty
1 TRACER 1.00E-09 1.00e-10 0. 1 .00 0.0

Figure 5-2b. **Example inert chemistry parameters file (requires chemistry flag to be set false**  **see the description of the CAMx control file).**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-22 Table 5-4a. **Rate constant expression types supported in CAMx and order of expression**  **parameters for the chemistry parameters file.**

Expression Type Description Expression

**1 Constant** 298 k k =

**2 UAM (Arrhenius expression)**       

    

− = T

E k k a 1

298 1 exp 298

**3 General temperature dependence**      

−        

= T

E

T T A k a

B

R exp

**4 Troe-type temperature and pressure** 

**dependence** 

## [ ] [ ] G F k M k M k k 

      

+ = ∞

/ 1 0 0



    

−        

= T

E

T T A k a

B

R exp 0



     ′

−        

′ ′
= ′

∞ T

E

T T A k a

B

R exp 

# ( ) 1

2 0 /] [ log 1 −

∞

  

 

  

 



      

+ = n k

M k G

**5 Equilibrium with a previously defined** 

**reaction (** k

ref **)**      

−        

= T

E

T T A k k a

B

R ref exp

**6 Lindemann - Hinshelwood as used for** 

**OH + HNO3** 2 3 3 0 /] [ 1 ] [

k M k M k k k

+ + =

**7 Simple pressure dependence used for** 

**OH + CO** 

### ] [ 2 1 

### M k k k + =

**Notes:**
T

**is temperature (K)**
T R **is reference temperature of 300 K**
E a **is an Arrhenius activation energy (K)**
k 0 **is the low pressure limit of the rate constant**
k ∞ **is the high pressure limit of the rate constant [**  M

**] is the concentration of air**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-23 Table 5-4b. **List of parameters that must be provided in the C AMx chemistry parameter file for**  **each of the seven types of rate constant expression s. Use ppm/minute units for A and Kelvin**  **for E a**  **and T R**  **. The variable O is the order of the reaction (1 t o 3).** 

Expression Type Parameters

1 2 3 4 5 6 7 8 9 10 11 12 13

1 **O k 298** 

2 **O k 298**  **E a** 

3 **O A E a**  **B T R** 

4 **O A E a**  **B T R**  **A’ E a**  **’ B’ T R**  **’ F n**

5 **O k ref** 

**A E a**  **B T R** 

6 **O A o**  **E a**  **o B**  **o T**  **R o**  **A 2**  **E a**  **2 B**  **2 T**  **R 2**  **A 3**  **E a**  **3 B**  **3 T**  **R 3** 

7 **O A 1**  **E a**  **1 B**  **1 T**  **R 1**  **A 2**  **E a**  **2 B**  **2 T**  **R 2** 

5.3 PHOTOLYSIS RATES FILE

The rates for the primary photolysis reactions are supplied via the photolysis rates file in units of
minute -1 . This file must be supplied if chemistry is invok ed, and it is essentially a large look-up table that provides the photolysis rates in five di mensions, including variations over solar zenith angle, height above ground, ultraviolet (UV) albedo of the ground, atmospheric turbidity, and total ozone column density. This table can be gene rated using the TUV radiative transfer model as a preprocessor, which can be configured by the u ser for the range of conditions expected during a specific CAMx application. Therefore, the user must supply the number of intervals (or “classes”) and associated values in each of the fiv e dimensions. Typically, TUV is run for a matrix of 11 heights, 10 solar zenith angles, 5 ozo ne column values, 5 albedo values, and 3 turbidity values.

The photolysis rates file is a readable text (ASCII ) format and it has the following structure:

where the variables in the photolysis rates file ha ve the following definitions: Loop from 1 to nozn ozone column intervals: Loop from 1 to nalb UV albedo intervals: Loop from 1 to nhaz haze turbidity intervals: ozcl,albcl,hazcl (12X,f7.3,8X,f7.3,11X,f7.3) Loop from 1 to nalt altitudes above ground: height (*) Loop from 1 to nphot photolysis reactions: (pk(n),n=1,nsol) (1X,10F12.0)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-24 ozcl Ozone column value for the current interval (Dob son units)

albcl UV albedo value for the current interval (unitles s)

hazcl Haze turbidity value for the current interval (un itless) height Altitude (km)
pk Photolysis rates (min -1 ) for nsol solar zenith angles

Figure 5-3 presents an example of a photolysis rate s file for the first several panels of data.

5.4 ALBEDO/HAZE/OZONE FILE

This file (commonly condensed to ALHZOZ or AHO) con tains the spatial and temporal distribution of surface UV albedo, ozone column den sity, and turbidity for a specific CAMx domain and episode. These parameters are essential for photochemical simulations as they determine the spatial and temporal variation of pho tolysis rates. Therefore, this file must be supplied if chemistry is invoked. Additionally, th e AHO file may also provide optional fields of snow cover, land/ocean mask, drought stress codes, and surface roughness.

The mandatory time-invariant UV albedo, and optiona l time-invariant land/ocean mask, drought stress, and surface roughness fields, must be suppl ied for each cell of the master grid, and optionally any nested grids. The mandatory time-va rying ozone and turbidity fields, and the optional time-varying snow cover field, are supplie d for the master grid only; CAMx internally assigns master cell values to all nested grids cell s for these three fields. The time-invariant field s must be located at the top of the file (below some definition records), followed by all timevarying fields.

Instead of supplying the actual values of these par ameters, the file contains the respective “codes” for each interval (or class). For ozone co lumn, UV albedo, and turbidity, the classes must exactly coincide with the values specified in preparing the photolysis rates file. For example, if 5 ozone column classes are specified in TUV, then the ozone column map in the ALHZOZ file must consist of a distribution of integ ers ranging from 1 to 5 and the value ranges of those 5 classes must match the value ranges used in TUV.

The optional surface roughness field allows the use r to override the default surface roughness that is set internally in CAMx according to season and the input landuse file. This effects dry deposition and PiG puff growth rates. If roughness is provided, the ALHZOZ header must include the definition of the surface roughness cla sses. CAMx is configured to read 9 roughness classes, and their definitions must range between t he limits of 1 × 10 -5 and 20 m, otherwise CAMx will stop with an error message.

Class interval definitions are not needed for land/ ocean mask, drought stress, or snow cover. The land/ocean mask is used for Hg chemistry and define s chloride concentrations; it is simply a map of 0 (land) and 1 (ocean). Drought stress indices coincide with the Palmer Drought Index (PDI) and range from 0 to 5 (corresponding to PDI values 0 [normal] to –5 [severe drought]). Drought stress impacts dry deposition calculations for non- water landuse categories. Snow cover is simply a map of 0 (no snow) and 1(snow); if snow ex ists, the season is overridden to “winter with snow” in the WESELY89 dry deposition algorithm . This impacts surface roughness, surface resistances for dry deposition, and chemist ry by setting UV albedo to the maximum
class.CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-25 O3 Column =0.285 Albedo =0.040 Haze O.D. =0.000
0.000 = Current altitude in km
4.562E-01 4.526E-01 4.416E-01 4.224E-01 3.934E-01 3.516E-01 2.921E-01 2.065E-01 1.1 48E-01 3.074E-02
1.670E-03 1.643E-03 1.563E-03 1.427E-03 1.236E-03 9.889E-04 6.936E-04 3.747E-04 1.5 02E-04 2.570E-05
2.211E-03 2.186E-03 2.107E-03 1.973E-03 1.776E-03 1.506E-03 1.152E-03 7.108E-04 3.3 52E-04 7.560E-05
2.078E-03 2.007E-03 1.801E-03 1.485E-03 1.097E-03 6.925E-04 3.382E-04 1.034E-04 2.2 01E-05 2.056E-06
3.131E-04 3.058E-04 2.841E-04 2.491E-04 2.027E-04 1.482E-04 9.100E-05 4.015E-05 1.2 64E-05 1.517E-06
8.728E-05 8.638E-05 8.364E-05 7.891E-05 7.189E-05 6.212E-05 4.892E-05 3.166E-05 1.5 80E-05 3.934E-06
0.150 = Current altitude in km
4.866E-01 4.830E-01 4.717E-01 4.521E-01 4.223E-01 3.794E-01 3.179E-01 2.284E-01 1.3 08E-01 3.760E-02
1.855E-03 1.827E-03 1.742E-03 1.598E-03 1.394E-03 1.130E-03 8.102E-04 4.566E-04 1.9 56E-04 3.701E-05
2.443E-03 2.416E-03 2.334E-03 2.193E-03 1.986E-03 1.701E-03 1.322E-03 8.415E-04 4.1 59E-04 1.000E-04
2.327E-03 2.249E-03 2.024E-03 1.677E-03 1.250E-03 8.008E-04 4.035E-04 1.337E-04 3.3 83E-05 4.166E-06
3.489E-04 3.410E-04 3.177E-04 2.799E-04 2.297E-04 1.704E-04 1.075E-04 5.024E-05 1.7 55E-05 2.556E-06
9.553E-05 9.460E-05 9.176E-05 8.684E-05 7.952E-05 6.928E-05 5.530E-05 3.672E-05 1.9 10E-05 5.068E-06
0.360 = Current altitude in km
5.050E-01 5.013E-01 4.900E-01 4.702E-01 4.402E-01 3.968E-01 3.343E-01 2.426E-01 1.4 12E-01 4.171E-02
1.974E-03 1.945E-03 1.857E-03 1.708E-03 1.497E-03 1.222E-03 8.862E-04 5.096E-04 2.2 45E-04 4.415E-05
2.587E-03 2.560E-03 2.477E-03 2.333E-03 2.120E-03 1.826E-03 1.432E-03 9.258E-04 4.6 73E-04 1.152E-04
2.494E-03 2.411E-03 2.174E-03 1.806E-03 1.353E-03 8.740E-04 4.474E-04 1.536E-04 4.1 29E-05 5.492E-06
3.722E-04 3.640E-04 3.396E-04 3.002E-04 2.475E-04 1.851E-04 1.183E-04 5.679E-05 2.0 67E-05 3.210E-06
1.006E-04 9.970E-05 9.681E-05 9.180E-05 8.433E-05 7.382E-05 5.940E-05 3.999E-05 2.1 22E-05 5.763E-06
0.640 = Current altitude in km
5.230E-01 5.193E-01 5.080E-01 4.882E-01 4.581E-01 4.143E-01 3.510E-01 2.574E-01 1.5 20E-01 4.577E-02
2.092E-03 2.062E-03 1.972E-03 1.819E-03 1.601E-03 1.315E-03 9.632E-04 5.633E-04 2.5 35E-04 5.126E-05
2.731E-03 2.703E-03 2.618E-03 2.471E-03 2.254E-03 1.951E-03 1.544E-03 1.011E-03 5.1 91E-04 1.304E-04
2.664E-03 2.577E-03 2.327E-03 1.939E-03 1.459E-03 9.497E-04 4.928E-04 1.738E-04 4.8 72E-05 6.809E-06
3.958E-04 3.872E-04 3.618E-04 3.207E-04 2.656E-04 2.000E-04 1.294E-04 6.343E-05 2.3 78E-05 3.860E-06
1.057E-04 1.047E-04 1.018E-04 9.672E-05 8.912E-05 7.839E-05 6.354E-05 4.331E-05 2.3 36E-05 6.448E-06
0.980 = Current altitude in km
5.409E-01 5.373E-01 5.260E-01 5.062E-01 4.760E-01 4.321E-01 3.682E-01 2.727E-01 1.6 35E-01 4.989E-02
2.210E-03 2.179E-03 2.087E-03 1.930E-03 1.705E-03 1.409E-03 1.041E-03 6.178E-04 2.8 27E-04 5.837E-05
2.873E-03 2.845E-03 2.758E-03 2.609E-03 2.387E-03 2.077E-03 1.656E-03 1.099E-03 5.7 18E-04 1.454E-04
2.837E-03 2.746E-03 2.483E-03 2.074E-03 1.567E-03 1.028E-03 5.396E-04 1.944E-04 5.6 11E-05 8.121E-06
4.195E-04 4.106E-04 3.842E-04 3.414E-04 2.840E-04 2.152E-04 1.407E-04 7.017E-05 2.6 89E-05 4.509E-06
1.107E-04 1.097E-04 1.068E-04 1.016E-04 9.392E-05 8.299E-05 6.775E-05 4.673E-05 2.5 56E-05 7.133E-06
1.380 = Current altitude in km
5.593E-01 5.556E-01 5.444E-01 5.247E-01 4.947E-01 4.507E-01 3.863E-01 2.892E-01 1.7 60E-01 5.427E-02
2.329E-03 2.298E-03 2.203E-03 2.042E-03 1.811E-03 1.505E-03 1.122E-03 6.743E-04 3.1 28E-04 6.563E-05
3.016E-03 2.988E-03 2.900E-03 2.749E-03 2.524E-03 2.207E-03 1.773E-03 1.190E-03 6.2 70E-04 1.609E-04
3.015E-03 2.920E-03 2.644E-03 2.215E-03 1.681E-03 1.109E-03 5.887E-04 2.158E-04 6.3 64E-05 9.454E-06
4.437E-04 4.345E-04 4.071E-04 3.627E-04 3.029E-04 2.310E-04 1.524E-04 7.716E-05 3.0 09E-05 5.169E-06
1.157E-04 1.148E-04 1.118E-04 1.066E-04 9.883E-05 8.772E-05 7.213E-05 5.031E-05 2.7 89E-05 7.835E-06
1.840 = Current altitude in km
5.782E-01 5.746E-01 5.635E-01 5.440E-01 5.141E-01 4.702E-01 4.056E-01 3.069E-01 1.8 97E-01 5.904E-02
2.451E-03 2.419E-03 2.322E-03 2.157E-03 1.920E-03 1.605E-03 1.206E-03 7.338E-04 3.4 45E-04 7.312E-05
3.162E-03 3.133E-03 3.045E-03 2.892E-03 2.664E-03 2.341E-03 1.894E-03 1.286E-03 6.8 56E-04 1.769E-04
3.200E-03 3.101E-03 2.811E-03 2.361E-03 1.799E-03 1.196E-03 6.407E-04 2.385E-04 7.1 41E-05 1.082E-05
4.685E-04 4.589E-04 4.307E-04 3.846E-04 3.224E-04 2.473E-04 1.647E-04 8.451E-05 3.3 41E-05 5.848E-06
1.209E-04 1.199E-04 1.169E-04 1.117E-04 1.039E-04 9.263E-05 7.671E-05 5.412E-05 3.0 39E-05 8.570E-06
2.350 = Current altitude in km
5.974E-01 5.939E-01 5.829E-01 5.636E-01 5.340E-01 4.904E-01 4.257E-01 3.258E-01 2.0 46E-01 6.425E-02
2.572E-03 2.540E-03 2.441E-03 2.273E-03 2.031E-03 1.706E-03 1.292E-03 7.955E-04 3.7 75E-04 8.077E-05
3.307E-03 3.278E-03 3.190E-03 3.036E-03 2.805E-03 2.477E-03 2.019E-03 1.386E-03 7.4 76E-04 1.934E-04
3.389E-03 3.285E-03 2.983E-03 2.512E-03 1.922E-03 1.285E-03 6.952E-04 2.622E-04 7.9 36E-05 1.221E-05
4.934E-04 4.836E-04 4.544E-04 4.068E-04 3.424E-04 2.641E-04 1.774E-04 9.214E-05 3.6 85E-05 6.538E-06
1.261E-04 1.251E-04 1.221E-04 1.169E-04 1.090E-04 9.763E-05 8.142E-05 5.811E-05 3.3 05E-05 9.333E-06
2.910 = Current altitude in km
6.169E-01 6.134E-01 6.026E-01 5.836E-01 5.543E-01 5.111E-01 4.466E-01 3.458E-01 2.2 09E-01 7.003E-02
2.694E-03 2.661E-03 2.561E-03 2.390E-03 2.143E-03 1.810E-03 1.382E-03 8.602E-04 4.1 24E-04 8.867E-05
3.452E-03 3.423E-03 3.335E-03 3.180E-03 2.948E-03 2.615E-03 2.148E-03 1.492E-03 8.1 40E-04 2.106E-04
3.582E-03 3.474E-03 3.159E-03 2.667E-03 2.049E-03 1.379E-03 7.528E-04 2.874E-04 8.7 61E-05 1.363E-05
5.187E-04 5.086E-04 4.786E-04 4.294E-04 3.628E-04 2.814E-04 1.906E-04 1.002E-04 4.0 45E-05 7.246E-06
1.312E-04 1.303E-04 1.273E-04 1.221E-04 1.142E-04 1.027E-04 8.630E-05 6.231E-05 3.5 93E-05 1.014E-05
3.530 = Current altitude in km
6.368E-01 6.334E-01 6.228E-01 6.041E-01 5.754E-01 5.328E-01 4.687E-01 3.673E-01 2.3 89E-01 7.670E-02
2.818E-03 2.784E-03 2.683E-03 2.510E-03 2.258E-03 1.917E-03 1.476E-03 9.296E-04 4.5 05E-04 9.704E-05
3.599E-03 3.571E-03 3.482E-03 3.328E-03 3.094E-03 2.759E-03 2.283E-03 1.605E-03 8.8 71E-04 2.291E-04
3.783E-03 3.670E-03 3.343E-03 2.830E-03 2.184E-03 1.478E-03 8.148E-04 3.147E-04 9.6 44E-05 1.512E-05
5.446E-04 5.343E-04 5.034E-04 4.528E-04 3.840E-04 2.996E-04 2.046E-04 1.088E-04 4.4 34E-05 7.989E-06
1.365E-04 1.355E-04 1.326E-04 1.274E-04 1.195E-04 1.080E-04 9.142E-05 6.681E-05 3.9 11E-05 1.103E-05
4.210 = Current altitude in km
6.569E-01 6.536E-01 6.433E-01 6.250E-01 5.969E-01 5.550E-01 4.917E-01 3.902E-01 2.5 88E-01 8.447E-02
2.943E-03 2.909E-03 2.807E-03 2.632E-03 2.376E-03 2.029E-03 1.574E-03 1.004E-03 4.9 25E-04 1.060E-04
3.747E-03 3.719E-03 3.631E-03 3.477E-03 3.243E-03 2.906E-03 2.424E-03 1.725E-03 9.6 79E-04 2.493E-04
3.992E-03 3.875E-03 3.534E-03 3.000E-03 2.325E-03 1.585E-03 8.818E-04 3.447E-04 1.0 60E-04 1.668E-05
5.711E-04 5.605E-04 5.289E-04 4.769E-04 4.060E-04 3.186E-04 2.195E-04 1.181E-04 4.8 59E-05 8.775E-06
1.417E-04 1.408E-04 1.379E-04 1.327E-04 1.249E-04 1.135E-04 9.674E-05 7.161E-05 4.2 62E-05 1.202E-05
O3 Column =0.285 Albedo =0.040 Haze O.D. =0.094
0.000 = Current altitude in km
4.655E-01 4.615E-01 4.493E-01 4.279E-01 3.958E-01 3.499E-01 2.855E-01 1.957E-01 1.0 48E-01 2.871E-02
1.688E-03 1.660E-03 1.576E-03 1.435E-03 1.237E-03 9.833E-04 6.824E-04 3.626E-04 1.4 14E-04 2.142E-05
2.240E-03 2.212E-03 2.129E-03 1.987E-03 1.780E-03 1.498E-03 1.132E-03 6.858E-04 3.1 59E-04 6.495E-05
2.098E-03 2.025E-03 1.815E-03 1.492E-03 1.098E-03 6.883E-04 3.334E-04 1.009E-04 2.1 01E-05 1.716E-06
3.162E-04 3.086E-04 2.863E-04 2.503E-04 2.029E-04 1.473E-04 8.961E-05 3.896E-05 1.1 93E-05 1.254E-06
8.857E-05 8.761E-05 8.467E-05 7.961E-05 7.213E-05 6.180E-05 4.802E-05 3.042E-05 1.4 83E-05 3.488E-06
0.150 = Current altitude in km
5.004E-01 4.963E-01 4.839E-01 4.623E-01 4.296E-01 3.828E-01 3.163E-01 2.220E-01 1.2 37E-01 3.1E-02
1.870E-03 1.841E-03 1.752E-03 1.604E-03 1.394E-03 1.123E-03 7.976E-04 4.424E-04 1.8 47E-04 3.188E-05
2.473E-03 2.445E-03 2.359E-03 2.211E-03 1.994E-03 1.697E-03 1.306E-03 8.179E-04 3.9 56E-04 8.840E-05
2.338E-03 2.259E-03 2.030E-03 1.677E-03 1.245E-03 7.929E-04 3.960E-04 1.294E-04 3.1 80E-05 3.572E-06
3.509E-04 3.428E-04 3.189E-04 2.803E-04 2.292E-04 1.690E-04 1.056E-04 4.859E-05 1.6 51E-05 2.187E-06
9.713E-05 9.614E-05 9.312E-05 8.789E-05 8.013E-05 6.934E-05 5.476E-05 3.574E-05 1.8 23E-05 4.615E-06

Figure 5-3. **Example of the first several panels of lookup dat a in the photolysis rates input file** 

.CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-26 Only one map of codes per grid is present in the AL HZOZ file for the time-invariant fields (UV albedo, land/water mask, drought stress, and surfac e roughness); these fields can be provided in any order. For time-varying fields (ozone column, turbidity, and snow cover) multiple maps of these codes may be provided for arbitrary time inte rvals, unique to each field, to span the entire simulation period. It is not necessary that the ti me spans of the ozone, turbidity, and snow cover maps match; for example, six maps of ozone column m ay be supplied for a six day simulation along with only one map for turbidity and snow cove r covering the same time span.

The ALHZOZ file is a readable text (ASCII) format a nd it has the following structure:

where the variables in the ALHZOZ file have the fol lowing definitions:

text Text identifying file and any messages

alname Text string “ALBEDO”

albcl UV albedo (unitless) for each of nalb albedo values

hzname Text string “HAZE”

hazcl Haze turbidity (unitless) for each of nhaz haze values

ozname Text string “OZONE COL” text (A) alname,(albcl(n),n=1,nalb) (A10,5F10.0) hzname,(hazcl(n),n=1,nhaz) (A10,3F10.0) ozname,(ozncl(n),n=1,nozn) (A10,5F10.0)
rlname,(ruflen(n),n=1,nruf) (A10,9f10.0) -- Optional
loname (A10) -- Optional
drname (A10) -- Optional
swname (A10) -- Optional alname,igrd,nx,ny (A10,3I10) Loop from ny grid rows to 1 (jalb(i,j),i=1,nx) (9999I1) alname,igrd,nx,ny (A10,3I10) rlname,igrd,nx,ny (A10,3I10) Loop from ny grid rows to 1 (jruf(i,j),i=1,nx) (9999I1) rlname,igrd,nx,ny (A10,3I10) loname,igrd,nx,ny (A10,3I10) Loop from ny grid rows to 1 (jocn(i,j),i=1,nx) (999I1) -- Optional loname,igrd,nx,ny (A10,3I10) drname,igrd,nx,ny (A10,3I10) Loop from ny grid rows to 1 (jdrt(i,j),i=1,nx) (999I1) drname,igrd,nx,ny (A10,3I10) name,idt1,tim1,idt2,tim2 (A10,I10,F10.0,I10,F10. 0) Loop from ny master grid rows to 1 (jozn(i,j),i=1,nx) or (9999I1) (jhaz(i,j),i=1,nx) or (9999I1) (jsno(i,j),i=1,nx) (9999I1) -- Optional ( name determines if

jozn, jhaz, or jsno is read)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-27 ozncl Ozone column (Dobson units) for each of nozn ozone values

rlname Text string “ROUGHNESS”

ruflen Roughness length (m) for each of nruf roughness values

loname Text string “OCEAN”

drname Text string “DROUGHT”

swname Text string “SNOW”
igrd Grid index (1 = master grid, 2+=nested grid, 0 = end of data)

nx Number of grid columns for this grid index

ny Number of grid rows for this grid index
jalb Grid igrd, row j albedo codes for nx grid columns jruf Grid igrd, row j roughness codes for nx grid columns
jocn Grid igrd, row j land/ocean codes for nx grid columns jdrt Grid igrd, row j drought codes for nx grid columns

name Text string “OZONE COL”, “HAZE”, or “SNOW”

idt1 Beginning date (YYMMDD or YYJJJ) of time span

tim1 Beginning hour (HHMM) of time span

idt2 Ending date of time span

tim2 Ending hour of time span jozn Master grid row j ozone column codes for nx master grid columns
jhaz Master grid row j haze codes for nx master grid columns jsno Master grid row j snow codes for nx master grid columns

An example of a small ALHZOZ file is given in Figur e 5-4.

5.5 FORTRAN BINARY INPUT FILES

What is a Fortran Binary File?

Large CAMx input and output data fields are contain ed within Fortran “unformatted” (binary)
files. This means that the data are read and writt en as represented in memory, without translation between binary and ASCII character sets as done for “text” files. Binary files reduce file volume and improve program read/write speed, but the user cannot directly view or manually edit them. There are two ways to represent binary information in memory: “big endian” and “little endian.” The difference between these is essentially the ord er of bits in a word, and which order is used depends on the computer chipset. Historically, big endian has been used in many Unix workstations (Sun, SGI, HP, and IBM). The x86 proc essors on personal computer platforms (e.g., Intel and AMD) use little endian, while Powe rPC chips are big endian.

CAMx can be compiled and run on machines using thei r native big or little endian binary representations, as long as the model and all of it s pre- and post-processors are consistently compiled and run on the same type of platform. If any component of the modeling system is compiled for a different platform using the opposit e binary representation, I/O files will not be properly read and will likely lead to a program cra sh. A typical run-time error message from trying to read the wrong binary format is “input re cord too long,” so if you get this error message, check for consistency between your binary files and Fortran compiler options.CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-28 Sample AHO file with all optional fields

ALBEDO 0.0400 0.0500 0.0600 0.0700 0.0800
HAZE 0.0000 0.0940 0.3950
OZONE COL 0.2850 0.3150 0.3450 0.3750 0.4050
ROUGHNESS 0.0010 0.0100 0.0200 0.0500 0.1000 0.2000 0.5000 ...

OCEAN

DROUGHT

SNOW
ALBEDO 1 64 10
222222222222222222221111111112222222222222222223333 3333333333333

222222222222222222222222222111111111222222222222222 2333333333333

222222222222222222222222111111111111122222222222222 2222233333333

222222222222222222222222222111111111111222222222222 2222222223333

222222222222222222222221111111111111112222222222222 2222222222222

222222222222222222222211111111111111111222222222222 2222222222222

222222222222222222222211111111111111111122222222222 2222222222222

222222222222222222222111111122222222211111111222222 2222222222222

222222222222222222221111111112222222221111111112222 2222222222222

222222222222222222222211111111112222222221111111111 2222222222222
ALBEDO 2 24 15

212111211111111111112222

222122222211111111111112

222222222222211111111111

222222222222111111111111

222222222211111111111111

222222222111111111111111

222222222111111111111111

222222222111111111111111

222222222112222221111111

222222211112222221111111

222222211122222221111111

222222211122222221111111

222222211122222221111111

222222211112222221111111

222222211112222221111111
ALBEDO 0 0 0
ROUGHNESS 1 64 10
444444444433333333337777777773333333333333333322222 2222222222222
444444444444433333333333333777777777333333333333333 3222222222222
444444444444443333333333777777777777733333333333333 3322222222222
444444444444444433333333333777777777777333333333333 3333333332222
444444444444443333333337777777777777773333333333333 3333333333333
444444444444444333333377777777777777777333333333333 3333333333333
444444444444333333333377777777777777777733333333333 3333333333333
444444444443333333333777777733333333377777777333333 3333333333333
444444444433333333337777777773333333337777777773333 3333333333333
444444444333333333333377777777773333333337777777777 3333333332222
ROUGHNESS 0 0 0
OCEAN 1 64 10
000000000000000000001111111110000000000000000000000 0000000000000
000000000000000000000000000111111111000000000000000 0000000000000
000000000000000000000000111111111111110000000000000 0000000000000
000000000000000000000000000111111111111000000000000 0000000000000
000000000000000000000001111111111111110000000000000 0000000000000
000000000000000000000011111111111111111000000000000 0000000000000
000000000000000000000011111111111111111100000000000 0000000000000
000000000000000000000111111100000000011111111000000 0000000000000
000000000000000000001111111110000000001111111110000 0000000000000
000000000000000000000011111111110000000001111111111 0000000000000
OCEAN 0 0 0

Figure 5-4. **Example of the structure of a 2-grid abedo/haze/o zone input file showing panels for**  **all mandatory and optional fields.**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-29 DROUGHT
222222222222222222222222222222222222222222222111111 1111111111111

222222222222222222222222222222222222222222222222211 1111111111111

222222222222222222222222222222222222222222222222222 2211111111111

222222222222222222222222222222222222222222222222222 2222211111111

222222222222222222222222222222222222222222222222222 2222222211111

222222222222222222222222222222222222222222222222222 2222222222111

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222
DROUGHT 0 0 0
HAZE 950707 0.00 950718 2400.00

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222

222222222222222222222222222222222222222222222222222 2222222222222
OZONE COL 950707 0.00 950718 2400.00
333333333333333333333333333333333333333333333333333 3333333333333
333333333333333333333333333333333333333333333333333 3333333333333
222222222222233333333333333333333333333333333333333 3333333333333
222222222222222333333333333333333333333333333333333 3333333333333
222222222222222333333333333333333333333333333333333 3333333333333
222222222222222333333333333333333333333333333333333 3333333333333
222222222222222333333333333333333333333333333333333 3333333333333
222222222222222333333333333333333333333333333333333 3333333333333
222222222222222333333333333333333333333333333333333 3333333333333
222222222222222222222233333333333333333333333333333 3333333333333
SNOW 950707 0.00 950718 2400.00
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000
000000000000000000000000000000000000000000000000000 0000000000000

Figure 5-4. **(concluded).**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-30 Compilers for little endian machines provide compil e-time switches that allow binary files to be read and written as big endian. The “makefile” com piler script that is distributed with CAMx supports many platforms and sets compiler flags to consistently use big endian to maximize platform portability. Therefore, use of the CAMx M akefile on any of the supported platforms will by default result in the model reading and wri ting big endian binary files. Additional information on this topic is provided in Section 11

.

Meteorological Input Files

Landuse File

The Fortran binary landuse input file contains a ti me-invariant two-dimensional gridded field of landuse distribution, and optionally two-dimensiona l gridded fields of leaf area index (LAI) and topographic elevation. This file must be developed for the master grid, and optionally any of the nested fine grids, if either dry deposition or the PiG submodel is invoked. The fractional distribution of 11 landuse categories must be suppl ied for each grid cell if the WESELY89 dry deposition option is selected in the user control f ile. Alternatively, the fractional distribution of 26 landuse categories must be supplied for each gri d cell if the ZHANG03 dry deposition option
is selected. These are used to define surface resi stances for dry deposition calculations and to set default surface roughness lengths, LAI, and UV albe do. The 11 landuse categories for the WESELY89 option follow the UAM-IV conventions (EPA, 1990), and are listed in Table 5-5. The 26 landuse categories for the ZHANG03 option ar e listed in Table 5-6. Both tables also show the default surface parameters assigned to eac h category. The topography field is not used by CAMx, but is simply passed through to the option al HDF5 output files.

The landuse file has the following structure, where each data record is preceded by an 8- character keyword (“ LUCAT11 ”, “ LUCAT26 ”, “ LAI ”, or “ TOPO ”) that identifies the data on the record. These keyword-d ata record pairs can be put in any order.

where the variables in the landuse file have the fo llowing definitions:

fland Fractional landuse distribution for nx grid columns, ny grid rows, and 11

landuse categories (WESELY89 dry deposition) or 26 landuse categories (ZHANG03 dry deposition)

lai Optional leaf area index (LAI) for nx grid columns and ny grid rows (used

for the ZHANG03 dry deposition option only)
topo Optional topographic elevation above sea level ( m) for nx grid columns

and ny grid rows

CAMx will properly read older (pre-v5.30) landuse f iles, but since that format only supported 11 landuse categories, CAMx will only be able to run w ith the WESELY89 dry deposition option. LUCAT11 (or LUCAT26) (((fland(i,j,k),i=1,nx),j=1,ny),k=1,11) (or k=1,2 6) LAI (optional) (((lai(i,j,k),i=1,nx),j=1,ny)) (optional) TOPO (optional) (((topo(i,j,k),i=1,nx),j=1,ny)) (optional)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-31 Table 5-5. **The 11 CAMx landuse categories and the default su rface roughness values (m)**  **assigned to each category by season for the WESELY8 9 dry deposition option. Winter is**  **defined for conditions where there is snow present; winter months with no snow are assigned to**  **the Fall category. Roughness for water is taken as the maximum of the baseline value given in**  **the table, and the function** 5.2 6
0 10 2 w z − × = **, where** w **is surface wind speed (m/s). The UV albedos are used in the albedo/haze/ozone (AHO) fil**  **e.**
Category
Number Land Cover Category Surface Roughness (meters) UV
Albedo Spring Summer Fall Winter

**1 Urban 1.0 1.0 1.0 1.0 0.08**

**2 Agricultural 0.03 0.2 0.05 0.01 0.05**

**3 Rangeland 0.05 0.1 0.01 0.001 0.05**

**4 Deciduous forest 1.0 1.3 0.8 0.5 0.05**

**5 Coniferous forest, wetland 1.3 1.3 1.3 1.3 0.05**

**6 Mixed forest* 1.15 1.3 1.05 0.9 0.05**

**7 Water 0.0001 0.0001 0.0001 0.0001 0.04**

**8 Barren land 0.002 0.002 0.002 0.002 0.08**

**9 Non-forested wetlands 0.2 0.2 0.2 0.05 0.05**

**10 Mixed agricultural/range* 0.04 0.15 0.03 0.006 0** **.05**

**11 Rocky (with low shrubs) 0.3 0.3 0.3 0.15 0.05**
*** Roughness for these categories is taken to be the average of their components.** CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-32 Table 5-6. **The 26 CAMx landuse categories and the default an nual minimum and maximum**  **LAI and surface roughness (m) ranges assigned to ea ch category for the ZHANG03 dry**  **deposition option. Roughness for water is calculat ed from the function**  5.2 6
0 10 2 w z − × = **, where**

w **is surface wind speed (m/s). The UV albedos are u sed in the albedo/haze/ozone (AHO) file.** 

Category
Number Land Cover Category Roughness (meters) LAI UV
Albedo Min Max Min Max

**1 Water f(**  w **) f(** w **) 0.0 0.0 0.04** 

**2 Ice 0.01 0.01 0.0 0.0 0.5**

**3 Inland lake f(**  w **) f(** w **) 0.0 0.0 0.04** 

**4 Evergreen needleleaf trees 0.9 0.9 5.0 5.0 0.05**

**5 Evergreen broadleaf trees 2.0 2.0 6.0 6.0 0.05**

**6 Deciduous needleleaf trees 0.4 0.9 0.1 5.0 0.05**

**7 Deciduous broadleaf trees 0.4 1.0 0.1 5.0 0.05**

**8 Tropical broadleaf trees 2.5 2.5 6.0 6.0 0.05**

**9 Drought deciduous trees 0.6 0.6 4.0 4.0 0.05**

**10 Evergreen broadleaf shrubs 0.2 0.2 3.0 3.0 0.05**

**11 Deciduous shrubs 0.05 0.2 0.5 3.0 0.05**

**12 Thorn shrubs 0.2 0.2 3.0 3.0 0.05**

**13 Short grass and forbs 0.04 0.04 1.0 1.0 0.05**

**14 Long grass 0.02 0.1 0.5 2.0 0.05**

**15 Crops 0.02 0.1 0.1 4.0 0.05**

**16 Rice 0.02 0.1 0.1 6.0 0.05**

**17 Sugar 0.02 0.1 0.1 5.0 0.05**

**18 Maize 0.02 0.1 0.1 4.0 0.05**

**19 Cotton 0.02 0.2 0.1 5.0 0.05**

**20 Irrigated crops 0.05 0.05 1.0 1.0 0.05**

**21 Urban 1.0 1.0 0.1 1.0 0.08**

**22 Tundra 0.03 0.03 0.1 2.0 0.05**

**23 Swamp 0.1 0.1 4.0 4.0 0.05**

**24 Desert 0.04 0.04 0.0 0.0 0.08**

**25 Mixed wood forest 0.9 0.9 3.0 5.0 0.05**

**26 Transitional forest 0.9 0.9 3.0 5.0 0.05**CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-33 Height/Pressure File

The Fortran binary height/pressure input file conta ins three-dimensional gridded fields of layer interface heights and layer-average pressure. This file must be developed for the master grid and optionally any fine grid nest specified for a given simulation. The layer interface heights define the vertical grid structure for each grid, as well as the vertical nesting structure for fine grid nests. For this reason, vertical nesting is not al lowed when flexi-nesting is used. The layer interface heights may be specified to vary in space and/or time (e.g., to follow the layer structure of meteorological models), or they may be set to a constant field. The pressure field is used for transport, plume rise, PiG, dry deposition, and che mistry calculations.

The height/pressure file has the following structur e, where the following records are repeated for each update time:

The variables in the height/pressure file have the following definitions:

hour Simulation time (HHMM: 1300.0 is 1:00 PM)

idate Simulation date (YYMMDD or YYJJJ)
height Layer k

interface height (m AGL) for nx grid columns, and ny grid rows

press Layer k

pressure (mb) for nx grid columns, and ny grid rows

CAMx time-interpolates the height and pressure arra ys to each model timestep for each grid, and so the model requires that data be available on fil e for an additional update time at the end of the simulation. For example, in the case of hourly fie lds, a 24-hour simulation requires 25 input
fields on file.

Wind File

The Fortran binary wind file contains three-dimensi onal gridded fields of layer average horizontal ( u and v ) wind components. This file must be developed for the master grid and optionally any of the fine grid nests. CAMx allows the user to optionally supply these components at cell center, in which case the model will interpolate the components to their respective positions on cell interfaces, or the use r may supply these components directly on the Arakawa C configuration (recommended). In any case , the user must supply a full nx×ny×nz array of wind values for each component (even thoug h for the Arakawa C configuration in CAMx, only (nx-1)×(ny-1) values are used in the horizontal). Wind fields a re used for transport, plume rise, PiG, and dry deposition calculations.

The wind file has the following structure, where th e following records are repeated for each update time: Loop from 1 to nlay layers: hour,idate,((height(i,j,k),i=1,nx),j=1,ny) hour,idate,((press(i,j,k),i=1,nx),j=1,ny)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-34 The variables in the wind file have the following d efinitions:

hour Simulation time (HHMM: 1300.0 is 1:00 PM)

idate Simulation date (YYMMDD or YYJJJ)
lstagger Staggered wind flag (T=input winds staggered to Ar akawa-C arrangement,

F=input winds are cell-centered)

uwind Layer k u -component (east-west) wind (m/s) for nx grid columns, and ny

grid rows

vwind Layer k v -component (north-south) wind (m/s) for nx grid columns, and ny

grid rows
dummy Dummy array

CAMx time-interpolates the wind components to each model timestep for each grid, and so the model requires that data be available on file for a n additional update time at the end of the simulation. For example, in the case of hourly fie lds, a 24-hour simulation requires 25 input
fields on file.

Temperature File

The Fortran binary temperature file contains three- dimensional gridded fields of layer average temperature and two-dimensional gridded fields of s urface temperature. This file must be developed for the master grid and optionally any of the fine grid nests. The three-dimensional temperature fields are used for transport, plume ri se, PiG, dry and wet deposition and chemistry calculations, while the surface temperatures are us ed for dry deposition calculations.

The temperature file has the following structure, w here the following records are repeated for each update time:

The variables in the temperature file have the foll owing definitions:

hour Simulation time (HHMM: 1300.0 is 1:00 PM)

idate Simulation date (YYMMDD or YYJJJ)

temps Surface temperature (K) for nx grid columns, and ny grid rows

temp Layer k

temperature (K) for nx grid columns, and ny grid rows hour,idate,lstagger Loop from 1 to nlay layers: ((uwind(i,j,k),i=1,nx),j=1,ny) ((vwind(i,j,k),i=1,nx),j=1,ny) ((dummy,i=1,nx),j=1,ny)

hour,idate,((temps(i,j),i=1,nx),j=1,ny) Loop from 1 to nlay layers: hour,idate,((temp(i,j,k),i=1,nx),j=1,ny)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-35 CAMx time-interpolates the temperature to each mode l timestep for each grid, and so the model requires that data be available on file for an addi tional update time at the end of the simulation. For example, in the case of hourly fields, a 24-hou r simulation requires 25 input fields on file.

Water Vapor File

The Fortran binary water vapor file contains three- dimensional gridded fields of layer average water vapor concentration. This file must be devel oped for the master grid, and optionally any fine grid nests, if chemistry, dry deposition, and/ or wet deposition are invoked. The threedimensional water vapor fields are used for dry and wet deposition and chemistry calculations.

The water vapor file has the following structure, w here the following record is repeated for each update time:

The variables in the water vapor file have the foll owing definitions:

hour Simulation time (HHMM: 1300.0 is 1:00 PM)

idate Simulation date (YYMMDD or YYJJJ)

water Layer k

water vapor concentration (ppm) for nx grid columns and ny grid

rows

CAMx time-interpolates the water vapor to each mode l timestep for each grid, and so the model requires that data be available on file for an addi tional update time at the end of the simulation. For example, in the case of hourly fields, a 24-hou r simulation requires 25 input fields on file.

Cloud/Rain File

The Fortran binary cloud/rain file contains three-d imensional gridded fields of cloud parameters to be used for chemistry and wet/dry deposition cal culations. Note that precipitation rate is not explicitly provided to the model; instead, it is in ternally calculated from the three precipitation water content forms provided on the cloud/rain file . Cloud optical depth is used to adjust clearsky photolysis rates for the presence of clouds. T his file must be developed for the master grid, and optionally any fine grid nests, if chemistry, d ry, and/or wet deposition are invoked.

The cloud/rain file contains a header record to def ine the file: Loop from 1 to nlay layers: hour,idate,((water(i,j,k),i=1,nx),j=1,ny)

cldhdr,nxcl,nycl,nzclCAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-36 The remainder of the file has the following structu re, where the following records are repeated for each update time:

The variables on the cloud/rain file have the follo wing definitions:

cldhdr Text string “CAMX_V5.3 CLOUD_RAIN” (new optical d epth definition)

or “CAMX_V4.3 CLOUD_RAIN” (old optical depth defini tion)

nxcl Number of master grid columns
nycl Number of master grid rows

nzcl Number of master grid layers

hour Simulation time (HHMM: 1300.0 is 1:00 PM)

idate Simulation date (YYMMDD or YYJJJ)

cldwtr Layer k

cloud water content (g/m 3 ) for nx grid columns, and ny grid rows

ranwtr Layer k

rain water content (g/m 3 ) for nx grid columns, and ny grid rows

snowtr Layer k

snow water content (g/m 3 ) for nx grid columns, and ny grid rows
gplwtr Layer k

graupel water content (g/m 3 ) for nx grid columns, and ny grid

rows
cldod Layer k

cloud optical depth (dimensionless) if v5.3 cloud/ rain file, or total integrated column cloud optical depth (dimensionles s) if v4.3 cloud/rain file, for nx grid columns, and ny grid rows

Vertical Diffusivity File

The Fortran binary vertical diffusivity file contai ns three-dimensional gridded fields of layerinterface vertical diffusivity (i.e., turbulent exc hange or diffusion coefficients). This file must b e developed for the master grid, and optionally any f ine grid nests. The three-dimensional diffusivity fields are used for vertical diffusion and PiG puff growth calculations.

The vertical diffusivity file has the following str ucture, where the following records are repeated for each update time:

The variables on the vertical diffusivity file have the following definitions:

hour Simulation time (HHMM: 1300.0 is 1:00 PM)

idate Simulation date (YYMMDD or YYJJJ)

rkv Layer k

interface diffusivity (m 2 /s) for nx grid columns, and ny grid rows hour,idate Loop from 1 to nz layers: ((cldwtr(i,j,k),i=1,nx),j=1,ny) ((ranwtr(i,j,k),i=1,nx),j=1,ny) ((snowtr(i,j,k),i=1,nx),j=1,ny) ((gplwtr(i,j,k),i=1,nx),j=1,ny) ((cldod(i,j,k),i=1,nx),j=1,ny)

Loop from 1 to nz layers: hour,idate,((rkv(i,j,k),i=1,nx),j=1,ny)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-37 CAMx time-interpolates the diffusivity to each mode l timestep for each grid, and so the model requires that data be available on file for an addi tional update time at the end of the simulation. For example, in the case of hourly fields, a 24-hou r simulation requires 25 input fields on file.

Initial Conditions File

The Fortran binary initial conditions file contains three-dimensional gridded concentration fields on the master grid. Initial concentration fields m ay be specified for a sub-set of the total number of modeled species. The format of this file follow s that defined for UAM-IV concentration files (EPA, 1990) so that pre-existing software may be us ed to develop these input files. An initial condition file must be developed for the master gri d, and contain concentration fields for at least one species. For those species not on the initial condition file, CAMx sets up uniform fields using the “lower bound” values specified in the che mistry parameters file. CAMx then interpolates all master grid initial conditions to each fine grid nest at the start of the simulation.

The initial conditions file contains a set of time- invariant header records, followed by a set of data records that contain time- and species-specifi c gridded concentration fields. The time span
of concentration data must contain the start time/d ate of the simulation.

The header portion of the initial conditions file c ontains four records with the following

structure:

The time-variant portion of the initial conditions file has the following structure, where the following records are repeated for each update time :

The variables on the initial conditions file have t he following definitions:

name Text string “AIRQUALITY” (character*4(10) array)

note Text string containing file description (charact er*4(60) array)

ione Dummy variable = 1

nspec Number of species on file

ibdate Beginning date (YYJJJ)

btime Beginning hour (HH)

iedate Ending date (YYJJJ)

etime Ending hour (HH) name,note,ione,nspec,ibdate,btime,iedate,etime rdum,rdum,iutm,xorg,yorg,delx,dely,nx,ny,nz,idum,i dum,rdum,rdum,rdum ione,ione,nx,ny (mspec(l),l=1,nspec)

ibdate,btime,iedate,etime Loop from 1 to nspec species: Loop from 1 to nz layers: ione,mspec(l),((conc(i,j,k,l),i=1,nx),j=1,ny)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-38 rdum Dummy real variable

iutm UTM zone (ignored for other projections)

xorg Grid x-origin at southwest corner of domain (m o r degrees longitude)

yorg Grid y-origin at southwest corner of domain (m o r degrees latitude)

delx Cell size in x-direction (m or degrees longitude )
dely Cell size in y-direction (m or degrees longitude )

nx Number of grid columns

ny Number of grid rows

nz Number of layers

idum Dummy integer variable

mspec Species names for nspec species (character*4(10,nspec) array)

conc Species l , layer k initial concentrations (ppm for gases, μg/m 3 for aerosols)
for nx grid columns and ny grid rows

Lateral Boundary Conditions File

The Fortran binary lateral boundary conditions file contains two-dimensional gridded
concentration fields on the lateral faces of the ma ster grid boundary. Boundary concentration fields may be specified for a sub-set of the total number of modeled species. However, if a boundary concentration is specified for a given spe cies, it must be supplied for all four boundaries. This file also defines any non-modeledportions of the master grid (i.e., a nonuniform boundary may be specified). The format of this file follows that defined for UAM-IV (EPA, 1990) so that pre-existing software may be us ed to develop these input files. The user
should refer to the UAM-IV User’s Guide for more in formation on specifying non-uniform boundaries. A boundary concentration file must be d eveloped for the master grid, and contain concentration fields for at least one species. For those species not on the boundary conditions file, CAMx sets up uniform fields using the “lower bound” values specified in the chemistry parameters file.

The boundary file contains a set of time-invariant header records, followed by a set of data records that contain the time- and species-varying gridded boundary concentration fields. The time span of each set of boundary data records may be set arbitrarily; e.g., a set of boundary conditions may be specified for a six hour span, fo llowed by a set spanning just an hour.

The header portion of the boundary conditions file contains eight records with the following

structure:

The time-variant portion of the boundary conditions file has the following structure, where the following records are repeated for each update time : name,note,ione,nspec,ibdate,btime,iedate,etime rdum,rdum,iutm,xorg,yorg,delx,dely,nx,ny,nz,idum,i dum,rdum,rdum,rdum ione,ione,nx,ny (mspec(l),l=1,nspec) Loop from 1 to 4 boundaries: ione,iedge,ncell,(icell(n),idum,idum,idum,n=1,nce ll)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-39 The variables that are unique to the boundary condi tions file have the following definitions:

name Text string “BOUNDARY” (character*4(10) array)

note Text string containing file description (charact er*4(60) array) iedge Boundary edge number (1=west, 2=east, 3=south, 4= north)

ncell Number of rows or columns on this edge

icell Index of first cell modeled (edges 1,3), or last cell modeled (edges 2,4):

if=0, this row/column is omitted from the simulatio n
bc Species l , edge iedge boundary concentrations (ppm for gases, μg/m 3 for

aerosols) for ncell

grid rows/columns, and nz layers

See the section describing the initial conditions f ile for a full list of variable definitions common to all UAM-IV type files.

Elevated Point Source File

The Fortran binary elevated point source emissions file contains stack parameters and emission rates for all elevated point sources, and for all e mitted species, to be modeled. The format of this file follows that defined for UAM-IV (EPA, 1990) so that pre-existing emission pre-processing software may be used to develop these input files. If elevated point sources are to be modeled, only one point source emissions file must be develo ped for the entire modeling domain. The point source file also flags the individual stacks to be treated by the CAMx PiG sub-model by setting the stack diameter as a negative value. Th e file offers the ability to optionally specify the effective plume height for each point source and to bypass the internal plume rise calculation.

The point source emissions file contains a set of t ime-invariant header records, followed by a set of data records that contain the time- and species- varying point source emissions data. The time span of each set of emission data records may be se t arbitrarily; e.g., a set of point emissions may be specified for a six hour span, followed by a set spanning just an hour.

The header portion of the elevated point source fil e contain 5+ nstk records with the following

structure: ibdate,btime,iedate,etime Loop from 1 to nspec species: Loop from 1 to 4 boundaries: ione,mspec(l),iedge,((bc(i,k,iedge,l),k=1,nz),i= 1,ncell)

name,note,ione,nspec,ibdate,btime,iedate,etime rdum,rdum,iutm,xorg,yorg,delx,dely,nx,ny,nz,idum,i dum,rdum,rdum,rdum ione,ione,nx,ny (mspec(l),l=1,nspec) ione,nstk (xstk(n),ystk(n),hstk(n),dstk(n),tstk(n),vstk(n),n =1,nstk)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-40 The time-variant portion of the elevated point sour ce file has the following structure, where the following records are repeated for each update time :

The variables that are unique to the elevated point source file have the following definitions:

name Text string “PTSOURCE” (character*4(10) array)

note Text string containing file description (charact er*4(60) array)

nstk Number of point sources

xstk Stack x-coordinate (m or degrees longitude) for nstk point sources
ystk Stack y-coordinate (m or degrees latitude) for nstk point sources

hstk Stack height (m) for nstk point sources

dstk Stack diameter (m, negative if PiG source) for nstk point sources

tstk Stack exit temperature (K) for nstk point sources

vstk Stack exit velocity (m/hr) for nstk point sources

kcell Ignored, except as flag for OSAT/PSAT point sourc e override (provide a

negative value for the assigned source region), for nstk point sources

flow Stack flow rate (m 3 /hr) for nstk point sources
plmht Effective plume height override (m, a negative va lue replaces the internal

plume rise calculation) for nstk point sources
ptems Species l

point emission rate (mol/time period for gases, g/ time period for aerosols) for nstk

point sources. Note that the emissions time perio d (the denominator for the emissions rate) is normally, bu t not necessarily, 1
hour.

See the section describing the initial conditions f ile for a full list of variable definitions common to all UAM-IV type files.

Gridded Emissions File

The Fortran binary emissions file contains two-dime nsional gridded fields of low-level (i.e., surface) emission rates for all emitted species to be modeled. The format of this file follows that defined for UAM-IV (EPA, 1990) so that pre-existing emission pre-processing software may be used to develop these input files. If gridded emis sions are to be modeled, a gridded emissions file must be developed for the master grid and opti onally any nested fine grids.

The gridded emissions file contains a set of time-i nvariant header records, followed by a set of data records that contain the time- and species-var ying gridded emission fields. The time span of each set of emission data records may be set arbitr arily; e.g., a set of emissions may be specified for a six hour span, followed by a set spanning jus t an hour.

The header portion of the gridded emissions file co ntain four records with the following

structure: ibdate,btime,iedate,etime ione,nstk (idum,idum,kcell(n),flow(n),plmht(n),n=1,nstk) Loop from 1 to nspec species: ione,mspec(l),(ptems(n,l),n=1,nstk)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-41 The time-variant portion of the initial conditions file has the following structure, where the following records are repeated for each update time :

The time-span of the emissions record must match th e input frequency of the emissions as specified in the CAMx.in file.

The variables that are unique to the gridded emissi on file have the following definitions:

name Text string “EMISSIONS” (character*4(10) array)

note Text string containing file description (charact er*4(60) array)

emiss Species l

gridded emission rate (mol/time period for gases, g/time period for aerosols) for nx grid columns and ny grid rows. Note that the emissions time period (the denominator for the emis sions rate) is normally, but not necessarily, 1 hour. See the section describing the initial conditions f ile for a full list of variable definitions common to all UAM-IV type files.

5.6 FORTRAN BINARY OUTPUT FILES

Master and nested grid instantaneous files contain full three-dimensional fields of all species modeled, while the gridded average and deposition f iles contain only those species specified in the run control file. For flexibility, CAMx offers the option to write full three-dimensional average concentration fields, or just surface layer two-dimensional fields (see the “3-D average file” option in the CAMx control file). It is perm issible to change the number of species on the average file, or change between 2-D and 3-D average files, from one CAMx simulation period to the next (provided the periods are configured as se parate CAMx runs). As the instantaneous concentration files are used for CAMx restarts, the model only writes instantaneous fields at the end of the simulation. PiG output files are unique to CAMx, and are used primarily for model
restarts. The PiG file contains all information ca rried by the sub-model for all simulation hours; therefore, there is sufficient information in the P iG file for subsequent postprocessing and analysis.

Gridded Output Files in UAM Format

The Fortran binary output average and deposition fi les for all grids, and the coarse (master) grid instantaneous file, are all written in the UAM form at, which is identical to the initial name,note,ione,nspec,ibdate,btime,iedate,etime rdum,rdum,iutm,xorg,yorg,delx,dely,nx,ny,nz,idum,i dum,rdum,rdum,rdum ione,ione,nx,ny (mspec(l),l=1,nspec)

ibdate,btime,iedate,etime Loop from 1 to nspec species: ione,mspec(l),((emiss(i,j,l),i=1,nx),j=1,ny)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-42 concentration file format described earlier. There are three differences between the output concentration files and the input initial concentra tion files. First, the file name given in the file description header record is “AVERAGE” for the aver age output file, “INSTANT” for the instantaneous output file, and “AIRQUALITY” for the input initial concentration file. Second, the “note” in the file description header record of the output concentration files is the message supplied in the first line of the CAMx run control file, whereas the “note” in the air quality file is set as part of the input file preparation. Third, the species lists can be different among the files: the output instantaneous file contains all species modeled (as specified in the chemistry parameters file), the average output file contains only the species specified in the run control file, and the input initial concentration file may contai n any subset of modeled species as determined when that file is prepared.

Two other differences exist between the average and instantaneous output files. As noted above, the average file may contain only surface-level fie lds or the entire three-dimensional fields, as selected by the user. Also, gas concentration fiel ds are output as ppm in average files, but as μmol/m 3 in instantaneous files (aerosols are in μg/m 3 in both files). Because of these differences, and because average files usually do n ot contain all modeled species, CAMx does not allow the average output concentration file to be used for simulation restarts.

Average output files contain concentrations of user -selected “state” species whose names are defined in the chemistry parameters file for the sp ecific chemistry mechanism employed. The average files can also contain concentrations of us er-selected steady-state “radical” species specific to each chemistry mechanism; the list of r adicals used in CB4, CB05, and SAPRC99
mechanisms is shown in Table 5-7.

Table 5-7. **List of steady-state radical species names used i n the CB4, CB05, and SAPRC99**  **gas-phase chemical mechanisms.**
CB4 (Mechanisms 3/4) CB05 (Mechanism 6) SAPRC99

(Mechanism 5)

O1D O1D O1D

O O O

OH OH OH
HO2 HO2 HO2

C2O3 C2O3 RO2R

XO2 XO2 R2O2

XO2N XO2N RO2N

TO2 CXO3 CCO3

ROR MEO2 RCO3

CRO TO2 MC03

ROR BZCO
HCO3 CXO2

CRO HCO3

TBUO

BZO

BZNOCAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-43 The output deposition file is identical in format t o the two-dimensional surface-level output average concentration file. The file name given on the first record of the deposition file is “AVERAGE” so that post-processing software will rec ognize the format for plotting (it will not recognize any other name, such as “DEPOSITION”, and therefore will not read the file). However, the output deposition file differs from th e output average concentration file in one important way. The species list is identical to th e list on the average concentration output files, except that four parameters are output for each spe cies:

species_DV Two-dimensional dry deposition velocity field for species (m/s) species_DD Two-dimensional dry deposited mass field for species (mol/ha for gases, g/ha for aerosols) species_WD Two-dimensional wet deposited mass field for species (mol/ha for gases, g/ha for aerosols) species_LC Two-dimensional precipitation liquid concentratio n for species

(mol/l for gases, g/l for aerosols)

Nested (Fine) Grid Instantaneous Output File

The nested (or “fine”) grid Fortran binary output i nstantaneous file contains the threedimensional concentration fields for all nested gri ds together, as opposed to separate files for each grid. All grid definition parameters given in these files are referenced relative to the master grid, so specific absolute information about grid c ell size or projection coordinates for each nested grid must be determined from master grid par ameters. If the user utilizes the Flexinesting capability of CAMx, then the gridded fields output to the nested fine grid files will change according to how nests are altered, added, a nd/or removed during the course of a
simulation.

The header portion of the nested grid output files contain 3+ nnest records with the following

structure:

The time-variant portion of the nested grid output files have the following structure, where the following records are repeated for each output time : message nnest,nspec (mspec(l),l=1,nspec) Loop from 1 to nnest grid nests ibeg,jbeg,iend,jend,mesh,ione,nx,ny,nz,iparnt,ile vel

time,idate Loop from 1 to nnest grid nests Loop from 1 to nspec species: Loop from 1 to nz layers: ((conc(i,j,k,l),i=1,nx),j=1,ny)CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-44 The variables on the nested grid output concentrati on files have the following definitions:

message Text string containing file description (characte r*60)

nnest Number of fine grid nests on file

nspec Number of species on file

mspec Species names for nspec species
ibeg Grid n x-direction starting index of grid (master grid ce ll) jbeg Grid n y-direction starting index of grid (master grid ce ll)

iend Grid n x-direction ending index of grid (master grid cell ) jend Grid n y-direction ending index of grid (master grid cell)

mesh Grid n meshing factor (number of nested cells per master)

ione Dummy integer = 1

nx Grid n number of grid rows

ny Grid n number of grid columns

nz Grid n number of layers iparnt Grid n’s parent grid (grid index within which this fine gri d is nested; 0 =

master grid)

ilevel Grid n’s grid level (depth at which this grid is nested; 1= master grid is

parent)

time Time of output (HHMM); ending hour for average o utput

idate Date of output (YYJJJ)

conc Grid n , species l , layer k concentrations (ppm for average gases, μg/m 3 for average aerosols, μmol/m 3 for instantaneous gas species) for nx grid columns, and ny grid rows

PiG Restart File

When the PiG option is invoked, CAMx outputs all pu ff parameters each hour for model restart capabilities. This file is Fortran binary and is a nalogous to the instantaneous gridded concentration output files in that it represents a “snapshot” of data at the top of each hour. The file contains information for each puff, including coordinates, grid location, size specifications, age, and mass of each of the chemical species carri ed. While this file contains PiG information for the entire simulation, it would be of limited u se for certain analyses such as plotting puff trajectories. This is because the instantaneous na ture of the data, and the dynamic memory allocation utilized in the PiG submodel, leads to i nsufficient information to identify and track individual puffs hour to hour.

The PiG restart file contains two records with the following structure, and these are repeated for each output time:

idatpig,timpig,npig,nreactr (ingrd(n),idpig(n),xpigf(n),xpigb(n),ypigf(n),ypig b(n),zpig(n), & axisy(n),axisz(n),sigy(n),sigx(n),sigz(n),pufft op(n),puffbot(n), & htfms(n),htfmb(n),vtfms(n),vtfmb(n),agepigf(n), agepigb(n),fmspig(n), & ipufmap(n),ipufgrp(n), & ((puffrad(i,nr,n),i=1,nrad),nr=1,nreactr), & ((puffmass(i,nr,n),i=1,nspec),nr=1,nreactr),n=1 ,npigCAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-45 The variables on the GREASD PiG restart file have t he following definitions: idatpig Date of output (YYJJJ)
timpig Time of output (HHMM) npig Number of PiG puffs active at this output time

nreactr Number of chemical reactors in each puff
ingrd Grid index for npig puffs idpig Point source index for npig puffs
xpigf x-coordinate of puff front (km from master grid o rigin) for npig puffs xpigb x-coordinate of puff back (km from master grid or igin) for npig puffs ypigf y-coordinate of puff front (km from master grid o rigin) for npig puffs
ypigb y-coordinate of puff back (km from master grid or igin) for npig puffs zpig Puff height (m AGL) for npig puffs
axisy Puff lateral width (m) for npig puffs

axisz Puff vertical depth (m) for npig puffs sigy Puff Gaussian lateral dimension (m) for npig puffs
sigx Puff Gaussian longitudinal dimension (m) for npig puffs sigz Puff Gaussian vertical dimension (m) for npig puffs
pufftop Puff top height (m AGL) for npig puffs
puffbot Puff bottom height (m AGL) for npig puffs

htfms Puff horizontal turbulent flux moment, shear (m 2 /s)

htfmb Puff horizontal turbulent flux moment, buoyancy ( m 2 /s)

vtfms Puff vertical turbulent flux moment, shear (m 2 /s)

vtfmb Puff vertical turbulent flux moment, buoyancy (m 2 /s)
agepigf Puff front age since release (s) for npig puffs agepigb Puff back age since release (s) for npig puffs
fmspig Puff volume parameter (unitless) for npig puffs ipufmap Puff OSAT region map pointer (unitless) for npig puffs ipufgrp Puff OSAT group poing (unitless) for npig puffs
puffrad Puff radical concentrations (ppm) for nrad species, nreactr

reactors, and npig puffs
puffmass Puff pollutant mass (μmol) for nspec species, nreactr reactors, and npig puffs

PiG Sample Grid Files

The optional PiG sampling grid concentrations are t ime-averaged in the same manner as the output average concentrations provided on the compu tational grids. The same user-defined set of output species are written to the sampling grid files, but only two-dimensional surface layer concentrations are reported. The sampling grid fil e format is identical to the average and deposition files (UAM format), with one file genera ted per sampling grid, so that they may be readily viewed and manipulated with CAMx post-proce ssing software.CAMx v5.30 User’s Guide, December 2010

Core Model Input/Output Structures

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_05_IO.docx 5-46 5.7 HDF5 OUTPUT FILES

CAMx provides the option to output two-dimensional or three-dimensional average
concentrations to HDF5 files in lieu of the standar d UAM Fortran binary format. The HDF conventions employed in CAMx were taken from the Re gional Atmospheric Modeling System (RAMS), version 6.0 ( http://www.atmet.com/html/docs/documentation.shtml ). Standard HDF5 utilities can be used to view and manipulate the CA Mx HDF5 average output files. Deposition and instantaneous restart files cannot be output in HDF5 format, and will continue to be written
in the standard formats as described above if the H DF option is invoked.

Two basic types of files are generated when this op tion is invoked:

• A text “header” file, containing various grid, spec ies and pointer information defining the data contained in each HDF concentration file (labe led “... head.txt ”);

• HDF5 format concentration files, one for each outpu t time and for each grid (labeled
“... g nn .h5 ”, where nn is grid index).

Each file is labeled first with a user-supplied roo t path/filename ( root ) and then the year, month, day, and time ( YYYY-MM-DD-HHMMSS ). The standard computational grid output fields a re written to files labeled with the letters “ CX ”. The optional PiG sampling grid output fields ar e written to files labeled “ CS ”.

Both types of files contain core model species, lab eled with a prefix of “ CH- ” to indicate standard chemistry species (e.g., NO and O3 are out put to HDF files as “ CH-NO ” and “ CHO3 ”). If the RTRAC Probing Tool is used, then both c omputational and PiG sampling grid files will also contain all the RTRAC species with a pref ix of “ PT- ” to indicate Probing Tool species (e.g., the arbitrary RTRAC species TRAC1 is output to HDF files as “ PT-TRAC1 ”).

See Section 11 for information on how to compile CA Mx with HDF5 libraries.CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-1

6. OZONE SOURCE APPORTIONMENT TECHNOLOGY

6.1 INTRODUCTION

Photochemical grid models are used to develop NO
x and/or VOC emission reduction strategies to attain ozone air quality objectives. Traditiona lly, the development of an ozone attainment strategy involves iteration through many photochemi cal grid model scenarios to identify which pollutants, source categories and source regions sh ould be controlled. It is impractical to analyze every potential control strategy and so there is po tential for implementing controls on sources that contribute little to the high ozone levels or, conversely, not controlling sources that do
contribute.

ENVIRON developed an ozone source attribution appro ach that has become known as the “Ozone Source Apportionment Technology”, or OSAT (Y arwood et al., 1996). This method was originally implemented in the Urban Airshed Mod el (UAM) and was then built into CAMx 1

. OSAT provides a method for estimating the contribut ions of multiple source areas, categories, and pollutant types to ozone formation in a single model run. OSAT also includes a methodology for diagnosing the temporal relationshi ps between ozone and emissions from groups of sources. The capabilities of OSAT are su mmarized below along with an assessment of the strengths and limitations of the approach.

The main challenges in developing and implementing a methodology to track the spatial and temporal relationships between separate groups of e mission sources and ozone formation are:

• Accounting not only for the presence of ozone precu rsors from a given source region at a given receptor location, but also accurately estima ting the cumulative contribution to ozone production of those precursors while they wer e en-route to the receptor.

• Insuring compatibility with the underlying air qual ity model formulation so that derived source-receptor relationships will be consistent wi th model response to emission changes.

• Providing sufficient spatial and temporal resolutio n while managing, within practical constraints, the computer resources required to run the software tool.

OSAT uses multiple tracer species to track the fate of ozone precursor emissions (VOC and
NO X ) and the ozone formation caused by these emissions within a simulation. The tracers operate as spectators to the normal CAMx calculatio ns so that the underlying CAMx predicted relationships between emission groups (sources) and ozone concentrations at specific locations (receptors) are not perturbed. Tracers of this typ e are conventionally referred to as “passive tracers,” however it is important to realize that t he tracers in the OSAT track the effects of chemical reaction, transport, diffusion, emissions and deposition within CAMx. In recognition of this, they are described here as “ozone reaction tracers.” The ozone reaction tracers allow ozone formation from multiple “source groupings” to be tracked simultaneously within a single

1 Note that with the release of CAMx v4.20, the form ulation of the Ozone Source Apportionment Technolog y differs slightly from earlier versions in that it accounts for the fact that ozone production and destruction occur simultaneously as it apportions ozone change among tracers. Previously, OSAT only considered the net change in ozone in the apportionment algorithm. The approach is described later in this section.CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-2 simulation. A source grouping can be defined in te rms of geographical area and/or emission category. Figure 6-1 provides an example of the wa y that a CAMx domain can be sub-divided into multiple source areas – 40 in this example. A lso, the emission inventory could be subdivided into several source categories – two emissi on categories over 40 source regions would produce 80 separate source groupings. So that all sources of ozone precursors are accounted, the CAMx boundary conditions and initial conditions are always tracked as separate source groupings.

Figure 6-1. **Example of the sub-division of a CAMx domain into separate areas for geographic**  **source apportionment.**

The methodology is designed so that all ozone and p recursor concentrations are attributed among the selected source groupings at all times. Thus, for all receptor locations and times, the ozone (or ozone precursor concentrations) predicted by CA Mx is attributed among the source groupings selected for OSAT. The methodology also estimates the fractions of ozone arriving at the receptor that were formed en-route under VOC- o r NO X -limited conditions. This information indicates how ozone concentrations at the receptor will respond to reductions in VOC and NO X precursor emissions.

In addition to using ozone reaction tracers to appo rtion ozone formation, the OSAT uses separate families of timing tracers to allow source-receptor transport times to be estimated. Unique timing tracers are released from each geographical area selected for ozone source apportionment. Thus, if the ozone formation tracers show that emis sions from a given source area contributed to

**-95 -90 -85 -80 -75 -70 30**  **35 40**  **45**

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4

6 7 8 9 10

11

12

13 14

15 16 17 18

19 20 21 22

23 24

25 26

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30 31

32

33 34

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39 40CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-3 ozone at any receptor of interest, the timing trace rs can then be used to estimate the time at which the emissions were released. This provides a way of investigating temporal features of the source-receptor relationships and even developing t emporally targeted control strategies.

An example display of the type of ozone source appo rtionment information available from OSAT is shown in Figure 6-2. The figure shows info rmation for the location of the region wide maximum hourly average ozone concentration (the hou rly peak) for an example OSAT run (this run had 17 geographical source areas and a single e mission group). Summary information for scenario and receptor is shown in the center. At t op left, a bar chart shows how the total amount of ozone was attributed between initial conditions, boundary conditions and emissions, with the emissions contribution being divided between ozone formed under VOC and NO X limited conditions. At top right, a pie chart shows the re lative ozone contributions from initial conditions, boundary conditions and emissions, with the emissions contributions broken out by numbered geographical source areas. Only the sourc e areas with the largest ozone contribution are shown, the remaining source areas are lumped to gether under “All Others.” At bottom, there is a tabular summary of ozone contributions underly ing the charts at the top. The tabular summary also shows the estimated release times for the emissions from different source areas impacting the receptor (under the “Time Emitted” co lumn). The release times are estimated using the timing tracers. The timing tracers are a lso used to estimate whether there is more than one significant source-receptor pathway between eac h source area and the receptor, i.e. if timing tracers of different ages from the same source area are present at the receptor it is likely that recirculation of emissions is occurring. The displ ay shown in Figure 6-2 is only one of many ways in which OSAT results can be displayed to gain insight into ozone source-receptor relationships predicted by CAMx. Figure 6-2 is sho wn here to illustrate the types of information available. Displays like Figure 6-2 are generated using post-processing software described later
in this section.

An important feature of the ozone reaction tracer a pproach used in OSAT is that the normal CAMx calculations are not perturbed; thus, OSAT est imates the same ozone and precursor concentrations as CAMx. Further, since the same in puts are used for meteorology, emissions etc., and the same numerical methods are employed t hroughout the model, the source-receptor relationships developed by OSAT inherently have a h igh degree of consistency with those present in CAMx. The level of consistency between the source-receptor relationships developed by OSAT and CAMx is limited by three factors:

• The completeness of the conceptual methodology unde rlying the source apportionments;

• The limitations of the numerical methods employed b y CAMx; and

• The non-linear nature of the ozone formation proces s.

The completeness of the source apportionment method ology was analyzed by performing a series of evaluations of OSAT results against UAM s ensitivity tests (Yarwood et al., 1996). These tests showed that the OSAT performed well in predicting both the spatial impacts of emissions from given source regions and the relativ e contributions of separate emission regions/groups to ozone. OSAT cannot overcome nume rical limitations inherent to CAMx (as well as other grid models). The UAM evaluation tes ts referred to above suggested that a potential serious numerical limitation was the Smol arkeiwicz horizontal advection solver (Smolarkeiwicz, 1983). As discussed below, the non -linear numerical limitations of advection algorithms were addressed in the OSAT design, and w e believe that they probably pose greater problems for the use of CAMx in conventional contro l strategy evaluations than for OSATCAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-4 Figure 6-2. **Example display of ozone source apportionment infor mation provided by OSAT for**  **a run with 17 source areas and 1 emission group. Contributions By Type**  **Contributions By Source Area**

Receptor = HourlyPeak

Time = 16 To 17

Date = 8/28/87

Scenario = Evaluation Test

Total Ozone = 183 ppb

**Detailed Ozone Apportionment**

Source Emission Percent Ozone from Time Recirculation Area Group NOx VOC Total Emitted Likely
**IC 12%** 
**BC 14%** 
**7 1 3% 17% 20% 8/28 5 am No**
**6 1 4% 13% 16% 8/28 6 am No**
**10 1 7% 7% 14% 8/28 7 am No 17 1 1% 6% 7% 8/27 1 pm No** 
**16 1 1% 6% 7% 8/27 6 am No 13 1 2% 1% 4% 8/28 2 pm No** 
**All Others 7%**  **0 20**  **40 60**  **80 100**  **120 140**  **160 180**  **200**Ozone (ppb) **NOx limited**

**VOC limited**

**Boundary**

**Initial IC** 
**12%**

**BC**

**14%**

**7, 1**
**19%**

**6, 1**
**16% 10, 1** 
**14% 17, 1** 
**7% 16, 1** 
**7% 13, 1** 
**4% All Others,** 

**7%**CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-5 (Yarwood et al., 1996). This issue was one of the primary reasons for incorporating the Bott (1989) and PPM (Colella and Woodward, 1984) advecti on algorithms into CAMx and redesigning the three-dimensional transport scheme to be more mass consistent 2 . Further, the tracer transport routines were modified to directly use the advective fluxes diagnosed by the regular model advection solvers to minimize inconsi stencies between tracers and regular model species.

The biggest limitation of this, or any other, ozone source apportionment approach likely results from the third factor mentioned above: namely, the non-linear nature of the chemical interactions between emissions from different source groupings. This means that as soon as the emission inventory is perturbed, the source receptor relatio nships begin to change. Thus, OSAT can only estimate the contribution to ozone from a specific source grouping under the current emissions scenario. It cannot directly quantify the ozone re ductions that will result from an emission control strategy because the CAMx response may well be non-linear with the magnitude of the control applied (e.g. 20 percent or 60 percent cont rol) and the presence/absence of other simultaneous controls on other source groupings.

However, this fundamental truth of photochemical mo deling does not limit the usefulness of OSAT: arguably it increases its usefulness. At any rate, it suggests the following approach to control strategy development with OSAT. For the ba se CAMx scenario, OSAT could be used to identify specific source groupings for which ozone reductions from emissions controls would be maximized. Just as important, OSAT will identify s ource groupings for which emissions controls are not effective at reducing ozone concen trations. The most effective and feasible control measures identified in this way can be appl ied to the emission inventory, leading to a revised CAMx scenario with different (lower) emissi ons. If additional controls are needed, OSAT could be applied again to identify the most ef fective control measures under the new conditions. Development of the control plan will s till proceed by an iterative process, but OSAT can be used to guide the process to develop (and ju stify) more refined, better-targeted and more cost-effective ozone control strategies.

6.2 OSAT FORMULATION

CAMx has been extended to accommodate extra tracer species needed to perform ozone source apportionment. For each user-specified source grou ping (geographical area/emissions category) there are two distinct types of tracer species, “ti ming tracers” and “ozone reaction tracers.” The timing tracers are used to track the temporal relat ionships between precursor emissions and
ozone formation. The ozone reaction tracers track the fate of ozone precursors (NO X and VOC) emissions from each source grouping plus the ozone formation attributed to those emissions. The methodology requires that all ozone precursors be tracked so that all contributions to ozone can be accounted for, thus ozone and precursors ori ginating from the model boundary and initial concentrations are also tracked as separate source groupings (however, no timing tracers are allocated to the initial or boundary conditions). The following sub-sections describe the methodologies employed for the timing and ozone rea ction tracers.

2 The Smolarkiewicz advection routine is no longer a vailable in CAMx.CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-6 Timing Tracers

There are two types of “timing” tracers called iner t and decay-companion tracers. Inert and decay-companion tracers are used in tandem to ident ify the age of emissions from any source area. Timing tracers are emitted into every surfac e grid cell at a constant rate. Timing tracers have unique names that identify the source area in which they were released. After release, the decay-companion tracers decay exponentially with a constant lifetime. Since the inert and decay-companion tracers are affected identically by transport and diffusion, differences in predicted concentrations between the two are due on ly to the first-order decay of the decaycompanion tracer. The presence of inert tracer fro m a given source area (A) at a receptor (B) indicates that a transport pathway exists from A to B. The amount of tracer decay that has occurred since release (i.e. the ratio of the decay companion tracer to its matched inert tracer) can be used to calculate the transport time from A to B and therefore the time period during which emissions at source area A can potentially impact r eceptor B.

To resolve ambiguities in timing calculations that could result from wind flow reversals (e.g., land/sea breezes) or other recirculation patterns, new sets of inert and decay-companion tracers can be released at user specified time periods. Wh en a new set of timing tracers (i.e. one pair for each source area) is released, emissions of the previous set cease. Old timing tracers are still
carried in the model to track emissions from those source areas and time period as they age. Thus, as a simulation progresses the number of timi ng tracers being carried increases as new sets are released. The names of timing tracer species i dentify the species type and source area/time period in which the tracer was released:

I i, t

= Inert timing tracer for region i and time period t. Emitted at a constant rate during that time period.

D i, t = Decay companion timing tracer for region i and time period t. Emitted at a

constant rate during that time period. Decays expo nentially with a constant
lifetime of 12 hours after release.

This method is more computationally efficient than the simpler alternative of releasing nondecaying tracers many short time periods, since the latter approach would rapidly lead to an impossibly large number of tracers.

Ozone Reaction Tracers

The second category of tracers is called “ozone-rea ction” tracers. There are four ozone-reaction tracers per source area to account for the contribu tions of emissions of separate source areas to the complex process of the ozone formation activity . It is important to recall that the mass of
NO X or VOC from a given source area that may be presen t at a given receptor at a selected hour is often not particularly relevant to the amount of ozone present there at that time. Rather, it is the contribution to ozone formation activity of sou rce areas’ NO X and VOC emissions en-route to a receptor that must be determined. The ozone-r eaction tracers are designed to integrate these en-route contributions to ozone production/destruct ion activity.

Ozone formation involves both NO X and VOCs, and the NO X and VOCs participating in ozone formation in any particular grid cell/time step may have originated from different sourceCAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-7 groupings. The ozone formation process can be cont rolled more by the availability of VOCs or
NO X , depending upon the relative abundance of both pre cursors, and ozone formation is
described either as VOC limited or NO X limited, respectively. When ozone production at a given location and time is NO X limited it makes sense to attribute ozone producti on to source groupings based on their contributions to the local NO X and similarly to allocate based on VOC
contributions when ozone formation is VOC limited. Consequently, separate tracers are used to track ozone formation occurring under NO X and VOC limited conditions. The criterion used to determine whether ozone formation is occurring unde r locally NO X or VOC limited conditions is described in detail in a separate section below.

The four types of ozone-reaction tracers that are t racked for each source grouping (i.e., geographic area/source category, boundary condition s or initial conditions) are explained below. The name of each tracer uniquely identifies the sou rce grouping it represents:

N i = NO X tracer for source grouping i . Emitted with the same spatial and temporal

distribution as NO X emissions for source grouping i . Decays with local
d NO X / d t for each grid cell/time step.

V i = VOC tracer for source grouping i . Emitted with the same spatial and temporal

distribution as VOC emissions for source grouping i . Decays with local
d VOC/ d t for each grid cell/time step. Note that VOC trace rs are defined as single carbon species, so their concentrations are numerically equal in ppb and ppbC units.

O3V i = Tracer of ozone formation under VOC limited cond itions attributed to source

grouping i . If ozone formation is determined to be VOC-limit ed for a given grid cell/time step, O3V i is formed in proportion to local d O 3 / d t in proportion weighted by the distribution of VOC precursors (V i ).

O3N i = Tracer of ozone formation under NO X limited conditions attributed to source

grouping i . If ozone formation is determined to be NO X -limited for a given grid cell/time step, O3N i is formed in proportion to local d O 3 / d t in proportion weighted by the distribution of NO X precursors (N i ).

Mass Consistency with CAMx

The ozone reaction tracer methodology was designed to be inherently mass consistent with CAMx. In other words, the sums of the individual o zone, NO X , and VOC tracers ( i ) should remain consistent with their standard CAMx counterp arts as follows:

3 3 3 2

O N O V O C VOC V NO NO N

i i j j i i

= + ×

= + =



## ∑ ∑ ∑ ∑ ∑CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-8 Note that VOC tracers are weighted by the carbon nu mber of each VOC species ( C j ). The validity of these relationships was evaluated and i t was found that the chemistry, emissions, deposition, horizontal diffusion, and vertical diff usion algorithms maintained mass consistency on an overall and cell-by-cell basis. However, it was found that the horizontal and vertical advection algorithms inherently maintained mass con sistency on an overall basis, but not a cellby-cell basis. Accordingly, the advection algorith ms for the ozone reaction tracers were
modified as described below to maintain mass consis tency.

Emissions

The tracer families, N i and V i , track NO X and VOC emissions from each source grouping to
allow attribution of ozone formation as it occurs t hroughout CAMx. The rates of emission of the
N i and V i tracers are set equal to the total NO X and VOC emissions in the inventory for a source grouping, respectively. For the V i tracers, the emissions are set equal on a ppmC bas is. For the initial condition (IC) source grouping, the N IC and V IC tracers are initialized from the CAMx
initial concentration fields and receive no more ma ss input after the start of the simulation. For the boundary condition (BC) source grouping, the fl uxes of NO X and VOC entering CAMx from the boundaries are effectively interpreted as emiss ions of the N BC and V BC tracers at the model boundaries. Unlike emission source groupings, boun dary and initial conditions also introduce ozone directly into CAMx. Since there is no way of determining whether the ozone in the boundary and initial conditions was formed under VO C or NO X limited conditions, this ozone is divided equally between O3N and O3V tracers. Howev er, subsequent ozone formation within CAMx from boundary and initial condition VOCs and N O X is allocated to O3V and O3N tracers
on the basis of whether ozone formation occurred un der VOC or NO X limited conditions (discussed below).

Deposition

The N i , V i , O3N i and O3V i tracers are deposited at rates determined by the s tandard CAMx deposition calculation on a surface grid cell by gr id cell basis. For NO X , the deposition velocity
for each tracer N i [ V d ( N i ) ] is set equal to the concentration weighted averag e of the deposition
velocities for NO and NO 2 :

Similarly, the deposition velocity for each tracer V i is set equal to the concentration weighted average of the deposition velocities for the VOC sp ecies (the concentration weighting is performed on a ppmC basis). The deposition velocit y for the O3N and O3V tracers is set equal to the ozone deposition velocity. Gridded fields o f deposited OSAT tracer mass can optionally be output to a file for viewing or reporting.



## ( ) ( ) ( ) )1(

2 2 2 

### NO NO NO

V NO NO V NO N V D D
i d 

### + × + ×

=CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-9 Transport and Diffusion

Originally, the N i , V i , O3N i and O3V i tracers were advected and diffused by the CAMx algorithms. As mentioned above, testing of OSAT sh owed that the CAMx advection routines did not accurately preserve the spatial relationshi ps between ozone reaction tracers and their corresponding CAMx species (i.e., between the sum o f the O3V and O3N species and CAMx
O 3 ). The distortion in any single model time-step wa s small, but the distortion built up over time to unacceptable levels. This was caused by non-lin ear dependences on the gradients of the advecting material, which can differ substantially between the standard model species and each of the OSAT tracers. This resulted in OSAT predict ions that differed from the standard CAMx
concentrations.

Such an outcome was inconsistent with the objective s in developing OSAT, so an alternative approach was adopted. As the horizontal and vertic al transport operations are carried out for all CAMx species, the individual advective fluxes for o zone and each NO
x and VOC species are reported by the advection solvers. The ozone and i ndividual NO
x and VOC fluxes are combined (and weighted by carbon number for VOC) and normali zed by their respective weighted mean concentrations prior to advection. The equations b elow summarize this process, where f i is the flux of the core model species into or out of cell i , C is VOC carbon number, and F i is bulk
normalized flux:

i i O i O OLE i PAR i OLE

i OLE PAR i PAR i VOC i

i i NO i NO i NOx



### O

f F C OLE C PAR C

f C f F NO

NO f f F

3 ... ... 2

3 3 2



### = + × + × +

× + ×

= + +

=

These bulk normalized fluxes are used to advect eac h tracer species directly, rather than sending the tracers through the advection algorithms. This approach has significantly improved consistency between tracer and regular model concen trations.

The OSAT tracers are sent to the standard CAMx rout ines for vertical diffusion and horizontal
diffusion. Since small deviations sometimes occur between the OSAT tracer concentrations and the regular CAMx species (due to numerical limitati ons), the ozone reaction tracer concentrations are renormalized after transport to be consistent with the standard CAMx concentration fields. The renormalization is appli ed in the same subroutine as chemical change because this is computationally efficient.

Chemical Change

The N i tracer mass in each grid cell at each time step de cays according to the chemical change in the CAMx predicted NO X ( ∆ NO X ) weighted by the tracer contribution to the total of NO X tracers from all source groupings:CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-10 The V i tracer mass in each grid cell at each time step de cays according to the chemical change in the CAMx predicted VOC ( ∆ VOC ) weighted by the tracer contribution to the total of VOC tracers from all source groupings. However, becaus e the reactivity of VOCs from different source groupings can be different, a weighting fact or based on the OH-reactivity of each V tracer ( kOH i ) is also introduced. The kOH i for each source grouping is calculated at the star t of each simulation period (typically one day) by averaging the OH rate constants of the speciated VOC emissions for each source grouping. The V tracer m ass in each grid cell at each time step decays at a rate determined by the following equation:

The O3N and O3V tracers for each source grouping ac cumulate a weighted fraction of the ozone production activity (

PO 3 ) and ozone destruction activity ( DO 3 ) that occurs in each grid cell at each time step. The process of apportioning PO 3 and DO 3 across O3N and O3V tracers occurs
as follows:

1. Determine whether the local (grid cell/time ste p) ozone production process is NO X or

VOC limited. As described in detail below, the det ermination is based on the ratio of
the local HNO 3 and H 2 O 2 production rates. If the H 2 O 2 /HNO 3 production ratio is greater than 1/3 during a time step, then ozone for mation is NO X limited. If this ratio is less than or equal to 1/3, then ozone formation is VOC limited.

2. For allocating ozone production under condition s determined to be:

(a) NO X limited: allocate PO 3 across O3N tracers

(b) VOC limited: allocate PO 3 across O3v tracers based on maximum incremental reactivity (MIR) factors

3. For ozone destruction, allocate across all ozon e tracers.

where X = N and V.

This is the algorithm used for OSAT. The GOAT and APCA methods use different algorithms for allocating ozone production, as described below

. 

### )2( 

# ∑ × ∆ + = old i old i

x old i new i 

### N N NO N N

)3(



# ∑ × × × ∆ + =

i old i i old i old i new i kOH V kOH V VOC V V



### )4(

3 3 3 

# ∑ × + = old i old i old i new i 

### N N PO N O N O

)5(

3 3 3 

# ∑ × × × + =

i old i i old i old i new i MIR V MIR V PO V O V O

)6(

3 3 3 

# ∑ × + = old i old i old i new i X X DO X O X OCAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-11 The MIRs used in equation (5) are the weighted aver age MIRs for the emissions in each source group. The MIR approach was developed by Carter (1 994) to approximate the ozone forming potential of VOCs accounting for both kinetic and m echanistic reactivity effects. The MIR represents the ozone formation potential of the VOC mixture accounting for the VOC reactions with OH as well as the effects of all of the VOC pr oducts that produce ozone formation. The
VOC + OH reaction is the main VOC destruction mechan ism so it is appropriate to weight the VOC destruction across the VOC reactive tracers by their composite reaction rate with OH (kOH). The MIR factors for the CAMx CB4, SAPRC99, and CB05 mechanisms are shown in Tables 3-4, 3-7, and 3-10, respectively.

Accounting for Ozone Destruction Reactions in OSAT

The original OSAT algorithm (i.e., CAMx v4.11s and earlier) allocated chemical change to ozone tracers based on the total change in ozone ( ∆ O 3 ) across a call to the gas-phase chemistry solver. A positive ∆

O 3 was equated to ozone production and ozone tracers were incremented according to the OSAT, APCA or GOAT rules for appor tioning ozone production. A negative

∆ O 3 was equated to ozone destruction and the destructi on was distributed equally among all ozone tracers. However, ozone production and destr uction mechanisms operate simultaneously within the chemical mechanism (e.g., CB4) and so th e net change in ozone is the balance of production and destruction. For example, VOC oxida tion can drive photochemical ozone production at the same time that O 3 + VOC reactions directly consume ozone, and these processes may lead to a net increase or decrease in ozone depending mainly upon availability of NOx and sunlight. OSAT has been modified to explic itly track several ozone destruction
mechanisms as follows:

1. O 3 + VOC reactions since these remove ozone;
2. O( 3 P) + VOC reactions since these effectively remove oz one; 3. O( 1 D) + H 2 O reaction since this effectively removes ozone;
4. HOx + O 3 reactions that do not re-form ozone.

Ozone production is then calculated from the differ ence between ∆ O 3 and total ozone destruction ( PO3 = ∆ O 3 – DO3 ). The OSAT ozone tracers are adjusted first for o zone destruction (applied to all tracers) and second for ozone production (ap plied using the OSAT, APCA or GOAT rules).

The amount of ozone destruction is calculated using integrated reaction rates obtained from the Process Analysis (PA) Probing Tool; see Section 9. It is easy to account for processes 1-3 using PA since the ozone destroyed is simply the integrat ed rates of the reactions involved. Process 4 is less easy to identify but is very important as a n ozone destruction mechanism in low NOx (rural) environments. Therefore, accounting for pr ocess 4 is important to understanding longrange ozone transport.

The main reaction pathways between ozone and HOx (O H and HO 2 ) are shown in Figure 6-3.CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-12 Figure 6-3 **. Daytime reactions of ozone with HOx (OH and HO 2**  **) showing potential for reformation of ozone or ozone destruction via peroxide formation.** 

Ozone reacts directly with both with OH and HO 2 :

O 3 + OH → O 2 + HO 2 (7)

O 3 + HO 2 → 2 O 2 + OH (8)

Since OH and HO 2 radicals inter-convert rapidly during the daytime they can be viewed as a radical pool, called HOx. During the daytime, ther e are two main sinks for HOx:

(1) propagation by reaction with NO, which reforms ozone:

NO + HO 2 → NO 2 + OH

NO 2 + h ν → NO + O( 3 P) O( 3 P) + O 2 → O 3 Net: HO 2 + O 2 + h ν → O 3 + OH

(2) termination by reaction with HO 2 or RO 2 to form peroxides and other organic products, which prevents HOx from propagating to re form ozone:

HO 2 + HO 2 → H 2 O 2 + O 2 HO 2 + RO 2 → ROOH + O 2 RO 2 + RO 2 → organic products + O 2

In the revised OSAT algorithms, the ozone destructi on rate due to O 3 + HOx reactions is
estimated as: OH
**HO2**

O3

**OH** HO2

**hV**
**NO** Peroxides
NO2 **HOx**
**Radical**
**Pool**

**HO2,**
**RO2**

( ) ( )

( ) ( ) 

      

+ × =

+

+

term HO NO HO term

HO

HOx O Rate

Rate Rate Rate Rate n Destructio O

2 2 2

3 3CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-13 This equation is the rate of O 3 + HOx reaction (i.e., the rates of [7] plus [8]) mu ltiplied by a branching ratio that estimates the fraction of O 3 + HOx reactions that result in loss of ozone. The
reaction of O 3 with HO 2 is the dominant destruction pathway for ozone unde r low NOx conditions at mid-latitudes (Wang et al., 2003).

Ozone destruction due to the O 3 + NO → NO 2 + O 2 titration reaction is explicitly excluded from the accounting of ozone destruction because it is h ard to disentangle its role in ozone destruction and production. Under high NOx (ozone titration) c onditions and at night this reaction becomes clearly an ozone destruction pathway, and in these situations ∆ O 3 becomes negative and ozone
destruction due to the O 3 + NO reaction is accounted for. Under most daytime conditions, this reaction is part of the O 3 , NO, NO 2 photo-stationary state and ozone can be reformed a gain when
NO 2 photolyzes so this reaction is not clearly an ozon e destruction pathway.

Distinguishing VOC-Limited from NO X -Limited Ozone Formation

The classic trajectory model EKMA diagram can be us ed to distinguish VOC-limited from NO X limited trajectories and develop a classification b ased on initial VOC-to-NO X ratio. However, this approach classifies VOC- or NO X -limited behavior based on the response of the peak ozone, which is the net response from several hours of pho tochemistry. In reality, many of the trajectories start out VOC-limited and become NO X -limited during the course of the day essentially because the NO X is depleted more rapidly than the VOC. For OSAT, a more fundamental indicator of the instantaneous state of ozone formation with regard to VOC- or
NO X -limitation is required.

The sensitivity of ozone formation to VOCs and NO X at any given time is attributable to the fate of radicals. The radical pool is often referred to as odd-hydrogen (HO X ) and is most usefully considered as the sum of OH, HO 2 and RO 2 radicals. When NO X is plentiful, the main radical termination (i.e., HO X removal) pathway is nitric acid formation, i.e.:

OH + NO 2 (+M) → HNO 3 (+M) (9)

Under these conditions, ozone formation is limited by the rate at which radicals can be formed, which is generally described as the VOC-limited con dition. Thus nitric acid (HNO 3 ) production is indicative of plentiful NO X and VOC-limited ozone formation.

When NO X is scarce, radical-radical reactions dominate HO X removal, e.g.:

HO 2 + HO 2 (+M) → H 2 O 2 + O 2 (+M) (10)

HO 2 + RO 2 → ROOH + O 2 (11)

Under these conditions, ozone formation is limited by the availability of NO to react with HO 2 and RO 2 radicals, which is described as the NO X -limited condition. HO 2 and RO 2 radicals that do not react with NO participate in peroxide format ion. Thus, peroxide formation (H 2 O 2 = hydrogen peroxide, or ROOH = organic hydroperoxides) is indicative of scarce NO X and NO X limited ozone formation.CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-14 Sillman (1995) has exploited this situation to deve lop useful indicators of VOC vs. NO X limited ozone formation based on the ratio of peroxide prod uction to nitric acid production. Sillman proposed that the transition between these conditio ns occurs when:

(P H2O2 + P

ROOH ) / P HNO3 = 0.5 (12)

The production rates of nitric acid (P HNO3 ) and hydrogen peroxide (P H2O2 ) in each CAMx grid cell at each time step are readily accessible, but the production rate of organic peroxides (P ROOH ) is not readily accessible because the CB4 mechanism does not explicitly track these species. The
balance between P ROOH and P H2O2 depends upon the relative production of HO 2 and RO 2 radicals, and Sillman suggests that it is not highly variable within/across simulations. Accordingly, he proposes that equation (12) is equivalent to a tran sition point of:

P H2O2 / P

HNO3 = 0.35 (13)

In other words, when this ratio exceeds 0.35 ozone formation is NO X -limited, and when this ratio
is less than 0.35 ozone formation is VOC-limited.

Evaluation of Sillman’s P H2O2 /P HNO3 Indicator

Equation (13) was evaluated as an indicator of VOC vs. NO X -limited ozone formation for several trajectory model scenarios using the CB4 mechanism as implemented in the UAM. 3 The ozone
concentration for a scenario with an initial VOC/NO X ratio of 10 (0.3 ppmC of VOC, 30 ppb of
NO X ) is shown in Figure 6-4. Also plotted, against th e right axis, is the ratio P H2O2 /P HNO3

.

Sillman’s proposed P H2O2 /P HNO3 transition point of 0.35 is shown by the dashed li ne. Before about 12 noon, P H2O2 /P HNO3 is less than 0.35 indicating VOC-limited ozone for mation, whereas
from about 12 noon to 7 PM P H2O2 /P HNO3 exceeds 0.35 indicating NO X -limited ozone formation. After 7 PM, P

H2O2 /P HNO3 falls below 0.35 again, but this is after the ozon e peak when ozone is decreasing and the concepts of VOC and NO X -limitation are no longer relevant.

In summary, the P H2O2 /P HNO3 indicator suggested a transition from VOC to NO X -limited ozone formation at about noon. To investigate whether th is prediction is corroborated by responses to emission controls, the trajectory model scenario wa s re-run with 30 percent reductions in initial
concentrations and emissions of VOC and NO X (Figure 6-5). Figure 6-5 shows that NO X controls started to be effective at about noon wher eas VOC controls had maximum effect before noon. This finding is even clearer in Figure 6-6, which shows the difference in ozone (controlbase) for the reduced VOC and NO X cases. Thus, the response of ozone to reducing VO C and
NO X was found to be consistent with the prediction of the P H2O2 /P HNO3 indicator.

The evaluation described above and shown in Figures 6-4 and 6-6 was repeated for two more
scenarios with different base VOC/NO X ratios. The results for a base VOC/NO X ratio of 20 (0.3 ppmC of VOC, 15 ppb of NO X ) are shown in Figure 6-7, and for a base VOC/NO X ratio of 5 (0.15 ppmC of VOC, 15 ppb of NO X ) are shown in Figure 6-8. For a base VOC/NO X ratio of 20,

3 The scenarios begin at sunrise at 6 a.m. with 10 pp b of O 3 plus initial VOCs and NO X . There is no dilution, and 5 percent of the initial VOCs and NO X are added in each hour as emissions.CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-15 Figure 6-4. **Box model simulation to evaluate** P H202 /P HNO3 **indicator for VOC vs. NOx limited ozone formation (see text). Ozone is plotted against the left axis,**  P H202 /P HNO3 **is plotted against the right axis. For**  P H202 /P HN03 **below 0.35 ozone formation is VOC limited. Thus, the transition form VOC to NOx limited ozone formation is predicted to occur about 12 noon.**  Initial VOC/NOx = 10:1.

Figure 6-5. **Effect on ozone of controlling VOC and NOx by 30 percent for the base scena rio**  **shown in Figure 6-4. The vertical dashed line shows the transition form VOC to NOx limited**  **ozone formation for the base case derived in Figure 6-4.** Initial base case VOC/NOx = 10:1. 0 0.05 0.1 0.15 0.2 0.25

6 9 12 15 18 21 24 3
Time (hours) **0.001 0.01**  **0.1 1**  **10 100**  **1000 10000**  **Ozone**
**p(H2O2) / p(HNO3)**

**NOx**
**limited VOC** 

**limited**

0 0.05 0.1 0.15 0.2 0.25

6 9 12 15 18 21 24 3 Time (hours) **Base**

**30% VOC control**

**30% NOx control**

**NOx**

**limited VOC** 
**limited**CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-16 Figure 6-6. **Change in ozone (control-base) for the VOC and NOx control scenarios shown i n**  **Figure 6-5. The vertical dashed line shows the transition from VOC to N Ox limited ozone**  **formation for the base case derived in Figure 6-4** . Initial base case VOC/NOx = 10:1.

Figure 6-7a. **As Figure 6-4, but for an** Initial VOC/NOx = 20:1. **0 0.02**  **0.04 0.06**  **0.08 0.1**  **0.12 0.14**  **0.16 0.18** 

**6 9 12 15 18 21 24 3 Time (hours)** **Ozone (ppm)**

**0.001 0.01**  **0.1 1**  **10 100**  **1000 10000** **ratio p(H2O2) / p(HNO3) Ozone** 
**p(H2O2) / p(HNO3)**

**NOx**

**limited VOC** 
**limited -0.07**  **-0.06 -0.05**  **-0.04 -0.03**  **-0.02 -0.01**  **0 0.01**  **0.02 0.03** **6 9** **12 15** **18 21** **24 3**  **Time (hours) Delta Ozone (Control - Base)** **(ppm) 30% NOx control** 

**30% VOC control**

**NOx**
**limited VOC** 
**limited**CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-17 Figure 6-7b. **As in Figure 6-6, but for an** Initial VOC/NOx = 20:1.

Figure 6-8a. **As Figure 6-4, but for an** Initial VOC/NOx = 5:1. **-0.04 -0.035**  **-0.03 -0.025**  **-0.02 -0.015**  **-0.01 -0.005**  **0 0.005**  **0.01 6** **9 12** **15 18** **21 24** **3**
**Time (hours) Delta Ozone (Control - Base)** **(ppm) 30% NOx control** 

**30% VOC control**

**NOx**
**limited VOC** 
**limited**

**0 0.02**  **0.04 0.06**  **0.08 0.1**  **0.12 0.14**  **0.16 0.18** 

**6 9 12 15 18 21 24 3 Time (hours)** **Ozone (ppm)**

**0.001 0.01**  **0.1 1**  **10 100**  **1000 10000** **ratio p(H2O2) / p(HNO3) Ozone** 
**p(H2O2) / p(HNO3)**CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-18 Figure 6-8b. **As Figure 6-6, but for an** Initial VOC/NOx = 5:1

Figure 6-7a shows that the transition from VOC to N O X -limited ozone formation is predicted to occur at about 9:30 AM. Figure 6-7b shows that thi s is confirmed by the response to VOC and
NO X controls. For a base VOC/NO X ratio of 5, Figure 6-8a shows that ozone formation is predicted to remain VOC-limited throughout the day. Figure 6-8b confirms this by showing no
net benefit from NO X control throughout the day.

Alternative Chemical Apportionment Techniques

The formulation of OSAT described above was first m ethodology implemented into CAMx.
Several variations on OSAT are now available that u se different algorithms to attribute ozone contributions. One advantage of the coding framewo rk for OSAT is that it allows for easy modification of the source attribution algorithm by modifying a single subroutine. The alternative methods currently available in CAMx are described below (other options are possible).

Geographic Ozone Assessment Technology (GOAT). GOAT does not attempt to trace ozone production back to the source of the precurso rs, but rather ozone formation is tracked based on the geographic location where it o ccurred. Thus ozone formation in a grid cell over “Area A” would be attributed to Area A even if the culpable emissions originated upwind in Area B. The disadvantage of G OAT is its simplistic assumption regarding the basis for ozone culpability. The adv antages of GOAT are: (1) its freedom from assumptions about whether ozone formation is N O X or VOC limited; and (2) that its results may be more directly comparable to other em erging source attribution methodologies (e.g., trajectories, tracers, and pos sibly the spatial analysis component of Process Analysis).

**-0.12 -0.1**  **-0.08 -0.06**  **-0.04 -0.02**  **0 0.02**  **0.04 0.06** **6 9** **12 15** **18 21** **24 3**  **Time (hours) Delta Ozone (Control - Base)** **(ppm) 30% NOx control** 

**30% VOC control**CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-19 Anthropogenic Precursor Culpability Assessment (APCA). APCA differs from OSAT in recognizing that certain emission groups are not co ntrollable (e.g., biogenic emissions) and that apportioning ozone production to these gro ups does not provide information that is relevant to control strategies. To address this , in situations where OSAT would attribute ozone production to non-controllable (i.e ., biogenic) emissions, APCA reallocates that ozone production to the controllable portion of precursors that participated in ozone formation with the non-controllable precur sor. In the case where biogenic emissions are the uncontrollable source category, A PCA would only attribute ozone production to biogenic emissions when ozone formati on is due to the interaction of biogenic VOC with biogenic NO X . When ozone formation is due to biogenic VOC and anthropogenic NO X under VOC-limited conditions (a situation in which OSAT would attribute ozone production to biogenic VOC), APCA r e-directs that attribution to the anthropogenic NO X precursors present. The use of APCA instead of OS AT results in more ozone formation attributed to anthropogenic NO X sources and less ozone formation attributed to biogenic sources. APCA is not really a “source apportionment” technique because it expresses biases as to which sources sho uld be implicated (i.e., those that are controllable), hence it is referred to as a “culpab ility assessment.” The APCA ozone
allocation scheme is described in more detail below

.

The APCA Technique

OSAT identifies which emission source regions, cate gories, and species (VOC and NO X ) that when reduced would have the greatest effect in redu cing ozone concentrations. For the OTAG database, OSAT correctly identified biogenic VOC em issions as a major contributor to ozone formation, and if controlled, would result in effic ient ozone reductions. However, biogenic emissions cannot be controlled; although scientific ally and chemically correct, this information is irrelevant for the design of an ozone control strat egy. The presence of a source category (biogenic emissions) that cannot be controlled requ ires special considerations when performing ozone source apportionment modeling for the purpose s of aiding in the design of anthropogenic emission control strategies, or when performing an ozone precursor culpability analysis. (Note that some biogenic NO X emissions are soil NO X emissions, which are partly from fertilizers and thus are not truly “biogenic” in nature).

Biogenic versus anthropogenic ozone source apportio nment, as a function of VOC and NO X precursor interactions, can be divided up into four ozone formation regimes as follows:

Biogenic NOx Anthropogenic NOx **Biogenic VOC 1 2 Anthropogenic VOC 3 4** 

Regime 1 involves ozone formation due to the intera ction between biogenic VOC and biogenic
NO X and is truly “biogenic ozone”. Regime 4 is true “ anthropogenic ozone” and the OSAT attribution scheme will provide control strategy re levant information by allocating ozone formed in Regime 4 to the proper anthropogenic ozone precu rsors (VOC or NO X ). Difficulties arise in the mixed anthropogenic/biogenic regimes (Regimes 2 and 3) where precursors from both biogenic and anthropogenic sources participate in t he ozone formation. Much of the ozone in the OTAG modeling is formed under Regime 2, although Re gime 1 may have some influence onCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-20 ozone formation in high biogenic NO X regions (e.g., the corn belt). To design effectiv e control strategies, an ozone allocation scheme is needed th at accounts for the special status of biogenic emissions as being uncontrollable.

In the APCA technique, only the true “biogenic ozon e” (i.e., ozone formed under Regime 1) would be attributed to biogenic emissions, while oz one formed in the remainder of the regimes (2-4) would be allocated to anthropogenic emissions . The standard OSAT approach is used to allocate ozone formed to anthropogenic VOC or NO X emissions when anthropogenic VOC and
NO X emissions interact (i.e., Regime 4), and to alloca te to biogenic VOC or NO X when these two precursors interact (Regime 1). However, when ozone forms due to the interaction of biogenic and anthropogenic emissions (i.e., Regimes 2 and 3), the APCA technique differs from OSAT by allocating ozone formed to anthropogenic em issions; the ozone increment is allocated to anthropogenic NO X (if VOC-limited) or anthropogenic VOC (if NO X -limited) precursors. Assuming that N and V are the NO X and reactivity-weighted VOC tracer concentrations in a grid cell from different source categories (e.g., biogen ic sources and several anthropogenic emissions categories) that participated in the formation of a n increment of ozone (O3), the APCA allocation scheme can be expressed mathematically a s follows.

First, APCA defines the fraction of ozone formed du e to the interaction of biogenic VOC with biogenic NO X , which will be attributed to biogenic emissions (R egime 1):

    

  

=
total bio

total bio bio N

N

V V F

,

min

under VOC-limited conditions, APCA attributes the fracti on of ozone formed to biogenic VOC emissions (Regime 1):

3 3 O

F V O bio bio × =

It then attributes the fraction of ozone increment to a nthropogenic VOC source groupings due to the interaction of anthropogenic VOC with anthropogenic and biogenic NO X (Regime 4):

nanthro i O V V V O

total anthro i

anthro i

,..., 1

, 3 3 = ×        

=

Finally, the remainder of the ozone increment ( ∆ O3 ), which would have been attributed to biogenic VOC in OSAT, is attributed to anthropogenic NO X (Regimes 2 and 3):



# ∑

− − = ∆

i anthro i
bio V O V O O O 3

3 3 3

nanthro i O N N N O total

anthro i anthro anthro i

,..., 1

, 3 3 = ∆ ×        

=

Under NO X -limited conditions, similar formulas are used, switchin g the N with V and O3N with O3V. Since it is expected that biogenic VOC precurso rs will almost always be greater thanCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-21 biogenic NO X precursors (i.e., F bio = N

bio /N total ), then under NO X -limited conditions the APCA ozone allocation scheme will produce identical resul ts to OSAT.

6.3 RUNNING CAMx WITH OSAT

CAMx Control File

OSAT and its derivatives are invoked similarly to the other Probing Tools within the CAMx
control file. In the &CAMx_Control namelist module, the variable Probing_Tool must be set to “OSAT”, “GOAT”, or “APCA”. An additional nam elist module called &SA_Control must then be provided in the control file to configure the OSAT portion of the model. The
additional namelist module is described below. The order of the variables follows the template available with the source code. Several examples o f the OSAT portion of the CAMx run control file are shown in Figures 6-9(a-d).

Description of OSAT Control in the CAMx Run Control File

&SA_Control Label for the Probing Tool namelist module that configures the OSAT option; it must begin in column 2

& Flag ending a namelist module; it must be in c olumn 2

SA_Summary_Output Logical flag used to limit the species written to the tracer
concentration file to a subset of the PSAT treated species. If set to true, the output will be restricted to the follo wing species: O3N, O3V

SA_File_Root Character root output path/filename

SA_Master_Sfc_Output Logical flag for master grid surface output (TRUE= OSAT concentration file will be output for all tracers, FALSE=OSAT concentration file will not be output)

SA_Nested_Sfc_Output Logical flag for nested grid surface output (TRUE= OSAT concentration file will be output for all tracers, FALSE=OSAT concentration file will not be output)

SA_Stratify_Boundary Logical flag to stratify boundary types (TRUE=sepa rate tracer types will be used for the N, S, E, W, and Top boun daries, FALSE=a single tracer type will be used for all 5 b oundaries)

SA_Deposition_Output Logical flag to output deposited tracer mass to a file (TRUE=output deposited tracer mass, FALSE=do not ge nerate a tracer deposition output file)

SA_Number_of_Source_Regions Integer number of source regions for this run. T his must
be the same as the number of source areas defined i n the SA_Source_Area_Map file.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-22 SA_Number_of_Source_Groups Integer number of emission groups for this run. T ogether with
the Use_Leftover_Group flag, this determines the number of paired emission files that must be supplied (add itional details below).

Use_Leftover_Group Logical flag to define a “leftover” emissions grou p (TRUE= calculate a “leftover” emissions group from the dif ference between the sum of the emission group files and the regular CAMx emission files, FALSE=do not calculate a “left over” emissions group)

Number_of_Timing_Releases Integer number of timing tracer releases per 24 h our period (set to zero to turn off timing releases)

SA_Receptor_Definitions Character input OSAT receptor definition path/file name. (This is an optional file).

SA_Source_Area_Map Character array (by CAMx grid) input OSAT source a rea definition path/filename (required for master grid, optional for nested grids)

SA_Master_Restart Character input master grid OSAT restart path/file name (ignored
if Restart=FALSE )

SA_Nested_Restart Character input nested grid OSAT restart path/file name (ignored

if Restart=FALSE or Number_of_Grids=1 )

SA_Points_Group Character array (by source group) input OSAT eleva ted point source emissions path/filename (optional, ignored i f Point_Emissions=FALSE )

SA_Emiss_Group_Grid Character array (by source group, by CAMx grid) in put OSAT gridded emissions path/filename (optional, ignored if Gridded_Emissions=FALSE )

Specifying Emission Groups

OSAT can apportion ozone and ozone precursor concentrati ons among several emission categories. To achieve this, the emissions for the se parate groups must be supplied as sets of separate emissions files for each source group; a low le vel (gridded) file for each fine grid nest, and one elevated point source file. The additional e mission files must be in the standard UAM emission file format (EPA, 1990), as described in Section 5. If the group being separated has no elevated emissions (e.g. biogenics), the point source file name for the group can be left blank. If the emission group being separated has no surface emis sions (e.g. a utility), the surface file name for the group can be left blank for all grids. APCA require s two or more emissions categories, with the first category being biogenic emissions.

Imagine a case where the emission inventory is split into three groups. Three sets of emission files could be supplied that together have the same t otal emissions as the regular CAMxCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-23 emissions files. Alternatively, two sets of files coul d be supplied and the third calculated from the “leftover” emissions (i.e., the regular CAMx emissio ns minus the two groups specified). Both approaches could be useful, depending upon the e missions files available to the user. Accordingly, OSAT reads an input flag “ Use_Leftover_Group ” that can be set true or false. If a leftover group is selected, the model verifies that the group is not too small to calculate within the numerical precision of the computer (this als o traps cases where the flag was set true in error). If no leftover group is selected, the model veri fies that the total emissions for the groups supplied are equal to the regular model emission s, i.e., that a leftover group is not needed. In both cases, if appropriate conditions are not met, t he model stops with a descriptive error

message.

The number of emission file sets that need to be suppl ied for different model configurations is
summarized in Table 6-1. Table 6-1 also shows how the emissions groups are numbered, which is reflected in the tracer species names (defined below).

Table 6-1. **Numbers of emission file sets (i.e., low level files and point so urce file) needed for**  **different model configurations.**
Number of
Emission Groups Use

Leftover Group Number of Emission

File Sets Needed Numbering of Emission Groups and Tracer Species

**1 Not Applicable 0 0**

**n>1 False n 1,2,3,...n**

**True n-1 1,2,3,...n**

When specifying additional point source files to reso lve source category groupings, the list of point sources on each file must be identical (i.e., sa me number of sources, same order) to the regular model point source file. This formal restriction is necessary to ensure that point sources are correctly cross-referenced within CAMx. Thus, a poin t source file for a specific source group may need to contain records for sources that are no t in the group: these records should
have zero emissions.

Geographic resolution is superimposed on emission group resolution using the source area map described below. The source area map is defined using master grid cells, which may not adequately resolve point source locations. To provid e finer control of point source assignments to geographic areas, the source area can be specified for any source by putting the source area index in the (unused) kcell value on the point source file (see file description in section 5). This feature is referred to as “point source override.”CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-24 &SA_Control

SA_File_Root = '$TRACOUT/CAMx.OSAT.95 07$today',

SA_Master_Sfc_Output = .true., SA_Nested_Sfc_Output = .true., SA_Summary_Output = .true., SA_Stratify_Boundary = .false., SA_Deposition_Output = .false., SA_Number_of_Source_Regions = 19, SA_Number_of_Source_Groups = 1, Use_Leftover_Group = .false., Number_of_Timing_Releases = 2,

SA_Receptor_Definitions = '$TRACIN/receptor.citie s ', SA_Source_Area_Map(1) = '$TRACIN/APCA.source.ar ea.map', SA_Source_Area_Map(2) = ' ',

SA_Master_Restart = ' ', SA_Nested_Restart = ' ',

SA_Points_Group(1) = ' ',

SA_Emiss_Group_Grid(1,1) = ' ', SA_Emiss_Group_Grid(1,2) = ' ',

&

Figure 6-9a. **An example of OSAT input records in the CAMx run control file. The opt ions for**  **this run are as follows: this is a two-grid run, master and nested grid surface concentrations are**  **written to file, a single tracer type is to be used for all boundaries, 19 source regions, one**  **emission group (i.e., zero additional emission files and no leftover group), and two timing tracer**  **releases per day. This is the first day of the simulation (i.e., r estart is false), so no OSAT restart**  **files are supplied.**CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-25 &SA_Control

SA_File_Root = '$TRACOUT/CAMx.OSAT.95 07$today',

SA_Master_Sfc_Output = .true., SA_Nested_Sfc_Output = .true., SA_Summary_Output = .true., SA_Stratify_Boundary = .false., SA_Deposition_Output = .false., SA_Number_of_Source_Regions = 19, SA_Number_of_Source_Groups = 1, Use_Leftover_Group = .false., Number_of_Timing_Releases = 2,

SA_Receptor_Definitions = '$TRACIN/receptor.citie s ', SA_Source_Area_Map(1) = '$TRACIN/APCA.source.ar ea.map', SA_Source_Area_Map(2) = ' ',

SA_Master_Restart = '$TRACOUT/CAMx.OSAT.950 7$yesterday.sa.inst', SA_Nested_Restart = '$TRACOUT/CAMx.OSAT.950 7$yesterday.sa.finst',

SA_Points_Group(1) = ' ',

SA_Emiss_Group_Grid(1,1) = ' ', SA_Emiss_Group_Grid(1,2) = ' ',

&

Figure 6-9b. **As in Figure 6-9(a), but in this case the run is a continuation, and s o the restart**  **flag is set to TRUE and a “Tracer Restart” files are supplied.**CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-26 &SA_Control

SA_File_Root = '$TRACOUT/CAMx.OSAT.95 07$today',

SA_Master_Sfc_Output = .true., SA_Nested_Sfc_Output = .true., SA_Summary_Output = .true., SA_Stratify_Boundary = .false., SA_Deposition_Output = .false., SA_Number_of_Source_Regions = 19, SA_Number_of_Source_Groups = 3, Use_Leftover_Group = .true., Number_of_Timing_Releases = 0,

SA_Receptor_Definitions = '$TRACIN/receptor.citie s ', SA_Source_Area_Map(1) = '$TRACIN/APCA.source.ar ea.map', SA_Source_Area_Map(2) = ' ',

SA_Master_Restart = '$TRACOUT/CAMx.OSAT.950 7$yesterday.sa.inst', SA_Nested_Restart = '$TRACOUT/CAMx.OSAT.950 7$yesterday.sa.finst',

SA_Points_Group(1) = ' ', SA_Points_Group(2) = '$PTSRCE/utilpt.ng.$tod ate-07.ag.07cem1A-run11',

SA_Emiss_Group_Grid(1,1) = '$TRACEMS/bio2.cc.$toda te-95', SA_Emiss_Group_Grid(1,2) = '$TRACEMS/bio2.ff.$toda te-95.ne', SA_Emiss_Group_Grid(2,1) = '$TRACEMS/util.cc.$toda te-07.ag.07cem1A-run11', SA_Emiss_Group_Grid(2,2) = '$TRACEMS/util.ff.$toda te-07.ag.07cem1A-run11',

&

Figure 6-9c. **As in Figure 6-9(a), but in this case the run is a continuation day of a run with**  **three emission groups. The three emission groups are defined by supplying tw o pairs of extra**  **emission files for each grid (AREA group 1, AREA group 2, and POINT group 2), and setting the**  **“Use_Leftover_Group” flag to TRUE for the model to calculate the thi rd group internally. The**  **POINT group 1 filename is blank because group 1 is a category with no point source emissions**  **(e.g., biogenics).**CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-27 &SA_Control

SA_Master_Sfc_Output = .true., SA_Nested_Sfc_Output = .true., SA_Summary_Output = .true., SA_Stratify_Boundary = .false., SA_Deposition_Output = .false., SA_Number_of_Source_Regions = 19, SA_Number_of_Source_Groups = 3, Use_Leftover_Group = .false., Number_of_Timing_Releases = 0,

SA_Receptor_Definitions = '$TRACIN/receptor.citie s ', SA_Source_Area_Map(1) = '$TRACIN/APCA.source.ar ea.map', SA_Source_Area_Map(2) = ' ',

SA_Master_Restart = '$TRACOUT/CAMx.OSAT.950 7$yesterday.sa.inst', SA_Nested_Restart = '$TRACOUT/CAMx.OSAT.950 7$yesterday.sa.finst',

SA_Points_Group(1) = ' ', SA_Points_Group(2) = '$PTSRCE/utilpt.ng.$tod ate-07.ag.07cem1A-run11', SA_Points_Group(3) = '$PTSRCE/remain.ng.$tod ate-07.ag.07cem1A-run11',

SA_Emiss_Group_Grid(1,1) = '$TRACEMS/bio2.cc.$toda te-95', SA_Emiss_Group_Grid(1,2) = '$TRACEMS/bio2.ff.$toda te-95.ne', SA_Emiss_Group_Grid(2,1) = '$TRACEMS/util.cc.$toda te-07.ag.07cem1A-run11', SA_Emiss_Group_Grid(2,2) = '$TRACEMS/util.ff.$toda te-07.ag.07cem1A-run11', SA_Emiss_Group_Grid(3,1) = '$TRACEMS/remain.cc.$to date-07.ag.07cem1A-run11', SA_Emiss_Group_Grid(3,2) = '$TRACEMS/remain.ff.$to date-07.ag.07cem1A-run11',

&

Figure 6-9d **. As in Figure 6-9(c) (i.e. a continuation day of a run with thr ee emission groups),**  **but in this case all three emission groups are defined explicitly by supplyi ng extra emission files**  **(AREA group 1, AREA group 2, POINT group 2, AREA group 3, and POINT group 3).**  **Therefore, the “Use_Leftover_Group” flag is set to FALSE. The PO INT group 1 filename is**  **blank because group 1 is a category with no point source emissions (e.g., biog enics).** CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-28 Running With Timing Tracers

Timing tracers will be released at hourly intervals acc ording to the

“ Number_of_Timing_Releases ” parameter. For example, 4 means a new release is started every six hours (hours 0, 6, 12 and 18). If the simulation is started in the middle of the day (e.g. at hour 15), the first tracer release is time sta mped with the model start time, subsequent
releases occur on a schedule as if the start time were a t hour zero.

OSAT can be run without timing tracers if the “ Number_of_Timing_Releases ” parameter is set to zero. Source apportionment of ozone and precu rsors is unaffected by this since these two functions of the model are independent. If timing tracers are omitted, the postprocessing software (“browser”) described below will not report estima ted release times for precursor emissions, nor will it estimate whether recirculation i s likely to have occurred. Users may choose to run without timing tracers in situations whe re temporal resolution is not required. Omitting timing tracers reduces the number of tracer spe cies employed and therefore may permit users to select relatively more geographic/emissions category resolution within the same computer memory limitations.

6.4 INPUT FILE FORMATS

Source Area Mapping

OSAT can apportion ozone and ozone precursor concentrati ons among several geographic regions within the modeling domain, as shown in Figu re 6-1. The “source area mapping” file assigns each grid cell to a specific geographic source region. The format of the file is an array of integer numbers (i3) corresponding to the CAMx domain. Figure 6-10 shows the source area mapping file corresponding to Figure 6-1. Since the CA Mx domain in Figure 6-1 has 63 rows and 64 columns of cells, the file shown in Figure 6-10 has 63 lines with 64 numbers on each line. The first number (top left) always corresponds to the Northw est corner of the domain. Note that the source area map is defined in terms of the master grid

.

If desired, a source area map may be created for each n est. This provides for more finely
resolved source areas within the nested domain. The source area map for a nest is in the same format as the master grid source area map described above, but contains a number of rows and columns corresponding to the nested grid, including the boundary (“buffer”) rows and
columns . Within a nest for which a nested grid source area map is supplied, the source areas as defined by this map will take precedence over the sourc e areas defined by the master grid source area map. If a source area map is not provided for a sp ecific nest the source area definition will be taken from the source area map of the parent grid.

Receptor Definition

Tracer concentrations for selected receptor locations a re output to an ASCII file every hour. The receptors for each model run are defined in the “Receptor d efinition” input file. Three types of receptors are supported:CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-29 POINT a point specified in the coordinate system of the CAM x grid.

Concentrations at the point are determined by bi-linear interpolation of the surrounding four master grid surface ce lls.

SINGLE CELL a single surface grid cell identified by number.

CELL AVERAGE a group of grid cells identified by number that are ave raged

together to provide multi-cell average tracer concentratio ns.

WALL OF CELLS a group of grid cells defining a wall (i.e., a flux plan e).

For the receptor types that are defined by grid cell it is necessary to specify the grid containing the receptor. This is done by including the grid numb er on the receptor definition record. Grid numbers are defined using the internal CAMx grid ordering. The grid numbering as defined by CAMx is shown in a table in the .diag file. Each re ceptor can be identified by a 10 character name. The formats for specifying each receptor type are given in Table 6-2. An example receptor file is shown below:

POINT City 1 461.520 3762.040

SINGLE CELL Cell 1 1 45 18 CELL AVERAGE Region 10 2 8
31 19
32 19
33 19
34 19
31 18
32 18
33 18

34 18 WALL OF CELLS Boundary1 2 10 20
18 18

1 5CAMx v5.30 User’s Guide, December 2010Ozone Source Apportionment TechnologyY:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.docx6-3031 31 31 31 18 18 18 18 18 18 18 18 18 18 37 37 3737 17 17 17 17 17 17 17 17 17 17 17 40 40 40 40 4040 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 4040 40 40 40 40 40 40 14 14 14 14 14 4031 31 31 31 31 18 18 18 18 18 18 18 18 37 37 37 3737 17 17 17 17 17 17 17 17 17 17 17 17 40 40 40 4040 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 4040 40 40 40 40 40 40 14 14 14 14 14 4031 31 31 31 31 18 18 18 18 18 18 18 18 37 37 37 3737 37 37 17 17 17 17 17 17 17 17 17 17 17 40 40 4040 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 4040 40 40 40 40 40 14 14 14 14 14 14 4031 31 31 31 31 18 18 18 18 18 18 18 18 37 37 37 3737 37 37 37 37 17 17 17 17 17 17 17 17 17 40 40 4040 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 4040 40 40 40 40 40 14 14 14 14 14 14 4031 31 31 31 31 18 18 18 18 18 18 18 18 37 37 37 3737 37 37 37 37 17 17 17 17 17 17 17 17 17 17 40 4040 40 40 40 40 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38 3833 33 33 33 33 33 33 39 39 39 39 39 39 39 39 39 3939 39 39 39 39 39 39 39 39 39 39 39 39 39 39 6 66 6 6 38 38 38 38 38 38 38 38 38 38 38 38 38 3838 38 38 38 38 38 38 38 38 38 38 38 3833 33 33 33 33 33 39 39 39 39 39 39 39 39 39 39 3939 39 39 39 39 39 39 39 39 39 39 39 39 39 39 6 66 6 6 38 38 38 38 38 38 38 38 38 38 38 38 38 3838 38 38 38 38 38 38 38 38 38 38 38 3833 33 33 33 33 39 39 39 39 39 39 39 39 39 39 39 3939 39 39 39 39 39 39 39 39 39 39 39 39 39 39 6 66 6 6 6 38 38 38 38 38 38 38 38 38 38 38 38 3838 38 38 38 38 38 38 38 38 38 38 38 3833 33 33 33 39 39 39 39 39 39 39 39 39 39 39 39 3939 39 39 39 39 39 39 39 39 39 39 39 39 39 39 6 66 6 6 6 38 38 38 38 38 38 38 38 38 38 38 38 3838 38 38 38 38 38 38 38 38 38 38 38 3833 33 33 33 39 39 39 39 39 39 39 39 39 39 39 39 3939 39 39 39 39 39 39 39 39 39 39 39 39 39 39 6 66 6 6 6 38 38 38 38 38 38 38 38 38 38 38 38 3838 38 38 38 38 38 38 38 38 38 38 38 3833 33 33 33 39 39 39 39 39 39 39 39 39 39 39 39 3939 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 66 6 6 6 38 38 38 38 38 38 38 38 38 38 38 38 3838 38 38 38 38 38 38 38 38 38 38 38 3833 33 33 33 39 39 39 39 39 39 39 39 39 39 39 39 3939 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 66 6 6 6 38 38 38 38 38 38 38 38 38 38 38 38 3838 38 38 38 38 38 38 38 38 38 38 38 3833 33 33 33 39 39 39 39 39 39 39 39 39 39 39 39 3939 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 396 6 6 6 38 38 38 38 38 38 38 38 38 38 38 38 3838 38 38 38 38 38 38 38 38 38 38 38 38Figure 6-10.**Example source area mapping file for the domain a nd source areas shown in Figure 6-1.** CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-31 Table 6-2. **Format for the receptor definition file.**

Receptor Type Line Columns Data

**POINT 1 1-15 The word “POINT”**

**1 21-30 Receptor name**

**1 31-40 X co-ordinate**

**1 41-50 Y co-ordinate**

**SINGLE CELL 1 1-15 The word “SINGLE CELL”**

**1 21-30 Receptor name**

**1 31-40 Grid Number**

**1 41-50 X cell number**

**1 51-60 Y cell number**

**CELL AVERAGE 1 1-15 The words “CELL AVERAGE”**

**1 21-30 Receptor name**

**1 31-40 Grid number**

**1 41-50 The number of cells to average**  **(M)**

**2-M 1-10 X cell number**

**2-M 11-20 Y cell number**

**WALL OF CELLS 1 1-15 The words “WALL OF CELLS”**

**1 21-30 Receptor name**

**1 31-40 Grid number**

**1 41-50 X-cell begin**

**1 51-60 X-cell end**

**2 41-50 Y-cell begin**

**2 51-60 Y-cell end**

**3 41-50 Z-cell begin**

**3 51-60 Z-cell end**CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-32 6.5 OUTPUT FILE FORMATS

OSAT writes several output files that are in Fortran bina ry format, as described in Section 5. These include the master and nested grid tracer instant aneous concentration files ( .sa.inst
and .sa.finst ), the grid-specific surface tracer average concentration file ( .sa.surf.grd nn ), and the grid-specific surface deposited mass file ( .sa.depn.grd nn ). These files are written in the same format as for the regul ar model species described in Section 5. In addition, OSAT writes out tracer concentrations for se lected receptor locations to an ASCII file ( .sa.receptor ). The naming conventions for tracer species and the form at of the receptor concentration file are discussed below.

Tracer Species Names

The names of tracer species uniquely identify the info rmation carried by each species and, taken as a group, identify the model configuration in any run . Species names have less than ten characters, consistent with the UAM convention. The naming conventions are as follows:

Timing Tracers

Inert Tracer Idddhhrrr Decay Companion Tracer Ddddhhrrr

where:

ddd

Julian day, e.g., 240

hh Hour tracer release started at, e.g., 12

rrr Region tracer released from, i.e., 001, 002, 003, etc

Examples: I24012001

, D24100012

Ozone Reaction Tracers

Emission Sources SSSeeerrr

where:

SSS Species type, i.e., NOX, VOC, O3V or O3N

eee Emissions group: Single group, always 000 Multiple groups, 001, 002, etc.

rrr Region tracer released from, 001, 002, 003, etc.

Initial/Boundary SSSeeerrrCAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-33 where:

SSS Species type, i.e., NOX, VOC, O3V or O3N

eee Initial Concentrations: always 000 Boundary Concentrations not stratified by boundary: always 000 Boundary Concentrations stratified by boundary: WST , EST, STH, NTH,

TOP indicating boundary of origin

rrr IC for Initial Concentrations, BC for Boundary Concentra tions

Examples: NOX000015

, VOC002015

, O3V000IC

, O3NTOPBC

Receptor Concentration File

Hourly average tracer concentrations at user specific rec eptor locations are output to the “receptor concentration” file. The file is in comma de limited ASCII format suitable for importing into a spreadsheet (a spreadsheet postproces sing tool is available to visualize the data in this file, as described below). An example output file is shown in Figure 6-11. Two header lines at the top of the file identify the model versio n and the date the run was performed. Next, two lines identify the time period covered by the fil e and the averaging interval (generally one hour, determined by the CAMx simulation control file). Next, three lines define the model configuration and seven lines specify the numbers of tra cer species that result from this configuration. The names of each tracer species are giv en on six lines ordered by tracer species type: the order in which species are listed here is th e same as the order in which tracer concentrations are given later in the file.

The tracer species names are followed by the number of re ceptors and receptor names. Note that the first receptor (receptor 1) is always the location of t he hourly peak ozone. Since the location of the hourly peak varies from hour to hour, the X and Y l ocations are left blank. Receptors 2 and higher are those specified in the “Receptor definit ion” file. The tracer concentrations are reported in blocks with a date and time stamp at the head of each block. Within each block, receptors are reported in numerical order. For each recepto r, there are six lines of data for the tracer species identified at the heading “Tracer Names” . All values are in CAMx units of ppm. At the bottom of the data block for each receptor are t wo lines with three numbers: these are the
total tracer NO X , VOC, and ozone (first line) and regular CAMx NO X , VOC and ozone (second line).

6.6 POSTPROCESSING

The tracer concentrations in the gridded surface concent ration files can be displayed using any post-processing software normally used for displaying CAM x average file output formats.

The receptor concentration file contains information for a ll receptors and all hours within the model run that created the file. A post-processing too l is available to allow the user to “browse” through the information contained in this file and prep are displays like Figure 6-2. The “browser” is a Microsoft Excel Spreadsheet macro that is controlled by a graphical user interface (GUI) allowing you to rapidly visualize results for any receptor. To use this software, the user must have Microsoft Excel (Office 2003). The browser is provided at www.camx.com

.CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-34 CAMx, SOURCE APPORTIONMENT, VERSION -- 960315 , Mon Oct 26 10:37:34 1998 ,

File Duration , 87238, 15.00, 87238, 24.00, Average Interval , 1.0000

Number of timing periods , 2 Number of source areas , 17 Number of emission groupings , 1 Number of tracer species , 144 Number of NOx species , 19 Number of VOC species , 19 Number of O3N species , 19 Number of O3V species , 19 Number of INERT TIME species , 34 Number of DECAY TIME species , 34

Tracer Names, NOX000IC ,NOX000BC ,NOX001001,NOX001002,NOX001003,N OX001004,NOX001005 .. VOC000IC ,VOC000BC ,VOC001001,VOC001002,VOC001003,V OC001004,VOC001005 .. O3N000IC ,O3N000BC ,O3N001001,O3N001002,O3N001003,O 3N001004,O3N001005 .. O3V000IC ,O3V000BC ,O3V001001,O3V001002,O3V001003,O 3V001004,O3V001005 .. I23815001,I23815002,I23815003,I23815004,I23815005,I 23815006,I23815007 .. D23815001,D23815002,D23815003,D23815004,D23815005,D 23815006,D23815007 ..

Number of receptors , 6 No, Name, Type, Xloc, Yloc, 1, HourlyPeak, 1, , , 2, Rubidoux , 0, 461.5, 3762.0, 3, Pasadena , 0, 396.4, 3778.5, 4, Lennox , 0, 373.0, 3755.0, 5, Peak , 1, 45, 18, 6, Region 10 , 8, 31, 19, 32, 19 33, 19 34, 19, 31, 18, 32, 18, 33, 18, 34, 18,

Time Varying Tracer Data,

Data for Period, 87238, 15.00, 87238, 16.00, Receptor, 1, 1.3265E-02, 1.3544E-09, 1.0000E-16, 1.0974E-15, 1.0 000E-16, 1.0000E-16 .. 1.2237E-01, 3.3869E-08, 1.0000E-16, 1.6165E-14, 1.0 000E-16, 1.0000E-16 .. 8.7304E-02, 1.1926E-08, 1.0000E-16, 1.0000E-16, 1.0 000E-16, 1.0000E-16 .. 9.0300E-02, 1.5269E-08, 1.0188E-16, 2.6997E-15, 1.0 213E-16, 1.0162E-16 .. 1.0036E-16, 4.0640E-15, 1.0036E-16, 1.0036E-16, 1.0 036E-16, 1.0029E-16 .. 1.0000E-16, 3.7563E-15, 1.0000E-16, 1.0000E-16, 1.0 000E-16, 1.0000E-16 .. 1.4500E-02, 1.2663E-01, 1.7770E-01, 1.4500E-02, 1.2663E-01, 1.7770E-01, Receptor, 2, (File continues with data for remaining receptors a nd hours)

Figure 6-11 **. Example Receptor Concentration File. Lines ending with “..” are truncated to fit**  **the page, and the file would continue with data for additional receptors and hours in the same**  **format.**CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-35 6.7 STEPS IN DEVELOPING INPUTS AND RUNNING OSAT

Below is a simple methodological list of steps to fo llow in setting up and running OSAT. The process is similar among the OSAT, PSAT, and DDM Probi ng Tools.

1) Define the source groups and regions that you wish to t rack. Keep in mind that memory resources increase dramatically as the number of tracers g rows. Probing Tool applications with large numbers of tracers, tracer class es, nested grids or grid cells may exceed available memory. 2) Build a source region map (Figure 6-10) that defines the s patial allocation of tracer emissions. For small domains or small number of region s, this can be done by hand. We suggest using GIS software to develop complex source r egion maps on large grids. 3) Process the emissions inventory into the separate so urce group files that you want to track (e.g., mobile, area, point, biogenic, etc.). a) Consideration of potential source apportionment or sensi tivity applications prior to any emissions processing can be very beneficial so th at files by group are available
for later use. b) Elevated point sources will automatically be assigne d to the source region in which they reside. However, you may override the region to w hich each individual point source is assigned (see the definition of kcell in Section 5, Elevated Point Source File). A point source region does not need to be defin ed in the source region map, e.g., you could have a map with two regions that spl it the domain in half, with a third region assigned arbitrarily to represent elevated point sources only. 4) Edit the CAMx control namelist file (Section 5). a) Set the Probing_Tool variable to the technology you wish to use (OSAT, A PCA, GOAT). This will activate the &SA_Control namelist module. b) Edit or add the &SA_Control namelist module (described earlier). Provide the required information, including:

• output paths

• whether to stratify boundary conditions

• number of source regions

• number of source groups

• whether to use the leftover group option

• whether to use timing tracers

• receptor definitions

• list of input emission files by group. c) Note that APCA requires that the biogenic emission files for each grid are list ed first. Several examples are shown in Figure 6-9. 5) Configure the CAMx source code to define the number of t racers, and build an executable. This will ensure that you have sufficient memory for the Probing Tool application. a) Edit the file Includes/camx.prm b) Change the parameter MXTRSP , following the instructions provided in the file.
CAMx is distributed with MXTRSP = 1 to minimize memory requirements for standard applications of the model. If you run OSAT wi th an insufficient value, the model will stop and tell you the required value of MXTRSP for your application. c) Execute the CAMx Makefile to build an executable program (Section 11).CAMx v5.30 User’s Guide, December 2010

Ozone Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_06_OSAT.doc x 6-36 6) Run CAMx and review the diagnostic output files to en sure that the model is correctly interpreting and running the Probing Tool configuration that you have specified. Ensure that CAMx is generating the proper output files that yo u are expecting. Review the table of emissions by source group and region. 7) Review gridded tracer fields using commonly available p lotting programs. Utilities such as PAVE or Verdi will read Probing Tool files directly. Use of any other software may require specialized re-formatting procedures. 8) You may post-process OSAT receptor files using an Excel macro (OSAT_browser) that
ENVIRON distributes at www.camx.com

. 9) Probing Tool gridded tracer output files are written in t he same Fortran binary format as the regular CAMx concentration output files. You can p ost-process gridded output fields using any software that reads CAMx files, or you can ad apt those programs or build your own software to generate specialized analysis and gr aphical products.CAMx v5.30 User’s Guide, December 2010

Particulate Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-1

7. PARTICULATE SOURCE APPORTIONMENT TECHNOLOGY

Airborne particulate matter (PM) is widely recogniz ed as an important pollution issue because it causes health problems and environmental degradatio n. In recent years the emphasis on controlling PM has shifted toward fine PM (or PM2.5 , with particle diameter less than 2.5 μm) because it is more strongly associated with serious health effects than coarse PM. Knowing
which sources contribute to PM2.5 is essential for developing effective control strategies. Many components of PM2.5 are secondary pollutants and so photochemical models are important tools for PM air quality planning. The Particulate Sourc e Apportioning Technology (PSAT) has been developed for CAMx to provide PM source apportionme nt among specific geographic regions and source categories. PM source apportionment inf ormation from PSAT is useful for:

1) Understanding model performance and thereby improvi ng model inputs/formulation; 2) Performing culpability assessments to identify sour ces that contribute significantly to PM pollution; 3) Designing the most effective and cost-effective PM control strategies.

Source apportionment for primary PM is relatively s imple to obtain from any air pollution model because their source-receptor relationships are ess entially linear. Gaussian steady-state models and Lagrangian puff models have been used extensive ly to model primary PM pollution from specific sources, which is a direct form of source apportionment. The Gaussian and Lagrangian approaches work for primary PM because the models c an assume that emissions from separate sources do not interact. This assumption breaks do wn for secondary PM pollutants (e.g., sulfate, nitrate, ammonium, secondary organic aerosol) and s o puff models may dramatically simplify the chemistry (to eliminate interactions between so urces) so that they can be applied to secondary PM. Eulerian photochemical grid models a re better suited to modeling secondary pollutants because they account for chemical intera ctions between sources. Grid models do not naturally provide source apportionment because the impact of all sources has been combined in the total pollutant concentration. PSAT has been d eveloped to retain the advantage of using a grid model to describe the chemistry of secondary P M formation and also to provide source apportionment.

This section starts by reviewing several approaches to PM source attribution in grid models that were considered when PSAT was designed. Next, the PSAT algorithms are explained, and then example PSAT results are presented and compared to results from some other methods.

7.1 APPROACHES TO PM SOURCE ATTRIBUTION IN GRID MO DELS

Several potential approaches were considered at the PSAT design stage and these are discussed below. The approaches considered fall into two gen eral categories, which we have called sensitivity analysis and reactive tracers. The rea ctive tracer approaches also could be called tagged species approaches. This section concludes with a discussion of an important fundamental difference between sensitivity analysis and source apportionment that explains why these two concepts should not be confused for secon dary pollutants.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-2 Sensitivity Analysis Methods

Sensitivity analysis methods measure the model outp ut response to an input change, e.g., the change in sulfate concentration due to a change in SOx emissions. In general, sensitivity methods will not provide source apportionment if th e relationship between model input and output is non-linear. For example, if sulfate form ation is non-linearly related to SOx emissions, the sum of sulfate (SO4) sensitivities over all SOx sources will not equal the model total sulfate concentration. This concept is discussed in more d epth later in this section.

Brute Force or Direct Method . The brute force method estimates first-order sen sitivity coefficients (e.g., ∆ SO4/ ∆ SOx) by making a small input change ( ∆ SOx) and measuring the change in model output ( ∆ SO4). This method is simple and can be applied to any model, but is inefficient because a complete model run is require d for each sensitivity coefficient to be determined. Accuracy also may be an issue for the brute force method because ideally the input change ( ∆ SOx) should be vanishingly small but for small inpu t changes the output change may be contaminated by numerical precision or model “no ise.” For larger input changes, first-order sensitivities estimated by the brute force method m ay be distorted by non-linear chemical effects, e.g., a brute force coefficient ( ∆ SO4/ ∆ SOx) may be a mixture of first-order (dSO4/dSOx) an d second-order (d 2 SO4/dSOx 2 ) derivatives. Higher-order sensitivity coefficie nts also can be estimated by the brute force method.

Zero-Out Modeling . The zero-out method differs from the brute force method in that a specific emissions input is set to zero and the change in ou tput measured. Zero-out modeling can be used with any model but is inefficient because a complet e model simulation is required for each source. Accuracy also may be an issue if the zero- out method is applied to a small emissions source. This method has been used extensively for source attribution because it seems intuitively obvious that removing a source should reveal the so urce’s impact. However, because zero-out modeling is a sensitivity method it does not provid e source apportionment for non-linear systems because the sum of zero-out impacts over all source s will not equal the total concentration, as discussed in more depth later in this section.

Decoupled Direct Method (DDM) . The DDM provides the same type of sensitivity in formation as the brute force method but using a computational method that is directly implemented in the host model (Dunker, 1981). The DDM has potential a dvantages of greater efficiency and accuracy relative to the brute force method (Dunker at al., 2002a) and the DDM implementation in CAMx (Dunker at al., 2002a,b) is currently being extended to PM species. Drawbacks of DDM are that the implementation is technically chal lenging, that using DDM for many sensitivities simultaneously requires large compute r memory, and because DDM is a sensitivity method, it does not provide source apportionment fo r non-linear systems unless higher-order
sensitivities are calculated.

Other Sensitivity Methods . There are other sensitivity methods that provide similar information to DDM such as adjoint methods (e.g., Menut at al., 2000; Elbern and Schmidt, 1999) and automatic differentiation in FORTRAN (ADIFOR). The y have similar advantages and disadvantages as DDM but may be less computationall y efficient (Dunker et al., 2002a).CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-3 Reactive Tracer Methods

Reactive tracers (or tagged species) are extra spec ies added to a grid model to track pollutants from specific sources. For example, a standard gri d model calculates concentrations for a species X that has many sources and so the concentration of X is the total concentration due to all sources. A reactive tracer ( x i ) is assigned to for each source ( i ) with the intention that the sum of the reactive tracers will equal total concentration ( X = Σ x i ). The challenge is to develop numerical algorithms for solving the reactive trace r concentrations that ensure that this equality is maintained. Depending upon the formulation of t he tracer algorithms, it may be possible to model tracers for a single source of interest and o mit tracers for all other sources, or it may be necessary to include tracers for all sources (as is the case for PSAT). Reactive tracers can potentially provide true source apportionment ( X = Σ x i ), however the numerical value of the source apportionment will depend upon assumptions w ithin the reactive tracer formulation. In particular, for any process that is non-linear in s pecies concentrations (e.g., chemistry) there is no unique way to assign the total concentration change to the reactive tracers. This issue is discussed in more depth later in this section.

Source Oriented External Mixture (SOEM) . Kleeman and Cass (2001) developed an approach called SOEM that tracks primary PM from different s ource categories/regions using tagged species that are considered to represent seed parti cles. Reactive tracers are added to track secondary PM and related gases from different sourc e categories/regions, and source apportioned secondary PM condenses onto the seed particles. Ch emical change for secondary PM and related gases is accounted for by expanding the che mical mechanism to treat different source regions/categories as separate precursor and produc t species. This requires thousands of
chemical reactions and hundreds to thousands of che mical species, depending upon the number of source regions/categories. The main advantage o f the SOEM method is that it is potentially accurate, and the main disadvantage is computationa l demand.

PSAT and TSSA . The PM Source Apportionment Technology (PSAT) us es reactive tracers to apportion primary PM, secondary PM and gaseous prec ursors to secondary PM among different source categories and source regions. The PSAT met hodology is described below. PSAT was developed from the related ozone source apportionme nt method (OSAT) already implemented in CAMx (Dunker at al., 2002b). Tonnesen and Wang (20 04) are independently developing a method very similar to PSAT called Tagged Species S ource Apportionment (TSSA) in the Community Multiscale Air Quality (CMAQ) modeling sy stem. Advantages of PSAT and TSSA are expected to be high efficiency and flexibility to study different source categories/regions. The accuracy of the PSAT (and TSSA) source apportio nment results must be evaluated, as for any other method.

Source Apportionment and Source Sensitivity

Consider a chemical species X that has two sources A and B (so X = x A + x B ) and which undergoes a second order self-reaction with rate co nstant k . The rate of chemical change is:CAMx v5.30 User’s Guide, December 2010

Particulate Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-4 The homogeneous rate terms kx A 2 and kx

B 2 clearly describe chemical change for pollutants fr om

sources A and B (

x A and x

B ), but the inhomogeneous rate term 2kx A x B is not uniquely associated
with either source A or B . A reactive tracer (or tagged species) source app ortionment method must deal with this inhomogeneous rate term either by developing rules to apportion the inhomogeneous term to sources A and B or by modifying the chemistry to eliminate the inhomogeneous term. For a sensitivity method, the homogeneous quadratic rate terms generate second-order homogeneous sensitivity coefficients ( s AA and s BB ) and the inhomogeneous rate term generates a second-order inhomogeneous sensiti vity coefficient ( s AB ). Consequently, the
total concentration of X

is incompletely described by the first-order sensi tivity coefficients ( s A and s B ) that resemble source apportionments.

The example presented above is a simple case of a n on-linear chemical system that illustrates why source apportionment and source sensitivity are not the same thing for nonlinear systems. The implications are:

1) There is no unique source apportionment for chemica l species that depend upon nonlinear reactions, such as secondary PM species and ozone. Nonetheless, reasonable source apportionment schemes can be developed and are usef ul tools for achieving the objectives listed in the introduction.

2) Sensitivity coefficients are not source apportionme nts for chemical species that depend upon nonlinear reactions, such as secondary PM spec ies and ozone. Sensitivity coefficients are most applicable for predicting the model response to an input change (e.g., control strategy) and are very useful for th is purpose (within the range of linear model response), but sensitivity coefficients shoul d be used with care for source apportionment or culpability assessment. Likewise, source apportionments should be used with care for predicting model response to inp ut changes.

7.2 PSAT METHODOLOGY

PSAT is designed to source apportion the following PM species modeled in CAMx:

• Sulfate (SO4)

• Particulate nitrate (NO3)

• Ammonium (NH4)

• Particulate mercury (Hg(p))

• Secondary organic aerosol (SOA)

• Six categories of primary PM: 

## ( )

B A B A B A

x

kx kx kx dt dX x

x k dt dX kX dt dX

2 2 2 2 2

− − − = +

− = − =CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-5

o Elemental carbon (EC)

o Primary organic aerosol (POA)

o Crustal fine

o Other fine

o Crustal coarse

o Other coarse

The PSAT “reactive tracers” that are added for each source category/region ( i ) are described below. In general, a single tracer can track prima ry PM species whereas secondary PM species require several tracers to track the relationship b etween gaseous precursors and the resulting PM. Particulate nitrate and secondary organics are the most complex species to apportion because the emitted precursor gases (NO, VOCs) are several steps removed from the resulting PM species (NO3, SOA). The PSAT tracers for each type of PM ar e listed below. There is a PSAT convention that tracer names for particulate specie s begin with the letter “P.”

Sulfur
SO2 i Primary SO 2 emissions
PS4 i Particulate sulfate ion from primary emissions plu s secondarily formed sulfate

Nitrogen
RGN i Reactive gaseous nitrogen including primary NOx (NO + NO 2 ) emissions plus nitrate radical (NO 3 ), nitrous acid (HONO) and dinitrogen pentoxide (N 2 O 5 ).
TPN i Gaseous peroxyl acetyl nitrate (PAN) plus peroxy ni tric acid (PNA)
NTR i Organic nitrates (RNO 3 )
HN3 i Gaseous nitric acid (HNO 3 )
PN3 i Particulate nitrate ion from primary emissions plu s secondarily formed nitrate
NH3 i Gaseous ammonia (NH 3 )
PN4 i Particulate ammonium (NH 4 )

Secondary Organic
ARO i Aromatic (toluene and xylene) secondary organic ae rosol precursors
ISP i Isoprene secondary organic aerosol precursors

TRP i Terpene secondary organic aerosol precursors SQT Sesquiterpene secondary organic aerosol precurs ors CG1 i Condensable gases from aromatics (low volatility p roducts)
CG2 i Condensable gases from aromatics (high volatility products)
CG3 i Condensable gases from isoprene (low volatility pr oducts)
CG4 i Condensable gases from isoprene (high volatility p roducts)
CG5 i Condensable gases from terpenes (low volatility pr oducts)
CG6 i Condensable gases from terpenes (high volatility p roducts)
CG7 i Condensable gases from sesqiterpenes
PO1 i Particulate organic aerosol associated with CG1
PO2 i Particulate organic aerosol associated with CG2
PO3 i Particulate organic aerosol associated with CG3
PO4 i Particulate organic aerosol associated with CG4
PO5 i Particulate organic aerosol associated with CG5
PO6 i Particulate organic aerosol associated with CG6
PO7 i Particulate organic aerosol associated with CG7CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-6 Mercury
HG0 i Elemental Mercury vapor
HG2 i Reactive gaseous Mercury vapor
PHG i Particulate Mercury

Primary Particulate
PEC i Primary Elemental Carbon
POA i Primary Organic Aerosol
PFC i Fine Crustal PM
PFN i Other Fine Particulate
PCC i Coarse Crustal PM
PCS i Other Coarse Particulate

PSAT includes a total of 32 tracers for each source group ( i ) if source apportionment is applied to all types of PM. Since source apportionment may not always be needed for all species, the PSAT implementation is flexible and allows source a pportionment for any or all of the chemical classes in each CAMx simulation (i.e. the SO4, NO3, NH4, SOA, Hg and primary PM classes listed above). For example, source apportionment f or sulfate/nitrate/ammonium requires just 9 tracers per source group.

One fundamental assumption in PSAT is that PM shoul d be apportioned to the primary precursor for each type of PM. For example, SO4 is apportion ed to SOx emissions, NO3 is apportioned to NOx emissions, NH4 is apportioned to NH3 emissions, et c. As a source apportionment method,
PSAT must account for all modeled sources of a PM s pecies. Consider two model species A and B that are apportioned by reactive tracers a i and b i , respectively. Reactive tracers must be
included for all sources of A and B including emissions, initial conditions and bounda ry conditions so that complete source apportionment is obtained, i.e., A = Σ a i and B = Σ b i

.

In PSAT, the general approach to modeling change ov er a model time step ∆ t is illustrated for a
chemical reaction A →

B . The general equation for species destruction is:

Here, the relative apportionment of A is preserved as the total amount changes. This eq uation applies to chemical removal of A and also to physical removal of A by processes such as deposition or transport out of a specific grid cell

.

The general equation for species production (e.g, c hemical production by the chemical reaction

A → B ) is:

Here, production of B inherits the apportionment of the precursor A . The same equation applies for “production” of B in a specific grid cell due to emissions or transp ort. For the case where B increases due to emissions, a i is the apportionment of the emissions inventory. For the case
where B increases due to transport, a i is the apportionment of the upwind grid cell. 

## ( ) ( )

i i
i i 

### a a A t a t t a ∑

∆ + = ∆ +



## ( ) ( )

i i

i i 

### a a B t b t t b ∑

∆ + = ∆ +CAMx v5.30 User’s Guide, December 2010

Particulate Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-7 In some cases, source category specific weighting f actors ( w i ) must be added to the equation for species destruction:

An example is chemical decay of the aromatic VOC tr acers (ARO), which must be weighted by the average OH rate constant of each ARO i . ARO tracers for different source groups have different average VOC reactivities because the rela tive amounts of toluenes and xylenes differ between source categories.

In some cases, source category specific weighting f actors ( w i ) must be added to the equation for species production:

An example is chemical production of condensable ga ses (CG1 or CG2) from aromatic VOC tracers, which must be weighted by aerosol yield we ighting factors. The aerosol yield weighting factors depend upon the relative amounts of toluene s and xylenes in each source group.

Several aerosol reactions are treated as equilibria

, A ↔ B . If A and B reach equilibrium at each time step, it follows that their source apportionme nts also reach equilibrium:

Examples are the equilibrium between gas phase nitr ic acid and aerosol nitrate, gas phase ammonium and aerosol ammonium, and condensable orga nic gases (CG) and secondary organic aerosols (SOA).

7.3 TESTING PSAT

Initial PSAT testing was conducted for primary PM s pecies (e.g., fine crustal material) because these species are the most straightforward to imple ment and test (i.e., no chemistry). The results for primary PM species are not shown here but the t ests were successful in that the sum of PSAT tracers over all source groups remained identical t o the total model concentration ( A = Σ a i ) for all grid cells and hours. The primary PM tests con firmed the proper implementation of PSAT for all CAMx algorithms except chemistry.

The next stage of PSAT testing included the chemist ry algorithms and results are shown here for two different types of testing for sulfate and nitr ate. Sulfate was tested by comparing PSAT to
zero out results in full 3-D CAMx simulations. As discussed above, zero out results are not expected to agree perfectly with PSAT because zero out does not give true source apportionment in non-linear systems. However, non-linearity in s ulfate formation chemistry is expected to be less important than for other secondary PM species making sulfate the best candidate for zero out testing. 

## ( ) ( )

i i i i

i i 

### a w a w A t a t t a ∑

∆ + = ∆ +



## ( ) ( )

i i i i

i i 

### a w a w B t b t t b ∑

∆ + = ∆ +



## ( ) ( ) ( ) 

## [ ]

( ) ( ) ( ) 

## [ ] 

    

+ + = ∆ +      

+ + = ∆ +

B A B t b t a t t b B A A t b t a t t a

i i i i i iCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-8 The most complex PSAT chemistry algorithm is for ni trate. Zero out tests were not used for nitrate because the relationship between NO emission s and nitric acid may be highly non-linear. Therefore, 1-D (box model) tests were used to evalu ate PSAT results for nitrate by comparing against the method used by Kleeman and Cass (2001) to track nitrate apportionment in the Source-Oriented External Mixture (SOEM) method, dis cussed above.

PSAT Sulfate Testing

The PSAT performance for sulfate was tested using a CAMx database developed by the Midwest RPO (MPRO) for PM and visibility modeling of the Ea stern US. The modeling was conducted from June 18 to July 21, 2001 and used a 36-km mode ling grid with meteorology developed using the mesoscale model version 5 (MM5). The mod eling domain was sub-divided to geographic areas according to regional planning org anizations (RPOs) responsible for developing regional visibility and PM control strat egies in the U.S. The sub division of the modeling domain to RPOs is shown in Figure 7-1 and the RPOs are labeled by their respective acronyms (MRPO, MANE-VU, VISTAS, CERAP and WRAP). The state of Illinois (IL) was split out from the Midwest RPO (MPRO) to test the a bility of PSAT to apportion the contribution from a single state. Four hypothetica l point sources were added near the middle of the MRPO, MANE-VU, VISTAS and CENRAP areas (shown by the + symbols in Figure 1) to test the ability of PSAT to track contributions fro m singe sources. The hypothetical point sources were chosen to be generally representative of a large coal-fired electricity generating unit but do not represent actual sources at these locati ons. In total, sulfate was apportioned to 11 source regions, including a remainder area for Cana da, Mexico and over water, plus initial and boundary conditions.

The sulfate impacts from the hypothetical MRPO poin t source are compared in Figure 7-2 at a single hour (hour 15) on 28 June 2001. The spatial distribution of sulfate impacts is very similar in the PSAT and zero out results as shown by the ed ge of the plume impacts (0.1 μ g/m 3 level). There are differences in the areas of larger impact s (e.g. the 1 and 2 μ g/m 3 levels) and these are due to the effects of non-linear chemistry in the z ero out test. As discussed above, sensitivity methods such as zero out do not provide accurate so urce apportionments for non-linear processes. Sulfate formation can be limited by the availability of oxidants, especially hydrogen peroxide in aqueous-phase chemistry, which will ten d to depress the maximum impact levels in zero out runs as well as shift impacts further down wind (to where oxidant availability is no longer limiting). The oxidant limiting effect on ze ro out sulfate impacts is most easily seen from
the 2 μ g/m 3 level extending further downwind over Lake Michiga n in the zero out result than the PSAT source apportionment result.

The episode average sulfate impacts from the hypoth etical MRPO point source are compared in Figure 7-3 for the entire 28 June to July 21, 2001 modeling period. The spatial distribution of sulfate impacts is very similar in the PSAT and zer o out results. The maximum impact occurs very close to the source and is higher in the PSAT result (2.2 μ g/m 3 ) than the zero out result (1.8

μ g/m 3 ) due to the effect of oxidant limitation on sulfat e impacts determined by the zero out
method.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-9 Figure 7-1 **. The CAMx modeling domain for PSAT testing showin g sub-division to geographic**  **areas and locations of four hypothetical point sour ces (+ symbols).** 

**(a) PSAT (b) Zero-out**

Figure 7-2 **. Comparison of sulfate contributions (μg/m 3**  **) from the hypothetical MRPO point source on 28 June 2001 at hour 15: (a) PSAT result;**  **(b) Zero-out result.**CAMx v5.30 User’s Guide, December 2010

Particulate Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-10 The PSAT sulfate tests provided a comparison of the efficiency of the PSAT method compared to zero out modeling. Zero out modeling requires a new model run for each source contribution determined, so the incremental time for each “appor tionment” is the same as for the model base case. In contrast, the marginal cost for each PSAT source apportionment was about 2% of the time required for the base case.

PSAT Nitrate Testing

The PSAT nitrate algorithms were tested using CAMx for a 1-D (box model) problem in order to focus upon the ability of the PSAT chemical algorit hms to track nitrate apportionment. The box model problem was for summer conditions and PSAT wa s used to apportion nitrate between 20 ppb of initial NOx and 100 ppb of NOx emission inject ed continuously through the 24 hour run. The PSAT results were compared to the SOEM algorith m of Kleeman and Cass (2001) where the reactive nitrogen (NOy) reactions in the chemic al mechanism are duplicated to provide two different types of NOy (think of them as “red” NOy a nd “blue” NOy). There was no ammonia in the box model so that nitric acid remained in the g as phase rather than forming PM nitrate.

The PSAT apportionment of NOy to initial conditions (ICs) and emissions during the 24 hour box model simulation is shown in Figure 7-4. The t otal NOy apportioned to ICs remains constant at 20 ppb throughout the simulation but th e apportionment changes over time from NOx (RGN-IC) at the start to PAN (TPN-IC), organic nitra tes (NTR-IC) and nitric acid (HN3-IC). The NOy apportioned to emissions increased linearly throughout the simulation and the apportionment also evolved as the NOx emissions (RGN -E) reacted. At the start of the simulation RGN (NOx) is dominated by ICs whereas late in the day (hour 18) RGN is dominated by emissions. The source apportionments shown in F igure 7-4 are reasonable and show necessary attributes (such as conserving the total 20 ppb of ICs).

The SOEM apportionment of NOy to ICs and emissions d uring the 24-hour box model simulation is shown in Figure 7-5. The time evolut ion of the source apportionments for the ICs and emissions is very similar for SOEM (Figure 7-5) and PSAT (Figure 7-4). It is not clear that either method is more “correct,” or indeed that a c orrect source apportionment result exists for this test, but the consistency between the PSAT and SOME results is encouraging.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-11 **(a) PSAT (b) Zero-out**

Figure 7-3 **. Comparison of episode average (June 18 to July 2 1, 2001) sulfate**  **contributions (μg/m 3**  **) from the hypothetical MRPO point source: (a) PSAT result; (b) Zero** 
**out result.**

Figure 7

- 4 **. PSAT apportionment of reactive nitrogen (NOy) to initial conditions and**  **emissions during a 24-hour box model simulation.**CAMx v5.30 User’s Guide, December 2010

Particulate Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-12 Figure 7

- 5 **. Source-Oriented External Mixture (SOEM) apportio nment of reactive**  **nitrogen (NOy) to initial conditions and emissions during a 24-hour box model** 
**simulation.**

7.4 RUNNING CAMx WITH PSAT

CAMx Control File

PSAT is invoked similarly to the OSAT Probing Tool within the CAMx control file. In the &CAMx_Control namelist module, the variable Probing_Tool must be set to “PSAT”. An
additional namelist module called &SA_Control must then be provided in the control file to configure the PSAT portion of the model – note that this is identically the same module as used
for OSAT. The PSAT/OSAT namelist module is describ ed in Section 6; the additional variables
needed for PSAT are described below. The order of the variables follow the template available with the source code. An example of the PSAT porti on of the CAMx run control file is shown in Figure 7-6. All other capabilities and I/O require ments for PSAT mirror those of OSAT.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-13

Description of PSAT Control in the CAMx Run Control File

&SA_Control Label for the Probing Tool namelist module that configures the PSAT and OSAT options; it must begin in column 2

& Flag ending a namelist module; it must be in c olumn 2

SA_Summary_Output Logical flag used to limit the species written to the tracer
concentration file to a subset of the PSAT treated species. If set to true, the output will be restricted to the follo wing species: O3N, O3V, PS4, PN3, PN4, PO1, PO2, PO3, PO4, PO5, P EC, POC, FCR, FPR, CCR, CPR, HG0, HG2, PHG

PSAT_Treat_SULFATE_Class Logical flag to turn on the sulfate class of trace r species.

PSAT_Treat_NITRATE_Class Logical flag to turn on the nitrate class of trace r species.

PSAT_Treat_SOA_Class Logical flag to turn on the SOA class of tracer sp ecies.

PSAT_Treat_PRIMARY_Class Logical flag to turn on the primary PM class of tr acer species.

PSAT_Treat_MERCURY_Class Logical flag to turn on the mercury class of trace r species.

PSAT_Treat_OZONE_Class Logical flag to turn on the ozone class of tracer species.CAMx v5.30 User’s Guide, December 2010

Particulate Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-14 &SA_Control

SA_File_Root = '$TRACOUT/CAMx.OSAT.95 07$today', SA_Summary_Output = .true.,

SA_Master_Sfc_Output = .true., SA_Nested_Sfc_Output = .true., SA_Stratify_Boundary = .false., SA_Deposition_Output = .false., SA_Number_of_Source_Regions = 19, SA_Number_of_Source_Groups = 3, Use_Leftover_Group = .false., Number_of_Timing_Releases = 0, PSAT_Treat_SULFATE_Class = .true., PSAT_Treat_NITRATE_Class = .true.,

SA_Receptor_Definitions = '$TRACIN/receptor.citie s ', SA_Source_Area_Map(1) = '$TRACIN/APCA.source.ar ea.map', SA_Source_Area_Map(2) = ' ',

SA_Master_Restart = '$TRACOUT/CAMx.OSAT.950 7$yesterday.sa.inst', SA_Nested_Restart = '$TRACOUT/CAMx.OSAT.950 7$yesterday.sa.finst',

SA_Points_Group(1) = ' ', SA_Points_Group(2) = '$PTSRCE/utilpt.ng.$tod ate-07.ag.07cem1A-run11', SA_Points_Group(3) = '$PTSRCE/remain.ng.$tod ate-07.ag.07cem1A-run11',

SA_Emiss_Group_Grid(1,1) = '$TRACEMS/bio2.cc.$toda te-95', SA_Emiss_Group_Grid(1,2) = '$TRACEMS/bio2.ff.$toda te-95.ne', SA_Emiss_Group_Grid(2,1) = '$TRACEMS/util.cc.$toda te-07.ag.07cem1A-run11', SA_Emiss_Group_Grid(2,2) = '$TRACEMS/util.ff.$toda te-07.ag.07cem1A-run11', SA_Emiss_Group_Grid(3,1) = '$TRACEMS/remain.cc.$to date-07.ag.07cem1A-run11', SA_Emiss_Group_Grid(3,2) = '$TRACEMS/remain.ff.$to date-07.ag.07cem1A-run11',

&

Figure 7-6 **. This figure follows from Figure 6-8(d): it is a continuation day of a 2-grid run with**  **three emission groups, and all three emission group s are defined explicitly by supplying extra**  **emission files (AREA group 1, AREA group 2, POINT g roup 2, AREA group 3, and POINT**  **group 3). Therefore, the “Use_Leftover_Group” flag is set to FALSE. The POINT group 1**  **filename is blank because group 1 is a category wit h no point source emissions (e.g.,**  **biogenics). PSAT will trace PM sulfate and nitrate species only.** CAMx v5.30 User’s Guide, December 2010

Particulate Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-15 7.5 STEPS IN DEVELOPING INPUTS AND RUNNING PSAT

Below is a simple methodological list of steps to f ollow in setting up and running PSAT. The process is similar among the OSAT, PSAT, and DDM Pr obing Tools.

1) Define the source groups and regions that you wish to track. Keep in mind that memory resources increase dramatically as the number of tr acers grows. Probing Tool applications with large numbers of tracers, tracer classes, nested grids or grid cells may exceed available memory. 2) Build a source region map (Figure 6-10) that define s the spatial allocation of tracer
emissions. For small domains or small number of re gions, this can be done by hand. We suggest using GIS software to develop complex sourc e region maps on large grids. 3) Process the emissions inventory into the separate s ource group files that you want to track (e.g., mobile, area, point, biogenic, etc.). a) Consideration of potential source apportionment or sensitivity applications prior to any emissions processing can be very beneficial so that files by group are available
for later use. b) Elevated point sources will automatically be assign ed to the source region in which they reside. However, you may override the region to which each individual point source is assigned (see the definition of kcell in Section 5, Elevated Point Source File). A point source region does not need to be d efined in the source region map, e.g., you could have a map with two regions that sp lit the domain in half, with a third region assigned arbitrarily to represent elevated p oint sources only. 4) Edit the CAMx control namelist file (Section 5). a) Set the Probing_Tool variable to the technology you wish to use (PSAT). This
will activate the &SA_Control namelist module. b) Edit or add the &SA_Control namelist module (described earlier). Provide the required information, including:

• output paths

• whether to stratify boundary conditions

• number of source regions

• number of source groups

• whether to use the leftover group option

• whether to use timing tracers

• receptor definitions

• flags to turn on specific PM classes

• list of input emission files by group. 5) Configure the CAMx source code to define the number of tracers, and build an executable. This will ensure that you have suffici ent memory for the Probing Tool application. a) Edit the file Includes/camx.prm b) Change the parameter MXTRSP , following the instructions provided in the file.
CAMx is distributed with MXTRSP = 1 to minimize memory requirements for standard applications of the model. If you run PSA T with an insufficient value, the model will stop and tell you the required value of MXTRSP for your application. c) Execute the CAMx Makefile to build an executable program (Section 11). 6) Run CAMx and review the diagnostic output files to ensure that the model is correctly interpreting and running the Probing Tool configura tion that you have specified. EnsureCAMx v5.30 User’s Guide, December 2010

Particulate Source Apportionment Technology

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_07_PSAT.doc x 7-16 that CAMx is generating the proper output files tha t you are expecting. Review the table of emissions by source group and region. 7) Review gridded tracer fields using commonly availab le plotting programs. Utilities such as PAVE or Verdi will read Probing Tool files direc tly. Use of any other software may require specialized re-formatting procedures. 8) You may post-process PSAT receptor files using an E xcel macro (OSAT_browser) that
ENVIRON distributes at www.camx.com

. 9) Probing Tool gridded tracer output files are writte n in the same Fortran binary format as the regular CAMx concentration output files. You c an post-process gridded output fields using any software that reads CAMx files, or you ca n adapt those programs or build your own software to generate specialized analysis and g raphical products.CAMx v5.30 User’s Guide, December 2010

Decoupled Direct Method

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-1

8. DECOUPLED DIRECT METHOD FOR SENSITIVITY ANALYSI S

Photochemical modelers have traditionally used sens itivity analysis both for model performance evaluation and emission control strategy design. T he simplest approach to sensitivity analysis, often referred to as the “brute-force” approach, in volves changing a model input parameter, rerunning the model, and then evaluating the change in model output. The change in output is quantified from the difference between the sensitiv ity case and base case. This approach requires a separate simulation for each parameter i nvestigated. For example, a model performance evaluation may use sensitivity simulati ons to evaluate the impact of changing the initial conditions (ICs), boundary conditions (BCs) , biogenic emissions, anthropogenic emissions, etc. Sensitivity analysis is also used to aid control strategy design. For example,
across-the-board VOC and NO
x emission reductions are used to determine whether VOC and/or
NO
x reduction strategies are the most effective path t o reduce ozone.

The advantages of the “brute force” method for sens itivity analysis are:

• Applicable to any model input parameter;

• Results are conceptually easy to explain and interp ret.

The limitations of the “brute force” method are:

• Computationally inefficient;

• Sensitivity depends upon the magnitude of the pertu rbation if the model response is nonlinear;

• Sensitivity derived from small perturbations may co ntain significant levels of uncertainty (numerical noise).

The last two points bear further explanation. If t he model response to an input parameter depends upon non-linear components within the model (e.g., chemistry), then the relative magnitude or even sign of the output response may c hange for perturbations of different sizes. An example is the response of ozone to NO
x reductions in a VOC-limited environment: smaller
reductions in NO
x emissions increase ozone levels whereas larger NO
x reductions decrease

ozone.

This situation can be illustrated mathematically. We define a “sensitivity coefficient” ( s ) which represents the change in concentration ( c ) with respect to some input parameter ( λ ), evaluated relative to the base state ( λ = λ 0 ),

In general, λ

can be a vector (denoted as λ ), which contains multiple parameters related to processes in the model (e.g., rate constants) or in puts to the model (e.g., emissions). The response of concentration to a change in λ can be represented by a Taylor series of sensitivi ty coefficients: o c s

λ λ ∂ ∂

=CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-2 where n is the number of λ vector elements, x is the spatial dimension vector, and t is time. In the example above, the non-linear response of ozone to large NO
x emission reductions indicates that higher-order sensitivity coefficients are sign ificant relative to the first order sensitivity. A s the magnitude of the input perturbation tends to ze ro, the output response will become dominated by the first-order sensitivity. Therefore, very sm all changes in the input parameter may be required to use the “brute force” method to estimat e the first-order (local) sensitivity. The practical limitation to this approach is that since the change in output must be determined from the difference between two simulations, small level s of numerical uncertainty (noise) in two very similar outputs will contaminate the sensitivity ca lculation.

An alternative methodology for evaluating model sen sitivity was developed by Dunker (1980 and 1981) called the decoupled direct method (DDM). The DDM can be used to calculate the same type of sensitivity coefficient as is availabl e from the “brute force” method. The difference is that with DDM, sensitivity coefficients are calc ulated explicitly by specialized algorithms implemented in the host model. Thus, the DDM offer s several advantages over the brute force
method:

• Improved computational efficiency;

• Improved accuracy since sensitivities need not be c ontaminated by numerical noise;

• Multiple sensitivities can be calculated simultaneo usly

The degree to which these advantages are realized d epends upon how the method is implemented, i.e., the algorithms employed and thei r coding. Fortunately, the performance of the DDM can be evaluated quantitatively by careful comparison of DDM sensitivities against
“brute-force” sensitivities.

The first implementation of the DDM into a three-di mensional air quality model used version II of the Urban Airshed Model (Dunker, 1981). This pr oject demonstrated the accuracy and capabilities of the method, but application was res tricted by the computing power available to the air quality modeling community at that time. Recen tly, Yang et al. (1997) described the implementation of the DDM into the California/Carne gie Institute of Technology airshed model, an application termed DDM-3D. The implementation o f the DDM into CAMx was performed in a joint project between Dr. Alan Dunker and ENVIRON with sponsorship from the Coordinating Research Council (http://www.crcao.com). A rigorou s analysis of the CAMx implementation of the DDM was performed as part of this project, and excellent agreement was demonstrated (Dunker et al., 2002a).



## ( ) ( ) ( )

( ) 

# ( )

... 2 1 ;, ;,

1 0

1 0 2 1 0 0

+ − − ∂ ∂ ∂
+ − ∂ ∂
+ =



# ∑∑

∑

= = =

n

i j j n

j i

i j i n

i i

i

i

o o

c c t x c t x c

λ λ λ λ λ λ λ λ λ λ λ

λ λCAMx v5.30 User’s Guide, December 2010

Decoupled Direct Method

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-3 The original DDM implementation considered only fir st-order sensitivity. Recently, High-order DDM (HDDM; Hakami et al., 2003; Cohan et al., 2005) was implemented in CAMx in collaboration with Dr. Daniel Cohan (Rice Universit y) with sponsorship from the Texas Commission on Environmental Quality (TCEQ), enablin g CAMx to calculate second-order sensitivities along with first-order values.

8.1 IMPLEMENTATION

The implementation of DDM into CAMx has focused on the evaluation of first-order sensitivity coefficients that relate predicted concentrations t o pollutant sources (i.e., emissions, initial conditions [ICs] and boundary conditions [BCs]). T he HDDM implementation in CAMx has recently been extended to calculate first- and seco nd-order sensitivities to chemical rate constants. The DDM has also been applied in the pa st to the calculation of sensitivities to other model input parameters (e.g., meteorological parame ters), but this was not attempted for the CAMx implementation because these sensitivities gen erally are less useful and because resources were limited. The CAMx implementation co uld be extended to other sensitivity parameters in the future.

The CAMx DDM permits the evaluation of sensitivity coefficients with respect to parameters related to emissions, BCs, ICs, or rate constants. The sensitivity to be evaluated may bear a simple relationship to the regular model input, suc h as scaling the ozone boundary concentration by a factor (

BC
new = λ × BC 0 ), or additively increasing the ozone boundary conc entration by a constant amount everywhere ( BC
new = λ + BC 0 ). To allow complete flexibility, the perturbation s are specified by providing additional IC, BC, and/o r emission input files with the same format as the regular model input files.

In mathematical terms, a regular model input file, for example the BC input file, represents some set of functions of space and time f i (x ,t) , where each chemical species can be defined by a u nique function. An additional input file provided for th e DDM represents another set of functions of space, time, and chemical species g i (x ,t) that can be different from the regular input file. The scalar parameter λ i is then defined by

Here, λ

i ×g i (x ,t) is the perturbation, and the user desires informat ion on how the model would respond if the input f i (x ,t) is replaced by the input F i (x ,t) . The HDDM calculates the first-order sensitivity s i (1) (x

,t) and second-order sensitivity s i (2) (x ,t) with respect to the scalar parameter λ i

. The Taylor series to second order then gives the es timate:

where c l (x ;t; λ

i ) is the estimated model result for species l when F

i (x ,t) is used as input, and

c l (x ,t; λ i =0) is the base case model result when f i (x ,t) is used as input.

For example, to calculate the sensitivity of the pr edicted ozone concentration to scaling boundary ozone by a factor, CAMx would be provided with a DD M BC file that has the same ozone values as the regular model BC file. The sensitivity coef ficient fields output by CAMx could then be used to estimate the resulting ozone concentration if the ozone BCs were increased by 20%, as 

## ( ) ( ) ( )

. , , , t x g t x f t x F i i i i ×

+ = λ



## ( ) ( ) ( ) ( ) t x s t x s t x c t x c i i i i i l i l , 2 1

, 0 ;, ;, )2( 2 )1( × + × + = = λ

λ λ λCAMx v5.30 User’s Guide, December 2010

Decoupled Direct Method

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-4 follows:

(For simplicity hereafter, the dependence on space, time, and chemical species will be omitted).

To calculate the sensitivity of the predicted ozone concentration to increasing boundary ozone by 10 ppb, CAMx would be provided with a DDM BC file t hat has a constant ozone value of 10 ppb. The sensitivity coefficient fields could be u sed to estimate the ozone concentration if the ozone BCs were increased by 10 ppb as follows:

An alternative approach here would be to provide CA Mx with a DDM BC file that has a constant ozone value of 1 ppb. This perturbation would be a ssociated with a new parameter λ′ and a new sensitivity coefficient s ′

. To estimate the ozone concentration if the ozone BC were increased by 10 ppb, one would use

This would give exactly the same estimate as the pr eceding approach in which CAMx is supplied with a DDM BC file that has an ozone value of 10 pp b everywhere because the two sensitivities are related by s ′

= s×0.1 (from the first to second approach, there has been only a change of the
scalar variable from λ

to λ′ ).

These are examples of relatively simple sensitiviti es. A more complex example would be to calculate the sensitivity to morning (6-9 AM) NO
x emissions in specific group of grid cells. In this case you would provide CAMx with a DDM emissio ns file where all values are zero except
for the NO
x emissions in the selected grid cells between 6 AM and 9 AM, which would have the same value as the regular emissions file. In this case the sensitivity coefficient for ozone might be interpreted as the increase (or decrease) in ozo ne concentration due to the selected emissions (however, caution is warranted because sensitivity coefficients are not directly equivalent to a source apportionment). More accurately, the sensit ivity coefficient could be used to predict the concentration after a scalar change ( λ ) in the morning NOx emissions using the equation:

Any type of sensitivity perturbation can be describ ed via an input file. However, the CAMx user interface also provides easy ways to define some se nsitivities that are likely to be used frequently. For example, in the first example abov e the DDM BC file was described as having the “same ozone values as the regular model BC file .” To make such a file you could copy the BC file and then remove (or zero out) all species b ut ozone. To avoid the effort of preparing an input file that is trivially different from the reg ular model file, the user interface allows you to select specific species from an input file to track - in this case ozone. It is possible to separatel y track the sensitivity to more than one species from the same file (e.g. ozone, and NO). It is also possible to track the combined sensitivity to a gro up of species, such as NO
x , VOC, HRVOC, or ALL. The user interface also provides a simple way to track sensitivities to emissions from specific grid cells or groups of cells (sub-regions ). )2( 2 )1(

0 2.0 2.0 5.0 2.0 s s c c × × + × + =

= = λ λ

)2( )1(
0 0.1 

### 5.0

0.1 s s c c × + × + =

= = λ λ

)2( 2 )1(

0 0.1 0. 10 5.0 0 10 s s

. c c ′ × × + ′ × + =

=

′ = ′ λ λ

)2( 2 )1(

0 5.0 λ s s c c ×

+ × + =

= λ λ λCAMx v5.30 User’s Guide, December 2010

Decoupled Direct Method

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-5 Tracking Sensitivity Coefficients Within CAMx

The conceptual approach in tracking DDM sensitivity coefficients is directly parallel to the way
in which CAMx calculates three-dimensional concentr ation fields. The concentration fields are calculated by a series of code modules that operate sequentially to represent the impact of different physical processes (emissions, advection, diffusion, chemistry, deposition, etc.) on the gridded concentrations. Sensitivity coefficients a re calculated by a parallel set of modules which operate in step with the CAMx modules to calculate gridded sensitivity coefficients. For some processes (e.g., chemistry and horizontal advection ), the sensitivity coefficient modules make use of information saved from the corresponding CAM x modules. The need to employ this approach arises in cases where the CAMx algorithm d epends non-linearly upon species concentrations. In other cases, the sensitivity al gorithm turns out to be identical to the CAMx algorithm (e.g., horizontal diffusion) and both con centrations and sensitivity coefficients can be processed by the single module. Finally, there are cases where the sensitivity algorithm turns out to be identical to the CAMx algorithm, but a specia lized module has been written for the sensitivity coefficients to improve the computation al efficiency (e.g., vertical advection).

Priorities in the DDM coding implementation were:

• Ensuring accuracy by using consistent numerical met hods for the concentrations and sensitivities;

• Ensuring accuracy by calculating the concentrations and sensitivities using the same time steps (for original DDM);

• Optimizing the efficiency of the sensitivity coeffi cient calculations without compromising accuracy;

• Providing a flexible User Interface that allows cal culation of sensitivities to all sources and precursors;

• Ensuring that the DDM algorithms have minimal impac t on computer resource requirements (memory and CPU time) when the DDM is not being used.

Further details of the implementation of the DDM in CAMx will be published in the near future.

Horizontal Advection and Chemistry

Specialized modules were developed to calculate the effects of chemistry and horizontal advection upon DDM sensitivity coefficients. These DDM modules are exactly matched to the numerical methods employed in the corresponding CAM x modules. CAMx has two separate options for the horizontal advection solver (Bott a nd PPM) and three options for the gas phase chemistry solver (the EBI solver, IEH solver, and L SODE [Gear] solver). With the resources available at the time, it was only possible to deve lop the DDM implementation for one advection solver (Bott) and one chemistry solver (EBI). The DDM option in CAMx cannot be used with other solver options at the present time. However, HDDM can be used with any advection solver and chemistry solver.

The implementation of DDM algorithms into the EBI c hemistry solver was performed using the chemical mechanism compiler (CMC). Therefore, the DDM option is available for the CB4, SAPRC, and CB05 chemical mechanisms, and will be av ailable for additional mechanisms implemented in CAMx in the future. The HDDM is als o available for all the chemistry mechanisms.CAMx v5.30 User’s Guide, December 2010

Decoupled Direct Method

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-6 Plume-in-Grid and Aerosol Processes

Resource constraints did not permit implementation of DDM algorithms for the Plume-in-Grid (PiG) and aerosol chemistry options currently avail able in CAMx.

Source Code

The coding of DDM algorithms involved the creation of several new subroutines for CAMx. These subroutines are all grouped in a separate dir ectory (called DDM) under the main CAMx source code directory. It was necessary to add add itional code for DDM in several existing CAMx subroutines, and the new code is clearly marke d by comments.

Flexi-DDM

Although DDM is computationally much more efficient than the BFM, it does require much more additional CPU time and memory space over and above a standard CAMx run, which can be significant especially when multiple grids are u sed in two-way nesting. The increased computational cost may not always be worthwhile if only part of the modeling domain is of interest. One way to enhance computational efficie ncy in such cases is to use one-way nesting, where boundary conditions (BCs) for a nested grid a re extracted from the parent grid, and so subsequent runs with sensitivities are performed wi thout outer grids. However, differences between the two nesting schemes (i.e. 1-way vs. 2-w ay) sometimes cause discrepancies in the model results. An alternative approach is to run t he full 2-way nested model while “turning off” sensitivity calculations outside nested grids of in terest.

CAMx provides a feature called “Flexi-DDM”, which a llows user to turn off sensitivity calculations for selected grids (normally grids far outside the area of interest) to improve computational efficiency of DDM runs. This reduces CPU times but will not reduce memory requirements. Also, note that turning off sensitiv ity calculations for outer grids is only appropriate for certain types of sensitivity calcul ations: e.g., sensitivity to BCs cannot be
calculated with Flexi-DDM.

8.2 RUNNING CAMx WITH DDM and HDDM

This section describes how to specify the sensitivi ty coefficients to be calculated and the additional input files that must be supplied. Sinc e CAMx already contained a methodology for probing source-receptor relationships called OSAT ( ozone source apportionment technology, see Section 6), the DDM user interface was designed alo ng similar lines to the existing OSAT user
interface. This makes it easier to learn how to us e both options and promotes consistency in analyses performed using OSAT and DDM.CAMx v5.30 User’s Guide, December 2010

Decoupled Direct Method

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-7 Inputs in the CAMx Control File

DDM is invoked similarly to the other Probing Tools within the CAMx control file. In the &CAMx_Control namelist module, the variable Probing_Tool must be set to either
“DDM” or “HDDM”. An additional namelist module cal led &DDM_Control must then be provided in the control file to configure the DDM p ortion of the model. The additional namelist
module is described below. The order of the variab les follow the template available with the source code. An example of the DDM portion of the CAMx run control file is shown in Figure
8-1.

Description of DDM Control in the CAMx Run Control File

&DDM_Control Label for the Probing Tool namelist module that configures the DDM option; it must begin in column 2

& Flag ending a namelist; it must be in column 2

DDM_File_Root Character root output path/filename

DDM_Master_Sfc_Output Logical flag for master grid surface output (TRUE= DDM file will be output for all sensitivities, FALSE=DDM fil e will not be output)

DDM_Nested_Sfc_Output Logical flag for nested grid surface output (TRUE= DDM file will be output for all sensitivities, FALSE=DDM fil e will not be output)

DDM_Stratify_Boundary Logical flag to stratify boundary types (TRUE=sepa rate sensitivity types will be used for the N, S, E, W, and Top boundaries, FALSE=a single sensitivity type will be used for all 5 boundaries)

DDM_Number_of_Source_Regions Integer number of source regions to be tracked. T his
must be the same as the number of source areas defi ned in the

DDM _Source_Area_Map file. This value may be zero.

DDM_Number_of_Source_Groups Integer number of emission groups to be tracked. This
determines the number of emission files that must b e supplied (additional details below). This value may be zero

.

Number_of_IC_Species_Groups Integer number of species or species groups in th e initial conditions to be tracked. This number may be betwe en zero and the number of species being simulated plus four (al lowing for the four species groups VOC, HRVOC, NOX, ALL).

IC_Species_Groups Character array (by IC group) names of the species or species groups in the initial conditions to be tracked. Al lowed names are any species being simulated by the mechanism in use (e.g., O3, PAR, NO, etc.) plus the species groups NOX, VOC

, HRVOC, and ALL. It is permissible to specify both a speciesCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-8 and a group containing that species, e.g., both NO and NOX. Each name may have up to 10 characters. Note that if you select a species that is not present on the IC file provid ed, the initial sensitivities for that species will be set to zero. This variable may be left blank if the number of initial conditio n species groups is zero.

Number_of_BC_Species_Groups Integer number of species or species groups in th e boundary conditions to be tracked. This number may be between zero and the number of species being simula ted plus four (allowing for the four species groups VOC, HRV OC, NOX, ALL).

BC_species_Groups Character array (by BC group) names of the species or species groups in the boundary conditions to be tracked. S ee description
for IC_Species_Group above.

Number_of_EM_Species_Groups Integer number of species or species groups in th e emissions to be tracked. This number may be betwee n zero and the number of species being simulated plus four (al lowing for the four species groups VOC, HRVOC, NOX, ALL).

Emis_Species_Groups Character array (by emissions group) names of the species or species groups in the emissions to be tracked. See description
for IC_Species_Group above.

Number_of_Rate_Const_Groups Integer number of reaction rate sensitivity groups to be tracked. This number may be zero.

Rate_Const_Groups Character string containing each reaction rate sens itivity group name and reaction numbers that belong to the group. Group name and reaction numbers are separated by colon (: ) and each reaction number is separated by comma (,).

Number_of_HDDM_Sens_Groups Integer number of HDDM sensitivity groups to be tr acked. This number may be zero.

HDDM_parameters Character array names of the first-order sensitivi ty parameters to which second-order sensitivity is computed. The na ming of the first-order parameters is the same as the long name of
sensitivities with the first 4 characters omitted. For each of HDDM sensitivity group, two first-order parameters are required (the same can be used twice). All the first-order parameters must be included in the modeling.

DDM_Receptor_Definitions Character input DDM receptor definition path/filen ame

DDM_Source_Area_Map Character array (by CAMx grid) input DDM source ar ea definition path/filename (required for master grid, optional for nested grids). Source regions are defined using a map in the same format as an OSAT source area map (Section 6); however, DDM does not require that all parts of the modeling domain beCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-9 tracked, therefore it is permissible to define an a rea numbered zero in the source area map (emissions from those a reas will not be tracked). The non-zero source region numbers mu st be between 1 and the number of regions.

DDM_Calc_Grid Logical array containing Flexi-DDM flag for each gr id (.TRUE. = calculate sensitivities in the grid; .FALSE. = do not calculate sensitivities in the grid).

DDM_Initial_Conditions The name of the sensitivity initial condition file . Leave the file name blank for restart days or if sensitivity to in itial conditions is not being calculated.

DDM_Boundary_Conditions The name of the sensitivity lateral boundary condi tion file. Leave the file name blank if sensitivity to boundar y conditions is not being calculated.

DDM_Master_Restart Character input master grid DDM restart path/filen ame (ignored
if Restart=FALSE )

DDM_Nested_Restart Character input nested grid DDM restart path/filen ame (ignored
if Restart=FALSE or Number_of_Grids=1 )

DDM_Points_Group Character array (by source group) input DDM elevat ed point source emissions path/filename (optional, ignored i f Point_Emissions=FALSE )

DDM_Emiss_Group_Grid Character array (by source group, by CAMx grid) in put DDM gridded emissions path/filename (optional, ignored if Gridded_Emissions=FALSE )CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-10 &DDM_Control

DDM_File_Root = '$DDM_OUT/CAMx.08ju l95.runA’, DDM_Master_Sfc_Output = .true., DDM_Nested_Sfc_Output = .true., DDM_Stratify_Boundary = .false., DDM_Number_of_Source_Regions = 4, DDM_Number_of_Source_Groups = 2,

Number_of_IC_Species_Groups = 1, IC_Species_Groups(1) = 'ALL', Number_of_BC_Species_Groups = 1, BC_species_Groups(1) = 'ALL', Number_of_EM_Species_Groups = 2, Emis_Species_Groups(1) = 'NOX', Emis_Species_Groups(2) = 'VOC', Number_of_Rate_Const_Groups = 1, Rate_Const_Groups(1) = 'RXN1: 120,121,122', Number_of_HDDM_Sens_Groups = 1, HDDM_parameters(1,1) = 'EM0101NOX_', HDDM_parameters(1,2) = 'EM0101VOC_',

DDM_Receptor_Definitions = '$DDM_IN/receptor.citi es', DDM_Source_Area_Map(1) = '$DDM_IN/source_map.DD M.4areas', DDM_Source_Area_Map(2) = ' ',

DDM_Calc_Grid(1) = .true., DDM_Calc_Grid(2) = .true.,

DDM_Initial_Conditions = ' ', DDM_Boundary_Conditions = '$DDM_IN/BC.08jul95', DDM_Master_Restart = '$DDM_OUT/CAMx.07jul95 .runA.sa.inst', DDM_Nested_Restart = '$DDM_OUT/CAMx.07jul95 .runA.sa.finst',

DDM_Points_Group(1) = ' ', DDM_Points_Group(2) = '$DDM_PT/ptsr.ng.08jul 95',

DDM_Emiss_Group_Grid(1,1) = '$DDM_EMIS/bio.cc.08ju l95', DDM_Emiss_Group_Grid(1,2) = '$DDM_EMIS/bio.ff.08ju l95', DDM_Emiss_Group_Grid(2,1) = '$DDM_EMIS/anthro.cc.0 8jul95', DDM_Emiss_Group_Grid(2,2) = '$DDM_EMIS/anthro.ff.0 8jul95',

&

Figure 8-1 **. Example input of DDM options and filenames via t he CAMx control file. In this**  **case, CAMx is run with two grids, and DDM is config ure to track emissions from four source**  **regions and two source groups (biogenic and anthrop ogenic). Initial and boundary conditions**  **are tracked as a single sensitivity called “ALL”, w hile emissions are tracked with three**  **sensitivities called “NOx”, “VOC”, and “ALL”. Addi tionally, second-order sensitivities to biogenic**  **VOC emissions from the first source region will be computed. No source region map is**  **provided for the nested grid (the region assignment s on the nest are defined by the master grid).**  **Initial condition sensitivity is not being tracked (no file provided), and only the group 2 point**  **sources are tracked (no biogenic point sources are available).** CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-11 8.3 APPROACHES TO DEFINE IC, BC AND EMISSIONS SENS ITIVITIES

The description above shows how to specify the IC, BC and emissions files for DDM sensitivity
calculations. The formats for each of these files are identical to the CAMx input files of these types. In many cases, users may choose to specify exactly the same file for a CAMx input file and a DDM input file. (Be aware that some computer systems do not allow a file to be opened twice by the same application. A work around for t his problem is to copy or link the file “filename” to another file of different name, such as “filename.copy”).

The user can perform many different sensitivity cal culations in a single run according to the content of the DDM input files. Take initial condi tions (ICs) as an example. If the same IC file is specified for both CAMx and DDM, the output sens itivity fields represent the sensitivity of the predicted concentrations to the ICs. Therefore, si mply scaling the output sensitivity coefficients fields provides the incremental concentrations resu lting from scaling the ICs.

As another example, if a DDM emissions file is supp lied to CAMx that has a constant VOC emission rate everywhere, the sensitivity will corr espond to a uniform absolute increase in the VOC emission rate rather than a percentage increase as described above. Another possibility would be a DDM emission file with a different spati al pattern than the CAMx input file. The sensitivity coefficient would then correspond to ch anging both the geographic distribution and magnitude of emissions. Still another possibility would be a DDM file that has zero emissions for all hours except midnight to 5 AM on the third simulated day. The program would then calculate the sensitivity to early morning emission s on the third day. In this case, just the
concentrations could be simulated for the first two days and the DDM calculation begun on the third day, because sensitivities on the first two d ays are identically zero.

In short, the DDM input file can be arbitrary – dif ferent from the CAMx input file in the overall magnitude of concentrations or emissions, different in the geographic and temporal distribution, and different in the relative proportions of the ch emical species. However, the user must understand what perturbations are being considered in order to properly interpret the resulting output sensitivity coefficient fields. Also, the u ser should recognize that as the size of perturbations increase, the second- and higher-orde r terms eventually become important, usually when the emissions are altered by more than 20-30%.

8.4 DDM OUTPUT FILES

The output file types for a DDM simulation are desc ribed in Table 8-1. These files have the same format as corresponding concentration output f iles, described in Section 5.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-12 Table 8-1 **. DDM output file suffix names.**

File Name Suffix DDM File Type

**.ddm.inst Binary master grid instantaneous sensitivity file a**  **t end of simulation (used**

**for restart), 3-D, all sensitivities, μmol m -3**  **parameter -1**  **units.**

**.ddm.finst Binary nested grid instantaneous sensitivity file a**  **t end of simulation (used**

**for restart), 3-D, all sensitivities, μmol m -3**  **parameter -1**  **units.**

**.ddm.grd** nn **Binary average sensitivity file for grid** nn **, 2-D, surface layer sensitivities**

**only for affected species requested in the CAMx ave rage file, ppm**  **parameter -1**  **units.**

**.ddm.receptor Text hourly average sensitivities at user specific**  **receptor locations. This**

**file is in comma delimited ASCII format suitable fo r importing into a**  **spreadsheet.**

8.5 DDM SENSITIVITY COEFFICIENT NAMES

Each DDM sensitivity coefficient tracks the influen ce of a species from a specific source (the influencing species ) on a predicted concentration (the affected species ). The sensitivity
coefficient names are constructed to show this rela tionship, as follows:

{Affected Species}{Pollutant Source}{Influencing Sp ecies}

This is a lot of information to encode in a name th at must conform to the ten character limit imposed by the binary I/O file formats. Because of this, two naming systems are used in CAMx:

• Long Names - these names are easy to read, but sinc e they are more than ten characters in length they cannot be used in sensitivity coefficie nt binary output files. If an alternate I/O format is implemented in the future it may be p ossible to use the long names on sensitivity output files.

• Short Names - these convey the same information as the long names but require more practice to learn. They are used in the sensitivit y coefficient binary output files.

At the start of each CAMx run a concordance of Long and Short sensitivity coefficient names is written to the diagnostic output file (.diag file). An example concordance is shown in Figure 8-2, and a detailed explanation of the naming convention follows.CAMx v5.30 User’s Guide, December 2010

Decoupled Direct Method

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-13 Affected Influencing Source Long Short Species Species Type Group Regio n Name Name

-------------------------------------------------- ----------------------------- NO NO EM 1 2 NO__EM0102NO__ 01E0102001
NO2 NO EM 1 2 NO2_EM0102NO__ 02E0102001
O3 NO EM 1 2 O3__EM0102NO__ 03E0102001
PAN NO EM 1 2 PAN_EM0102NO__ 04E0102001
NXOY NO EM 1 2 NXOYEM0102NO__ 05E0102001
OLE NO EM 1 2 OLE_EM0102NO__ 06E0102001
PAR NO EM 1 2 PAR_EM0102NO__ 07E0102001
TOL NO EM 1 2 TOL_EM0102NO__ 08E0102001
XYL NO EM 1 2 XYL_EM0102NO__ 09E0102001
FORM NO EM 1 2 FORMEM0102NO__ 10E0102001
ALD2 NO EM 1 2 ALD2EM0102NO__ 11E0102001
ETH NO EM 1 2 ETH_EM0102NO__ 12E0102001
CRES NO EM 1 2 CRESEM0102NO__ 13E0102001
MGLY NO EM 1 2 MGLYEM0102NO__ 14E0102001
OPEN NO EM 1 2 OPENEM0102NO__ 15E0102001
PNA NO EM 1 2 PNA_EM0102NO__ 16E0102001
CO NO EM 1 2 CO__EM0102NO__ 17E0102001
HONO NO EM 1 2 HONOEM0102NO__ 18E0102001
H2O2 NO EM 1 2 H2O2EM0102NO__ 19E0102001
HNO3 NO EM 1 2 HNO3EM0102NO__ 20E0102001
ISOP NO EM 1 2 ISOPEM0102NO__ 21E0102001
MEOH NO EM 1 2 MEOHEM0102NO__ 22E0102001
ETOH NO EM 1 2 ETOHEM0102NO__ 23E0102001
ISPD NO EM 1 2 ISPDEM0102NO__ 24E0102001

NTR NO EM 1 2 NTR_EM0102NO__ 25E0102001
Figure 8-2 **. Example concordance of long and short sensitivit y coefficient names from the**  **CAMx diagnostic output file.**

Initial Condition Sensitivity Names

Long Name NNNNIC____MMMM

where:

NNNN Affected species name with trailing underscore to pad blanks

IC Indicates the sensitivity coefficient is for init ial conditions

____ Four underscores to pad the name to 14 characters

MMMM Influencing species name with trailing underscore to pad blanks

Examples: FORMIC____VOC_ O3__IC____O3__

Short Name nnI____mmm

where:

nn Affected species number

I Indicates the sensitivity coefficient is for init ial conditions

____ Four underscores to pad the name to 10 characters

mmm Influencing species number or name of a species g roup (NOX,

VOC, HRVOC or ALL).

Examples: 10I____VOC (where FORM is species number 10)CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-14 03I____003 (where O3 is species number 3)

Boundary Condition Sensitivity Names

Long Name NNNNBCRRR_MMMM

where:

NNNN Affected species name with trailing underscore to pad blanks

BC Indicates the sensitivity coefficient is for boun dary conditions

RRR NTH, STH, EST, WST or TOP if stratified by bounda ry; ALL if

not stratified by boundary _ Underscore to pad the name to 14 characters

MMMM Influencing species name with trailing underscore to pad blanks

Examples: FORMBCALL_VOC_ O3__BCTOP_O3__

Short Name nnBRRR_mmm

where:

nn Affected species number

B Indicates the sensitivity coefficient is for init ial conditions

RRR NTH, STH, EST, WST or TOP if stratified by bounda ry; ALL if

not stratified by boundary _ Underscore to pad the name to 10 characters

mmm Influencing species number or name of a species g roup (NOX,

VOC, HRVOC or ALL)

Examples: 10BALL_VOC (where FORM is species number 10) 03BTOP_003 (where O3 is species number 3)

Emissions Sensitivity Names

Long Name NNNNEMGGRRMMMM

where:

NNNN Affected species name with trailing underscore to pad blanks

EM Indicates the sensitivity coefficient is for emis sions

GG Emissions group number

RR Emissions region number

MMMM Influencing species name with trailing underscore to pad blanks

Examples: FORMEM0103VOC_ O3__EM0201O3__CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-15 Short Name nnEGGRRmmm

where:

nn Affected species number

E Indicates the sensitivity coefficient is for emis sions

GG Emissions group number

RR Emissions region number

mmm Influencing species number or name of a species g roup (NOX,

VOC, HRVOC or ALL)

Examples: 10E0103VOC (where FORM is species number 10)

03E0201003 (where O3 is species number 3)

Reaction Rate Sensitivity Names

Long Name NNNNRATE__MMMM

where:

NNNN Affected species name with trailing underscore to pad blanks

RATE Indicates the sensitivity coefficient is for rate constants

__ Two underscores to pad the name to 14 characters

MMMM Reaction rate sensitivity group name with trailin g underscore to pad blanks

Examples: NO__RATE__RXN1 O3__RATE__R28_

Short Name nnRATE_mmm

where:

nn Affected species number

RATE Indicates the sensitivity coefficient is for rate constants

_ Underscore to pad the name to 10 characters

mmm Reaction rate sensitivity group number

Examples: 01RATE_001 (where NO is species number 1) 03RATE_002 (where O3 is species number 3)

HDDM Sensitivity Names

Long Name NNNNHDDMLLLMMM

where:

NNNN Affected species name with trailing underscore to pad blanks

HDDM Indicates the sensitivity coefficient is second-o rderCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-16

LLL The index of the first 1 st -order sensitivity parameter in the internal

list of the 1st-order parameters

MMM The index of the second 1 st -order sensitivity parameter in the

internal list of the 1st-order parameters

Examples: NO__HDDM001001 O3__HDDM001002

Short Name nnEGGRRmmm

where:

nn Affected species number

HDDM Indicates the sensitivity coefficient is second-o rder

ll The index of the first 1 st -order sensitivity parameter in the internal

list of the 1st-order parameters

mm The index of the second 1 st -order sensitivity parameter in the

internal list of the 1st-order parameters

Examples: 01HDDM0101 (where NO is species number 1)

03HDDM0102 (where O3 is species number 3)

8.6 STEPS IN DEVELOPING INPUTS AND RUNNING DDM

Below is a simple methodological list of steps to f ollow in setting up and running DDM. The process is similar among the OSAT, PSAT, and DDM Pr obing Tools.

1) Define the source groups and regions that you wish to track. Keep in mind that memory resources increase dramatically as the number of se nsitivities grows. Probing Tool applications with large numbers of sensitivities, n ested grids or grid cells may exceed available memory. 2) Build a source region map (Figure 6-10) that define s the spatial allocation of emission
sensitivities. For small domains or small number o f regions, this can be done by hand. We suggest using GIS software to develop complex so urce region maps on large grids. 3) Process the emissions inventory into the separate s ource group files that you want to track (e.g., mobile, area, point, biogenic, etc.). a) Consideration of potential source apportionment or sensitivity applications prior to any emissions processing can be very beneficial so that files by group are available
for later use. b) Elevated point sources will automatically be assign ed to the source region in which they reside. However, you may override the region to which each individual point source is assigned (see the definition of kcell in Section 5, Elevated Point Source File). A point source region does not need to be d efined in the source region map, e.g., you could have a map with two regions that sp lit the domain in half, with a third region assigned arbitrarily to represent elevated p oint sources only. 4) Edit the CAMx control namelist file (Section 5). a) Set the Probing_Tool variable to the technology you wish to use (DDM, H DDM).
This will activate the &DDM_Control namelist module.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_08_DDM.docx 8-17 b) Edit or add the &DDM_Control namelist module (described earlier). Provide the required information, including:

• output paths

• whether to stratify boundary conditions

• number of source regions

• number of source groups

• numbers and names of IC, BC, emissions, rate consta nt, and HDDM groups

• receptor definitions

• IC/BC input files

• list of input emission files by group. 5) Configure the CAMx source code to define the number of tracers, and build an executable. This will ensure that you have suffici ent memory for the Probing Tool application. a) Edit the file Includes/camx.prm b) Change the parameter MXTRSP , following the instructions provided in the file.
CAMx is distributed with MXTRSP = 1 to minimize memory requirements for standard applications of the model. If you run DDM with an insufficient value, the model will stop and tell you the required value of MXTRSP for your application. c) Execute the CAMx Makefile to build an executable program (Section 11). 6) Run CAMx and review the diagnostic output files to ensure that the model is correctly interpreting and running the Probing Tool configura tion that you have specified. Ensure that CAMx is generating the proper output files tha t you are expecting. Review the table of emissions by source group and region. 7) Review gridded tracer fields using commonly availab le plotting programs. Utilities such as PAVE or Verdi will read Probing Tool files direc tly. Use of any other software may require specialized re-formatting procedures. 8) Probing Tool gridded tracer output files are writte n in the same Fortran binary format as the regular CAMx concentration output files. You c an post-process gridded output fields using any software that reads CAMx files, or you ca n adapt those programs or build your own software to generate specialized analysis and g raphical products.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-1 9. PROCESS ANALYSIS

Process Analysis (PA) allows for in-depth analysis of photochemical model performance by revealing the contributions from individual physica l and chemical processes operating within the model (Jeffries and Tonnesen, 1994). Using PA, one can more fully understand the complex interactions between the different processes, expla in simulation results within the context of the model formulation, and improve the design of contro l strategies.

A conventional model performance evaluation employs statistical and graphical methods to analyze predicted concentrations against observed c oncentrations. This answers the basic question: “How well is the model replicating measur ements?” While such comparisons are necessary to summarize performance, they are far fr om sufficient to determine whether the model is adequately representing the real situation . This is because compensating errors among various model processes can result in predictions t hat serendipitously agree with limited observations but for the wrong reasons. In contras t PA provides information on how the specific model predictions were obtained, which can be inter preted to improve model performance and/or inform control strategy decisions.

9.1 PROCESS ANALYSIS IN CAMx

Three components of PA are implemented in CAMx:

1. Integrated Processes Rate (IPR) analysis. The IPR method provides detailed process rate information for each physical process in CAMx (i.e., advection, diffusion, deposition, emissions, and chemistry) for selected grid cells and selected species (Wang, Langstaff, and Jeffries, 1995). The IPR outputs ca n be analyzed to determine what processes governed the model-predicted concentratio ns at any time and place. IPR information has often been plotted as a time series of process contributions for specific cells or groups of cells. IPR outputs have also be en used to check the mass balance in the host model, i.e., to determine whether model concen trations are fully explained by the diagnosed process information or whether unexpected artifacts are occurring. The IPR data are relatively easy to interpret and can be an alyzed using simple tools such as spreadsheets. Currently, only the IPR analysis has been extended to the PM modules in
CAMx.

2. Integrated Reaction Rate (IRR) analysis . The IRR method provides detailed reaction
rate information for all reactions in the chemical mechanism for selected grid cells (Jeffries and Tonnesen, 1994). The IRR data can be analyzed to determine how the chemical changes occurring in the model are related to the chemical mechanism. For example, by analyzing rate information over groups of reactions it is possible to quantify chemically meaningful attributes such as radical in itiation rates, radical propagation efficiencies, chain lengths, etc. Since these anal yses tend to be complex, IRR data generally require post-processing to be useful. IR R is implemented for the CB4, CB05, and SAPRC99 chemical mechanisms (mechanism numbers 3 through 6: see Chapter 3, Table 3-1).CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-2 3. Chemical Process Analysis (CPA) . CPA is closely related to the IRR method but is designed to be more user friendly and accessible. As mentioned above, IRR data are generally only useful after the data have been post -processed into chemically meaningful parameters. With CPA, a selection of useful parame ters is calculated within CAMx and then output to gridded files (Tonnesen and Dennis, 2000). The gridded CPA files use the same format as the gridded concentration outputs an d can be visualized and processed using standard post-processing tools. CPA is imple mented for the CB4, CB05, and SAPRC99 chemical mechanisms (mechanism numbers 3 th rough 6: see Chapter 3, Table 3-1).

Integrated Process Rate Analysis

The specific processes that are reported by IPR are listed in Table 9-1. This information is output for each chemical species selected for inclu sion in the average concentration output file, and for each grid cell selected for analysis. The process rates are integrated across each model output time interval (normally hourly). Taken toge ther, this information provides a complete description of how the species concentration change d across the output time interval and the magnitude of all of the processes that caused this change. Information is output in the concentration units used internally within CAMx (μm ole/m 3 for gases, μg/m 3 for PM species). A gas conversion factor (ppm/μmole/m 3 ) specific to the grid cell/time period is also out put to allow conversion to mixing ratio (ppm) for comparis on of gas species with CAMx average concentration outputs. For PM species, the convers ion factor is always 1. Grid cell volume is also output to allow aggregation across grid cells.

For most of the process rates listed in Table 9-1 t he interpretation is straightforward, the rate is simply the concentration change caused by the named process across the output time interval. The sign convention is such that a positive flux al ways tends to increase the cell concentration. Further explanation is provided for several process es below:

Plume-in-Grid change : The grid cell concentration change caused by Plu me-in-Grid puffs that transferred mass to the grid cell during the output time interval.

Point source emissions : Does not include point sources selected for PiG treatment as these are reported in Plume-in-Grid Change.

Dilution in the vertical : CAMx allows for layer interface heights to chang e over time which can lead to a “dilution” term for affected gr id cells.

Boundary diffusion : In some cases this term will be zero by definiti on, namely: the bottom boundary of surface layer grid cells; the to p boundary of top layer grid cells; any lateral boundary that coincides with a nest boundar y.

Dry deposition : This term is zero by definition for all grid cel ls above the surface layer.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-3

Table 9-1 **. Process information reported by the IPR option.**
IPR Parameter Process Information Units a

**1 Initial concentration** μ **mole/m 3**  **(** μ **g/m 3**  **) 2 Gas phase chemistry**  μ **mole/m 3**  **(** μ **g/m 3**  **)**
**3 Gridded emissions** μ **mole/m 3**  **(** μ **g/m 3**  **)**
**4 Point source emissions** μ **mole/m 3**  **(** μ **g/m 3**  **) 5 Plume-in-Grid change**  μ **mole/m 3**  **(** μ **g/m 3**  **) 6 West boundary advection**  μ **mole/m 3**  **(** μ **g/m 3**  **) 7 East boundary advection**  μ **mole/m 3**  **(** μ **g/m 3**  **) 8 South boundary advection**  μ **mole/m 3**  **(** μ **g/m 3**  **) 9 North boundary advection**  μ **mole/m 3**  **(** μ **g/m 3**  **) 10 Bottom boundary advection**  μ **mole/m 3**  **(** μ **g/m 3**  **) 11 Top boundary advection**  μ **mole/m 3**  **(** μ **g/m 3**  **)**
**12 Dilution in the vertical** μ **mole/m 3**  **(** μ **g/m 3**  **) 13 West boundary diffusion**  μ **mole/m 3**  **(** μ **g/m 3**  **) 14 East boundary diffusion**  μ **mole/m 3**  **(** μ **g/m 3**  **) 15 South boundary diffusion**  μ **mole/m 3**  **(** μ **g/m 3**  **) 16 North boundary diffusion**  μ **mole/m 3**  **(** μ **g/m 3**  **) 17 Bottom boundary diffusion**  μ **mole/m 3**  **(** μ **g/m 3**  **) 18 Top boundary diffusion**  μ **mole/m 3**  **(** μ **g/m 3**  **) 19 Dry deposition**  μ **mole/m 3**  **(** μ **g/m 3**  **) 20 Wet deposition**  μ **mole/m 3**  **(** μ **g/m 3**  **) 21 Inorganic aerosol chemistry**  μ **mole/m 3**  **(** μ **g/m 3**  **) 22 Oganic aerosol chemistry**  μ **mole/m 3**  **(** μ **g/m 3**  **) 23 Aqueous aerosol chemistry**  μ **mole/m 3**  **(** μ **g/m 3**  **)**
**24 Final concentration** μ **mole/m 3**  **(** μ **g/m 3**  **) 25 Units conversion ppm/(μmole/m**  **3 ) (N/A)**  **b 26 Average cell volume m**  **3**

**a Units in the parentheses are for PM species.** 

**b Unit conversion factor for PM species is always 1.** 

Integrated Reaction Rate Analysis

IRR provides the integrated rate of each gas-phase chemical reaction in units of ppm hr -1 for each grid cell selected for process analysis. Reaction rates are accumulated (integrated) within the chemistry solver at the time steps being used to so lve the chemical equations, and output at the CAMx output time interval (usually 1 hour).

Chemical Process Analysis

The CPA method calculates a pre-determined set of p arameters as listed in Table 9-2. The CPA parameters are calculated for all grid cells in eit her the surface layer or all layers. The selection between surface layer or all layer CPA outputs is d etermined by the “3-D average file” flag specified in the CAMx Control File (see Section 5). This is based on the premise that 3-D CPA information will be interpreted in conjunction with 3-D concentration fields.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-4 Table 9-2 **. Chemical Process Analysis (CPA) variables calcul ated in CAMx for the CB4, CB05**  **and SAPRC99 mechanisms.**

CB4 CB05 SAPRC99 Description

Ozone and Oxidant Production and Loss
**OxProd OxProd OxProd Production of Ox = Ozone + NOy -NO** 

**OxLoss OxLoss OxLoss Destruction of Ox**
**PO3_net PO3_net PO3_net Net ozone produced**
**PO3_VOCsns PO3_VOCsns PO3_VOCsns Net ozone produced under VOC sensitive conditions** 
**PO3_NOxsns PO3_NOxsns PO3_NOxsns Net ozone produced under NOx sensitive conditions** 

**PH2O2_PHN3 PH2O2_PHN3 PH2O2_PHN3 Ratio of H**  **2 O**  **2 produced / HNO**  **3 produced. Greater**  **than 0.35 means NOx sensitive ozone production O3_dest O3_dest O3_dest Ozone destruction by chemic**  **al reactions**

Radical Initiation
**OH_new OH_new OH_new New OH produced (initiated)** 

**HO2_new HO2_new HO2_new**  **New HO 2**  **produced**
**HOx_new HOx_new HOx_new New HOx (HOx = OH+HO 2**  **) produced**
**newOH_O1D newOH_O1D newOH_O1D Production of OH from ozone photolysis** 
**newOH_HONO newOH_HONO**  **newOH_HONO Production of OH from HONO photolysis** 
**nOH_O3_OLE nOH_O3_OLE nOH_O3_OLE Production of OH f rom ozone-alkene reactions** 
**nwHO2_HCHO nwHO2_HCHO**  **nwHO2_HCHO Production of HO**  **2 from formaldehyde photolysis** 
**RO2_new RO2_new RO2_new New RO 2**  **produced**
Radical Propagation
**OHw_CO OHw_CO OHw_CO OH reacted with carbon monoxid e**  **OHw_CH4 OHw_CH4 OHw_CH4 OH reacted with methane**
**OHw_PAR OHw_ETHA OHw_ALK1**

**OH reacted with alkanes OHw_PAR OHw_ALK2** 
**OHw_ALK3**
**OHw_ALK4**
**OHw_ALK5**

**OHw_TOL OHw_TOL OHw_ARO1 OH reacted with toluene and mono-substituted** 
**aromatics**

**OHw_XYL OHw_XYL OHw_ARO2 OH reacted with xylenes and poly-substituted** 
**aromatics**
**OHw_ETH OHw_ETH OHw_ETHE OH reacted with ethane**

**OHw_OLE OHw_OLE OHw_OLE1 OH reacted with terminal alkenes (R–HC=CH**  **2 , e.g.**  **propene)**

**OHw_IOLE OHw_OLE2 OH reacted with internal alkenes (R–HC=CH–R, e.g.**  **2-butene)**
**OHw_ISOP OHw_ISOP OHw_ISOP OH reacted with isoprene**
**OHw_TERP OHw_TERP OH reacted with terpenes**

**OHw_all_HC OHw_all_HC OHw_all_HC OH reacted with all organic compounds (including**  **CO)**
**ISOPwOx ISOPwOx ISOPwOx Isoprene reacted with O 3**  **, NO 3**  **and O( 3**  **P)**
**TERPwOx TERPwOx Terpenes reacted with O 3**  **, NO 3**  **and O( 3**  **P)**
**OH_rctd OH_rctd OH_rctd Total OH reacted**
**HO2_rctd HO2_rctd HO2_rctd Total HO2 reacted**CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-5 CB4 CB05 SAPRC99 Description

Ozone and Oxidant Production and Loss
**HOx_rctd HOx_rctd HOx_rctd Total HOx reacted**
**RO2_rctd RO2_rctd RO2_rctd Total RO 2**  **reacted**
**OHfromHO2 OHfromHO2 OHfromHO2 OH produced from reac tions of HO**  **2**
**Y_OHperHO2 Y_OHperHO2 Y_OHperHO2 Yield of OH per HO**  **2 reacted (= OHfromHO2 /**  **HO2_rctd)** Radical Termination and HOx Chain Length
**OH_term OH_term OH_term OH terminated**
**HO2_term HO2_term HO2_term HO 2**  **terminated**
**HOx_term HOx_term HOx_term HOx terminated**
**RO2_term RO2_term RO2_term RO 2**  **terminated**
**HOx_CL HOx_CL HOx_CL HOx chain length ( = HOx_rctd / {2 x HOx_new} )** 
Formaldehyde Production
**HCHOp_eth HCHOp_eth HCHOp_ethe Formaldehyde produce d from ethene** 

**HCHO_ole HCHO_ole HCHO_ole1 Formaldehyde from terminal alkenes (R–HC=CH**  **2 ,**  **e.g. propene) in the first generation of products**

**HCHO_iole HCHO_ole2 Formaldehyde from internal alkenes (R–HC=CH–R,**  **e.g. 2-butene) in the first generation of products**

**HCHO_terp HCHO_terp Formaldehyde from terpenes in the first generation**  **of products** 

**HCHOp_isop HCHOp_isop HCHOp_isop Formaldehyde from isoprene in the first generation**  **of products** 

**HCHOp_ispd HCHOp_ispd HCHOp_ispd Formaldehyde from isoprene daughter products**  **(isoprod, methacrolein and methylvinylketone) HCHOp_Tot HCHOp_Tot HCHOp_Tot Total formaldehyde pr**  **oduced**
NOy Reactions
**HNO3_OHNO2 HNO3_OHNO2**  **HNO3_OHNO2 Nitric acid produced from OH reacting with NO**  **2 HNO3_NO3HC**  **HNO3_NO3HC HNO3_NO3HC**  **Nitric acid produced from NO 3**  **reacting with organics**
**HNO3_N2O5 HNO3_N2O5 HNO3_N2O5 Nitric acid produced from N**  **2 O**  **5 reacting with water** 
**PANprodNet PANprodNet PANSprdNet Net PAN produced ( sum of several for SAPRC99)** 
**PANlossNet PANlossNet PANSlosNet Net PAN destroyed (sum of several for SAPRC99)** 
**RNO3_prod RNO3_prod RNO3prod Organic nitrates (RNO 3**  **) produced**
**NOxrecycl NOxrecycl NOxrecycl Nitrates (HNO 3**  **and RNO 3**  **) recycled to NOx**
**NOw_HO2 NOw_HO2 NOw_HO2 NO reacted with HO 2**  **(forming NO 2**  **)**
**NOw_RO2s NOw_RO2s NOw_RO2s NO reacted with RO 2**  **(forming NO 2**  **)**
**NOw_RCO3s NOw_RCO3s NOw_RCO3s NO reacted with RCO 3**  **(forming NO 2**  **)**
Photolysis
**J_NO2 J_NO2 J_NO2 NO 2**  **photolysis rate**
**J_O3O1D J_O3O1D J_O3O1D O 3**  **photolysis rate to O( 1**  **D) atoms**
**J_Cld_Adj J_Cld_Adj J_Cld_Adj Adjustment factor** 

Radical Concentrations

**OH OH OH OH radical concentration**

**HO2 HO2 HO2 HO 2**  **radical concentration**

**NO3 NO3 NO3 NO 3**  **radical concentration**

**N2O5 N2O5 N2O5 N 2**  **O 5**  **concentration**CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-6 9.2 RUNNING PROCESS ANALYSIS

PA can be used with most of the physical options av ailable for the “core” CAMx model, e.g., the various advection and chemistry mechanisms/solvers. However, PA cannot be used at the same time as the other CAMx “Probing Tool” options (e.g. , OSAT, PSAT, DDM, or RTRAC) because the Probing Tools share internal data structures to minimize the total memory resources required by CAMx.

PA is invoked similarly to the other Probing Tools within the CAMx control file. In the &CAMx_Control namelist module, the variable Probing_Tool must be set to either “PA” (generates all PA output), “IPR”, or “IRR”. Table 9-3 summarizes the types of process analysis performed for each keyword and the output files tha t are produced.

Table 9-3. **Process analysis keywords and associated CAMx outpu t files.**  Process Analysis Key Word Output
Filename File

Contains IPR IRR PA

**Yes No**  **Yes** *.ipr **Integrated process rate (IPR) information for all selected cells** 

**No Yes**  **Yes** *.irr **Integrated reaction rate (IRR) information for all selected cells** 

**No Yes**  **Yes** *.cpa.grd nn **Chemical process analysis (CPA) parameters for grid**  nn

An additional namelist module called &PA_Control mu st then be provided in the control file to configure the PA portion of the model. The additio nal namelist module is described below. The order of the variables follow the template availabl e with the source code. An example of the PA portion of the CAMx run control file is shown in Fi gure 9-1.

Description of PA Control in the CAMx Run Control F ile

&PA_Control Label for the Probing Tool namelist module that configures the PA option; it must begin in column 2

& Flag ending a namelist; it must be in column 2

PA_File_Root Character root output path/filename

Number_of_PA_Domains Integer number of PA analysis domains to be evalua ted during
the simulation.

Within_CAMx_Grid Integer array (by PA domain) pointer into the CAMx grid within which the PA domain exists (1=master grid, etc.). Use the CAMx internal grid number reported in the *.diag fi le. Note that this may differ from the nest order provided by the user in the
CAMx control file.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-7 PA_Beg_I_Index Integer array (by PA domain) grid column containin g western edge of PA domain.

PA_End_I_Index Integer array (by PA domain) grid column containin g eastern edge of PA domain.

PA_Beg_J_Index Integer array (by PA domain) grid row containing s outhern edge
of PA domain.

PA_End_J_Index Integer array (by PA domain) grid row containing n orthern edge
of PA domain.

PA_Beg_K_Index Integer array (by PA domain) grid layer containing bottom of
PA domain.

PA_End_K_Index Integer array (by PA domain) grid layer containing top of PA
domain.

Figure 9-1. **Example section of a CAMx control file specifying options for Process Analysis.**  &PA_Control

PA_File_Root = 'CAMx.OTAG.950707.PA',

Number_of_PA_Domains = 2, Within_CAMx_Grid(1) = 1, PA_Beg_I_Index(1) = 8, PA_End_I_Index(1) = 12, PA_Beg_J_Index(1) = 9, PA_End_J_Index(1) = 13, PA_Beg_K_Index(1) = 1, PA_End_K_Index(1) = 5,

Within_CAMx_Grid(2) = 2, PA_Beg_I_Index(2) = 107, PA_End_I_Index(2) = 110, PA_Beg_J_Index(2) = 78, PA_End_J_Index(2) = 82, PA_Beg_K_Index(2) = 1, PA_End_K_Index(2) = 7,

&CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-8 The rules for defining PA domains are as follows:

1. They must be contained within a single CAMx grid;
2. They may not include cells that contain a nested gr id;
3. They may contain as few as 1 grid cells;
4. They may contain up to all of the grid cells in a C AMx grid provided that this does not violate the second rule;
5. They may intersect or overlap – the same grid cell may be in several process analysis
domains.

Setting CAMx Parameters

PA stores information in data structures that are d imensioned using FORTRAN parameter statements. These parameters must be large enough to accommodate the PA configuration specified in the CAMx control file. If one of the se parameters is exceeded CAMx will stop with an error message stating that a parameter must be c hanged and the model recompiled. It is always a good idea to do a complete rebuild (use th e Unix command “make clean”) when a parameter is changed. The parameters that may need to be changed are in two include files,

“ procan.inc ” and “ camx.prm ”.

procan.inc

MXPADOM – The maximum number of Process Analysis domains.

MXPACEL – The maximum number of Process Analysis cells ove r all domains

camx.prm

MXTRSP – This parameter defines gridded data structures t hat are used by several probing tools. For PA the data structures store ch emical process analysis (CPA) variables, so MXTRSP must be set to at least the value of MXCPA (set in procan.inc )
which is 99.

Output File Formats

CAMx may output up to four files containing PA info rmation according to the PA option selected (see Table 9-3). All of these files are i n FORTRAN binary format to conserve disk space. Two files (the *.ipr and *.irr files) contain information for just the grid cells
selected for PA. The formats for these files are n ot described here since two FORTRAN postprocessor programs are provided to extract data fro m these files in a comma-delimited ASCII format. The files containing CPA information (the *.cpa.grd nn ) are gridded files covering the same area as the regular model average files ( *.avrg.grd nn ). The gridded CPA files have the same format as a regular model average fil e as described in Section 5.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-9 9.3 POSTPROCESSING

Two post-processors are provided to read the binary *.ipr and *.irr output files and extract PA data for further analysis. The CPA output files can be visualized directly.

IPR Output Files

The FORTRAN77 program “ ext_ipr ” extracts IPR data from one or more CAMx *.ipr binary files and reformats the data to comma delimi ted ASCII format ( .csv ) suitable for subsequent analysis (e.g., using spreadsheets). Th e “ ext_ipr ” program performs the following
tasks:

• Reads and outputs the descriptive header of the *.ipr file;

• Optionally combines data from several consecutive *.ipr files to provide multi-day output;

• Selects data for an individual cell within a PA sub -domain or aggregates data over multiple cells within a PA sub-domain;

• Outputs the selected IPR data in .csv format in either ppb or molar units for gas specie s; PM species are in either μ g/m 3 or mass units..

A sample script to run the “ ext_ipr ” program is provided with its source code, and the script includes a description of how to use the program. Also included is a spreadsheet macro that can
read the .csv format and prepare several plots. This macro is d escribed below.

Aggregating Cells

The “ ext_ipr ” program can combine IPR information across severa l cells. This is useful for analyzing the contributions of model processes to a geographic area that spans multiple cells (e.g., an urban area). For simplicity, the multi-c ell area must be defined as a rectangular box. The capability of aggregating IPR information acros s vertical layers is particularly important during the day because vertical columns of cells wi thin the mixed layer become strongly coupled on time scales shorter than one hour. Thus, if the process contributions for a surface grid cell are analyzed during the day vertical diffusion will oft en completely dominate all other processes. In this situation, it is more informative to analyze a column of cells extending from the surface to the approximate height of the mixed layer.

When the “ ext_ipr ” program aggregates information across grid cells it accounts for differences in cell volume. If the output for aggr egate cells is requested in ppb units, the output from CAMx in molar units is converted to ppb using the volume-weighted average units conversion factor for the cells being aggregated.

IPR Time Series Analysis

One useful approach to analyzing IPR data is to plo t the contributions of several processes as a time series. Figures 9-2 through 9-4 present examp les of this approach from the CAMx test case that is distributed with the model. The figures sh ow process contributions to the PM 2.5 sulfateCAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-10 and nitrate concentrations in a multi-cell area aro und Chicago on June 13 and 14, 2002, and are intended solely as an illustration of how PA can be used. The figures were prepared using the

“ ext_ipr ” program described above and the spreadsheet macro

“ process_analysis_macro.xls .” The macro reads an output file from “ ext_ipr ” and prepares three charts:

1. Bar chart of the contribution from each process averaged over the duration of the PA data. This is useful to see which processes were i mportant (Figure 9-2).

2. Bar chart showing the time series of contributions from all major processes. To reduce the number of processes being displayed, advection and diffusion information for each boundary is combined into a single transport term, and any series with zero contribution is excluded from the chart. Also, the three PM che mistry processes (organic, inorganic and aqueous-phase aerosol chemistry) are aggregated to a single net term (Heterogeneous Chemistry). An example of this chart is shown in F igure 9-3.

3. Bar chart showing the time series of contributions from major processes with lateral boundary transport and all chemistry processes show n as a single net term. An example of this chart is shown in Figure 9-4. For a small region lateral boundary transport often leads to large positive and negative terms that approximately cancel (e.g., inflow from the east and outflow to the west). This chart provides a comparison of the net effect of lateral boundary transport relative to other processes, whe reas the second chart resolves the transport by boundary. Comparing Figures 9-3 and 9 -4, based on the same data, shows the difference between these two analysis approache s.

IRR Output Files

The FORTRAN77 program “ ext_irr ” extracts IRR data from one or more CAMx *.irr binary files and reformats the data for subsequent analysis. The “ ext_irr ” program performs the following tasks:

• Reads and outputs the descriptive header of the *.irr file;

• Optionally combines data from several consecutive *.irr files to provide multi-day
output;

• Selects data for an individual cell or multiple cel ls within a PA sub-domain;

• Optionally, outputs the selected IRR data to a .csv format ASCII file;

• Optionally, outputs the selected IRR data to a UAM average format binary file.

The ASCII IRR data are suitable for subsequent anal ysis (e.g., using spreadsheets) but at this time there is no “pre-packaged” analysis available comparable to the macro for analyzing IPR data. The binary IRR output from “ext_irr” can be displayed using any post-processing software that can display CAMx average concentration outputs

.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-11 Figure 9-2. **Time-averaged IPR analysis for PSO4 and PNO3 on J une 13 and 14, 2002; all of**  **the processes are plotted individually.** Time-Averaged PSO4 Change from the Different Proces ses in Chicago Area. Run = postproc_test Grid cells used from grid number 1: (43, 47) to (52 , 56) using layers 1 to 5

**-0.2 -0.15**  **-0.1 -0.05**  **0 0.05**  **0.1 0.15**  **0.2**

**G a**  **s -**  **p h**  **a s**  **e c**  **h e**  **m i**  **s t**  **r y** 

**A r**  **e a**  **e m**  **i s**  **s i**  **o n**  **s**

**P o**  **i n**  **t s**  **o u**  **r c**  **e e**  **m i**  **s s**  **i o**  **n s** 

**P l**  **u m**  **e -**  **i n**  **- G**  **r i**  **d c**  **h a**  **n g**  **e**

**W e**  **s t**  **b o**  **u n**  **d a**  **r y**  **a d**  **v e**  **c t**  **i o**  **n**

**E a**  **s t**  **b o**  **u n**  **d a**  **r y**  **a d**  **v e**  **c t**  **i o**  **n**

**S o**  **u t**  **h b**  **o u**  **n d**  **a r**  **y a**  **d v**  **e c**  **t i**  **o n** 

**N o**  **r t**  **h b**  **o u**  **n d**  **a r**  **y a**  **d v**  **e c**  **t i**  **o n** 

**B o**  **t t**  **o m**  **b o**  **u n**  **d a**  **r y**  **a d**  **v e**  **c t**  **i o**  **n**

**T o**  **p b**  **o u**  **n d**  **a r**  **y a**  **d v**  **e c**  **t i**  **o n** 

**D i**  **l u**  **t i**  **o n**  **i n**  **t h**  **e v**  **e r**  **t i**  **c a**  **l**

**W e**  **s t**  **b o**  **u n**  **d a**  **r y**  **d i**  **f f**  **u s**  **i o**  **n**

**E a**  **s t**  **b o**  **u n**  **d a**  **r y**  **d i**  **f f**  **u s**  **i o**  **n**

**S o**  **u t**  **h b**  **o u**  **n d**  **a r**  **y d**  **i f**  **f u**  **s i**  **o n** 

**N o**  **r t**  **h b**  **o u**  **n d**  **a r**  **y d**  **i f**  **f u**  **s i**  **o n** 

**B o**  **t t**  **o m**  **b o**  **u n**  **d a**  **r y**  **d i**  **f f**  **u s**  **i o**  **n**

**T o**  **p b**  **o u**  **n d**  **a r**  **y d**  **i f**  **f u**  **s i**  **o n** 

**D r**  **y d**  **e p**  **o s**  **i t**  **i o**  **n**

**W e**  **t d**  **e p**  **o s**  **i t**  **i o**  **n**

**I n**  **o r**  **g a**  **n i**  **c a**  **e r**  **o s**  **o l**  **c h**  **e m** 

**O r**  **g a**  **n i**  **c a**  **e r**  **o s**  **o l**  **c h**  **e m** 

**A q**  **u e**  **o u**  **s c**  **h e**  **m i**  **s t**  **r y** Change in PSO4 (ug/m3)

Time-Averaged PNO3 Change from the Different Proces ses in Chicago Area. Run = postproc_test Grid cells used from grid number 1: (43, 47) to (52 , 56) using layers 1 to 5

**-0.25 -0.2**  **-0.15 -0.1**  **-0.05 0**  **0.05 0.1**  **0.15 0.2**  **0.25**

**G**

**a s**  **- p**  **h a**  **s e**  **c h**  **e m**  **i s**  **t r**  **y**

**A r**  **e a**  **e m**  **i s**  **s i**  **o n**  **s**

**P o**  **i n**  **t s**  **o u**  **r c**  **e e**  **m i**  **s s**  **i o**  **n s** 

**P l**  **u m**  **e -**  **i n**  **- G**  **r i**  **d c**  **h a**  **n g**  **e**

**W e**  **s t**  **b o**  **u n**  **d a**  **r y**  **a d**  **v e**  **c t**  **i o**  **n**

**E a**  **s t**  **b o**  **u n**  **d a**  **r y**  **a d**  **v e**  **c t**  **i o**  **n**

**S o**  **u t**  **h b**  **o u**  **n d**  **a r**  **y a**  **d v**  **e c**  **t i**  **o n** 

**N o**  **r t**  **h b**  **o u**  **n d**  **a r**  **y a**  **d v**  **e c**  **t i**  **o n** 

**B o**  **t t**  **o m**  **b o**  **u n**  **d a**  **r y**  **a d**  **v e**  **c t**  **i o**  **n**

**T o**  **p b**  **o u**  **n d**  **a r**  **y a**  **d v**  **e c**  **t i**  **o n** 

**D i**  **l u**  **t i**  **o n**  **i n**  **t h**  **e v**  **e r**  **t i**  **c a**  **l**

**W e**  **s t**  **b o**  **u n**  **d a**  **r y**  **d i**  **f f**  **u s**  **i o**  **n**

**E a**  **s t**  **b o**  **u n**  **d a**  **r y**  **d i**  **f f**  **u s**  **i o**  **n**

**S o**  **u t**  **h b**  **o u**  **n d**  **a r**  **y d**  **i f**  **f u**  **s i**  **o n** 

**N o**  **r t**  **h b**  **o u**  **n d**  **a r**  **y d**  **i f**  **f u**  **s i**  **o n** 

**B o**  **t t**  **o m**  **b o**  **u n**  **d a**  **r y**  **d i**  **f f**  **u s**  **i o**  **n**

**T o**  **p b**  **o u**  **n d**  **a r**  **y d**  **i f**  **f u**  **s i**  **o n** 

**D r**  **y d**  **e p**  **o s**  **i t**  **i o**  **n**

**W e**  **t d**  **e p**  **o s**  **i t**  **i o**  **n**

**I n**  **o r**  **g a**  **n i**  **c a**  **e r**  **o s**  **o l**  **c h**  **e m** 

**O r**  **g a**  **n i**  **c a**  **e r**  **o s**  **o l**  **c h**  **e m** 

**A q**  **u e**  **o u**  **s c**  **h e**  **m i**  **s t**  **r y** Change in PNO3 (ug/m3)CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-12 Figure 9-3. **IPR time series analysis for PSO4 and PNO3; Later al boundary/Chemistry terms**  **are not aggregated.** Hourly PSO4 Change from Different Processes in Chic ago Area. Run = postproc_test Grid cells used from grid number 1: (43, 47) to (52 , 56) using layers 1 to 5

**-1.5 -1**  **-0.5 0**  **0.5 1** 

**Jun 13, 2002 Jun 14, 2002** Change in PSO4 (ug/m3) **Top Boundary Deposition**  **Emissions Gas-Phase Chemistry**  **Heterogeneous Chemistry West Boundary**  **East Boundary South Boundary**  **North Boundary**

Hourly PNO3 Change from Different Processes in Chic ago Area. Run = postproc_test Grid cells used from grid number 1: (43, 47) to (52 , 56) using layers 1 to 5

**-2.5 -2**  **-1.5 -1**  **-0.5 0**  **0.5 1**  **1.5 2** 

**Jun 13, 2002 Jun 14, 2002** Change in PNO3 (ug/m3) **Top Boundary Deposition**  **Emissions Gas-Phase Chemistry**  **Heterogeneous Chemistry West Boundary**  **East Boundary South Boundary**  **North Boundary**CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-13 Figure 9-4. **IPR time series analysis for PSO4 and PNO3; Lateral boundary/Chemistry terms**  **are aggregated to a single net term, in contrast to Figure 9-3.**  Hourly PSO4 Change from Different Processes in Chic ago Area. Run = postproc_test Grid cells used from grid number 1: (43, 47) to (52 , 56) using layers 1 to 5

**-1 -0.8**  **-0.6 -0.4**  **-0.2 0**  **0.2 0.4**  **0.6 0.8** 

**Jun 13, 2002 Jun 14, 2002** Change in PSO4 (ug/m3) **Top Boundary Deposition**  **Emissions Chemistry**  **Lateral Boundaries**

Hourly PNO3 Change from Different Processes in Chicago Area. Run = postproc_test Grid cells used from grid number 1: (43, 47) to (52, 56 ) using layers 1 to 5

**-2.5 -2**  **-1.5 -1**  **-0.5 0**  **0.5 1**  **1.5**

**Jun 13, 2002 Jun 14, 2002** Change in PNO3 (ug/m3) **Top Boundary Deposition**  **Emissions Chemistry**  **Lateral Boundaries**CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-14 CPA Output Files

CPA results are output from CAMx as gridded files i n the same format as the average concentration files, and therefore can be visualize d using any post-processing software that can display CAMx concentration outputs. These files ma y optionally include just the surface layer or all layers according to how the “3-D output” fla g is set in the CAMx control file. Surface layer species concentrations reflect the balance be tween several model processes including surface emissions/deposition, vertical mixing, and chemical reactions in surface (and possibly aloft) grid cells. In contrast, the CPA output da ta are grid cell specific and reflect chemical change in single grid cells. The fact that vertica l mixing tends to average species concentrations over multiple layers whereas CPA variables are laye r specific may complicate and bias the interpretation of CPA results. A solution is to pl ace both the concentrations and CPA variables on a comparable basis by averaging them over all la yers within the planetary boundary layer.

A post-processor (VERTAVG) was developed to average CPA variables and concentrations over multiple layers contained within the depth of the p lanetary boundary layer (PBL). The PBL depth varies in space and time according to the str ength and vertical extent of turbulent mixing. Vertical turbulent mixing is specified for CAMx by the input diffusivity (Kv) fields. The VERTAVG processor reads CAMx Kv, height, temperatur e, and pressure input files, and then calculates the PBL depth for each grid column at ea ch hour. VERTAVG also reads a CAMx 3-D output file of CPA variables (or species concentrat ions) and calculates air-mass weighted PBL values for each grid column at each hour. The outp ut from VERTAVG is a 2-D file in average file format where the single layer represents the P BL average values rather than surface layer
values.

VERTAVG appends two extra variables to the file bei ng processed to aid with interpretation and to make clear that the data have been vertically av eraged. The added variables are:

• PBL_Z is the diagnosed height of the PBL for each g rid column.

• PBL_I is the layer index of the top layer within th e diagnosed PBL for each grid column.

CAMx must be run using the option to create 3-D spe cies concentration (and therefore CPA) output files in order to use VERTAVG.

An example of how CPA can be used to evaluate a par ticular CAMx modeling application is provided later in Section 9.5.

9.4 CHEMICAL PROCESS ANALYSIS AND GRID NESTING

CAMx user’s should already be aware that the horizo ntal and vertical resolution of CAMx grids can impact model results such as predicted concentr ations. Such impacts may be considered modeling artifacts since they result from choices i n modeling system design. These artifacts are rarely apparent in the course of a typical CAMx app lication because modeling grid design conventions have evolved that reduce grid resolutio n artifacts to acceptable levels (e.g., EPA guidance for 8-hour ozone modeling), and because gr id resolution is usually held fixed throughout a modeling project. However, there are technical reasons why grid resolution artifacts are more likely to be apparent for proces s analysis (CPA and IRR) outputs than theCAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-15 normal CAMx concentration (average file) outputs. The purpose of this discussion is to alert users’s to this situation and provide some technica l background. An example is given below.

The sensitivity of grid model concentrations to gri d resolution depends upon competition between processes that tend to smooth out concentra tion gradients (i.e., horizontal and vertical mixing) and processes that tend to create concentra tion gradients (i.e., emissions, chemical reactions, deposition). Hence, long lived species with slow production and removal rates will tend to show little spatial variation in mixing rat io provided that some atmospheric mixing is occurring. For example, carbon monoxide mixing rat ios tend to be spatially uniform in grid model simulations, except where there are large dif ferences in emission strength between neighboring cells.

Many of the chemical process analysis (CPA) terms i nvolve or describe radical concentrations. Radicals are distinguished from the state species i n CAMx (i.e., the species that are listed in the chemistry parameters file and are available for out put to average concentration file) by having very short lifetimes (sub-second). Short lifetime means that the radicals cannot be “transported” in the atmosphere since they react before they can travel farther than fractions of a centimeter. Consequently, CAMx does not transport radicals and there is no direct physical coupling between radical concentrations in neighboring grid cells. This suggests a greater potential for spatial variability in radical concentrations than exists for state species. However, the radical concentrations are determined by the state species concentrations in each grid cell, and since the concentrations of state species in neighboring cell s are coupled by transport, the radical species concentrations in neighboring cells are indirectly coupled. Therefore, differences in radical concentrations between neighboring cells must refle ct differences in the state species concentrations or differences in the steady state r elationships between the radicals and the state species. The example below shows that these steady state relationships can show systematic dependencies on grid resolution that are directly v isible in model outputs.

Vertical Nesting in the OTAG Modeling

The grid system developed for the OTAG modeling in the mid-1990’s used vertical nesting to economize on model resources in the master grid are a. This means that the nested grid had seven layers but the master grid had only five layers. V ertical nesting was achieved by aligning the first two nested grid layers with the first master grid layer, and the third and fourth nested grid layers with the second master grid layer. The surf ace layer in the master grid was 100 m deep whereas in the nested grid the surface layer was 50 m deep.

The surface OH concentration is shown in Figure 9-5 (a) for noon on July 8. This output was prepared from the master grid CPA output file, and so the hourly OH is the time weighted average of the mixing ratio at each sub-hourly time step. In the area occupied by the nested grid, the OH concentrations from 18 nested grid cells (i. e., a block of 3 by 3 by 2 cells) have been aggregated to the corresponding master grid cell us ing a volume weighted average. The area of the nested grid is clearly visible with a box shape d outline in Figure 9-5(a). The OH concentrations are systematically lower in the nest ed grid than the master grid. The area of the nested grid is also visible in other CPA outputs re lated to radical concentrations. Figure 9-6(a) shows the corresponding ozone distribution, and the area of the master grid cannot be distinguished.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-16 Figure 9-5 **. Surface OH concentrations for the OTAG master gr id area with (a) and without (b)**  **vertical nesting in the nested grid area.**

Figure 9-6. **Surface ozone concentrations for the OTAG master grid area with (a) and without**  **(b) vertical nesting in the nested grid area.**CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-17 A simple experiment shows that the difference in OH concentrations between the master and nested grids results from the vertical nesting. CA Mx was run using a 5 layer nested grid (no vertical nesting) using the nested grid emissions b ut interpolating the meteorology from the master grid using the CAMx “flexi-nesting” capabili ty. The surface OH concentration without vertical nesting is shown in Figure 9-5(b). Withou t vertical nesting, the area of the nested grid cannot be distinguished in the OH concentration fie ld. This shows that the surface OH concentration is very sensitive to the thickness of the surface layer, much more so than ozone (Figure 9-6).

While results such as Figure 9-5(a) may appear “wro ng” at first sight, a more considered response is a realization that CPA is simply provid ing new insight into relationships between modeling system design and function. Such informat ion can be used to improve the design of a modeling system or to assist in the interpretation of model results. A modeler might conclude from these results that vertical nesting is undesir able in the surface layer because it provides relatively little saving in CPU and memory requirem ents at the cost of artifacts that are difficult to explain.

9.5 CPA DEMONSTRATION

SAPRC99 and CB4 simulations were performed with CPA for an existing Los Angeles modeling database (Yarwood et al., 2003). The analysis focu sed on chemical explanations for differences in ozone predictions with the CB4 and SAPRC99 mecha nisms. CPA was used to investigate several aspects of atmospheric chemistry that are i mportant to understanding model performance and control strategy development:

• Determining geographic areas of high ozone producti vity;

• Classifying geographic areas as being “VOC-sensitiv e” or “NOx- sensitive” with respect to ozone production;

• Quantifying the initiation, propagation and termina tion of radical species such as hydroxyl (OH) radical that drive ozone formation ch emistry;

• Investigating the efficiency of radical utilization in ozone production as measured by the “HOx chain length”;

• Determining the contribution of biogenic (isoprene) emissions to VOC reactivity and ozone production;

• Determining geographic areas of photochemical ozone destruction;

• Investigating how NOx emissions are converted to NO z;

• Quantifying recycling of NOy from chemically inacti ve (NOz) to active (NOx) forms.

The Los Angeles (LA) modeling episode was August 3– 7, 1997 during the Southern California Ozone Study (SCOS). The modeling domain covered 65 by 40, 5-km grid cells as shown in Figure 9-7. CAMx was run with 10 layers extending between a surface layer of 60 m and a model top at 4 km. The photochemistry of ozone for mation was simulated in CAMx using two
different condensed chemical mechanisms:CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-18 **300 350 400 450 500 550 600**
**UTM Easting (km) 3700**  **3750 3800**  **3850 U** **T M** **N o** **r t** **h i** **n g** **( k** **m )** 

**-200 m 0 m**  **200 m 400 m**  **600 m 800 m**  **1000 m 1200 m**  **1400 m 1600 m**  **1800 m 2000 m**  **2200 m 2400 m**  **2600 m**

**Pacific Ocean**  **Los Angeles County Kern County** 

**Orange**

**County Riverside County** 

**San Diego County San Bernardino County** 

**Ventura County**  Crestline

LAX Hawthorn Long Beach Van Nuys Diamond Bar

Anaheim Lake Perris

Figure 9-7 **. The CAMx Los Angeles modeling domain (Yarwood et al., 2003).** 

• The Carbon Bond 4 (CB4) mechanism (Gery et al., 198 9) with updates for low-NO
x conditions and isoprene reactions. This is impleme nted in CAMx as “Mechanism number 3” and has 37 species and 96 reactions.

• The fixed stoichiometry version of the SAPRC99 mech anism (Carter, 2000). This is implemented in CAMx as “Mechanism 5” and has 74 spe cies and 211 reactions.

The analysis of CPA results for Los Angeles is show n for 2 PM on August 5, 1997. This was the day with the highest observed ozone during the epis ode period and 2 PM is the time when modeled ozone production rates were high in many ar eas just before the peak concentration levels were modeled. The CPA results were averaged over all layers within the PBL using the VRTAVG post-processor described above. Figure 9-8 shows the diagnosed PBL depth for the modeling domain at this time.

Ozone Production

The PBL average ozone levels with CB4 and SAPRC99 a re compared in Figure 9-9. SAPRC99 (right) produces up to about 30 ppb more ozone thro ughout the Los Angeles basin. The chemical process analysis sought to explain how SAP RC99 makes more ozone than CB4 and the potential consequences. One consequence is respons e to emission changes. The NOx emission inventory was reduced by 50% and the ozone response measured as a relative reduction factor (RRF = Control Case ozone / Base Case ozone) is sho wn in Figure 9-10. CB4 showed both greater ozone increases (yellow/red) and decreases (green/blue) than SAPRC99 in response to
the 50% NOx emissions cut.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-19 Figure 9-8 **. PBL depth (m) diagnosed by VRTAVG for 14:00 on A ugust 5, 1997.** 

CB4 SAPRC99

Figure 9

- 9 **. PBL average ozone (ppb) at 14:00 on August 5, 19 97.** CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-20

CB4 SAPRC99

Figure 9

- 10 **. Relative response of ozone to a 50% NOx emission reduction at 14:00 on August**  **5, 1997.**

The ozone production rate (ppb/hr) during this same time is compared in Figure 9-11. As expected from the ozone concentrations (Figure 9-9) , SAPRC99 has higher peak ozone production rates than CB4 by about 5 to 10 ppb/hr. Comparing the ozone production rates (Figure 9-12) reveals that SAPRC99 produces ozone f aster especially in the central area of the Los Angeles basin and directs the chemical process analysis toward understanding this phenomenon.

Ozone Sensitivity to NOx and VOC

Understanding the sensitivity of ozone to NOx and V OC precursors is important to control strategy design. Several methods have been propose d for differentiating ozone sensitivity to precursors based on chemical indicators, such as th e ratio of the production rates of hydrogen peroxide and nitric acid (P H2O2 /P HNO3 ). This method is implemented in CPA and used to designate grid cells as VOC-sensitive or NOx-sensit ive at each model time step (i.e., every few minutes) by defining P H2O2 /P HNO3 of 0.35 and higher as the NOx-sensitive regime. T he

P H2O2 /P HNO3 indicator ratio is reported as the CPA variable (P H2O2_PHN3; Figure 9-13) and the NOx-sensitive and VOC-sensitive ozone production ra tes are accumulated as the CPA variables PO3_NOxsns (Figure 9-14) and PO3_VOCsns (Figure 9-1 5), respectively.

The P H2O2 /P HNO3 indicator ratio at 14:00 on August 5, 1997 (Figure 9-13) shows a sharp transition from VOC-sensitive in high NOx emission urban/suburban areas to NOx-sensitive in
lower NOx emission rural areas. The transition in indicator ratio occurs at almost the same
location with the CB4 and SAPRC99 mechanisms even t hough the ozone production rates differ
between the mechanisms.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-21

CB4 SAPRC99

Figure 9

- 11 **. Ozone production rate (ppb/hr) at 14:00 on Augus t 5, 1997.** 

Figure 9

- 12 **. Difference (CB4-SAPRC99) in ozone production rat e (ppb/hr) at 14:00 on August**  **5, 1997.**CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-22

CB4 SAPRC99

Figure 9

- 1 3 **. Ratio of instantaneous production rates for hydr ogen peroxide to nitric acid (H**  **2 O**  **2 produced / HNO**  **3 produced) at 14:00 on August 5, 1997.** 

CB4 SAPRC99

Figure 9

- 1 4 **. Ozone production rate in areas defined as NOx-se nsitive by the P**  **H2O2 /P**  **HNO3 indicator ratio at 14:00 on August 5, 1997.** CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-23

CB4 SAPRC99

Figure 9

- 1 5 **. Ozone production rate in areas defined as VOC-se nsitive by the P**  **H2O2 /P**  **HNO3 indicator ratio at 14:00 on August 5, 1997.** 

Ozone production rates are much lower in the NOx-se nsitive areas than VOC-sensitive areas (Figures 9-14 and 9-15). SAPRC99 predicts much hig her ozone production rates than CB4 in VOC-sensitive areas, and this is where the major di fference between the mechanisms is found. The important question is: “how is SAPRC99 more rea ctive than CB4 under VOC-sensitive conditions?” Potential explanations include:

• SAPRC99 has greater radical production for VOC-sens itive conditions and therefore converts NO more rapidly to NO 2

.

• SAPRC99 has stronger NOx removal mechanisms (NOx si nks) than CB4 for VOCsensitive conditions and therefore more rapidly ach ieves NOx concentrations that maximize ozone production.

CPA can be used to evaluate these (and other) poten tial hypotheses for how SAPRC99 and CB4
are different.

Radical Production

Radicals are molecular fragments with an unpaired e lectron and they generally are highly reactive in the atmosphere. The hydroxyl radical ( OH) initiates many VOC degradation reactions whereas peroxy radicals (HO 2 and RO 2 ) convert NO to NO 2 thereby triggering ozone production.

The CAMx implementation of CPA calculates initiatio n rates of OH, HO 2 and RO 2 radicals.
Initiation means the creation of “new” radicals fro m reactions of stable molecules such as photolysis or ozonolysis. Figure 9-16 compares the production of new HOx (the sum of OH and
HO 2 ) by CB4 and SAPRC99 (HOx_new). SAPRC99 has strong er HOx initiation than CB4 in
the VOC-sensitive area where the mechanisms differ. However, HOx radicals can be formed in propagation reactions as well as initiation reactio ns. The total production of HO 2 can beCAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-24

CB4 SAPRC99

Figure 9

- 16 **. HOx radical initiation rate (ppb/hour) at 14:00 on August 5, 1997.** 

determined from the amount of HOx reacting because these quantities will always be in balance (because HOx radicals have lifetimes much shorter t han a second).

The HOx radical reaction rate (HOx_rctd) is compare d in Figure 9-17. Once again, SAPRC99 has more HOx radical reaction (and therefore HOx ra dical production) than CB4 in VOCsensitive areas.

The amount of HOx radicals reacting (Figure 9-17) i s greater than the amount of new HOx radicals initiated (Figure 9-16) because of chain r eactions that propagate radicals, such as:

1. OH and HO 2 inter-conversion, e.g.

OH + O 3 → HO 2 + O 2 HO 2 + O 3 → OH + O 2

2. OH and HO 2 propagation through RO 2 radicals, e.g.

OH + VOC → RO 2 + products
RO 2 + NO → HO 2 + products
HO 2 + NO → OH + NO 2

Every time that an OH radical propagates via a chai n of reactions and reforms OH (or HO 2 propagates back to HO 2 ) may be considered as one cycle of HOx propagation . The HOx chain length is the number of cycles completed by the ave rage HOx radical and depends upon atmospheric conditions. HOx propagation could be r elatively more efficient for OH than HO 2 , or vice-versa, but this distinction disappears as t he HOx chain length becomes large (> 2) and HOx can cycle between OH and HO 2 many times. Therefore, CAMx CPA just calculates a combined HOx chain length that averages OH and HO 2 together.

The HOx chain length can be calculated simply as HO x_rctd/(2 x HOx_new). The number 2 appears here because one chain is considered to req uire two HOx reactions to return the HOx radical back to its initial form (e.g., OH → HO 2 → OH). Using this approach enables consistent HOx chain length calculations for the CB4 and SAPRC 99 mechanisms.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-25

CB4 SAPRC99

Figure 9

- 17 **. HOx radical reaction rate (ppb/hour) at 14:00 on August 5, 1997.** 

The CB4 and SAPRC99 chain lengths (HOx_CL) for 14:0 0 on August 5, 1997 are compared in Figures 9-18 and 9-19. The HOx chain length for SA PRC99 is longer than for CB4 in the VOCsensitive areas (Figure 9-18) and this combines wit h the greater HOx initiation for SAPRC99 than CB4 (Figure 9-16) to give SAPRC99 more overall HOx production in the VOC-sensitive

area.

The HOx chain length values calculated by CPA may b e interpreted using the following guidelines:

• HOx_CL should never be less than 0.5 because of the way chain length is being defined.

o Chain length of 0.5 means that all newly produced H Ox “terminates” at the first
reaction.

• HOx_CL is low (1-2) in areas where NO is scarce (ra dicals can not propagate) or NO 2 is very high (radicals are scavenged).

o OH or HO 2 terminating immediately is likely to result in ozo ne destruction.

• HOx_CL is high (>2) in areas where NO levels favor efficient radical propagation and rapid ozone production.

o The area of HOx_CL of ~2.5 and higher corresponds to the VOC-sensitive area.

Organic peroxy (RO 2 ) radicals also are important because they convert NO to NO 2 and because they are potential sources of HOx radicals. RO 2 is comprised of many different organic radicals produced from different VOCs and the CB4 and SAPRC9 9 mechanisms classify RO 2 radicals using different lumping schemes and names. Accordi ngly, the CAMx CPA just compares the
total amount of RO 2 radicals reacting (RO2_rctd) as a measure of the i nfluence of RO 2 on ozone production chemistry.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-26

CB4 SAPRC99

Figure 9

- 1 8 **. HOx chain length at 14:00 on August 5, 1997.**

Figure 9

- 1 9 **. Ratio of the HOx chain length for CB4 to SAPRC99 at 14:00 on August 5, 1997.** 

The RO 2 reaction rates for CB4 and SAPRC99 are compared in Figure 9-20. SAPRC99 has a much higher RO 2 reaction rate, and therefore RO 2 production rate, than CB4 in the VOCsensitive area. The area where SAPRC99 has higher RO 2 production closely corresponds with the area where SAPRC99 has higher ozone production (Figure 9-15). The RO 2 radical production rate is the most pronounced difference b etween the SAPRC99 and CB4 mechanisms related to ozone production and should be further i nvestigated.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-27

CB4 SAPRC99

Figure 9-20 **. R** O 2 **organic peroxy radical reaction rate (ppb/hour) at 14:00 on August 5, 1997.** 

NOy Reactions

Reactive nitrogen (NOy) comprises NOx and its react ion products (NOz):

• NOx = NO + NO 2

• NOz = HONO + PAN + HNO 3 organic nitrates + other minor N-species

• NOy = NOx + NOz

CAMx CPA reports several parameters that track the conversion of oxidized nitrogen between active (NOx) and inactive forms (NOz). Here, activ e means able to participate directly in ozone producing reactions, i.e., NO and NO 2

.

OH + NO 2 Reaction

The most important atmospheric reaction converting NOx to NOz is the reaction of OH with
NO 2 to for nitric acid (HNO 3 ). Converting NO 2 to HNO 3 promotes ozone formation in high NOx environments by overcoming the NOx inhibition e ffect. The rate constant for the OH +
NO 2 reaction has changed in recent years and CB4 has a slightly higer (~10%) value than SAPRC99. However, the rate at which OH radicals co nvert NO 2 to HNO 3 depends upon both
the reaction rate constant and the OH concentration . Figure 9-21 compares the CB4 and
SAPRC99 conversion rate for NO 2 to HNO 3 by reaction with OH (HNO3_OHNO2). The conversion rate is higher in SAPRC99 than CB4 becau se the higher OH concentration in
SAPRC99 more than offsets the lower OH + NO 2 reaction rate in SAPRC99 than CB4. This
shows that the OH + NO 2 reaction rate alone does not explain differences i n reactivity between
SAPRC99 and CB4.CAMx v5.30 User’s Guide, December 2010

Process Analysis

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_09_PA.docx 9-28

CB4 SAPRC99

Figure 9

- 2 1 **. Conversion rate (ppb/hr) for NO 2**  **to HNO 3**  **by reaction with OH at 14:00 on August 5, 1997.** 

NOx Recycling

Both the CB4 and SAPRC99 mechanisms include reactio ns that slowly convert reactive nitrogen from inactive (NOz) back to active (NOx) forms. Pot entially important “NOx recycling”
reactions are:

• OH reaction with nitric acid

• Nitric acid photolysis

• Organic nitrate photolysis

• OH reaction with organic nitrates

The original CB4 mechanism (Gery et al., 1989) incl udes only the first of these reactions,
whereas SAPRC99 includes the first three reactions. The CB05 mechanism and CB4xi mechanism (CAMx Mechanism number 4) include all fou r of these reactions.

The amount of NOz recycled back to NOx (NOxrecycl) for the CB4 and SAPRC99 mechanisms is compared in Figure 9-22. As expected, NOx recyc ling is greater for SAPRC99 than CB4 because SAPRC99 includes more reaction pathways. T he NOx recycling rate is slow (tenths of ppb/hour of NOx produced) and these reactions will only influence ozone chemistry in regions where NOx emissions are low. The NOx recycling rea ctions play little role in ozone production for large urban areas. NOx recycling will be more important in rural areas between urban areas and may influence ozone transport.CAMx v5.30 User’s Guide, December 2010

Process Analysis

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CB4 SAPRC99

Figure 9

- 2 2 **. Rate of NOx recycling (ppb/hr) from NOz at 14:00 on August 5, 1997.** 

Ozone Destruction

The CB4 and SAPRC99 mechanisms include reactions th at can destroy ozone as well as produce ozone. Some ozone destruction pathways are well-kn own, e.g., ozone titration by NO and ozone
reaction with alkenes:

NO + O 3 → NO 2 + O 2 alkene + O 3 → products

Reactions with radicals such as the HOx cycle discu ssed above also can destroy ozone:

OH + O 3 → HO 2 + O 2 HO 2 + O 3 → OH + O 2

Whether or not the HOx cycle results in net ozone d estruction or production depends upon the availability of NOx.

The CAMx CPA calculates the ozone destruction rate (O3_dest) due to these processes. Figure 9-23 compares the ozone destruction rate for CB4 an d SAPRC99. There is rapid ozone destruction by NO-titration in high NOx emission ar eas for both CB4 and SAPRC99. Outside of high NOx emission areas, ozone destruction occurs a t moderate rates of about 1-4 ppb/hr even as net ozone production is occurring at the same time (Figure 9-11). The net ozone production shown in Figure 9-11 is the sum of ozone production and destruction reactions. Simultaneous ozone destruction and production represents a repla cement of “old ozone” by “new ozone.” Since “old ozone” is effectively transported ozone, the rate of ozone destruction has implications for ozone transport analyses and evaluating the bal ance between ozone transport and local ozone production in causing ozone pollution. The ozone d estruction rates seen here outside high NOx emission areas of a few ppb/hour are small compared to the ozone production rates of many ppb/hour, but would be significant if combined with multi-hour, or multi-day, transport times.CAMx v5.30 User’s Guide, December 2010

Process Analysis

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CB4 SAPRC99

Figure 9

- 2 3 **. Ozone destruction rate by chemical reactions (pp b/hr) at 14:00 on August 5,** 
**1997.**

Role of Biogenic VOC

The CAMx CPA reports the amount of OH reacting with VOCs included in the reaction
mechanism. The VOCs in the CB4 and SAPRC99 mechani sm are different because of differences in the lumping approach. Direct compar isons are possible for explicit species (e.g., isoprene, ethylene), for species that are effective ly the same (TOL and ALK1; XYL and ALK2) and for groups of lumped compounds (PAR and the sum of ALK1 to ALK5).

One way to use the CPA variables for OH + VOC react ions is to evaluate the role of biogenic isoprene. Figure 9-24 shows the fractional contrib ution of isoprene to all OH + VOC reactions. The contribution of isoprene to OH + VOC reactions is very similar for the CB4 and SAPRC99 mechanisms. Isoprene is much less important as an OH reaction partner for the Los Angeles area than in the Eastern US because the area is dry and has relatively low biomass, and also has relatively high anthropogenic VOC emissions. Note that the analysis shown in Figure 9-24 indicates where isoprene reactions are occurring, b ut not where ozone is produced as a consequence of isoprene reactions.CAMx v5.30 User’s Guide, December 2010

Process Analysis

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CB4 SAPRC99

Figure 9

- 2 4 **. OH + isoprene as a fraction of OH + VOC reaction s at 14:00 on August 5, 1997.** CAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-1 10. REACTIVE TRACERS

The CAMx reactive tracer algorithm provides a flexi ble approach for simulating the emission, dispersion, chemistry, and deposition of multiple t race gas and particulate tracers on the computational grid(s) and within the IRON PiG treat ment. Originally developed to model air toxics, it was extended and generalized to be able to track a variety of user-defined organic and inorganic species.

Reactive tracers are carried separately from the co re model photochemical/PM chemistry mechanisms, and thus can be used to address a varie ty of issues, separately or in combination:

• Reactive and inert gaseous and particle air toxic c ompounds;

• Chemical decay of individual VOC compounds into mul tiple generations of daughter products;

• Source tagging of primary emitted inert and reactiv e compounds from specific source types/classes, or from individual stacks, facilitie s and/or complexes.

The reactive tracer algorithm is implemented as a C AMx “Probing Tool” and thus shares model data structures with other Probing Tools such as th e Ozone/Particulate Source Apportionment Technology (OSAT/PSAT) and the Decoupled Direct Met hod (DDM/HDDM). This streamlines the CAMx code, improves efficiency, and maximizes c onsistency with the core model since it allows reactive tracer calculations for emissions, transport, and deposition to use the existing CAMx algorithms. However, this means that reactive tracers cannot be used simultaneously with other Probing Tools.

The reactive tracer implementation employs two appr oaches to define tracer chemistry. The original approach, referred to as RTRAC, allows tra cers to decay and form multiple generations of daughter products through photolysis and user-sp ecified thermal reactions with ozone and radicals (OH, NO3) that are extracted from the core gas-phase chemistry (CB4, CB05, or SAPRC99). The newer approach, referred to as the R TRAC Chemical Mechanism Compiler (RTCMC), allows the user to externally define a ful l chemistry mechanism with no limits on complexity (within available computer resources). RTCMC can also access any gas-phase concentrations from the core gas-phase mechanism as well. Neither option allows chemistry for particulate tracers.

10.1 DESCRIPTION OF RTRAC

Reactive tracers are defined for each CAMx run by p roviding an RTRAC chemistry parameters
file similar to that used for the core model. The examples in Figure 10-1 and 10-2 illustrate an RTRAC air toxics application using the CB4 and SAPR C99 chemical mechanisms, respectively (ENVIRON, 2002; Morris et al., 2003). The number a nd names of the tracers are arbitrary; i.e. information on the tracer species’ chemical identit ies, structure, reaction pathways, and kinetics are kept separate from the core model. Consistent with the chemistry parameters files used for CB4 and SAPRC99, the physical characteristics for e ach reactive tracer must be specified for deposition calculations, and their reaction pathway s and rates must be defined. The structure of the RTRAC tracer definition provides complete flexi bility in the selection of the compounds andCAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-2 Figure 10-1 . Example RTRAC chemistry input file for modeling the MATES toxic species using CB4 as the host chemical mechanism. CAMx Version |VERSION5.2 Description |MATES Toxics for CRC project A- 42 No of gas tracers |6 No of aero tracers |8 No photolysis rxns |4 No thermal rxns |12

Gas Tracers No. Name P/S SNAM lower bnd H-law T- fact Diffrat Reactvty Rscale

1 PACET PRIM 1.00E-12 6.30e+03 - 6492. 1.56 0.0 1.0
2 HCHO PRIM 1.00E-12 6.30e+03 - 6492. 1.29 0.0 1.0
3 BENZ PRIM 1.00E-12 1.80e-01 0. 2.08 0.0 1.0
4 BUTA PRIM 1.00E-12 1.00e-02 0. 1.73 0.0 1.0
5 SACET SEC ALD2 1.00E-12 6.30e+03 - 6492. 1.56 0.0 1.0
6 SFORM SEC FORM 1.00E-12 6.30e+03 - 6492. 1.29 0.0 1.0

Aero Tracers No. Name lower bnd Density Low cut Uppe r cut
7 DSLF 1.00E-09 1.5 0.10 2.50
8 ECF 1.00E-09 1.5 0.10 2.50
9 CRF 1.00E-09 1.5 0.10 2.50
10 CR6F 1.00E-09 1.5 0.10 2.50
11 DSLC 1.00E-09 1.5 2.50 10.00
12 ECC 1.00E-09 1.5 2.50 10.00
13 CRC 1.00E-09 1.5 2.50 10.00
14 CR6C 1.00E-09 1.5 2.50 10.00 Photolysis reactions Toxic Rxn # Factor

PACET 45 1.0
SACET 45 1.0
HCHO 39 1.6
SFORM 39 1.6
Thermal reactions and rates Toxic React A(ppm-1min-1) Ea(K) B Tref
PACET OH 8.2015E+03 -3.1099E+02 0.0 300.0
PACET NO3 2.0689E+03 1.8599E+03 0.0 300.0
HCHO OH 1.6699E+03 -6.4815E+02 2.0 300.0
HCHO NO3 4.1377E+03 2.5161E+03 0.0 300.0
BENZ OH 3.6944E+03 1.9978E+02 0.0 300.0
BUTA OH 2.1871E+04 -4.4787E+02 0.0 300.0
BUTA O3 4.8766E+01 2.5000E+03 0.0 300.0
BUTA NO3 2.1871E+04 1.4890E+03 0.0 300.0
SACET OH 8.2015E+03 -3.1099E+02 0.0 300.0
SACET NO3 2.0689E+03 1.8599E+03 0.0 300.0
SFORM OH 1.6699E+03 -6.4815E+02 2.0 300.0
SFORM NO3 4.1377E+03 2.5161E+03 0.0 300.0CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-3 Figure 10-2 . Example RTRAC chemistry input file for modeling t oxic species with SAPRC99 as
the host chemical mechanism. CAMx Version |VERSION5.0 Description |Toxics for CRC project A-42 wit h SAPRC99 host mechanism No of gas tracers |14 No of aero tracers |0 No photolysis rxns |8 No thermal rxns |28

Gas Tracers No. Name P/S SNAM lower bnd H-law T- fact Diffrat Reactvty Rscale

1 mPACET PRIM 1.00E-12 6.30e+03 - 6492. 1.56 0.0 1.0
2 mPFORM PRIM 1.00E-12 6.30e+03 - 6492. 1.29 0.0 1.0
3 mBENZ PRIM 1.00E-12 1.80e-01 0. 2.08 0.0 1.0
4 mBUTA PRIM 1.00E-12 1.00e-02 0. 1.73 0.0 1.0
5 SACET SEC CCHO 1.00E-12 6.30e+03 - 6492. 1.56 0.0 1.0
6 SFORM SEC HCHO 1.00E-12 6.30e+03 - 6492. 1.29 0.0 1.0
7 oPACET PRIM 1.00E-12 6.30e+03 - 6492. 1.56 0.0 1.0
8 oPFORM PRIM 1.00E-12 6.30e+03 - 6492. 1.29 0.0 1.0
9 oBENZ PRIM 1.00E-12 1.80e-01 0. 2.08 0.0 1.0
10 oBUTA PRIM 1.00E-12 1.00e-02 0. 1.73 0.0 1.0 11 pPACET PRIM 1.00E-12 6.30e+03 - 6492. 1.56 0.0 1.0 12 pPFORM PRIM 1.00E-12 6.30e+03 - 6492. 1.29 0.0 1.0 13 pBENZ PRIM 1.00E-12 1.80e-01 0. 2.08 0.0 1.0 14 pBUTA PRIM 1.00E-12 1.00e-02 0. 1.73 0.0 1.0 Photolysis reactions Toxic Rxn # Factor

mPACET 131 1.0
oPACET 131 1.0 pPACET 131 1.0
SACET 131 1.0
mPFORM 123 1.6
oPFORM 123 1.6 pPFORM 123 1.6
SFORM 123 1.6
Thermal reactions and rates Toxic React A(ppm-1min-1) Ea(K) B Tref
mPACET OH 8.2015E+03 -3.1099E+02 0.0 300.0
mPACET NO3 2.0689E+03 1.8599E+03 0.0 300.0
oPACET OH 8.2015E+03 -3.1099E+02 0.0 300.0
oPACET NO3 2.0689E+03 1.8599E+03 0.0 300.0 pPACET OH 8.2015E+03 -3.1099E+02 0.0 300.0 pPACET NO3 2.0689E+03 1.8599E+03 0.0 300.0
mPFORM OH 1.6699E+03 -6.4815E+02 2.0 300.0
mPFORM NO3 4.1377E+03 2.5161E+03 0.0 300.0
oPFORM OH 1.6699E+03 -6.4815E+02 2.0 300.0
oPFORM NO3 4.1377E+03 2.5161E+03 0.0 300.0 pPFORM OH 1.6699E+03 -6.4815E+02 2.0 300.0 pPFORM NO3 4.1377E+03 2.5161E+03 0.0 300.0
mBENZ OH 3.6944E+03 1.9978E+02 0.0 300.0
oBENZ OH 3.6944E+03 1.9978E+02 0.0 300.0 pBENZ OH 3.6944E+03 1.9978E+02 0.0 300.0
mBUTA OH 2.1871E+04 -4.4787E+02 0.0 300.0
mBUTA O3 4.8766E+01 2.5000E+03 0.0 300.0
mBUTA NO3 2.1871E+04 1.4890E+03 0.0 300.0
oBUTA OH 2.1871E+04 -4.4787E+02 0.0 300.0
oBUTA O3 4.8766E+01 2.5000E+03 0.0 300.0
oBUTA NO3 2.1871E+04 1.4890E+03 0.0 300.0 pBUTA OH 2.1871E+04 -4.4787E+02 0.0 300.0 pBUTA O3 4.8766E+01 2.5000E+03 0.0 300.0 pBUTA NO3 2.1871E+04 1.4890E+03 0.0 300.0
SACET OH 8.2015E+03 -3.1099E+02 0.0 300.0
SACET NO3 2.0689E+03 1.8599E+03 0.0 300.0
SFORM OH 1.6699E+03 -6.4815E+02 2.0 300.0
SFORM NO3 4.1377E+03 2.5161E+03 0.0 300.0CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-4 tracers to be included in each analysis. The user is able to easily alter or expand the compounds
as needed.

For gas species, the required deposition parameters are the Henry’s Law constant and a diffusivity parameter (“Diffrat”), which is defined by the ratio their molecular weights to that of

water, O H g M M

2 / . The deposition calculation for gases that react in plant tissue also needs a
reactivity parameter that describes whether a speci es reacts when dissolved inside leaf tissues (Wesely, 1989). This parameter is intended for mod eling the deposition of reactive species, such as ozone, and should be set to zero for air toxics. The deposition calculation for gasses also uses an “Rscale” factor to adjust the surface resistance . This is used to set the surface resistance to zero for strong acids (e.g., HNO 3 ) and should be set to 1.0 for modeling air toxics.

The deposition calculation for particles requires t he particle density and size associated with each species. The particle size is calculated as the ge ometric mean of the lower and upper cut points (see Figure 10-1). If possible, the particle size and density should be based on the measured size of particles associated with each RTRAC species (e. g., for a compound associated with soot particles, use a density and size representing the soot).

The emission rates for the RTRAC species are provid ed by an extra set of emission files (surface and/or point source). Emissions of gases are in mo les per time period (normally moles/hour), whereas particles are in micro-grams per time perio d. The emissions file format is the same as for a regular CAMx emissions file (i.e., the UAM-IV format), as described in Section 5.

RTRAC Gas-Phase Chemistry

The RTRAC chemistry calculations use a special chem istry module. Chemistry may be modeled for primary and secondary gas species, meaning that tracers can be formed from the decay of primary tracers or from the decay of host model spe cies (e.g., secondary formaldehyde). The chemical decay of gaseous tracers can account for t hermal reactions with ozone (O 3 ), hydroxyl radical (OH) and nitrate radical (NO 3 ), as well as photolysis. The algorithms are coded so that all chemical decay pathways are zero by default and only become non-zero if decay rates are explicitly specified in the input file (see Figure 10-1). The example RTRAC chemistry input file in Figure 10-1 shows how thermal reactions are spec ified by naming the tracer and oxidant, and providing reaction rate parameters. Note that the RTRAC chemical reaction rates depend on the rates and parameters provided in the RTRAC input fi le, and not the rates in the host model chemical mechanism; however the host model does pro vide the oxidizing species concentrations (i.e., O 3 , OH, and NO 3 ).

Thermal Reactions

Thermal reactions with oxidants are modeled as seco nd order reactions:

where R is the decay rate and the rate constant k is defined using the generalized temperature dependent rate expression: 

## [ ] [ ] oxidant tracer k R =CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-5 The Arrhenius factor (

A ) must be in units (ppm -1 min -1 ), the activation energy ( E
a ) must be Kelvin
and B is dimensionless. This is the same as expression 3 in Table 5-4a. Oxidant concentrations for the decay calculation are obtained from the CAM x photochemical simulation for each grid cell at each time step. RTRAC can be used with eit her the CB4, CB05, or SAPRC99 chemical
mechanisms that are available in the current versio n of CAMx (see Section 3). Choosing
between the core mechanisms will influence the RTRA C chemical decay rates by changing the
oxidant concentrations in the host model.

Photolysis

Photolysis reactions are specified by naming the tr acer undergoing photolysis and providing a ratio of the tracer photolysis rate to one of the p hotolysis reactions in the host photochemical mechanism (i.e., CB4, CB05 or SAPRC99). For exampl e, Figure 10-1 shows that there are both primary and secondary acetaldehyde reactive tracers (PACET and SACET) and the photolysis rate for both species is set equal to reaction 45. Reaction 45 in the CB4 mechanism is photolysis of ALD2, which is based on acetaldehyde. Figure 10 -1 also shows that there are two types of formaldehyde in the MATES toxics modeling (PFORM an d SFORM). Modeling the photolysis of formaldehyde with RTRAC is complicated by the fa ct that the CB4 mechanism includes two photolysis reactions for formaldehyde (reactions 38 and 39). The solution shown in Figure 10-1 is to model formaldehyde photolysis as 1.6 times th e rate of CB4 reaction 39. The CAMx host
mechanisms are discussed in Section 3 and are defin ed by the ASCII chemistry parameters files and mechanism listings distributed with CAMx and av ailable from the CAMx web page (www.camx.com).

Secondary Species

RTRAC allows for formation of secondary/daughter pr oducts related to the chemical decay of one of the primary tracers. Secondary species can also be subject to chemical decay, just like primary species, if the user desires. Therefore, t he RTRAC chemistry module allows decay reactions (thermal and photolysis) to be specified for secondary species using the same method as for primary species. In this manner, concentrat ions of secondary species are determined by the balance between chemical production and destruc tion. RTRAC requires that any secondary daughter tracers must be specified after their pare nt tracer in the chemistry parameters input file.

RTRAC also allows tracers that track the secondary formation of any species that is included in the host chemical mechanism (CB4, CB05, or SAPRC99) . For example, in Figure 10-1 the species SFORM is used to track secondary formaldehy de, and so SFORM is defined as a secondary species and identified with the host spec ies FORM. This means that the RTRAC chemistry module will identify the chemical product ion of FORM in each grid cell at each time step, and add this chemical production to the SFORM tracer. Since SFORM is intended to track only secondary formaldehyde, no primary emissions s hould be included for SFORM. 

###      

−      

= T

E T A k a B


### exp 300CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-6 Example for Defining Chemical Rate Data

Reaction rate data for many gases can be found in r ate constant compilations such as IUPAC (2001), JPL (2001) and Calvert et al. (2000, 2002). For the examples shown in Figures 10-1 and 10-2, rate constants were obtained from a study by Harley and Cass (1995) that modeled detailed VOCs for Los Angeles. Photolysis rates were calcul ated using the Tropospheric Ultra-violet Visible (TUV) model (Madronich, 2002), which is ava ilable as a CAMx pre-processor. TUV includes photolysis data (cross-sections and quantu m yields) for many species, and data for other species are included in IUPAC (2001), JPL (2001) an d Calvert et al. (2002).

Chemical Decay Rates for Near-Source Modeling

The RTRAC algorithm can output hourly chemical deca y rates at user-specified locations for input to an external point-source dispersion model (e.g., a Gaussian plume/puff model) for subsequent sub-grid scale “fenceline” modeling. Th e objective is to allow the grid model (CAMx) to provide local decay rate information to a ny plume-scale model of the user’s choice.

If subgrid-scale near-source modeling is to be cond ucted using an external model, then the user provides the locations of each of the target emissi on complexes using the CAMx Probing Tools receptor file input format. Figure 10-3 displays a n example RTRAC receptor input file for the five point source complexes modeled by ENVIRON (200 2) and Morris et al. (2003). At each grid cell, hourly decay rates for each RTRAC compou nd and every vertical layer are output and can then be interfaced with the user-selected plume model.

Figure 10-3 . Example RTRAC receptor input file identifying th e grid cells with locations of point source complexes where hourly decay rates will be o utput for subgrid-scale point source modeling (see format for OSAT receptor file in Tabl e 6-2).

RTRAC Source-Receptor Relationships and Double Coun ting

Separate families of reactive tracer compounds can be simulated by providing separate emission inputs similarly to OSAT/PSAT (see Section 6). Tra cking separate families of RTRAC tracers allows for source apportionment and can be used to avoid double counting when an external plume model is used to obtain near-source impacts. For example, in the RTRAC air toxics example application discussed previously (ENVIRON, 2002; Morris et al., 2003), separate air toxic emission inputs were provided to source appor tion on-road mobile sources, area plus nonroad sources, point sources, and secondarily formed species. Furthermore, separate families of air toxic tracers were also specified for each poin t source complex to be modeled by the external SINGLE CELL Test Cell 1 42 44

SINGLE CELL Test Cell 1 41 36
SINGLE CELL Test Cell 1 39 36
SINGLE CELL Test Cell 1 50 43
SINGLE CELL Test Cell 1 34 48CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-7 plume model, so that total concentrations could inc lude the local point source impacts (plume model) plus the regional contributions from all oth er sources (CAMx RTRAC).

10.2 DESCRIPTION OF RTCMC

Like RTRAC, the purpose of RTCMC is to add tracer s pecies to a CAMx “core model” simulation and have the tracers undergo chemical ch anges that depend, in part, upon the evolution of CAMx core model species. The RTCMC ap proach differs from the original RTRAC approach by allowing arbitrarily complex chem ical reaction schemes, but it is exactly like RTRAC in every other respect. The current imp lementation of RTCMC is for gas-phase reactions, i.e., gas-phase tracers reacting with ea ch other and/or gas-phase host model species. The core model’s photochemical mechanisms (CB4, CB0 5, and SAPRC99) remain intact and separate from the reactive tracer chemistry.

RTCMC Gas-Phase Chemistry

The RTCMC allows users to input, in a text-based (A SCII) format, a set of chemical reactions (mechanism) for certain target species to be treate d by the CAMx Reactive Tracer Probing Tool. RTCMC is an extension of the original RTRAC algorit hm that reads (and solves) a completely independent, user-defined chemical mechanism for re active tracers that can utilize concentrations of any photochemical species from th e core model mechanism. Upon startup, RTCMC compiles information on the chemical mechanis m and configures the reactive tracer chemistry solver. During the model simulation, the RTCMC chemistry solver receives ambient pollutant information from the core photochemical m echanism and uses this to calculate the evolution of RTRAC species.

The format of the RTCMC input file is essentially t he same as the “IMC” input file format of the SCICHEM Lagrangian puff model (EPRI, 2000). An exa mple IMC format file is shown in Figure 10-4. There are four sections in an IMC fil e that are identified by a keyword at the start of each section, as follows:

#Control Configuration information identified by keywords #Species Names of chemical species and associated data
#Table Photolysis rate data for any photolytic reactions #Equations Chemical reactions and thermal rate constants

The IMC file uses space-delimited ASCII format. Le ading white space at the start of any line will be ignored. CAMx reads the IMC file as case insensitive

.

The hash symbol ( # ) before each section keyword marks the start of a section and should be reserved for this purpose. The four sections shoul d appear in the order shown above. The only section that may be unnecessary in some cases (i.e. , if there are no photolytic reactions) is the
#Table section and guidance on handling this case is prov ided below.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-8

Figure 10-4. Example RTCMC chemistry input file

.

The Control Section

According to the SCICHEM documentation, the #Control section of the IMC file must always have at least three lines, as follows:

#CONTROL

&CONTROL

&END

The first line is the keyword identifying the contr ol section. The second and third lines are additional keywords denoting the start and end of t he control section. Note, however, that CAMx ignores the &CONTROL and &END lines.

One or more control options may be specified using keywords, inserted one per line, ahead of the closing &END line, like this:

#CONTROL

&CONTROL Keyword = ‘option’ Keyword = ‘option’

&END

The keywords used by CAMx and SCICHEM are listed in Table 10-1 and are case insensitive. Not all keywords are used by both models, and CAMx will ignore any non-recognized keyword.

A “ = ” symbol must separate each keyword and option. Th e option must be enclosed within single quotes. In practice, only the first six let ters of each keyword and the first three letters of each option are considered and you may abbreviate a ccordingly (i.e., keywor = ‘opt’ ). #Control rate_species_units = 'ppm' rate_time_units = 'min'

solver = 'dlsode'
Jacobian = 'numeric' #Species, Type, Ambient, Tolerance, deposition vel, wet scav
O3 A 1.0 1.0E-12 0.0 0.0
OH A 1.0 1.0E-12 0.0 0.0
ATRAC F 1.0 1.0E-12 0.010 0.0

BTRAC F 1.0 1.0E-12 0.001 0.0
CTRAC F 1.0 1.0E-12 0.020 0.0

DTRAC F 1.0 1.0E-12 0.001 0.0
ETRAC F 1.0 1.0E-12 0.030 0.0
FTRAC F 1.0 1.0E-12 0.001 0.0 #Table

0 0. 15. 30. 45. 60. 75.

80. 86. 87. 88.
1 4.1590E-04 4.0600E-04 3.7540E-04 3.27E-04 2.604 0E-04 9.4990E-05
2.9930E-05 4.8590E-06 8.3030E-08 1.0000E-09 #Equations 1 [ATRAC] -> (2.0)[BTRAC] ; 0 0.000E -00 2 (1.5)[CTRAC] + [OH] -> (0.5)[DTRAC] ; 1 4.2000 E+04 3 [ETRAC] + [O3] -> [FTRAC] ; 1 1.8000 E-02CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-9 Table 10-1 . Keywords, options and default values for the Con trol section of the IMC file.

Keyword Used by SCICHEM Used by

CAMx Options Allowed by CAMx

Ambient file ● n/a Species_units ● n/a Emission_units ● n/a

Rate_species_units ● ● molecules/cm3 ( default )

ppm

Rate_time_units ● ● seconds ( default )
minutes hours

Solver ● DLSODE ( default ) SLSODE
Rosenbrock

Rtol ● ● Real number ( default = 1.0E-5 )

Atol ● Real number ( default = 1.0E-18 )

Jacobian ● Numeric ( default ) Algebraic

All CAMx recognized keywords have a default option that will be used if the keyword is omitted, meaning that the CAMx RTCMC may be run wit hout specifying any keywords provided that that all other input data (e.g., rate constants) are consistent with the defaults. The allowed keyword options in Table 10-1 are discussed below:

Rate_species_units
The concentration units for thermal rate constant e xpressions.

Rate_time_units The time units for photolysis and thermal rate cons tant expressions.

Solver The name of the numerical integrator to be used as the chemistry solver. SLSODE and DLSODE are, respectively, the single and double pre cision versions of the Livermore Solver for Ordinary Differential Equations (Hindmar sh 1980). The Rosenbrock solver is the double precision RODAS solver (Hairer and Wanne r, 1991).

Rtol The relative error tolerance (convergence criterion ) employed for all chemical species by the chemistry solver.

Atol The absolute error tolerance (convergence criterion ) employed for all chemical species by the chemistry solver. CAMx does not use species-sp ecific Atol values that may appear in the species section, as discussed below.

Jacobian The chemistry solvers employ a Jacobian matrix of f irst-order derivatives of each chemical species with respect to all species. The Jacobian matrix is constructed automatically by the RTCMC. This option controls w hether the Jacobian is constructed algebraically or numerically. Both options may be used with the double precision solvers and numeric may be more efficient. The algebraic o ption is strongly recommended forCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-10 the single precision SLSODE solver (because single precision may be inadequate for constructing a numeric Jacobian by finite differenc e).

Concentration Units

CAMx does not use the Species_units or Emission_units keywords and will ignore them if they are present. CAMx will output RTCMC s pecies average concentrations in ppm units. Emissions of RTCMC species must be provided in moles/hour.

Setting Error Tolerances

All three RTCMC chemistry solvers use the Rtol and Atol parameters specified in the control section to manage errors in predicted concentration s. CAMx does not use the species-specific

Atol values that may appear in the species section beca use it is difficult to select reliable Atol values for each species. The error ( err ) in the predicted concentration ( con ) for species i should be roughly less than:

err(i) = rtol × con(i) + atol

The combined Rtol and Atol determine accuracy. Setting Atol to zero will result in pure
relative error control. Relative error control has the advantage of being easily understood (the errors should be smaller than X percent) but suffer s the disadvantage of excessive computational resources that may be expended to manage errors in species concentrations that are essentially
zero. Note that RTCMC sets a concentration floor o f 1.0E-16 ppm.

The default settings for Rtol and Atol listed in Table 10-1 should be generally applicabl e because they are conservative and effectively resul t in pure relative error control. We recommend against setting Rtol greater than 0.001. Appropriate settings for Atol depend upon the magnitude of concentration predictions and the need for accurate predictions in high vs. low concentration areas (e.g., plume centerline vs. out of plume).

Do not request infeasible accuracy from single prec ision SLSODE by setting Rtol and Atol smaller than about 1.0E-7.

The Species Section

The species section of the IMC file lists chemical species and associated data. All chemical species referred to in the equation section must ap pear in the species section. Extra species may appear in the species section, but including numero us extra species may cause a run-time error by exceeding the memory available for storing speci es information (if this happens, delete some of the unused species from the species section).

The first line is the keyword identifying the speci es section. The following information must be provided for each listed species:CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-11 Name Species names may be up to 8 characters and must st art with a letter. They are case insensitive. Accurate names are important because other CAMx input data (e.g., emissions, boundary conditions) will be matched to RTRAC species by name.
Type There are four permissible species types identified by first letter: Ambient ( A ), Fast ( F ), Slow ( S ), and Equilibrium ( E ). Setting the species type is discussed in more d etail
below.

Ambient The ambient value is not used by CAMx, but is used by SCICHEM. Provide a real number (e.g., 0.0).

Atol Species specific error tolerances are not used by C AMx, but are used by SCICHEM. Provide a real number (e.g., 0.0).
Dep The species deposition velocity in m/s. This depos ition velocity will be used for all land surface types, which is a simplification compared t o the CAMx dry deposition scheme. Provide a real number (e.g., 0.0).

Wet Scav The wet scavenging coefficient is not used by CAMx. For SCICHEM, this is a washout
ratio. CAMx does not use the washout ratio because this approach is incompatible with the CAMx wet deposition algorithms. Provide a real number (e.g., 0.0).

MW The molecular weight is not needed by CAMx. Provid e a real number (e.g., 1.0).

Choosing the Species Type

The Type for each RTCMC species should be set according to: (a) whether the species
concentration should be obtained from the core mode l or modeled using the RTCMC; and (b) the most accurate and efficient numerical method for pe rforming chemistry within RTCMC.

All species to be obtained from the core model (e.g ., O 3 , OH, NO, NO 2 , H 2 O, M, O 2 ) must be set
to type Ambient . This rule will be enforced by CAMx and, for exam ple, the species O3 must be set to type A , because it is part of all the core chemical mecha nisms (CB4, CB05, SAPRC99).

Species that are solved by the RTCMC may be type F

, S or E . The recommended default type
is F (fast) in which case chemistry will be performed u sing the selected chemistry solver (e.g., DLSODE). Species that undergo slow chemical change (lifetime of hours or longer) may be set
to type S (slow) with potential gain in efficiency but some loss in accuracy. Species that undergo extremely rapid chemical change (lifetime s maller than a second) may be set to type E (equilibrium) and solved using a steady-state appro ximation with some gain in efficiency but some loss in accuracy. The Rosenbrock solver does not work well with species types S or E

. Equilibrium species may be used effectively with th e single precision SLSODE solver to avoid the need for double precision. You should use type s S or E with caution and evaluate both computational speed and concentration accuracy by c omparing against results with using type F

.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-12 The Table Section

The table section of the IMC file provides photolys is rates for any photolytic reactions in the
RTCMC mechanism. It must contain at least two line s:

#Table 0 zenith1, zenith2, zenith3, ...

The first line is the keyword identifying the table section. The second line must begin with 0 (zero) followed by a list of space-delimited zenith angles (in degrees) starting with zero degrees and ascending to the largest angle. If the largest zenith angle specified is less than 90 degrees a value of 90 degrees is implicitly added to the list . By default, up to 15 zenith angles are allowed (this may be changed as described under adjustable parameters, below). If the final zenith angle is not 90 degrees, no more than 14 angles should be listed to allow the 15 th angle to be implicitly set to 90 degrees.

If the reaction mechanism has no photolytic reactio ns, include just the first two lines (keyword followed by zenith angles) in the table section. I f the reaction mechanism includes photolytic reactions, list them one reaction per space-delimit ed line after the zenith angles:

reaction_ID, rate1, rate2, rate3, ...

The r eaction_ID must be the integer identification number of the p hotolytic reaction followed by the photolysis rate at each zenith angl e, from zero to the largest angle. Photolysis
reactions are first order and have rate units of re ciprocal time to be provided in the Rate_time_units specified in the control section of the IMC file. A photolysis rate of zero is implicitly assumed at 90 degrees unless you spec ify otherwise.

The Equations Section

The equations section of the IMC file lists the che mical reactions and rate constants for the
RTCMC reaction mechanism and must contain at least two lines:

#Equations reaction_ID [Reactants] > (Stoichiometry) [Products ] ; Rate_Constant

The first line is the keyword identifying the equat ions section and must be followed by at least
one reaction line. Reaction lines list reactions a nd rate constants and are delimited by white space and separators. The reaction_ID and the Reactants must be separated by white space. The Reactants and Products must be separated by a right arrow symbol (the rig ht arrow may be preceded by characters, e.g., => or −> ). The Products and the Rate_Constant must be separated by a semi-colon.

The reaction_ID must be an integer value that uniquely identifies each reaction. Reactions
identifiers need not be in order or continuous.CAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-13 The name of Reactants and Products must be enclosed within square brackets, begin wit h a letter, and not exceed 8 characters in length. A ll species names used in the equations section must also appear in the species section. Zero to t hree reactants are allowed. Zero to 20 products are allowed (the maximum is a user adjustable param eter). Reactant and product names may be preceded by a stoichiometric coefficient enclosed w ithin round brackets. If the stoichiometric coefficient is omitted it is assumed to be unity.

Rate constants are specified using SCICHEM conventi ons and must be in the units specified by the keywords Rate_species_units and Rate_time_units in the control section (the
defaults are molecules cm -3 and seconds, respectively). SCICHEM supports nume rous rate constant expression types and CAMx should interpret all of them correctly, although not all have been thoroughly tested. Table 10-2 defines a sub-s et of the SCICHEM rate constant expression types that are recommended for use with CAMx. The format for specifying rate expressions is the integer expression type followed by a list of t he numerical values required by that expression type. It is important that rate expressions are de fined in units that are consistent with the reactio n order, and Table 10-3 defines how the reaction orde r and rate constant unit dimensions may be
determined.

The CAMx output “diag” file lists diagnostic inform ation on the mechanism and rate constant expressions read by from the IMC file. You should review this diagnostic output to ensure that CAMx correctly read and configured the RTCMC chemis try mechanism.

10.3 REACTIVE TRACERS IN IRON PiG

RTRAC/RTCMC calculations for emissions and chemistr y have been integrated into the IRON PiG algorithms. There are two ways in which RTRAC tracers may enter a PiG plume: as primary emissions from specifically flagged sources within the RTRAC point source file, or by formation of secondary species from decay of primar y plume emissions. There is no entrainment of tracers from the grid to the plume as this is li kely to result in negative tracer concentrations, especially if the entrained tracer is a secondary p roduct of a host model species (e.g., secondary formaldehyde). Tracers are assumed to have negligi ble impact on PiG puff chemistry or oxidant levels. If the tracer concentration in the plume i s high enough to enhance or suppress the plume oxidant levels, then the photochemical impacts of t he tracer can be accounted for by separately adding the tracer emissions into the host model lum ped emissions; e.g., for tracing high concentrations of propene and butene in a plume, on e would track the propene/butene concentrations using RTRAC tracers but also add CB4 -OLE or SAPRC99-OLE1 emissions to the plume to account for the oxidant impacts.

RTRAC checks to ensure that it is reading its own i nput point source file. RTRAC and host model point source files must have the same number of sources in the same order; however, the list of species on each file may be different, and the sources flagged to receive the PiG treatment may vary. A pre-processor program was coded to hel p prepare consistent RTRAC and host model point source files.CAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-14 Table 10-2a . Recommended SCICHEM rate constant expression typ es for use in CAMx. Expression Type Description Expression

0 Photolysis k = 0

1 Constant 0 

### k k =

2 General temperature

dependence 

### ( ) T B c 

### e T A k /

− 

### =

3 Troe-type temperature and

pressure dependence G F k M k M k k 

  

   

+ = ∞

/] [ 1 ] [

0 0

B 

### T A k = 0

D 

### T C k = ∞

6.0 = F 

# ( ) 

# [ ] 1

2 0 /] [ log 1 −

∞      

+ = k

M k G

8 Equilibrium with a previously

defined reaction ( k ref ) 

### ( ) T B ref e A k k /

=

13 Lindemann - Hinshelwood

as used for OH + HNO3 2 3 3 0 /} [ 1 ] [

k M k M k k k

+ + =


### ( )

T B e A k / 0 = 

### ( )

T D 

### e C k / 2 

### = 

### ( )

T F e E k / 3 =

7 Simple pressure

dependence used for OH + CO 

## ( ) P k k 6.0 1 0 

### + =CAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-15 Table 10-2b. Parameters required by SCICHEM rate constant expr ession types. Expression Type Parameters

1 2 3 4 5 6

0 0

1 k o

2 A B C

3 A B C D

8 k ref A B

13 A B C D E F

7 k o

Table 10-3 . Determining the reaction order and consequent un it dimensions for rate constants.

Number of
Reactants Reaction Order Concentration Unit
Dimension Time Unit
Dimension

0 Zero None Time - 1

1 First None Time - 1

2 Second Concentration - 1 Time - 1

3 Third Concentration - 2 Time - 1

Tracers released from PiG sources decay according t o the oxidant and photolytic environment of the plume using user-supplied chemical rate paramet ers (as described earlier). Oxidant concentrations for the decay calculation are obtain ed from the CAMx PiG incremental photochemical simulation for each puff at each time step. RTRAC tracers in each puff reactor are updated based on the total oxidant concentrations for the reactor, i.e., puff increment plus puff ambient/background. RTRAC enforces a rule tha t no secondary tracer formation from the decay of host model species are allowed if IRON PiG is active (e.g., no secondary formaldehyde tracer formation is allowed with IRON PiG). Second ary tracer production from primary tracer decay is allowed.

Tracers are transferred from the PiG to the grid us ing the same approach as for any other host model species (see Section 4). Tracer concentratio ns at any point are the superposition of the grid concentration plus any collocated PiG puffs.

Integration of RTRAC Puff Sampling

RTRAC optionally employs surface-layer IRON puff sa mpling of tracers on a user-defined sampling grid (see Section 4). Sampling grids are entirely passive, and intended to provide a display of the reactive tracer plume concentrations at scales much smaller than typically used for the finest computational grids (i.e., <1 km).CAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-16 Figure 10-5. Example RTRAC tracer plume emanating from a large point source and displayed on a surface sampling grid with 1-km grid spacing. The figure illustrates the entire extent of the sampling grid.

Figure 10-5 shows an example plume of hypothetical tracer “TRAC1” emitted from a point source in the center of the image as displayed on a 1-km sampling grid. The finest core model computational mesh contains 4-km grid spacing and c overs a much broader area than shown in the figure.

10.4 RUNNING CAM X WITH REACTIVE TRACERS

CAMx Control File

RTRAC is invoked similarly to the other Probing Too ls within the CAMx control file. In the &CAMx_Control namelist module, the variable Probing_Tool must be set to “ RTRAC ” or

“ RTCMC ”. An additional namelist module called &RT_Control must then be provided in the control file to configure the RTRAC portion of the model. The additional namelist module is
described below. The order of the variables follows the template available with the source code. Figure 10-6 provides an example of the RTRAC contro l module.CAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-17

Description of RTRAC Control in the CAMx Run Contro l File

&RT_Control Label for the Probing Tool namelist module that configures the RTRAC option; it must begin in column 2

& Flag ending a namelist; it must be in column 2

RT_File_Root Character root output path/filename

RT_Initial_Conditions Character input master grid RTRAC initial conditio ns path/filename (optional, ignored if Restart=TRUE )

RT_Boundary_Conditions Character input master grid RTRAC boundary conditi ons path/filename (optional)

RT_Master_Restart Character input master grid RTRAC restart path/fil ename (ignored if Restart=FALSE )

RT_Nested_Restart Character input nested grid RTRAC restart path/fil ename (ignored if Restart=FALSE or Number_of_Grids=1 )

RT_Chemistry_Parameters Character input RTRAC chemistry parameters path/fi lename, or RTCMC IMC chemistry definition path/filename

RT_Receptor_Definitions Character input RTRAC receptor definition path/fil ename (optional)

RT_Point_Sources Character input RTRAC elevated point source emissi ons path/filename (optional, ignored if Point_Emissions=FALSE )

RT_Emiss_Grid Character array (by CAMx grid) input RTRAC gridded emissions path/filename (optional, ignored if Gridded_Emissions=FALSE )

RT_PiG_Sample Logical sampling grid flag for RTRAC IRON PiG outp ut; sampling grids are defined in the main &CAMx_Control namelist (TRUE=sampling grid output will be generat ed, FALSE=sampling grid output will not be generated)CAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-18 Figure 10-6. Example input of RTRAC options and filenames with in the CAMx control file

.

As with the output for the host model and other Pro bing Tools, a “root” file name is specified and suffixes are added depending upon the type of o utput produced. A separate root name for RTRAC (and other Probing Tools) allows the user to direct the output to a completely different path. RTRAC writes several output files that are i n Fortran binary format, as described in Section 5. These include the master and nested gri d tracer instantaneous concentration files ( .rt.inst and .rt.finst ), the grid-specific surface tracer average concent ration file ( .rt.surf.grd nn ). These files are written in the same format as f or the regular model species described in Section 5.

The “ RT_Chemistry_Parameter ” namelist variable specifies the path/filename of either the RTRAC chemistry parameters file or the RTCMC IM C chemistry definition file. The choice of which type of file format is read is set accordi ng the main “ Probing_Tool ” variable (i.e.,

RTRAC or RTCMC ).

RTRAC/IRON PiG sampling grids are invoked in the RT RAC namelist by setting a logical flag. If set to TRUE, the user must provide the number of sampling grids and the grid parameters of
each in the main &CAMx_Control namelist. Sampling grids are set identically to t he way nested grids are specified for the host model, with one exception: there are no vertical levels to define (sampling grids are currently only 2-D surfa ce fields). The same rules that apply for the specification of nested grids holds for the specifi cation of all sampling grids (see Sections 2, 4, and 5 of the User’s Guide). The “mesh factor” sets the resolution or cell size of the sampling grid relative to the master grid. The CAMx diagnos tic output file provides information on the location and size of each sampling grid to help ens ure proper setup. &RT_Control

RT_File_Root = 'CAMx4.test.020614',

RT_Initial_Conditions = ' ', RT_Boundary_Conditions = ' ', RT_Master_Restart = 'CAMx4.test.020613.rt.in st', RT_Nested_Restart = 'CAMx4.test.020613.rt.fi nst',

RT_Chemistry_Parameters = 'CAMx4.chemparam.rtrac_t est', RT_Receptor_Definitions = 'receptor.rtrac.test', RT_Point_Sources = 'pt.rtrac.test', RT_Emiss_Grid(1) = 'emiss.rtrac.36km', RT_Emiss_Grid(2) = 'emiss.rtrac.12km', RT_Emiss_Grid(3) = 'emiss.rtrac.04km',

RT_PiG_Sample = .true.,

&CAMx v5.30 User’s Guide, December 2010

Reactive Tracers

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_10_RTRAC.do cx 10-19 User Adjustable Parameters

Once the RTRAC/RTCMC chemistry parameters/definitio n file is established, the user should be sure that a sufficient allocation of memory is prov ided for this Probing Tool. This is done by examining the main Probing Tool parameter and commo n block file in /Includes/camx.prm . The parameter MXTRSP should be set to the total number of species defined in the chemistry parameters file. If sampl ing grids are to be used, the user should ensure that sufficient memory is available to define the s ize of sampling grid arrays. This is set in /Includes/camx.prm

.

User adjustable parameters for RTCMC are set in the CAMx include file
/Includes/rtcmcchm.inc . If an error is encountered at model start up bec ause one of these RTCMC parameter has been exceeded, consult th e list of parameters in Table 10-4 and then change the parameter appropriately in the rtcmcchm.inc include file. Rebuild the CAMx executable (we recommend performing a “ make clean ” before making a new CAMx executable) after changing any RTCMC parameter.

Table 10-4 . RTCMC parameters default settings in the Includes/rtcmcchm.inc include
file.
Name Description Default

MXRX maximum number of RTCMC reactions 20 MXPHOT maximum number of photolysis reactions 10 MXZEN maximum number of photolysis reaction zeni th angles 15
MXRCT Maximum number of reactants in each reaction 3 MXPRD maximum number of products in each reaction 20 MXEQM maximum number of equilibrium species 5 MXSLO maximum number of slow species 25CAMx v5.30 User’s Guide, December 2010

CAMx Modeling System

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-1 11. THE CAMx MODELING SYSTEM

CAMx comprises the core component of an overall air quality modeling system, as illustrated in Figure 11-1. CAMx inputs are developed using indep endent third-party models and processing tools that characterize meteorology, emissions, and various other environmental conditions (land cover, radiative/photolysis properties, and initial /boundary conditions). Interface programs are needed to translate the products of each of these m odels/processors into the specific input fields and formats required by CAMx. After the air qualit y simulation is completed, additional programs are used to post-process the concentration fields, develop model performance statistics and measures, manipulate Probing Tool output into v arious reportable formats, and further translate raw results into forms necessary for regu latory purposes. While third-party visualization software, meteorological models, and emission processors are not distributed with CAMx, ENVIRON does provide many of the necessary in terface programs and post-processors on the CAMx web site ( www.camx.com ). A brief description of each of these is provide d at the
end of this section.

Figure 11-1. **Schematic diagram of the CAMx modeling system. S ee Table 5-1 for a detailed**  **list of specific model input requirements for the f ive major data classes shown at the top of the**  **figure. Certain pre- and post-processor programs s hown in the figure are described in this**  **section. Third-party models, processors, and visua lization software are not described in this**  **User’s Guide and are not distributed with CAMx. Data** 

**Models & Pre-processors** 

**Interface Programs** 

**Core Model**

**Post-processors**

**File Formatting Regulatory**  **Probing Tools Performance**  **Emissions Air Quality**  **Geographic Photolysis**  **Meteorology**

**SMOKE MM5** 

**TUV**

CAMx **EPS3**

**CONCEPT**

**EMS-95 RAMS** 

**WRF**

**PiGSET, WINDOW,** 
**PREPBIOE MM5CAMx,** 

**RAMSCAMx,**
**WRFCAMx Ozone Column,** 

**Haze Opacity**

**Landcover maps Measurements**  **Emission Inventory Analyses,**  **Measurements, Topographic Data** 

**ICBCPREP**

**BNDEXT AHOMAP** 

**CAMxTRCT**
**CAMx2IOAPI AVGDIF** 

**CAMxPOST**
**EPASTAT MATS**  **PA Tools**
**OSAT Browser Drought Index maps,**  **Snow Cover maps**

**GEOSCHEM,**
**MOZART**

**Global Model Interface Programs** CAMx v5.30 User’s Guide, December 2010

CAMx Modeling System

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-2 11.1 CAMx PROGRAM STRUCTURE

The core CAMx model is written in Fortran 90, but i ncludes some utilities written in C to interface with MPI and HDF5. The program is highly modular to allow for easy substitution of alternate routines for specific processes. Subrout ines are well documented to include such information as the tasks performed, the input/outpu t arguments, a history of modifications, etc. The source code is arranged in several directories, grouped according to function. The main source directory contains release notes, the licens e, the “Makefile” compile utility, and a control file namelist template, while sub-directories conta in core model and ancillary routines according to the following:

CAMx/ Source code for the main driver routine CAMx.f and core model routines.

CF_AERO/ Source code for inorganic aerosol chemistry (aqueo us and thermodynamic

partitioning) for the 2-mode CF scheme.

CMC/ Source code for the gas-phase chemical mechanism routines.

CMU_AERO/ Source code for inorganic aerosol chemistry (aqueo us and thermodynamic

partitioning) for the multiple size CMU scheme.

DDM/ Source code for the DDM Probing Tool, consisting of I/O and core

routines that are unique to DDM/HDDM.

HG/ Source code for the mercury chemistry routines.

Includes/ F90 “include” files, consisting of program paramet ers and memory

common blocks.

IO_bin/ Source code for Fortran binary (unformatted) I/O.

IO_hdf/ Source code for HDF5 output format.

Mod_src/ Source code for core model F90 modules.

MPI/ Source code for routines specific to the MPI impl ementation.

OSAT/ Source code for the OSAT/PSAT Probing Tools, consi sting of I/O and

core routines that are unique to OSAT and PSAT.

PA/ Source code for the Process Analysis Probing Tool , consisting of I/O and

core routines that are unique to PA.

PiG/ Source code for the Plume-in-Grid sub-model, cons isting of I/O and core

routines that are unique to PiG.

RTRAC/ Source code for the Reactive Tracer Probing Tool, consisting of I/O and

core routines that are unique to RTRAC/RTCMC.CAMx v5.30 User’s Guide, December 2010

CAMx Modeling System

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-3 SOAP/ Source code for secondary organic aerosol thermodyn amic partitioning.

Memory Management

All of the model’s global data structures are dynam ically allocated during the model setup. This alleviates the need to customize the CAMx parameter s file ( Includes/camx.prm ) for a particular application as required in previous vers ions of CAMx. The data necessary to allocate memory space for a given grid configuration are rea d from the CAMx control file (see Section 5.1). However, we discovered in our performance co mparisons that certain fortran compilers exhibit a significant speed/performance slowdown wh en local data array structures within subroutines are dynamically allocated, which is as a result of considerable overhead in allocating and de-allocating these local arrays each time the subroutine is called. To alleviate this compiler dependence on speed performance, CAMx continues to utilize some basic parameters to statically allocate local arrays in low-level subro utines. All of these parameters are defined in
the camx.prm file. The distribution version of this “include” file sets key array parameters to default values that should be sufficiently large to accommodate most applications (see Table 11- 1 for a description of parameters and their default values). However, you may want to customize these values to ensure that they are large enough t o accommodate all of your model configurations, or to exactly match your specific a pplication, thus preventing wasted memory. If any parameter is set to a value that is too smal l to support your application the model will stop, displaying an informative error message.

Table 11-1. **Parameters and their defaults in** camx.prm **used to statically dimension local arrays in low-level subroutines.** 

Parameter Name Description Default
Value

MXCELLS **Number of cells in X/Y direction for any grid 200**

MXLAYER **Number of layers 28** 

MXSPEC **Number of species (could be number of radicals, num ber of**  **input species, or number of model species) 125**

MXREACT **Number of reactions (depends on the mechanism; see the**  **user's guide for the value for each mechanism) 217**

MXGRID **Number of grids 10** 

MXPTSRC **Number of point sources 100000** 

MXTRSP **Number of Probing Tool tracer species 1**

MXPIG **Number of PiG puffs 50000** 

MXSAMPLE **Number of PiG sampling grids 1** 

MXCOLSMP **Number of PiG sampling grid columns 1**

MXROWSMP **Number of PiG sampling grid rows 1**

To conserve memory, default values of 1 are set for Probing Tool tracers, PiG sampling grids, and sampling grid dimensions. These must be increa sed accordingly if Probing Tools or sampling grids are to be used. All of the paramete rs in the table above can be determined before starting a simulation except for MXPIG . A value of 10,000 is usually sufficient for most applications in which PiG is used; set this paramet er to 1 if PiG is not used to conserve memory. If this parameter is exceeded during a simulation, the model will halt. If this happens, simplyCAMx v5.30 User’s Guide, December 2010

CAMx Modeling System

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-4 increase MXPIG , recompile the model executable, and restart the s imulation. The other parameters in camx.prm will not normally need to be changed and are not d iscussed further.

11.2 COMPILING CAMx

A single “Makefile” script is provided in the main source directory. The Makefile will compile the CAMx source code into an executable program for a number of computer platforms, including: Compaq/DEC, Sun, SGI, HP, IBM, Linux PC (Portland Group and Intel compilers) and Macintosh OSX (Absoft compiler). All gas-phase chemistry mechanisms and solvers are compiled together into the model executable from th e /CMC sub-directory. The choice of which chemistry mechanism and solver to use is selected a t model run-time as defined in the chemistry parameters and run control files, respectively. Si milarly, all OSAT, PSAT, DDM, Process Analysis, and RTRAC source code is compiled into th e model executable from their respective sub-directories. The choice of which (or none) of these “Probing Tools” to use is selected at
model run-time in the run control file.

CAMx is compiled by issuing the following command a t a shell prompt within the main source directory:

make platform

<DOMAIN= domain_name > <MPI=true> <HDF=true>

where the text within the brackets “ <> ” is optional, and platform is one of the following:

dec

sun hp
ibm sgi or sgiomp pg_linux or pg_linuxomp (Portland Group compiler for Linux) i_linux or i_linuxomp (Intel compiler for Linux) ab_linux (Absoft compiler for Macintosh OSX)

These keywords inform the Makefile of the type of m achine being used to compile and run the model, so that the Makefile can invoke the proper c ompiler-specific commands and flags. The

“ omp ” platform options are available for only three com pilers: SGI, Portland Group, and
Intel. If an OMP platform is not specified then CAMx wil l not be able to run with OMP parallelization.

The optional domain_name allows the CAMx executable program to be labeled f or a specific memory configuration as defined within the CAMx par ameters file ( camx.prm ) described above. The Makefile will search for a CAMx paramet ers file in the /Includes sub-directory
called

camx.prm.domain_name

.

If the variable “ DOMAIN ” is not defined on the Makefile command line, the Makefile will compile CAMx using the default parameters file, camx.prm.v5.20 . This feature is retainedCAMx v5.30 User’s Guide, December 2010

CAMx Modeling System

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-5 for legacy purposes. Since it is no longer necessa ry to configure the grid parameters specifically for your modeling domain, the default parameters fi le distributed with the source code should be sufficient for your application. However, you may want to customize some applications, for example to configure Probing Tools, and it is conve nient to be able to distinguish between these
executables.

The optional MPI=true flag will compile CAMx to utilize MPI parallel pro cessing. This requires that the MPICH package has been installed and built on the machine that is running this Makefile script and compiling CAMx. You should che ck that the MPI_INST variable is correctly set to your system's MPICH installation p ath in the standard CAMx Makefile, as well as the Makefile in the MPI/util directory. CAMx su pports both MPICH1 and MPICH2. If the

MPI flag is not set on the command line, the Makefile script will ignore the MPICH library and CAMx will not be able to run with MPI parallelizati on. Currently, the Makefile only supports building an MPI version of CAMx using the Portland Group and Intel Fortran90 compilers.

The optional HDF=true flag will compile CAMx to include HDF5 I/O librari es. This requires
that HDF5 libraries have been installed and built o n the machine that is running this Makefile script and compiling CAMx. You should check that t he variables HDF_LIB and HDF_INC in the Makefile are set correctly. If the HDF flag is not set on the command line, the Makefile script will ignore the HDF libraries and CAMx will not be able to write HDF5 files.

The Makefile will generate a CAMx executable progra m named

CAMx.< domain_name>.MPI_configuration.platform

which will reside in the main source directory. Th e variable MPI_configuration will be set
to either MPI or noMPI , depending on how the MPI flag was set on the Make file command line. For example, a default compilation using the Portla nd Group compiler wil result in an executable
named CAMx.v5.20 .noMPI.pg_linux

. If you need to rebuild CAMx using different Makefile arguments we recommend typing “ make clean ” between builds. Make clean will delete all existing object files forcing a complete re-build.

Notes on Compiling for MPI

Currently, the Makefile provided with the CAMx sour ce code only supports building an MPI version of CAMx using the Portland Group compiler o r the Intel compiler. The steps needed to compile CAMx are as follows:

1. Modify the Makefile and set the variable MPI_INST to the path of the installation of
the mpich package. This is necessary so that the compiler c an find the needed header
files and libraries for MPI.

2. If the model is compiled using the Portland Group P GF90 compiler and will be run on a 64-bit operating system, you will probably need to remove the “ -Bstatic ” option from the compiler flags of the “ pg_linux ” and “ pg_linuxomp ” sections of the

Makefile . We have found that an executable built with MPI on a 32-bit compiler will
fail to run on a 64-bit OS if the -Bstatic option is included. This option will build theCAMx v5.30 User’s Guide, December 2010

CAMx Modeling System

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-6 compiler libraries into the executable, rather than use the default mode of relying on the dynamic libraries installed on the operating system . The -Bstatic option generates a portable executable program. The inconvenience is that excluding the -Bstatic option will create the necessity of having the Portland Gr oup dynamic libraries installed on each computer that will serve as a master or compute nod e in the model application. To remove the static option just delete the -Bstatic from the following lines:

FLGS="-I$(CAMx_INC) –I$(MPI_INC) ... –Bstatic"

3. CAMx supports the use of both OMP and MPI in a s ingle run. To utilize OMP in your MPI application, run the command “ make pg_linuxomp MPI=true ” or “ make i_linuxomp MPI=true ”. In this case, the executable will have the OMP directives for shared-memory parallelization built in.

A Note on Binary Input/Output Files

Large CAMx input and output data fields are contain ed within Fortran “unformatted” (binary)
files. This means that the data are read and writt en as represented in memory, without translation between binary and ASCII character sets as done for “text” files. Binary files reduce file volume and improve program read/write speed, but the user cannot directly view or manually edit them. There are two ways to represent binary information in memory: “big endian” and “little endian.” The difference between these is essentially the ord er of bits in a word, and which order is used depends on the computer chipset. Historically, big endian has been used in many Unix workstations (Sun, SGI, HP, and IBM). The x86 proc essors on personal computer platforms (e.g., Intel and AMD) use little endian, while Powe rPC chips are big endian.

CAMx can be compiled and run on machines that use e ither big or little endian binary representations, as long as the model and all of it s pre- and post-processors are consistently compiled and run on the same type of platform. If any component of the modeling system is compiled on a different platform using the opposite binary representation, I/O files will not be properly read and will likely lead to a program cra sh. A typical run-time error message from trying to read the wrong binary format is “input re cord too long,” so if you get this error message, check for consistency between your binary files and Fortran compiler options.

In practice, users should use the default binary fo rmat (big endian) that is built into the CAMx
Makefile and that is used for the CAMx distribution test case. Compilers for little endian machines (e.g., x86 PC chipsets) provide compile-ti me switches that allow binary files to be read and written as big endian. The Portland Group comp iler option is “ -byteswapio ”, whereas the Intel compiler option is “ -convert big_endian .” The CAMx Makefile supports many platforms and sets compiler flags to consistently u se big endian to maximize platform portability. Therefore, use of the CAMx Makefile on any of the s upported platforms will by default result in the model reading and writing big endian binary fil es.CAMx v5.30 User’s Guide, December 2010

CAMx Modeling System

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-7 11.3 RUNNING CAMx

Control File Namelist Input

CAMx reads a text (ASCII) run control file named “ CAMx.in ” that must exist locally in the directory from which the model is run. This file i s in the Fortran “namelist” format, and contains all user-specified control parameters for a given s imulation, including model configuration, option-specific inputs, and I/O filenames. The run control file must contain the primary namelist
module labeled “ &CAMx_Control ”, which provides all of the information to configu re and run the core model. Additional namelist modules may be provided in the run control file to configure and run the various CAMx Probing Tools. These optional namelist modules are ignored if none of the Probing Tools are selected i n the primary namelist. A detailed description of the format and contents of this file is provided in Section 5 (core model) and Sections 6 through 10 (Probing Tools).

Each record in the CAMx control file contains a var iable name that is explicitly set to a numerical, logical, or character value. The variab le names are used by the program directly, and therefore cannot be changed without source code mod ifications. Character strings must be enclosed by single quotes, and all variable assignm ents must be delimited with commas. The order of the records may be arranged in any fashion that the user prefers. Any number of comment statements may be included anywhere within the namelists, provided that they do not interrupt “ variable_name = value, ” assignments. The “ ! ” character is the Fortran
namelist comment delimiter.

Certain variables are multi-dimension arrays; the u ser may provide a comma-delimited list of values to fill the array or assign values to specif ic array elements. Certain other variables are optional or associated with option flags; these do not need to appear in the namelist if their associated options are not invoked, and they will b e ignored if they remain in the file. If the user does not provide necessary inputs, the model will s top with a descriptive error message. We suggest that users start with the CAMx.in template that is provided with the source code.

On computer platforms running Unix or Linux the gen eration of the run control file is most easily accomplished in the job script that actually runs the model (see Figure 5-1 as an example using the Unix/Linux c-shell syntax). Alternativel y, the run control file could be written separately with a name specific to a given simulati on, then linked or copied to the standard

“ CAMx.in ” filename before the model is executed at a comman d line or in a job script.

Common Errors When Creating a Namelist

Fortran programs ingest the entire contents of name list modules using a single READ statement. If the program experiences an error reading the nam elist, it echoes a simple error message like “error reading namelist” and stops. It is therefor e difficult to determine why the read of the namelist has caused an error, especially if the nam elist is lengthy and contains a variety of data types. When experiencing an error reading the CAMx control file namelist, you must carefully inspect the file for any syntax errors. These erro rs can be subtle and difficult to spot. Here are a
few of the common reasons an error occurs when read ing a namelist:CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-8 • Mistyped variable name : All variables to be assigned within a namelist must be recognized as a declared namelist variable within the reading program. If a variable is misspelled or an unknown variable is assigned a value, a read error will occur.

• Incorrect data type for the assigned variable : If the data type of the value assigned to a namelis t variable does not match the variable’s declared data type within the reading program, an e rror will occur. Some compilers will allow real type variables to be assigned to integer values, but not the converse.

• Missing period around a logical value : The logical values .true. and .false. must be surrounded by a period.

• Missing quotes around a character variable : Any character data type must be surrounded by quote s.

• Overflow when assigning values to an array : The values in an array can be assigned using array index notation. If the index used to assign an array value exceeds the declared dimensio n of the array, a read error occurs.
Check the “ MXNAM ” parameter in the ./Includes/namelist.inc include file to
see if this value needs to be increased. Alternati vely, check your namelist file to be sure all of your array indices are correct.

• Wrong number of dimensions when assigning values to a multi-dimensional array : When assigning values to an array using array index notation, the number of subscripts in the assignment must match the declared dimensions o f the array (e.g., assignments to an array dimensioned var(i,j) must be referenced using two indices).

• Missing comma following a variable definition : A comma must be the last character in a “ variable = value ,” assignment. A comment may be placed after the comma (delimited us ing the “ ! ” symbol, see below) on
the same file record. This restriction on the use of commas is ignored on some compilers.

• Too many commas following a scalar variable definit ion : More than one comma following a scalar variable ass ignment will result in a read error.

• Too many commas following the variable assignment l ist for an array : The entire contents of an array can be assigned usi ng a single statement by listing the values of each element separated by commas. The re ad will fail if there are more commas than the dimension of the array.

• Comment does not begin with ! : The character that delimits a comment in a namelist is the exclamation point. Comments can appear anywhere within the namelist. However, all text in the namelist must either be part of a namelist variable assignment or part o f an identified comment.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-9 Parallel Processing

Parallel-processing refers to distributing a model application to multiple processors (CPUs) that share the computational load in integrating the mod el solution forward. Previous versions of CAMx only supported parallelization across multiple CPUs on a single server using the Open Multi-Processing (OpenMP or OMP) protocol for share d-memory machines (e.g., a multi-core PC). OMP distributes calculations for single grid cells (chemistry) or rows/columns of cells (advection/diffusion) to each CPU within the server . PC platforms are available with multiple (2 to 8) cores that access common memory; we find that OMP works well for this number of CPUs. A computer cluster (i.e., multiple networked server s that work together virtually as a single computer) can provide many more CPUs but requires a different multi-processing management system called the Message Passing Interface (MPI). With MPI, the CAMx modeling grid is divided into sub-regions that are processed on sepa rate servers on a network and integrated by passing large data messages among the servers. MPI CH is an open source implementation of MPI widely used in the atmospheric modeling communi ty.

MPI Parallelization

The MPI version of CAMx was designed using a “maste r/slave” parallel processing approach. The CPU on which the program is launched (process I D 0 under MPI) serves as the master node and will not make any actual model integration comp utations for any part of the modeling domain. This process will perform all of the model setup, the vast majority of I/O, and manage the communication between the compute nodes. Since the master node handles the important I/O it is the only CPU that needs access to the dis k volume containing the input files and the location of the output directory. This approach al lows for minimal amount of network traffic to the nodes on the cluster by eliminating the need fo r the compute nodes to manage NFS mounts. The master node may need access to the LAN for data access, but the compute nodes only need access to the internal cluster network. However, t he compute nodes will need access to a copy of the executable program. This can be accomplished i n a number of ways: (1) have an NFS mount
on the master node accessible to the internal clust er network and launch the model from that location; or (2) port a copy of the executable prog ram, using rcp or scp , to the user’s home directory on each compute node and launch the model from the user’s home directory on the
master node.

During each model time step, when computations are performed by the compute nodes, some information is written to the diagnostic ( *.diag ) and message output ( *.out ) files. Rather than just eliminate this information altogether, we decided to create node-specific versions of each of these two files and have each compute node write the information to its own version. However, in order to prevent the need to have the o utput directory available to the compute node across the network, we have designed the model so t hat the node-specific files are created in the current working directory. This means that if the model is launched from an NFS-mounted directory, all of the node-specific files will all be created in that location. On the other hand, if the model is launched from a user's home directory on the compute nodes, you will have to log in to the specific compute node to view the files.

Using CAMx with MPI requires the same job script as the standard version, with a few modifications to support multi-processing. Here, w e explain the approach we have taken. WeCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-10 provide this as guidance; it is not necessary to fo llow these steps to run CAMx with MPI. Others may have a different way of handling the MPI portio n of the execution.

1. Put the CAMx input files somewhere on the network t hat is accessible by the computer that will serve as the master node. Also, identify a location where the output will be
written.

2. Create a directory on the local disk of the master node that will serve as the launching point for the CAMx execution. Copy the standard CA Mx job script, and any scripting support programs to this location.

3. Modify the job script so that the variable identify ing the executable program points to the local directory.

4. Modify the job script so that the pathnames for inp ut and output files are correct.

5. Add a section, similar to the one shown below, to t he top of the job script:

For MPICH1 use the following:

cat << ieof > nodes
10.1.4.2
10.1.4.3

10.1.4.4
10.1.4.5
10.1.4.6

10.1.4.2
10.1.4.3
10.1.4.4
ieof set NUMPROCS = `wc -l nodes | awk '{print $1+1}'`

This will create a file containing the IP addresses of the computers that will get a computational slice of the domain on which to work. NOTE: THE MASTER NODE SHOULD NOT BE LISTED and will not get a computation al slice. The computer on which the program is launched is included in the MP I simulation by default and is given a process ID of 0. Notice that some computers are li sted twice. This example is utilizing a cluster containing dual processor computers. The c omputer must be listed once for each
CPU that is to be used in the simulation. Notice t hat the NUMPROCS variable is assigned to a value equal to one more than the number of com puters listed. This is because the master node is included in the count of processors. This example will utilize 9 processors: one to serve as the master node and 8 t hat will receive a computational slice. If the names of the computers are known to the mast er node, hostnames can be used in place of IP addresses.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-11 For MPICH2 use the following:

cat << ieof > nodes
10.1.4.2:3

10.1.4.3:2
10.1.4.4:2
10.1.4.5:2
ieof
set NUMPROCS = 9 set RING = `wc -l nodes | awk '{print $1}'

Notice that the syntax for each entry differs from the MPICH1 version in that the addresses are followed by a colon and a numeric val ue. This is the convention adopted in MPICH2 for utilizing several processors on a single host. Do not list a machine multiple
times in the nodes file. Unlike MPICH1 the NUMPROCS variable is set to the number
the total nodes listed after the colon. In this ex ample, the nodes file will also be used to launch a node “ring” using the MPICH2 utilities. F or this reason you must include the master node in the list – the master node must be p art of the MPICH ring.

6. Change the line that launches the executable progra m, at the bottom of the script.

For MPICH1 use the following:

mpirun -v -machinefile nodes -np $NUMPROCS $EXEC

This will use the mpirun utility to launch the CAMx model using the file ca lled

“ nodes ” to identify the computers to be included. Of cou rse, this assumes that the mpirun utility is in the current user's path. In fact, t he mpich package must be installed on each compute node and the mpirun utility must be available in the user's path on each system. A complete path to mpirun , such as /usr/local/bin/mpirun , could be used instead.

For MPICH2 use the following:

mpdboot -n $RING -f nodes --verbose mpiexec -machinefile nodes -np $NUMPROCS $EXEC mpdallexit

The mpdboot command will initialize the MPD environment, establ ishing a ring using
all of the machines listed in the nodes file. The mpiexec command will launch the model using the specified number of processors, uti lizing the machines in the established ring. The mpdallexit command will terminate the ring. If your script is looping over several days, which is typical, you should put the mpdboot command before the start of the loop, the mpiexec command inside the loop, and the mpdallexit command after the point where the loop is terminate d.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-12 OMP Parallelization

You may implement OMP parallel processing alone or in combination with MPI. In either case, it is necessary to add environment variables to the CAMx job script that define the number of OMP threads (the examples below use the c-shell syn tax):

setenv OMP_NUM_THREADS 4

There have been problems with some parallel program s on Linux systems when the per-thread stack size is set to the default (2 MB). If you ha ve unexplained failures such as segmentation faults/violations, try setting the MPSTKZ environment variable to a larger value, for exampl e:

setenv MPSTKZ 32M

For large applications, you will probably also need to increase the stack size available to the shell executing the model by using the limit command as s hown below:

limit stacksize unlimited

To use a hybrid MPI/OMP approach, follow all of the steps for running an MPI application and include the OMP_NUM_THREADS environment variable (a nd others if needed). The domain will be divided into slices in the usual way as par t of MPI, but when operating on a particular slice, the host will spawn multiple OMP threads to parallelize the portions of the code where
OMP directives have been included.

Benchmarking Model Run Times

Table 11-2 provides some example non-MPI CAMx confi gurations and associated system requirements when run on a dual-processor Linux PC (with and without OMP). All of these tests utilized the original CMC fast solver (since replac ed by the EBI solver) with the CB4 gas-phase
mechanism.

Two cases with very different model configurations were used to test the CAMx/MPI code. The
first consists of the standard CAMx test case avail able for download from www.camx.com : it employs two relatively small grids at 36 and 12 km resolution, centered over St Louis, Missouri:

• Grids: 68 × 68 and 92 × 115

• 16 layers

• No PiG

• Mechanism 6 (CB05, no PM)

The second was provided by the EPA/OAQPS, and emplo ys two larger grids, the 36-km RPO domain covering most of North America and a 12-km g rid covering the entire eastern U.S.:

• Grids: 148 × 112 and 281 × 242

• 14 layers

• No PiG

• Mechanism 4 CF (CB4, with PM)CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-13 Results from both test cases are shown in tables 11 -3 and 11-4. These results consistently show that MPI is most advantageous for larger applicatio ns in which overhead processes (e.g., model setup, I/O, etc.) are a much smaller fraction of to tal model run time. In other words, CAMx applications on expansive multiple grids, employing extensive chemistry (PM and RTRAC), and/or including Probing Tools (OSAT, PSAT, DDM) wo uld benefit most from MPI parallelization.

Table 11-2. **Example of computer requirements for several non- MPI CAMx configurations on a**  **dual-processor Athlon 2800+ (2.1Ghz) PC; compiled u sing the Portland Group compiler.** 

Configuration Memory Disk Usage CPUs Execution
Time **1 grid MRPO domain (97 x 90 x 14)**  **~123,000 grid cells Mechanism 3 (25 species)**  **112 mB 564 mB/episode**  **day 1**  **24 min/ episode day** 

**1 grid MRPO domain (97 x 90 x 14)**  **~123,000 grid cells Mechanism 3 (25 species)**  **112 mB 564 mB/episode**  **day 2**  **16 min/ episode day** 

**2 grids MRPO domain (97 x 90 x 14 ) and (119 x 134 x 14)**  **~ 346,000 grid cells Mechanism 3 (25 species)**  **248mB 960 mB/epidode**  **day 2**  **1 hr/ epsiode day** 

**2 grids MRPO domain (97 x 90 x 14 ) and (119 x 134 x 14)**  **~ 346,000 grid cells Mechanism 4 CF (50 species)**  **306 mB 1.2 gB/epidode day 1**  **2.5 hrs/ epsiode day** 

**2 grids MRPO domain with OSAT (97 x 90 x 14 ) and (119 x 134 x 14)**  **~ 346,000 grid cells Mechanism 3 (25 species)**  **16 OSAT source regions 2 OSAT source groups**  **Total OSAT tracers = 136 506 mB 1.6 gB/episode day**  **2 4 hrs/**  **episode day**

**2 grids VISTAS domain (148 x 112 x 19 ) and (170 x 179 x**  **19) ~ 608,000 grid cells**  **Mechanism 4 CMU (4 sections, 86 total species)**  **1 gB 2.3 gB/episode day 1 8 hrs/**  **episode day**CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-14 Table 11-3. **CAMx speed performance with MPI and OMP for the d istribution test case** 
**available at www.camx.com**  **(processed on 2.83 Ghz Intel Xeon chipsets).**

MPI Slices OMP threads / Slice Total CPU Minutes /Sim Day Equivalent
Processors
**1 1 1 106**
**2 1 2 57 1.9**
**4 1 4 32 3.3**
**8 1 8 21 5.0**
**16 1 16 18 5.9**
**1 4 4 43 2.5**
**2 4 8 25 4.3**
**4 4 16 17 6.2**

Table 11-4. **CAMx speed performance with MPI and OMP for the E PA/OAQPS test case**  **(processed on 2.83 Ghz Intel Xeon chipsets).**

MPI Slices OMP threads / Slice Total CPU Minutes / Sim Day Equivalent Processors*
**1 1 1 503**
**2 1 2 266 1.9**
**4 1 4 138 3.6**
**8 1 8 81 6.2**
**12 1 12 76 7.0**
**16 1 16 61 8.2**
**24 1 24 49 10.3**
**1 8 8 111 4.5**
**2 8 16 79 6.4**
**4 4 16 71 7.1**
**3 8 24 52 9.7** ***Equvalent Processors is defined as the ratio of ru ntime for multiple processors to the runtime for a**  **single processor.**

11.4 CAMx PRE- AND POST-PROCESSORS

This section describes a variety of CAMx pre- and p ost-processors that we have made available to the user community. Since they support CAMx I/O , these programs are written in Fortran 77 and 90 and distributed by ENVIRON as free software. As such, they contain notices like this in
the source code:

ccccccccccccccccccccccccccccccccccccccccccccccccccc cccccccccccccc c Copyright (C) 2003 ENVIRON

c c This program is free software; you can redistrib ute it and/or c modify it under the terms of the GNU General Pub lic License c as published by the Free Software Foundation; ei ther version 2 c of the License, or (at your option) any later ve rsion.

c c This program is distributed in the hope that it will be useful, c but WITHOUT ANY WARRANTY; without even the impli ed warranty of

c MERCHANTABILITY or FITNESS FOR A PARTICULAR PURP OSE. See the
c GNU General Public License for more details.

c c To obtain a copy of the GNU General Public Licen se c write to the Free Software Foundation, Inc., c 59 Temple Place - Suite 330, Boston, MA 02111-1 307, USA.

cccccccccccccccccccccccccccccccccccccccccccccccccc cccccccccccccccCAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-15 ENVIRON occasionally posts updates for certain wide ly-used programs when necessary, but does not actively support or maintain every one. U sers can e-mail questions, comments, suggestions or improvements to ask-camx@environ.org . A few of the more important pre- and post-processors are briefly described below. Each come with README files, makefiles, and sample job scripts that document their use.

Emissions

Most emission models (those shown in Figure 11-1) c an provide speciated, temporally-allocated, gridded and point source emission input files in th e CAMx-ready format. Further processing may be required, however, to select point sources f or the PiG treatment, translate the point
source files from a common ASCII text format to CAM x-ready binary files, or to refine the domain size/resolution for gridded emissions. Ther e are three interface programs that perform these operations.

PIGSET: This program allows the user to select andset certain point sources for the Plume-inGrid (PiG) treatment in a CAMx simulation. It also converts ASCII point source files commonly generated by emission models to CAMx-ready binary format. See the source code for more information, and the sample job for u sage. Also see Section 4 for guidance in selecting PiG point sources and manipul ating day-specific point source files.

WINDOW: This program is used to “window” out a sub -section of the surface emissions grid for use on a smaller CAMx grid. It can also be use d to aggregate or interpolate surface emissions to coarser or finer resolution, respectiv ely. See the sample job for usage.

PREPBIOE: This is a converter to generate emission s for additional species needed by the updated CAMx SOA module (v4.50 and later). It read s in an existing biogenic emissions file and generates emissions for new spec ies by scaling from existing species.

Meteorology

The recommended approach to develop meteorological inputs for CAMx is through the use of prognostic meteorological models. Currently, the m ost common of these is MM5, RAMS, and WRF; ENVIRON provides interface programs for these specific models. This does not necessarily preclude other meteorological models to be used, but users will need to develop interface programs on their own.

MM5CAMx: This program generates CAMx v4 and v5 met eorological input files from MM5 v3 output files. See the README in the archive for a description of the program and how it is applied.

RAMSCAMx: This program generates CAMx v4 and v5 me teorological input files from RAMS v4 through v6 output files. See the README in the archive for a description of the program and how it is applied.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-16 WRFCAMx: This program generates CAMx v4 and v5 met eorological input files from WRF (ARW core) v2 and v3 output files. See the README in the archive for a description of the program and how it is applied.

KVPATCH: This program applies minimum Kv values to layers below 100 m based on an input landuse grid. Minimum Kv's are determined from a w eighted average of landuse for each cell, where each landuse is assigned a Kv valu e. See the source code for more information. Use of this program to adjust Kv inpu ts is entirely optional.

Photolysis Rates

The development of photolysis rate inputs for CAMx is crucial for the photochemical mechanisms, but is not needed for inert or simple c hemistry (e.g., Mechanism 10) applications. Two programs are available to assist the user in de veloping photolysis and albedo/haze/ozone input files.

AHOMAP: This program prepares albedo/haze/ozone co lumn input files for CAMx, and must be run prior to running the TUV model as it defines the albedo, haze, and ozone column intervals based on input data. A CAMx-ready landus e file and ozone column data files ( http://toms.gsfc.nasa.gov/ozone/ozone.html ) must be supplied as input. The program assumes a constant haze turbidity value for the ent ire grid. NOTE: gaps in day-specific ozone column data at low latitudes, and high latitu des during winter, may result in zero ozone column values over portions of the modeling g rid (see Figure 11-2). This results from the inability of polar-orbiting satellite plat forms to cover the entire globe on a daily basis. The user may instead use seasonal-average o zone column input files (no data gaps); alternatively, AHOMAP provides an option to fill these day-specific gaps with an average determined from valid data processed for th e extraction domain. See the Readme file and job script in the archive for usage

.

TUV: This program develops photolysis rate inputs for all CAMx CB4, CB05, and SAPRC99 photochemical mechanisms via the TUV radiative tran sfer model (Madronich, 2002). TUV originates from NCAR ( http://cprm.acd.ucar.edu/Models/TUV/ ). The program uses the albedo, ozone column, and haze turbidity i ntervals defined in AHOMAP. See the sample.job in the archive for usage.

Air Quality

There are numerous approaches for defining initial and boundary concentration inputs for CAMx. The level of possible detail ranges from tim e- and space-constant values for all pollutants, to specific time and space profiles for each. The level of detail depends on available measurements or modeling datasets, focus and detail of the modeling exercise, and scale considerations (e.g., local vs. continental domains ). ENVIRON provides one simple program, as described below. Another approach gaining populari ty is to extract initial and boundary conditions for large regional applications from glo bal-scale chemical transport models, such as Harvard’s GEOS-CHEM or NCAR’s MOZART. Interface pr ograms have been developed for this purpose, but are not distributed by ENVIRON. Users will need to develop their own programs if they choose an alternative methodology to generate initial/boundary conditions.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-17 Figure 11-2. **An example of global ozone column from the Ozone Monitoring Instrument (OMI)**  **platform. White areas denote missing data. From http://jwocky.gsfc.nasa.gov/** 

**.**

ICBCPREP: This program prepares CAMx initial condi tion (IC) and boundary condition (BC) files. Values are constant in space and time, but unique values may be specified for each chemical species to be modeled; they are defined in a simple text file. See the file sample.job in the archive for usage.

Landuse

Approaches for developing landuse/landcover inputs for CAMx (and for AHOMAP) are about as varied as they are for initial/boundary conditions. Popular approaches have included: (1) translating gridded spatial allocation surrogates d eveloped during emissions processing into the CAMx categories described in Section 5; (2) transla ting the gridded landuse/landcover fields from the meteorogical model; or (3) separately deve loping landuse input fields from raw data (such as from the USGS or NLCD) using GIS or other programs. The advantages of (1) are consistency with input emissions estimation methodo logies, and the fact that land cover processing is done just once. The advantages of (2 ) are similar consistency with the meteorological modeling and one-time processing, bu t most models assign a single dominant landuse to each grid cell, which may not be represe ntative for coarse-resolution grids (CAMx allows a distribution of multiple land cover types per cell). ENVIRON provides meteorological interface programs (described above) that translate the meteorological model landuse/landcover fields to the CAMx definitions and grid configurati on.CAMx v5.30 User’s Guide, December 2010

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Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_11_system.d ocx 11-18 Post-processors

Most of the CAMx post-processors provided by ENVIRO N provide some manner of concentration file manipulation, either to extract certain information from the raw output files, to re-format for use in other programs and application s, to concatenate files, etc. A few others are provided to assist in evaluating Probing Tool outpu t. As stated above, ENVIRON does not provide any visualization or graphics software othe r than a few Excel® spreadsheet macros.

AVGDIF: This program is used to compare two UAM-IV format files and print a table of differences. This is useful for checking differenc es between CAMx runs on different machines or different compile options for the test case. Note that the CAMx master grid output file is in UAM-IV format, but the nested gri d output file needs to first be
translated into that format before AVGDIF can be us ed (see FINE2UAM below). See the file sample.job in the archive for usage.

BNDEXTR: Use this program to extract boundary cond itions for a nested grid when you apply CAMx in one-way nesting mode. One-way nesting mean s that CAMx is run successively for each grid, with BNDEXTR as the int erface between each run. This program is not needed when CAMx is run in the more standard two-way nesting mode, where all grids are run in a single simulations. S ee the example job for usage.

CAMxPOST: This is a suite of post-processing utili ties designed to facilitate the evaluation of model performance. It is used to combine observati ons and predictions, calculate statistics, and plot time series. See the README f ile in the archive for usage.

CAMxTRCT: This program extracts specified chemical species for specified grids from the output average concentration and deposition files, and from input emission files. Output from this program can be written in the standard UA M-IV format (master grid output format), or alternatively to a text (ASCII) format in Surfer “GRD” format for subsequent plotting. It also has the capability to convert un its. See the file sample.job in the archive for usage.

CAMx2IOAPI: This program converts CAMx output aver age concentration and deposition files
to I/O-API format. You will need I/O-API and NetCD F libraries to compile and run this program. This program allows you to use various Mo dels-3 post-processors and visualization programs that only handle I/O-API for mat.

PA_Tools: This is a suite of post-processing utili ties designed to extract IPR, IRR, and CPA data from one CAMx Process Analysis output files an d reformats the data suitable for subsequent analysis (e.g. using spreadsheets).

OSAT/PSAT_Browser: These are separate suites of Fo rtran and Excel macro tools that support the evaluation of the source apportionment output a t receptor locations. The macro plots ranked source contributions at receptor locations u sing Excel (compatible with MS Office 2003). Note that the macro is untested for the latest version of MS Office. See the README file for usage.CAMx v5.30 User’s Guide, December 2010

References

Y:\CAMx\UsersGuide\v5.30\New Folder\Sec_12_referenc es.docx 12-1 12. REFERENCES

Anthes, R.A. and T.T. Warner. 1978. Development o f Hydrodynamic Models Suitable for Air Pollution and Other Mesometeorological Studies. Mon. Wea. Rev.

, 106 , 1045-1078.

Ariya, P.A., A. Khalizov and A. Gidas. 2002. Reac tions of gaseous mercury with atomic and molecular halogens: kinetics, product studies, and atmospheric implications. J. Phys.
Chem.

, 106 , 7310-7320.

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