ENVIRON International Corporation 

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

Revised Report – August 2009 

AIR QUALITY MODELING STUDY 

FOR THE FOUR CORNERS REGION 

Prepared for 

New Mexico Environment Department 

Air Quality Bureau 

1301 Siler Road Building B 

P.O. Box 26110 

Santa Fe, NM 87507 

Prepared by 

Till E. Stoeckenius 

Christian A. Emery 

Tejas P. Shah 

Jeremiah R. Johnson 

Lynsey K. Parker 

Alison K. Pollack 

ENVIRON International Corporation 

Novato, CA 94998 

30 June 2009 
Note to Readers 

This report was originally prepared 30 June 2009. Upon subsequent review it was 

discovered that a figure in Section 4 summarizing the impacts of emission mitigation 

scenarios on the annual fourth highest 8-hour average ozone levels had been 

inadvertently mixed up with another similar figure depicting 1-hour average ozone 

impacts during the peak afternoon hour of a typical high ozone summer day (18 July). 

This error has been corrected in this revised version of the report. For the sake of clarity, 

figures for both the annual fourth highest 8-hour ozone impacts and the 18 July impacts 

are included in this revised version. Several minor typographical errors have also been 

corrected. 
June 2009 

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TABLE OF CONTENTS 

Page 

ACKNOWLEDGEMENT 

GLOSSARY 

EXECUTIVE SUMMARY .................................................................................................... ES-1 

1. INTRODUCTION................................................................................................................ 1-1 

1.1 Background...................................................................................................................... 1-1 

1.2 Approach.......................................................................................................................... 1-1 

2. EMISSIONS INVENTORY................................................................................................ 2-1 

2.1 Data Sources and Model-Ready Inventory Development ............................................... 2-1 

2.2 Emission Summaries: 2005 and 2018 Base Cases........................................................... 2-3 

2.3 Mitigation Scenarios ...................................................................................................... 2-10 

3. AIR QUALITY MODELING............................................................................................. 3-1 

3.1 Meteorological Modeling................................................................................................. 3-1 

3.2 Ozone and PM Modeling................................................................................................. 3-1 

4. RESULTS ............................................................................................................................. 4-1 

4.1 2018 Base Case................................................................................................................ 4-1 

4.2 Mitigation Scenario Results........................................................................................... 4-15 

4.3 Calculation of Ozone Sensitivities via HDDM.............................................................. 4-28 

5. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ..................................... 5-1 

5.1 Summary and Conclusions .............................................................................................. 5-1 

5.2 Recommendations............................................................................................................ 5-2

APPENDICES 

Appendix A : Modeling Protocol Document 

Appendix B : Oil & Gas Emissions Inventory Development 

Appendix C : Four Corners Area Emission Inventory Development 

Appendix D : Biogenic Emissions 

Appendix E : Development of Western U.S. Fire Emissions Inventory 

Appendix F : Meteorological Modeling 
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TABLES 

Table 2-1 2005 NOx emissions (t/yr) within the 4 km modeling domain by 

state and source category ..................................................................................... 2-3 

Table 2-2 2005 SO2 emissions (t/yr) within the 4 km modeling domain by 

state and source category ..................................................................................... 2-3 

Table 2-3 2005 VOC emissions (t/yr) within the 4 km modeling domain by 

state and source category ..................................................................................... 2-4 

Table 2-4 2005 PM emissions (t/yr) within the 4 km modeling domain by 

state and source category ..................................................................................... 2-4 

Table 2-5 2018 NOx emissions (t/yr) within the 4 km modeling domain by 

state and source category ..................................................................................... 2-4 

Table 2-6 2018 SO2 emissions (t/yr) within the 4 km modeling domain by 

state and source category ..................................................................................... 2-5 

Table 2-7 2018 VOC emissions (t/yr) within the 4 km modeling domain by 

state and source category ..................................................................................... 2-5 

Table 2-8 2018 PM emissions (t/yr) within the 4 km modeling domain by 

state and source category ..................................................................................... 2-5 

Table 2-9. Emission changes under mitigation scenario #1................................................ 2-11 

Table 2-10. EGU emission (tons/year) under the 2018 base case and 

 scenario #1 ......................................................................................................... 2-11 

Table 2-11. Oil and gas area source emissions (tons/year) under the 2018 base 

case and scenario #2........................................................................................... 2-13 

Table 2-12. Oil and gas point source emissions under the 2018 base case and 

mitigation scenario #3........................................................................................ 2-14 

Table 2-13. EGU and oil and gas source emissions (tons/year) under the 2018 

base case and mitigation scenario #4................................................................. 2-15 

Table 2-14. Oil and gas area and point source emissions (tons/year) under the 

2018 base case and mitigation scenario #5. ..................................................... 2-16 

Table 3-1 CAMx meteorological input data requirements................................................... 3-3 

Table 3-2. Core statistical measures used in the four corners air quality model 

evaluation with ground-level data .................................................................... 3-10 

Table 3-3. Model performance criteria................................................................................ 3-11 

Table 3-4. Monitoring sites used in the model performance evaluation for 

ozone.................................................................................................................. 3-12 

Table 3-5. PM monitoring sites used in the model performance evaluation....................... 3-13 

Table 4-1. Definitions of the 24 source groups used in 2018 base case ozone 

and PM source apportionment runs ..................................................................... 4-4 

Table 4-2. Average ozone source contributions calculated by OSAT at the 

Navajo Lake monitoring location for all running 8-hour periods 

with average ozone greater than 65 ppb (top), 70 ppb (middle) and 

75 ppb (bottom). ................................................................................................. 4-8 

Table 4-3. Daily average visibility summary statistics for 2018 base case at 

Mesa Verde ........................................................................................................ 4-30 

Table 4-4. Summary statistics for changes in daily average visibility at Mesa 

Verde under each mitigation scenario (changes greater than 0.5 dV 

absolute value in bold)....................................................................................... 4-31 
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Table 4-5. Daily average visibility summary statistics for 2018 base case at 

Weminuche ........................................................................................................ 4-31 

Table 4-6. Summary statistics for changes in daily average visibility at 

Weminuche under each mitigation scenario (changes greater than 

0.5 dV absolute value in bold). .......................................................................... 4-31 

FIGURES 

Figure ES-1 CAMx 36/12/4 km modeling domain used in the Four Corners Air 

Quality Modeling Study.....................................................................................ES-3 

Figure 1-1. CAMx 36/12/4 km modeling domain used in the Four Corners Air 

Quality Modeling Study....................................................................................... 1-3 

Figure 2-1. Oil and gas and area source gridded emissions in the 4 km 

modeling domain ................................................................................................. 2-6 

Figure 2-2. Mobile source gridded emissions in the 4 km modeling domain ........................ 2-7 

Figure 2-3 Comparative summaries of annual emissions within the 4 km 

domain for the 2005 and 2018 base case inventories .......................................... 2-8 

Figure 3-1. Air quality monitoring sites within the CAMx 12/4 km modeling 

domain.................................................................................................................. 3-9 

Figure 3-2. Ozone monitoring sites in the 4 km domain ...................................................... 3-15 

Figure 3-3. CASTNET ozone monitoring sites in the western U.S. used for 

model performance evaluation........................................................................... 3-16 

Figure 3-4. Monthly mean fractional errors for ozone monitoring sites in the 4 

km domain (top) and 12 km domain (bottom)................................................... 3-17 

Figure 3-5. Monthly mean fractional bias for ozone monitoring sites in the 4 

km domain (top) and 12 km domain (bottom)................................................... 3-18 

Figure 3-6. Monthly mean gross bias (top) and error (bottom) for ozone 

monitoring sites in the 4 km domain. Bias and error statistics are 

based on data pairing in which 1-hour observations are above 40 

ppb. Historical EPA acceptance criteria are shown for reference as 

red horizontal lines............................................................................................. 3-19 

Figure 3-7. Ozone time series from each monitoring location in the 4 km 

domain and associated CAMx prediction for the months of April 

(left) and July (right), 2005................................................................................ 3-20 

Figure 3-8. PM monitoring sites within the 4 km modeling domain.................................... 3-22 

Figure 3-9. Monthly fractional bias (top) and error (bottom) for PM2.5 by site 

relative to monthly-mean observations and to RPO performance 

goals and criteria................................................................................................ 3-23 

Figure 3-10. Monthly fractional bias (top) and error (bottom) for sulfate by site 

relative to monthly-mean observations and to RPO performance 

goals and criteria................................................................................................ 3-24 

Figure 3-11. Monthly fractional bias (top) and error (bottom) for nitrate by site 

relative to monthly-mean observations and to RPO performance 

goals and criteria................................................................................................ 3-25 

Figure 3-12. Monthly fractional bias (top) and error (bottom) for ammonium by 

site relative to monthly-mean observations and to RPO 

performance goals and criteria........................................................................... 3-26 
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Figure 3-13. Monthly fractional bias (top) and error (bottom) for elemental 

carbon by site relative to monthly-mean observations and to RPO 

performance goals and criteria........................................................................... 3-27 

Figure 3-14. Monthly fractional bias (top) and error (bottom) for organic carbon 

by site relative to monthly-mean observations and to RPO 

performance goals and criteria........................................................................... 3-28 

Figure 3-15. Monthly fractional bias (top) and error (bottom) for soil by site 

relative to monthly-mean observations and to RPO performance 

goals and criteria................................................................................................ 3-29 

Figure 4-1. Annual fourth highest daily maximum 8-hour average ozone 

predicted in each model surface grid cell for the 2005 base case 

(top left), 2018 base case (bottom left) and 2018 minus 2005 

(bottom right)....................................................................................................... 4-2 

Figure 4-2. OSAT source apportionment results for Navajo Lake monitoring 

location in July..................................................................................................... 4-5 

Figure 4-3. OSAT source apportionment results at Farmington monitoring 

location in July..................................................................................................... 4-5 

Figure 4-4. OSAT source apportionment results at Mesa Verde monitoring 

location in April ................................................................................................... 4-6 

Figure 4-5. OSAT source apportionment results at Mesa Verde monitoring 

location in July..................................................................................................... 4-6 

Figure 4-6. Average ozone source contributions from sources within the 4 km 

modeling domain as calculated by OSAT at the Navajo Lake 

monitoring location for all 8-hour periods greater than 70 ppb. Not 

shown here are contributions from boundary and initial conditions 

(41.2 ppb) and sources in the 12 km domain (3.8 ppb). The 

average impact from all sources combined for these 8-hour periods 

is 71.5 ppb............................................................................................................ 4-9 

Figure 4-7. Average ozone source contributions from sources within the 4 km 

modeling domain as calculated by OSAT at the Mesa Verde 

monitoring location for all 8-hour periods greater than 70 ppb. Not 

shown here are contributions from boundary and initial conditions 

(64.6 ppb) and sources in the 12 km domain (0.8 ppb). The 

average impact from all sources combined for these 8-hour periods 

is 70.4 ppb............................................................................................................ 4-9 

Figure 4-8. July average calculated source contributions to ozone for all 8-hour 

periods with average ozone greater than 65 ppb at the Navajo Lake 

monitoring location: comparison of OSAT and APCA results ......................... 4-11 

Figure 4-9. July average calculated source contributions to ozone for all 8-hour 

periods with average ozone greater than 65 ppb at the Mesa Verde 

monitoring location: comparison of OSAT and APCA results ......................... 4-11 

Figure 4-10. Quarterly and annual average PM source apportionment results at 

the Mesa Verde monitoring location calculated with PSAT for 

sulfate, nitrate, ammonium, elemental carbon and total PM2.5 mass ................. 4-13 

Figure 4-11. Quarterly and annual average PM source apportionment results at 

the Weminuche monitoring location calculated with PSAT for 

sulfate, nitrate, ammonium, elemental carbon and total PM2.5 mass ................. 4-14 

Figure 4-12. Time series of predicted hourly ozone under the 2018 base case 

and mitigation scenario #1 during April at Mesa Verde (top) and 

July at Navajo Lake (bottom) ............................................................................ 4-17 
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Figure 4-13. Time series of predicted hourly ozone under the 2018 base case 

and mitigation scenario #2 during April at Mesa Verde (top) and 

July at Navajo Lake (bottom) ............................................................................ 4-18 

Figure 4-14. Time series of predicted hourly ozone under the 2018 base case 

and mitigation scenario #3 during April at Mesa Verde (top) and 

July at Navajo Lake (bottom). ........................................................................... 4-19 

Figure 4-15. Time series of predicted hourly ozone under the 2018 base case 

and mitigation scenario #4 during April at Mesa Verde (top) and 

July at Navajo Lake (bottom). ......................................................................... 4-20 

Figure 4-16. Time series of predicted hourly ozone under the 2018 base case 

and mitigation scenario #5 during April at Mesa Verde (top) and 

July at Navajo Lake (bottom) ............................................................................ 4-21 

Figure 4-17. Predicted change in hourly ozone (mitigation scenario – base case) 

versus base case ozone (values matched in time and space) at Mesa 

Verde in April (left column) and Navajo Lake in July (right 

column) for mitigation scenarios 1, 2 and 3 (top row) and 

mitigation scenarios 4 and 5 (bottom row). Note that only hours 

with base case ozone greater than 50 ppb are shown......................................... 4-22 

Figure 4-18. Predicted hourly average ozone at 3:00 pm MST on 18 July under 

the 2018 base case and mitigation scenarios #1 and #2 (left 

column) and differences between each mitigation scenario and the 

2018 base case (right column). .......................................................................... 4-23 

Figure 4-19. Predicted hourly average ozone at 3:00 pm MST on 18 July under 

mitigation scenarios #3, #4 and #5 (left column) and differences 

between each mitigation scenario and the 2018 base case (right 

column) .............................................................................................................. 4-24 

Figure 4-20. Annual fourth highest daily maximum 8-hour ozone in each model 

grid cell under the 2018 base case and mitigation scenarios #1 and 

#2 (left column) and differences between each mitigation scenario 

and the 2018 base case (right column)............................................................... 4-26 

Figure 4-21. Annual fourth highest daily maximum 8-hour ozone in each model 

grid cell under mitigation scenarios #3, #4 and #5 (left column) 

and differences between each mitigation scenario and the 2018 

base case (right column). ................................................................................... 4-27 

+Figure 4-22. Predicted change in visibility compared to predicted 2018 base 

case visibility for each day of the year at Mesa Verde under each 

mitigation scenario (negative values for the change in visibility 

indicate visibility improvement)........................................................................ 4-25 

Figure 4-23. Predicted change in visibility compared to predicted 2018 base 

case visibility for each day of the year at Weminuche under each 

mitigation scenario (negative values for the change in visibility 

indicate visibility improvement)........................................................................ 4-26 
ACKNOWLEDGEMENTS 

ENVIRON would like to acknowledge members of the Four Corners Air Quality Task Force and 

the Four Corners Modeling Group for their thoughtful comments and suggestions which played a 

significant role in the success of this study. Mark Jones of the New Mexico Environment 

Department provided invaluable assistance in defining the emission mitigation strategies to be 

analyzed, reviewing results and facilitating the dissemination of results to and interactions with 

the Four Corners Modeling Group. In addition, Dennis McNally of Alpine Geophysics provided 

significant assistance with preparation of the model performance evaluation results and, as part 

of a previous study, developed the MM5 meteorological fields used in this analysis. Funding for 

this study was provided by the New Mexico Environment Department under the direction of 

Mary Uhl via a grant provided by the U.S. Environmental Protection Agency, Region VI. 

Additional funding for emission inventory development and model sensitivity evaluations was 

provided by BP North America Production Co under the direction of Reid Smith and Dana 

Wood. 
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GLOSSARY 

APCA Anthropogenic Precursor Culpability Assessment 

BART Best Available Retrofit Technology 

BC Boundary Conditions 

CAMx Comprehensive Air Quality Model with Extensions 

CBM Coal Bed Methane 

CMAQ Community Multi-scale Air Quality model 

EGU Electric Generating Unit 

EIS Environmental Impact Statement 

FCAQTF Four Corners Air Quality Task Force 

FCMG Four Corners Air Quality Modeling Group 

FCPP Four Corners Power Plant 

IC Initial Conditions 

NCAR National Center for Atmospheric Research 

NMED New Mexico Environment Department 

OSAT Ozone Source Apportionment Technology 

PM Particulate Matter 

PNM (PNM is the New Mexico electric utility company that operates the San Juan 

Generating Station) 

PSAT Particulate matter Source Apportionment Technology 

RPO Regional Planning Organization 

SCR Selective Catalytic Reduction 

SJGS San Juan Generating Station 

SMOKE Sparse Matrix Operator Kernel Emissions processing system 

SUIT Southern Ute Indian Tribe 

TOG Total organic gasses 

TPY Tons per year 

VOC Volatile Organic Compounds 

WRAP Western Regional Air Partnership 
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EXECUTIVE SUMMARY 

In response to concerns regarding the air quality impacts of growth in many types of sources, 

especially oil and gas sources and power generation, on Class I and surrounding Class II areas in 

the Four Corners region, the New Mexico Environmental Department (NMED), together with 

the Colorado Department of Public Health and Environment, convened the Four Corners Air 

Quality Task Force (FCAQTF). States, Tribes, Federal Land Managers and other stakeholders 

were brought together under the FCAQTF to develop strategies for air quality management in the 

region. In connection with this effort, the FCAQTF identified a need to model the air quality 

impacts of potential alternative mitigation strategies being developed by various FCAQTF work 

groups. Estimates of mitigation scenario impacts on criteria pollutants (ozone, particulate matter 

[PM]) and air quality related values (visibility and deposition) were needed with peak ozone and 

visibility impacts in the Mesa Verde, San Pedro Parks, and Weminuche Class I areas and 

surrounding Class II areas of primary interest. In response to these requirements, NMED 

obtained funding for a comprehensive atmospheric modeling study designed to address these 

issues. A Four Corners Modeling Group (FCMG) consisting of representatives from NMED, BP 

and other interested stakeholders from the FCAQTF was formed to guide and monitor the 

modeling study. 

A high resolution (4 x 4 km horizontal grid cell size), regional scale air quality photochemical 

modeling study of the Four Corners region was conducted by ENVIRON under the direction of 

the FCMG, resulting in a regional air quality planning tool which can be used to evaluate 

impacts of both future development projects and alternative emission reduction strategies.1

 

Development of emission inventories needed for the modeling analysis was funded by BP North 

America Production Co. BP also funded several model sensitivity analyses. Results of the 

modeling study are intended to inform the direction of future air quality management efforts in 

the Four Corners region. 

Emission Inventory Development 

An updated emission inventory for 2005, and a projected inventory for 2018 were developed for 

use in this study. Inventories were developed for a series of nested grids with 36, 12, and 4 km 

horizontal grid spacing (resolution) as shown in Figure ES-1. Inventories previously developed 

for the Western Regional Air Partnership (WRAP) were used as the main starting point for this 

work. Emissions outside of the Four Corners region from anthropogenic sources were based on 

the WRAP 2002 and 2018 (PRP18) inventories with linear interpolation to 2005. Revised 

biogenic emission estimates were developed for the entire modeling domain and 2005 fire 

emissions were developed for the western U.S. domain including the Four Corners region. 

Additional emissions development and modeling was conducted specifically for the 4 km 

modeling domain located over the Four Corners area, including spatial allocation of oil and gas 

emissions at 4 km resolution (see below), use of measured hourly emissions for 2005 from 

electric generating units, 2005 and 2018 mobile source emissions resolved at 4 km resolution and 

ammonia and wind blown dust emissions modeling. The resulting model-ready emissions were 

used to examine the air quality impacts of five alternative mitigation scenarios focused on 

 

1 http://www.nmenv.state.nm.us/aqb/4C/Modeling.html 
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various combinations of emissions reductions from electric generating units (EGUs) and fuel 

combustion and evaporative sources associated with oil and gas exploration and production 

activities in the Four Corners region (see below). 

Oil and gas emissions estimates used in this analysis are based on the most recently available 

information at the time the analysis was conducted, including the 2002 WRAP Phase II 

inventory which was scaled to 2005 based on activity data, the NMED ozone precursor inventory 

for San Juan and Rio Arriba counties and the 2005 updated Southern Ute inventory. With 

respect to sources in southwestern Colorado, these are essentially the same emissions that will be 

included in the WRAP Phase III inventory. For sources in northwestern New Mexico, however, 

the WRAP Phase III study includes development of a new “bottom up” inventory based on 

updated survey data and other information. It is not presently known to what extent the updated 

Phase III inventory will differ from the inventory used in this study. Emission estimates used in 

this analysis for oil and gas sources outside of the Four Corners region are also being updated as 

part of the WRAP Phase III study. Overall, inventories used in this study are sufficient for 

examining impacts of Four Corners region mitigation scenarios but additional modeling with 

updated inventories will be needed to further evaluate possible air quality impacts of future 

emissions growth and control. 

Air Quality Modeling 

Air quality modeling with the 2005 base case, 2018 base case and five mitigation scenario 

emission inventories was conducted using the Comprehensive Air quality Model with 

eXtensions (CAMx, v4.51). CAMx is a publicly available (www.camx.com) three-dimensional 

multi-scale photochemical/aerosol grid model that is developed and maintained by ENVIRON 

International Corporation. CAMx is able to evaluate a variety of air quality issues related to 

ozone, particulate matter (PM), visibility, acid deposition, and air toxics impacts. CAMx has 

been widely used in recent years by a variety of regulatory agencies for 1-hr and 8 hour ozone 

and PM SIP modeling studies, as well as by several Regional Planning Organizations (RPOs) for 

regional haze modeling. It is currently being used to evaluate air quality impacts of several oil 

and gas development projects in the western U.S. 

Base Case Modeling

CAMx was run for the 2005 base case over a modeling domain comprised of a series of nested 

grids with 36, 12, and 4 km grid spacing (resolution) as shown in Figure ES-1. Meteorological 

data needed for the CAMx simulation was based on a previously prepared full annual 

meteorological modeling simulation using the MM5 mesoscale model that used the domain 

structure shown in Figure ES-1. The 2005 MM5 meteorological data fields were processed into 

CAMx inputs for this domain configuration and quality-assured. CAMx was run first on the 

single 36 km grid for the entire year; gridded hourly concentrations of all chemical species from 

the 36 km run were then used to generate initial conditions (ICs) and boundary conditions (BCs) 

for the 12 km grid. CAMx was then exercised on the combined 12/4 km grid system in a fully 

two-way interactive manner for 2005. 
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-2880-2520-2160-1800-1440-1080 -720 -360 0 360 720 1080 1440 1800 2160 2520 2880

-2160

-1800

-1440

-1080

-720

-360

0

360

720

1080

1440

1800

2160

New Mexico Modeling Domain

CAMx 36 km: 148 x 112 (-2736, -2088) to (2592, 1944)

CAMx 12 km: 167 x 137* (-2316, -912) to ( -312, 732)

CAMx 04 km: 101 x 92* (-1192, -508) to ( -788, -140)

* includes buffer cells

MM5 36 km: 165 x 129 dot points (-2952, -2304) to (2952, 2304)

MM5 12 km: 178 x 157 (-2376, -1080) to (-252, 792)

MM5 04 km: 172 x 167 (-1272, -672) to (-588, -8)

36 km

12 km

4 km

Figure ES-1. CAMx 36/12/4 km modeling domain used in the Four Corners Air Quality 

Modeling Study. 

Model Performance Evaluation

A comprehensive model performance evaluation was conducted on the 2005 base case 

simulation. Available measurements for ozone, speciated PM and total PM mass were compared 

with model output over the Four Corners region and for other remote rural sites in the western 

U.S. Well-established model evaluation software and techniques were employed, which have 

been developed from regional modeling conducted for WRAP and other urban and regional-scale 

modeling programs. Statistical performance metrics were compared to acceptance goals and 

criteria established over the past several years by the EPA and Regional Planning Organizations 

(RPOs). Graphical displays of model performance were generated, including scatter plots, time 

series plots, spatial maps of model predictions, “bugle” plots and other displays. 
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Analyses of model performance for the 2005 base case showed reasonably good performance for 

ozone with some over prediction at higher elevation locations during the spring time and a slight 

under prediction bias during the summer. While predictions of elevated spring time ozone levels 

in the mountains are generally consistent with seasonal trends observed at remote western U.S. 

monitoring sites, some concerns remain regarding the accuracy of ozone levels predicted at high 

elevation sites during spring in the Four Corners area. Diagnostic analyses showed that these 

predicted ozone peaks result from the long range transport of elevated upper tropospheric or 

lower stratospheric ozone and subsequent enhanced downward mixing in the model over the 

mountains. As a result, these ozone peaks are not very sensitive to controls on local sources of 

emissions. Efforts are currently underway to improve the treatment of vertical circulations over 

elevated terrain in the model. 

In terms of total PM2.5, model performance was good except for a few months at IMPROVE 

sites, which show an under prediction tendency. Typical of the more remote western U.S. 

locations, observed total PM2.5 concentrations do not exceed 10 μg/m3

 on a monthly basis. 

Model performance for sulfate PM showed good agreement with observations, consistent with 

the expected accurate representation of power plant SO2 emissions provided by the EPA Acid 

Rain database. Nitrate PM model performance was mixed with some large under prediction 

biases but concentrations are very low and thus do not contribute significantly to total PM mass. 

Although a consistent under prediction bias was noted for elemental and organic carbon PM, this 

bias is not of primary concern in the present study which focuses mostly on the impacts of 

reductions in SO2 and NOx emissions which are effectively independent of organic and 

elemental carbon impacts. Additional follow-up analyses will be needed to understand the 

source of the elemental and organic carbon under prediction biases. 

Future-Year Modeling

After completing the 2005 base case analysis, a 2018 base case scenario was run using the 2018 

base case emission inventory. A source apportionment analysis was conducted for the 2018 

base case to inform the selection of mitigation scenarios to be analyzed. In addition, CAMx was 

run with inventories corresponding to each of five mitigation scenarios described below. As 

with the 2005 base case, these future year modeling scenarios were run on the 12/4 km nested 

grid system. Boundary conditions for these runs were kept the same as in the 2005 base case 

run. Model results were used to evaluate future year air quality impacts for ozone and PM 

concentrations and visibility. 

Given that emissions changed very little overall between the 2005 and 2018 base case, the ozone 

results are nearly identical between these two base cases. Results of an ozone and PM source 

apportionment analysis performed on the 2018 base case scenario showed that local oil & gas 

and EGU sources are the biggest contributors to local ozone episodes and to PM after 

contributions from outside the 4 km domain are accounted for. Further study is needed to better 

understand sources of regional (i.e., western US outside the 4 km modeling domain) 

contributions to ozone and PM impacts in the Four Corners area. 

A series of five emissions mitigation scenarios focused on EGU and oil and gas sources within 

the Four Corners area were examined in this study. These scenarios included a 70% NOx 

reduction and a 16% SO2 reduction from local (i.e., 4 km domain) EGU sources as well as an 

approximate 50% VOC reduction and a 16% NOx reduction from local oil and gas sources. 
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EGU and oil & gas together account for 75% of 4 km domain NOx emissions (oil and gas alone 

accounts for 37% of 4 km domain NOx emissions) and 33% of 4 km domain VOC emissions in 

the 2018 base case inventory. Thus, the combined EGU and oil and gas NOx controls 

considered here amount to roughly a 50% reduction in local NOx emissions and a 16% reduction 

in local VOC emissions. These mitigation scenarios resulted in peak predicted ozone changes 

generally limited to less than about 5 ppb. Some of the largest reductions are predicted to occur 

at the Navajo Lake monitoring location during the summer months on days with predicted peak 

hourly ozone above about 70 ppb. A combination of VOC and NOx controls on oil and gas 

sources is more effective at reducing ozone than VOC or NOx controls alone. There also 

appears to be a synergistic effect resulting in enhanced ozone reduction when the EGU controls 

are combined with the oil and gas controls: EGU controls alone result in inconsistent reductions 

from day to day depending on meteorological conditions but combining them with the oil and 

gas controls appears to put a “floor” under the reductions resulting in larger and more consistent 

reductions. 

Predicted visibility impacts under the mitigation scenarios are greatest at Mesa Verde. Oil and 

gas mitigations examined in this study don’t have any significant visibility impacts. EGU 

emissions reductions result in some days with visibility improvements of 0.5 to 1 dV but more 

than 98% of days during the year have predicted improvements of less than 0.5 dV. 

Recommendations 

This study provides a wealth of information about the linkages between sources of air pollutants 

and resulting air quality and air quality related value impacts in the Four Corners region. 

Additional investigations of these results beyond those presented here can be carried out to 

support future air quality management activities. In particular, the modeling databases from this 

study could be used to examine the impacts of additional specific regulatory strategies beyond 

the five mitigation scenarios included in this report. Several improvements to the modeling 

analyses are recommended, including: 

• Incorporation of updated oil and gas emissions estimates currently being developed via 

the WRAP Phase III study. 

• Further investigation of the apparent underestimation of elemental carbon emissions and 

incorporation of any resulting updated emissions estimates into the modeling database. 

• Evaluate if updated modeling algorithms for secondary organic aerosol currently under 

development are able to proved more realistic prediction of organic carbon. 

• Further evaluation of individual impacts of major source categories located within the 12 

km (western U.S.) modeling domain on ozone and PM levels in the Four Corners area, 

e.g., oil & gas, electric generating units, urban area emissions and biogenic and fire 

emissions. This could be done via application of source apportionment technology with 

emissions reprocessed to split out these major 12 km source categories. 

• Incorporation of model improvements for setting stratospheric boundary condition inputs and 

reducing vertical circulations over elevated terrain that are currently under development. 

• Review of recent western U.S. modeling analyses of biogenic and other natural emissions 

impacts for potential improvements to emissions estimates and modeling methodologies. In 

particular, evaluate the possibility of including NOx formed by lightning in the analysis. 
June 2009 

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1.0 INTRODUCTION 

1.1 BACKGROUND 

In response to concerns regarding the air quality impacts of growth in many types of sources, 

especially oil and gas sources and power generation, on Class I and surrounding Class II areas in 

the Four Corners region, the New Mexico Environmental Department (NMED), together with 

the Colorado Department of Public Health and Environment, convened the Four Corners Air 

Quality Task Force (FCAQTF). States, Tribes, Federal Land Managers and other stakeholders 

were brought together under the FCAQTF to develop strategies for air quality management in the 

region. In connection with this effort, the FCAQTF identified a need to model the air quality 

impacts of potential alternative mitigation strategies being developed by various FCAQTF work 

groups. Estimates of mitigation scenario impacts on criteria pollutants (ozone, particulate matter 

[PM]) and air quality related values (visibility and deposition) were needed with peak ozone and 

visibility impacts in the Mesa Verde, San Pedro Parks, and Weminuche Class I areas and 

surrounding Class II areas of primary interest. In response to these requirements, NMED 

obtained funding for a comprehensive atmospheric modeling study designed to address these 

issues. Development of emission inventories needed for the modeling analysis was funded by 

BP North America Production Co. BP also funded several model sensitivity analyses. A Four 

Corners Modeling Group (FCMG) consisting of representatives from NMED, BP and other 

interested stakeholders from the FCAQTF was formed to guide and monitor the modeling study. 

Results of the modeling study are intended to inform the direction of future air quality 

management efforts in the Four Corners region. 

1.2 APPROACH 

A comprehensive, high resolution (4 x 4 km horizontal grid cell size), regional scale air quality 

photochemical modeling study of the Four Corners region was conducted by ENVIRON under 

the direction of the FCMG as described above. Extensive development of air quality modeling 

methods and data bases was conducted by ENVIRON for the FCMG for this purpose, resulting 

in a regional air quality planning tool which can be used to evaluate impacts of both future 

development projects and alternative emission reduction strategies.1

 Regional modeling of the 

Four Corners region consisted of the following tasks: 

1. Development of a modeling protocol which set forth the procedures, data sources and 

modeling approach used in performing the air quality modeling for evaluation by the 

FCMG prior to the start of any modeling work (See Appendix A); 

2. Development of a model-ready 2005 base case emission inventory suitable for ozone and 

PM modeling with suitable photochemical grid model (CAMx); 

3. Development of a 2018 base case emission inventory representing the combined effects 

of growth of emission generating activities in the region between 2005 and 2018 and 

current “on the books” emission control regulations, 

4. Application of CAMx with the 2005 base case inventory using 2005 meteorological data 

and evaluation of model performance based on comparisons of measured and predicted 

air quality as described in Section 3; 

 

1 http://www.nmenv.state.nm.us/aqb/4C/Modeling.html 
June 2009 

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5. Application of CAMx to a 2018 base case emissions inventory (i.e. with no mitigation 

options) 

6. Source apportionment modeling with the 2018 base case emissions to estimate the key 

source types and locations contributing to ozone and PM formation in the Four Corners 

area as described in Section 4. 

7. Use of CAMx as described in Section 4 to evaluate changes in ambient air quality 

estimated to result from implementation of five alternative mitigation options developed 

by the FCAQTF. 

A modeling domain comprised of a series of nested grids with 36, 12, and 4 km grid spacing 

(resolution) was defined as shown in Figure 1-1. The rationale for the modeling domain 

configuration and vertical layer structure is described in the Modeling Protocol (Appendix A). 

Development of the emission inventories used in this study is described in Section 2. Emissions 

inventory development and the source apportionment modeling performed in Step 6 were funded 

by BP America Production Company. All other work was conducted by ENVIRON and Alpine 

Geophysics under contract to NMED. Air quality modeling for this study was based on an 

existing annual meteorological simulation for 2005 which was performed, quality assured, and 

evaluated by Alpine Geophysics using the MM5 mesoscale meteorological model as part of an 

earlier study conducted by ENVIRON and Alpine Geophysics (see Appendix F). Results of the 

study are summarized in Section 5 along with conclusions and recommendations for further 

analysis. 
June 2009 

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-2880-2520-2160-1800-1440-1080 -720 -360 0 360 720 1080 1440 1800 2160 2520 2880

-2160

-1800

-1440

-1080

-720

-360

0

360

720

1080

1440

1800

2160

New Mexico Modeling Domain

CAMx 36 km: 148 x 112 (-2736, -2088) to (2592, 1944)

CAMx 12 km: 167 x 137* (-2316, -912) to ( -312, 732)

CAMx 04 km: 101 x 92* (-1192, -508) to ( -788, -140)

* includes buffer cells

MM5 36 km: 165 x 129 dot points (-2952, -2304) to (2952, 2304)

MM5 12 km: 178 x 157 (-2376, -1080) to (-252, 792)

MM5 04 km: 172 x 167 (-1272, -672) to (-588, -8)

36 km

12 km

4 km

Figure 1-1. CAMx 36/12/4 km modeling domain used in the Four Corners Air Quality Modeling 

Study. 
June 2009 

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2.0 EMISSIONS INVENTORY 

A regional inventory representative of 2005 emissions suitable for use with the CAMx 

photochemical grid model was developed for this study. This 2005 base case inventory was 

developed to establish a current year base case CAMx simulation for evaluation of the 

emissions/modeling system via comparison of model predictions with observed air contaminant 

concentrations. This inventory is based on work conducted by ENVIRON and others for the 

Western Regional Air Partnership (WRAP). 

In addition to the 2005 base case inventory, two types of future year inventories were developed: 

1. A future year base case inventory that reflects both increases and decreases in emissions 

projected to occur in the region within the next decade. Given the need to model longterm control strategies and the availability of a 2018 inventory developed under the 

auspices of WRAP, 2018 was chosen as the future year for this analysis. 

2. A set of five future year inventories corresponding to five mitigation scenarios defined by 

the FCMG. These mitigation scenario inventories are identical to the 2018 base case 

inventory but with emissions from selected source categories reduced to reflect the 

expected impact of each mitigation scenario. The mitigation scenarios are defined in 

Section 2.3. 

2.1 DATA SOURCES AND MODEL-READY INVENTORY DEVELOPMENT 

Emission inventories were prepared for sources within the 4 km, 12 km and 36 km modeling 

domains shown in Figure 1-1. The inventories contain estimates of anthropogenic PM, SOx, 

NOx, VOC, CO, NH3 and windblown dust emissions as well as biogenic VOC and NOx 

emissions and fire emissions (wildfires and prescribed burns). Primary emissions data sources 

used in developing the inventories included: 

WRAP Regional Inventory Development and Modeling: WRAP funded 

development of a 2002 emissions inventory processed for use in the CMAQ and 

CAMx air quality models using the SMOKE emissions processing system. This 

inventory covers the entire continental U.S. at 36 km resolution. A similar 

model-ready inventory for 2018 which includes the latest available updates and is 

known as the PRP18 inventory was also prepared for WRAP. 

WRAP Phase II Oil and Gas Emissions Updates: ENVIRON developed a regionwide oil and gas emissions inventory for the western U.S. under contract to 

WRAP. ENVIRON recently completed updating this inventory for the years 

2002, 2005 and 2018. Emissions data for 2002 developed for the Southern Ute 

Indian Reservation and other areas in connection with the Northern San Juan Coal 

Bed Methane (CBM) EIS are included in the updated inventory. 

Southern Ute 2005 Oil & Gas Emissions: An updated emissions inventory for 

2005 for oil & gas sources on the Southern Ute lands compiled by the Southern 

Ute Indian Tribe (SUIT, 2005) was used to replace the older SUIT inventory 

included in the WRAP Phase II inventory. 
June 2009 

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At the time of this study, the inventories listed above represented the most accurate estimate of 

emissions in the region. A limitation in the inventories is that the WRAP Phase II Oil and Gas 

Inventory did not consider VOC emissions associated with production facilities. However, as 

part of the New Mexico Ozone Early Action Compact, Environ developed a VOC inventory for 

the region which was used in this analysis. It should also be noted that the 2005 Southern Ute 

Oil and Gas Inventory used in this study is also being used in the WRAP Phase III Inventory. 

Model-ready (gridded, hourly) emissions for the 2005 base year for all area sources outside of 

the 4 km modeling domain were obtained by linearly interpolating between the WRAP 2018 

(PRP18) and WRAP 2002 model-ready (gridded, hourly) emissions. Area source emissions on 

the portion of the 36 km grid that is overlapped by the 12 km modeling domain but outside the 4 

km domain were disaggregated to 12 km resolution with emissions evenly divided over the nine 

12 x 12 km grid cells within each 36 x 36 km grid cell. Model ready point source emissions for 

2005 for sources outside of the 4 km modeling domain were also obtained via linear 

interpolation between the WRAP 2018 (PRP18) and WRAP 2002 inventories. 

Model-ready (gridded, hourly) emissions for 2018 for all point and area sources outside of the 4 

km domain were obtained directly from the WRAP 2018 (PRP18) inventory. As the WRAP 

modeling was done at 36 km resolution, area source emissions on the portion of the 36 km grid 

that is overlapped by the 12 km modeling domain but outside the 4 km domain were 

disaggregated to 12 km resolution with emissions evenly divided over the nine 12 x 12 km grid 

cells within each 36 x 36 km grid cell as was done for the 2005 base year inventory. 

ENVIRON performed new revised emissions modeling for this study based on the 2005 and 

2018 inventories resolved at 4 km resolution over the 4 km modeling domain using SMOKE and 

related tools to obtain model-ready inventories. This provided a more detailed and up to date 

inventory for the innermost and most important modeling domain. Part of this effort included 

developing updated emissions estimates for oil & gas activities and electric generating units 

(EGUs) within the 4 km domain. Development of the model-ready oil & gas inventories is 

described in Appendix B; development of the EGU inventory as well as inventories for other 

point sources, mobile sources and other area sources (including ammonia and wind blown dust) 

is described in Appendix C. Revised biogenic emissions estimates were developed for all three 

modeling domains as described in Appendix D. In addition, emissions from fires (wildfires and 

prescribed fires) of CO, NOx, VOC, SO2, and PM were obtained from the National Center for 

Atmospheric Research (NCAR) 2005 fire database which is derived by NCAR from satellite 

data. Fire emissions were processed for use in this study over the 12 km western U.S. domain 

(see Appendix E for details). 
June 2009 

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2.2 EMISSION SUMMARIES: 2005 AND 2018 BASE CASES 

Annual emissions in the Four Corners 4 km domain are summarized by State and major source 

category for 2005 in Tables 2-1 through 2-4 and for the 2018 base case inventory in Tables 2-5 

through 2-8. In these tables, road dust and fugitive dust emissions are included within the area 

source category whereas windblown dust was included within the biogenic source category. 

Locomotive, aircraft and other non-road sources are included in the off-road emissions category. 

In the point source inventory, tribal sources were distinguished from the state sources and hence 

tribal point source emissions were reported separately from state emissions. For all other source 

categories, tribal emissions were combined with state emissions. Point sources associated with 

oil & gas production were separated from other point sources and reported separately in the 

tables below. Spatial distributions of annual emissions for oil and gas and other area sources are 

shown in Figure 2-1. Spatial distributions of annual emissions for on-road and off-road mobile 

sources are shown in Figure 2-2. Comparisons of the 2005 and 2018 base case inventories are 

provided in Figure 2-3. 

Table 2-1. 2005 NOx emissions (t/yr) within the 4 km modeling domain by state and source 

category.

STATE/Tribe Area On-road Off-road Biogenic

Area Oil 

& Gas 

Point Oil & 

Gas EGU Non EGU Total 

Arizona 97 4,661 2,407 211 13 7,389 

Colorado 302 3,757 1,910 659 921 2,548 535 10,632 

New Mexico 16,036 30,182 11,219 833 37,848 19,834 30,925 3,615 150,492 

Utah 42 741 181 130 51 352 78 1,575 

Tribes 7,264 41,743 2,770 51,777 

Grand Total 16,477 39,340 15,717 1,834 38,832 29,998 72,668 6,997 221,863 

Table 2-2. 2005 SO2 emissions (t/yr) within the 4 km modeling domain by state and source 

category. 

STATE/Tribe Area On-road Off-road Biogenic

Area Oil 

& Gas 

Point Oil & 

Gas EGU Non EGU Total 

Arizona 20 52 119 191 

Colorado 135 62 53 19 14 105 388 

New Mexico 5,580 543 625 116 552 17,866 3,020 28,302 

Utah 54 12 13 1 1,581 1,661 

Tribes 35 12,653 232 12,920 

Grand Total 5,789 669 809 136 602 30,518 4,938 43,461 
June 2009 

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Table 2-3. 2005 VOC emissions (t/yr) within the 4 km modeling domain by state and source 

category.

STATE/Tribe Area On-road Off-road Biogenic

Area Oil 

& Gas 

Point Oil 

& Gas EGU Non EGU Total 

Arizona 2,204 3,314 728 29,202 37 35,485 

Colorado 3,632 2,616 4,884 84,822 891 1,257 348 98,450 

New Mexico 26,675 17,079 5,690 108,515 109,480 7,857 7 1,849 277,152 

Utah 479 490 388 15,931 455 77 52 17,872 

Tribes 2,219 292 180 2,691 

Grand Total 32,989 23,499 11,690 238,471 110,862 11,410 299 2,429 431,649 

Table 2-4. 2005 PM emissions (t/yr) within the 4 km modeling domain by state and source 

category. 

STATE/Tribe Area On-road Off-road Biogenic

Area Oil 

& Gas 

Point Oil 

& Gas EGU Non EGU Total 

Arizona 4282 131 110 21074 25,597 

Colorado 2227 119 311 9766 24 34 687 13,168 

New Mexico 30,324 925 772 54744 123 25 2,238 89,151 

Utah 390 22 31 13057 12 13,512 

Tribes 11 965 81 1,057 

Grand Total 37,224 1,197 1,224 98,640 24 168 990 3,018 142,485 

Table 2-5. 2018 NOx emissions (t/yr) within the 4 km modeling domain by state and source 

category. 

STATE/Tribe Area On-road Off-road Biogenic

Area Oil 

& Gas 

Point Oil 

& Gas EGU Non EGU Total 

Arizona 117 1,934 1,217 211 13 1,340 0 4,834 

Colorado 366 1,456 1,269 659 946 2,939 701 8,335 

New Mexico 20,700 9,658 6,142 833 38,630 26,913 21,934 3,777 128,588 

Utah 47 337 115 130 50 233 89 1,002 

Tribes 8,316 54,306 3,202 65,824 

Total 21,231 13,385 8,743 1,834 39,639 38,401 77,580 7,770 208,583 
June 2009 

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Table 2-6. 2018 SO2 emissions (t/yr) within the 4 km modeling domain by state and source 

category. 

State Area On-road Off-road Biogenics

Area Oil 

& Gas 

Point Oil 

& Gas EGU NonEGU Total 

Arizona 24 20 1 0 0 1,452 0 1,496 

Colorado 146 16 5 0 19 12 141 340 

New Mexico 13,204 140 60 0 122 548 12,607 3,180 29,861

Utah 54 4 0 0 1 0 2,122 2,182 

Tribes 155 21,253 319 21,728

Total 13,428 180 66 0 142 715 35,312 5,763 55,606

Table 2-7. 2018 VOC emissions (t/yr) within the 4 km modeling domain by state and source 

category.

State Area On-road Off-road Biogenics 

Area Oil 

& Gas 

Point Oil 

& Gas EGU NonEGU Total 

Arizona 2,602 1,848 469 29,202 37 41 0 34,200 

Colorado 4,341 1,217 3,299 84,822 892 1,730 413 96,714 

New Mexico 34,313 7,753 4,179 108,515 131,900 11,150 356 2,153 300,319

Utah 651 277 246 15,931 453 103 72 17,732 

Tribes 2,962 184 100 3,245 

Total 41,906 11,094 8,193 238,471 133,282 15,945 581 2,738 452,210

Table 2-8. 2018 PM emissions (t/yr) within the 4 km modeling domain by state and source 

category. 

State Area On-road Off-road Biogenics Area Oil & Gas Point Oil & Gas EGU NonEGU Total 

Arizona 5,393 107 47 21,074 0 261 0 26,880

Colorado 2,678 88 174 9,766 26 28 871 13,630

New Mexico 46,424 670 420 54,744 0 106 1,047 833 104,244

Utah 440 18 15 13,057 0 0 13 13,543

Tribes 2 4,581 96 4,679 

Total 54,934 883 655 98,640 26 136 5,889 1,814 162,976
June 2009 

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Figure 2-1. Oil and gas and area source gridded emissions in the 4 km modeling domain. 
June 2009 

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Figure 2-2. Mobile source gridded emissions in the 4 km modeling domain. 
June 2009 

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NOx Emissions

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

Area On-road Off-road Biogenic Oil & Gas EGU NonEGU

Source Categories

TPY

2005

2018

SO2 Emissions

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

Area On-road Off-road Biogenic Oil & Gas EGU NonEGU

Source Categories

TPY

2005

2018

Figure 2-3. Comparative summaries of annual emissions within the 4 km domain for the 2005 

and 2018 base case inventories. 
June 2009 

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VOC Emissions

0

50,000

100,000

150,000

200,000

250,000

300,000

Area On-road Off-road Biogenic Oil & Gas EGU NonEGU

Source Categories

TPY

2005

2018

PM Emissions

0

20,000

40,000

60,000

80,000

100,000

120,000

Area On-road Off-road Biogenic Oil & Gas EGU NonEGU

Source Categories

TPY

2005

2018

Figure 2-3. (Concluded) Comparative summaries of annual emissions within the 4 km domain 

for the 2005 and 2018 base case inventories.
June 2009 

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2.3 Mitigation Scenarios 

A set of five mitigation scenarios were defined by the FCMG in terms of across the board 

emission reductions by source category: 

1. Scenario #1 focused on EGU SO2 and NOx emission reductions 

2. Scenario #2 focused on application of VOC controls to oil and gas sources 

3. Scenario #3 focused on application of NOx controls to oil and gas sources 

4. Scenario #4 combined emission controls from scenarios 1 – 3. 

5. Scenario #5 combined the oil and gas VOC and NOx controls from scenarios 2 and 3. 

Descriptions of each mitigation scenario and resulting emission reductions are provided in the 

following subsections. 

2.3.1 Scenario #1 

Mitigation scenario #1 is based on application of aggressive control measures for NOx and SO2

emissions from coal fired power plants in the Four Corners region. NOx reductions were based 

on the application of selective catalytic reduction (SCR) at the Four Corners Power Plant (FCPP) 

and the San Juan Generating Station (SJGS). Application of SCR at these two power plants was 

estimated to achieve emission levels of: 

• 0.1 lbs/MMBtu NOx at Four Corners Power Plant (based on feedback from Steve Frey, 

EPA Region IX indicating that a rate of 0.07 lbs/MMBtu would be difficult to obtain and 

that 0.1 lbs/MMBtu would be more reasonable) 

• 0.07 lbs/MMBTU at San Juan Generating Station based on BART analysis 

Ammonia emissions were increased at these two EGUs to account for an estimated SCR 

ammonia slip of approximately 2 ppm (based on estimate received from PNM). 

SO2 reductions were based on the presumptive BART limit of 0.15 lb/MMBtu at FCPP. Since 

the San Juan Generating Station Consent Decree limits were close to or more stringent than 

presumptive BART, the Consent Decree limits were included in the 2018 base case scenario for 

SJGS and no further controls were modeled as part of this mitigation scenario. 

Table 2-9 summarizes the emission changes under this mitigation scenario. 
June 2009 

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Table 2-9. Emission changes under mitigation scenario #1. 

NOx SO2 NH3

2018 

Base 

Mitigation 

#1 

2018 

Base 

Mitigation 

#1 

2018 

Base 

Mitigation 

#1 

Four Corners Power 

Plant – Unit 1 

4,558 742 1,603 1,113 0 14.6

Four Corners Power 

Plant – Unit 2 

5,106 832 1,796 1,247 0 16.4

Four Corners Power 

Plant – Unit 3 

6,101 994 2,146 1,491 0 19.6

Four Corners Power 

Plant – Unit 4 

17,784 2,896 6,256 4,345 0 57.1

Four Corners Power 

Plant – Unit 5 

17,431 2,839 6,132 4,258 0 56

San Juan Generating 

Station - Unit 1 

3,102 944 1,753 1,753 0 28

San Juan Generating 

Station - Unit 2 

3,158 961 1,785 1,785 0 28

San Juan Generating 

Station - Unit 3 

5,044 1,535 2,851 2,851 0 43.4

San Juan Generating 

Station - Unit 4 

5,242 1,595 2,963 2,963 0 42.5

Total 67,526 13,338 27,285 21,806 0 306

A comparison of total EGU emissions in the 4 km modeling domain under the 2018 base case 

and mitigation scenario #1 is shown in Table 2-10. Note that the 41 tons/year NH3 emissions 

included in the 2018 base case represent emissions from three power plants: Reeves, Delta 

Person, and Escalante. 

Table 2-10. EGU emission (tons/year) under the 2018 base case and scenario #1. 

4Corner 2018 Base 

State NOX SO2 NH3 VOC PM10 CO 

Arizona 1,340 1,452 0 41 261 2,662 

Navajo Nation 54,306 21,253 0 184 4,581 7,910 

New Mexico 21,934 12,607 41 356 1,047 36,377 

Grand Total 77,580 35,312 41 581 5,889 46,949 

EGU Mitigation Scenario #1

State NOX SO2 NH3 VOC PM10 CO 

 Arizona 1,340 1,452 0 41 261 2,662 

 Navajo 

Nation 11,629 15,774 164 184 4,581 7,910 

 New Mexico 10,423 12,607 183 356 1,047 36,377 

Grand Total 23,392 29,833 346 581 5,889 46,949 

% Difference

State NOX SO2 NH3 VOC PM10 CO 

Arizona 0% 0% 0% 0% 0% 

Navajo Nation -79% -26% 0% 0% 0% 

New Mexico -52% 0% 349% 0% 0% 0% 

Grand Total -70% -16% 751% 0% 0% 0% 
June 2009 

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2.3.2 Scenario #2 

Mitigation scenario #2 is based on the application of VOC control technologies to oil and gas 

sources in San Juan and Rio Arriba counties, New Mexico. 

The 2005 oil and gas VOC emission inventory for San Juan and Rio Arriba counties in 

northwestern New Mexico used for this study indicated that process fugitive emissions (leaking 

components) represented 35 % of the total VOC emissions. In addition, estimated VOC 

emissions from pneumatic equipment was 0.02 percent of the total emissions. The FCMG 

determined, based on typical component counts for a typical well site, the number of total wells 

and EPA emission factors, that the estimate for process fugitives was too high. Similarly, based 

on the engineering judgment, members of the FCMG determined that the estimated VOC 

emissions from pneumatic equipment was too low. 

A more realistic estimate of fugitive emissions was developed by multiplying an estimated 0.4 

tons/year fugitive VOC emissions per well1

 by the number of wells in these two counties. This 

results in a lowering of the estimated fugitive VOC emissions from 37,563 tons/year to a more 

realistic 8,087 tons/year. It was assumed out of necessity that the total VOC emissions from oil 

and gas sources used in the base case modeling was correct (105,966 tons per year). Therefore, 

the difference between the original and revised fugitive VOC emission estimates was added to 

the pneumatic VOC emissions. The resulting revised pneumatic VOC emission estimate of 

29,500 tons/year2

 is highly uncertain but is considered more reasonable than the very low value 

(24 tons/year) contained in the original inventory. 

The resulting revised 2005 VOC inventory was carried forward to 2018 by applying the revised 

apportionment of VOCs between pneumatics and fugitives to the 2018 base case inventory. The 

revised 2018 base case inventory was then used to estimate VOC emission reductions associated 

with potential control measures of interest to the FCMG. After considering several control 

options, the group ultimately selected a “high” VOC control option based on: 

• 90% control of pneumatic emissions achieved via use of low- or no-bleed pneumatics 

(assumes nearly all existing equipment is high-bleed which is likely an overestimate); 

• 45% control of completion emissions based on the assumption that 50% of wells can be 

routed to flares capable of 98% VOC control for completions (it is unlikely all well 

completions can be routed to flares due to low reservoir pressure in the Farmington area); 

• 50% control of venting emissions (activities used to remove water in the well bore) via 

changes in operating practices. 

These controls were applied to the revised 2018 oil and gas VOC inventory to derive the modelready VOC emissions under mitigation Scenario #2. 

A comparison of oil and gas area source emissions under the 2018 base case and scenario #2 is 

provided in Table 2-11. 

 

1

 Doug Blewitt, personal communication, 12 February 2009. 

2

 Equals 15,805 tons/year in San Juan county and 13,695 tons/year in Rio Arriba county. 
June 2009 

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Table 2-11. Oil and gas area source emissions (tons/year) under the 2018 base case and 

scenario #2. 

4Corner 2018 Base (tpy)

State CO NOx TOG NH3 SO2 PM 

Arizona 1 13 206 0 0 0

Colorado 338 946 895,114 0 19 0

New Mexico 29,550 38,629 752,219 0 122 0

Utah 9 50 2,532 0 1 0

Grand Total 29,899 39,638 1,650,070 0 142 0

O&G High TOG Mitigation Scenario #2 (tpy)

State CO NOx TOG NH3 SO2 PM 

Arizona 1 13 206 0 0 0

Colorado 338 946 895,114 0 19 0

New Mexico 29,550 38,629 367,488 0 122 0

Utah 9 50 2,532 0 1 0

Grand Total 29,899 39,638 1,265,340 0 142 0

% Difference

State CO NOx TOG NH3 SO2 PM 

Arizona 0% 

Colorado 0% 

New Mexico -51% 

Utah 0% 

Grand Total -23% 

2.3.3 Scenario #3 

Mitigation scenario #3 assumes a reduction in oil & gas combustion source NOx emissions due 

to a 50% penetration of NSPS compliant engines into the existing engine fleet. Table 2-12 

compares oil and gas point source emissions under the 2018 base case with scenario #3. 
June 2009 

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Table 2-12. Oil and gas point source emissions under the 2018 base case and mitigation 

scenario #3. 

4Corner 2018 Base

State NOX SO2 CO 

Arizona 13 0 1

Colorado 3,401 31 1,857

New Mexico 59,426 670 46,823

Utah 284 1 287

Southern Ute 4,694 0 6,324

Navajo Nation 3,622 155 0

Northern San Juan CBM Project 

Area 483 0 1,561

Farmington RMP Area 6,117 0 6,482

Grand Total 78,040 858 63,335

O&G High NOx Mitigation Scenario #3 

State NOX SO2 CO 

Arizona 13 0 1

Colorado 3,203 31 1,857

New Mexico 47,933 670 46,823

Utah 284 1 287

Southern Ute 4,235 0 6,324

Navajo Nation 3,622 155 0

NSJCBM 483 0 1,561

Farmington RMP 6,117 0 6,482

Grand Total 65,890 858 63,335

% Difference

State NOX SO2 CO 

Arizona 0% -- 0%

Colorado -6% 0% 0%

New Mexico -19% 0% 0%

Utah 0% 0% 0%

Southern Ute -10% 0%

Navajo Nation 0% 0% 

NSJCBM 0% 0%

Farmington RMP 0% 0%

Grand Total -16% 0% 0%

2.3.4 Scenario #4 

Mitigation scenario #4 represents the combination of EGU controls from scenario #1 with the oil 

and gas VOC and NOx controls from scenarios #2 and #3. Table 2-13 compares emissions 

under this scenario with the 2018 base case. 
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Table 2-13. EGU and oil and gas source emissions (tons/year) under the 2018 base case and 

mitigation scenario #4. 

4Corner 2018 Base (EGU + O&G Area + O&G Point) 

State CO NOx TOG PM 

Arizona 2,661 1,352 255 260 

Colorado 3,418 3,884 1,261,028 0 

Navajo Nation 7,892 57,799 4,267 4,570 

New Mexico 89,657 87,462 846,114 1,200 

Southern Ute 6,324 4,694 20,964 0 

Utah 287 284 2,997 0 

Grand Total 110,238 155,476 2,135,625 6,031 

EGU & OG Controls ( Mitigation# 1, 2 & 3 ) Mitigation Scenario #4 

State CO NOx TOG PM 

Arizona 2,661 1,352 255 260 

Colorado 3,418 3,686 1,261,028 0 

Navajo Nation 7,892 15,223 4,267 4,570 

New Mexico 89,657 64,466 461,383 1,200 

Southern Ute 6,324 4,235 20,964 0 

Utah 287 284 2,997 0 

Grand Total 110,238 89,247 1,750,894 6,031 

%Difference 

State CO NOx TOG PM 

Arizona 0% 0% 0% 0% 

Colorado 0% -5% 0% 

Navajo Nation 0% -74% 0% 0% 

New Mexico 0% -26% -45% 0% 

Southern Ute 0% -10% 0% 

Utah 0% 0% 0% 0% 

Grand Total 0% -43% -18% 0% 

2.3.5 Scenario #5 

Mitigation scenario #5 is based on implementation of the oil and gas source VOC controls from 

scenario #2 and the oil and gas NOx controls from scenario #3. Table 2-14 compares emissions 

under this scenario with emissions under the 2018 base case. 
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Table 2-14. Oil and gas area and point source emissions (tons/year) under the 2018 base case 

and mitigation scenario #5. 

4Corner 2018 Base (O&G Area + O&G Point) 

State CO NOx TOG PM 

Arizona 1 13 206 0

Colorado 3,418 3,884 1,261,028 0

Navajo Nation 0 3,622 4,048 0

New Mexico 53,305 65,542 845,571 63

Southern Ute 6,324 4,694 20,964 0

Utah 287 284 2,997 0

Grand Total 63,335 78,039 2,134,814 63

OG Controls ( Mitigation# 2 & 3 ) Mitigation Scenario #5 

State CO NOx TOG PM 

Arizona 1 13 206 0

Colorado 3,418 3,686 1,261,028 0

Navajo Nation 0 3,622 4,048 0

New Mexico 53,305 54,050 460,840 63

Southern Ute 6,324 4,235 20,964 0

Utah 287 284 2,997 0

Grand Total 63,335 65,890 1,750,082 63

%Difference 

State CO NOx TOG PM 

Arizona 0% 0% 0% 

Colorado 0% -5% 0% 

Navajo Nation 0% 0% 

New Mexico 0% -18% -45% 0%

Southern Ute 0% -10% 0% 

Utah 0% 0% 0% 0%

Grand Total 0% -16% -18% 0%
June 2009 

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3.0 AIR QUALITY MODELING 

Modeling of regional ozone, PM and air quality related value impacts for the Four Corners Air 

Quality Task Force was conducted using the Comprehensive Air quality Model with extensions 

(CAMx, v4.51) in accordance with the previously established modeling protocol (Appendix A). 

CAMx is a publicly available (www.camx.com) three-dimensional multi-scale 

photochemical/aerosol grid model that is developed and maintained by ENVIRON International 

Corporation (ENVIRON, 2008). CAMx has been widely used in recent years by a variety 

regulatory agencies for 1-hour and 8-hour ozone and PM SIP modeling studies, as well as by the 

Western Regional Air Partnership and other Regional Planning Organizations (RPOs) for 

regional haze modeling. It is currently being used to evaluate air quality impacts of several oil 

and gas development projects in the western U.S. 

3.1 METEOROLOGICAL MODELING 

CAMx modeling was based on a complete annual simulation of gridded high resolution 3-

dimensional meteorological fields developed for the 2005 base year. An existing application of 

the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) 

Mesoscale Model (MM5) was selected for this purpose (see Appendix F). MM5 (Dudhia, 1993; 

Grell et al., 1994: www.mmm.ucar.edu/mm5) is a limited-area, non-hydrostatic, terrainfollowing model designed to simulate mesoscale atmospheric circulation. The model is 

supported by several pre- and post-processing programs which are referred to collectively as the 

MM5 modeling system. MM5 was applied for the calendar year 2005 over a set of nested 

modeling domains that cover the continental United States at a 36 km grid spacing, the 

southwestern United States at a 12 km spacing, and the Four Corners Region (New Mexico, 

Utah, Arizona, and Colorado) at a 4 km spacing (see Figure 1-1). Additional details of the MM5 

modeling procedure and input data sources are provided in Appendix F. This appendix also 

includes results of an evaluation of the MM5 model performance with respect to the model’s 

ability to reproduce observed wind, temperature, water vapor mixing ratio and precipitation 

patterns. Also included in Appendix F is a comparison of the 2005 MM5 model performance 

with MM5 performance for two other years (2003 and 2004). Model performance for 2005 is 

similar to performance in 2003 and 2004 and is generally as good as or better than model 

performance achieved in other recent MM5 applications, with generally good agreement in wind 

and temperature fields. As in other MM5 applications, the amount of summer precipitation in 

the Four Corners region is overestimated, which in principal may lead to overestimation of wet 

deposition and excessive removal of airborne pollutants in the air quality modeling. As 

described in Section 3.2, however, a sensitivity analysis in which wet deposition was turned off 

showed no major impacts on predicted pollutant concentrations. 

3.2 OZONE AND PM MODELING 

CAMx was applied to a 2005 base case emissions inventory for purposes of establishing model 

performance prior to simulation with future year (2018) inventories. Steps in the modeling 

analysis are outlined below: 
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Base Case Modeling: A modeling domain comprised of a series of nested grids with 36, 12, and 

4 km grid spacing (resolution) was defined as previously shown in Figure 1-1. The rationale for 

the modeling domain configuration and vertical layer structure is described in the Modeling 

Protocol (Appendix A). The 2005 MM5 meteorological data fields, developed as described 

above, were processed into CAMx inputs for this domain configuration and quality-assured. 

Emission inputs for CAMx were developed as described in Section 2. CAMx was run first on 

the national 36 km grid for the entire year; each quarter was run separately, which included a 15-

day model spin-up period before the first day of each quarter. Gridded hourly concentrations of 

all chemical species from the 36 km run were used to generate initial conditions (ICs) and 

boundary conditions (BCs) for the western U.S. 12 km grid. CAMx was then exercised on the 

combined 12/4 km grid system in a fully two-way interactive manner for each quarter of 2005, 

each with a 5-day model spin-up period. 

Model Performance Evaluation: A comprehensive model performance evaluation was conducted 

on the 2005 base case simulation. Available measurements for ozone, speciated PM and total 

PM mass were compared with model output over the 4 km domain. Well-established model 

evaluation software and techniques were employed, which have been developed from regional 

modeling conducted for the Western Regional Air Partnership (WRAP) and other urban and 

regional-scale modeling programs. Statistical performance metrics were compared to acceptance 

goals and criteria established over the past several years by the EPA and RPOs. Graphical 

displays of model performance were generated, including scatter plots, time series plots, spatial 

maps of model predictions, “bugle” plots and other displays. 

Future Year (2018) Modeling: CAMx was applied to a 2018 base case emissions inventory 

(with no mitigation options) developed as described in Section 2. Results were compared with 

the 2005 base case simulation. 

Source Apportionment Modeling: A source apportionment modeling analysis was conducted for 

the 2018 base case run to estimate the key source types and locations contributing to ozone and 

PM formation in the Four Corners area. Results of this analysis were used to guide the selection 

of emissions mitigation scenarios to be modeled. 

Mitigation Scenario Modeling: A set of mitigation scenarios were modeled to evaluate changes 

in ozone, PM and air quality related values estimated to occur under each of five alternative 

emissions mitigation scenarios developed by the FCMG based on emission control measures 

developed previously by the FCAQTF. 

Procedures for the 2005 base case modeling and results of the model performance evaluation are 

described in more detail in the following subsections. Results from the 2018 base case 

modeling, ozone and PM source apportionment modeling and mitigation scenario modeling are 

presented in Section 4. 

3.2.1 2005 Base Case Modeling 

Databases required to configure and operate CAMx for the Four Corners Air Quality Modeling 

Study are as follows: 

• Three-dimensional hourly meteorological fields generated by MM5 and prepared using the 

MM5CAMx interface processor (see Section 3); 
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• Two-dimensional land use/land cover and topography, as prepared for MM5, and generated 

using the MM5CAMx interface processor; 

• Two-dimensional low-level (surface layer) emissions and elevated point source emissions 

generated via the SMOKE emissions processor (see Section 2); 

• Initial/boundary (IC/BC) inputs for the coarsest (master) 36 km grid as prepared by WRAP 

from GEOS-CHEM global model output; 

• Two-dimensional albedo/haze/ozone column fields developed using the CAMx AHOMAP 

pre-processor; 

• Photolysis rates look up table developed using the albedo/haze/ozone column fields and the 

TUV radiative transfer model. 

Meteorological Inputs 

Meteorological data for this analysis were derived from MM5 modeling of the calendar year 

2005 on a similar 36/12/4 km nested grid structure (as described in Section 3). It is necessary to 

convert raw output from the MM5 meteorological model to formats and variables used by CAMx 

specifically. The MM5CAMx translation processor was used to complete this task. MM5CAMx 

includes the ability to interpolate data from the native map projections used by the 

meteorological model to any projection to be specified for the air quality model (CAMx may be 

applied on Lambert Conformal, Polar Stereographic, or UTM Cartesian projections, or in 

geodetic latitude/longitude). 

CAMx requires meteorological input data for the parameters described in Table 3-1. All of these 

input data are derived from the MM5 results. MM5CAMx performs several functions: 

1. Extracts data from the MM5 grids to the corresponding CAMx grids; in this study, the 

extraction includes a simple one-to-one mapping from the MM5 Lambert Conformal grid 

to the CAMx Lambert Conformal grid, with appropriate windowing to remove the extra 

row/columns in the MM5 grids. 

2. Performs mass-weighted vertical aggregation of data for CAMx layers that span multiple 

MM5 layers – in this project 34 MM5 layers were aggregated to 19 CAMx layers 

spanning the depth between the surface and ~15 km MSL. 

3. Applies diagnostic analysis techniques to derive key variables required by CAMx that are 

not directly output by MM5 (e.g., vertical diffusion coefficients and some cloud 

information). 

Table 3-1. CAMx meteorological input data requirements. 

CAMx Input Parameter Description 

Layer interface height (m) 3-D gridded hourly time-varying layer heights 

Winds (m/s) 3-D gridded hourly wind vectors (u,v) 

Temperature (K) 3-D gridded hourly temperature and 2-D gridded surface temperature 

Pressure (mb) 3-D gridded hourly pressure 

Vertical Diffusivity (m2/s) 3-D gridded hourly vertical exchange coefficients 

Water Vapor (ppm) 3-D gridded hourly water vapor mixing ratio 

Cloud Cover 3-D gridded hourly cloud and precip water contents 

Land use Distribution 2-D gridded static landuse/landcover distribution 
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The MM5CAMx program has been written to carefully preserve the consistency of the predicted 

wind, temperature and pressure fields output by MM5. This is the key to preparing massconsistent inputs for CAMx, and therefore for obtaining high quality performance from CAMx. 

The MM5CAMx processor was used to process the 2005 MM5 output data fields from each 

modeling grid to the CAMx grids, variables and formats. Layer collapsing was employed to 

reduce the number of vertical layers from the 34 used in the MM5 modeling to 19 used in the 

CAMx modeling (as shown in Appendix E, Table 1). 

Vertical diffusivities (Kv) are an important input to the CAMx simulation since they determine 

the rate and depth of mixing in the planetary boundary layer (PBL) and above. The MM5CAMx 

program offers three options to determine Kv fields from MM5 meteorological parameters, 

depending on the physics options set in MM5. Given the configuration of MM5 used for the 

Four Corners modeling, two Kv options were available in MM5CAMx for this project: the 

CMAQ method and the O’Brien (1970) profile method. The O’Brien approach was used 

throughout all developmental and final modeling simulations. The O’Brien method yields 

generally lower mixing rates and slightly lower mixing depths than the more vigorous CMAQ 

method. 

Developmental CAMx runs indicated a bias toward under predictions for most PM species; this 

was attributed to many issues, including under estimation of emissions (dust, fires) and poor 

meteorological performance for precipitation and boundary layer mixing. Early sequential tests 

investigated sensitivity to precipitation and boundary layer depths, in which wet deposition was 

turned off and the mixing depth as diagnosed using the O’Brien method in MM5CAMx was 

reduced artificially. Results of both tests showed that PM species concentrations were not 

sensitive to either change. Therefore the under prediction biases were attributed to potential 

emission uncertainties, specifically related to under estimation of primary combustion (carbon). 

Fire emissions, which were not included in the initial developmental runs, were added to the 

final model run. This improved but did not eliminate the underestimation of PM concentrations. 

Emission Inputs 

Model-ready emission files for CAMx simulations were prepared as described in Section 2. 

Initial and Boundary Conditions 

For the WRAP modeling of the 2002 year, boundary conditions for the continental U.S. 36 km 

RPO domain were based on a 2002 simulation of the Harvard GEOS-CHEM global transport and 

chemistry model. The GEOS-CHEM 2002 output was processed as 3 hourly spatially varying 

boundary conditions along the edges of the 36 km RPO grid. For modeling years other than 

2002, ENVIRON processed the 2002 GEOS-CHEM data into 12 sets of monthly-averaged 

diurnally varying boundary conditions. This approach has been successfully used for several 

recent SIP modeling efforts in the Southwest U.S. (e.g., Phoenix, Las Vegas, Denver), and was 

similarly used to provide 36 km grid boundary conditions for this study. Boundary conditions 

for the 12/4 km nested grid run were extracted from the 36 km CAMx results. 

Developmental CAMx runs indicated very high ozone concentrations over the Rocky Mountains 

of Colorado during the mid-spring period, often reaching as high as 90 ppb for daily maximum 8 

hour averages. Peak observed concentrations during this period rarely exceeded 65 ppb. This 
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problem was apparent on all three CAMx domains (36, 12, and 4 km grids), and in fact mirrored 

a similar result from 2002 36 km WRAP CMAQ modeling. After significant effort to identify 

the cause, it was found that the lateral boundary conditions extracted from GEOS-CHEM in the 

top-most layers (layers 17-19, 8-15 km MSL) were reflecting stratospheric ozone levels in excess 

of 200 ppb during the springtime, and these high concentrations were being vertically transported 

downward over the highest terrain. This further indicated that vigorous vertical circulation 

systems were being generated over complex terrain in both CAMx and CMAQ. To overcome 

this problem, the 36 km ozone boundary conditions in the uppermost layer were artificially 

reduced to tropospheric levels by assigning each grid cell in layer 19 to the average ozone in 

layers 18 and 19. Ozone performance was dramatically improved during the springtime, with 

only a minor impact on summertime ozone levels. 

Default initial concentrations developed for the 2002 WRAP CMAQ simulations were also used 

to specify CAMx initial conditions for each quarter of the 2005 CAMx simulation. A 15-day 

spin-up period was run before each quarter to eliminate any significant influence of these 

arbitrary initial conditions. Initial conditions for the 12/4 km nested grid simulations were 

extracted from the 36 km grid results 5 days prior to the beginning of each quarter. 

Ancillary Inputs 

Additional CAMx model inputs were prepared using standard data sources and processors. For 

example, total integrated ozone column data for 2005 were obtained from the TOMS satellite 

database1

 and processed as input into CAMx using the AHOMAP preprocessor. Ozone column 

data were processed for each month of 2005, according to the monthly average files obtained 

from that web site. Surface characteristics, including UV albedo and daily snowcover, were 

defined based on data output by the MM5 simulation (as processed by MM5CAMx). The 

photolysis rates lookup table was prepared using the NCAR TUV radiative transfer pre-processor 

according to the range of ozone column and surface characteristics data described above. TUV 

outputs a monthly clear-sky photolysis look up table that is directly input to CAMx; the table 

defines photolysis rates for six photolytic reactions over a range of solar zenith angles, altitudes, 

ozone column, surface UV albedo, and haze turbidity. CAMx internally adjusts the photolysis 

rates for cloud cover according to the cloud inputs provided to CAMx (from MM5 via 

MM5CAMx). 

CAMx Model Options 

The latest public-released version of CAMx at the time if the study (v4.51) was employed. The 

CAMx configuration options included the following: 

• CAMx was run separately on the 36 km grid (resulting in “one-way” grid nesting between 

the 36 km grid and the 12/4 km nests); 

• CAMx was run on the 12/4 km nested grid systems (resulting in interactive “two-way” grid 

nesting between the 12 and 4 km grids); 

• The CB05 gas-phase chemistry was employed, and solved using the fast CMC hybrid 

solver; 

• The Coarse/Fine (CF) static two-mode aerosol chemistry mechanism was employed, which 

uses RADM aqueous-phase chemistry, ISORROPIA inorganic aerosol thermodynamics 

 

1 http://jwocky.gsfc.nasa.gov/
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(sulfate/nitrate/ammonium equilibrium), and the latest updates to the SOAP secondary 

organic aerosol chemistry module; 

• The Plume-in-Grid (PiG) subgrid-scale plume module was not used given that the regional 

scales in this study were addressed with a high-resolution 4 km grid; 

• The PPM advection solver was employed; 

• Dry and wet deposition were both active; 

Modeling Strategy 

An initial CAMx simulation was performed for the entirety of 2005 on the 36 km continental 

U.S. RPO domain. Hourly gridded output from this run was then processed to generate initial 

and boundary conditions for the interactive two-way 12/4 km model simulations. The strategy 

for performing the annual 36 and 12/4 km grid simulations was to run CAMx separately for each 

of four quarters of the year (January-March, April-June, July-September, and OctoberDecember). The CAMx simulation for each quarter was comprised of a series of single-day 

simulations, in which the model is restarted at midnight UTC (1700 local standard time). This 

facilitated the use of various day-of-week specific emissions and other inputs that needed to be 

provided to the model on a daily basis. 

A 15 day “spinup” period was added prior to the start of each quarter for the 36 km grid run as a 

way to remove the influence of initial conditions. A single set of WRAP initial conditions were 

used to cold-start the model at beginning of all four 15-day spinup periods. Prior tests of CAMx 

on the RPO grid suggest that at least two weeks are needed to remove a significant fraction of the 

initial conditions from such large domains. Alternatively, a 5 day spinup period was used to 

initialize the 12/4 km grid quarterly simulations. The initial conditions used for each of the four 

5-day spinups were extracted from the 36 km grid output to remain consistent with the manner in 

which 12 km boundary conditions were generated. 

As was done in several recent annual modeling studies, we initially selected two representative 

monthly periods to perform diagnostic and sensitivity testing with CAMx on the 12/4 km nested 

grid system: a summer month characterized by high ozone and anthropogenic PM (e.g., SO4) and 

a winter month characterized by high NO3 (note that EC and OC occur year round and are 

heavily associated with natural emissions). Using the 12/4 km emissions and meteorology, and 

boundary conditions generated from the 36 km 2005 annual run, initial 2005 base case 

simulations were run for the chosen summer and winter months of 2005, and a preliminary 

model performance evaluation was conducted. Results of this performance evaluation were used 

to guide a series of diagnostic and sensitivity tests designed to identify the optimal model 

configuration for simulating ozone and PM air quality in the Four Corners region. 

3.2.2 CAMx Performance Evaluation 

A critical component of every air quality modeling study is the model performance evaluation, 

where the modeled estimates for the base year are compared against observed values to assess 

the model’s accuracy and provide an indication of its reliability as a tool to guide effective air 

quality management. The Four Corners modeling protocol (ENVIRON, 2007) discussed a 

general evaluation approach based upon the methods, data, and analyses recommended in the 

EPA modeling guidance (U.S. EPA, 2007). The protocol also delineated the specific analyses 
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and products that were to be generated under the Four Corners modeling program according to 

schedule and available resources. 

All mathematical models possess inherent limitations owing to the necessary simplifications and 

approximations made in formulating the governing equations, implementing them for numerical 

solution on fast computers, and in supplying them with input data sets and parameters that are 

themselves approximations of the full state of the atmosphere and emissions processes. Like all 

air quality models, a major limitation of CAMx rests with the input fields that characterize 

emissions, meteorology, and initial/boundary conditions. Key science limitations in the model 

itself include the nitrate formation chemistry and the secondary organic aerosol (SOA) module. 

Preliminary modeling by the RPOs (e.g., WRAP, VISTAS and CENRAP) found both CAMx 

and CMAQ nitrate performance suspect with winter overestimations and summer 

underestimations (Morris et al., 2004, 2005). While not as poor as CMAQ, the VISTAS and 

CENRAP modeling also found CAMx performance for Organic Carbon (OC) to be less than 

ideal; much of the OC performance problems have been due to deficiencies in the SOA module 

that in the past has failed to account for several known processes important to SOA (e.g., 

polymerization). Some of these limitations have been addressed in an improved SOA module 

now available in the version of CAMx used in this analysis (version 4.51); additional research in 

this area is ongoing. 

Overview and Context 

The Four Corners modeling protocol laid out the “roadmap” for achieving an adequately tested 

modeling system for regulatory use. This does not mean that every analysis identified was 

carried out or even possible according to available resources, the existing aerometric databases, 

and present technology constraints. Hence, the protocol describes a range of model testing 

methodologies potentially available to adequately evaluate the performance of the CAMx air 

quality modeling system for the 2005 annual period. Procedures for evaluating PM models are 

much less established than for ozone, and research is ongoing. 

The evaluation of the CAMx modeling system for the annual 2005 simulation was consistent with 

EPA’s modeling guidance, which essentially calls for an operational evaluation of the model 

focusing on a specific set of gas phase and aerosol chemical species and a suite of statistical metrics 

for quantifying model response over the annual cycle. Emphasis was placed on assessing: (a) how 

accurately the model predicts observed concentrations; and (b) how accurately the model predicts 

responses of predicted air quality to changes in inputs. Over the past 20 years, a substantial body of 

information and analytical techniques has been developed to address the first aspect. Unfortunately, 

even today there are few rigorous methods available for quantifying the accuracy and precision of a 

model’s predicted concentration changes as the result of emissions changes. 

When designing a model performance evaluation, it is important to understand how the modeling 

results will ultimately be used. EPA modeling guidance not only provides a framework for the 

Four Corners model performance evaluation approach, but just as importantly describes the 

methodology by which to project base-year pollutant levels to target years. A key concept in 

EPA’s guidance is that the modeling projections are used in a relative sense to scale or roll back 

the observed individual PM species concentrations. The model-derived ratios of future-year to 

current-year concentrations are called relative response factors (RRFs). Since the model is used 

to project future year PM2.5 species components rather than total PM2.5 mass, then the model 

performance for each of the components is actually more important than for total PM2.5 mass for 
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which the standard was written. These PM2.5 species components are: 

• Sulfate (PSO4); 

• Nitrate (PNO3); 

• Ammonium (PNH4); 

• Organic Carbon (OC); 

• Elemental Carbon (EC); and 

• Other Inorganic fine Particulate (FPRM and FCRS). 

Therefore, the model testing concentrated on an operational evaluation of the model predictions 

for those PM components listed above. We also evaluated the modeling system for its ability to 

accurately estimate ozone. The correct simulation of gas-phase oxidants is needed for PM since 

correct, unbiased simulation of gas-phase photochemistry is a necessary element of reliable 

secondary PM predictions. This evaluation was carried out across the 4 km grid for the entire 

year and also on a month-by-month to daily basis to help build confidence that the modeling 

system operated correctly. 

Evaluation Datasets 

The CAMx model performance evaluation for the 2005 base year included analyses of 

predictions against available measurements at ground-level monitors throughout the 4 km 

modeling domain. Unfortunately, there were no aloft data for the 2005 period in the Four 

Corners Area. Concentration measurements from a number of monitoring networks were used to 

the fullest extent possible in the CAMx model performance evaluation. Drawn from available 

state and federal monitoring networks in New Mexico, Colorado, Arizona, and Utah as well as in 

surrounding states, these surface measurements included ozone, NOx, SO2, total PM mass and 

PM species components. Routine gas-phase concentration measurements for ozone, NOx and 

CO are archived in EPA’s Air Quality Subsystem (AQS) database. Additional ozone 

measurements obtained from a Forest Service monitoring site at the Shamrock Mine site in La 

Plata County, Colorado. Other sources of information were the various PM monitoring networks 

including the: (a) Interagency Monitoring of Protected Visual Environments (IMPROVE), (b) 

Clean Air Status and Trends Network (CASTNET), (c) EPA PM2.5 and PM10 Mass Networks 

(EPA-FRM), (d) EPA Speciation Trends Network (STN); and (e) National Acid Deposition 

Network (NADP). Typically, these networks provide ozone, other gas phase precursors and 

product species, PM, and visibility measurements. Figure 3-1 shows locations of the standard 

monitoring network sites within the 12 km modeling domain. 
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-2280-2160-2040-1920-1800-1680-1560-1440-1320-1200-1080 -960 -840 -720 -600 -480 -360

-840

-720

-600

-480

-360

-240

-120

0

120

240

360

480

600

720

New Mexico CAMx 12 km Nested Domain

IMPROVE

CASTNET

NADP

12 km

4 km

CAMx 12 km: 167 x 137* (-2316, -912) to ( -312, 732)

CAMx 04 km: 101 x 92* (-1192, -508) to ( -788, -140)

* includes buffer cells

Figure 3-1. Air quality monitoring sites within the CAMx 12/4 km modeling domain. 

Statistical Performance Metrics 

Table 3-2 lists a standard set of EPA recommended statistical performance measures that were 

used during this study to evaluate CAMx performance (U.S. EPA, 1991, 2001, 2007). Typically, 

the statistical metrics are calculated for all monitoring sites across the full computational domain 

for all simulation days. In this evaluation, we stratified the performance statistics across relevant 

space and time scales. As part of the operational evaluation, the gas-phase and aerosol statistical 

measures shown in Table 3-2 were computed for the full 4 km domain and for specific sites. 

Temporally, we computed the statistical measures for the appropriate averaging times: 8 hourly 

for ozone, and 24 hour for total PM2.5, sulfate, nitrate, EC, OC, and other aerosol species. 
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Statistics are reported at daily, monthly, and annual time scales. 

Table 3-2. Core statistical measures used in the four corners air quality model evaluation with 

ground-level data 

Statistical 

Measure 

Mathematical 

Expression Notes 

Coefficient of 

determination (r

2

) 

∑ ∑

∑

= =

=

− −

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

−−

N

i

N

i

i i

N

i

ii

OOPP

OOPP

1 1

2 2

2

1

)()(

))((

Pi = prediction at time/location i; 

Oi = observation at time/location i; 

P = arithmetic average of Pi; 

O = arithmetic average of Oi

Normalized Gross 

Error (NGE) ∑=

− N

i i

ii

O

OP

N 1

1 Reported as % 

Normalized Bias 

(NB) ( ) ∑=

− N

i i

ii

O

OP

N 1

1 Reported as % 

Fractional Gross 

Error (FGE) ∑= +

− N

i ii

ii

OP

OP

N 1

2 Reported as % 

Fractional Bias 

(FB) ∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

− N

i ii

ii

OP

OP

N 1

2 Reported as % 

Establishment of performance goals and criteria for modeling is a necessary but difficult activity, 

and has been an area of ongoing research and debate (Morris et al., 2005). Here, performance 

goals refer to targets that we believe a good performing PM model should achieve, whereas less 

stringent performance criteria represent a minimal level of model performance that a PM model 

should achieve for use in regulatory modeling. Performance goals are necessary in order to 

provide consistency in model applications and expectations across the country, while criteria 

provide standardization in how much weight may be accorded modeling study results in the 

decision-making process. It is a problematic activity, though, because many areas present unique 

challenges and no one set of performance goals is likely to fit all needs. Equally concerning is 

the very real danger that modeling studies will be truncated when the “statistics look right” 

before full assessment of the model’s reliability is made. This has the potential for breeding 

built-in compensating errors as modelers strive to achieve good statistics as opposed to searching 

for the explanations for poor performance and then rectifying them. 
June 2009 

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Decades ago EPA (1991) established performance goals for 1-hour ozone centered on the use of 

normalized bias (<±15 percent) and error (≤35 percent). However, when these evaluation 

metrics were later adapted to PM and its components, difficulties arose because performance 

statistics that divide by low concentration observations (such as nitrate, which is often zero) 

become practically meaningless. In time, this has led to the introduction of the fractional bias 

and error metrics. EPA modeling guidance notes that PM models may not be able to achieve 

goals similar to those of ozone, and that better performance should be achieved for those PM 

components that make up the major fraction of total PM mass than those that are minor 

contributors. In fact, differences in measurement techniques for some PM species likely exceed 

the more stringent ozone performance goals. For example, recent comparisons of PM 

measurements using the IMPROVE and STN technologies found differences of ~20 percent for 

sulfate and ~50 percent for elemental carbon (Morris et al., 2005). 

As with ozone in the 1980s, actual experience with PM models has led to the development of the 

current performance expectations for these models. For example, PM10 SIP model performance 

goals of 30 percent and 50 percent for normalized gross error have been used for southern 

California (SCAQMD, 1997; 2003) and Phoenix (MAG, 1997), respectively. Boylan and 

Russell (2006) have proposed fractional bias and error goals of ±30 percent and 50 percent, and 

fractional bias and error criteria of ±60 percent and 75 percent, respectively. Furthermore, they 

proposed that these goals and criteria values vary as a function of concentration, such that below 

2 μg/m3, they expand exponentially to 200 percent (the maximum of fractional bias and error) at 

zero observed concentrations. The following levels of model performance criteria (Table 3-3) 

have been adopted for RPO regional visibility modeling using CMAQ, and we carry these forth 

into the Four Corners modeling assessment. We regard the above goals and criteria not as a 

pass/fail test, but rather as a basis of inter-comparing model performance across studies, 

sensitivity tests and models. 

Table 3-3. Model performance criteria.

Fractional 

Bias 

Fractional 

Error Qualitative Performance 

≤ ±15% ≤ 35% Excellent (original U.S. EPA 1991 ozone goal) 

≤ ±30% ≤ 50% Good 

≤ ±60% ≤ 75% Average, each PM component should meet for regulatory modeling 

> ±60% > 75% Poor, indicating fundamental problems with the modeling system 

Model Performance Evaluation Results: Ozone 

Model performance for ozone was evaluated primarily using monitoring sites located within the 

4 km modeling domain (Figure 3-2). Additional evaluation of ozone prediction performance was 

conducted using data from CASTNET sites located throughout the intermountain western U.S. 

within the 12 km domain (Figure 3-3). Use of only CASTNET sites in the 12 km domain 

avoided inclusion of data from urban sites which might bias the performance statistics as the 

relatively coarse grid 12 km domain simulation was not designed to reproduce the finer scale 

features of these distant urban locations. Both sets of monitoring sites are listed in Table 3-4. 
June 2009 

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Table 3-4. Monitoring sites used in the model performance evaluation for ozone. 

4 km Domain 

Site ID Site Name County State 

04-017-0119 Petrified Forest Navajo CO 

08-067-SHAM Shamrock La Plata CO 

08-067-7001 Ignacio La Plata CO 

08-067-7003 Bondad La Plata CO 

08-083-0101 Mesa Verde Montezuma CO 

35-045-0009 Bloomfield San Juan NM 

35-045-1005 Farmington San Juan NM 

12 km Domain 

Site ID Site Name County State 

YEL408 Yellowstone WY 

PND165 Pinedale WY 

CNT169 Centennial WY 

GRB411 Great Basin NV 

GTH161 Gothic CO 

CAN407 Canyonlands UT 

DEV412 Devils Postpile CA 

GRC474 Grand Canyon AZ 

CHA467 Chiricahua NM 

ROM206 Rocky Mountain CO 

PET427 Petrified Forest AZ 

Monthly mean fractional error statistics for hourly ozone at sites in the 4 km and 12 km domains, 

respectively, are summarized in Figure 3-4. Monthly mean fractional bias statistics are shown in 

Figure 3-5. Fractional metrics were chosen to evaluate performance for all hours of each month 

of the year since many observations reach zero concentrations, especially in winter months. 

Results from two model runs are shown: “original” refers to an initial CAMx simulation that 

used unmodified 36 km grid boundary conditions taken directly from the 2002 WRAP modeling 

(derived from GEOS-CHEM global model results as explained earlier); “final” refer to the final 

CAMx configuration in which the 36 km grid boundary conditions were modified to ensure 

tropospheric ozone levels in the topmost layers (as explained earlier). The performance 

sensitivity from this change is substantial, especially in winter months and within the 4 km grid. 

The winter months were most impacted because stratospheric ozone levels (reaching well above 

100 ppb) exist much lower in the atmosphere and in combination with vigorous wintertime 

weather systems, this ozone is easily transported to the surface in the grid models. The 4 km grid 

was more impacted than the 12 km grid because the highest and most complex terrain exists in 

the 4 km grid area where both CAMx and CMAQ showed the most impact from the GEOSCHEM boundary conditions. 

A more informative approach is to determine gross bias and error for observation-prediction 

pairings above a minimum ozone concentration, as recommended by EPA guidance (EPA, 

2007). Figure 3-6 shows monthly gross bias and error based on all hours for which the observed 

hourly ozone was above 40 ppb. Relative to established EPA acceptance criteria, most months 

are within the ±15 percent bias envelope and all months are within the 35 percent error envelope. 

The bias decreases during the summer months; ozone hours above 40 ppb tend to be under 

predicted while all ozone hours are near a zero bias relative to the rest of the year. This indicates 

that CAMx exhibits the largest under predictions for the highest ozone concentrations. 
June 2009 

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Figure 3-7 shows time series of hourly observed ozone and co-located CAMx predictions at sites 

within the 4 km grid for April and July, 2005. CAMx results were taken from the final 2005 

base case simulation. The more rural sites show little diurnal variation, while the more urbaninfluenced sites (such as around Farmington, NM) show strong diurnal variations associated with 

local NOx emissions that remove ozone to near zero concentrations at night. CAMx cannot be 

expected to match the strong local NOx influences on ozone since much of that occurs at scales 

below the resolution of the CAMx grid. CAMx also does not capture the rather large diurnal 

ozone variations at the Shamrock and Gothic rural sites. In the latter case, Gothic is located in 

the 12 km grid, so model resolution has an even greater impact on performance at that site. Even 

so, it is difficult to say what the causes of the observed diurnal ozone patterns would be at these 

sites; apparently local emissions and meteorological influences have some effect there that the 

model cannot replicate according to the procedures used to process emissions and to simulate the 

meteorology. 

Model Performance Evaluation Results: PM 

Monitoring sites used in the model performance evaluation for PM are shown in Figure 3-8 and 

listed in Table 3-5. This includes speciated particulate matter monitoring at IMPROVE and 

CASTNET sites, and federal reference method (FRM) monitoring of total PM2.5 and PM10 mass 

at FRM sites. 

Table 3-5. PM monitoring sites used in the model performance evaluation. 

Site ID Site Name County State Type 

04-017-0119 Petrified Forest (PEFO1) Navajo CO IMPROVE 

08-113-0004 San Miguel CO FRM 

08-083-0101 

Mesa Verde (MEVE1) Montezuma CO IMPROVE 

 Weminuche (WEMI1) CO IMPROVE 

 Bandelier (BAND1) NM IMPROVE 

35-045-0006 San Juan NM FRM 

35-043-9004 Sandoval NM FRM 

35-043-9011 Sandoval NM FRM 

35-043-1003 Sandoval NM FRM 

35-001-0023 Bernalillo NM FRM 

35-001-0024 Bernalillo NM FRM 

One of the best ways to summarize monthly/annual speciated PM performance is through the use 

of “bugle” plots, an approach first developed by the VISTAS RPO and now widely used in many 

regional PM modeling studies throughout the U.S. In these plots, monthly fractional bias and 

error statistics by site and month are plotted in relation to the respective monthly-averaged 

observed concentrations (i.e., each plotted point represents bias or error for one site and one 

month). Sites are color-coded by network to facilitate comparison among networks. The PM 

performance goals and criteria values are also plotted to show how the field of bias/error points 

fall within these ranges. As noted earlier, VISTAS proposed that these goals/criteria vary as a 

function of observed concentration, such that below 2 μg/m3

, they expand exponentially to 200 

percent (the maximum of fractional bias and error) at zero observed concentrations. Hence these 

goal/criteria lines take on a “bugle” appearance at low concentrations, giving more leeway for a 

wider range of acceptable model performance. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-14

Figures 3-9 through 3-15 presents monthly fractional error/bias performance for total and 

speciated PM2.5 concentrations in the form of bugle plots. Only sites within the 4 km grid are 

evaluated. In terms of total PM2.5, the model performs well except for a few months at 

IMPROVE sites, which show an under prediction tendency. Typical of the cleaner western U.S., 

observed total PM2.5 concentrations do not exceed 10 μg/m3

 on a monthly basis. 

The best performing PM species is sulfate, which is within acceptance criteria for the entire year 

and for both networks. Nitrate is observed at very low concentrations, and CAMx shows a wide 

range of over and under predictions but these are nearly all at or within the performance criteria 

limits. Ammonium is driven primarily by the sulfate concentrations, and is replicated reasonably 

well although with an overall under prediction bias. Elemental carbon shows an under prediction 

bias, but at very low concentrations and is therefore not important for the overall PM mass 

budget. Since there is no chemistry involved with elemental carbon, its performance is entirely 

related to inaccuracies in emissions characterization and dispersion. Of more significant concern 

is the under prediction of organic carbon, a component that dominates the PM2.5 mass budget. 

Most months are outside the performance goals, primarily in late spring and early summer. 

Under prediction of organic carbon was identified as a concern during initial model simulations 

in which fire emissions were not included. However, adding wildfire emissions did not result in 

a significant performance improvement as demonstrated by the results in Figure 3-14. As stated 

earlier, the science of organic aerosol chemistry is complex and currently not well characterized 

in models. The remaining PM component “soil”, which is a catch-all for all remaining fine PM 

dominated by crustal components, is rooted mostly in wind-blown dust emissions in the western 

U.S. It too comprises a large fraction of the mass budget (on par with sulfate), and although it 

exhibits an under prediction tendency, soil is generally well replicated given the obvious 

uncertainties in emission estimates. 
June 2009 

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Figure 3-2. Ozone monitoring sites in the 4 km domain. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-16

Figure 3-3. CASTNET ozone monitoring sites in the western U.S. used for model performance 

evaluation. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-17

Figure 3-4. Monthly mean fractional errors for ozone monitoring sites in the 4 km domain (top) 

and 12 km domain (bottom). 

Fractional Error

CASTNET Sites in 12-km Grid

0

10

20

30

40

50

60

70

January

February

March

April

May

June

July

August

September

October

November

December

Error (%)

Original Run

Final Run

Fractional Error

Sites in 4-km Grid

0

10

20

30

40

50

60

70

January

February

March

April

May

June

July

August

September

October

November

December

Error (%)

Original Run

Final Run
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-18

Figure 3-5. Monthly mean fractional bias for ozone monitoring sites in the 4 km domain (top) 

and 12 km domain (bottom). 

Fractional Bias

CASTNET Sites in 12-km Grid

-60

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

January

February

March

April

May

June

July

August

September

October

November

December

Bias (%)

Original Run

Final Run

Fractional Bias

Sites in 4-km Grid

-60

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

January

February

March

April

May

June

July

August

September

October

November

December

Bias (%)`

Original Run

Final Run
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-19

Figure 3-6. Monthly mean gross bias (top) and error (bottom) for ozone monitoring sites in the 

4 km domain. Bias and error statistics are based on data pairing in which 1-hour observations 

are above 40 ppb. Historical EPA acceptance criteria are shown for reference as red horizontal 

lines. 

Normalized Bias

-30

-20

-10

0

10

20

30

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

(%)

fc2005

+15%

-15%

Normalized Error

0

10

20

30

40

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

(%)

fc2005

35%
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-20

Mesa Verde: April Mesa Verde: July 

Shamrock: April Shamrock: July 

Gothic: April Gothic: July 

Figure 3-7. Ozone time series from each monitoring location in the 4 km domain and 

associated CAMx prediction for the months of April (left) and July (right), 2005. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-21

San Juan 0009: April San Juan 0009: July 

San Juan 1005: April San Juan 1005: July 

Figure 3-7. (Concluded) Ozone time series from each monitoring location in the 4 km domain 

and associated CAMx prediction for the months of April (left) and July (right), 2005. 
June 2009 

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Figure 3-8. PM monitoring sites within the 4 km modeling domain. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-23

Bugle Plot of Monthly PM2.5 Fractional Bias 

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria

CASTNET

IMPROVE

FRM

Bugle Plot of Monthly PM2.5 Fractional Error

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Average Concentration (ug/m3)

Fractional Error (%)

Goal

Criteria

CASTNET

IMPROVE

FRM

Figure 3-9. Monthly fractional bias (top) and error (bottom) for PM2.5 by site relative to monthlymean observations and to RPO performance goals and criteria.
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-24

Bugle Plot of Monthly SO4 Fractional Bias 

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria

CASTNET

IMPROVE

Bugle Plot of Monthly SO4 Fractional Error

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Error (%)

Goal

Criteria

CASTNET

IMPROVE

Figure 3-10. Monthly fractional bias (top) and error (bottom) for sulfate by site relative to 

monthly-mean observations and to RPO performance goals and criteria. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-25

Bugle Plot of Monthly NO3 Fractional Bias 

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria

CASTNET

IMPROVE

Bugle Plot of Monthly NO3 Fractional Error

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Error (%)

Goal

Criteria

CASTNET

IMPROVE

Figure 3-11. Monthly fractional bias (top) and error (bottom) for nitrate by site relative to 

monthly-mean observations and to RPO performance goals and criteria.
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-26

Bugle Plot of Monthly NH4 Fractional Bias 

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria

CASTNET

IMPROVE

Bugle Plot of Monthly NH4 Fractional Error

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Error (%)

Goal

Criteria

CASTNET

IMPROVE

Figure 3-12. Monthly fractional bias (top) and error (bottom) for ammonium by site relative to 

monthly-mean observations and to RPO performance goals and criteria. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-27

Bugle Plot of Monthly EC Fractional Bias 

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria

CASTNET

IMPROVE

Bugle Plot of Monthly EC Fractional Error

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Error (%)

Goal

Criteria

CASTNET

IMPROVE

Figure 3-13. Monthly fractional bias (top) and error (bottom) for elemental carbon by site 

relative to monthly-mean observations and to RPO performance goals and criteria. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-28

Bugle Plot of Monthly OC Fractional Bias 

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8 10

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria

CASTNET

IMPROVE

Bugle Plot of Monthly OC Fractional Error

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Error (%)

Goal

Criteria

CASTNET

IMPROVE

Figure 3-14. Monthly fractional bias (top) and error (bottom) for organic carbon by site relative 

to monthly-mean observations and to RPO performance goals and criteria. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 3-29

Bugle Plot of Monthly SOIL Fractional Bias 

-200

-150

-100

-50

0

50

100

150

200

0 2 4 6 8 10

Average Concentration (ug/m3)

Fractional Bias (%)

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria

IMPROVE

Bugle Plot of Monthly SOIL Fractional Error

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 810

Average Concentration (ug/m3)

Fractional Error (%)

Goal

Criteria

IMPROVE

Figure 3-15. Monthly fractional bias (top) and error (bottom) for soil by site relative to monthlymean observations and to RPO performance goals and criteria. 
June 2009 

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Conclusions Regarding the CAMx Model Performance Evaluation 

As part of the modeling analysis, a compressive evaluation of the CAMx model was conducted 

which compared model predictions to ambient air measurements. In terms of ozone, the model 

performed adequately in replicating rural levels of ozone throughout the year and over the 4 km 

resolution grid that covered the Four Corners area. CAMx could not resolve some very localized 

nighttime NOx impacts on ozone levels (as seen in the hourly measurements), and CAMx tended 

to under predict the highest ozone levels observed during the summer season. An obvious 

sensitivity to lateral ozone boundary conditions in connection with vigorous vertical circulations 

over the mountains in spring was effectively removed by raising stratospheric ozone levels to 

higher altitudes in the boundary conditions inputs. With the exception of organic carbon (one of 

the most significant PM components of western rural mass budgets), the CAMx model generally 

exceeded EPA model performance guidelines for the more abundant PM species. The best 

performing PM component was sulfate and associated ammonium. Nitrate exhibited mixed 

performance, but at such low concentrations its contribution to overall modeled PM was not 

significant. A similar conclusion is reached for elemental carbon, which performed poorly but 

contributes very little to the PM mass budget (although potentially much more to the visibility 

budget). Based on sensitivity analysis results, the performance issues noted for carbon (both 

elemental and organic) and dust were attributed to remaining uncertainties in emissions and, in 

the case of organic carbon, to chemical processing uncertainties in the model. 
June 2009 

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4.0 RESULTS 

In this section we summarize results of the CAMx modeling of air quality impacts over the “4 

km” modeling domain depicted in Figure 1-1. CAMx results for the 2005 base case, the 2018 

base case and the 2018 mitigation scenarios as described in Section 2 were used in this analysis. 

Atmospheric chemistry resulting in ozone and secondary PM formation from directly emitted 

precursor species is complex and non-linear and as a result it is necessary to perform modeling 

that accounts for the simultaneous changes in emissions at all sources within the region. 

Increases in emissions of ozone and PM precursors can result in disproportionate changes in 

ozone and secondary PM. Under certain conditions, precursor emission decreases can even 

result in increases in ozone and some secondary PM species. 

4.1 2018 BASE CASE 

4.1.1 2018 Base Case 

CAMx was exercised with the 2018 base case emissions and the 2005 meteorological data as 

described in Section 3. Given that emissions changed very little overall between the 2005 and 

2018 base case, the ozone results are nearly identical between these two base cases. Annual 

fourth highest daily maximum 8-hour average ozone values predicted in each grid cell are shown 

for both base cases along with the difference between the two in Figure 4-1. Note that the date 

of occurrence of the 4th highest values shown here can vary from one grid cell to the next so 

these comparisons are not matched in time. Most locations show either no change or a decline of 

1 or 2 ppb in the 4th highest modeled daily maximum 8-hour ozone concentrations, with a few 

localized increases. 
June 2009 

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Figure 4-1. Annual fourth highest daily maximum 8-hour average ozone predicted in each 

model surface grid cell for the 2005 base case (top left), 2018 base case (bottom left) and 2018 

minus 2005 (bottom right). 

4.1.2 Source Apportionment Modeling Results 

4.1.2.1 Methodology 

Prior to defining mitigation scenarios to be examined, source apportionment modeling was 

conducted on the 2018 base case scenario to assist with understanding the contributions of 

different source groups to ozone and PM impacts. These analyses were prepared using ozone 

and PM source apportionment technology (OSAT and PSAT, respectively). In OSAT/PSAT, 

precursor emissions are stratified by source category and by source region; tracers are used to tag 

emissions from each category-region pair and track transport, chemical evolution, and removal. 

In OSAT, NOx and VOC tracers are run to apportion total ozone throughout the grid to NOx and 

VOC precursor source categories and regions. NOx and VOC are attributed to ozone formation 

according to local NOx- or VOC-limited chemical conditions along the transport route. In 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-3

PSAT, tracers can be run, separately or in combination, for a sulfur group (sulfur dioxide gas 

[SO2] and particulate sulfate [PSO4]); a nitrogen group (nitrogen oxide gas [NOx], nitrogen 

oxide chemical products [NOy], nitric acid gas [HNO3], ammonia [NH3], particulate nitrate 

[PNO3], and particulate ammonium [PNH4]); an organic group (volatile organic compounds 

[VOC], condensable hydrocarbon products [CG], and SOA components); and a primary PM 

group (carbonaceous, fine/coarse dust, and fine/coarse other PM). 

When interpreting results from an OSAT or PSAT run, it is important to understand that the 

apportionment of ozone and PM in this manner is defined by the particular emission scenario run 

during the OSAT/PSAT simulation (in this case the 2018 base case scenario). Use of a different 

emission scenario would result in a different set of relative source contributions as non-linear 

chemistry changes the source apportionment matrix. It should also be noted that source 

apportionment is not the same as conducting source reduction sensitivity tests which are used 

specifically to understand how changes in emissions for a certain source type/location impact 

chemistry and ultimately ozone and PM concentration patterns. Thus, a source reduction 

sensitivity test is not an attribution or apportionment of ozone or PM, but more of a measure of 

how sensitive ozone or PM formation chemistry is to emission changes for the source being 

tested under a given emission scenario. The difference between emission sensitivity and source 

apportionment is the non-linear chemistry. The definitions of source sensitivity and source 

apportionment would be equivalent only if the chemistry component were to be removed (e.g., in 

a situation involving only inert pollutants). Additional information and a more detailed 

description of OSAT and PSAT is provided in ENVIRON (2008). 

A set of source groups defined as combinations of source types (e.g., electric generating units) 

and source regions (e.g., portion of Arizona contained within the 4 km modeling domain) were 

selected for analysis with OSAT and PSAT. The selected source groups are defined in Table 

4-1. One fairly detailed set of source groups was defined for sources within the 4 km modeling 

domain and a broader grouping of sources was defined for portions of the 12 km modeling 

domain falling outside of the 4 km domain. Oil and gas sources were divided into two types: 

combustion sources such as compressors, etc. which emit primarily NOx along with some other 

pollutants and non-combustion sources such as completions, etc. which result in VOC emissions. 

Electric generating units (EGUs) were grouped separately from other (non oil and gas) point 

sources. Because of the way emissions from fires were modeled, the other point source category 

includes fire emissions. The other area source group includes all area sources not included in 

any of the other source groups. As such, this group includes windblown dust emissions. In 

addition to the 24 source groups defined in Table 4-1, OSAT and PSAT produce source 

contribution estimates for initial conditions and for boundary conditions. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-4

Table 4-1. Definitions of the 24 source groups used in 2018 base case ozone and PM source 

apportionment runs1

4 km Modeling Domain 

Source Category/Region Colorado New Mexico Arizona & Utah 

12 km 

Modeling 

Domain2

Oil & gas combustion sources 

(compressors, drill rigs, 

 heaters, all other engines) 

Area & 

Point3 Area Area & Point4

Oil & gas large point sources in 

NM -- Point -- 

Oil & gas non-combustion 

sources (fugitives, completions, 

venting) 

Area Area Area 

Mobile sources (on-road and 

non-road) Area Area Area 

Biogenics Area Area Area 

Other area sources Area Area Area 

All Area Sources 

Electric Generating Units Point Point Point 

Other (non-EGU, non-O&G) 

point sources Point Point Point 

All Point 

Sources 

4.1.2.2 Ozone Source Apportionment Results 

Source contributions to hourly ozone at the Navajo Lake, Farmington and Mesa Verde 

monitoring sites during April and July are illustrated by the time series in Figures 4-2 to 4-5. 

Source groups shown in these time series are as defined in Table 4-1 except that sources in the 4 

km domain are not split out by state, the oil and gas group includes all point and area oil and gas 

related sources in the 4 km domain and the 12 km sources include all point and area sources in 

the 12 km modeling domain outside of the 4 km domain. Note that the “NonEGUs” source 

group represents the total “other point source category” for the 4 km domain and that this 

category includes fire emissions. 

 

1

 In addition to these 23 source categories, CAMx will compute contributions from 1) 12 km domain boundary 

conditions and 2) initial conditions 

2

 Excluding emission sources within the 4 km domain 

3

 All NOx sources greater than 2 tons/year included as point sources 

4

 Only large NOx sources included as point sources, small sources are included as area sources 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc Figure 4-2. OSAT source apportionment results for Navajo Lake monitoring location in July. Figure 4-3. OSAT source apportionment results at Farmington monitoring location in July. 

2018 OSAT Farmington ozone contributions

July

0

102030405060708090

100

7/01 7/02 7/03 7/04 7/05 7/06 7/07 7/08 7/09 7/10 7/11 7/12 7/13 7/14 7/15 7/16 7/17 7/18 7/19 7/20 7/21 7/22 7/23 7/24 7/25 7/26 7/27 

Date

1-hour Ozone (ppb)

2018 OSAT Navajo Lake ozone contributions

July

0

102030405060708090

100

7/01 7/02 7/03 7/04 7/05 7/06 7/07 7/08 7/09 7/10 7/11 7/12 7/13 7/14 7/15 7/16 7/17 7/18 7/19 7/20 7/21 7/22 7/23 7/24 7/25 7/26 7/27 

Date

1-hour Ozone (ppb)
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 

2018 OSAT Mesa Verde ozone contributions

April

0

102030405060708090

4/01 4/02 4/03 4/04 4/05 4/06 4/07 4/08 4/09 4/10 4/11 4/12 4/13 4/14 4/15 4/16 4/17 4/18 4/19 4/20 4/21 4/22 4/23 4/24 4/25 4/26 

Date

1-hour Ozone (ppb)

Figure 4-4. OSAT source apportionment results at Mesa Verde monitoring location in April. 

2018 OSAT Mesa Verde ozone contributions

July

0

102030405060708090

100

7/01 7/02 7/03 7/04 7/05 7/06 7/07 7/08 7/09 7/10 7/11 7/12 7/13 7/14 7/15 7/16 7/17 7/18 7/19 7/20 7/21 7/22 7/23 7/24 7/25 7/26 7/27 

Date

1-hour Ozone (ppb)

Figure 4-5. OSAT source apportionment results at Mesa Verde monitoring location in July. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-7

As expected, boundary conditions (BCs), representing the contributions of ozone and precursors 

transported into the Four Corners area from sources outside of the western US (i.e., outside of 

the 12 km modeling domain) are the single largest contributing source group. These 

contributions can be conceptualized as the “background” conditions to which emissions from 

sources in the Four Corners region and other western US locations contribute additional ozone 

and ozone precursor loadings. During April, nearly all of the predicted ozone is attributed to 

boundary conditions most of the time with occasional small contributions from oil and gas 

sources. As compared to July, the dominance of boundary condition influence in April reflects 

the relatively higher April boundary conditions, rapid transport from the upwind boundary, and 

limited amount of local ozone production due to cooler temperatures and lower sun angles. 

Boundary condition influence wanes in July with local sources becoming more important. 

Different source groups are found to be the major contributor at different times, depending on 

meteorological conditions (and hourly and daily emission variations in the case of the EGUs). 

Apart from significant contributions from 12 km domain sources, EGUs, oil and gas and 

biogenics are typically the major contributors on days with elevated ozone. 

Average source contributions to all 8-hour running average periods with 8-hour average ozone 

levels exceeding selected threshold levels (50, 60, 65, 70 and 75 ppb) were calculated at key 

monitoring site locations. An example for 8-hour periods greater than 75 ppb at Navajo Lake is 

provided in Table 4-2. Key features of these results with respect to sources within the 4 km 

modeling domain are illustrated in Figure 4-6 which shows the average source contributions for 

all 8-hour periods exceeding 70 ppb. Contributions from sources within the 4 km domain in 

New Mexico dominate those from other states with oil and gas combustion sources representing 

the single biggest contribution. Corresponding results for Mesa Verde are presented in Figure 4-

7. Local source contributions are much lower at Mesa Verde and the estimated oil and gas 

combustion source impact from Colorado sources is about the same as at Navajo Lake while the 

impact from New Mexico sources is on the order of a factor of 10 less. When considering the 

combined impact from all oil and gas sources (first three categories on the left), New Mexico 

sources are still the dominant contributors at Mesa Verde although their total contribution is only 

2.4 ppb as compared to 15.4 ppb at Navajo Lake. As the time series in Figures 4-4 and 4-5 

suggest, local sources, including oil and gas sources, contribute more at Mesa Verde during the 

summer (July) but most of the predicted ozone greater than 70 ppb occurs during the spring 

(April) at this location. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc Table 4-2. Average ozone source contributions calculated by OSAT at the Navajo Lake monitoring location for periods with average ozone greater than 65 ppb (top), 70 ppb (middle) and 75 ppb (bottom). 

Navajo Lake hourly O3 contributions for 8-hr O3 average > 75 ppb

O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total

New Mexico 7.60 1.06 8.67 0.00 2.24 2.24 3.64 0.57 4.21 3.15 0.12 0.32 

Colorado 1.16 0.16 1.32 0.00 0.04 0.04 0.00 0.09 0.20 AZ & UT 0.03 0.01 0.04 0.00 0.00 0.00 0.00 0.09 0.18 12km 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.45 0.00 IC 0.00 

BC 38.35 Grand Total 0.00 38.35 8.79 1.23 10.03 0.00 2.28 2.28 3.64 0.57 4.21 3.15 3.75 0.70 

Oil & Gas combustion Oil & Gas non-combustion Oil & Gas NM large points

IC BC EGUs 

other points 

(incl. fires) mobile other 4km 

Navajo Lake hourly O3 contributions for 8-hr O3 average > 70 ppb

O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total

New Mexico 7.05 1.34 8.39 0.00 3.02 3.02 2.55 0.62 3.17 1.96 0.16 0.43 

Colorado 0.64 0.13 0.77 0.00 0.03 0.03 0.00 0.06 0.20 AZ & UT 0.06 0.00 0.06 0.00 0.00 0.00 0.01 0.02 0.13 12km 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.32 0.00 IC 0.05 

BC 41.21 Grand Total 0.05 41.21 7.75 1.47 9.22 0.00 3.05 3.05 2.55 0.62 3.17 1.97 2.56 0.76 

IC BC EGUs 

other points 

(incl. fires) mobile other 4km 

Oil & Gas combustion Oil & Gas non-combustion Oil & Gas NM large points

Navajo Lake hourly O3 contributions for 8-hr O3 average > 65 ppb

O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total

New Mexico 5.15 1.08 6.22 0.00 2.39 2.39 1.93 0.57 2.50 1.57 0.13 0.30 

Colorado 0.54 0.13 0.66 0.00 0.02 0.02 0.00 0.06 0.17 AZ & UT 0.04 0.00 0.04 0.00 0.00 0.00 0.01 0.02 0.10 12km 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.49 0.00 IC 0.04 

BC 42.41 Grand Total 0.04 42.41 5.73 1.21 6.92 0.00 2.41 2.41 1.93 0.57 2.50 1.58 2.70 0.57 

Oil & Gas combustion Oil & Gas non-combustion Oil & Gas NM large points

IC BC EGUs 

other points 

(incl. fires) mobile other 4km 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-9

Figure 4-6. Average ozone source contributions from sources within the 4 km modeling domain as 

calculated by OSAT at the Navajo Lake monitoring location for all 8-hour periods greater than 70 

ppb. Not shown here are contributions from boundary and initial conditions (41.2 ppb) and sources 

in the 12 km domain (3.8 ppb). The average impact from all sources combined for these 8-hour 

periods is 71.5 ppb. 

Figure 4-7. Average ozone source contributions from sources within the 4 km modeling domain as 

calculated by OSAT at the Mesa Verde monitoring location for all 8-hour periods greater than 70 

ppb. Not shown here are contributions from boundary and initial conditions (64.6 ppb) and sources 

in the 12 km domain (0.8 ppb). The average impact from all sources combined for these 8-hour 

periods is 70.4 ppb.

Avg Source Contributions to 8hr O3 > 70 ppb: Mesa Verde

(sources in 4 km modeling domain)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Oil & Gas combustion Oil & Gas noncombustionOil & Gas NM large

points

EGUs Other

ppb

New Mexico

Colorado 

AZ & UT 

Avg Source Contributions to 8hr O3 > 70 ppb: Navajo Lake

(sources in 4 km modeling domain)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

Oil & Gas combustion Oil & Gas noncombustionOil & Gas NM large

points

EGUs Other

ppb

New Mexico

Colorado 

AZ & UT 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-10

A significant feature of the OSAT probing tool used to derive the results described above is that 

biogenic emissions are treated in the same manner as emissions from any other source group. 

Thus, ozone is apportioned to biogenic sources whenever it is formed from a combination of 

biogenic VOC and anthropogenic NOx under VOC limited conditions or from biogenic NOx and 

anthropogenic VOC under NOx limited conditions. Since biogenic emissions are not directly 

controllable, however, apportioning ozone to biogenics in this manner does not give a directly 

relevant picture of the potential importance of anthropogenic source controls in reducing ozone. 

An alternative approach, under which ozone formed from anthropogenic NOx (VOC) and 

biogenic VOC (NOx) is always assigned to the anthropogenic source, even under VOC (NOx) 

limited conditions is available to address this situation. This methodology is known as 

Anthropogenic Precursor Culpability Assessment (APCA). Under APCA, ozone is only 

apportioned to the biogenic source if it is formed from biogenic VOC and biogenic NOx. In this 

sense, APCA provides a more policy-relevant picture of the culpability of anthropogenic sources 

in ozone formation. Additional information about OSAT and APCA are provided by ENVIRON 

(2008). 

CAMx was rerun for the 2018 base case using APCA for comparison with the previously 

generated OSAT results. OSAT and APCA results were compared for the summer season when 

biogenic emissions are generally highest. A comparison of average source contributions to 

ozone formation under OSAT and APCA in July is provided for the Navajo Lake monitoring site 

in Figure 4-8 and for the Mesa Verde monitoring site in Figure 4-9. Note that the biogenics 

source group (left most bar pair in each figure) represents biogenic sources within the 4 km 

domain; biogenics in the 12 km domain are included with all other 12 km domain sources in the 

“12 km sources” group and CAMx treated this entire source group as anthropogenic even though 

a significant portion of VOC emissions in the 12 km domain are biogenic. Under OSAT, the 

biogenic source contributions are on par with those of the major anthropogenic source groups. 

Under APCA, however, the 4 km domain biogenic contribution is near zero, indicating that most 

of the ozone apportioned under OSAT to biogenics is formed from a combination of biogenic 

emissions (mostly VOCs) with anthropogenic emissions (mostly NOx from EGUs and oil and 

gas combustion sources). 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-11

Figure 4-8. July average calculated source contributions to ozone for all 8-hour periods with 

average ozone greater than 65 ppb at the Navajo Lake monitoring location: comparison of 

OSAT and APCA results. 

Figure 4-9. July average calculated source contributions to ozone for all 8-hour periods with 

average ozone greater than 65 ppb at the Mesa Verde monitoring location: comparison of 

OSAT and APCA results. 

OSAT Vs. APCA O3 Source Apportionment

July Avg Source Contributions to 8hr O3 > 65 ppb: Mesa Verde

0

5

10

15

20

25

30

35

40

ppb

OSAT

APCA

OSAT 5.2 7.6 1.7 6.1 4.4 6.4 35.1

APCA 0.4 9.8 1.6 8.8 4.5 6.2 35.1

Biogenics O&G Comb O&G Non-Comb EGUs Other 4 km 12 km Sources IC&BC

OSAT Vs. APCA O3 Source Apportionment

July Avg Source Contributions to 8hr O3 > 65 ppb: Navajo Lake

0

5

10

15

20

25

30

35

40

45

ppb

OSAT

APCA

OSAT 5.0 11.3 2.5 1.9 4.3 4.7 38.0

APCA 0.4 14.9 2.4 2.7 4.5 4.7 38.1

Biogenics O&G Comb O&G Non-Comb EGUs Other 4 km 12 km Sources IC&BC
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-12

4.1.2.3 PM Source Apportionment 

Apportionment of predicted PM to source groups listed in Table 4-1 was calculated using the 

CAMx PSAT probing tool described by ENVIRON (2008). PSAT was used to apportion each 

PM species carried in the model to each of the 24 source groups plus initial and boundary 

conditions. Although PSAT also tracks organics, it was not to run the organic group in PSAT 

since this PM component is dominated by biogenic VOC in the rural west and the core CAMx 

PM chemistry already tracks organic aerosols by their anthropogenic and biogenic precursors. 

For purposes of displaying the PSAT results, quarterly and annual average source 

apportionments were calculated for four key PM species (sulfate, nitrate, ammonium, elemental 

carbon) as well for total PM2.5 mass (within which the separate anthropogenic and biogenic 

contributions to PM2.5 are displayed). These results are summarized in Figures 4-10 for Mesa 

Verde and in Figure 4-11 for Weminuche. Initial/Boundary conditions and 12 km source 

influences are greater at Weminuche which is located further away than Mesa Verde from the 4 

km domain sources. EGU and oil and gas combustion sources contribute in roughly equal 

amounts to nitrate loadings at Mesa Verde and together these two 4 km source groups contribute 

41% of the total nitrate in the fourth quarter. Of the 4 km source groups, EGUs are the only 

significant contributor to sulfate at Mesa Verde but their contribution is only 14% of the total 

sulfate on an annual average basis with nearly all the rest attributed to sources outside the 4 km 

domain, including boundary conditions. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-13

Four-Corner PSAT Analysis (4km), MesaVerde

0

0.1

0.2

0.3

0.4

0.5

0.6

Q1 Q2 Q3 Q4 ANN

μg/m3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Sulfate Four-Corner PSAT Analysis (4km), MesaVerde

0

0.1

0.2

0.3

0.4

0.5

0.6

Q1 Q2 Q3 Q4 ANN

μg/m3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Nitrate

Four-Corner PSAT Analysis (4km), MesaVerde

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Q1 Q2 Q3 Q4 ANN

μg/m3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Ammonium Four-Corner PSAT Analysis (4km), MesaVerde

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Q1 Q2 Q3 Q4 ANN

μg/m3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Elemental Carbon

Four-Corner PSAT Analysis (4km), MesaVerde

0

0.5

1

1.5

2

2.5

3

Q1 Q2 Q3 Q4 ANN

μg/m3

PCL

SOA (Biogenic)

SOA (Anthropogenic)

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

PM 2.5

Figure 4-10. Quarterly and annual average PM source apportionment results at the Mesa 

Verde monitoring location calculated with PSAT for sulfate, nitrate, ammonium, elemental 

carbon and total PM2.5 mass. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-14

Four-Corner PSAT Analysis (4km), Weminuche

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Q1 Q2 Q3 Q4 ANN

μg/m3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Sulfate Four-Corner PSAT Analysis (4km), Weminuche

0

0.05

0.1

0.15

0.2

0.25

0.3

Q1 Q2 Q3 Q4 ANN

μg/m3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Nitrate

Four-Corner PSAT Analysis (4km), Weminuche

0

0.02

0.04

0.06

0.08

0.1

0.12

Q1 Q2 Q3 Q4 ANN

μg/m3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Ammonium Four-Corner PSAT Analysis (4km), Weminuche

0

0.01

0.02

0.03

0.04

0.05

0.06

Q1 Q2 Q3 Q4 ANN

μg/m3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Elemental Carbon

Four-Corner PSAT Analysis (4km), Weminuche

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Q1 Q2 Q3 Q4 ANN

μg/m3

PCL

SOA (Biogenic)

SOA (Anthropogenic)

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

PM 2.5

Figure 4-11. Quarterly and annual average PM source apportionment results at the 

Weminuche monitoring location calculated with PSAT for sulfate, nitrate, ammonium, elemental 

carbon and total PM2.5 mass. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-15

4.2 MITIGATION SCENARIO RESULTS 

4.2.1 Mitigation Scenarios 

A set of five mitigation scenarios were defined by the FCAQTF for analysis as described in 

Section 2.3: 

Scenario #1: An overall 16% reduction in 4 km domain EGU SO2 emissions to presumptive 

BART limits and a 70% reduction of NOx emissions from these sources via installation of 

selective catalytic reduction (SCR) at the Four Corners Power Plant and the San Juan Generating 

Station. This scenario includes a 305 tons/year increase in ammonia emissions to account for 

SCR ammonia slip. 

Scenario #2: Application of various VOC controls to oil and gas sources in New Mexico 

resulting in a 51% reduction in total organics (TOG) from these New Mexico sources 

(corresponds to a 23% reduction in oil and gas TOG over the entire 4 km domain). 

Scenario #3: A 16% reduction in 4 km domain oil and gas NOx emissions resulting from 

penetration of NSPS compliant engines into half of the existing engine fleet. 

Scenario #4: EGU SO2 and NOx reductions from scenario #1 plus oil and gas VOC and NOx 

reductions from scenario #2 and scenario #3, respectively. 

Scenario #5: Oil and gas VOC and NOx reductions from scenario #2 and scenario #3, 

respectively. 

4.2.2 Mitigation Scenario Results: Ozone 

Ozone concentrations predicted under the 2018 base case and each mitigation scenario described 

above were compared for two key months: April (when high ozone is predicted at higher 

elevations in the San Juans and other mountain ranges) and July (when high ozone is predicted at 

lower elevations). Time series of hourly predicted ozone under each scenario for these two 

months/locations are provided in Figures 4-12 thru 4-16. Ozone reductions under the mitigation 

scenarios are generally less than 5 ppb with the exception of a few hours under scenario #4 in 

July at Navajo Lake. Some increases in ozone are evident during some off-peak hours due to 

disbenefits of NOx reductions under VOC limited conditions. This is better illustrated by the 

scatter plots in Figure 4-17. Reductions in April are limited to well less than 5 ppb but 

somewhat larger reductions (up to the 5 – 10 ppb range) are achieved in July. These seasonal 

differences are consistent with the source apportionment results presented in Section 4.1.2 which 

showed that the higher ozone values observed during the late spring are more heavily influenced 

by precursor sources and ozone transport from outside of the 4 km domain as compared to 

summer. The largest ozone reductions during peak ozone hours are predicted for scenario #4 

with hours greater than 65 ppb during July at Navajo Lake reduced by as much as 8 ppb. 

Ozone changes under each mitigation scenario relative to the 2018 base case vary both spatially 

and temporally as suggested by the results described above for Mesa Verde and Navajo lake. 

Ozone changes over the 4 km modeling domain during a typical summer afternoon high ozone 

event (3:00 pm MST on 18 July which corresponds to the time of the daily maximum hourly 

ozone concentration on the highest predicted July ozone day at Navajo Lake) are illustrated in 

Figures 4-18 and 4-19. Figure 4-18 shows ozone during this hour under the 2018 base case and 

mitigation scenarios #1 and #2 along with plots showing the differences in values between the 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-16

mitigation scenarios and the base case (negative values indicate ozone reductions). 

Corresponding results for scenarios #3, #4 and #5 are shown in Figure 4-19. Ozone reductions 

under the oil and gas mitigation scenarios (#2, #3 and #5) are predicted to be largest in northern 

Rio Arriba County. Power plant NOx reductions (included in scenarios #1 and #4) result in a 

narrow band of ozone increases downwind of the Four Corners and San Juan power plants with 

ozone reductions on either side. The largest and most widespread ozone reductions are predicted 

under Scenario #4, with a maximum reduction of nearly 9 ppb during this hour. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc Figure 4-12. Time series of predicted hourly ozone under the 2018 base case and mitigation scenario #1 during Ap

(top) and July at Navajo Lake (bottom). 

Navajo Lake

-10

0

1020304050607080

7/1

7/10 Date

7/19

O3 [ppb]

base Mitigation Scenario #1 Mitigation Scena

Mesa Verde: April

-10

0

1020304050607080

4/1 4/10 4/19 

Date

1-Hr O3 [ppb]

base Mitigation Scenario #1 Mitigation Scenar

O

3 (pp

b)

O

3 (pp

b)
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 

Figure 4-13. Time series of predicted hourly ozone under the 2018 base case and mitigation scenario #2 during Ap(top) and July at Navajo Lake (bottom). 

Navajo Lake

-10

0

1020304050607080

7/1 7/10 7/19 

Date

1-Hr O3 [ppb]

base Mitigation Scenario #2 Mitigation Scenario 

Mesa Verde: April

-10

0

1020304050607080

4/1 4/10 4/19 

Date

1-Hr O3 [ppb]

base Mitigation Scenario #2 Mitigation Scenario #2 

O

3 (pp

b)O

3 (pp

b)
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc Figure 4-14. Time series of predicted hourly ozone under the 2018 base case and mitigation scenario #3 during Ap(top) and July at Navajo Lake (bottom). 

Navajo Lake

-10

0

1020304050607080

7/1 7/10 7/19 

Date

1-Hr O3 [ppb]

base Mitigation Scenario #3 Mitigation Scenario 

Mesa Verde

-10

0

1020304050607080

4/1 4/10 4/19 

Date

1-Hr O3 [ppb]

base Mitigation Scenario #3 Mitigation Scena

O

3 (pp

b)O

3 (pp

b)
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc Figure 4-15. Time series of predicted hourly ozone under the 2018 base case and mitigation scenario #4 during Ap(top) and July at Navajo Lake (bottom). 

-10

0

10203040506070

80

7/1 7/10 7/19 

Date

1-Hr O3 [ppb]

Mesa Verde: April

-10

0

1020304050607080

4/1 4/10 4/19 

Date

1-Hr O3 [ppb]

base Mitigation Scenario #4 Mitigation Scenario #4 - base

O

3 (pp

b)O

3 (pp

b)
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc Figure 4-16. Time series of predicted hourly ozone under the 2018 base case and mitigation scenario #5 during Ap

(top) and July at Navajo Lake (bottom). 

Navajo Lake

-10

0

1020304050607080

7/1 7/10 7/19 

Date

1-Hr O3 [ppb]

base Mitigation Scenario #5 Mitigation Scenario #5 - base

Mesa Verde: April

-10

0

1020304050607080

4/1 4/10 4/19 

Date

1-Hr O3 [ppb]

base Mitigation Scenario #5 Mitigation Scenario #5 - b

O

3 (pp

b)O

3 (pp

b)
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-22

Mesa Verde: April 1-Hour O3 (ppb)

-10

-5

0

5

10

15

50 55 60 65 70 75 80

2018 Base Case

Mitigation Scenario - Base

Scenario #1

Scenario #2

Scenario #3

Navajo Lake: July 1-Hour O3 (ppb)

-10

-5

0

5

10

15

50 55 60 65 70 75 80

2018 Base Case

Mitigation Scenario - Base

Scenario #1

Scenario #2

Scenario #3

Mesa Verde: April 1-Hour O3 (ppb)

-10

-5

0

5

10

15

50 55 60 65 70 75 80

2018 Base Case

Mitigation Sceanrio - Base

Scenario #4

Scenario #5

Navajo Lake: July 1-Hr O3 (ppb)

-10

-5

0

5

10

15

50 55 60 65 70 75 80

2018 Base Case

Mitigation Scenario - Base

Scenario #4

Scenario #5

Figure 4- 17. Predicted change in hourly ozone (mitigation scenario – base case) versus base 

case ozone (values matched in time and space) at Mesa Verde in April (left column) and Navajo 

Lake in July (right column) for mitigation scenarios 1, 2 and 3 (top row) and mitigation scenarios 

4 and 5 (bottom row). Note that only hours with base case ozone greater than 50 ppb are 

shown. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-23

2018 Base Case 

Scenario #1 Scenario #1 – 2018 Base 

Scenario #2 Scenario #2 – 2018 Base 

Figure 4-18. Predicted hourly average ozone at 3:00 pm MST on 18 July under the 2018 base 

case and mitigation scenarios #1 and #2 (left column) and differences between each mitigation 

scenario and the 2018 base case (right column).
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-24

 Scenario #3 Scenario #3 – 2018 Base 

Scenario #4 Scenario #4 – 2018 Base 

Scenario #5 Scenario #5 – 2018 Base 

Figure 4-19. Predicted hourly average ozone at 3:00 pm MST on 18 July under mitigation 

scenarios #3, #4 and #5 (left column) and differences between each mitigation scenario and the 

2018 base case (right column). 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-25

Ozone impacts of the five mitigation scenarios were also analyzed in terms of changes in the 

predicted annual 4th highest daily maximum 8-hour average ozone concentration in each model 

grid cell. Color plots of these ozone design values are provided for the 2018 base case and 

mitigation scenarios #1 and #2 in Figure 4-20 along with plots showing the differences in values 

between the mitigation scenario and the base case (negative values indicate ozone reductions). 

Results for scenarios #3, #4 and #5 are shown in Figure 4-21. Note that these results are not 

paired in time as the date of the 4th highest 8-hour value can change from one grid cell to the 

next. Both the overall largest ozone design value reductions (maximum decrease of 5.0 ppb) and 

the most wide-spread reductions are predicted to occur under scenario #4. Some of the larger 

reductions occur in the area of the local design value peak in Rio Arriba County. There is a 

narrow band of design value increases in San Juan County downwind of the Four Corners and 

San Juan power plants but it does not coincide with the highest base case design value locations. 

Results for scenario #5 indicate that a combination of VOC and NOx controls on oil and gas 

sources are more effective at reducing ozone than VOC or NOx controls alone. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-26

2018 Base Case 

Scenario #1 Scenario #1 – 2018 Base 

Scenario #2 Scenario #2 – 2018 Base 

Figure 4-20. Annual fourth highest daily maximum 8-hour ozone in each model grid cell under 

the 2018 base case and mitigation scenarios #1 and #2 (left column) and differences between 

each mitigation scenario and the 2018 base case (right column). 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-27

Scenario #3 Scenario #3 – 2018 Base 

Scenario #4 Scenario #4 – 2018 Base 

Scenario #5 Scenario #5 – 2018 Base 

Figure 4-21. Annual fourth highest daily maximum 8-hour ozone in each model grid cell under 

mitigation scenarios #3, #4 and #5 (left column) and differences between each mitigation 

scenario and the 2018 base case (right column). 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-28

4.2.3 Mitigation Scenario Results: PM and Visibility 

Mitigation scenario impacts on visibility in Class I areas within the 4 km domain were calculated 

based on differences in predicted pollutant concentrations between the 2018 base case and each 

mitigation scenario. For the sake of simplicity, visibility under each scenario was calculated 

using the original IMPROVE equation for light extinction: 

Bext = Bext(SO4) + Bext(NO3) + Bext(OC) + Bext(EC) + Bext(PMF) + Bext(PMC) 

Where: 

Bext is the light extinction (Mm-1), 

Bext(SO4) = 3f(RH)[SO4], 

Bext(NO3) = 3f(RH)[NO3], 

Bext(OC) = 4 [OC], 

Bext(EC) = 10[EC], 

Bext(PMF) = 1[PMF], 

Bext(PMC) = 0.6[PMC], 

and 

F(RH) = relative humidity factor, 

[SO4] = sulfate concentration, 

[NO3] = nitrate concentration, 

[OC] = organic carbon concentration, 

[EC] = elemental carbon concentration (LAC), 

[PMF] = fine particulate matter concentration (soil), 

[PMC] = coarse particulate matter concentration (diameter between 2.5 and 10 microns), 

Daily average predicted pollutant concentrations averaged over all model grid squares wholly or 

partially contained within each Class I area were used in the calculations. Monthly default 

values of f(RH) provided by EPA (EPA, 2003, Appendix A) were used to compute Bext values. 

Resulting light extinction coefficient, Bext, was then converted to deciview haze index (DHI) 

expressed in deciview units (dV): 

DHI = 10In [(Bext + 10)/10]. 

Where: 

Bext = light extinction as calculated above, 

ln = natural logarithm and 10 represents light extinction in Mm-1 due to Rayleigh 

scattering. 

The DHI was calculated for the 2018 base case and each mitigation scenario. Changes in 

visibility were then calculated by subtracting the base case DHI from the mitigation scenario 

DHI. Thus, negative values for the change in DHI represent visibility improvement while 

positive values represent visibility degradation. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-29

Visibility changes predicted under each mitigation scenario for the two Class I areas closest to 

the Four Corners (Mesa Verde and Weminuche) are summarized in Figures 4-22 and 4-23, 

respectively. Key summary statistics from these results are listed in Tables 4-3 and 4-4 (for 

Mesa Verde) and 4-5 and 4-6 (for Weminuche). Note that visibility impairment increases on 

some days reflecting situations in which NOx emission reductions result in particulate nitrate 

concentration increases at certain times and locations. This effect is similar to the ozone 

disbenefit associated with NOx emission reductions described above. 

Mesa Verde NP Mitigation Scenario 1

Worst 20%-->

<--Best 20%

<--Best 20%

Worst 20%-->

-1

-0.5

0

0.5

1

0 2 4 6 8 10 12 14

Haze Index 2018 baseline (dv)

Haze Index 2018 miti1 - Haze Index 2018 

baseline (dv)

Mesa Verde NP Mitigation Scenario 2

<--Best 20%

Worst 20%-->

Worst 20%-->

<--Best 20%

-1

-0.5

0

0.5

1

0 2 4 6 8 10 12 14

Haze Index 2018 baseline (dv)

Haze Index 2018 miti2 - Haze Index 2018 

baseline (dv)

Mesa Verde NP Mitigation Scenario 3

<--Best 20%

Worst 20%-->

Worst 20%-->

<--Best 20%

-1

-0.5

0

0.5

1

0 2 4 6 8 10 12 14

Haze Index 2018 baseline (dv)

Haze Index 2018 miti3 - Haze Index 2018 

baseline (dv)

Mesa Verde NP Mitigation Scenario 4

<--Best 20%

Worst 20%-->

Worst 20%-->

<--Best 20% -1

-0.5

0

0.5

1

0 2 4 6 8 10 12 14

Haze Index 2018 baseline (dv)

Haze Index 2018 miti4 - Haze Index 2018 

baseline (dv)

Mesa Verde NP Mitigation Scenario 5

<--Best 20%

Worst 20%-->

Worst 20%-->

<--Best 20%

-1

-0.5

0

0.5

1

0 2 4 6 8 10 12 14

Haze Index 2018 baseline (dv)

Haze Index 2018 miti5 - Haze Index 2018 

baseline (dv)

Figure 4-22. Predicted change in visibility compared to predicted 2018 base case visibility for 

each day of the year at Mesa Verde under each mitigation scenario (negative values for the 

change in visibility indicate visibility improvement). 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-30

Weminuche WA Mitigation Scenario 1

Worst 20%-->

<--Best 20%

<--Best 20%

Worst 20%-->

-1

-0.5

0

0.5

1

0 2 4 6 810

Haze Index 2018 baseline (dv)

Haze Index 2018 miti1 - Haze Index 2018 

baseline (dv)

Weminuche WA Mitigation Scenario 2

<--Best 20% Worst 20%-->

<--Best 20% Worst 20%-->

-1

-0.5

0

0.5

1

0 2 4 6 810

Haze Index 2018 baseline (dv)

Haze Index 2018 miti2 - Haze Index 2018 

baseline (dv)

Weminuche WA Mitigation Scenario 3

Worst 20%-->

<--Best 20%

<--Best 20%

Worst 20%-->

-1

-0.5

0

0.5

1

0 2 4 6 810

Haze Index 2018 baseline (dv)

Haze Index 2018 miti3 - Haze Index 2018 

baseline (dv)

Weminuche WA Mitigation Scenario 4

<--Best 20% Worst 20%-->

<--Best 20% Worst 20%-->

-1

-0.5

0

0.5

1

0 2 4 6 810

Haze Index 2018 baseline (dv)

Haze Index 2018 miti4 - Haze Index 2018 

baseline (dv)

Weminuche WA Mitigation Scenario 5

Worst 20%-->

<--Best 20%

<--Best 20%

Worst 20%-->

-1

-0.5

0

0.5

1

0 2 4 6 810

Haze Index 2018 baseline (dv)

Haze Index 2018 miti5 - Haze Index 2018 

baseline (dv)

Figure 4-23. Predicted change in visibility compared to predicted 2018 base case visibility for 

each day of the year at Weminuche under each mitigation scenario (negative values for the 

change in visibility indicate visibility improvement). 

Table 4-3. Daily average visibility summary statistics for 2018 base case at Mesa Verde. 

Mesa Verde 

2018 Base Visibility 

Impairment (dV) 

Worst day 12.4849

98th Percentile (8th worst day) 11.2189

2nd Percentile (8th best day) 2.9046

Best day 1.7941

Avg over 20% best days 3.6925

Avg over 20% worst days 9.3698
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-31

Table 4-4. Summary statistics for changes in daily average visibility at Mesa Verde under each 

mitigation scenario (changes greater than 0.5 dV absolute value in bold). 

Change in daily visibility impairment (dV): Mitigation 

scenario - 2018 Baseline 

Mesa Verde 

Scenario 

#1 

Scenario 

#2 

Scenario 

#3 

Scenario 

#4 

Scenario 

#5 

Max 0.4492 0.1052 0.1858 0.3093 0.1969

98th Percentile (8th highest) 0.1733 0.0392 0.0243 0.1991 0.0349

2nd Percentile (8th lowest) -0.4267 -0.0198 -0.0958 -0.5025 -0.0909

Min -0.6921 -0.06 -0.2379 -0.8484 -0.3007

Avg over 20% Best Base Case Visibility 

Days -0.0064 0.0021 0.0036 -0.0008 -0.0008

Avg over 20% Worst Base Case 

Visibility Days -0.1197 0.0053 -0.0320 -0.1574 -0.0275

Table 4-5. Daily average visibility summary statistics for 2018 base case at Weminuche. 

Weminuche 

2018 Base Visibility 

Impairment (dV) 

Worst day 9.1316

98th Percentile (8th worst day) 7.9503

2nd Percentile (8th best day) 2.1073

Best day 1.6126

Avg over 20% best days 2.6584

Avg over 20% worst days 6.5110

Table 4-6. Summary statistics for changes in daily average visibility at Weminuche under each 

mitigation scenario (changes greater than 0.5 dV absolute value in bold). 

Change in daily visibility impairment (dV): Mitigation 

scenario - 2018 Baseline 

Weminuche 

Scenario 

#1 

Scenario 

#2 

Scenario 

#3 

Scenario 

#4 

Scenario 

#5 

Max 0.0207 0.0511 0.0074 0.0248 0.0149

98th Percentile (8th highest) 0.0010 0.0149 0.0011 0.0000 0.0061

2nd Percentile (8th lowest) -0.2453 -0.0005 -0.04 -0.2497 -0.0351

Min -0.4457 -0.0219 -0.0693 -0.4764 -0.0658

Avg over 20% Best Base Case Visibility 

Days -0.0185 0.0011 -0.0019 -0.0192 -0.0007

Avg over 20% Worst Base Case 

Visibility Days -0.0580 0.0022 -0.0103 -0.0672 -0.0077

Examination of the visibility results shows that both base case visibility impacts and predicted 

visibility changes are larger at Mesa Verde as compared to the more remote Weminuche 

Wilderness. In both locations, the largest visibility improvements are predicted to occur under 

mitigation strategy #4 (which represents the combination of mitigations #1, #2 and #3) but nearly 

all of these visibility improvements stem from the EGU emission reductions (mitigation #1). 

Visibility improvements greater than 0.5 dV (but less than 1 dV) are predicted on seven days 

during the year At Mesa Verde under scenario #4 and on three days under scenario #1. VOC and 

NOx emission reductions from oil & gas sources examined in this analysis are predicted to have 

only very minor impacts on visibility in these Class I areas. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc 4-32

4.3 CALCULATION OF OZONE SENSITIVITIES VIA HDDM 

Results from the mitigation scenario modeling presented above suggest that application of what 

are currently regarded as maximum achievable levels of emission reductions from the two major 

source groups in the 4 km modeling domain (EGUs and oil and gas) will reduce ozone design 

values by at most 5 to 10 ppb. Achieving additional ozone reductions would require reducing 

emissions from sources outside the 4 km domain which the source apportionment results 

presented in Section 4.1.2 suggest are substantial contributors to ozone in the Four Corners area. 

However, formulating a realistic mitigation scenario for these sources is a difficult undertaking 

that falls outside the scope of the FCAQTF. Nevertheless, it is possible to estimate the 

sensitivity of ozone in the 4 km domain to changes in VOC and NOx emissions from 12 km 

domain sources (and other sources) using the Higher-Order Direct Decoupled (HDDM) method 

(ENVIRON, 2008). Ozone sensitivity coefficients calculated with this method represent an 

estimate of the change in ozone given a small change in VOC or NOx emissions individually or a 

combined change in both VOC and NOx. Similar to the source apportionment analysis, HDDM 

sensitivities can be calculated for any source group defined by either source category and/or 

location. Since HDDM computes first and second-order sensitivities, nonlinearities in ozone 

formation chemistry are treated to second order and it is thus feasible to generate diagrams that 

show the approximate change in ozone corresponding to different combinations of VOC and 

NOx emission reductions (or increases) from each source group. 

Results of the CAMx HDDM analysis, while not officially a part of this study, will be presented 

to the Four Corners Air Quality Group when available. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_5_Conclusion.doc 5-1

5.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 

5.1 SUMMARY AND CONCLUSIONS 

Detailed annual modeling of the Four Corners region for ozone and particulate matter impacts 

was conducted with updated emissions estimates for 2005, and a projected inventory for 2018. 

The resulting modeling database was used to examine the air quality impacts of five alternative 

mitigation scenarios focused on various combinations of emissions reductions from electric 

generating units (EGUs) and fuel combustion and evaporative sources associated with oil and 

gas exploration and production activities in the Four Corners region. 

Oil and gas emissions estimates used in this analysis are based on the most recently available 

information at the time the analysis was conducted, including the 2002 WRAP Phase II 

inventory scaled to 2005 based on activity data, the New Mexico Environmental Department 

ozone precursor inventory for San Juan and Rio Arriba counties and the 2005 updated Southern 

Ute inventory. In southwestern Colorado, these are essentially the same emissions that will be 

included in the WRAP Phase III inventory. For sources in northwestern New Mexico, however, 

the WRAP Phase III study includes development of a new “bottom up” inventory based on 

updated survey data and other information. It is not presently known to what extent the updated 

Phase III inventory will differ from the inventory used in this study. Emission estimates used in 

this analysis for oil and gas sources outside of the Four Corners region are also being updated as 

part of the WRAP Phase III study. Overall, inventories used in this study are sufficient for 

examining impacts of Four Corners region mitigation scenarios but additional modeling with 

updated inventories will be needed to further evaluate possible air quality impacts of future 

emissions growth and control. 

Analyses of model performance for the 2005 base case showed reasonably good performance for 

ozone with some over prediction at higher elevation locations during the spring time and a slight 

under prediction bias during the summer. While predictions of elevated spring time ozone levels 

in the mountains are generally consistent with seasonal trends observed at remote western U.S. 

monitoring sites, some concerns remain regarding the accuracy of ozone levels predicted at high 

elevation sites during spring in the Four Corners area. Diagnostic analyses showed that these 

predicted ozone peaks result from the long range transport of elevated upper tropospheric or 

lower stratospheric ozone and subsequent enhanced downward mixing in the model over the 

mountains. As a result, these ozone peaks are not very sensitive to controls on local sources of 

emissions. Efforts are currently underway to improve the treatment of vertical circulations over 

elevated terrain in the model. Model performance for sulfate PM showed good agreement with 

observations, consistent with the expected accurate representation of EGU SO2 emissions 

provided by the EPA Acid Rain database. Nitrate PM model performance was mixed but 

concentrations are low so nitrate contributions to overall modeled PM was not significant. 

Although a consistent under prediction bias was noted for elemental and organic carbon PM, this 

bias is not of primary concern in the present study which focuses mostly on the impacts of 

reductions in SO2 and NOx emissions which are effectively independent of organic and 

elemental carbon impacts. Additional follow-up analyses will be needed to understand the 

source of the EC and OC under prediction bias. 

CAMx was exercised with the 2018 base case emissions and the 2005 meteorological data as 

described in Section 3. Given that emissions changed very little overall between the 2005 and 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_5_Conclusion.doc 5-2

2018 base case, the ozone results are nearly identical between these two base cases. Results of 

an ozone and PM source apportionment analysis performed on the 2018 base case scenario 

showed that local oil & gas and EGU sources are the biggest contributors to local ozone episodes 

and to PM after contributions from outside the 4 km domain are accounted for. Further study is 

needed to better understand sources of regional (i.e, western US outside the 4 km modeling 

domain) contributions to ozone and PM impacts in the Four Corners area. 

A series of five emissions mitigation scenarios focused on EGU and oil and gas sources within 

the Four Corners area were examined in this study. These scenarios included a 70% NOx 

reduction and a 16% SO2 reduction from local (i.e., 4 km domain) EGU sources as well as an 

approximate 50% VOC reduction and a 16% NOx reduction from local oil and gas sources. 

EGU and oil & gas together account for 75% of 4 km domain NOx emissions (oil and gas alone 

accounts for 37% of 4 km domain NOx emissions) and 33% of 4 km domain VOC emissions in 

the 2018 base case inventory. Thus, the combined EGU and oil and gas NOx controls 

considered here amount to roughly a 50% reduction in local NOx emissions and a 16% reduction 

in local VOC emissions. These mitigation scenarios resulted in peak predicted ozone changes 

generally limited to less than about 5 ppb. Some of the largest reductions are predicted to occur 

at the Navajo Lake monitoring location during the summer months on days with predicted peak 

hourly ozone above about 70 ppb. A combination of VOC and NOx controls on oil and gas 

sources is more effective at reducing ozone than VOC or NOx controls alone. There also 

appears to be a synergistic effect resulting in enhanced ozone reduction when the EGU controls 

are combined with the oil and gas controls: EGU controls alone result in inconsistent reductions 

from day to day depending on meteorological conditions but combining them with the oil and 

gas controls appears to put a “floor” under the reductions resulting in larger and more consistent 

reductions. 

Predicted visibility impacts under the mitigation scenarios are greatest at Mesa Verde. Oil and 

gas mitigations examined in this study don’t have any significant visibility impacts. EGU 

emissions reductions result in some days with visibility improvements of 0.5 to 1 dV but more 

than 98% of days during the year have predicted improvements of less than 0.5 dV. 

5.2 RECOMMENDATIONS 

This study provides a wealth of information about the linkages between sources of air pollutants 

and resulting air quality and air quality related value impacts in the Four Corners region. 

Additional investigations of these results beyond those presented here can be carried out to 

support future air quality management activities. In particular, the modeling databases from this 

study could be used to examine the impacts of additional specific regulatory strategies beyond 

the five mitigation scenarios included in this report. Several improvements to the modeling 

analyses are recommended, including: 

• Incorporation of updated oil and gas emissions estimates currently being developed via 

the WRAP Phase III study. 

• Further investigation of the apparent underestimation of elemental carbon emissions and 

incorporation of any resulting updated emissions estimates into the modeling database. 

• Further evaluation of individual impacts of major source categories located within the 12 

km (western U.S.) modeling domain on ozone and PM levels in the Four Corners area, 

e.g., oil & gas, EGU, urban area emissions and biogenic and fire emissions. This could 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_5_Conclusion.doc 5-3

be done via application of source apportionment technology with emissions reprocessed 

to split out these major 12 km source categories. 

• Evaluation of whether or not updated modeling algorithms currently under development 

for secondary organic aerosols are able to provide more realistic predictions of organic 

carbon. 

• Incorporation of model improvements for setting stratospheric boundary condition inputs 

and reducing vertical circulations over elevated terrain that are currently under 

development. 

• Review of recent western U.S. modeling analyses of biogenic and other natural emissions 

impacts for potential improvements to emissions estimates and modeling methodologies. 

In particular, evaluate the possibility of including NOx formed by lightning in the 

analysis. 
June 2009 

G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_6_References.doc 6-1 

6.0 REFERENCES 

Abt. 2008. Modeled Attainment Test Software: User’s Manual. Abt Associates, Inc., Bethesda, 

MD, June. 

Boylan, J.W. and Russell, A.G. 2006. PM and Light extinction model performance metrics, 

goals, and criteria for three-dimensional air quality models. Atmospheric Environment, 

40, 4946-4959. 

Dudhia, J., 1993. "A Non-hydrostatic Version of the Penn State/NCAR Mesoscale Model: 

Validation Tests and Simulation of an Atlantic Cyclone and Cold Front", Mon. Wea. 

Rev., Vol. 121. pp. 1493-1513. 

ENVIRON, 2007. Air Quality Modeling Study of the Four Corners Region. ENVIRON 

International Corp., Alpine Geophysics, LLC, 29 June. 

ENVIRON, 2008 CAMx User’s Guide: Comprehensive Air Quality Model with Extensions, 

ENVIRON International Corp., Novato, CA, May. 

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994. "A Description of the Fifth Generation Penn 

State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN-398-STR, 138 pp. 

MAG. 1997. "Technical Support Document for Regional PM10 Modeling in Support of the 

1997 Serious Area PM10 Plan for Maricopa County Nonattainment Area." Prepared by 

the Maricopa Association of Governments, Phoenix, Arizona. 

Morris, R., D. McNally, T.W. Tesche, G. Tonnesen, J. Boylan and P. Brewer. 2005. 

“Preliminary Evaluation of the Community Multiscale Air Quality Model for 2002 over 

the Southeastern United States.” J. Air & Waste Man. Assoc., Vol 55, No. 11, pp1694-

1708. November. 

SCAQMD. 1997. “1997 Air Quality Management Plan.” Prepared by the South Coast Air 

Quality Management District, Diamond Bar, CA. 

SCAQMD. 2003. “2003 Air Quality Management Plan.” Prepared by the South Coast Air 

Quality Management District, Diamond Bar, CA. 

U.S. EPA. 1991. "Guidance for Regulatory Application of the Urban Airshed Model (UAM), " 

EPA-450/4-91-013, Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, N.C. 

U.S. EPA. 2001. "Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and 

Regional Haze", Draft Report, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. 

U.S. EPA. 2003. “Guidance for Tracking Progress Under the Regional Haze Rule”, 

EPA-454/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

U.S. EPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. EPA -454/B-07-

002, U.S. Environmental Protection Agency, April. 
APPENDIX A 

Modeling Protocol Document 
 International Corporation 

Air Sciences

Modeling Protocol 

AIR QUALITY MODELING STUDY 

FOR THE FOUR CORNERS REGION 

Prepared for 

New Mexico Environment Department 

Air Quality Bureau 

2048 Galisteo St. 

Santa Fe, NM 87505 

Prepared by 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, CA 94945 

Alpine Geophysics, LLC 

341 Poppy Way 

Arvada, CO 80007 

29 June 2007 

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 
 

June 2007 

i 

TABLE OF CONTENTS 

Page 

1. INTRODUCTION.......................................................................................................... 1-1 

1.1 Background............................................................................................................... 1-1 

 1.1.1 Purpose of the Modeling Protocol ................................................................... 1-1 

1.2 Project Overview ...................................................................................................... 1-2 

 1.2.1 CAMx Overview.............................................................................................. 1-3 

1.3 Project Management ................................................................................................. 1-5 

 1.3.1 Data Gatekeepers ............................................................................................. 1-5 

 1.3.2 Communications Plan ...................................................................................... 1-6 

 1.3.3 Schedule and Deliverables............................................................................... 1-7 

2. MODELING DOMAIN................................................................................................. 2-1 

3. BASE YEAR MODELING ........................................................................................... 3-1 

3.1 Context for Modeling and Evaluation Approach...................................................... 3-1 

3.2 2005 Base Case Modeling ........................................................................................ 3-3 

 3.2.1 Meteorological Inputs ...................................................................................... 3-3 

 3.2.2 Emission Inputs................................................................................................ 3-5 

 3.2.3 Initial/Boundary Conditions............................................................................. 3-5 

 3.2.4 Ancillary Model Inputs.................................................................................... 3-5 

 3.2.5 CAMx Model Options ..................................................................................... 3-6 

 3.2.6 Modeling Strategy............................................................................................ 3-6 

3.3 Model Performance Evaluation ................................................................................ 3-7 

 3.3.1 Available Aerometric Databases for Model Evaluation .................................. 3-8 

 3.3.2 Model Evaluation Process................................................................................ 3-9 

 3.3.3 Statistical Performance Metrics ..................................................................... 3-11 

 3.3.4 Evaluation of CAMx Model Performance for the 2005 Base Case............... 3-14 

 3.3.5 Performance Goals and Benchmarks............................................................. 3-16 

4. FUTURE YEAR MODELING ..................................................................................... 4-1 

4.1 PM Speciated Model Attainment Test...................................................................... 4-1 

5. REFERENCES............................................................................................................... 5-1 

TABLES 

Table 1-1. Schedule for the Four Corners Air Quality Modeling Study............................... 1-7 

Table 2-1. MM5 vertical layer definitions and mapping to CAMx vertical layers............... 2-8 

Table 3-1. CAMx meteorological input data requirements................................................... 3-4 

Table 3-2. Routine statistical measures used in evaluating air quality models 

 against observational data.................................................................................. 3-13 
 

June 2007 

ii 

FIGURES 

Figure 1-1. Project management for the Four Corners Air Quality Study ............................. 1-6 

Figure 2-1. 2005 36/12/4 km MM5 modeling domain used in the NMED 

 Giant PSD Increment Consumption Study. ......................................................... 2-2 

Figure 2-2(a). Maximum potential CAMx 36/12/4 km modeling domain according 

 to the 2005 36/12/4 km MM5 domain from NMED Giant PSD 

 Increment Consumption Study ............................................................................ 2-3 

Figure 2-2(b). Maximum potential CAMx 12/4 km modeling domain according 

 to the 2005 12/4 km MM5 domain from NMED Giant PSD Increment 

 Consumption Study.............................................................................................. 2-4 

Figure 2-3(a). CAMx 36/12/4 km modeling domain to be used for the Four Corners 

 Air Quality Modeling Study ................................................................................ 2-6 

Figure 2-3(b). CAMx 12/4 km modeling domain to be used for the Four Corners 

 Air Quality Modeling Study ................................................................................ 2-7 

Figure 3-1. The humidity growth function f(RH) taken from the IMPROVE 

 methodology for reconstructing extinction from ammonium sulfate 

 and ammonium nitrate concentrations............................................................... 3-12
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec1.doc 1-1 

1. INTRODUCTION 

This document constitutes the modeling protocol for the Four Corners Air Quality Modeling 

Study, which is being carried out by the contractor team of ENVIRON International Corporation 

and Alpine Geophysics, LLC. This particular component of the study will apply the 

Comprehensive Air Quality Model with extensions (CAMx), a photochemical/PM dispersion 

grid model, to project and evaluate criteria pollutants and air quality related values in the Mesa 

Verde, San Pedro Parks, and Weminuche Class I areas and surrounding Class II areas. The air 

quality model will be supplied with high-resolution meteorological fields for the entire year of 

2005, along with gridded, model-ready emissions for the year 2005 and for several 2018 future 

year scenarios. The meteorological and emission datasets have been developed under separate 

projects funded by local industrial stakeholders. 

 

1.1 BACKGROUND 

In response to concerns regarding the air quality impacts of growth in many types of sources, 

especially oil and gas sources and power generation, on Class I and surrounding Class II areas in 

the Four Corners region, the New Mexico Environmental Department (NMED), together with 

the Colorado Department of Public Health and Environment, convened the Four Corners Air 

Quality Task Force (FCAQTF). States, Tribes, Federal Land Managers and other stakeholders 

have been brought together under the FCAQTF to develop strategies for air quality management 

in the region. In connection with this effort, the FCAQTF identified a need to model the air 

quality impacts of potential alternative mitigation strategies being developed by various 

FCAQTF work groups. Impacts on criteria pollutants (ozone, particulate matter [PM]) and air 

quality related values (visibility and deposition) are to be evaluated. Impacts in the Mesa Verde, 

San Pedro Parks, and Weminuche Class I areas and surrounding Class II areas are of primary 

interest. To facilitate this effort, ENVIRON was contracted by BP America Production 

Company to develop a gridded, model-ready base case emissions for 2005 and a future year 

(2018) base case together with five mitigation options to be specified by the FCAQTF. In 

addition, a high resolution (4 km) MM5 annual simulation for 2005 has been completed, quality 

assured, and evaluated by Alpine Geophysics, working under subcontract to ENVIRON, at the 

direction of the NMED in a Prevention of Significant Deterioration (PSD) increment 

consumption study funded by Giant Petroleum. 

In this particular component of the air quality study, the contractor team of ENVIRON and 

Alpine Geophysics will complete the mitigation option analysis in support of the FCAQTF by 

applying the CAMx photochemical grid model to the Four Corners region using the emission 

inventories and meteorological data we have developed as described above. 

1.1.1 Purpose of the Modeling Protocol 

This Modeling Protocol sets forth the procedures, data sources and modeling approach to be used 

in performing the air quality modeling and evaluation of air quality related values for the various 

Class I and Class II areas in the Four Corners area. Its main function is to serve as a means for 

planning and communicating how the modeling will be performed before it occurs. The protocol 
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec1.doc 1-2 

guides the technical details of a modeling study and provides a formal framework within which 

the scientific assumptions, operational details, commitments and expectations of the various 

participants can be set forth explicitly and means for resolution of potential differences of 

technical and policy opinion can be worked out openly and within prescribed time and budget 

constraints. Although the air quality modeling of the Four Corners area is not currently planned 

to be part of a SIP attainment demonstration, much of EPA’s guidance related to the content of 

Modeling Protocols is directly relevant and provide a road map for our work under this task 

(EPA, 1991; 2001; 2006; 2007). Note that because some aspects of the Four Corners Air Quality 

Modeling Study are predetermined (e.g., CAMx has been previously chosen for these analyses, 

and MM5 meteorological model outputs and emission inputs will be provided from other 

projects), this Modeling Protocol describes only the air quality modeling and analysis 

component. 

The procedures described herein will be reviewed by the FCAQTF and stakeholders so that a full 

and complete understanding of the modeling approach will be understood by all. Agencies and 

Stakeholders are invited to comment on the procedures outlined in the Modeling Protocol; 

revisions to the protocol will be developed to address issues on an as-needed basis. 

1.2 PROJECT OVERVIEW 

Details of the technical approach are described in detail in the following sections of this 

Modeling Protocol. The air quality modeling to be conducted in this project will provide 

estimates of ozone, PM, sulfur and nitrogen deposition, and visibility impacts in the Four 

Corners area for 2005 and 2018 base cases, and up to five alternative mitigation scenarios to be 

defined by NMED/FCAQTF. 

The components of the Four Corners Air Quality Study are as follows: 

Modeling Protocol: A Modeling Protocol (this document) will be prepared that conforms to the 

recommendations on Modeling Protocols in EPA’s guidance for 8-hour ozone, PM and regional 

haze modeling (EPA, 2007). The protocol will be provided to the NMED for review, and based 

on comments received from NMED, a revised Modeling Protocol will be prepared and 

submitted. Additional Modeling Protocol revisions will be prepared as needed during the course 

of the study. 

Base Case Modeling: A modeling domain comprised of nested grids with 36, 12, and 4 km grid 

spacing (resolution) will be defined. The 2005 MM5 meteorological data fields will be 

processed into CAMx inputs for this domain configuration and quality-assured. Emission inputs 

will be quality assured, and other CAMx 2005 Base Case inputs will be developed. The model 

will be run for a winter and summer period (e.g., January and July 2005), followed by a 

preliminary performance evaluation and diagnostic testing (as needed), to identify the optimal 

model configuration that will be used to perform the full 2005 36/12/4 km CAMx annual base 

case simulation. CAMx will be run first on the 36 km grid for the full annual period; results 

from this run will be used to provide boundary conditions (BCs) to the 12 km grid (this is 

referred to as a “one-way” nesting technique). Then the 12/4 km grids will be run together in a 

fully two-way interactive manner. A Technical Report and associated presentation will be 

prepared that documents the Base Case modeling inputs and final configuration. 
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec1.doc 1-3 

Model Performance Evaluation: A detailed and comprehensive model performance evaluation 

will then be conducted on the 2005 Base Case simulation. Available measurements for ozone 

and other gas-phase species, speciated PM and total PM mass, deposition and visibility will be 

compared with model output. Existing model evaluation software and techniques will be 

employed, which have been developed from regional modeling conducted for WRAP and other 

urban and larger-scale modeling programs. Graphical displays of model performance will be 

generated including scatter plots, time series plots, quantile-quantile plots, spatial maps of model 

predictions with superimposed observations, “soccer goal” plots, “bugle” plots and other 

displays. A Technical Report and associated presentation will be prepared that documents the 

2005 CAMx Base Case performance evaluation. 

Future-Year Modeling: A 2018 Base Case and 5 future-year mitigation strategies will be 

simulated using the 2005 36/12/4 CAMx modeling databases and configurations developed 

above. We will employ EPA guidance techniques to project future-year ozone, visibility and 

PM2.5 concentrations. These techniques use the model in a relative sense to scale the observed 

values using relative response factors (RRFs). Spatial maps, bar charts and other graphical 

displays would be used extensively to convey the results. A Technical Report and associated 

presentation will be prepared that documents the 2018 CAMx Future Year results. 

Final Report and Presentation: The results from the 2005 Base Case and 2018 Future Year 

modeling analyses will be documented in a draft Final Report and submitted to NMED for 

review by the FCAQTF. After receipt of comments, the report will be updated and resubmitted. 

A presentation on the study will be developed and presented at the Four Corners Task Force 

meeting in November 2007. 

1.2.1 CAMx Overview 

The CAMx modeling system is a publicly available (www.camx.com) three-dimensional multiscale photochemical/aerosol grid modeling system that is developed and maintained by 

ENVIRON International Corporation (ENVIRON, 2006). CAMx was developed with all new 

code during the late 1990s using modern and modular coding practices. This has made the 

model an ideal platform for the extension to treat a variety of air quality issues including ozone, 

particulate matter (PM), visibility, acid deposition, and air toxics. The flexible CAMx 

framework has also made it a convenient and robust host model for the implementation of a 

variety of mass balance and sensitivity analysis techniques (referred to as “Probing Tools”), 

including Process Analysis (IRR, IPR, and CPA), Decoupled Direct Method (DDM), and the 

Ozone and Particulate Source Apportionment Technology (OSAT/PSAT). CAMx has been 

widely used in recent years by a variety regulatory agencies for 1-hr and 8-hr ozone and PM10

SIP modeling studies as well as by several RPOs for regional haze modeling. Key attributes of 

the CAMx model include the following: 

• Fully interactive two-way grid nesting that supports multiple levels of grids (e.g., 36/12/4 

km); 

• CB4, CB05 or SAPRC99 gas-phase photochemical mechanisms; 

• Two gas-phase chemical solvers, the CAMx Chemical Mechanism Compiler (CMC) Fast 

Solver or the highly accurate Implicit Explicit Hybrid (IEH) solver; 
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec1.doc 1-4 

• Two separate treatments of PM using the same ISOROPIA and RADM chemistry 

algorithms as CMAQ: 

o A static two-mode (coarse/fine) option comparable to the approach in CMAQ; 

o A multi-section “full-science” approach using the Multi-component Aerosol 

Dynamics Model (MADM) that treats the effects of condensation/evaporation, 

coagulation and nucleation upon the particle size distribution. 

• Secondary organic aerosol thermodynamics represented using a semi-volatile scheme; 

• Multiple numerical algorithms for horizontal transport including the Piecewise Parabolic 

Method (PPM) and Bott advection solvers; 

• Subgrid-scale Plume-in-Grid (PiG) algorithm to treat the near-source plume dynamics 

and chemistry from point sources; 

• Ability to interface with a variety of meteorological models including the MM5, RAMS, 

and WRF prognostic hydrostatic meteorological models and the CALMET diagnostic 

meteorological model (others also compatible); 

CAMx provides two key options to users interested in simulating PM. For CPU-efficient PM 

modeling applications, CAMx may be run using a two mode size representation (fine and coarse) 

similar to the treatment in CMAQ. Alternatively, more rigorous aerosol simulations (perhaps for 

shorter episodes) may be addressed using the version that treats N-size sections (N is typically 

10) and the rigorous, but computationally-extensive MADM multi-section chemistry module. 

The databases required to set up and operate CAMx for the Four Corners Air Quality Modeling 

Study are as follows: 

• Three-dimensional hourly meteorological fields generated by MM5 and prepared using 

the MM5CAMx interface processor; 

• Two-dimensional low-level (surface layer) emissions and elevated point source emissions 

generated by an emissions processor; 

• Initial/boundary (IC/BC) inputs for the coarsest (master) grid as prepared by WRAP; 

• Photolysis rates look up table; 

• Monthly Albedo/Haze/Ozone Column input file prepared from several available global 

satellite-derived datasets; 

• Land use and topography, as prepared for MM5. 

All mathematical models possess inherent limitations owing to the necessary simplifications and 

approximations made in formulating the governing equations, implementing them for numerical 

solution on fast computers, and in supplying them with input data sets and parameters that are 

themselves approximations of the full state of the atmosphere and emissions processes. Like all 

air quality models, a major limitation of CAMx rests with the input fields that characterize 

emissions, meteorology, and IC/BCs. Key science limitations in the model itself include the 

nitrate formation chemistry and the secondary organic aerosol (SOA) module. Preliminary 

modeling by the RPOs (e.g., WRAP, VISTAS and CENRAP) found both CAMx and CMAQ 

nitrate performance suspect with winter overestimations and summer underestimations (Morris et 

al., 2004, 2005). While not as poor as CMAQ, the VISTAS and CENRAP modeling also found 

CAMx performance for Organic Carbon (OC) to be less than ideal; much of the OC performance 

problems have been due to deficiencies in the SOA module that in the past has failed to account 

for several known processes important to SOA (e.g., polymerization). Much of these limitations 
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec1.doc 1-5 

have been addressed in an improved SOA module now available in CAMx; additional research 

in this area is on-going. 

1.3 PROJECT MANAGEMENT 

The Four Corners Air Quality Modeling Study will be conducted by ENVIRON International 

Corporation and Alpine Geophysics, with input from FCAQTF participants. Organizational 

commitment is an essential element for developing and implementing a successful research 

project. The overall project organization is shown in Figure 1-1. 

The project management organization established for the ENVIRON/Alpine team is based on the 

respective expertise of the individual team members. ENVIRON is the prime contractor, and 

Mr. Till Stoeckenius in ENVIRON’s Novato, CA office will serve as the Project Manager; in 

this role he will be the main point of contact for the NMED, and will be responsible for the 

timeliness and quality of all project deliverables. Mr. Ralph Morris (also of ENVIRON’s Novato 

office) will serve as Principal-in-Charge for the project. Mr. Morris will ensure that staffing and 

other resources are made available to the project to meet the needs of NMED. Mr. Dennis 

McNally will serve as Alpine Geophysics’ subcontractor point of contact; he will direct all of 

Alpine’s work on preparation of the MM5 model output for use in CAMx and CAMx model 

evaluation. ENVIRON has a substantial track record of successfully working together with 

Alpine Geophysics on complex and high profile air sciences modeling projects and generating 

high-quality deliverables. 

1.3.1 Data Gatekeepers 

For quality assurance purposes, we have designated certain key individuals to serve as 

gatekeepers for data flowing into the modeling analysis. These individuals will be responsible 

for ensuring that the data provided to the model fulfill the modeling requirement, are consistent 

with the modeling protocol, have been adequately quality assured, and are ready to be used in the 

model when needed. Separate air quality, meteorological and emissions gatekeepers have been 

identified whose roles are defined below. 

• Meteorological Gatekeeper: As necessary, obtain meteorological data as appropriate 

for annual 2005 modeling runs and perform data quality checks, together with 

appropriate documentation of model performance evaluation activities. Chris Emery will 

serve as the Meteorological Gatekeeper. 

• Emissions Gatekeeper: Obtain emissions inventory data necessary to support annual 

2005 and future year modeling. Assure quality of all emissions data received is 

consistent with the Modeling Protocol. Ms. Ou Nopmongcol will serve as the Emissions 

Gatekeeper. 
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec1.doc 1-6 

Model

Performance

Evaluation

Dennis McNally

Task Manager

State of New Mexico

Environmental Dept.

Modeling

Protocol

Chris Emery

Task Manager

CAMx Modeling

Chris Emery

Task Manager

Reporting

Till Stoeckenius

Task Manager

Ralph Morris

Principal-in-Charge

Till Stoeckenius

Project Manager

Figure 1-1. Project management for the Four Corners Air Quality Study.

• Air Quality Data Gatekeeper: Compile, QA/QC, and maintain the observational 

database used in the model performance evaluation. Maintain the modeling results and 

other documents as requested by the FCAQTF to support the modeling tasks. This 

includes, for example, the storage of model inputs and outputs for annual runs and the 

transfer (via USB/firewire portable disk or alternative media) of electronic files to NMED 

and other interested parties. Till Stoeckenius will serve as the Air Quality Data 

Gatekeeper. 

1.3.2 Communications Plan 

The ENVIRON team will conduct several activities to ensure smooth project management and 

communications throughout the project, including: 

• Weekly or bi-weekly conference calls among ENVIRON and Alpine Geophysics 

personnel. 

• Monthly calls between the ENVIRON team and NMED personnel; ENVIRON team task 

managers will participate as appropriate. 

• Monthly progress reports prepared by ENVIRON with input as needed from Alpine 

Geophysics; these reports will summarize project progress, results to date, problems 

encountered and necessary action items, and plans for the upcoming reporting period. 

• Continuous, regular e-mail and telephone contacts as necessary to ensure the 

communication of progress, issues and their resolution, accuracy of the results, and 

timely completion of the tasks. 
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec1.doc 1-7 

The modeling team members and agency representatives will utilize e-mail as the primary means 

of routine correspondence and data exchange. Larger data file transfer will require FTP; the 

largest modeling files are quite volumous and will require shipment via external disk drives. 

Unscheduled meetings or conference calls will also be held concerning specific issues as the 

needs arise. 

Miscellaneous documents, presentation material, reports, and small data files will be provided on 

the project FTP site. A web-enabled anonymous FTP site will be set up at: 

ftp://ftp.environ.org/pub/webaccess/FourCorners. 

1.3.3 Schedule and Deliverables

The current schedule for this project is detailed in Table 1-1. All work is to be completed by 30 

November 2007. While this is a fairly ambitious schedule, we will be able to complete all of the 

requested work within this timeframe by combining resources available at ENVIRON and at 

Alpine Geophysics. 

Table 1-1. Schedule for the Four Corners Air Quality Modeling Study. 

Task Description Due Date 

1 Modeling Protocol 

Progress Reports 

30 June 2007 

Monthly 

2 Draft Technical Report 

 Base Year Modeling 

15 September 2007 

3 Draft Technical Report 

 Model Performance Evaluation 

15 September 2007 

4 Draft Technical Report 

 Future Year Modeling 

31 October 2007 

5 Draft Presentation 

Final Presentation 

31 October 2007 

7 November 2007 

 Draft Final Report 

Final Report 

16 November 2007 

30 November 2007 
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec2.doc 2-1 

2. MODELING DOMAIN 

The Four Corners air quality modeling will be conducted using CAMx with 2005 meteorological 

data developed from a 36/12/4 km MM5 simulation conducted as part of the NMED Giant PSD 

Increment Consumption study. Figure 2-1 displays the MM5 36/12/4 km modeling domain used 

in the NMED Giant modeling. CAMx will also be similarly applied on a 36/12/4 km grid system 

that will need to match the MM5 Lambert projection and fit within the respective MM5 grids. 

CAMx will be run first on the 36 km grid for the full annual period; results from this run will be 

used to provide boundary conditions (BCs) to the 12 km grid (this is referred to as a “one-way” 

nesting technique). Then the 12/4 km grids will be run together in a fully two-way interactive 

manner. 

When nesting a CAMx modeling domain within an MM5 modeling domain there are certain 

rules that must be followed: (1) there must be a buffer of at least 5 grid cells from the MM5 

domain boundaries in order to limit artifacts of the MM5 solution near the boundary; and (2) 

when using two-way grid nesting the modeling domain boundaries of the fine grids must align 

with the coarsest grid used. Figure 2-2(a) displays the maximum possible CAMx 36/12/4 km 

modeling domain that can be set within the existing 2005 36/12/4 km MM5 configuration. Also 

plotted in Figure 2-2(b) are the locations of the IMPROVE and CASTNet monitoring sites in the 

Four Corners states. 

The CAMx 36 km grid will correspond to the Regional Planning Organizations (RPO) 

continental U.S. domain that is being used by WRAP, CENRAP, MRPO, and VISTAS for their 

2002 CAMx/CMAQ regional haze modeling. This will allow the use of WRAP emission 

databases on the 36 km grid. Note that we considered whether the 36 km grid should be reduced, 

given our focus on the Four Corners area. However, we took note of recent 8-hour ozone State 

Implementation Plan (SIP) modeling performed by Clark County (Las Vegas), who reduced the 

size of the 36 km grid by eliminating the eastern U.S. They found that some of their episodic 

ozone days were heavily influenced by the eastern boundary conditions. Use of the full 36 km 

RPO modeling grid is highly advantageous and so will remain unchanged. 

However, it is desirable to reduce the size of the 12 km and 4 km domains to increase 

computational efficiency. Concerning the 12 km CAMx domain, the eastern, western, and 

northern boundaries will remain at their maximum extent as shown in Figure 2-2. We see no 

reason to bring in the eastern boundary to reduce its size. It may be important to include 

transport from California, so the western boundary appears to be appropriate as well. Finally, it 

will be advantageous to include the Yellowstone and Grand Teton IMPROVE monitors in the 

model evaluation, so the northern boundary remains unchanged. However, the southern 

boundary will be moved northward somewhat to make it consistent with 12 km emissions 

surrogate distributions that ENVIRON has already prepared. 

The 4 km domain is reduced from the maximum shown in Figure 2-2 as follows: 

• The domain extends sufficiently west and south to include the IMPROVE and nearby 

CASTNet sites in the Petrified Forest National Park; 

• The domain extends as far east to include San Pedro Parks Wilderness Area; and 
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec2.doc 2-2 

Figure 2-1. 2005 36/12/4 km MM5 modeling domain used in the NMED Giant PSD 

Increment Consumption Study. 
 

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-2880-2520-2160-1800-1440-1080 -720 -360 0 360 720 1080 1440 1800 2160 2520 2880

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0

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720

1080

1440

1800

2160

New Mexico Proposed Modeling Domain

CAMx 36 km: 148 x 112 (-2736, -2088) to (2592, 1944)

CAMx 12 km: 167 x 146* (-2316, -1020) to ( -312, 732)

CAMx 04 km: 155 x 155* (-1228, -652) to ( -608, -32)

* includes buffer cells

MM5 36 km: 165 x 129 dot points (-2952, -2304) to (2952, 2304)

MM5 12 km: 178 x 157 (-2376, -1080) to (-252, 792)

MM5 04 km: 172 x 167 (-1272, -672) to (-588, -8)

36 km

12 km

4 km

Figure 2-2(a). Maximum potential CAMx 36/12/4 km modeling domain according 

to the 2005 36/12/4 km MM5 domain from NMED Giant PSD Increment 

Consumption Study. 
 

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-2280-2160-2040-1920-1800-1680-1560-1440-1320-1200-1080 -960 -840 -720 -600 -480 -360

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-840

-720

-600

-480

-360

-240

-120

0

120

240

360

480

600

720

New Mexico CAMx 12 Nested Domain

IMPROVE

CASTNET

12 km

4 km

Figure 2-2(b). Maximum potential CAMx 12/4 km modeling domain according to 

the 2005 12/4 km MM5 domain from NMED Giant PSD Increment Consumption 

Study.
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec2.doc 2-5 

• The domain extends as far north to include Mesa Verde National Park and Weminuche 

Wilderness Area. 

The resultant CAMx 36/12/4 km modeling domain for the 2005 Four Corners Air Quality 

Modeling Study is displayed in Figure 2-3(a). Figure 2-3(b) displays the 12/4 grid domain and 

the locations of the IMPROVE, CASTNet and NADP monitoring sites. The 4 km domain 

includes 5 IMPROVE, 5 CASTNet and 2 NADP monitoring sites. 

Table 2-1 displays how the CAMx vertical grid will be meshed with the MM5 vertical grid 

structure. This mapping is taken from the methodology used by WRAP. Note that 19 CAMx 

layers will comprise an aggregation of 34 MM5 layers. This is done to increase computational 

efficiency, while maintaining higher resolution in the planetary boundary layer (lowest ~3 km of 

the atmosphere) where the bulk of pollutant chemistry and transport take place. Such layer 

collapsing is performed in nearly every regulatory modeling exercise, and is especially necessary 

to maximize model speed for annual simulations. Early testing by WRAP found nearly identical 

air quality modeling results were obtained using no layer collapsing (i.e., 34 vertical layers that 

exactly match MM5) and 19 vertical layers. However, more aggressive layer collapsing (e.g., 12 

layers) affected the surface predicted concentrations. Thus, in Table 2-1 we have proposed a 19 

vertical layer structure that is consistent with WRAP, CENRAP and VISTAS. 
 

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New Mexico Modeling Domain

CAMx 36 km: 148 x 112 (-2736, -2088) to (2592, 1944)

CAMx 12 km: 167 x 137* (-2316, -912) to ( -312, 732)

CAMx 04 km: 101 x 92* (-1192, -508) to ( -788, -140)

* includes buffer cells

MM5 36 km: 165 x 129 dot points (-2952, -2304) to (2952, 2304)

MM5 12 km: 178 x 157 (-2376, -1080) to (-252, 792)

MM5 04 km: 172 x 167 (-1272, -672) to (-588, -8)

36 km

12 km

4 km

Figure 2-3(a). CAMx 36/12/4 km modeling domain to be used for the Four 

Corners Air Quality Modeling Study.
 

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-2280-2160-2040-1920-1800-1680-1560-1440-1320-1200-1080 -960 -840 -720 -600 -480 -360

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New Mexico CAMx 12 km Nested Domain

IMPROVE

CASTNET

NADP

12 km

4 km

CAMx 12 km: 167 x 137* (-2316, -912) to ( -312, 732)

CAMx 04 km: 101 x 92* (-1192, -508) to ( -788, -140)

* includes buffer cells

Figure 2-3(b). CAMx 12/4 km modeling domain to be used for the Four Corners 

Air Quality Modeling Study.
 

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G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec2.doc 2-8 

Table 2-1. MM5 vertical layer definitions and mapping to CAMx vertical layers. 

MM5 Vertical Layers CAMx Vertical Layers 

K 

(MM5) sigma Press (mb x 100) 

Height 

(m) 

Depth 

(m) 

K 

(CAMx) 

Height 

(m) 

Depth 

(m) 

34 0.000 10000 15674 2004 19 15674 7053 

33 0.050 14500 13670 1585 

32 0.100 19000 12085 1321 

31 0.150 23500 10764 1139 

30 0.200 28000 9625 1004 

29 0.250 32500 8621 900 18 8621 2467 

28 0.300 37000 7720 817 

27 0.350 41500 6903 750 

26 0.400 46000 6153 693 17 6153 1942 

25 0.450 50500 5461 645 

24 0.500 55000 4816 604 

23 0.550 59500 4212 568 16 4212 1104 

22 0.600 64000 3644 536 

21 0.650 68500 3108 508 15 3108 896 

20 0.700 73000 2600 388 

19 0.740 76600 2212 282 14 2212 556 

18 0.770 79300 1930 274 

17 0.800 82000 1657 178 13 1657 353 

16 0.820 83800 1478 175 

15 0.840 85600 1303 172 12 1303 341 

14 0.860 87400 1130 169 

13 0.880 89200 961 167 11 961 249 

12 0.900 91000 794 82 

11 0.910 91900 712 82 10 712 163 

10 0.920 92800 631 81 

9 0.930 93700 550 80 9 550 80 

8 0.940 94600 469 80 8 469 80 

7 0.950 95500 389 79 7 389 79 

6 0.960 96400 310 78 6 310 78 

5 0.970 97300 232 78 5 232 78 

4 0.980 98200 154 39 4 154 39 

3 0.985 98650 115 39 3 115 39 

2 0.990 99100 77 38 2 77 38 

1 0.995 99550 38 38 1 38 38 

0 1.000 100000 0 0 0 0 0 
 

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3. BASE YEAR MODELING 

3.1 CONTEXT FOR MODELING AND EVALUATION APPROACH 

Although the emergence of affordable and fast Linux PCs allows for annual/continental air 

quality modeling using photochemical grid models on nested grids, it remains a challenge to 

achieve the best model performance possible given: the many factors associated with evaluating 

performance for ozone, multiple PM2.5 species, and wet/dry deposition; uncertainties associated 

with the meteorology and emissions; and variations in the important physio-chemical processes 

associated with different locations and times of the year. With the limitations imposed by the 

schedule and resource availability, as well as the extensive computer requirements associated 

with annual ozone/PM modeling, the number of model iterations required to develop a well 

performing base case, as done for episodic SIP modeling, will not be possible1

. Thus, much of 

the technical approach to be applied in this project builds from our past experience with 

ozone/PM/visibility modeling, tailored to the specific and unique conditions of the Four Corners 

region. Some elements in our recent past experience that we would put to use in the Four 

Corners air quality study include the following: 

• We have amassed a history of model performance expectations for ozone, PM2.5 and 

regional haze from which general model performance attributes can be gleaned: 

Ozone: Current photochemical grid models generally perform well for ozone in most 

urban and rural applications, with perhaps a tendency for under prediction. In most cases 

ozone model performance achieves EPA’s performance goals and adequate ozone model 

performance is attainable. Reasons for ozone underestimation tendencies likely include 

misrepresented emission rates and VOC speciation, limitations in the photochemical 

mechanisms themselves, improperly characterized vertical mixing, and/or inadequate 

treatment of other processes, often specific to each area, that are important to ozone 

formation. 

Sulfate (SO4): Model performance for SO4 has been fairly good, which is probably due 

to well characterized emissions (e.g., known point source emissions rates) and relatively 

simple chemistry. 

Nitrate (NO3): Performance for NO3 has been challenging for the current models. This 

is due to the complex chemical cycle that transforms NOx to total nitrate, the partitioning 

of total nitrate between gaseous nitric acid and particle NO3 (which depends strongly on 

ambient SO4, ammonia and meteorology), very large uncertainties in ammonia 

emissions, and the strong roll of wet and dry deposition pathways. It is also very difficult 

to measure, given its volatility. This is an area that will be evaluated very closely in the 

Four Corners Modeling Study. 

Ammonium (NH4): Performance for NH4 has been mixed and generally follows the 

performance for SO4 and NO3 in a particular application, depending upon which acid is 

 

1

 For example, ENVIRON’s work with the State of Texas for a recent ozone SIP required over 40 base case 

simulations in order to achieve an adequate level of model performance. 
 

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dominant. Performance evaluations have been limited by the fact that many PM 

monitoring networks do not measure NH4 (such as IMPROVE), and complicated by the 

fact that ammonia emissions are very uncertain and that NH4 salts can take on many 

forms depending upon the relative quantities of the various acids and environmental 

conditions. 

Elemental Carbon (EC): With the exception of fire events, performance for EC has been 

fairly good. Again, well characterized emissions from combustion sources and no 

chemistry are likely causes for this good performance. 

Organic Carbon (OC): Performance for OC has been mixed, and depends upon the 

model formulation, the region of interest, and the mix of contributing sources. OC is 

typically one of the largest components of PM2.5 and regional haze; it is often dominated 

by wildfire emissions in the western U.S. OC is complicated because there are both 

primary (e.g., combustion) and secondary contributors, and its volatile components are 

difficult to measure. There is currently an intense area of research investigating the 

sources and chemistry of secondary organic aerosol (SOA), and the SOA chemistry 

modules in both CMAQ and CAMx have been updated to include new biogenic sources 

of SOA. The modeling team will use the latest version of CAMx, which incorporates a 

new SOA module that includes SOA pathways from biogenic sesquiterpene, isoprene, 

and terpenes. However, this would require that biogenic emissions be generated with the 

new MEGAN biogenic emissions model. 

Inorganic Primary Fine PM (IP): Performance for IP has been poor. This is due to: (1) 

PM speciation profiles that allocate unidentified PM to this species; (2) miss-matches 

between what the modeled and measured values represent; and (3) emission sources that 

are poorly characterized, episodic and uncertain (e.g., wind blown and fugitive dust). 

Coarse Mass (CM): CM performance has also been poor as a large part of the measured 

contribution is due to local fugitive dust sources whose emissions are poorly 

characterized and not appropriately modeled with regional models (e.g., the fact that the 

vast majority of CM from specific sources settles upon nearby vegetation and structures 

soon after being emitted cannot be handled in grid models, since emissions are 

instantaneously diluted to grid scale). 

• An important issue to consider is that different PM monitoring networks may use 

different measurement approaches that “measure” different amounts of the same species, 

which further differ from the form of the modeled species. It is often difficult to gauge 

model performance against specific measurements, since we must emulate “how” the PM 

is measured (i.e., the form of sulfate reported, loss of carbon and nitrate via volatilization, 

etc.). Such measurement issues can introduce an apparent model bias that may not be 

truly there. In other words, measurements do not necessarily always represent “truth”; as 

modelers, we must build in a large margin for what is “acceptable” performance when 

comparing model results to measurement data. 

• It is difficult to perform comprehensive diagnostic testing on annual simulations due to 

the computational requirements and the vast amount of data generated. Consequently, we 

will perform initial diagnostic model testing over two representative months to identify 
 

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an optimal model configuration for the annual run. We have used this same approach for 

the VISTAS (Morris et al., 2004), and other studies (e.g., CENRAP, WRAP and the 

ongoing St. Louis 8-hour ozone and PM2.5 modeling study). 

The CAMx model evaluation protocol concentrates on an operational evaluation of 1-hour and 8-

hour ozone concentrations and PM species concentrations that are consistent between the model 

and measurements (i.e., SO4, NO3, NH4, OC, EC, IP, and CM). Where feasible and supported by 

sufficient measurement data, we will also evaluate the modeling system for its ability to 

accurately estimate gas-phase oxidant and precursor/product species since correct, unbiased 

simulation of gas-phase photochemistry is a necessary element of reliable secondary PM 

formation. This evaluation will focus on available measurements within the 4 km Four Corners 

domain; however an evaluation across the full 12/4 km western U.S. on a month-by-month basis 

will be relied upon heavily to build confidence that the modeling system is operating correctly in 

the Four Corners region. With this context in mind, we next turn to the actual steps in the CAMx 

Base Case model application and evaluation process. 

3.2 2005 BASE CASE MODELING 

 

The modeling team will develop CAMx modeling inputs on the 36/12/4-km nested modeling 

domain for a 2005 Base Case simulation. Preliminary model testing and evaluation will be 

performed over two months of the year to establish the model configuration and any input 

modifications needed, after which a full 2005 annual simulation will be performed. 

3.2.1 Meteorological Inputs 

Meteorological data for this project will be derived from MM5 modeling of the calendar year 

2005 on a similar 36/12/4 km nested grid structure (as described in Section 2). The MM5 

simulation has been completed by the ENVIRON/Alpine Team for the Giant/NMED project. 

Details of the MM5 application are described in the Giant/NMED work plan and final report 

(ENVIRON and Alpine Geophysics, 2006; McNally and Schewe, 2006). 

It is necessary to convert raw output from the MM5 meteorological model to formats and 

variables used by CAMx specifically. The MM5CAMx translation processor will be used to 

complete this task. The software includes the ability to interpolate data from the native map 

projections used by the meteorological model to any projection to be specified for the air quality 

model (CAMx may be applied on Lambert Conformal, Polar Stereographic, or UTM Cartesian 

projections, or in geodetic latitude/longitude). 

CAMx requires meteorological input data for the parameters described in Table 3-1. All of these 

input data are derived from the MM5 results. MM5CAMx performs several functions: 
 

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Table 3-1. CAMx meteorological input data requirements. 

CAMx Input Parameter Description 

Layer interface height (m) 3-D gridded hourly time-varying layer heights 

Winds (m/s) 3-D gridded hourly wind vectors (u,v) 

Temperature (K) 3-D gridded hourly temperature and 2-D gridded surface 

temperature 

Pressure (mb) 3-D gridded hourly pressure 

Vertical Diffusivity (m2/s) 3-D gridded hourly vertical exchange coefficients 

Water Vapor (ppm) 3-D gridded hourly water vapor mixing ratio 

Cloud Cover 3-D gridded hourly cloud and precip water contents 

Landuse Distribution 2-D gridded static landuse/landcover distribution 

1. Extracts data from the MM5 grids to the corresponding CAMx grids; in this study, the 

extraction includes a simple one-to-one mapping from the MM5 Lambert Conformal grid 

to the CAMx Lambert Conformal grid, with appropriate windowing to remove the extra 

row/columns in the MM5 grids. 

2. Performs mass-weighted vertical aggregation of data for CAMx layers that span multiple 

MM5 layers. 

3. Diagnoses key variables that are not directly output by MM5 (e.g., vertical diffusion 

coefficients and some cloud information). 

The MM5CAMx program has been written to carefully preserve the consistency of the predicted 

wind, temperature and pressure fields output by MM5. This is the key to preparing massconsistent inputs for CAMx, and therefore for obtaining high quality performance from CAMx. 

The MM5CAMx processor will be used to process the 2005 MM5 output data fields from each 

modeling grid to the CAMx grids, variables and formats. We will employ layer collapsing to 

reduce the number of vertical layers from the 34 used in the MM5 modeling (as described in 

Section 2). Vertical diffusivities (Kv) are an important input to the CAMx simulation since they 

determine the rate and depth of mixing in the planetary boundary layer (PBL) and above. In 

general, our experience has been that diffusivities from meteorological models require careful 

examination before they are used in air quality modeling. This may be because the air quality 

model results are much more sensitive to diffusivities than the meteorological model results. We 

will evaluate the CAMx diffusion inputs by comparing the Kv values from several diagnostic 

calculation approaches. Two sets of vertical turbulent diffusivity files will be generated by 

MM5CAMx: 

• Use of the O’Brien scheme (OB70); 

• Use of the CMAQ scheme. 

Sensitivity simulations will be undertaken with the two variations. Additionally, MM5CAMx 

will be set up to apply both 0.1 m2

/s and 1.0 m2/s minimum Kv values, which will be evaluated 

in these same sensitivity tests. The choice of minimum Kv value is an area of ongoing 

investigation by both the CMAQ and CAMx developers. The problem relates to simulating the 

proper degree of the pollutant buildup during stable (e.g., nighttime) conditions. A value that is 

too small often results in over predictions of PM and ozone precursors such as NOx that can 
 

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artificially remove ozone, while a value that is too large may lead to significant under 

predictions. These problems have been seen to impact daytime photochemistry and the 

calculation of 24-hour PM levels. 

As described in Section 1, we have established a Meteorological Gatekeeper to provide 

independent review and QA/QC of the meteorological inputs. Mr. Chris Emery will serve as the 

Meteorological Gatekeeper for this study. He will provide a final level of QA/QC on the modelready meteorological inputs processed by the MM5CAMx program, and ensure that data are 

transferred to the model correctly. 

3.2.2 Emissions Inputs 

CAMx emissions inputs for the study are being developed by ENVIRON under separate funding. 

Documentation on the development of model-ready emissions will be prepared at the conclusion 

of that project. Thus, for this work effort, we are expecting that speciated, hourly, model-ready 

emissions for 2005 on the 36/12/4 km CAMx grids will be fully available. 

As described in Section 1, we have established an Emissions Gatekeeper to provide independent 

review and QA/QC of the emission inputs. Dr. Ou Nopmongcol will serve as the Emissions 

Gatekeeper for this study. She will provide a final level of QA/QC on the model-ready 

emissions, and ensure that data are transferred to the model correctly. 

3.2.3 Initial/Boundary Conditions 

For the WRAP/CENRAP/VISTAS modeling of the 2002 year, boundary conditions for the 

continental U.S. 36 km RPO domain were based on a 2002 simulation of the GEOS-CHEM 

global transport and chemistry model. The GEOS-CHEM 2002 output was processed as 3-

hourly spatially varying boundary conditions along the edges of the 36 km RPO grid. For 

modeling years other than 2002, we have processed the GEOS-CHEM 2002 data into monthly 

average, diurnally varying boundary conditions (BCs). This approach has been successfully used 

for several recent SIP modeling efforts in the Southwest U.S. (e.g., Phoenix, Las Vegas). We 

will similarly use the 2002 monthly average, diurnally varying BCs for this study. 

Default initial concentrations developed for RPO CMAQ simulations will be used to specify 

CAMx initial conditions (ICs). A 15-day spin-up period will be run before each quarter to 

eliminate any significant influence of the arbitrary initial conditions. 

3.2.4 Ancillary Model Inputs 

Additional CAMx model inputs will be prepared using standard data sources and processors. 

For example, total integrated ozone column data for 2005 will be obtained from the TOMS 

satellite database (http://jwocky.gsfc.nasa.gov/) and processed as input into CAMx using the 

AHOMAP preprocessor. Ozone column data will be processed for each month of 2005, 

according to the monthly average files obtained from that web site. Surface characteristics will 

be defined based on land cover data taken from the MM5 simulation (as processed by 
 

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MM5CAMx). This will include monthly snow cover. Photolysis rates will be prepared using 

the NCAR TUV radiation transfer pre-processor, which will ingest the monthly ozone column 

and surface characteristics data described above. TUV will prepare a monthly photolysis look up 

table that is directly input to CAMx; the table defines photolysis rates for six photolytic reactions 

over a range of solar zenith angles, altitudes, ozone column, surface ultraviolet albedo, and haze 

turbidity. 

3.2.5 CAMx Model Options 

The latest version of CAMx (ver 4.50 or newer) will be employed. The CAMx configuration 

options for the preliminary model performance will include the following: 

• CAMx will be run separately on the 36-km grid (resulting in “one-way” grid nesting 

between the 36 and 12 km grids); 

• CAMx will be run on the 12/4-km nested grid system (resulting in interactive “two-way” 

grid nesting between the 12 and 4 km grids); 

• Plume-in-Grid (PiG) subgrid-scale plume chemistry and dynamics module will be used 

for major stationary sources (e.g., San Juan and Four Corners EGUs); 

• PPM advection solver will be employed; 

• CB05 gas-phase chemistry will be employed (requiring the VOC emissions inventory to 

be prepared according to the speciation needed by CB05); 

• CMC chemical solver will be employed; 

• CF static two-mode aerosol chemistry mechanism will be employed, which invokes 

RADM aqueous-phase chemistry, ISORROPIA inorganic aerosol thermodynamics 

(sulfate/nitrate/ammonium equilibrium), and the latest updates to the SOAP secondary 

organic aerosol chemistry module; 

• Dry and wet deposition will be active; 

• Probing Tools will not be employed (these include source apportionment, decoupled 

direct method of sensitivity analysis, process analysis, and reactive tracers). 

3.2.6 Modeling Strategy 

An initial CAMx simulation will be performed for the entirety of 2005 on the 36 km continental 

RPO domain. Output will be processed to generate BCs for the interactive two-way 12/4 km 

model simulations. The strategy for performing the annual simulation will be to run CAMx 

separately for each of four quarters of the year (January-March, April-June, July-September, and 

October-December). A 15 day “spinup” period will be added prior to the start of each quarter as 

a way to remove the influence of initial conditions, which as described above, will be specified 

in very simple terms. Our tests on the RPO grid suggest that at least two weeks are needed to 

remove a significant fraction of the initial conditions from such large domains. The CAMx 

simulation for each quarter will comprise a series of single-day simulations, in which the model 

is restarted at midnight local time. This will facilitate the use of various day-of-week specific 

emissions that need to be provided to the model on a daily basis. 
 

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As was done in several recent annual modeling studies (e.g., VISTAS, CENRAP, WRAP, St. 

Louis PM2.5 SIP, etc.), we will initially select two representative monthly periods to perform 

diagnostic and sensitivity testing with CAMx on the 12/4-km nested grid system. We will select 

a summer month characterized by high ozone and anthropogenic PM (e.g., SO4) and a winter 

month characterized by high NO3 (note that EC and OC occur year round and are heavily 

associated with natural emissions). The actual modeling periods will be chosen according to an 

analysis of the 2005 air quality data in the Four Corners region. Using the 12/4-km emissions 

and meteorology, and the BCs generated from the 36 km 2005 annual run, we will perform an 

initial 2005 Base Case simulation for the chosen summer and winter months of 2005, and 

conduct a preliminary model performance evaluation. We will then perform a series of 

diagnostic and sensitivity tests designed to identify the optimal model configuration for 

simulating ozone and PM air quality in the Four Corners region. The specific types of tests to be 

performed will depend upon the initial model performance and our assessment of the quality and 

uncertainties of the model inputs. 

The summer and winter diagnostic tests could include, but not be limited to, the following: 

• Vertical Mixing 

o O’Brien Kz profile from MM5CAMx 

o CMAQ Kz profile from MM5CAMx 

o Various versions of minimum surface Kz 

• Other Meteorological Variables 

o Account for bias in MM5 temperature 

o Precipitation Adjustments 

o Other 

• Emissions (increases/decreases) 

o Ammonia 

o VOC 

o NOx 

o Other 

• Boundary Conditions 

• Photolysis Rates 

Once we have identified an optimum configuration for the summer and winter 2005 periods, we 

will perform a full annual CAMx simulation on the 12/4 km grids to establish the 2005 Base 

Case scenario. The 12/4-km annual simulation will follow the technique used for the 36-km run, 

with four quarterly simulations and 15 days of model spinup prior to each. 

3.3 MODEL PERFORMANCE EVALUATION 

In general, a model performance evaluation (MPE) is the process of testing a model’s ability to 

accurately estimate observed atmospheric properties over a range of synoptic and geophysical 

conditions. To the extent possible given available data, the MPE should provide evidence that 

the modeling system is properly simulating the observed conditions for the correct reasons, and 

not through a serendipitous combination of compensatory errors (i.e., arriving at the right answer 

for the wrong reason). Likewise, the MPE process should also provide evidence that the model 
 

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properly responds to changes in emissions, allowing us to judge the suitability of the CAMx air 

quality modeling system as an effective tool to evaluate alternative mitigation strategies. When 

conducted thoughtfully and thoroughly, the process focuses and directs the continuing cycle of 

model development, data collection, model testing, diagnostic analysis, refinement, and retesting. 

The objective of the model performance evaluation (MPE) to be conducted for this project will 

be to evaluate the ability of CAMx, through the use of common statistical measures and 

graphical procedures, to appropriately replicate the spatial/temporal distribution of ozone and 

PM species concentrations, and deposited mass, as measured at various monitoring sites in the 

western U.S. over the 2005 period. Below we summarize the philosophy and objectives that will 

govern the evaluation of the CAMx regional air quality modeling system (and supporting 

preprocessor models) for the Four Corners Air Quality Modeling Study. To the extent possible 

given data availability in the Four Corners region, this evaluation plan conforms to the final 

procedures published by the EPA (2007) for 8-hour ozone, PM2.5, and regional haze attainment 

demonstration modeling. 

3.3.1 Available Aerometric Databases for Model Evaluation 

Ideally, concentration measurements from a number of monitoring networks will be used to the 

fullest extent possible in the CAMx model performance evaluation. Drawn from a wide variety 

of state and federal monitoring networks in New Mexico, Colorado, Arizona, and Utah as well as 

in surrounding states, these surface measurements include ozone, NOx, CO, SO2, some VOCs, 

PM10, PM2.5, PM species components, light scattering/extinction, and deposited mass. The first 

is the routine gas-phase concentration measurements for ozone, NOx and CO archived in EPA’s 

Aerometric Information Retrieval System (AIRS/AQS) database. Other sources of information 

come from the various PM monitoring networks in the U.S. These include the: (a) Interagency 

Monitoring of Protected Visual Environments (IMPROVE), (b) Clean Air Status and Trends 

Network (CASTNET), (c) EPA PM2.5 and PM10 Mass Networks (EPA-FRM), (d) EPA 

Speciation Trends Network (STN); and (e) National Acid Deposition Network (NADP). 

Typically, these networks provide ozone, other gas phase precursors and product species, PM, 

and visibility measurements. 

Unfortunately, in our previous modeling work for the NMED in the San Juan/Four Corners 8-

hour ozone EAC SIP study (Tesche et al., 2003), the available photochemical data sets for 

CAMx evaluation were extremely limited. In particular, hourly surface observations provided by 

EPA’s AQS database were available from only 17 ozone monitoring sites in the 4 km grid used 

in the EAC modeling, and from only 2 to 5 NOx and CO sites. Within the San Juan Basin (a 

smaller region of the 4 km domain focusing on just the Four Corners area), no CO data were 

available and there were only two NO monitors (Bondad and Ignacio). NO2 monitoring data 

were available at four monitors (Bondad, Ignacio, Substation, and Bloomfield). No ambient 

monitoring of volatile organic compounds (VOCs), either speciated or total mass, was available 

within the 4 km modeling domain. 

Care must be taken in the PM model performance evaluation to assure the PM measurements and 

modeled concentrations are consistent with the measured quantities, as discuss previously. 

Although the focus of the CAMx PM evaluation will be on PM component performance in the 
 

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Four Corners region, since data are lacking in this area, it will be important to evaluate the model 

for PM and gas species throughout the entire 12/4-km modeling domains to assure that the “right 

answer is obtained for the right reason”. Fortunately, gas, PM, and visibility data are available 

within the 12 km grid domain and these will likely serve as the principal basis upon which the 

model’s performance for secondary aerosol and visibility impacts will be based. 

As described in Section 1, we have established an Air Quality Data Gatekeeper to provide 

independent review and QA/QC of the various types of ambient measurement data. Mr. Till 

Stoeckenius will serve as the Air Quality Data Gatekeeper for this study. He will provide a final 

level of QA/QC on the data, and ensure that data are used in the model performance evaluation 

correctly. 

3.3.2 Model Evaluation Process 

The CAMx regional photochemical modeling system will be evaluated through a logical, proven 

process of model performance evaluation and diagnostic analysis. The operational evaluation of 

the model’s reliability in simulating 8-hour ozone, 24-hour and annual PM concentrations, 

regional haze (visibility), and deposition for the 2005 base year will be quantified through the 

following model testing process: 

• Exercise the modeling system for the base case, attempting to replicate the time and 

space behavior of 1-hour and 8-hour ozone concentration fields as well as concentrations 

of gas-phase and particulate precursor and product species; primary (e.g., EC, OC, IP) 

and secondary aerosol species (e.g., nitrate, sulfate, SOA); total PM, and visibility; 

 

• Evaluate the model’s fidelity in simulating gas-phase (e.g., ozone) and particulate 

precursor/product species using a two-step process consisting of: (a) an initial “screening 

model performance evaluation” (SMPE) process; and if the modeling results pass the 

screening analysis, (b) a “refined model performance evaluation” (RMPE) consisting of 

progressively more stressful testing procedure involving multi-species, multi-scale (e.g., 

36/12/4 km grids) surface model evaluations; 

• Identify sources of error and/or compensating biases, through examination of 

preprocessor models (e.g., MM5), air quality model inputs, mass budgets and 

conservation checks, process analysis, etc. (as time and budget permit); 

• Through a documented process of diagnostic and sensitivity investigation, pinpoint and 

correct specific performance problems through model refinement, additional data 

collection and/or analysis, or theoretical considerations (as time and budget permit); 

• Re-run the model for the base case and re-evaluate performance until adequate 

performance is achieved consistent with project time and resource allocations. 

It is stressed that the process outlined above should avoid a model “tuning” exercise, wherein 

specific non-physical, unrealistic, or uncorroborated adjustments are made simply to obtain a 

better answer. 
 

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In addition to 1-hour and 8-hour ozone performance, the performance evaluation will focus on 

the evaluation of CAMx to estimate PM concentrations and PM components (SO4, NO3, NH4, 

EC, OC, IP and CM) at different averaging times (e.g., 24-hour, weekly, monthly, quarterly and 

annually). Model performance for wet deposition of sulfur and nitrogen species at the NADP 

monitoring sites will also be made. The ability of the model to simulate visibility impairment 

will also be evaluated using the IMPROVE reconstructed mass extinction equation and direct 

visibility measurements as available (e.g., via comparison against nephelometer and 

transmissometer measurements). 

When designing a model performance evaluation, it is important to understand how the modeling 

results will ultimately be used. EPA has published draft guidance for fine particulate and 

regional haze modeling (EPA, 2001), utilizing a Fine Particulate Guidance Workgroup to 

provide technical input in its development2

. More recently, EPA provided an informal update on 

the PM/regional haze modeling guidance (Timin, 2002) and conducted a PM model evaluation 

workshop (see, for example, Timin, 2004; Boylan, 2004) shedding additional light on what the 

final guidance document would contain (final guidance was released by EPA in April 2007). A 

key concept in the guidance for addressing annual PM issues is that the modeling results should 

be used in a relative sense to scale or roll back the observed ozone and PM concentrations from 

the current observed levels to the projected future-year levels. 

Here, the total PM mass is assumed to be the sum of the component concentrations: 

PM = [SO4] + [NO3] + [NH4] + [OC] + [EC] + [IP] + [PBW] + [CM] + [Blank] 

where, 

 [SO4] is the sulfate concentration; 

 [NO3] is the particulate nitrate concentration; 

 [NH4] is the ammonium concentration; 

 [OC] is the organic mass carbon concentration (=POA+SOA); 

 [EC] is the elemental carbon concentration; 

[IP] is the inorganic primary fine particulate (< 2.5:) concentration excluding primary 

sulfates and nitrates; 

[PBW] is fine mass associated with particle bound water; 

[CM] is the total coarse mass; and 

[Blank] is the passive blank corrections for the measurement. 

CAMx does not directly estimate visibility, instead it estimates PM and gaseous species 

concentrations from which visibility can be estimated, usually in a post-processing step. The 

most frequent equation to convert PM species concentrations to light extinction is the original 

IMPROVE reconstructed mass equation: 

 

2 Members of the Four Corners modeling team participated on the EPA fine particulate modeling work group over 

the two-year span of its activities.
 

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where: 

bext is the estimated total light extinction coefficient (Mm-1); 

 [(NH4)2SO4] is the sulfate concentration assumed to be ammonium sulfate; 

 [NH4NO3] is the particulate nitrate concentration assumed to be ammonium nitrate; 

 [OC] is the total organic mass concentration (=POA+SOA); 

 [EC] is the elemental carbon concentration; 

[IP] is the inorganic primary fine particulate (< 2.5 μm) concentration excluding primary 

sulfates and nitrates; 

[CM] is the primary coarse particulate (> 2.5 μm and <10 μm) concentration; 

brayleigh is the natural atmospheric Rayleigh scattering (assumed to be 10 Mm-1); 

f(RH) is a relative humidity adjustment factor for the sulfate and nitrates; and 

f′(RH) is a relative humidity adjustment factor for OC (set to a constant 1.0). 

The numerical factors in front of each term of the extinction equation are referred to as 

“extinction efficiencies.” A unique efficiency is used for each PM species, and converts 

concentration to an extinction coefficient in units of inverse megameters (1/Mm). Total 

extinction is determined from the sum of scattering components (SO4, NO3, OC, IP, CM, and 

natural atmospheric Rayleigh scattering) and absorbing components (EC). The relative humidity 

adjustment factor f(RH) accounts for the growth of sulfate and nitrate aerosols as they hydrate 

with increasing humidity. As these salts absorb water, they grow into sizes that are more 

efficient at scattering light, and as they continue to take on water near 100% relative humidity, 

they transform form large haze particles to small cloud droplets. The humidity adjustment 

function defined by IMPROVE is shown in Figure 3-1. 

3.3.3 Statistical Performance Metrics 

The current PM2.5 guidance document (EPA, 2007) focuses more on a holistic model evaluation 

approach compared to the original 1-hour ozone and draft PM guidance (EPA, 1991; 2001). Not 

only should we assess how well the model matches the observation, but we also need to 

determine whether the model is correctly simulating the processes that produce the elevated 

concentrations, which includes comparing against a conceptual model. Table 3-2 lists a standard 

set of statistical performance measures that can be used to evaluate fine particulate models. 

These performance measures will be calculated using several model performance evaluation 

software tools, including: 

UCR Analysis Tool: Operates on a Linux platform, performs species and temporal 

matching of the predictions and observations and generates statistical performance 

measures, scatter plots and time series plots for user specified subdomains and across all 

sites and all days, for each site and all days and for each day across all sites. 

[( ) ] [ ] [ ]

[ ] [] [ ] rayleigh

ext

EC IP bCM

b SONHRHf NONHRHf OCRHf

+×+×+×+

×= + × + × ′

10 1 6.0

)(3 4 )(3 34 )(4 4 2
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec3.doc 3-12 

 

Figure 3-1. The humidity growth function f(RH) taken from the IMPROVE methodology for 

reconstructing extinction from ammonium sulfate and ammonium nitrate concentrations. Two 

alternative curves are shown; (1) the cap at 50% is intended to mirror the behavior of “dry” 

nephelometer scattering, and (2) the cap at 90% is used to compare to “wet” nephelometers 

and for reconstructing IMPROVE extinction. 

Alpine Geophysics MAPS Software: Also operates on a Linux platform generating 

statistical measures, scatter plots, time series plots and spatial comparisons of predictions 

and observations. MAPS also produces spatially averaged performance summaries (e.g., 

time series of bias and error) that are useful for synthesizing model performance. 

PAVE by MCNC: Used on a Linux platform to generate spatial maps (tile plots) of 

model predictions with super imposed observations as colored symbols. 

ENVIRON Performance Software: Calculates performance statistics and exports them 

along with the predictions and observations to be used with macros operating standard 

Windows software such as Excel and SURFER to generate graphical displays of model 

performance that can be customized by the user where the data are also available for 

further analysis if desired. 

The Four Corners CAMx evaluation will strive to use each of these evaluation packages to some 

extent to elucidate model performance. 

f(RH)

0

2

4

6

8

10

12

14

16

18

20

0 10 20 30 40 50 60 70 80 90 100

RH (%)

f(RH)

f(RH) dry (50% RH cap) f(RH) wet (90% RH cap) f(RH)
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec3.doc 3-13 

Table 3-2. Routine statistical measures used in evaluating air quality models against 

observational data. 

Statistical 

Measure 

Shorthand 

Notation 

Mathematical 

Expression Notes 

Accuracy of 

paired peak (Ap) 

Paired_Peak 

peak

peak

O

− OP Ppeak = paired (in 

both time and 

space) peak 

prediction 

Coefficient of 

determination (r

2

) 

Coef_Determ 

∑ ∑

∑

= =

=

− −

⎥

⎦

⎤ ⎢

⎣

⎡ −−

N

i

N

i

i i

N

i

i i

OOPP

OOPP

1 1

2 2

2

1

)()(

))((

Pi = prediction at 

time and location 

i; 

Oi = observation 

at time and 

location i; 

P = arithmetic 

average of Pi, 

i=1,2,..., N; 

O = arithmetic 

average of Oi, 

i=1,2,...,N 

Normalized Mean 

Error (NME) 

Norm_Mean_Err 

∑

∑

=

=

−

N

i

i

N

i

ii

O

OP

1

1

Reported as % 

Root Mean 

Square Error 

(RMSE) 

Rt_Mean_Sqr_Err 

( )

2

1

1

1 2

⎥

⎦

⎤ ⎢

⎣

⎡ ∑ − =

N

i

OP ii N

Reported as % 

Fractional Gross 

Error (FE) 

Frac_Gross_Err 

∑= +

− N

i ii

ii

OP

OP

N 1

2 Reported as % 

Mean Absolute 

Gross Error 

(MAGE) 

Mean_Abs_G_Err 

∑=

−

N

i

OP ii N 1

1

Mean Normalized 

Gross Error 

(MNGE) 

Mean_Norm_G_Err

∑=

− N

i i

ii

O

OP

N 1

1 Reported as % 

Mean Bias (MB) Mean_Bias 

∑( ) =

−

N

i

OP ii N 1

1 Reported as 

concentration 

(e.g., μg/m3

) 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec3.doc 3-14 

Statistical 

Measure 

Shorthand 

Notation 

Mathematical 

Expression Notes 

Mean Normalized 

Bias (MNB) 

Mean_Norm_Bias ( ) ∑=

− N

i i

ii

O

OP

N 1

1 Reported as % 

Mean 

Fractionalized 

Bias (Fractional 

Bias, MFB) 

Mean_Fract_Bias 

∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

− N

i ii

ii

OP

OP

N 1

2 Reported as % 

Normalized Mean 

Bias (NMB)

Norm_Mean_Bias 

∑

∑

=

=

−

N

i

i

N

i

ii

O

OP

1

1

)(

Reported as % 

Bias Factor (BF) Bias Factor 

1

1 N

i

i i

P

N O =

⎛ ⎞ ⎜ ⎟ ⎝ ⎠ ∑

Reported as 

BF:1 or 1: BF or 

in fractional 

notation (BF/1 or 

1/BF). 

3.3.4 Evaluation of CAMx Model Performance for the 2005 Base Case 

The CAMx performance evaluation will be carried out in two sequential phases, beginning with 

the SMPE of modeled and observed ground-level ozone and aerosol species concentrations 

(SMPE), progressing to the RMPE with potentially more illuminating analyses if necessary (e.g., 

examination of precursor and product species, comparisons of pollutant ratios and groupings). 

The formal procedures outlined in EPA’s final ozone, PM2.5, and regional haze modeling 

guidance (EPA, 2007) will be used. In particular, we will consider all six means for assessing 

photochemical model performance as specified in the guidance, to the extent feasible given 

available measurement data bases and project time/schedule constraints. These methods include: 

• Use of computer generated graphics; 

• Use of ozone and PM metrics in statistical comparisons; 

• Comparison of predicted and observed precursor emissions or species concentrations; 

• Comparison of observed and predicted ratios of indicator species; 

• Comparison of predicted source category contribution factors with estimates obtained 

using observational models; and 

• Use of retrospective analyses in which air quality differences predicted by the model are 

compared with observed trends. 

Obviously, a comprehensive measurement database for gas phase species including ozone and 

particulate aerosol precursors requires an extensive monitoring network to support all six of 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec3.doc 3-15 

these analyses. This is clearly not possible with the current 2005 Four Corners data base, 

particularly in regards to precursor measurements, since no intensive field measurements were 

conducted in this area during the 2005 year. Therefore, the evaluation approach will consist of a 

blend of those points above and the steps outlined below. To the extent possible, each of the 

performance procedures described by EPA’s final guidance will be addressed, and at a 

minimum, an explanation of why certain components cannot be fulfilled will be provided. 

Initial screening of the CAMx base case ozone and PM predictions (i.e., the SMPE) will be 

performed for the 2005 base case in an attempt to identify obviously flawed model simulations 

and to implement improvements to the model input files in a logical, defensible manner. If the 

initial screening phase reveals no obvious flaws or compensating errors exist in the simulation(s), 

we will progress to the refined model performance evaluation (RMPE) operational evaluation. 

The screening SMPE will employ some of the more appropriate ozone performance statistics and 

plots; examples of the types of graphical displays that may be helpful in the SMPE include the 

following: 

• Spatial mean ozone and PM2.5 time series plots; 

• Ozone and aerosol time series plots; 

• Ground-level ozone and particulate aerosol isopleths; 

• Ozone and PM2.5 concentration scatterplots; 

• Bias and error stratified by concentration; and 

• Bias and error stratified by time. 

This screening is intended to identify obviously flawed simulations. Experience in 

photochemical modeling is the best basis upon which to identify obviously flawed simulation 

results. Efforts to improve photochemical model performance, where necessary and warranted 

(i.e., to reduce the discrepancies between model estimates and observations), should be based on 

sound scientific principles. A "curve-fitting" or "tuning" activity is to be avoided. 

The following principals will govern the model performance improvement process (to the fullest 

extent possible given the project schedule): 

• Any significant changes to the model or its inputs must be documented; 

• Any significant changes to the model or its inputs must be supported by scientific 

evidence, analysis of new data, or by re-analysis of the existing data where errors or 

misjudgments may have occurred; 

• All significant changes to the model or its inputs should be reviewed by the project 

sponsors and/or other advisory group(s); and 

• Changes to the modeling approach need to be documented in the Modeling Protocol. 

If the initial screening of the CAMx ozone and particulate aerosol results does not reveal obvious 

flaws, the refined model performance evaluation will be carried out. If the SMPE is not passed, 

the period will be subjected to further model diagnosis, quality assurance of the input files and 

related model performance improvement analyses. That is, the full refined model performance 

evaluation will not be carried out on an obviously flawed model simulation as it would be 

wasteful of project resources and schedule. 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec3.doc 3-16 

Assuming the SMPE is passed, the formal operational evaluation in the RMPE will consist of 

three steps. First, the graphical displays utilized previously for ozone may be generated for NOx, 

VOC, and key product species (e.g., HNOx, PAN). Note that model performance for VOC and 

many product species may not be tested since there is a limited quantity of relevant ambient 

measurements collected in the Four Corners region. The graphical displays for ozone precursor 

and product species will be examined for obvious flaws. Should these be detected, the model 

diagnosis and performance improvement efforts may be needed to fully identify and correct (if 

possible) the noted problems. Second, the ozone and NOx predictions will be examined both at 

the ground and aloft. Where aloft data are lacking or in short supply, the modeled fields should 

nevertheless be examined to assess their reasonableness. Finally, a limited number of model 

sensitivity and/or uncertainty simulations may be performed to help elucidate model 

performance and response to changes in key inputs. Sensitivity analysis, often an important 

component of the evaluation process, may be performed to aid in understanding the air quality 

model’s response to key input parameter uncertainties. 

The extent to which sensitivity simulations with CAMx will be needed can only be assessed after 

the initial model evaluations are performed. Sensitivity experiments may be conducted as part of 

the CAMx model performance evaluation analysis as appropriate. However, much of these 

diagnostic tests will have been performed using the Jan/Jul 2005 diagnostic testing. 

3.3.5 Performance Goals and Benchmarks 

Establishment of performance goals and criteria for modeling is a necessary but difficult activity, 

and has been an area of ongoing research and debate (Morris et al., 2005). Here, performance 

goals refer to targets that we believe a good performing model should achieve, whereas less 

stringent performance criteria represent a minimal level of model performance that a model 

should achieve for use in regulatory modeling. Performance goals are necessary in order to 

provide consistency in model applications and expectations across the country, while criteria 

provide standardization in how much weight may be accorded modeling study results in the 

decision-making process. It is a problematic activity, though, because many areas present unique 

challenges and no one set of performance goals is likely to fit all needs. Equally concerning is 

the very real danger that modeling studies will be truncated when the “statistics look right” 

before full assessment of the model’s reliability is made. This has the potential for breeding 

built-in compensating errors as modelers strive to achieve good statistics as opposed to searching 

for the explanations for poor performance and then rectifying them. 

 

Decades ago EPA established performance goals for 1-hour ozone centered on the use of 

normalized bias (<15%) and error (<35%). However, when these evaluation metrics were later 

adapted to PM and its components, difficulties arose because performance statistics that divide 

by low concentration observations (such as nitrate, which is often zero) become practically 

meaningless. In time, this has led to the introduction of the fractional bias and error metrics. 

EPA draft fine PM modeling guidance (EPA, 2001) notes that PM models may not be able to 

achieve goals similar to those of ozone, and that better performance should be achieved for those 

PM components that make up the major fraction of total PM mass than those that are minor 

contributors. In fact, differences in measurement techniques for some PM species likely exceed 

the more stringent ozone performance goals. For example, recent comparisons of PM 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec3.doc 3-17 

measurements using the IMPROVE and STN technologies found differences of ~20% for sulfate 

and ~50% for elemental carbon (Morris et al., 2005). 

As with ozone in the 1980s, actual experience with PM models has led to the development of the 

current performance expectations for these models. For example, PM10 SIP model performance 

goals of 30% and 50% (normalized gross error) have been used for southern California and 

Phoenix, respectively. Boyland and Russell (2006) have proposed fractional bias and error goals 

of 30% and 50%, and fractional bias and error criteria of 60% and 75%, respectively. 

Furthermore, they proposed that these goals and criteria values vary as a function of 

concentration, such that below 2 μg/m3

, they expand exponentially to 200% (the maximum of 

fractional bias and error) at zero observed concentrations. 

The following levels of model performance criteria have been adopted for VISTAS regional 

visibility modeling using CMAQ, and we carry these forth into the Four Corners modeling 

assessment: 

Fractional 

Bias 

Fractional 

Error Qualitative Performance 

≤ ±15% ≤ 35% Excellent 

≤ ±30% ≤ 50% Good 

≤ ±60% ≤ 75% Average, each PM component should meet for regulatory modeling

> ±60% > 75% Poor, indicating fundamental problems with the modeling system 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec4.doc 4-1 

4. FUTURE YEAR MODELING 

Future year CAMx model-ready emission inputs will be available on the 36/12/4 km grid 

configuration for a 2018 Base Case and up to five (5) mitigation scenarios. Thus, the modeling 

team will be running a total of six (6) annual CAMx simulations. To maximize efficiency, our 

approach will be to run the 2018 Base Case similarly to the 2005 Base Case, with a separate 

simulation on the 36 km, which will provide BCs to a nested 12/4 km grid simulation. The 

mitigation runs will only be run on the 12/4 grids using the same 2018 Base Case BCs, since we 

expect that the mitigation scenarios will likely focus more on “local” controls in the Four 

Corners states (the exact definitions will be determined by the NMED). 

The meteorological inputs will be held constant, based on the 2005 36/12/4 km MM5 

simulations described in Sections 2 and 3. The BCs around the edges of the 36 km domain will 

also be held constant, based on the 2002 GEOS-CHEM simulation. 

The 2018 modeling results will be interpreted using both relative and absolute methodologies. 

EPA guidance suggests using the relative changes in the modeling results to project future-year 

ozone, PM2.5 and regional haze measurements (EPA, 2007). EPA has developed a new 

Modeled Attainment Test Software (MATS) that incorporates 8-hour ozone Design Value 

projection techniques and the Speciated Modeled Attainment Test (SMAT) for PM2.5 and 

regional haze (visibility). MATS will be used with the 2018 modeling results. These approaches 

use the relative changes in the modeling results between the 2005 Base Case and 2018 emission 

scenarios to project or scale the observed concentrations (e.g., Design Values). MATS also 

includes procedures for interpolating the observed concentrations to make projections for 

unmonitored areas (which are important for Class II assessments). 

We will also analyze the absolute modeling results for the 2018 Base Case and mitigation 

scenarios. When looking at changes in emissions from specific sources, the absolute modeled 

incremental impact may be enlightening in addition to analyzing the model results in a relative 

sense. The types of displays will include the following: 

• Spatial maps of modeled ozone, PM speciated and total mass, regional haze (visibility), 

wet deposition, and differences between the modeled values for the different scenarios; 

• Time series of species concentrations and changes in visibility at specific monitoring sites 

and locations; 

• Comparison of changes in concentrations, visibility and deposition against thresholds that 

are used to judge significance; 

• Development of visibility trend lines in terms of total light extinction and deciview; 

• Other displays to be determined. 

4.1 PM SPECIATED MODEL ATTAINMENT TEST 

EPA guidance for projecting PM2.5 and regional haze to future year conditions relies on the 

SMAT technique, which applies model results in a relative sense to scale observed PM2.5

concentrations from the current to future year. The scaling factors are called Relative Response 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec4.doc 4-2 

Factors (RRFs) and are specific to each monitor and PM species; they are based on the ratio of 

the future-year to current-year modeling results. The SMAT procedures consists of two 

components: (1) the combination of total PM2.5 mass measurements from the FRM network with 

speciated PM2.5 measurements such as those from the STN or IMPROVE networks; and (2) the 

use of modeling results to scale the speciated FRM PM2.5 observations to obtain future-year 

projected PM2.5 levels. 

Speciated PM2.5 measurements are routinely collected on the same 1-in-3 day sampling schedule 

as used by the FRM network. The FRM, STN, and IMPROVE networks use different 

measurement technologies and each exhibits its own measurement artifacts. For example, FRM 

uses a single Teflon filter to measure total PM2.5 mass, and includes water in the measurement 

(after equilibration at ~35% relative humidity). Particulate nitrate may volatilize off of the FRM 

Teflon filter. The STN measurement technology uses Teflon, Nylon and Quartz filters for 

measuring the speciated PM and does not measure the water component. The STN Quartz filters 

are also not blank-corrected which results in inaccurate OC measurements. IMPROVE also uses 

multiple filters and does not include ammonium in its measurements. 

As the FRM is the de-facto regulatory definition of PM2.5, EPA has developed procedures for 

adjusting the STN and IMPROVE speciated PM2.5 measurements to account for the 

measurement artifacts of the different networks and to make the speciated PM measurements 

consistent with the FRM PM2.5 mass measurements. These adjustments include the following: 

• Adjust nitrates downward to account for volatilization off of the FRM nylon filter; 

• Add particle-bound water (PBW) that is assumed to be associated with nitrate and sulfate 

in the FRM measurements (hydroscopic species); and 

• Estimate total carbonaceous mass accounting for the lack of blank-correction in the STN 

measurements. 

The resultant fine particle chemical speciation approach has been named the Sulfates, Adjusted 

Nitrates, Derived Water, Inferred Carbonaceous mass and estimated aerosol acidity (H+), or 

SANDWICH. Details on the SANDWICH procedures are given in EPA (2007). 

The SMAT scaling/projection procedure involves the following steps: 

1. Use the ratio of base to future year model-estimated PM components at each monitor and 

for each of the four annual quarters to develop monitor-, quarter- and PM species-specific 

Relative Response Factors (RRFs). 

2. Apply the monitor-, quarter- and species-specific RRF to each observed quarterly PM 

species concentrations to obtain quarterly PM species concentrations representative of the 

future year. 

3. Recalculate the Particle Bound Water (PRB) component from the future year projected 

quarterly sulfate and nitrate concentrations. 

4. Average the four quarterly future year species concentrations to derive annual average 

PM species concentrations at each monitor. 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec4.doc 4-3 

5. Compare the projected annual average PM2.5 concentrations against the NAAQS PM2.5 in 

the attainment test, and use the scaled PM species concentrations to calculate total 

extinction and deciview levels to assess visibility changes. 

The SANDWICH and SMAT RRF projection techniques are both included in EPA’s MATS 

analysis software package. 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec5.doc 5-1 

5. REFERENCES 

Boylan, J. W. 2004. “Calculating Statistics: Concentration Related Performance Goals.” Paper 

presented at the EPA PM Model Performance Workshop, Chapel Hill, NC (February 11, 

2004). 

ENVIRON and Alpine Geophysics. 2006. “Work Plan: Four Corners Cumulative Prevention of 

Significant Deterioration (PSD) Increment Consumption Modeling Study.” Prepared by 

ENVIRON International Corporation, Novato, CA and Alpine Geophysics, LLC, Arvada, 

CO (7 July 2006). 

ENVIRON. 2006. User’s Guide: Comprehensive Air quality Model with extensions, Version 

4.40. Prepared by ENVIRON International Corporation, Novato, CA (September 2006). 

EPA. 1991. "Guidance for Regulatory Application of the Urban Airshed Model (UAM).” 

Prepared by the Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, N.C. 

EPA. 2001. “Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and 

Regional Haze,” Draft Report, U.S. Environmental Protection Agency, Research 

Triangle Park, NC. 

EPA. 2006. “Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze.” Draft 3.2 – 

September 2006. Prepared by the U.S. Environmental Protection Agency, Office of Air 

Quality Planning and Standards, Research Triangle Park, North Carolina. 

EPA. 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment 

of Air Quality Goals for Ozone, PM2.5, and Regional Haze. Prepared by the U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, 

Research Triangle Park, North Carolina. (EPA-454/B-07-002; April 2007). 

NcNally, D., and George Schewe. 2006. “Evaluation of 36/12/4 km MM5 for Calendar Year 

2005.” Prepared for the New Mexico Environment Department, Santa Fe, NM, prepared 

by Alpine Geophysics, Arvada, CO (19 October 2006). 

Morris, R.E., B. Koo, S. Lau, T.W. Tesche, D. McNally, C. Loomis, G. Stella, G. Tonnesen and 

Z. Wang. 2004. “VISTAS Emissions and Air Quality Modeling – Phase I Task 4cd 

Report: Model Performance Evaluation and Model Sensitivity Tests for Three Phase I 

Episodes.” Prepared for the VISTAS Technical Analysis Committee, prepared by 

ENVIRON International Corporation, Alpine Geophysics, LLC, and the University of 

California, Riverside (CE-CERT). 

Morris, R.E., D.E. McNally, T.W. Tesche, G.S. Tonnesen, J.W. Boylan, and P. Brewer. 2005. 

Preliminary Evaluation of the Community Multiscale Air Quality Model for 2002 over 

the Southeastern United States. J. Air and Waste Manage. Assoc. 55:1694-1708. 

November. 
 

June 2007 

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec5.doc 5-2 

Tesche, T.W., D.E. McNally, C. Loomis, R.E. Morris and G. Mansell. 2003. Air Quality 

Modeling Analysis for the San Juan County Early Action Compact: Ozone Modeling 

Protocol. Prepared by Alpine Geophysics and ENVIRON. Prepared for the New Mexico 

Environmental Department (June 16, 2003). 

Timin, B. 2002. “PM2.5 and Regional Haze Modeling Guidance.” Prepared by the U.S. 

EPA/OAQPS (April 2002). 

Timin, B. 2004. “PM2.5 Model Performance Evaluation: Purpose and Goals.” Presented at PM 

Model Evaluation Workshop, Chapel Hill, NC (February 2004). 
APPENDIX B 

Oil & Gas Emissions Inventory Development 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-1 

(Appendix B) 

OIL & GAS EMISSIONS INVENTORY DEVELOPMENT 

Oil & Gas Emissions – 2005 

The main basis for the oil and gas emissions inventory was the year 2002 WRAP Phase II 

inventory prepared by ENVIRON under contract to the WRAP (Bar-Ilan et al., 2007). All four 

states in the 4 km modeling domain were included in the WRAP inventory. The WRAP Phase 

II inventory was focused on improving compressor and drill rigs emissions from the previous 

WRAP inventory. In addition, the Phase II inventory incorporated the oil and gas emissions for 

San Juan and Rio Arriba Counties from the New Mexico Environmental Department (NMED) 

ozone precursors study (Pollack, et al., 2006), as well as updated emissions for oil and gas 

sources on SUIT lands. The WRAP Phase II emissions inventory for year 2005 were developed 

by applying scaling factors, derived on the basis of the state OGC databases for spuds, well 

location and production, to the 2002 emission inventory (Bar-Ilan et al., 2007). 

The NMED oil and gas inventory was developed by ENVIRON based on detailed surveys of oil 

and gas producers (Pollack, et al., 2006). The producers provided activity data and emissions 

factors that were used to generate more refined emissions estimates in San Juan and Rio Arriba 

counties, New Mexico. Oil and gas source categories in the NMED inventory include drill rigs, 

compressors, heaters, tanks, pneumatic devices, fugitives, truck loading, dehydration, completion 

and venting, CBM pump engines, artificial lift engines, and saltwater disposal engines. 

After the WRAP Phase II inventory project was completed, an error was identified in the heater 

emissions calculations for Colorado. For the inventory used in this project, the Colorado heater 

emissions were corrected. 

SUIT 2005 Inventory

An inventory of oil and gas emissions was compiled by the Southern Ute Indian Tribe (SUIT, 

2005) for the calendar year 2005. Oil and gas source categories in the Southern Ute inventory 

include drill rigs, compressors, heaters, other engines, venting, flaring, and process fugitives. 

ENVIRON reviewed that inventory and incorporated the emissions into the inventory used for 

this modeling project. 

The new SUIT emissions inventory is the result of a detailed survey and inventory effort for all 

sources on the SUIT land, whereas the previous WRAP Phase II inventory used broader 

assumptions for estimating the emissions in the counties that contain the SUIT lands. In order to 

incorporate these updated and more detailed 2005 SUIT emissions into the modeling inventory, 

the 2002 SUIT emissions in the WRAP Phase II inventory were removed using GIS analysis. 

Table B-1 summarizes 2005 SUIT emissions by county and SCC tpd). 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-2 

Table B-1. SUIT 2005 emissions by county and SCC (tpd). 

County 

Code SCC SCC Description 

County 

Emissions 

SUIT 

Emissions 

Archuletta 2310000220 

Industrial Processes, Oil and Gas Production, All 

Processes, Drill Rigs 0.0136 0.0130 

Archuletta 2310010100 

Industrial Processes, Oil and Gas Production, Crude 

Petroleum, Heaters 0.0000 0.0000 

Archuletta 2310010200 

Industrial Processes, Oil and Gas Production, Crude 

Petroleum, Tanks - Flashing and 

Standing/Working/Breathing 0.0000 0.0000 

Archuletta 2310010300 

Industrial Processes, Oil and Gas Production, Crude 

Petroleum, Pneumatic Devices 0.0000 0.0000 

Archuletta 2310020600 

Industrial Processes, Oil and Gas Production, Natural 

Gas, Compressor Engines 0.1639 0.1619 

Archuletta 2310021100 

Industrial Processes, Oil and Gas Production, Natural 

Gas, Heaters 0.0039 0.0038 

Archuletta 2310021300 

Industrial Processes, Oil and Gas Production, Natural 

Gas, Pneumatic Devices 0.0000 0.0000 

Archuletta 2310023000 

Industrial Processes, Oil and Gas Production, Natural 

Gas, CBM - Dewatering pump engines 0.0133 0.0130 

La Plata 2310000220 

Industrial Processes, Oil and Gas Production, All 

Processes, Drill Rigs 0.1269 0.1033 

La Plata 2310010100 

Industrial Processes, Oil and Gas Production, Crude 

Petroleum, Heaters 0.0002 0.0001 

La Plata 2310010200 

Industrial Processes, Oil and Gas Production, Crude 

Petroleum, Tanks - Flashing and 

Standing/Working/Breathing 0.0000 0.0000 

La Plata 2310010300 

Industrial Processes, Oil and Gas Production, Crude 

Petroleum, Pneumatic Devices 0.0000 0.0000 

La Plata 2310020600 

Industrial Processes, Oil and Gas Production, Natural 

Gas, Compressor Engines 8.8824 8.2431 

La Plata 2310021100 

Industrial Processes, Oil and Gas Production, Natural 

Gas, Heaters 2.4708 2.2930 

La Plata 2310021300 

Industrial Processes, Oil and Gas Production, Natural 

Gas, Pneumatic Devices 0.0000 0.0000 

La Plata 2310021400 

Industrial Processes, Oil and Gas Production, Natural 

Gas, Dehydrators 0.0000 0.0000 

La Plata 2310021500 

Industrial Processes, Oil and Gas Production, Natural 

Gas, Completion - Flaring and Venting 0.0239 0.0222 

La Plata 2310023000 

Industrial Processes, Oil and Gas Production, Natural 

Gas, CBM - Dewatering pump engines 1.0473 0.5672 

La Plata 2310030210 

Industrial Processes, Oil and Gas Production, Natural 

Gas Liquids, Tanks - Flashing and 

Standing/Working/Breathing, Uncontrolled 0.0000 0.0000 

Overall change in Oil and Gas Emissions from the WRAP Phase II Inventory 

The overall change in the oil and gas NOx emissions between the WRAP Phase II inventory and 

the revised inventory was an increase of 300 tons from 2002 to 2005 over the entire 4 km 

modeling domain. The changes are shown in Table B-2 by source category. Although the 

emissions reported in the table are for the entire 4 km domain, most of the changes are a result of 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-3 

changes made to emissions estimates for sources on SUIT lands, except that heater emissions 

were revised for all of Colorado. For the heaters on SUIT lands, the previous inventory had 

about 1,000 tpy NOx, and the revised inventory has about 200 tpy NOx. 

Table B-2. Changes in emissions estimates between WRAP Phase II inventory and revised 

inventory.

Source 

Current version 

(tpy) 

Previous 

version (tpy) 

Current – 

Previous (tpy) 

Compressor engines 4,694 3,067 1,627 

Fugitives and flaring 3.4 220 -216.6 

Drilling rigs 207 42.5 164.5 

Heaters 7,410 8,272 -862 

Removal of duplicate point sources -1,013 -1,013 

Total 11,301 11,601 -300 

VOC Speciation 

Photochemical modeling requires that the chemical composition VOC emissions included in the 

emissions inventory be identified. The process of allocating the reported VOC emissions into 

individual VOC species is known as VOC speciation. Different VOC speciation profiles were 

used for each of the various oil and gas source categories as described below. Speciation profiles 

were chosen using best engineering judgment and were reviewed by the Four Corners Air 

Quality Task Force modeling group. 

Four VOC speciation groupings was identified, each of which used different VOC speciation 

profiles: 

1. Drilling Rigs – drilling rigs were assumed to all use large diesel-powered internal 

combustion engines. For drilling rigs, ENVIRON used an EPA speciation profile for a 

diesel-powered internal combustion engine (see Table B-3 - SPECIATE4 profile#0009). 

Although this speciation profile contains no formaldehyde emissions, these were 

expected to be negligible from this type of engine. 

2. Compressor Engines, Artificial Lift Engines, Salt-Water Disposal Engines, CBM Pump 

Engines – these engines were all assumed to be natural-gas fired spark-ignition engines. 

ENVIRON used an EPA speciation profile for a natural gas-fired internal combustion 

engine (see Table B-3 - SPECIATE4 profile#1001). 

3. Heaters – these were assumed to be natural-gas fired external combustion sources. 

ENVIRON used an EPA speciation profile for natural gas-fired external combustion (see 

Table B-3 - SPECIATE4 profile#0003). 

4. Venting, Flaring, Pneumatics, Fugitive, Tank, Dehydrators and Truck Loading

Speciation profiles for these source categories were derived from gas composition 

analyses. Although tank, truck loading, dehydrator and flaring VOC speciation was 

expected to be somewhat different from VOC speciation for fugitives, pneumatics and 

venting, accounting for this difference was not feasible. 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-4 

VOC speciation was handled differently for CBM versus conventional (non-CBM) gas 

wells. There are few true oil wells in the modeling domain, therefore gas well speciation 

profiles were used throughout. 

• COLORADO – In Colorado it was assumed that all wells in the 4 km modeling 

domain were CBM wells, therefore there are only minimal VOC emissions from 

venting, flaring, pneumatics, and fugitives. Gas composition analysis files provided 

by Doug Blewitt from BP-operated wells on SUIT land (Blewitt, 2007) were 

averaged to represent a single CBM VOC speciation profile for all Colorado CBM 

wells. There are no emissions from tanks, dehydrators, and truck loading, as there is 

no condensate production at these wells (See Table B-4 – VOC speciation for CBM 

wells in Colorado). 

• NEW MEXICO – In New Mexico it was assumed that all wells in the 4 km modeling 

domain are conventional gas wells. A conventional gas well VOC speciation profile 

was developed based on averaging gas composition analyses provided by BP for 

several formations in San Juan and Rio Arriba counties, and gas composition analyses 

provided by NMOGA for this same region (Pollack et al., 2006). Table B-5 lists the 

VOC speciation for conventional gas wells in New Mexico.

Table B-3. SPECIATE4 profiles used for oil and gas sources. 

POLLUTANT 

SPECIATE4 - 0003

External 

Combustion Boiler 

- Natural Gas 

SPECIATE4 - 0009 

Reciprocating 

Distillate Oil Engine 

SPECIATE4 - 1001 

Internal Combustion 

Engine - Natural Gas 

1,2,3-TRIMETHYLBENZENE 0.01 

1,2,4-TRIMETHYLBENZENE 0.01 

1,3-BUTADIENE 7.00 

1,3,5-TRIMETHYLBENZENE 0.02 

1-BUTENE 13.40 

1-NONENE 0.01 

1-PENTENE 0.01 

2,2-DIMETHYLBUTANE 0.01 

2,4-DIMETHYLPENTANE 0.01 

2-METHYL-1-PENTENE 0.02 

2-METHYL-2-BUTENE 0.01 

3-METHYLHEPTANE 0.02 

3-METHYLHEXANE 0.01 

3-METHYLPENTANE 0.02 

ACETALDEHYDE 0.03 

ACETONE 

ACETYLENE 11.30 0.32 

BENZENE 4.00 7.90 0.11 

C10 AROMATIC 0.01 

C10 OLEFINS 0.02 

C3/C4/C5 ALKYLBENZENES 0.01 

C-7 CYCLOPARAFFINS 

C-8 CYCLOPARAFFINS 

C-9 CYCLOPARAFFINS 

C9 OLEFINS 0.04 

CIS-2-BUTENE 0.02 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-5 

POLLUTANT 

SPECIATE4 - 0003

External 

Combustion Boiler 

- Natural Gas 

SPECIATE4 - 0009 

Reciprocating 

Distillate Oil Engine 

SPECIATE4 - 1001 

Internal Combustion 

Engine - Natural Gas 

CYCLOHEXANE 1.00 0.01 

CYCLOPENTANE 0.02 

ETHANE 2.80 14.00 

ETHYLBENZENE 0.01 

ETHYLENE 28.70 0.63 

FORMALDEHYDE 8.00 0.81 

HEPTENE 0.01 

ISOBUTANE 0.43 

ISOBUTYLENE 0.02 

ISOBUTYRALDEHYDE 0.02 

ISOMERS OF BUTENE 0.26 

ISOMERS OF DECANE 0.02 

ISOMERS OF HEPTANE 0.04 

ISOMERS OF HEXANE 1.00 0.02 

ISOMERS OF NONANE 0.01 

ISOMERS OF OCTANE 0.02 

ISOMERS OF PENTANE 9.00 0.13 

ISOMERS OF XYLENE 0.02 

METHANE 56.00 11.60 76.69 

METHYLCYCLOHEXANE 0.02 

METHYLCYCLOPENTANE 0.04 

M-ETHYLTOLUENE 0.01 

M-XYLENE 0.01 

N-BUTANE 9.00 1.00 

N-DECANE 0.01 

N-HEPTANE 0.02 

N-HEXANE 0.02 

N-NONANE 0.01 

N-OCTANE 0.02 

N-PENTANE 6.00 0.13 

N-UNDECANE 0.01 

OCTENE 0.01 

O-ETHYLTOLUENE 0.01 

O-XYLENE 0.01 

PROPANE 4.00 2.91 

PROPYLENE 17.30 1.69 

TOLUENE 2.00 0.04 

TRANS-2-BUTENE 0.13 

TRANS-2-PENTENE 0.01 

Total 100.00 100.00 100.00 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-6 

Table B-4. VOC Speciation for venting, flaring, pneumatic devices and fugitive emissions from 

CBM wells in Colorado.

COMPONENT SPECID 

Normalized 

Weight Percentage 

Methane C1 529 99.6%

Ethane C2 438 0.34%

Propane C3 671 0.03%

i-Butane i-C4 491 0.01%

n-Butane n-C4 592 0.01%

i-Pentane iC5 508 0.01%

n-Pentane nC5 605 0.00%

Hexane+ 2127 0.03%

Benzene 302 0.00%

Toluene 717 0.00%

Ethyl Benzene 449 0.00%

Xylene 522 0.00%

Table B-5. VOC Speciation for venting, flaring, pneumatic devices, fugitive emissions, and 

condensate tanks from conventional gas wells in New Mexico. 

COMPONENT SPECID 

Normalized 

Weight Percentage 

Methane C1 529 68.54%

Ethane C2 438 13.10%

Propane C3 671 9.00%

i-Butane i-C4 491 1.98%

n-Butane n-C4 592 3.02%

i-Pentane iC5 508 1.33%

n-Pentane nC5 605 0.99%

n-Hexane n-C6 601 2.01%

Benzene 302 0.02%

Toluene 717 0.01%

Ethyl Benzene 449 0.00%

Xylene 522 0.00%
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-7 

Oil & Gas Emissions – 2018 

To project 2005 oil and gas emissions to 2018, different methodologies were used for each of the 

following areas shown in Figure B-1: 

• SUIT EIS area 

• Farmington RMP area 

• New Mexico portion of the 4 km domain outside the Farmington RMP area 

• Northern San Juan Basin (NSJB) EIS area 

• Colorado portion of the 4 km domain outside NSJB and SUIT EIS areas 

• Utah and Arizona 

The approach used in each area is described below. 

SUIT EIS area

Oil and gas emissions on SUIT lands were assumed to remain constant at 2005 levels. 

Farmington RMP area 

In the Farmington area, there were several sources of data used to generate the 2018 growth 

inventory: 

• The WRAP Phase II emissions from NOx sources were held constant at 2005 levels. 

• Compressors (with NSPS incorporated), separators and dehydrators were added and 

modeled as point sources (with unique coordinates and appropriate stack parameters). 

• For drill rigs, WRAP 2005 emissions were grown based on the ratio of the number of 

wells drilled in 2018 (per the RMP) to the number of wells drilled in 2005, adjusted for 

an assumed 90% success rate. 

• VOC emissions were grown using the 2007 Energy Information Agency Annual Energy 

Outlook (http://www.eia.doe.gov/oiaf/forecasting.html) gas production growth factor of 

1.21. 

New Mexico portion of the 4 km domain outside the Farmington RMP area

The 2005 oil and gas emissions in New Mexico outside the Farmington RMP area were grown 

using the 2007 Annual Energy Outlook (AEO) projections. Emissions from sources related to 

gas production were grown using the AEO growth factor of 1.21, and sources related to oil 

production were grown using the AEO growth factor of 1.55. Drilling rig emissions were grown 

using growth factor of 1.07 (this AEO growth factor is for all well drilling in the continental 

U.S., as regional forecasts were not available). 

The resulting area source NOx emissions were then reduced to account for implementation of the 

small stationary source New Source Performance Standard (NSPS) finalized in December, 2007 

(71 FR 38482). To do so, it was assumed that small wellhead compressor engines would be 

installed on 50 percent of the new wells, each with a capacity of 69 hp, running at 54% load, 

with a NOx emission factor of 2 g/hp-hr as required by the NSPS. The number of new wells was 

estimated based on the average production per well for 2002-2005 in San Juan and Rio Arriba 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-8 

Counties, grown by the EIA gas production factor (1.21), and calculating the number of wells 

required to sustain that production (using the calculated production/well). 

Northern San Juan Basin (NSJB) EIS area

In this area, WRAP Phase II oil and gas area and point source emissions were held constant at 

2005 levels. Additional emissions for all sources, except drilling, reflecting growth were 

provided and modeled as individual point sources in a spreadsheet from the NSJB EIS 

development (BLM, 2004) as points except drilling. Drilling emissions in the NSJB growth 

inventory were small and no stack parameters or location information was included in the RMP 

spreadsheet, and they were therefore modeled as area sources. 

Colorado portion of 4 km domain - Outside northern San Juan Basin and SUIT EIS areas 

The oil and gas area and point source emissions in this area were left constant at 2005 emissions 

levels. As a conservative assumption, no turnover of engines was assumed, and so no NSPS 

reduction was applied. 

Utah and Arizona 

Oil and gas emissions estimates in the 2005 inventory are very small in the Utah and Arizona 

portion of the 4 km modeling domain and were kept constant at WRAP Phase II 2005 levels. 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_B_O&G Emis_20090806.doc B-9 

Figure B-1. Modeling domain showing regions for which different growth methodologies were 

applied to estimate 2018 oil and gas emissions. 
APPENDIX C 

Four Corners Area Emission Inventory Development 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc C-1 

(Appendix C) 

FOUR CORNERS AREA EMISSION INVENTORY DEVELOPMENT 

POINT SOURCE EMISSIONS 

Electric Generating Units 

Hourly emissions in 2005 for electric generating units (EGUs) in the Four Corners states were 

obtained from EPA’s Clean Air Markets Division (CAMD) database of Continuous Emissions 

Monitoring (CEM) data (http://camddataandmaps.epa.gov/gdm/). The CEM database provides 

hourly values of NOx and SO2 emissions as measured by in-stack monitoring equipment. Table 

C-1 lists the resulting total annual emissions from all EGUs located within the 4 km modeling 

domain. EGU emissions of other pollutants not reported in the CAMD database were estimated 

by linearly interpolating between the WRAP 2002 (Base02b1) and 2018 (PRP182) emissions for 

these sources to 2005 and temporally allocating the resulting interpolated annual emissions to 

hourly values using ratios of hourly heat input reported in CAMD to the annual heat input. 

EGU emissions for 2018 were provided by the New Mexico Environmental Department (Jones, 

2008). Future year 2018 base case emissions inventory for the largest sources (i.e. coal-fired 

power plants) was constructed by scaling up 2005 estimates according to increased heat input 

information and other assumptions in the WRAP 2018 preliminary reasonable progress 

emissions inventory. Rules on the books as of 2008 were included in the future 2018 base case 

emissions inventory including the estimated emissions limits in the San Juan Generating Station 

Consent Decree. Neither of the two major coal fired power plants in the Four Corners region 

(San Juan Generating Station and Four Corners Power Plant) had reached a BART determination 

under the regional haze rule. Since the San Juan Generating Station (SJGS) was in process of 

construction on emissions control technology under the Consent Decree requirements that would 

result in emissions reductions close to presumptive BART, the Consent Decree enforceable 

condition of 90% SO2 reduction and the presumptive BART limit of 0.23 lbs/MMBtu were used 

as base case 2018 emissions estimates for SJGS. For the Four Corners Power Plant (FCPP), the 

modeling team consulted with Steve Frey of EPA Region IX to estimate how the rules on the 

books would limit emissions as it was suggested that limits in the FCPP Federal Implementation 

Plan (FIP) were close to or at BART levels already. Steve Frey estimated that existing rules 

would reasonably limit FCPP to a conservative estimate of 0.216 lb/MMBtu SO2 in 2018 from 

the FIP 88% SO2 control required on 365 day rolling average bubble (this is also close to or 

equivalent to emission rate based on current U.S. EPA BART proposal for FCPP). The 2018 

FCPP NOx emissions rate was obtained from rolling average of 0.614 lbs/MMBtu from Acid 

Rain Permit which is to be included in the new Title V permit for this plant. 

EGU temporal emission profiles by state, fuel type, and technology category developed for use 

in WRAP modeling (Fields, et al., 2006) were used to temporally allocate the 2018 EGU 

emissions within the SMOKE emissions processing system. As shown in Table C-1, the 2018 

future year inventory includes the proposed new coal-fired Desert Rock Energy Facility. In 

addition to Desert Rock, the WRAP PRP18 inventory includes new generic coal-fired units that 

are assumed to have been built and begun operation by 2018. The assumed new units are 

 

1

 BASE02b is the WRAP 2002 version b base case inventory. 

2

 PRP18 is the WRAP 2018 projected point emissions inventory developed by ERG for the Preliminary Reasonable 

Progress analysis. 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc C-2 

intended to represent additional capacity needed to meet future projected electricity demand as 

determined from analysis of Energy Information Administration (EIA) projections released in 

February 2007 

(http://www.wrapair.org/forums/ssjf/documents/eictts/Projections/PRP18_EI_tech%20memo_06

1607.pdf). WRAP assumed that a typical future coal-fired EGU has a nameplate capacity of 500 

MW and operates at the capacity threshold of 85%. A total of 11 such new EGUs were 

estimated to be required in the WRAP states to meet future demand. The state-level allocation of 

the future coal-fired EGUs were based upon current state-level capacity (i.e., sum of capacity at 

existing, under construction, and permitted facilities); county-level allocations were based upon 

announcements of plans to build coal-fired EGUs and locations of existing coal-fired EGUs and 

associated infrastructure. 

Table C-1. Annual emissions from electric generating units located within the 4 km modeling 

domain. 

2005 2018 Facility Name NOx SO2 NOx SO2 

Four Corners Power Plant 41,7431a 12,6531a 50,9812a 17,9352a

San Juan 26,8091a 16,5691a 16,5462b** 9,3522b**

Prewitt Escalante Generating Station 3,7971a 1,2931a 3,7292c** 1,7962c**

Reeves Generating Station 1511a 01a* 1512d 02d

Milagro 1101a 31a 1102d 32d

Animas 541b 01b 542d 02d

Person Generating Project 41a 01a* 42d 02d

Desert Rock n/a n/a 3,3252e 3,3192e

Future Coal Units n/a n/a 2,6802f 2,9042f

Total Emissions (tons)3 72,668 30,518 77,580 35,310 

1. 2005 base case emissions data are from: (a) EPA Facility & Unit Emissions reports 2005 

(CEMS) data, (b) NMED 2005 emissions inventory, & (c) EPA 9 emissions 2005 inventory info 

estimate for PM emissions. 

* for Reeves & Person SO2 is <0.5 Tons (gas turbine plants) 

2. 2018 base case emissions data are estimated using: (a) per EPA9/Steve Frey, NOx from Acid 

Rain Permit, SO2 conservative estimate from FIP with 88% control; (b) Presumptive BART limits; 

(c) WRAP PRP18a; (d) assuming constant emissions rate from 2005 to 2018, gas plants,(e) Desert 

Rock Energy Facility PSD Permit Application; (f) WRAP PRP based on EIA projections. 

**Prorated heat input 2005-2018 is accounted for in the calculations for San Juan Generating 

Station 

3. Bluffview gas-fired power plant (59 tons/year of NOx) is not included in total because this plant 

does not appear in the the WRAP inventory. 

Non-EGU Point Sources 

Non-EGU point source emissions for 2005 in the Four Corner states were obtained by linearly 

interpolating between the WRAP region 2002 (Base02b) and latest 2018 (PRP18) point source 

inventories as described for EGUs above. Emission source records in the two inventories were 

matched on state/county code, plant ID, point ID, stack ID, point segment and SCC fields for 

interpolation. Emissions were processed using the same SMOKE settings used in WRAP 

regional modeling (Tonnesen et al., 2005). Point sources associated with the oil and gas sector 

were extracted and processed separately so that appropriate basin-specific VOC speciation 

profiles could be applied. 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc C-3 

MOBILE SOURCES 

Mobile sources include on-road and off-road vehicles and engines. On-road mobile sources 

include vehicles certified for highway use – cars, buses, trucks, and motorcycles. Off-road 

mobile equipment encompasses a wide variety of equipment types that either move under their 

own power or are capable of being moved from site to site. Off-road mobile sources consist of 

vehicles and engines in the following categories: 

• Agricultural equipment, such as tractors, combines, and balers; 

• Aircraft, jet and piston engines; 

• Airport ground support equipment, such as terminal tractors; 

• Commercial and industrial equipment, such as fork lifts and sweepers; 

• Construction and mining equipment, such as graders and back hoes; 

• Lawn and garden equipment, such as leaf and snow blowers; 

• Locomotives, switching and line-haul trains; 

• Logging equipment, such as shredders and large chain saws; 

• Pleasure craft, such as power boats and personal watercraft; 

• Railway maintenance equipment, such as rail straighteners; 

• Recreational equipment, such as all-terrain vehicles and off-road motorcycles; and 

• Underground mining and oil field equipment, such as mechanical drilling engines. 

Mobile source emissions were taken from the 2005 and 2018 mobile source inventories 

originally developed for WRAP regional modeling (Pollack et al., 2006). Emissions were 

estimated by county for an average weekday in each of the four seasons, and for an average 

annual weekday. Seasons were defined as: Spring (March–May), Summer (June-August), Fall 

(September-November), and Winter (December-January). Emissions were estimated for PM10, 

PM2.5, NOx, SOx, VOCs, carbon monoxide (CO), NH3, elemental and organic carbon (EC/OC), 

and sulfate (SO4). For all pollutants, emissions were estimated separately for gasoline and 

diesel-fueled engines. Details of the emission inventory development methodology are provided 

in (Pollack et al., 2006). 

After the WRAP on-road mobile source emissions were generated and compiled, an error was 

discovered for three counties in New Mexico: San Juan, Sandoval, and San Miguel. For these 

three counties, for both the 2002 and 2018 on-road emissions, the vehicle miles traveled (VMT) 

data were applied incorrectly in generating the emissions. Specifically, Sandoval County VMT 

was applied to generate San Miguel County emissions, San Juan County VMT was applied to 

generate Sandoval County emissions, and San Miguel County VMT was applied to generate San 

Juan County emissions. These errors were fixed and the emissions recalculated for use in this 

modeling project. 

The 2018 WRAP regional modeling (PRP18) inventories were used for both on-road and offroad source categories. The WRAP PRPa8 locomotive emissions in 2018 were reduced to 

account for the effects of new standards for locomotive and marine diesel emissions (finalized in 

March, 2008), based on EPA’s estimate of emissions reductions in their Regulatory Impact 

Analysis (EPA, 2008). 

All of the mobile source seasonal county-level emissions were processed using SMOKE to 

generate gridded model-ready emissions. 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc C-4 

AREA SOURCES 

Area source emissions (aside from oil and gas sources that are modeled as area sources) include 

ammonia source categories, windblown dust and other area sources such as fugitive dust, 

residential fuel combustion, etc. Development of emissions inventories for each of these source 

categories is described in the following subsections. 

Ammonia Emissions 

Ammonia emissions for Four Corners 4 km domain were estimated using a GIS-based ammonia 

emissions modeling system developed for the Western Regional Air Partnership (WRAP). The 

development of the model, including data sources and estimation methodology, is documented in 

Chitjian and Mansell (2004). The model treats the source categories of primary significance in 

the overall emission inventory (excluding the mobile, industrial point and fire source categories) 

as described below. Ammonia emission source categories include livestock, fertilizer 

application, natural soils and domestic sources. Where possible, the model considers 

environmental conditions (wind speed, temperature, soil moisture and pH) in developing the 

emission factors as well as the temporal allocation of the ammonia emissions. Meteorological 

data was obtained from the 2005 MM5 output. Spatial allocation was based on application of 

EPA gridding surrogates (EPA, 2006). 

Livestock: Ammonia emissions from livestock were developed using county-level head 

counts and emission factors based on a literature review performed by Chinkin, et al. 

(2003). Estimates were developed for beef and dairy cattle, poultry, swine, sheep and 

horses. Animal headcounts for 2002 are based on the National Agricultural Statistics 

Service (NASS) county livestock files (NASS, 2003). 

Fertilizer Application: Ammonia emission estimates from fertilizer application were 

developed using emission factors from the European Environment Agency (EEA, 2002) 

as recommended in the development and application of the WRAP NH3 model. 

Ammonia emissions from fertilizer application were developed using county-level 

fertilizer sales data obtained from the latest release of the CMU model. 

Natural Soils: Natural soils can be both a source and a sink of ammonia emissions 

depending on the ambient NH3 concentrations, climatic conditions and the conditions of 

the soils. While there are a number of researchers considering this issue, ammonia 

emission from natural soils remains highly uncertain. For the current inventory, 

ammonia emission from natural soils were estimated based on emission factors 

developed or recommended by Battye et al., (2003) and Chinkin et al., (2003). Landuse 

data used for the inventory were developed from the North American Land Cover 

Database (www.gvm.jrv.it/glc2000) 

Domestic Sources: Ammonia emissions from domestic source considered in the current 

inventory include human respiration and perspiration, disposable and cloth diapers and 

domestic pets (cats and dogs). The emission factors are from the report by Chitjian and 

Mansell (2004). Activity data for domestic sources are based on the most recent US 

Census (2000), and pet ratios based on recommendations of Dickson et al. (1991). 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc C-5 

Windblown Dust Emissions 

The windblown fugitive dust PM emission inventory for the Four Corners 4 km modeling 

domain was developed using the estimation methodology developed for the Western Regional 

Air Partnership (WRAP) by a team of contractors led by ENVIRON and subsequently revised by 

Mansell and others (Chitjian and Mansell, 2003a; 2003b; Mansell, 2005). The methodology is 

based on the results of wind tunnel studies and a detailed characterization of vacant lands. 

Windblown dust emissions are estimated hourly on a gridded modeling domain using hourly 

averaged wind speeds and other meteorological parameters. Estimates are developed for every 

hour of the year 2005. 

There are two important factors for characterizing the dust emission process from an erodible 

surface. They are (a) the threshold friction velocity that defines the inception of the emission process 

as a function of the wind speed as influenced by the surface characteristics, and (b) the strength of the 

emissions that follow the commencement of particle movement. The two critical factors affecting 

emission strength are the wind speed (wind friction velocity) that drives the saltation system, and the 

soil characteristics.

Friction Velocities

Surface friction velocities are determined from the aerodynamic surface roughness lengths and 

the 10-meter wind speeds based on MM5 model simulations. Friction velocity, u*, is related to 

the slope of the velocity versus the natural logarithm of height through the relationship: 

 

o

z

z

z

u

u ln 1

* κ = 

where uz = wind velocity at height z (m/s) 

 u* = friction velocity (m/s) 

κ = von Karman's constant (0.4) 

 z0 = aerodynamic roughness height (m) 

The threshold friction velocities, u*t, are determined from the relationships developed by 

Marticorena et al. (1997) as a function of the aerodynamic surface roughness length, z0. Surface 

friction velocities, including the threshold friction velocity, are a function of the aerodynamic 

surface roughness lengths. The surface friction velocities are in turn dependent on surface 

characteristics, particularly land use/land cover. 

Emission Fluxes 

Emission fluxes, or emission rates, are determined as a function of surface friction velocity and 

soil texture. The relationships that Chatenet et al, (1996) established between the 12 soil types in 

the classical soil texture triangle and their four dry soil types (silt [FSS], sandy silt [FS], silty 

sand [MS], and sand [CS]) are of key importance. The relationships developed by Alfaro et al, 

(2001; 2003) for each of the soil texture groups are used to estimate dust emission fluxes. 

Reservoir Characteristics

 Reservoirs are classified as limited for stable land parcels and unlimited for unstable land 

parcels. Classification of reservoirs as limited or unlimited has implications with respect to the 

duration of time over which the dust emissions are generated. In general, the reservoirs should 

be classified in terms of the type of soils, the depth of the soil layer, soil moisture content and 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc C-6 

meteorological parameters. Finally, the time required for a reservoir to recharge following a 

wind event is influenced by a number of factors including precipitation and snow events and 

freezing conditions of the soils. A recharge time of 24 hours is assigned to all surfaces. In 

addition, it is assumed that no surface will generate emissions for more than 10 hours in any 24-

hour period. 

The duration and amount of precipitation and snow and freeze events will also affect the dust 

emissions from wind erosion. Barnard (2003) has compiled a set of conditions for treating these 

events based on seasons, soil characteristics and the amounts of rainfall and snow cover. The 

time necessary to re-initiate wind erosion after a precipitation event ranges from 1 to 10 days, 

depending on the soil type, season of the year and whether the rainfall amount exceeds 2 inches. 

Soil Disturbance 

The disturbance level of a surface more appropriately has the effect of lowering the threshold 

surface friction velocity. Except for agricultural lands, which are treated separately in the model 

as described below, vacant land parcels are typically undisturbed unless some activity is present 

such as to cause a disturbance (e.g., off-road vehicle activity in desert lands, or animal grazing on 

rangelands). It is recommended that all non-agricultural land types be considered undisturbed, 

since there is no a priori information to indicate otherwise for the regional scale modeling 

domain to be considered. 

Soil Characteristics

Application of the emission factor relations described above requires the characterization of soil 

texture in terms of the four soil groups considered by the model. The characteristics or type of 

soil is one of the parameters of primary importance for the application of the emission estimation 

relations derived from wind tunnel study results. The State Soil Geographic Database 

(STATSGO) available from the USDA (1994) is used to determine the type of soils present in 

the modeling domain for which the emission inventory is developed. The classification of soil 

textures and soil group codes is based on the standard soil triangle that classifies soil texture in 

terms of percent sand, silt and clay. Combining the soil groups defined by the work of Alfaro et 

al, (2001; 2003) and Chatenet et al, (1996) and the standard soil triangle provides the mapping of 

the 12 soil textures to the four soil groups considered in their study. The soil texture mappings 

are summarized in Table C-2. 

Table C-2. STATSGO soil texture and soil group codes.

STATSGO Soil 

Texture 

Soil Texture 

Code 

Soil 

Group 

Soil Group 

Code 

No Data 0 N/A 0 

Sand 1 CS 4 

Loamy Sand 2 CS 4 

Sandy Loam 3 MS 3 

Silt Loam 4 FS 1 

Silt 5 FSS 2 

Loam 6 MS 3 

Sandy Clay Loam 7 MS 3 

Silty Clay Loam 8 FSS 1 

Clay Loam 9 MS 3 

Sandy Clay 10 MS 3 

Silty Clay 11 FSS 1 

Clay 12 FS 2 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc C-7 

Surface Roughness Lengths

Surface roughness lengths can vary considerably for a given land type, and are assigned as a 

function of land use type based on a review of information reported in the literature. The 

disturbance level of various surfaces has the effect of altering the surface roughness lengths, 

which in turn impact the potential for vacant lands to emit dust from wind erosion. 

An examination of the relationship between the threshold surface friction velocity and the 

aerodynamic surface roughness length, reveals that for surface roughness lengths larger than 

approximately 0.1 cm, the threshold friction velocities increase rapidly above values that can be 

realistically expected to occur in the meteorological data used in the model implementation. 

Therefore to simplify the model implementation, only those land types with roughness length 

less than or equal to 0.1 cm are considered as potentially erodible surfaces. 

For a given surface roughness, as determined by the land use type, the threshold friction velocity 

has a constant value. Thus, the land use data are mapped to an internal dust code used within the 

model to minimize computer resource requirements and coding efforts. The mapping of land use 

types to dust codes 3 and above (except for code 5 that applies to orchards and vineyards) is 

presented in Table C-3, which summarizes the surface characteristics by dust code. [Note: Dust 

codes 1 and 2 refer to water/wetlands and forest/urban, respectively.] 

Table C-3. Surface characteristics by dust code and land use category. 

Dust Code 3 4 6 7 

Land use category Agricultural Grassland Shrubland Barren 

Surface roughness length, Z0 (cm) 0.031 0.1 0.05 0.002 

Threshold friction velocity (m/s) 3.72 6.17 4.30 3.04 

Threshold wind velocity at 10 

meter height (m/s [mph]) 

13.2 

[29.5] 

19.8 

[44.3] 

14.6 

[32.8] 

12.7 

[28.5] 

Meteorology 

Gridded hourly meteorological data, which is required for the dust estimation methodology is 

based on MM5 model simulation results. Data fields required include wind speeds, precipitation 

rates, soil temperatures and ice/snow cover. 

Agricultural Land Adjustments 

Unlike other types of vacant land, windblown dust emissions from agricultural land are subject 

to a number of non-climatic influences, including irrigation and seasonal crop growth. As a 

result, several non-climatic correction or adjustment factors were developed for applicability to 

the agricultural wind erosion emissions. These factors included: 

• Long-term effects of irrigation (i.e., soil “clodiness”) 

• Crop canopy cover 

• Post-harvest vegetative cover (i.e., residue) 

• Bare soil (i.e., barren areas within an agriculture field that do not develop crop canopy for 

various reasons, etc.) 

• Field borders (i.e., bare areas surrounding and adjacent to agricultural fields) 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc C-8 

The methodology used to develop individual non-climatic correction factors was based upon 

previous work performed by the California Air Resources Board in their development of 

California-specific adjustment factors for the USDA’s Wind Erosion Equation (CARB, 1997)

Other Adjustments

Two other adjustments to modeled air quality impacts relate to fugitive dust transportability and 

partitioning between fine and coarse fractions of PM10. Transportability fractions as a function 

of land use are assigned on the basis of the methodology described by Pace (2003; 2005). New 

fine fraction values developed by Cowherd (MRI, 2005) from controlled wind tunnel studies of 

western soils are applied to determine the fine and coarse fractions of wind-generated fugitive 

dust emissions. 

Model Application 

The windblown fugitive dust model was applied for the calendar year 2005 at a spatial resolution 

of 4 km for Four Corners. The model generates estimates of PM10 dust emissions. The fine 

fraction of dust is obtained by using a nominal PM2.5 of 0.10, as used in the implementation of 

the model for the WRAP. 

Other Area Source Emissions

Emissions from numerous small sources treated as area sources such as commercial and 

residential fuel combustion, architectural coatings, etc. that are not included in the other source 

categories described above, were obtained from the WRAP inventories. This category of 

emissions includes road dust and fugitive dust but not wind blown dust. Area source emissions 

for 2005 were estimated via linear interpolation between the WRAP 2002 and latest WRAP 2018 

(PRP18) emission inventories at the county level. The WRAP 2018 (PRP18) inventory was used 

to represent 2018 area source emissions. 

Spatial allocation of area source emissions to model grid cells requires the use of spatial gridding 

surrogates. Within the 4 km domain, a new set of gridding surrogates were developed from the 

EPA population and landuse/landcover distributions (EPA, 2006) that had previously been 

aggregated by WRAP to 36 km resolution. These 4 km gridding surrogates were then applied to 

the interpolated 2005 county-level WRAP area source inventory. Temporal allocations were 

then applied as in the WRAP modeling to obtain hourly gridded emissions for input to CAMx. 

All emissions processing was done using SMOKE. 
APPENDIX D 

Biogenic Emissions 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_D_eibio(Biogenic Emissions).doc D-1

(Appendix D) 

BIOGENIC EMISSIONS 

Gridded hourly biogenic emission inventories suitable for input to CAMx were developed using 

the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.0 emissions 

model, with modifications made by ENVIRON (Guenther et al, 2006; Guenther and 

Wiedinmyer, 2007; Mansell et al, 2007). MEGAN accounts for spatial variability by using high 

resolution estimates of vegetation type and quantity. Key MEGAN variables include weather 

data, Leaf Area Index (LAI), plant functional type (PFT) cover, and compound specific emission 

factors that are based on plant species composition. All of these variables are provided in a georeferenced gridded database in several formats (e.g., netcdf, ESRI GRID). The inputs to 

MEGAN model are: 

• Landcover: The land cover available in MEGAN database has global coverage at 30 sec 

(~ 1 km) spatial resolution (Guenther et al, 2006). 

• Surface Temperature Data: Gridded, hourly temperature fields were extracted from the 

2005 MM5 predictions for each day for each grid resolution. 

• Photosynthetically active radiation (PAR): The PAR data represents the spectral range of 

solar radiation that is used by plants for the photosynthesis process. The data were 

downloaded from the University of Maryland (UMD; 2006) and a FORTRAN program 

was used to reformat the data. Some of the PAR data were missing. As part of the QA 

process, the PAR data were inspected, and the missing data were replaced by 

interpolating the missing data between hours. 

Biogenic emissions were generated as described above for all three modeling domains. Spatial 

distributions of the annual total organic compounds (TOG) and NOx in the 4 km domain are 

shown in Figures D-1 and D-2, respectively. Biogenic emissions are generally highest in the 

higher elevation areas, including the San Juan Mountains of southwestern Colorado, and lowest 

in the arid lower elevation mesas and plains, including much of San Juan County in northwestern 

New Mexico. Annual biogenic emissions are summarized in Table D-1. 

Table D-1. Annual biogenic emissions (t/yr) within the 4 km modeling domain by state/tribal 

area. 

STATE/Tribe VOC NOx 

Arizona 29,202 211 

Colorado 84,822 659 

New Mexico 108,515 833 

Utah 15,931 130 

Tribes 

Grand Total 238,471 1,834 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_D_eibio(Biogenic Emissions).doc D-2

Figure D-1. Annual TOG (tpy) in 4 km domain Figure D-2. Annual NOx (tpy) in 4 km domain 
APPENDIX E 

Development of Western U.S. Fire Emissions Inventory 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_E_eifire(fire emissions).doc E-1

(Appendix E) 

DEVELOPMENT OF WESTERN U.S. FIRE EMISSIONS INVENTORY 

The National Center for Atmospheric Research (NCAR) generates annual fire databases that are 

derived from MODIS satellite data. The MODIS platform is a polar-orbiting satellite that passes 

over a given point on the globe four times per day. The raw infrared data are processed at 1 km 

pixel resolution to identify “hot” pixels that indicate significant fire activity. High resolution 

land coverage and fuel type databases are overlaid onto the 1 km fire pixel data to determine fuel 

loading, and in combination with fuel-specific emission factors, daily fire emission estimates are 

estimated for criteria pollutants (CO, NOx, VOC, SO2, PM) and greenhouse gasses. 

The 2005 NCAR/MODIS fire emissions dataset was processed to generate emission inputs for 

CAMx. For each day, the 1 km fire pixels were aggregated to the 12 km Four Corners modeling 

grid. Each 12 km “fire” cell was assigned multiple co-located point sources that inject a fraction 

of each fire’s emissions into each CAMx vertical layer. The plume rise and diurnal activity 

profiles were determined from the approach developed by the WRAP Fire Emissions Joint 

Forum (FEJF). The FEJF approach assigns diurnal intensity profiles and plume rise according to 

fire size in acres; since size is not given in the NCAR fire dataset, fire size was determined from 

daily PM emissions rates aggregated to each 12 km “fire” cell. VOC emissions were speciated 

to CB05 according to profiles derived from the TROFEE study (Karl et al., 2007). Resulting 

hourly point source emissions for speciated SO2, NOx, VOC, CO, and PM (primary EC and OC) 

were compiled into a CAMx point source file format and merged with the anthropogenic point 

source inventory. 

Fire emissions vary widely from day to day and month to month. During 2005, fires were most 

prevalent in the general vicinity of the Four Corners region during June and July. Emissions of 

NOx and PM for these months are shown in Figure E-1. 
G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\App_E_eifire(fire emissions).doc E-2

Figure E-1. Fire emissions during June (left column) and July (right column) 2005 for NOx (top 

row) and PM (bottom row). 
Appendix F

Meteorological Modeling
Draft Topical Report 

Evaluation of 36/12/4 km MM5 

for Calendar Year 2005

Prepared for:

New Mexico Environment Department 

1190 St. Francis Drive

Suite #N4050 Santa 

Fe, New Mexico 87502

Prepared by:

Dennis McNally

George Schewe

Alpine Geophysics, LLC

7341 Poppy Way 

Arvada, Colorado 80007

19 October 2006
Table of Contents 

Section No.____________________________________________________________________Page No.

1 INTRODUCTION ........................................................................................................................... 1-1 

2 METHODOLOGY ........................................ 2-1 

2.1 MODEL SELECTION AND APPLICATION..................................................................................................................... 2-1 

2.2 EVALUATION APPROACH.......................................................................................................................................... 2-2 

3 MM5 PERFORMANCE EVALUATION RESULTS................................................................... 3-6

3.1 QUANTITATIVE MODEL EVALUATION RESULTS ........................................................................................................ 3-6 

3.1.1 Temperature Bias and Error................................................................................................................................ 3-6 

3.1.2 Mixing Ratio Bias and Error............................................................................................................................... 3-6 

3.1.3 Wind Index of Agreement .................................................................................................................................. 3-7 

3.2 MONTHLY PRECIPITATION ANALYSIS....................................................................................................................... 3-7 

4 COMPARISON WITH OTHER ANNUAL MM5 SIMULATIONS ........................................... 4-1

4.1 COMPARISON TO OTHER ANNUAL 36KM SIMULATIONS ............................................................................................. 4-1 

4.2 COMPARISON TO OTHER ANNUAL 12KM SIMULATIONS ............................................................................................. 4-3 

4.3 COMPARISON TO OTHER ANNUAL 4KM SIMULATIONS ............................................................................................... 4-4 

5 REFERENCES ................................................................................................................................ 5-1

i
List of Figures 

Figure No. ____________________________________________________________________Page No.

Figure 2-1: 36km (D01) and 12km (D02) and 4km (D03) MM5 Domains................................................................................ 2-5 

Figure 3-1: Regional Planning Organization (RPO) Boundaries ............................................................................................. 3-24 

Figure 3-2: CPC Analyzed Precipitation for January 2005 over the 36km Domain.................................................................. 3-25 

Figure 3-3: MM5 Estimated Precipitation for January 2005 over the 36km Domain .............................................................. 3-25 

Figure 3-4: CPC Analyzed Precipitation for February 2005 over the 36km Domain................................................................ 3-26 

Figure 3-5: MM5 Estimated Precipitation for February 2005 over the 36km Domain ............................................................ 3-26 

Figure 3-6: CPC Analyzed Precipitation for March 2005 over the 36km Domain.................................................................... 3-27 

Figure 3-7: MM5 Estimated Precipitation for March 2005 over the 36km Domain ................................................................ 3-27 

Figure 3-8: CPC Analyzed Precipitation for April 2005 over the 36km Domain...................................................................... 3-28 

Figure 3-9: MM5 Estimated Precipitation for April 2005 over the 36km Domain .................................................................. 3-28 

Figure 3-10: CPC Analyzed Precipitation for May 2005 over the 36km Domain..................................................................... 3-29 

Figure 3-11: MM5 Estimated Precipitation for May 2005 over the 36km Domain ................................................................. 3-29 

Figure 3-12: CPC Analy zed Precipitation for June 2005 over the 36km Domain ..................................................................... 3-30 

Figure 3-13: MM5 Estimated Precipitation for June 2005 over the 36km Domain ................................................................. 3-30 

Figure 3-14: CPC Analyzed Precipitation for July 2005 over the 36km Domain...................................................................... 3-31 

Figure 3-15: MM5 Estimated Precipitation for July 2005 over the 36km Domain.................................................................. 3-31 

Figure 3-16: CPC Analyzed Precipitation for August 2005 over the 36km Domain................................................................. 3-32 

Figure 3-17: MM5 Estimated Precipitation for August 2005 over the 36km Domain ............................................................. 3-32 

Figure 3-18: CPC Analyzed Precipitation for September 2005 over the 36km Domain ........................................................... 3-33 

Figure 3-19: MM5 Estimated Precipitation for September 2005 over the 36km Domain........................................................ 3-33 

Figure 3-20: CPC Analyzed Precipitation for October 2005 over the 36km Domain ............................................................... 3-34 

Figure 3-21: MM5 Estimated Precipitation for October 2005 over the 36km Domain............................................................ 3-34 

Figure 3-22: CPC Analyzed Precipitation for November 2005 over the 36km Domain ........................................................... 3-35 

Figure 3-23: MM5 Estimated Precipitation for November 2005 over the 36km Domain........................................................ 3-35 

Figure 3-24: CPC Analyzed Precipitation for December 2005 over the 36km Domain............................................................ 3-36 

Figure 3-25: MM5 Estimated Precipitation for December 2005 over the 36km Domain ........................................................ 3-36 

Figure 3-26: CPC Analyzed Precipitation for January 2005 over the 12km Domain................................................................ 3-37 

Figure 3-27: MM5 Estimated Precipitation for January 2005 over the 12km Domain ............................................................ 3-37 

Figure 3-28: CPC Analyzed Precipitation for February 2005 over the 12km Domain.............................................................. 3-38 

Figure 3-29: MM5 Estimated Precipitation for February 2005 over the 12km Domain .......................................................... 3-38 

Figure 3-30: CPC Analyzed Precipitation for March 2005 over the 12km Domain.................................................................. 3-39 

Figure 3-31: MM5 Estimated Precipitation for March 2005 over the 12km Domain .............................................................. 3-39 

Figure 3-32: CPC Analyzed Precipitation for April 2005 over the 12km Domain.................................................................... 3-40 

Figure 3-33: MM5 Estimated Precipitation for April 2005 over the 12km Domain ................................................................ 3-40 

Figure 3-34: CPC Analyzed Precipitation for May 2005 over the 12km Domain..................................................................... 3-41 

Figure 3-35: MM5 Estimated Precipitation for May 2005 over the 12km Domain ................................................................. 3-41 

Figure 3-36: CPC Analyzed Precipitation for June 2005 over the 12km Domain..................................................................... 3-42 

Figure 3-37: MM5 Estimated Precipitation for June 2005 over the 12km Domain ................................................................. 3-42 

Figure 3-38: CPC Analyzed Precipitation for July 2005 over the 12km Domain...................................................................... 3-43 

Figure 3-39: MM5 Estimated Precipitation for July 2005 over the 12km Domain.................................................................. 3-43 

Figure 3-40: CPC Analyzed Precipitation for August 2005 over the 12km Domain................................................................. 3-44 

Figure 3-41: MM5 Estimated Precipitation for August 2005 over the 12km Domain ............................................................. 3-44 

Figure 3-42: CPC Analyzed Precipitation for September 2005 over the 12km Domain ........................................................... 3-45 

Figure 3-43: MM5 Estimated Precipitation for September 2005 over the 12km Domain........................................................ 3-45 

Figure 3-44: CPC Analyzed Precipitation for October 2005 over the 12km Domain ............................................................... 3-46 

Figure 3-45: MM5 Estimated Precipitation for October 2005 over the 12km Domain............................................................ 3-46 

Figure 3-46: CPC Analyzed Precipitation for November 2005 over the 12km Domain ........................................................... 3-47 

Figure 3-47: MM5 Estimated Precipitation for November 2005 over the 12km Domain........................................................ 3-47 

Figure 3-48: CPC Analyzed Precipitation for December 2005 over the 12km Domain............................................................ 3-48 

ii
Figure 3-49: MM5 Estimated Precipitation for December 2005 over the 12km Domain ........................................................ 3-48 

Figure 3-50: CPC Analyzed Precipitation for January 2005 over the 4km Domain.................................................................. 3-49 

Figure 3-51: MM5 Estimated Precipitation for January 2005 over the 4km Domain .............................................................. 3-49 

Figure 3-52: CPC Analyzed Precipitation for February 2005 over the 4km Domain................................................................ 3-50 

Figure 3-53: MM5 Estimated Precipitation for February 2005 over the 4km Domain ............................................................ 3-50 

Figure 3-54: CPC Analyzed Precipitation for March 2005 over the 4km Domain.................................................................... 3-51 

Figure 3-55: MM5 Estimated Precipitation for March 2005 over the 4km Domain ................................................................ 3-51 

Figure 3-56: CPC Analyzed Precipitation for April 2005 over the 4km Domain...................................................................... 3-52 

Figure 3-57: MM5 Estimated Precipitation for April 2005 over the 4km Domain .................................................................. 3-52 

Figure 3-58: CPC Analyzed Precipitation for May 2005 over the 4km Domain....................................................................... 3-53 

Figure 3-59: MM5 Estimated Precipitation for May 2005 over the 4km Domain ................................................................... 3-53 

Figure 3-60: CPC Analyzed Precipitation for June 2005 over the 4km Domain....................................................................... 3-54 

Figure 3-61: MM5 Estimated Precipitation for June 2005 over the 4km Domain ................................................................... 3-54 

Figure 3-62: CPC Analyzed Precipitation for July 2005 over the 4km Domain ....................................................................... 3-55 

Figure 3-63: MM5 Estimated Precipitation for July 2005 over the 4km Domain.................................................................... 3-55 

Figure 3-64: CPC Analyzed Precipitation for August 2005 over the 4km Domain................................................................... 3-56 

Figure 3-65: MM5 Estimated Precipitation for August 2005 over the 4km Domain ............................................................... 3-56 

Figure 3-66: CPC Analyzed Precipitation for September 2005 over the 4km Domain ............................................................. 3-57 

Figure 3-67: MM5 Estimated Precipitation for September 2005 over the 4km Domain.......................................................... 3-57 

Figure 3-68: CPC Analyzed Precipitation for October 2005 over the 4km Domain ................................................................. 3-58 

Figure 3-69: MM5 Estimated Precipitation for October 2005 over the 4km Domain.............................................................. 3-58 

Figure 3-70: CPC Analyzed Precipitation for November 2005 over the 4km Domain ............................................................. 3-59 

Figure 3-71: MM5 Estimated Precipitation for November 2005 over the 4km Domain.......................................................... 3-59 

Figure 3-72: CPC Analyzed Precipitation for December 2005 over the 4km Domain.............................................................. 3-60 

Figure 3-73: MM5 Estimated Precipitation for December 2005 over the 4km Domain .......................................................... 3-60 

List of Tables 

Table No. _____________________________________________________________________Page No.

Table 2-1: MM5 Vertical Domain Specification........................................................................................................................ 2-4 

Table 3-1: Temperature Bias (K) by Month and by State and Region in the 36km Domain....................................................... 3-9 

Table 3-2: Temperature Bias (K) by Month and by State and Region in the 12km Domain..................................................... 3-11 

Table 3-3: Temperature Bias (K) by Month for the 4km Domain............................................................................................. 3-11 

Table 3-4: Temperature Error (K) by Month and by State and Region in the 36km Domain ................................................... 3-12 

Table 3-5: Temperature Error (K) by Month and by State and Region in the 12km Domain.................................................. 3-14 

Table 3-6: Temperature Error (K) by Month for the 4km Domain ........................................................................................... 3-14 

Table 3-7: Mixing Ratio Bias (g/kg) by Month and by State and Region in the 36km Domain................................................ 3-15 

Table 3-8: Mixing Ratio Bias (g/kg) by Month and by State and Region in the 12km Domain................................................ 3-17 

Table 3-9: Mixing Ratio Bias (g/kg) by Month for the 4km Domain ....................................................................................... 3-17 

Table 3-10: Mixing Ratio Error (g/kg) by Month and by State and Region in the 36km Domain ............................................ 3-18 

Table 3-11 Mixing Ratio Error (g/kg) by Month and by State and Region in the 12km Domain.............................................. 3-20 

Table 3-12 Mixing Ratio Error (g/kg) by Month for the 4km Domain ..................................................................................... 3-20 

Table 3-13: Wind Index of Agreement by Month and by State and Region in the 36km Domain............................................ 3-21 

Table 3-14: Wind Index of Agreement by Month and by State and Region in the in 12km Domain........................................ 3-23 

Table 3-15: Wind Index of Agreement by Month in the 4km Domain.................................................................................... 3-23 

Table 4-1: Temperature Bias (K) For 36km Annual MM5 Simulations .................................................................................... 4-6 

Table 4-2: Temperature Error (K) for 36km Annual MM5 Simulations..................................................................................... 4-6 

Table 4-3: Mixing Ratio Bias (g/kg) for 36km Annual MM5 Simulations ................................................................................. 4-6 

Table 4-4: Mixing Ratio Error (g/kg) for 36km Annual MM5 Simulations................................................................................ 4-6 

Table 4-5: Wind Index of Agreement for 36km Annual MM5 Simulation ............................................................................... 4-7 

Table 4-6: Mixing Ratio Bias (g/kg) over the 12km MM5 Domain and Four-Corner States...................................................... 4-8 

iii
Table 4-7: Mixing Ratio Error (g/kg) over the 12km MM5 Domain and Four-Corner States..................................................... 4-9 

Table 4-8: Temperature Bias (K) over the 12km MM5 Domain and Four-Corner States ....................................................... 4-10 

Table 4-9: Temperature Error (K) over the 12km MM5 Domain and Four-Corner States........................................................ 4-11 

Table 4-10: Wind Index of Agreement over the 12km MM5 Domain and Four-Corner States............................................... 4-12 

Table 4-11: Mixing Ratio Bias (g/kg) over 4km MM5 Domain .............................................................................................. 4-13 

Table 4-12: Mixing Ratio Error (g/kg) over the 4km MM5 Domain ........................................................................................ 4-13 

Table 4-13: Temperature Bias (K) over the 4km MM5 Domain............................................................................................... 4-13 

Table 4-14: Temperature Error (K) over the 4km MM5 Domain ............................................................................................. 4-13 

Table 4-15: Wind Index of Agreement over the 4km MM5 Domain........................................................................................ 4-14 

iv
1 INTRODUCTION

Over the past half decade, emergent requirements for direct numerical simulation of 

urban and regional scale photochemical and secondary aerosol air quality—spawned 

largely by the new particulate matter (PM2.5) and regional haze regulations—have led to 

intensified efforts to construct high-resolution emissions, meteorological and air quality 

data sets. The concomitant increase in computational throughput of low-cost modern 

scientific workstations has ushered in a new era of regional air quality modeling. It is 

now possible, for example, to exercise sophisticated mesoscale prognostic meteorological 

models and Eulerian and Lagrangian photochemical/aerosol models for the full annual 

period, simulating ozone, sulfate and nitrate deposition, and secondary organic aerosols 

(SOA) across the entire United States (U.S.) or over discrete subregions.

One such model is the Pennsylvania State University/National Center for Atmospheric 

Research (PSU/NCAR) Mesoscale Model (MM5) (Dudhia, 1993; Grell et al., 1994: 

www.mmm.ucar.edu/mm5). MM5 is a limited-area, non-hydrostatic, terrain-following 

model designed to simulate mesoscale atmospheric circulation. The model is supported 

by several pre- and post-processing programs which are referred to collectively as the 

MM5 modeling system. This report describes an application and performance evaluation 

of MM5 for an atmospheric simulation for calendar 2005 over a modeling domain that 

covers the continental United States at a 36km grid spacing, the southwestern United 

States at a 12km spacing, and the Four Corners region (New Mexico, Utah, Arizona, and 

Colorado) at a 4km spacing.

1- 1
2 METHODOLOGY

The methodology for this approach is very straightforward. The basic methodology was 

to apply the MM5 model for the annual period (2005 in this case) and the model results 

(wind speeds, wind directions, temperatures, etc.) were compared with available surface 

meteorological observations.

2.1 Model Selection and Application

Below we give a brief summary of the MM5 input data preparation procedure used for 

this annual modeling exercise.

Model Selection: The publicly available non-hydrostatic version of MM5 (version 3.7.2) 

was used for this modeling study. Preprocessor programs of the MM5 modeling system 

including TERRAIN, REGRID, LITTLE_R, and INTERPF were used to develop model 

inputs.

Horizontal Domain Definition: The computational grids are presented in Figure 2-1. The 

outer 36km domain (D01) has 165 x 129 grid cells, selected to maximize the coverage of 

the ETA analysis region. The 12km nested grid domain (D02) has 178 x 157 grid cells 

and the 4km nested grid domain (D03) has 172 x 169 grid cells. The projection is 

Lambert Conformal with the "national RPO" grid projection pole of 40°, -97° with true 

latitudes of 33° and 45°.

Vertical Domain Definition: The MM5 modeling was based on 34 vertical layers with an 

approximately 38 meter deep surface layer. The MM5 vertical domain is presented in 

both sigma and height coordinates in Table 2-1.

Topographic Inputs: Topographic information for the MM5 was developed using the 

NCAR and the United States Geological Survey (USGS) terrain databases. The grid was 

based on the 2 min (~4 km) Geophysical Data Center global data. Terrain data was 

interpolated to the model grid using a Cressman-type objective analysis scheme. To 

avoid interpolating elevated terrain over water bodies, after the terrain databases were 

interpolated onto the MM5 grid, the NCAR graphic water body database was used to 

correct elevations over water bodies.

Vegetation Type and Land Use Inputs: Vegetation type and land use information was 

developed using the most recently released PSU/NCAR databases provided with the 

MM5 distribution. Standard MM5 surface characteristics corresponding to each land use 

category were employed.

Atmospheric Data Inputs: The first guess fields were taken from the NCAR ETA 

archives. Surface and upper-air observations used in the objective analyses, following the 

procedures outlined by Stauffer and Seaman at PSU, were quality-inspected by MM5

2- 1
pre-processors using automated gross-error checks and "buddy" checks. In addition, 

rawinsonde soundings were subject to vertical consistency checks. The synoptic-scale 

data used for this initialization (and in the analysis nudging discussed below) were 

obtained from the conventional National Weather Service (NWS) twice-daily 

radiosondes and 3-hr NWS surface observations.

Water Temperature Inputs: The ETA database contains a "skin temperature" field. This 

can be and was used as the water temperature input to these MM5 simulations. Past 

studies have shown that these skin temperatures, the water temperature surrogates, can 

lead to temperature errors along coastlines. However, for this analysis which focuses on 

bulk continental scale transport in the Four Corners area, this issue is likely not important 

and the skin temperatures were used.

FDDA Data Assimilation: This simulation used a combination of analysis and 

observation-based nudging. For these simulations analysis nudging coefficients of 

2.5x10-4 and 1.0x10-4 were used for winds and temperature at 36km and 12km, 

respectively. An analysis nudging coefficient of 1x10-5 was used for mixing ratio. 

Thermodynamic variables were not nudged within the boundary layer. For January 

through November, observation nudging of the NOAA Techniques Development Lab 

(TDL) surface observation database (NCAR DS472.0) was used for winds with a 

nudging coefficient of 4x10-4. No observation nudging was performed for December 

because the TDL dataset was not available.

Physics Options: The MM5 model physics options in this simulation were as follows:

Betts-Miller Cumulus Parameterization PleimXiu PBL and Land Surface Schemes Reisner 1 

Mixed Phase Moisture Scheme RRTM 

Atmospheric Radiation Scheme

Application Methodology: The MM5 model was executed in 5-day blocks initialized at 

12Z every 5 days with a 90 second time step. Model results were output every 60 

minutes and output files were split at 24 hour intervals. Twelve (12) hours of spin-up is 

included in each 5-day block before the data was used in this evaluation.

2.2 Evaluation Approach

The model evaluation approach was based on a combination of qualitative and 

quantitative analyses. The qualitative approach was to compare the model estimated 

monthly total precipitation with the monthly Center for Prediction of Climate (CPC) 

precipitation analysis. The statistical approach was to examine the model bias and error 

for temperature, and mixing ratio and the Index of Agreement for the wind fields.

Interpretation of bulk statistics over a continental scale domain is problematic. To detect 

if the model is missing important sub-regional features is difficult. For this analysis the

2- 2
statistics are performed on a state by state basis, a Regional Planning Organization (RPO) 

basis for the continental 36km domain, and on a domain-wide basis.

The observed database for winds, temperature, and water mixing ratio used in this 

analysis was the NOAA Techniques Development Lab (TDL) Surface Hourly 

Observation database obtained from the NCAR archives. The TDL data for December 

2005 was not available in time to be used for this analysis. The rain observations are 

taken from the CPC retrospective rainfall archives available at:

http://www.cpc.ncep.noaa.gov/products/precip/realtime/retro.shtml.

- 3
Table 2-1: MM5 Vertical Domain in Specification.

k(MM5) sigma press.(mb) height(m) depth(m)

34 0.000 10000 15674 2004

33 0.050 14500 13670 1585

32 0.100 19000 12085 1321

31 0.150 23500 10764 1139

30 0.200 28000 9625 1004

29 0.250 32500 8621 900

28 0.300 37000 7720 817

27 0.350 41500 6903 750

26 0.400 46000 6153 693

25 0.450 50500 5461 645

24 0.500 55000 4816 604

23 0.550 59500 4212 568

22 0.600 64000 3644 536

21 0.650 68500 3108 508

20 0.700 73000 2600 388

19 0.740 76600 2212 282

18 0.770 79300 1930 274

17 0.800 82000 1657 178

16 0.820 83800 1478 175

15 0.840 85600 1303 172

14 0.860 87400 1130 169

13 0.880 89200 961 167

12 0.900 91000 794 82

11 0.910 91900 712 82

10 0.920 92800 631 81

9 0.930 93700 550 80

8 0.940 94600 469 80

7 0.950 95500 389 79

6 0.960 96400 310 78

5 0.970 97300 232 78

4 0.980 98200 154 39

3 0.985 98650 115 39

2 0.990 99100 77 38

1 0.995 99550 38 38

0 1.000 100000 0 0

2- 4
Figure 2-1: 36km (D01) and 12km (D02) and 4km (D03) MM5 Domains.

2- 5
3 MM5 PERFORMANCE EVALUATION RESULTS

3.1 Quantitative Model Evaluation Results

Statistical model evaluation results are presented in this section. A full annual model 

evaluation is very difficult to summarize in a single document, especially a simulation 

that could be used for many different purposes. With this in mind, this section presents 

results so potential data users can independently judge the adequacy of the model 

simulation.

The tables present the statistical metric for each state, for each Regional Planning 

Organization (RPO), and for the United States portion of the modeling domain. A 

graphic of RPO boundaries is presented if Figure 3-1. In this comparison the vertical 

level 1 (~19m) model estimates are compared directly with the nominal ~2m temperature 

and moisture and ~10m wind measurements.

3.1.1 Temperature Bias and Error

Temperature bias statistics are presented in Tables 3-1, 3-2 and 3-3 for the 36km, 12km 

and 4km domains, respectively. As can be seen in Table 3-1, when the temperatures are 

averaged over the entire 2005 period and the entire modeling domain (ALL), the model 

has a bias of 0.52 °C for the 36km domain and 0.14 °C for the 12km domain. The model 

tends to have positive bias (overestimate) temperatures throughout the year on the 36km 

domain, and to underestimate temperatures from March through July in the NMED4KM 

region. Temperatures are overestimated the remainder of the year on the 12km domain 

for the NMED4KM region. Table 3-3 shows that temperatures are generally 

overestimated for the 4KM grid for all months except March.

Temperature error data are presented in Tables 3-4 through 3-6 for the 36km, 12km, and 

4km grids, respectively. The overall temperature error (ALL category) is 2.28°C on the 

36km domain, 2.72°C on the 12km domain, and 3.34°C on the 4km domain. The mean 

error of 3.34°C for the 4km grid was somewhat consistent across all 12 months with 

February being the lowest temperature error at 2.59°C. All temperature errors were 

typically greater than 3.0°C for the 4km domain, 2.0°C for the 12km domain, and greater 

than about 1.5°C for the 36km domain.

3.1.2 Mixing Ratio Bias and Error

Mixing ratio bias data are presented in Tables 3-7 through 3-9 for the three modeling 

domains. Averaged over the entire year, at all stations, the model has a bias of 0.17 g/kg 

and 0.03 g/kg for the 36km and 12km domains, respectively, as shown by the "ALL" 

category shown in Tables 3-7 and 3-8.. For the 36km domain, the model tends to

3- 6
perform better in the western (WRAP) and Central States (CENRAP) than for the 

southeast (VISTAS) and east (MANE_VU). For the 4km grid the mixing ratio bias 0.24 

g/kg with underestimates h January through April and overestimates in May through 

November (except October where the bias was 0.0 g/kg).

Mixing ratio error results are presented in Tables 3-10 through 3.12. The mean error is 

1.12 g/kg for the 36km domain, 1.07 g/kg for the 12km domain, and 1.10 g/kg for the 

4km domain. The model has a positive error (overestimates) throughout the year in each 

domain and shows the highest error values in the more moist summertime months of June 

through August in all cases.

3.1.3 Wind Index of Agreement

Comparisons of the Wind Index of Agreement (IA) are presented in Tables 3-13 through 

3-15. The domain-wide episode average IA is 0.87 for both the 36km and 12km domains 

and 0.8 for the 4km domain. No significant monthly trends were discerned in any of the 

month to month variations or by State, Region, or area reviewed. For the 36km domain, 

the model is tending to perform better in the western portion of the domain than the 

eastern portion. Performance across the 12km domain is consistent and commensurate 

with the 4km performance.

3.2 Monthly Precipitation Analysis

This section presents qualitative comparisons of MM5 estimated precipitation with the 

CPC retrospective analysis data. When comparing the CPC and MM5 precipitation data, 

note should be taken that the CPC analysis covers only the Continental U.S. and does not 

extend offshore or into Canada or Mexico. The MM5 fields cover the entire domain. 

Also note that the CPC analysis is based on a 0.25 x 0.25 degree (~40 x 40 km) grid 

which does not capture small precipitation features.

Monthly total precipitation comparisons for the 36km domain are presented in Figures 3-

2 through 3-25. For each month, the first plot presents the CPC analysis data (i.e., Figure 

3-2) and the second plot represents the MM5 total precipitation (i.e., Figure 3-3). If the 

CPC analysis data are considered to be the standard for precipitation, MM5 does a 

reasonably good job representing both the spatial coverage and magnitude of the 

precipitation in the Western U.S. throughout the year. The MM5 model did tend to 

overestimate precipitation in Arizona and New Mexico in July and August especially in 

the Four Corners region. In the Central and Eastern U.S., MM5 performs well during the 

fall, winter and spring which are the cooler months (Sept. through May), but 

overestimates precipitation from June through August especially in the southeast U.S.

Monthly total precipitation comparisons for the 12km domain are presented in Figures 3-

26 through 3-49. As with the 36km grid, MM5 does a reasonably good job representing 

both the spatial coverage and magnitude of the precipitation in the Western U.S. 

throughout the year. The refinement of the 12km grid size is obvious when comparing

3- 7
the CPC precipitation to that of MM5. The tendency is for the MM5 precipitation data to 

have a smaller, more well defined footprint than the CPC data which is at the 40km by 

40km spacing. Features like terrain appear more well defined in the MM5 data where the 

terrain elevations are considered.

Comparison of the CPC data and the 12km MM5 data indicates that MM5 precipitation is 

somewhat representative of the western region. Where MM5 does not agree well with 

the CPC data is in the summer months. The MM5 modeling indicates more precipitation 

in the Four Corners region than the CPC data for May through July as shown in Figures 

3-34 through 3-39. A comparison of Figures 3-38 and 3-40 for CPC and 3-39 and 3-41 

for MM5 show a marked overestimate by MM5 over the Colorado River basin in 

southeast Nevada, in eastern New Mexico, and across Arizona. Other months are 

comparable for the study area.

Figures 3-50 through 3-73 present the monthly total precipitation comparisons for the 

4km domain. While the general patterns of precipitation over the Four Corners region 

are similar between the CPC and MM5 data, the magnitude of the precipitation is highly 

variable between the two data sets. Generally the MM5 model overestimated monthly 

total precipitation when compared to that of the CPC analysis. Review of Figures 3-57 

through 3-67 for April through September show much higher precipitation in New 

Mexico and Colorado in the MM5 over the CPC data. Even the late fall and winter 

months show more precipitation although the spatial extent is reduced where the 

differences appear. January, November, and December show the most representative 

results from MM5 versus CPC. Considering that this area in the Four Corners is the focal 

point of the intended dispersion modeling analysis, the use of the MM5 data may not be 

the best representation of the precipitation. The higher precipitation could lead to higher 

deposition due to precipitation and subsequent lower air concentrations at the nearby 

Class I areas. Comparative review of the other meteorological data sets (other years and 

at all spatial grid sizes) should be considered prior to the decision to use or not use the 

4km data in modeling studies.

3- 8
Table 3-1: Temperature Bias (K) by Month and by State and Region in the 36km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

AK -0.46 0.73 -0.36 -0.09 -1.03 -1.22 -0.74 0.28 -0.07 0AL 0.84 1.31 1.02 1.02 1.51 1.21 1.45 1.11 1.12 ALL 0.30 0.54 0.19 0.50 0.80 0.40 0.45 0.65 0.54 0AR 0.66 1.65 1.12 0.95 1.45 1.19 1.53 1.22 1.11 AZ 0.98 0.28 -0.04 -0.82 -1.36 -1.57 -1.20 -0.33 -0.34 -0

CA 1.76 0.42 0.29 -0.46 -1.13 -1.73 -0.85 -0.30 -0.07 0CENRAP 0.27 0.95 0.64 1.10 1.31 0.92 0.98 1.08 0.80 0CO 0.33 -0.46 -1.67 -0.98 -0.28 -0.21 -0.46 0.07 0.02 0CT -0.20 -0.77 -1.26 0.60 1.08 0.54 0.36 0.25 0.24 0

DC -0.25 -0.06 -0.22 0.48 0.74 0.28 0.27 0.07 -0.68 -0DE -1.00 0.05 0.20 0.06 1.26 0.24 -0.23 0.05 -0.05 0FL 0.85 0.74 0.44 0.09 -0.08 0.51 -0.04 0.40 0.26 0

GA 0.86 1.27 0.96 0.78 1.24 1.00 1.15 1.05 0.76 

IA -1.01 0.20 0.90 1.72 1.81 1.10 1.40 1.70 1.13 0ID 0.92 0.52 -0.31 -0.45 0.46 0.59 0.44 0.78 1.34 IL -0.19 0.72 0.96 1.47 1.67 1.16 1.34 1.83 1.49 IN -1.06 0.41 0.87 1.33 1.65 0.88 1.03 1.39 1.08 KS 0.88 1.90 1.71 1.16 1.02 0.68 0.45 0.69 0.26 0KY -0.38 1.28 1.11 0.99 1.08 0.73 0.78 1.05 0.88 LA 0.58 1.04 0.84 0.22 0.49 0.52 0.56 0.90 0.46 

MA -0.52 -0.75 -1.66 0.46 0.99 0.32 0.03 0.12 0.56 0MANE VU -0.28 -0.46 -0.98 0.16 1.02 0.48 0.28 0.34 0.31 0MD -0.05 0.48 0.16 0.44 1.10 0.34 0.25 0.46 0.07 0ME -0.43 -0.64 -1.61 -0.45 1.15 0.87 0.53 0.57 0.70 0MI -0.09 -0.48 -0.32 0.45 1.22 0.45 0.50 0.55 0.58 MN -1.36 -1.03 -1.46 1.28 1.92 1.40 0.96 1.03 1.00 0MO 0.42 1.28 1.15 0.97 0.96 0.91 0.64 1.18 1.00 0MS 1.23 1.70 1.53 1.14 1.59 1.35 1.64 1.41 1.17 MT 1.13 0.39 -0.14 -0.50 0.38 0.37 -0.05 0.38 0.18 0MW -0.61 -0.28 -0.05 1.01 1.48 0.79 0.87 1.03 0.92 

3- 9
NC 0.56 0.98 0.75 0.35 0.97 0.45 0.59 0.55 0.46 0

ND -0.73 0.64 0.74 1.86 1.74 1.41 1.28 1.06 1.13 1NE 0.41 1.91 1.72 1.95 1.33 0.72 0.58 1.15 0.49 0NH -0.72 -0.41 -1.62 0.89 1.77 1.29 0.76 1.01 0.90 0NJ -0.20 -0.58 -0.51 0.12 0.75 0.10 0.18 0.24 0.08 0NM 1.10 0.81 0.23 0.14 0.30 -0.30 -0.43 0.32 0.25 0NMED4KM 0.16 -0.47 -1.19 -0.96 -0.42 -0.52 -0.71 0.07 -0.02 0NV 0.64 -1.15 -1.68 -2.19 -1.83 -2.47 -2.19 -1.60 -1.28 -0NY 0.06 -0.48 -1.27 -0.10 0.63 0.19 -0.02 0.07 0.06 0

OH -0.78 0.16 0.10 1.00 1.62 0.71 0.76 1.04 0.97 1OK 1.55 1.86 1.14 0.73 1.34 1.15 1.07 1.18 0.85 0OR 0.62 1.35 0.50 -0.48 -0.54 -1.23 -1.31 -1.12 -0.16 0

PA -0.15 -0.25 -0.04 0.03 1.11 0.60 0.56 0.46 0.15 0RI -0.39 -0.85 -0.95 0.79 1.24 0.36 0.43 0.35 0.67 0

SC 1.08 1.59 0.86 0.52 1.17 0.88 0.87 0.82 0.41 0SD 0.04 1.89 1.13 1.93 1.80 1.33 1.07 1.29 0.87 1

TN 0.13 1.29 0.43 0.74 0.97 0.76 0.97 0.95 0.88 0TX 1.28 1.72 1.12 0.77 0.86 0.54 1.02 0.89 0.67 0

UT 0.74 0.27 -0.76 -1.10 -0.42 -0.47 -0.32 0.21 0.33 0

VA -0.05 0.55 0.32 0.36 0.89 0.39 0.57 0.64 0.39 0VISTAS 0.56 1.01 0.71 0.51 0.89 0.69 0.71 0.74 0.57 0VT -0.79 -0.84 -2.22 0.05 1.00 0.78 0.18 0.49 0.39 0

WA 0.70 1.57 0.57 0.17 0.01 -0.59 -0.44 -0.03 0.17 0WI -1.48 -1.69 -1.32 1.09 1.45 0.83 0.78 0.59 0.62 0WRAP 0.92 0.64 -0.04 -0.31 -0.30 -0.65 -0.48 0.04 0.16 0WV -0.25 0.34 0.26 0.38 1.36 0.74 0.67 0.60 0.53 0WY 0.67 0.70 -1.54 -1.48 -0.24 -0.14 -0.65 0.42 -0.07 0

3-10
Table 3-2: Temperature Bias (K) by Month and by State and Region in the 12km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 0.94 0.41 -0.16 -0.42 -0.41 -0.53 -0.21 0.32 0.26 0AZ 0.59 -0.30 -0.36 -0.97 -1.43 -1.70 -1.44 -0.48 -0.44 -0

CA 1.36 0.16 0.20 -0.34 -0.83 -1.15 -0.03 0.40 0.15 0CO 0.68 -0.06 -1.42 -0.94 -0.30 -0.01 -0.14 0.45 0.38 0

ID 1.37 0.32 -0.92 -1.14 -0.05 0.29 0.58 0.70 1.64 1NM 0.64 0.08 -0.15 -0.06 -0.07 -0.40 -0.66 0.12 0.03 0NMED4KM 0.25 -0.43 -1.19 -0.90 -0.39 -0.33 -0.48 0.27 0.21 0NV 0.72 -1.09 -1.55 -2.01 -1.75 -2.20 -1.61 -1.19 -0.79 -0UT 0.76 0.34 -0.63 -0.97 -0.25 -0.32 0.07 0.45 0.87 1

WY 0.73 1.56 -0.80 -0.93 0.09 0.45 0.17 1.05 0.68 0

Table 3-3 Temperature Bias (K) by 

Month

for the 4km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun 05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 1.45 0.95 -0.03 0.72 1.19 0.94 0.58 1.22 1.27 1.54 

3-11
Table 3-4: Temperature Error (K) by Month and by State and Region 

i

n the 36km Domain.

•

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

AK 1.94 1.52 1.35 1.74 1.88 1.86 1.29 1.65 1.14 1.2AL 2.17 2.11 2.35 2.27 2.41 1.95 2.08 2.07 2.23 2.4ALL 2.36 2.32 2.41 2.37 2.26 2.10 2.17 2.20 2.26 2.2AR 1.99 2.35 2.14 2.03 2.18 1.99 2.19 2.14 2.02 2.1AZ 2.27 1.98 2.50 2.97 3.12 3.26 3.28 2.81 2.88 2.8

CA 2.94 2.28 2.49 2.63 2.66 2.86 2.94 2.89 2.96 2.8CENRAP 2.35 2.40 2.40 2.27 2.18 1.90 1.99 2.09 2.08 2.2CO 3.44 3.29 3.58 3.43 3.09 2.90 3.23 2.88 3.02 2.9CT 1.82 2.13 2.37 1.97 1.71 1.75 1.51 1.55 1.88 1.7

DC 2.26 1.23 1.36 1.43 1.48 1.15 1.21 1.22 1.33 1.4DE 2.59 2.40 1.77 2.22 2.25 1.65 1.81 1.56 1.72 1.6FL 2.07 2.05 2.11 1.94 1.87 1.75 1.72 1.91 1.74 1.9

GA 2.41 2.28 2.51 2.39 2.44 1.91 2.08 1.92 2.10 2.3

IA 2.06 1.94 2.17 2.48 2.49 1.98 2.03 2.25 2.27 2.2ID 2.92 2.87 2.99 2.97 2.73 2.80 3.44 3.42 3.22 2.9IL 1.86 1.73 1.95 2.23 2.24 2.01 1.89 2.27 2.26 2.1IN 2.07 1.67 1.85 2.28 2.22 1.80 1.75 1.91 1.97 1.9KS 2.52 2.62 2.56 2.16 2.05 1.86 1.90 1.93 1.89 2.2KY 1.73 1.87 1.85 1.94 1.79 1.69 1.49 1.96 1.83 2.0LA 2.26 2.14 2.14 2.15 2.03 1.99 2.01 2.20 2.00 2.5

MA 2.11 2.42 2.53 2.23 1.88 2.11 1.81 1.85 2.13 1.8MANE_VU 2.11 2.25 2.30 2.32 1.97 1.94 1.78 1.88 2.08 1.8MD 2.27 1.96 1.80 2.06 2.11 1.66 1.58 1.79 2.16 2.0ME 2.36 2.60 2.47 2.64 1.88 2.13 1.94 1.89 1.98 1.7MI 1.96 1.83 2.19 2.42 2.29 2.07 2.16 2.00 2.14 2.0MN 2.49 2.56 2.86 2.62 2.50 2.16 1.87 2.00 2.18 2.2MO 2.00 2.11 2.23 2.02 1.95 1.83 1.82 2.05 1.87 2.0MS 2.14 2.29 2.37 2.21 2.40 2.06 2.23 2.25 2.18 2.6

3-12
MT 4.07 3.40 3.40 2.97 2.50 2.16 2.95 2.75 2.70 2.7

MW 2.03 1.90 2.15 2.32 2.25 2.00 1.94 2.05 2.13 2.0

NC 2.21 2.21 2.38 2.18 2.17 1.89 1.90 1.79 1.90 2.1ND 2.59 2.40 2.61 2.76 2.46 1.97 2.09 2.21 2.40 2.5NE 2.71 2.84 2.70 2.61 2.38 1.83 2.06 2.23 2.32 2.5NH 2.80 3.23 3.57 3.31 2.68 2.80 2.54 2.59 2.84 2.3NJ 1.82 2.02 1.94 2.13 1.86 1.65 1.51 1.74 2.16 1.8NM 2.79 2.21 2.61 2.86 2.64 2.67 2.58 2.41 2.57 2.4NMED4KM 3.02 2.67 3.24 3.32 3.08 3.05 3.17 2.77 2.91 2.7NV 2.95 2.68 3.13 3.39 3.37 3.92 4.29 3.92 4.20 3.5NY 1.96 2.22 2.28 2.29 1.84 1.88 1.75 1.92 2.02 1.8

OH 1.95 1.73 1.98 2.17 2.26 1.95 1.71 1.95 2.00 2.0OK 2.68 2.57 2.20 1.96 2.05 1.99 2.04 2.18 2.15 2.2OR 2.59 2.96 2.67 2.08 2.11 2.54 3.11 3.14 2.98 2.4

PA 1.94 1.83 1.92 2.20 2.00 1.76 1.67 1.81 1.96 1.7RI 1.85 1.96 1.95 1.88 1.91 1.84 1.75 1.85 1.82 1.7

SC 2.28 2.35 2.36 2.10 2.09 1.74 1.85 1.74 1.73 2.1SD 2.41 2.87 2.67 2.69 2.46 2.04 2.21 2.28 2.36 2.5

TN 1.80 2.11 2.02 2.07 2.09 1.77 1.68 2.03 2.13 2.1TX 2.28 2.38 2.24 2.06 1.93 1.68 2.04 2.02 1.93 2.2

UT 2.72 2.72 2.95 2.89 2.57 2.78 3.07 2.92 3.07 2.7

VA 2.14 1.97 2.17 2.20 2.11 1.89 1.82 1.85 2.18 2.0VISTAS 2.15 2.13 2.27 2.16 2.15 1.86 1.88 1.91 1.98 2.1VT 2.51 2.61 3.01 2.63 2.02 2.16 1.83 2.01 2.26 2.0

WA 2.20 2.75 2.25 1.94 1.97 2.03 2.40 2.65 2.37 1.8WI 2.34 2.40 2.54 2.41 2.23 2.01 1.94 1.96 2.09 2.0WRAP 2.86 2.67 2.73 2.71 2.57 2.62 2.87 2.79 2.80 2.6WV 2.11 1.92 2.21 2.30 2.35 1.98 1.87 1.94 2.22 2.0WY 3.55 3.59 3.20 3.19 2.40 2.53 3.04 2.92 3.00 2.7

3-13
Table 3-5: Temperature Error (K) by Month and by State and Region in the 12km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 2.83 2.56 2.70 2.69 2.47 2.52 2.77 2.64 2.81 2.7AZ 2.21 1.85 2.48 2.88 3.04 3.19 3.16 2.57 2.77 2.7

CA 2.73 2.20 2.38 2.43 2.33 2.35 2.56 2.56 2.73 2.7CO 3.26 3.06 3.29 3.17 2.93 2.80 3.11 2.69 2.93 2.9

ID 3.06 3.09 3.05 3.06 2.58 2.64 3.38 3.45 3.35 3.0NM 2.75 2.11 2.58 2.86 2.54 2.66 2.55 2.37 2.65 2.5NMED4KM 3.01 2.53 3.12 3.28 3.02 3.02 3.04 2.63 2.90 2.8NV 3.10 2.73 3.14 3.23 3.06 3.58 3.89 3.61 4.00 3.6UT 2.74 2.70 2.88 2.75 2.46 2.68 3.15 2.90 3.19 2.9

WY 3.30 3.57 2.81 2.75 2.17 2.36 2.86 2.92 2.90 2.7

Table 3-6 Temperature Error (K) by 

Month

for t he 4km Domain.

Region Jan '05 Feb '05 Mar '05 Apr 05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 3.09 2.59 3.03 3.26 3.32 3.51 3.71 3.25 3.55 3.50 

3-14
Table 3-7: Mixing Ratio Bias (g/kg) by Month and by State and Region in the 36km 

Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

AK 0.05 0.15 0.29 0.19 0.55 0.53 0.17 0.37 0.42 0.5AL 1.29 1.16 1.12 1.16 0.83 -0.07 0.03 -0.15 0.39 0.2ALL 0.39 0.39 0.35 0.44 0.05 0.06 -0.17 -0.24 0.19 0.1AR 0.86 0.76 0.41 0.59 0.17 0.00 -0.32 -0.59 -0.04 -0.2AZ -0.31 -0.34 -0.34 0.40 1.04 1.41 1.37 0.22 1.07 0.5

CA 0.02 -0.21 -0.40 -0.39 -0.32 -0.48 -0.34 -0.03 -0.48 -0.4CENRAP 0.36 0.36 0.15 0.19 -0.08 -0.09 -0.51 -0.55 0.06 -0.0CO 0.15 0.06 0.10 0.13 -0.33 -0.89 -0.25 -0.85 -0.34 -0.5CT 0.37 0.44 0.54 0.59 0.27 0.60 0.10 0.22 0.56 0.6

DC 0.98 0.86 0.81 0.45 0.16 0.13 -0.49 -0.35 0.24 0.5DE 0.43 0.53 0.55 0.48 0.22 -0.11 -0.49 -0.17 0.29 0.4FL 1.07 0.93 0.88 0.84 0.68 0.01 -0.10 0.19 0.55 0.3

GA 1.16 1.05 1.23 0.99 0.62 -0.25 -0.16 -0.33 0.21 0.0

IA 0.09 0.25 0.03 -0.16 -0.33 0.26 -0.69 -0.85 0.04 -0.0ID 0.23 0.11 0.47 -0.01 -0.60 -0.69 -0.38 -0.06 -0.46 -0.2IL 0.31 0.43 0.21 0.38 -0.08 0.51 -0.60 -1.08 -0.32 -0.0IN 0.00 0.50 0.44 0.65 -0.14 0.46 -0.15 -0.66 0.29 0.1KS 0.35 0.28 -0.16 -0.03 0.17 0.27 -0.19 0.11 0.34 0.1KY 0.46 0.70 0.46 1.10 0.23 0.48 0.37 0.08 0.44 0.1LA 0.96 0.96 0.85 0.72 0.43 0.41 0.14 -0.04 0.77 0.2

MA 0.19 0.24 0.35 0.64 0.30 0.73 0.48 0.80 0.96 0.8MANE VU 0.35 0.39 0.51 0.82 0.24 0.66 0.22 0.49 0.73 0.7MD 0.65 0.75 0.64 0.38 -0.17 0.09 -0.55 -0.41 0.44 0.5ME 0.11 0.15 0.39 0.92 0.56 1.22 0.70 0.62 0.85 0.7MI 0.12 0.20 0.45 1.04 0.10 0.71 0.22 0.04 0.45 0.4MN -0.07 0.02 0.02 0.67 -0.05 0.41 0.22 -0.17 0.32 0.1MO 0.62 0.66 0.15 0.22 0.27 0.64 0.06 -0.17 0.10 -0.0

3-15
MS 1.06 0.96 0.90 0.83 0.45 0.06 -0.14 -0.09 0.37 0.1

MT 0.29 0.34 0.45 0.03 -0.42 -0.90 -0.73 -0.10 -0.09 -0.0MW 0.14 0.31 0.38 0.76 0.04 0.73 0.00 -0.35 0.15 0.2

NC 1.12 0.89 1.10 1.20 0.83 0.13 0.10 -0.21 0.54 0.5ND -0.04 0.14 0.05 0.05 -0.35 -0.31 -0.16 0.06 0.45 0.1NE 0.23 0.29 0.01 0.05 0.08 0.36 0.22 0.05 0.69 0.1NH 0.13 0.16 0.32 0.96 0.26 1.08 0.56 0.56 0.73 0.7NJ 0.59 0.62 0.76 1.12 0.23 0.30 -0.10 0.44 0.77 0.7NM 0.08 -0.07 -0.17 0.05 -0.04 0.38 0.11 -1.02 0.34 -0.1NMED4KM 0.23 0.09 0.17 0.22 -0.08 -0.08 0.10 -0.76 0.21 -0.2NV 0.17 0.10 0.39 -0.03 -0.23 0.16 0.88 0.51 0.85 0.1NY 0.27 0.30 0.37 0.85 0.07 0.41 0.05 0.36 0.64 0.6

OH 0.13 0.52 0.50 0.66 -0.01 0.80 0.27 0.18 0.54 0.4OK 0.57 0.19 -0.24 -0.33 -0.67 -1.34 -2.22 -1.19 -0.68 -0.6OR 0.20 0.39 0.25 -0.10 -0.47 -0.48 0.27 0.34 0.08 0.0

PA 0.58 0.63 0.75 0.96 0.30 0.74 0.17 0.66 0.77 0.6RI 0.25 0.30 0.46 0.52 0.41 1.04 1.00 0.90 0.82 0.8

SC 1.13 1.00 1.35 1.28 0.78 0.15 0.12 -0.24 0.63 0.3SD 0.12 0.22 0.03 -0.01 -0.38 -0.08 0.01 -0.18 0.29 0.0

TN 0.85 0.84 0.64 0.91 0.29 0.12 -0.04 -0.37 0.32 0.2TX 0.49 0.50 0.36 0.04 -0.13 -0.61 -0.96 -0.97 -0.20 -0.2

UT 0.21 0.18 0.37 -0.05 -0.62 -0.54 0.27 -0.19 -0.02 -0.3

VA 0.71 0.70 0.62 0.52 -0.17 -0.51 -0.86 -1.12 -0.04 0.2VISTAS 1.01 0.91 0.95 0.96 0.53 -0.03 -0.14 -0.27 0.39 0.3VT 0.11 0.17 0.30 0.88 0.19 0.96 0.58 0.60 0.70 0.7

WA 0.16 0.19 0.33 0.04 -0.31 -0.52 -0.11 -0.03 -0.10 0.0WI 0.03 0.11 0.39 0.97 0.20 1.07 0.30 -0.19 0.01 0.1WRAP 0.10 0.04 0.01 -0.05 -0.26 -0.37 -0.07 -0.15 -0.06 -0.1WV 0.77 0.80 0.75 1.19 0.44 0.31 -0.08 -0.17 0.64 0.5WY 0.31 0.25 0.16 0.09 -0.74 -1.33 -0.42 -0.25 -0.07 -0.3

3-16
Table 3-8: Mixing Ratio Bias (g/kg) by Month and by State and Region in the 12km 

Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 0.11 0.02 0.01 0.01 -0.07 -0.10 0.29 0.03 0.13 -0.1AZ -0.24 -0.16 -0.13 0.54 1.17 1.55 1.75 0.32 1.07 0.7

CA 0.02 -0.19 -0.29 -0.35 -0.20 -0.36 -0.03 0.23 -0.26 -0.3CO 0.15 0.08 0.10 0.23 -0.12 -0.46 0.24 -0.57 -0.10 -0.3

ID 0.25 -0.01 0.49 0.20 -0.60 -0.47 -0.22 0.29 -0.32 -0.1NM 0.10 -0.08 -0.07 0.22 0.22 0.60 0.60 -0.81 0.43 -0.0NMED4KM 0.21 0.08 0.20 0.34 0.17 0.27 0.69 -0.42 0.41 -0.0NV 0.07 -0.06 0.34 0.12 -0.09 0.45 0.87 0.69 0.96 0.2UT 0.10 0.04 0.37 0.11 -0.35 -0.13 0.68 0.25 0.22 -0.0

WY 0.23 0.22 0.19 0.13 -0.59 -0.89 -0.10 0.00 0.11 -0.2

Table 3-9 Mixing Ratio Bias (g/kg) 

by

Month for

the 4km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '0 Aug '05 Sep '05 Oct '05 

ALL -0.10 -0.29 -0.28 -0.20 0.05 0.52 1 37 0.33 1.08 0.00 

3-17
Table 3-10: Mixing Ratio Error (g/kg) by Month and 

b

Stat and Region 

i

the 36km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul ' 05 Aug '05 Sep '05 Oct '05 

AK 0.45 0.39 0.46 0.54 1.05 0.98 0.60 0.70 0.64 0.6AL 1.35 1.26 1.27 1.36 1.39 1.45 1.59 1.60 1.37 0.9ALL 0.71 0.74 0.81 1.11 1.16 1.49 1.59 1.52 1.33 1.0AR 0.97 1.02 0.91 1.16 1.28 1.53 1.81 1.86 1.43 0.9AZ 0.95 0.94 0.94 0.88 1.43 1.83 2.54 2.00 2.06 1.2

CA 0.79 0.85 1.07 1.03 1.01 1.12 1.44 1.48 1.41 1.CENRAP 0.67 0.77 0.76 1.15 1.33 1.70 1.84 1.69 1.43 1.0CO 0.55 0.59 0.59 0.83 1.05 1.53 1.53 1.53 1.24 1.0CT 0.46 0.54 0.61 0.84 0.87 1.14 1.24 1.14 1.12 0.8

DC 1.03 0.90 0.88 1.01 0.99 1.36 1.50 1.34 1.10 0.9DE 0.54 0.62 0.62 0.83 0.87 1.06 1.26 1.17 1.10 0.7FL 1.38 1.30 1.34 1.28 1.49 1.41 1.52 1.44 1.55 1.2

GA 1.31 1.23 1.41 1.34 1.32 1.57 1.72 1.56 1.61 1.

IA 0.35 0.57 0.65 1.16 1.37 1.84 1.90 1.91 1.35 0.8ID 0.55 0.52 0.81 0.79 1.16 1.28 1.31 1.34 1.02 0.8IL 0.55 0.58 0.61 1.11 1.12 1.52 1.70 1.82 1.38 0.9IN 0.58 0.61 0.66 1.20 1.04 1.44 1.35 1.51 1.21 0.9KS 0.60 0.71 0.62 0.99 1.32 1.51 1.48 1.41 1.25 0.9KY 0.80 0.81 0.82 1.44 1.11 1.45 1.46 1.51 1.21 0.9LA 1.23 1.23 1.23 1.23 1.42 1.63 1.68 1.54 1.60 1.0

MA 0.42 0.41 0.49 0.89 0.78 1.28 1.21 1.34 1.26 0.9MANE_VU 0.50 0.52 0.61 1.03 0.88 1.32 1.29 1.29 1.23 0.9MD 0.79 0.83 0.79 0.98 1.03 1.42 1.47 1.52 1.44 1.0ME 0.31 0.33 0.48 1.03 0.84 1.53 1.27 1.18 1.17 0.8MI 0.35 0.35 0.55 1.20 0.79 1.38 1.29 1.18 1.09 0.8MN 0.23 0.36 0.42 1.23 1.07 1.52 1.47 1.28 1.27 0.8MO 0.73 0.80 0.64 1.07 1.24 1.57 1.48 1.59 1.29 0.9MS 1.22 1.18 1.20 1.24 1.33 1.54 1.72 1.64 1.55 1.0

3-18
MT 0.47 0.51 0.73 0.75 0.98 1.48 1.36 1.22 0.92 0.8

MW 0.43 0.46 0.58 1.16 0.94 1.48 1.44 1.41 1.21 0.8

NC 1.19 0.99 1.23 1.53 1.37 1.56 1.69 1.57 1.41 1.ND 0.25 0.36 0.45 0.86 1.03 1.28 1.39 1.20 1.13 0.8NE 0.49 0.59 0.56 1.02 1.22 1.47 1.66 1.60 1.32 0.8NH 0.37 0.38 0.48 1.12 0.94 1.61 1.29 1.26 1.22 0.8NJ 0.65 0.71 0.79 1.22 0.85 1.15 1.19 1.21 1.32 1.0NM 0.77 0.78 0.80 0.79 1.15 1.57 1.78 1.84 1.65 1.0NMED4KM 0.60 0.59 0.66 0.80 1.00 1.45 1.56 1.52 1.41 0.9NV 0.65 0.61 0.97 0.70 1.22 1.10 1.73 1.46 1.25 0.8NY 0.42 0.43 0.51 1.01 0.84 1.19 1.30 1.24 1.17 0.9

OH 0.55 0.64 0.65 1.03 0.95 1.41 1.28 1.34 1.17 0.9OK 0.88 0.85 0.79 1.09 1.51 2.17 2.61 1.96 1.56 1.OR 0.66 0.68 0.80 0.77 0.95 0.90 1.16 1.16 0.91 0.9

PA 0.67 0.70 0.82 1.14 0.91 1.34 1.29 1.39 1.26 0.9RI 0.41 0.46 0.55 0.78 0.93 1.32 1.57 1.51 1.29 1.0

SC 1.21 1.10 1.43 1.46 1.29 1.36 1.43 1.41 1.35 1.SD 0.33 0.52 0.52 0.89 1.07 1.54 1.58 1.44 1.15 0.8

TN 0.96 0.96 0.93 1.33 1.09 1.33 1.61 1.68 1.30 0.9TX 0.94 1.05 1.05 1.17 1.51 1.80 2.06 1.90 1.63 1.2

UT 0.56 0.54 0.74 0.75 1.22 1.48 1.58 1.41 1.10 0.9

VA 0.88 0.83 0.84 1.20 1.25 1.64 1.86 1.87 1.43 1.0VISTAS 1.17 1.08 1.18 1.36 1.33 1.50 1.65 1.58 1.44 1.0VT 0.33 0.33 0.46 1.18 0.96 1.55 1.24 1.23 1.17 0.9

WA 0.56 0.56 0.72 0.72 0.79 0.89 1.02 1.04 0.89 0.8WI 0.27 0.30 0.51 1.26 0.90 1.64 1.44 1.22 1.19 0.8WRAP 0.64 0.67 0.80 0.85 1.05 1.31 1.47 1.42 1.25 1.0WV 0.85 0.88 0.84 1.48 1.09 1.43 1.53 1.52 1.13 0.9WY 0.54 0.51 0.55 0.75 1.14 1.74 1.41 1.31 1.02 0.8

3-19
Table 3-11 Mixing Ratio Error (g/kg) by Month and by State and Region in the 12km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 0.66 0.68 0.77 0.86 1.09 1.38 1.67 1.52 1.34 0.9AZ 0.88 0.79 0.95 0.89 1.46 1.88 2.69 1.84 2.02 1.3

CA 0.79 0.83 0.97 1.01 0.98 1.06 1.48 1.49 1.35 1.CO 0.51 0.58 0.56 0.80 0.95 1.39 1.57 1.46 1.19 0.8

ID 0.48 0.45 0.74 0.69 1.09 1.22 1.26 1.38 0.95 0.7NM 0.74 0.70 0.75 0.79 1.12 1.59 1.91 1.89 1.67 1.0NMED4KM 0.57 0.56 0.64 0.80 0.93 1.42 1.71 1.53 1.42 0.8NV 0.60 0.57 0.95 0.68 1.12 1.22 1.77 1.56 1.29 0.9UT 0.55 0.52 0.71 0.72 1.12 1.38 1.74 1.54 1.16 0.8

WY 0.46 0.51 0.55 0.73 1.01 1.52 1.39 1.28 1.03 0.7

Table 3-12 Mixing Rati

o

Error

(g/kg) by

r Month for 

the

4km

i Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May 05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 0.59 0.70 0.68 0.82 1.01 1.49 1.91 1.49 1.70 1.01 

3-20
Table 3-13: Wind Index of Agreement by Month and 

b

Stat and Region 

i

the 36km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul ' 05 Aug '05 Sep '05 Oct '05 

AK 0.55 0.59 0.55 0.60 0.50 0.47 0.53 0.52 0.52 0.4ALL 0.85 0.87 0.87 0.87 0.86 0.88 0.88 0.90 0.87 0.8AL 0.64 0.64 0.64 0.63 0.65 0.61 0.64 0.63 0.65 0.6AR 0.66 0.68 0.68 0.68 0.67 0.67 0.71 0.72 0.70 0.7AZ 0.72 0.67 0.72 0.72 0.73 0.71 0.68 0.75 0.73 0.7

CA 0.73 0.75 0.76 0.73 0.76 0.76 0.77 0.78 0.78 0.7CENRAP 0.81 0.83 0.84 0.86 0.82 0.85 0.85 0.87 0.83 0.8CO 0.79 0.75 0.76 0.79 0.78 0.77 0.78 0.78 0.76 0.7CT 0.55 0.55 0.54 0.56 0.52 0.56 0.57 0.57 0.52 0.5

DE 0.74 0.79 0.76 0.73 0.71 0.74 0.79 0.79 0.73 0.7FL 0.71 0.77 0.71 0.72 0.71 0.73 0.69 0.70 0.68 0.7

GA 0.61 0.58 0.61 0.62 0.58 0.57 0.53 0.59 0.56 0.5

IA 0.61 0.66 0.66 0.61 0.68 0.67 0.62 0.68 0.69 0.6ID 0.74 0.77 0.65 0.72 0.67 0.77 0.76 0.75 0.67 0.7IL 0.63 0.64 0.63 0.65 0.59 0.68 0.67 0.67 0.66 0.6IN 0.59 0.58 0.62 0.55 0.60 0.63 0.62 0.62 0.66 0.6KS 0.73 0.68 0.70 0.73 0.73 0.75 0.75 0.76 0.74 0.7KY 0.52 0.56 0.52 0.57 0.56 0.56 0.58 0.59 0.62 0.5LA 0.66 0.66 0.67 0.67 0.63 0.65 0.66 0.66 0.67 0.6

MA 0.61 0.69 0.58 0.62 0.68 0.60 0.59 0.65 0.57 0.6MANE VU 0.70 0.76 0.72 0.66 0.77 0.70 0.76 0.69 0.70 0.6MD 0.58 0.60 0.59 0.59 0.61 0.56 0.56 0.52 0.58 0.6ME 0.55 0.57 0.60 0.53 0.53 0.54 0.59 0.53 0.56 0.5MI 0.66 0.63 0.67 0.63 0.65 0.68 0.59 0.66 0.70 0.6MN 0.66 0.67 0.71 0.68 0.66 0.70 0.69 0.64 0.70 0.6MO 0.67 0.67 0.67 0.68 0.62 0.70 0.68 0.67 0.68 0.6MS 0.62 0.59 0.60 0.61 0.60 0.54 0.56 0.63 0.65 0.6MT 0.77 0.79 0.80 0.77 0.73 0.76 0.77 0.79 0.76 0.7

3-21
MW 0.70 0.72 0.70 0.72 0.67 0.75 0.72 0.79 0.74 0.7

NC 0.56 0.60 0.59 0.58 0.54 0.63 0.61 0.59 0.58 0.6ND 0.72 0.72 0.69 0.72 0.73 0.75 0.67 0.70 0.72 0.7NE 0.73 0.74 0.75 0.76 0.76 0.75 0.77 0.78 0.76 0.7NH 0.56 0.48 0.43 0.51 0.42 0.34 0.36 0.28 0.39 0.3NJ 0.56 0.58 0.54 0.61 0.59 0.52 0.52 0.54 0.53 0.6NMED4KM 0.79 0.77 0.77 0.79 0.79 0.78 0.78 0.79 0.78 0.7NM 0.77 0.79 0.77 0.77 0.78 0.78 0.78 0.77 0.78 0.7NV 0.72 0.74 0.77 0.72 0.74 0.71 0.74 0.74 0.74 0.7NY 0.71 0.71 0.73 0.69 0.75 0.72 0.74 0.69 0.71 0.6

OH 0.62 0.60 0.61 0.62 0.62 0.64 0.60 0.66 0.63 0.6OK 0.68 0.67 0.71 0.66 0.65 0.67 0.69 0.72 0.71 0.6OR 0.75 0.72 0.74 0.73 0.72 0.75 0.77 0.77 0.73 0.7

PA 0.62 0.64 0.62 0.64 0.65 0.65 0.66 0.68 0.66 0.6RI 0.68 0.68 0.67 0.67 0.69 0.71 0.71 0.76 0.72 0.7

SC 0.58 0.61 0.61 0.64 0.55 0.57 0.61 0.59 0.58 0.5SD 0.76 0.75 0.76 0.77 0.77 0.77 0.77 0.76 0.73 0.7

TN 0.57 0.57 0.61 0.60 0.61 0.55 0.66 0.64 0.60 0.6TX 0.73 0.76 0.77 0.76 0.79 0.76 0.75 0.77 0.81 0.7

UT 0.66 0.66 0.73 0.75 0.74 0.69 0.75 0.74 0.73 0.7

VA 0.58 0.61 0.63 0.61 0.65 0.62 0.65 0.65 0.58 0.6VISTAS 0.71 0.76 0.77 0.73 0.75 0.73 0.74 0.76 0.79 0.7VT 0.49 0.51 0.50 0.50 0.49 0.50 0.50 0.51 0.51 0.5

WA 0.71 0.78 0.74 0.80 0.72 0.72 0.79 0.76 0.80 0.7WI 0.61 0.62 0.60 0.59 0.64 0.62 0.63 0.61 0.65 0.5WRAP 0.85 0.84 0.86 0.85 0.86 0.85 0.84 0.87 0.86 0.8WV 0.53 0.49 0.55 0.52 0.54 0.55 0.57 0.58 0.58 0.5WY 0.77 0.72 0.76 0.75 0.69 0.74 0.76 0.76 0.71 0.7

3-22
Table 3-14: Wind Index of Agreement by Month and by 

St t

and Region in the in 12km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 0.87 0.86 0.88 0.87 0.85 0.87 0.89 0.85 0.88 0.8AZ 0.73 0.67 0.73 0.72 0.72 0.71 0.68 0.74 0.72 0.7

CA 0.77 0.79 0.78 0.76 0.79 0.80 0.80 0.81 0.81 0.8CO 0.83 0.82 0.81 0.83 0.81 0.81 0.80 0.81 0.79 0.7

ID 0.73 0.65 0.71 0.76 0.61 0.78 0.66 0.76 0.75 0.7NMED4KM 0.81 0.80 0.81 0.81 0.82 0.80 0.83 0.82 0.80 0.8NM 0.77 0.81 0.78 0.80 0.79 0.81 0.81 0.81 0.80 0.7NV 0.71 0.71 0.76 0.71 0.75 0.71 0.76 0.74 0.74 0.7UT 0.71 0.72 0.77 0.77 0.74 0.76 0.78 0.76 0.74 0.7

WY 0.78 0.79 0.79 0.78 0.78 0.79 0.80 0.78 0.75 0.7

Table 3-15 : Wind Inde of Agreement by Month in the 4km Domain.

Region Jan '05 Feb '05 Mar '05 Apr '05 May '05 Jun '05 Jul '05 Aug '05 Sep '05 Oct '05 

ALL 0.83 0.82 0.79 0.78 0.80 0.80 0.80 0.81 0.80 0.81 

3-23
Figure 3-1: Regional Planning Organization (RPO) Boundaries.

3-24
Figure 3-2: CPC Analyzed Precipitation for January 2005 over the 36km Domain.

Figure 3-3: MM5 Estimated Precipitation for January 2005 over the 36km Domain.

3-25
Figure 3-4: CPC Analyzed Precipitation for February 2005 over the 36km Domain.

Figure 3-5: MM5 Estimated Precipitation for February 2005 over the 36km Domain.

3-26
Figure 3-6: CPC Analyzed Precipitation for March 2005 over the 36km Domain.

Figure 3-7: MM5 Estimated Precipitation for March 2005 over the 36km Domain.

3-27
Figure 3-8: CPC Analyzed Precipitation for April 2005 over the 36km Domain.

Figure 3-9: MM5 Estimated Precipitation for April 2005 over the 36km Domain.

3-28
Figure 3-10: CPC Analyzed Precipitation for May 2005 over the 36km Domain.

Figure 3-11: MM5 Estimated Precipitation for May 2005 over the 36km Domain.

3-29
Figure 3-12: CPC Analyzed Precipitation for June 2005 over the 36km Domain.

Figure 3-13: MM5 Estimated Precipitation for June 2005 over the 36km Domain.

3-30
Figure 3-14: CPC Analyzed Precipitation for July 2005 over the 36km Domain.

Figure 3-15: MM5 Estimated Precipitation for July 2005 over the 36km Domain.

3-3
Figure 3-16: CPC Analyzed Precipitation for August 2005 over the 36km Domain.

Figure 3-17: MM5 Estimated Precipitation for August 2005 over the 36km Domain.

3-32
Figure 3-18: CPC Analyzed Precipitation for September 2005 over the 36km Domain.

Figure 3-19: MM5 Estimated Precipitation for September 2005 over the 36km Domain.

3-33
Figure 3-20: CPC Analyzed Precipitation for October 2005 over the 36km Domain.

Figure 3-21: MM5 Estimated Precipitation for October 2005 over the 36km Domain.

3-34
Figure 3-22: CPC Analyzed Precipitation for November 2005 over the 36km Domain.

Figure 3-23: MM5 Estimated Precipitation for November 2005 over the 36km Domain.
Figure 3-24: CPC Analyzed Precipitation for December 2005 over the 36km Domain.

Figure 3-25: MM5 Estimated Precipitation for December 2005 over the 36km Domain.

3-36
Figure 3-26: CPC Analyzed Precipitation for January 2005 over the 12km Domain.

Figure 3-27: MM5 Estimated Precipitation for January 2005 over the 12km Domain.

3-3 7
Figure 3-28: CPC Analyzed Precipitation for February 2005 over the 12km Domain.

Figure 3-29: MM5 Estimated Precipitation for February 2005 over the 12km Domain.
Figure 3-30: CPC Analyzed Precipitation for March 2005 over the 12km Domain.

Figure 3-31: MM5 Estimated Precipitation for March 2005 over the 12km Domain.

3-39
Figure 3-32: CPC Analyzed Precipitation for April 2005 over the 12km Domain.

Figure 3-33: MM5 Estimated Precipitation for April 2005 over the 12km Domain.

3-40
Figure 3-34: CPC Analyzed Precipitation for May 2005 over the 12km Domain.

Figure 3-35: MM5 Estimated Precipitation for May 2005 over the 12km Domain.

3-41
Figure 3-36: CPC Analyzed Precipitation for June 2005 over the 12km Domain.

Figure 3-37: MM5 Estimated Precipitation for June 2005 over the 12km Domain.

3-42
Figure 3-38: CPC Analyzed Precipitation for July 2005 over the 12km Domain.

Figure 3-39: MM5 Estimated Precipitation for July 2005 over the 12km Domain.

3-43
Figure 3-40: CPC Analyzed Precipitation for August 2005 over the 12km Domain.

Figure 3-41: MM5 Estimated Precipitation for August 2005 over the 12km Domain.

3-44
Figure 3-42: CPC Analyzed Precipitation for September 2005 over the 12km Domain.

Figure 3-43: MM5 Estimated Precipitation for September 2005 over the 12km Domain.

3-45
Figure 3-44: CPC Analyzed Precipitation for October 2005 over the 12km Domain.

Figure 3-45: MM5 Estimated Precipitation for October 2005 over the 12km Domain.

3-46
Figure 3-46: CPC Analyzed Precipitation for November 2005 over the 12km Domain.

Figure 3-47: MM5 Estimated Precipitation for November 2005 over the 12km Domain.

3-47
Figure 3-48: CPC Analyzed Precipitation for December 2005 over the 12km Domain.

Figure 3-49: MM5 Estimated Precipitation for December 2005 over the 12km Domain.

3-48
Figure 3-50: CPC Analyzed Precipitation for January 2005 over the 4km Domain.

Figure 3-51: MM5 Estimated Precipitation for January 2005 over the 4km Domain.

3-49
Figure 3-52: CPC Analyzed Precipitation for February 2005 over the 4km Domain.

Figure 3-53: MM5 Estimated Precipitation for February 2005 over the 4km Domain.

3-50
Figure 3-54: CPC Analyzed Precipitation for March 2005 over the 4km Domain.

Figure 3-55: MM5 Estimated Precipitation for March 2005 over the 4km Domain.

3-51
Figure 3-56: CPC Analyzed Precipitation for April 2005 over the 4km Domain.

Figure 3-57: MM5 Estimated Precipitation for April 2005 over the 4km Domain.

3-52
Figure 3-58: CPC Analyzed Precipitation for May 2005 over the 4km Domain.

Figure 3-59: MM5 Estimated Precipitation for May 2005 over the 4km Domain.

3-53
Figure 3-60: CPC Analyzed Precipitation for June 2005 over the 4km Domain.

Figure 3-61: MM5 Estimated Precipitation for June 2005 over the 4km Domain.

3-54
Figure 3-62: CPC Analyzed Precipitation for July 2005 over the 4km Domain.

Figure 3-63: MM5 Estimated Precipitation for July 2005 over the 4 km Domain.

3-55
Figure 3-64: CPC Analyzed Precipitation for August 2005 over the 4km Domain.

Figure 3-65: MM5 Estimated Precipitation for August 2005 over the 4km Domain.

3-56
Figure 3-66: CPC Analyzed Precipitation for September 2005 over the 4km Domain.

Figure 3-67: MM5 Estimated Precipitation for September 2005 over the 4km Domain.

3-57
Figure 3-68: CPC Analyzed Precipitation for October 2005 over the 4km Domain.

Figure 3-69: MM5 Estimated Precipitation for October 2005 over the 4km Domain.

3-58
Figure 3-70: CPC Analyzed Precipitation for November 2005 over the 4km Domain.

Figure 3-71: MM5 Estimated Precipitation for November 2005 over the 4km Domain.

3-59
Figure 3-72: CPC Analyzed Precipitation for December 2005 over the 4km Domain.

Figure 3-73: MM5 Estimated Precipitation for December 2005 over the 4km Domain.

3-60
4 Comparison with Other Annual MM5 Simulations

This section presents a comparison of this 36 km MM5 simulation with other 36km 

annual meteorological simulations that have been completed during the past several years 

by Alpine Geophysics and other researchers (Tables 4-1 through 4-5). This section also 

compares the performance of this 2005 simulation with two other years of MM5 

simulation for 2003 and 2004 for the same 36km, 12km, and 4km grid domains (Tables 

4-1 through 4-10).

4.1 Comparison to Other Annual 36km Simulations

Comparisons between the Alpine MM5 simulations and those of contemporaneous 

researchers were conducted. All of the Alpine MM5 simulations as well as those of the 

other researchers were performed at a 36km grid resolution using the same horizontal and 

vertical grid definitions as the 36km grid simulations presented in this report. The 

simulations compared include the 2001 EPA (McNally and Tesche, 2003), 2002 WRAP 

(Kemball-Cook, Jia, et. al., 2005), 2002 VISTAS (Olerud and Sims, 2004) and the 2003 

Midwest RPO (Baker and Johnson, 2005) studies. The current study will be referred 

herein as the NMED 2005 study because the MM5 application was performed under 

contract to the New Mexico Environment Department (NMED) (and funded by GIANT 

Refining) and was performed for the 2005 data set. The analysis of these simulations was 

performed using the TDL surface observation database subdivided by region (CENRAP, 

MANE_VU, MW, VISTAS, and WRAP) and the Alpine Geophysics, MAPS analysis 

package (McNally and Tesche, 1994).

Emery and co-workers (2001), have derived and proposed a set of daily performance 

"benchmarks" for typical meteorological model performance. These standards were 

based upon the evaluation of about 30 MM5 and RAMS meteorological simulations in 

support of air quality applications performed over several years and reported by Tesche et 

al. (2001). The purpose of these benchmarks was not to give a passing or failing grade to 

any one particular meteorological model application, but rather to put its results into the 

proper context of other models and meteorological data sets. The key to the benchmarks 

is to understand how good or poor the results are relative to other model applications run 

for various areas of the U.S. These benchmarks include bias and error in temperature and 

mixing ratio as well the Wind Speed Index of Agreement (IA) between the models and 

data bases. The benchmark for acceptability for each variable was:

• Temperature bias - +/- 0.5 K 

• Temperature error - 2.0 K 

• Mixing ratio bias - +/- 1.0 g/kg 

• Mixing ratio error - 2.0 g/kg 

• Wind Speed Index of Agreement - 0 = worst, 1 = best 

4- 1
Temperature bias for both the entire domain and for each RPO for the five studies and the 

three years of NMED data is presented in Table 4-1. This NMED 2005 MM5 application 

was just greater than the temperature bias benchmark of +/- 0.5 K with a 0.52 K average 

over all of the regions (ALL in Table 4-1). When comparing the NMED 2005 

performance to other study simulations, this NMED 2005 simulation slightly 

overestimated the temperature bias for the Western U.S., 0.13 K (see the WRAP column 

in Table 4-1), but within the benchmark which is important in this evaluation to 

determine viability of the data for use in the companion dispersion modeling that will 

ensue. This NMED 2005 simulation performed satisfactorily in comparison to other 

studies in other parts of the U.S., but was greater than the benchmark in three regions, 

namely, the CENRAP, MW, and VISTAS regions at 0.86, 0.58 and 0.75 K, respectively.

Temperature error is presented in Table 4-2. For this NMED 2005 application of MM5 

the temperature error was generally somewhat higher than the other annual simulation 

studies over each region but consistent within the three years of simulation produced 

within this NMED study. As with the other simulations the MM5 results for this analysis 

are somewhat greater than the benchmark of 2.0 K. Table 4-2 shows the temperature 

error for the NMED 2005 MM5 simulation was 2.28 K over ALL study areas. The 

2.74K for the WRAP RPO was comparable to other simulations. As with the other 

studies, the temperature error in this NMED 2005 study is rather consistent across all 

regions and varied the most in the WRAP region.

Mixing ratio bias is presented in Table 4-3. The domain-wide bias for this NMED 2005 

MM5 simulation was 0.17 g/kg (see the ALL category in Table 4-3) which is much less 

than the benchmark of +/- 1.0 g/kg. The NMED mixing ratio bias was comparable to 

overall performance of the other studies and other years in this NMED study. On a subregional basis this NMED 2005 simulation was comparable with the other simulations.

Table 4-4 presents the mixing ratio error comparisons between the five studies, the other 

two years of NMED simulation, and the five regions. As with the mixing ratio bias, the 

domain-wide and sub-regional values for the NMED 2005 simulations are well under the 

benchmark of 2.0 g/kg and thus, expected to be reasonable representations of the mixing 

ratios. The NMED 2005 MM5 simulations resulted in mixing ratio errors that were 

comparable with the other annual MM5 applications by other researchers and to the other 

NMED simulation years.

Wind Speed Index of Agreement (IA) is presented in Table 4-5. The domain-wide IA for 

the NMED 2005 simulations was 0.87 (shown under ALL in Table 4-5) which is higher 

than the minimally acceptable benchmark of 0.6 and close to the best performing IA 

statistic of 1.0. This was comparable to all other annual simulations. The NMED 2005 

simulation is comparable in performance to other studies over each of the sub-regions.

4- 2
4.2 Comparison to Other Annual 12km Simulations

No other consistent model evaluations for 12km scale grid domain simulations over this 

domain were available for comparison to those conducted in this study. The results of 

these 2005 annual simulations could, however, be compared to other years of simulation 

in this study (NMED) whereby the annual temperature, mixing ratio, and wind speed 

indices were compared to observations. This would give an indication of the 

representativeness of the data in terms of the benchmarks as well as between years. 

Tables 4-6 through 4-10 present the comparisons for the 12 km grid MM5 simulations.

Mixing ratio bias over the 12km simulation domain is presented in Table 4-6. The 

domain-wide bias for this NMED 2005 MM5 simulation was 0.03 g/kg (see the ALL 

category under Mean for NMED 2005 in Table 4-6) which is much less than the 

benchmark of +/- 1.0 g/kg. The mean mixing ratio bias was within the range of the 

benchmark for of four of the Four corners states for NMED 2005. For Arizona the 

mixing ratio bias was greater than 1.0 g/kg in May, June, July, and September with an 

acceptable overall annual average of 0.66 g/kg. A comparison to other years of MM5 

simulation data for NMED 2003 and NMED 2004 show similar results for the overall 

12km domain as well as each state.

Mixing ratio error over the 12km simulation domain is presented in Table 4-7. The 

domain-wide bias for this NMED 2005 MM5 simulation was 1.07 g/kg (see the ALL 

category under Mean for NMED 2005 in Table 4-7) which is less than the benchmark of 

2.0 g/kg. The mixing ratio error is well within the range of the benchmark for all of the 

Four Corners states. For Arizona the mixing ratio error is slightly greater than the 

benchmark for this NMED 2005 data set in July and September. All other months for 

Arizona are less than the benchmark as is the mean over all months. A comparison to 

other years of MM5 simulation data for NMED 2003 and NMED 2004 shows similar 

mixing ratio error results for the overall 12km domain as well as each state.

Temperature bias over both the entire 12km domain and for each State in the Four 

Corners region is presented in Table 4-8. This NMED 2005 MM5 simulation had a 

temperature bias on an annual average of 0.14 K (ALL and Mean in Table 4-8, well 

within the acceptability benchmark of +/- 0.5 K. Month-to-month variability of the 

temperature bias was within the benchmark with January and November being 

overestimated and June underestimated. Review of the state temperature bias indicated 

that the summer months were underestimated and the winter months overestimated for 

the four states. When comparing temperature bias for the NMED 2005 simulations to 

other years, the NMED 2005 simulations generally were closer to the temperatures than 

the other years. On a month-to-month comparison between the years of simulation, the 

NMED 2005 results were generally comparable.

Temperature error over the 12km domain is presented in Table 4-9. For this comparison 

of MM5 simulation the temperature error was similar for all three years of simulation 

across each month and for each state as well as the overall 12km domain. In all cases 

(except February in Arizona for the NMED 2005 MM5 simulation) the temperature error

4- 3
was greater than the benchmark of 2.0 K. The temperature error in this NMED 2005 

simulation is consistent across the months of simulation in each State.

Wind Speed Index of Agreement (IA) is presented in Table 4-10 over the 12km domain. 

The domain-wide IA for the NMED 2005 12km simulations was 0.87 (shown under ALL 

and Mean in Table 4-10 for the NMED 2005 data set) which compares favorably with the 

best score of 1.0. The IA was comparable across all states for the NMED 2005 and also 

comparable to the NMED 2003 and NMED 2004 data sets.

4.3 Comparison to Other Annual 4km Simulations

The 4km domain covered only portions of each of the Four Corners states. Thus, no state 

temperature, mixing ratio, or winds were available for an individual state. Rather 

comparisons were made for the overall 4km domain and are shown in Tables 4-11 

through 4-15.

Mixing ratio bias over the 4km simulation domain is presented in Table 4-11. The 

domain-wide bias for this NMED 2005 MM5 simulation was 0.24 g/kg (see NMED 2005 

in Table 4-11) which is much less than the benchmark of +/- 1.0 g/kg. The mixing ratio 

bias means are similar for the three years of simulation. On a monthly basis the mixing 

ratio bias is just greater than the benchmarks for NMED 2005 in July and September and 

for NMED 2003 in July. All other months for the individual years of simulation are less 

than the benchmark of +/- 1.0 g/kg.

Mixing ratio error over the 4km simulation domain is presented in Table 4-12 The 

domain-wide bias for this NMED 2005 MM5 simulation was 1.10 g/kg (see NMED 2005 

in Table 4-12) which is less than the benchmark of 2.0 g/kg. The mixing ratio bias is well 

within the range of the benchmark for all of the years of data and for each month.

Temperature bias over both the entire 4km domain is presented in Table 4-13. The 

NMED 2005 MM5 simulation had a temperature bias on an annual average of 1.07 K as 

shown in Table 4-13, greater than the guideline benchmark of+/- 0.5 K. Several months 

in the NMED 2005 data set had a temperature bias greater than 1.0 K. Comparison to 

NMED 2003 and NMED 2004 data show a similar month-to-month pattern of 

temperature bias.

Temperature error over the 4km domain is presented in Table 4-12. For this NMED 2005 

application of MM5 the temperature error was similar over all the months of the 

simulation. Comparison of the means for the three years shows comparable results as 

does the month-to-month variation. In all cases the temperature error was greater than 

the benchmark of 2.0 K.

4- 4
Wind Speed Index of Agreement (IA) is presented in Table 4-15 over the 4km domain. 

The domain-wide IA mean for the NMED 2005 4km simulations was 0.80 as it also was 

for the NMED 2003 and NMED 2004 simulations which compares favorably with the 

best score of 1.0 and is higher than the acceptability benchmark of 0.6. The IA was 

comparable across all three years of data simulation for each month.

4- 5
Table 4-1: Temperature Bias (K) For 36km Annual MM5 Simulations.

ALL CENRAP MANE_VU MW VISTAS WRAP

EPA 2001 -0.51 -0.26 -0.40 -0.31 -0.25 -1.10

WRAP 2002 -0.12 0.14 -0.15 -0.11 0.05 -0.49

VISTAS 2002 -0.05 0.14 0.00 0.05 0.24 -0.55

MRPO 2003 -0.15 0.11 -0.17 -0.10 0.18 -0.67

NMED 2005 0.52 0.86 0.15 0.58 0.75 0.13

NMED 2004 0.49 0.79 0.27 0.55 0.73 0.07

NMED 2003 0.27 0.54 0.21 0.28 0.65 -0.26

Table 4-2: Temperature Error (K) for 36km Annual MM5 Simulations.

ALL CENRAP MANE_VU MW VISTAS WRAP

EPA 2001 2.04 1.77 1.85 1.63 1.92 2.70

WRAP 2002 2.10 1.85 1.80 1.74 1.93 2.79

VISTAS 2002 2.02 1.76 1.80 1.72 1.84 2.67

MRPO 2003 2.17 1.94 1.86 1.92 1.98 2.82

NMED 2005 2.28 2.20 2.05 2.05 2.10 2.74

NMED 2004 2.26 2.13 1.99 2.01 2.11 2.75

NMED 2003 2.23 2.07 1.97 1.97 2.06 2.73

Table 4-3: Mixing Ratio Bias (g/kg) for 36km Annual MM5 Simulations.

ALL CENRAP MANE_VU MW VISTAS WRAP

EPA 2001 -0.11 -0.24 -0.06 -0.22 0.06 -0.08

WRAP 2002 -0.09 -0.34 0.08 -0.11 0.20 -0.09

VISTAS 2002 0.01 -0.07 0.19 0.13 0.02 -0.04

MRPO 2003 0.22 0.11 0.30 0.29 0.49 0.05

NMED 2005 0.17 -0.02 0.54 0.24 0.47 -0.08

NMED 2004 0.07 -0.09 0.36 0.19 0.38 -0.20

NMED 2003 0.05 -0.18 0.35 0.17 0.35 -0.13

Table 4-4: Mixing Ratio Error (g/kg) for 

36k

Annual MM5 Simulations.

ALL CENRAP MANE VU MW VISTAS WRAP

EPA 2001 1.02 1.09 0.80 0.85 1.13 1.04

WRAP 2002 1.03 1.17 0.82 0.93 1.16 0.94

VISTAS 2002 0.94 0.98 0.78 0.82 1.13 0.90

MRPO 2003 0.96 0.98 0.78 0.82 1.14 0.97

NMED 2005 1.12 1.20 0.96 0.97 1.32 1.03

NMED 2004 1.05 1.11 0.89 0.85 1.29 0.99

NMED 2003 1.03 1.09 0.86 0.85 1.22 1.00

4- 6
Table 4-5: Wind Index of Agreement for 36km Annual MM5 Simulation.

ALL CENRAP MANE VU MW VISTAS WRAP

EPA 2001 0.88 0.85 0.69 0.75 0.77 0.86

WRAP 2002 0.93 0.92 0.81 0.84 0.84 0.92

VISTAS 2002 0.90 0.88 0.71 0.78 0.79 0.89

MRPO 2003 0.90 0.88 0.72 0.78 0.80 0.88

NMED 2005 0.87 0.84 0.71 0.73 0.75 0.86

NMED 2004 0.90 0.88 0.76 0.77 0.79 0.88

NMED 2003 0.90 0.88 0.76 0.77 0.79 0.88

4- 7
Table 4-6: Mixing Ratio Bias (g/kg) over the 12km MM5 Domain and Four-Corner States.

Region Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DeNMED 2003

ALL 0.09 -0.04 -0.01 0.08 -0.14 -0.18 0.08 -0.02 -0.34 -0.12 -0.19 AZ -0.12 -0.15 -0.26 0.26 0.53 0.57 0.54 0.26 0.18 0.48 -0.20 

CO 0.26 0.04 0.10 0.15 -0.09 -0.14 0.63 -0.05 -0.36 0.04 -0.15 

NM 0.48 0.29 0.26 0.41 0.57 0.85 0.96 0.34 0.12 0.39 0.10 UT 0.15 0.04 0.27 0.25 0.06 0.47 1.24 0.46 0.46 0.42 -0.06 NMED 2004

ALL -0.07 -0.08 0.03 -0.12 -0.12 -0.16 -0.23 -0.38 -0.33 -0.08 -0.15 -AZ -0.29 -0.14 -0.06 0.08 0.86 1.04 0.77 0.39 0.13 0.20 -0.21 -

CO -0.09 -0.09 0.26 -0.05 -0.42 -0.13 -0.54 -0.76 -0.60 -0.20 -0.10 

NM 0.03 0.08 0.20 -0.03 0.52 0.93 0.22 -0.09 -0.13 -0.06 -0.04 -UT 0.24 0.10 0.77 0.27 -0.05 0.48 0.84 0.50 0.47 0.26 -0.13 NMED 2005

ALL 0.11 0.02 0.01 0.01 -0.07 -0.10 0.29 0.03 0.13 -0.10 0.02 AZ -0.24 -0.16 -0.13 0.54 1.17 1.55 1.75 0.32 1.07 0.70 0.71 

CO 0.15 0.08 0.10 0.23 -0.12 -0.46 0.24 -0.57 -0.10 -0.37 -0.08 

NM 0.10 -0.08 -0.07 0.22 0.22 0.60 0.60 -0.81 0.43 -0.08 0.47 UT 0.10 0.04 0.37 0.11 -0.35 -0.13 0.68 0.25 0.22 -0.07 -0.14 

4- 8
Table 4-7: Mixing Ratio Error (g/kg) over the 12km MM5 Domain and Four-Corner States.

Region Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DeNMED 2003

ALL 0.68 0.63 0.75 0.79 1.07 1.30 1.73 1.53 1.31 1.15 0.76 AZ 0.74 0.76 0.84 0.75 1.17 1.38 1.99 2.04 1.73 1.44 0.80 

CO 0.55 0.47 0.61 0.85 1.14 1.23 1.73 1.46 1.05 0.79 0.56 

NM 0.72 0.66 0.73 0.86 1.19 1.59 1.94 1.51 1.34 1.18 0.85 UT 0.57 0.51 0.62 0.66 0.99 1.25 2.13 1.64 1.16 0.93 0.61 NMED 2004

ALL 0.63 0.61 0.97 0.92 1.08 1.36 1.61 1.45 1.31 0.94 0.72 AZ 0.74 0.64 1.00 0.91 1.19 1.49 1.81 1.80 1.59 1.02 0.76 

CO 0.46 0.48 0.77 0.78 1.05 1.27 1.64 1.47 1.15 0.79 0.56 

NM 0.53 0.61 0.86 0.95 1.30 1.75 1.79 1.53 1.34 1.10 0.66 UT 0.45 0.40 1.11 0.78 0.92 1.44 1.74 1.42 1.20 0.77 0.59 NMED 2005

ALL 0.66 0.68 0.77 0.86 1.09 1.38 1.67 1.52 1.34 0.99 0.85 AZ 0.88 0.79 0.95 0.89 1.46 1.88 2.69 1.84 2.02 1.30 1.00 

CO 0.51 0.58 0.56 0.80 0.95 1.39 1.57 1.46 1.19 0.88 0.62 

NM 0.74 0.70 0.75 0.79 1.12 1.59 1.91 1.89 1.67 1.01 0.68 UT 0.55 0.52 0.71 0.72 1.12 1.38 1.74 1.54 1.16 0.87 0.69 

4- 9
Table 4-8: Temperature Bias (K) over the 12km MM5 Domai in and Four-Corner States.

Region Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DeNMED 2003

ALL 0.66 -0.10 -0.68 -1.15 -0.71 -0.88 -0.91 -0.85 0.17 0.87 0.19 AZ 0.65 -0.12 -0.25 -1.49 -1.20 -1.59 -1.66 -1.36 -0.36 0.29 0.15 

CO 0.55 -0.68 -2.11 -2.20 -0.75 -0.59 -0.75 -0.63 0.33 0.80 -0.14 

NM 0.32 -0.13 -0.53 -1.06 -0.81 -1.05 -1.32 -0.96 -0.45 0.52 0.11 UT 0.91 -1.04 -1.78 -1.35 -0.61 -1.20 -1.27 -0.70 0.54 1.51 -0.02 NMED 2004

ALL 1.02 0.20 -0.08 -0.34 -0.66 -0.59 -0.27 -0.02 0.11 0.22 0.72 AZ 0.73 0.14 -0.03 -0.98 -1.63 -1.32 -0.87 -0.52 -0.78 -0.41 -0.07 

CO 1.47 -0.04 -1.04 -1.08 -0.17 0.00 0.25 0.22 0.07 -0.17 0.26 

NM 0.70 0.48 0.17 -0.03 -0.24 -0.68 -0.11 0.19 -0.09 0.02 0.34 UT 1.90 0.14 -0.72 -0.45 -0.53 -0.45 -0.44 -0.16 0.18 0.33 1.03 NMED 2005

ALL 0.94 0.41 -0.16 -0.42 -0.41 -0.53 -0.21 0.32 0.26 0.39 0.90 AZ 0.59 -0.30 -0.36 -0.97 -1.43 -1.70 -1.44 -0.48 -0.44 -0.52 0.77 

CO 0.68 -0.06 -1.42 -0.94 -0.30 -0.01 -0.14 0.45 0.38 0.32 0.46 

NM 0.64 0.08 -0.15 -0.06 -0.07 -0.40 -0.66 0.12 0.03 0.25 0.89 UT 0.76 0.34 -0.63 -0.97 -0.25 -0.32 0.07 0.45 0.87 1.00 1.40 

4-10
Table 4-9: Temperature Error (K) over the 12km MM5 Domain and Four-Corner States.

Region Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DeNMED 2003

ALL 2.98 2.48 2.68 2.67 2.52 2.57 2.84 2.64 2.84 3.27 2.62 AZ 3.00 2.20 2.39 2.71 2.75 2.75 2.83 2.77 2.88 3.06 2.62 

CO 3.69 2.79 3.50 3.64 2.82 2.73 3.23 2.62 2.86 3.50 2.94 

NM 3.14 2.40 2.70 2.94 2.76 2.73 2.70 2.46 2.49 2.89 2.78 UT 2.93 2.46 2.87 2.85 2.81 3.06 3.52 2.85 3.31 3.62 2.17 NMED 2004

ALL 2.99 2.52 2.93 2.57 2.69 2.62 2.61 2.60 2.74 2.55 2.61 AZ 2.36 2.26 2.64 2.43 2.98 2.79 2.59 2.56 2.61 2.53 2.24 

CO 3.71 2.92 3.33 2.91 3.04 2.88 2.85 2.78 2.75 2.82 2.73 

NM 2.74 2.51 2.52 2.24 2.79 2.78 2.62 2.38 2.46 2.51 2.36 UT 3.67 2.58 3.42 2.42 2.78 3.02 3.05 2.95 3.04 2.55 2.53 NMED 2005

ALL 2.83 2.56 2.70 2.69 2.47 2.52 2.77 2.64 2.81 2.78 3.12 AZ 2.21 1.85 2.48 2.88 3.04 3.19 3.16 2.57 2.77 2.77 3.08 

CO 3.26 3.06 3.29 3.17 2.93 2.80 3.11 2.69 2.93 2.90 3.32 

NM 2.75 2.11 2.58 2.86 2.54 2.66 2.55 2.37 2.65 2.55 3.30 UT 2.74 2.70 2.88 2.75 2.46 2.68 3.15 2.90 3.19 2.96 3.11 

4-11
Table 4-10: Wind Index of Agreement over 

th

; 12km MM5 Domain and Four-Corner States.

Region Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DeNMED 2003

ALL 0.88 0.89 0.88 0.89 0.90 0.90 0.90 0.90 0.89 0.89 0.88 AZ 0.73 0.77 0.77 0.78 0.74 0.76 0.74 0.72 0.73 0.74 0.75 

CO 0.84 0.83 0.83 0.80 0.83 0.84 0.85 0.84 0.85 0.87 0.84 

NM 0.86 0.83 0.83 0.80 0.80 0.81 0.80 0.80 0.84 0.84 0.82 UT 0.79 0.76 0.77 0.80 0.79 0.81 0.78 0.80 0.81 0.81 0.78 NMED 2004

ALL 0.88 0.89 0.89 0.9 0.9 0.91 0.89 0.89 0.89 0.88 0.89 AZ 0.75 0.77 0.76 0.77 0.76 0.72 0.71 0.7 0.73 0.75 0.75 

CO 0.84 0.84 0.82 0.8 0.84 0.85 0.83 0.82 0.82 0.82 0.84 

NM 0.85 0.83 0.85 0.82 0.82 0.82 0.8 0.8 0.82 0.82 0.86 UT 0.72 0.78 0.78 0.81 0.81 0.79 0.77 0.79 0.79 0.79 0.78 NMED 2005

ALL 0.87 0.86 0.88 0.87 0.85 0.87 0.89 0.85 0.88 0.87 0.88 AZ 0.73 0.67 0.73 0.72 0.72 0.71 0.68 0.74 0.72 0.73 0.74 

CO 0.83 0.82 0.81 0.83 0.81 0.81 0.80 0.81 0.79 0.79 0.79 

NM 0.77 0.81 0.78 0.80 0.79 0.81 0.81 0.81 0.80 0.79 0.78 UT 0.71 0.72 0.77 0.77 0.74 0.76 0.78 0.76 0.74 0.76 0.78 

4-12
Table 4-11: Mixing Ratio Bias (g/kg) over 4km MM5 Domain.

Simulation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov De

NMED 2003 0 .0 -0.09 -0. 20 0 .0 0.43 0.51 1.24 0 .76 0.32 0.49 -0.21 NMED 2004 -0 .1 -0.12 -0. 18 -0 .2 -0.07 0.72 0.62 0 .15 0.13 -0.05 -0.25 NMED 2005 -0 .1 -0.29 -0. 28 -0 .2 0.05 0.52 1.37 0 .33 1.08 0.00 0.14 

Table 4-12: Mixin Ratio Error (g/kg) ove

r

the

4km MM5 Domain.

Simulation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov De

NMED 2003 0 .5 0.55 0.67 0 .7 1.18 1.37 1.88 1 .56 1.18 1.05 0 72 NMED 2004 0 .5 0.55 0.78 0 .8 0.99 1.50 1.73 1 .48 1.24 0.84 0 66 NMED 2005 0 .5 0.70 0.68 0 .8 1.01 1.49 1.91 1 .49 1.70 1.01 0 69 

Table 4-13: Temperature Bias (K) over the 4km MM5 Domain.

Simulation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov De

NMED 2003 0 .9 0.73 -0. 24 -0.48 0.58 0.49 -0.11 0 .27 1.49 2.06 1 27 NMED 2004 1 .8 0.89 0. 50 0.83 1.26 0.87 0.97 1 .31 1.26 1.39 1 40 NMED 2005 1 .4 0.95 -0. 03 0.72 1.19 0.94 0.58 1 .22 1.27 1.54 1 91 

Table 4-14: Temperature Error (K) over the 4km MM5 Domain.

Simulation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov De

NMED 2003 3.82 2.70 3.23 3.50 3.36 3.36 3.70 3.06 3.69 4.20 3.22 NMED 2004 3.58 2.80 3.37 3.01 3.54 3.51 3.28 3.23 3.38 3.13 2.87 NMED 2005 3.09 2.59 3.03 3.26 3.32 3.51 3.71 3.25 3.55 3.50 3.98 

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Table 4-15: Wind Index of Agreement over the 4km MM5 Domain.

Simulation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov De

NMED 2003 0 .8 0.81 0. 79 0 .7 0.79 0.80 0.80 0 .80 0.82 0.82 0 79 NMED 2004 0 .8 0.82 0. 80 0 .7 0.80 0.81 0.81 0 .77 0.75 0.79 0 81 NMED 2005 0 .8 0.82 0. 79 0 .7 0.80 0.80 0.80 0 .81 0.80 0.81 0 79 

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