### 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 summa rizing the impacts of emission mitigation

scenarios on the annual f ourth highest 8-hour averag e ozone levels had been

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

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

This error has been corrected in this revised ve rsion of the report. Fo r 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 ve rsion. Several minor typographi cal errors have also been

corrected.

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# i 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 Wester n U.S. Fire Emissions Inventory

Appendix F : Meteorological Modeling

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# ii 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 SO 2 

# 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 SO 2 

# 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 unde r mitigation scenario #1................................................ 2-11

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

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

Table 2-11. Oil and gas area source emi ssions (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 sour ce emissions (tons/year) under the 2018

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

Table 2-14. Oil and gas area and poin t 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 us ed in the four corn ers 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 sour ce groups used in 2018 base case ozone

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

Table 4-2. Average ozone source cont ributions 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 su mmary statistics for 2018 base case at

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

Table 4-4. Summary statistics for change s 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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# iii Table 4-5. Daily average visibility su mmary statistics for 2018 base case at

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

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

Weminuche under each mitigation s cenario (changes greater than

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

FIGURES

Figure ES-1 CAMx 36/12/4 km modeling dom ain 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 so urce gridded emissions in the 4 km

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

Figure 2-2. Mobile source gridded emi ssions 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 s ites 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 erro rs for ozone monitoring sites in the 4

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

Figure 3-5. Monthly mean fractional bi as 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 domai n. Bias and error statistics are

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

ppb. Historical EPA acceptance crit eria 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 pred iction for the months of April

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

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

Figure 3-9. Monthly fractional bias (top) and error (bottom) for PM 2.5 

# by site

relative to monthly-mean observa tions and to RPO performance

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

Figure 3-10. Monthly fractiona l bias (top) and error (bot tom) for sulfate by site

relative to monthly-mean observa tions and to RPO performance

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

Figure 3-11. Monthly fractiona l bias (top) and error (bot tom) for nitrate by site

relative to monthly-mean observa tions and to RPO performance

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

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

site relative to monthly-m ean observations and to RPO

performance goals and criteria........................................................................... 3-26

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# iv Figure 3-13. Monthly fracti onal bias (top) and error (bottom) for elemental

carbon by site relative to monthl y-mean observations and to RPO

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

Figure 3-14. Monthly fractiona l 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 fracti onal bias (top) and error (bottom) for soil by site

relative to monthly-mean observa tions 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 gr id 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 apportionme nt 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 contri butions from sources within the 4 km

modeling domain as calculated by OSAT at the Navajo Lake

monitoring location for all 8-hour pe riods 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 contri butions from sources within the 4 km

modeling domain as calculated by OSAT at the Mesa Verde

monitoring location for all 8-hour pe riods 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 greate r 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 greate r than 65 ppb at the Mesa Verde

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

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

the Mesa Verde monitoring location calculated with PSAT for

sulfate, nitrate, ammonium , elemental carbon and total PM 2.5 

# mass ................. 4-13

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

the Weminuche monitoring location calculated with PSAT for

sulfate, nitrate, ammonium , elemental carbon and total PM 2.5 

# mass ................. 4-14

Figure 4-12. Time series of predicte d 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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# v Figure 4-13. Time series of predicte d 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 predicte d 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 predicte d 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 predicte d 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 matc hed 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 mitigati on scenario and the 2018 base case (right

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

Figure 4-20. Annual fourth highest da ily 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 da ily 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 visi bility 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 visib ility 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 th e Four Corners Air Quality Task Force and

the Four Corners Modeling Group for their thought ful 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 de fining the emission mitigation strategies to be

analyzed, reviewing results and f acilitating the dissemination of results to and interactions with

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

significant assistance with prepar ation 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 Envi ronment 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 devel opment and model sensitivity evaluations was

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

Wood.G:\Four Corners Task Force - Modeli ng\FinalReport\Rev20090806\Glossary.doc 

# GLOSSARY

APCA Anthropogenic Precursor Culpability Assessment

BART Best Availabl e 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 u tility 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 Ke rnel 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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# ES-1 EXECUTIVE SUMMARY

In response to concerns regardi ng the air quality imp acts of growth in many types of sources,

especially oil and gas sources and power genera tion, on Class I and surro unding Class II areas in

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

the Colorado Department of Public Health an d 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 w ith this effort, the FCAQTF identifie d a need to model the air quality

impacts of potential alternative mitigation stra tegies being developed by various FCAQTF work

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

[PM]) and air quality related values (visibility an d 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 atmosphe ric modeling study designed to address these

issues. A Four Corners Modeling Group (FCMG) c onsisting of representa tives from NMED, BP

and other interested stakehol ders from the FCAQTF was formed to guide and monitor the

modeling study.

A high resolution (4 x 4 km horiz ontal 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 regiona l air quality planning tool whic h can be used to evaluate

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



# Development of emission invent ories needed for the modeling analysis was funded by BP North

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

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

the Four Corners region.

Emission Inventory Development

An updated emission inventory fo r 2005, and a projected inventor y 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 (resolutio n) as shown in Figure ES-1. I nventories 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 fo r 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 modeli ng 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), us e of measured hourly emissions for 2005 from

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

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

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

1 

### http://www.nmenv.state.nm.us/aqb/4C/Modeling.html

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# ES-2 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 an alysis are based on the most recently available

information at the time the analysis wa s conducted, including th e 2002 WRAP Phase II

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

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

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

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

the WRAP Phase III study include s 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 st udy. Emission estimates used in

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

part of the WRAP Phase III study. Overall, i nventories 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 ev aluate possible air qual ity 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), vi sibility, acid deposition, and ai r toxics impacts. CAMx has

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

and PM SIP modeling studies, as well as by seve ral Regional Planning Orga nizations (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 modeli ng domain comprised of a series of nested

grids with 36, 12, and 4 km grid spacing (resolu tion) 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 init ial 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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# ES-3 -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 evalua tion 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 Co rners region and for other remote rural sites in the western

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

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

modeling programs. Statistical performance me trics were compared to acceptance goals and

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

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

series plots, spatial maps of model pred ictions, “bugle” plots and other displays.

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# ES-4 Analyses of model performance for the 2005 ba se case showed reasonably good performance for

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

under prediction bias during the summe r. While predictions of elev ated 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 th e Four Corners area. Diagnostic analyses showed that these

predicted ozone peaks result from the long rang e 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 no t very sensitive to controls on local sources of

emissions. Efforts are currently underway to improve th e treatment of verti cal circulations over

elevated terrain in the model.

In terms of total PM 2.5 

# , 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 PM 2.5 

# concentrations do not exceed 10 μ g/m 3 

# on a monthly basis.

Model performance for sulfate PM showed good ag reement with observations, consistent with

the expected accurate representation of power plant SO 2 

# emissions provided by the EPA Acid

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

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

Although a consistent under predic tion bias was noted for elementa l and organic carbon PM, this

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

reductions in SO 2 

# and NOx emissions which are effec tively independent of organic and

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

source of the elemental and organi c 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 an alysis was conducted for the 2018

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

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

with the 2005 base case, these future year m odeling 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 futu re year air quality impacts for ozone and PM

concentrations and visibility.

Given that emissions changed very little overa ll between the 2005 and 20 18 base case, the ozone

results are nearly identical between these two ba se 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. Fu rther study is needed to better

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

contributions to ozone and PM im pacts in the Four Corners area.

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

the Four Corners area were examined in th is study. These scenar ios included a 70% NOx

reduction and a 16% SO 2 

# reduction from local (i.e., 4 km do main) EGU sources as well as an

approximate 50% VOC reduction a nd a 16% NOx reduction from lo cal oil and gas sources.

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# ES-5 EGU and oil & gas together account for 75% of 4 km domain NOx emissi ons (oil and gas alone

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

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

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

in local VOC emissions. These mitigation scenar ios 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 combinati on 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 c onditions but combining them with the oil and

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

reductions.

Predicted visibility impacts unde r the mitigation scenarios are gr eatest at Mesa Verde. Oil and

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

emissions reductions result in some days with vi sibility 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 relate d value impacts in the Four Corners region.

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

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

study could be used to examine the impacts of a dditional 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 a nd gas emissions estimates cu rrently being developed via

the WRAP Phase III study.

• Further investigation of the apparent undere stimation of elemental carbon emissions and

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

• Evaluate if updated modeling algorithms for secondary organic aeros ol currently under

development are able to proved more r ealistic prediction of organic carbon.

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

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

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

emissions. This could be done via applica tion of source apportionm ent technology with

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

• Incorporation of model improvements for setting stratosphe ric boundary condition inputs and

reducing vertical circulations over elevated terrain that ar e currently unde r development.

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

impacts for potential improvemen ts to emissions es timates and modeling methodologies. In

particular, evaluate the possibility of includ ing NOx formed by lightning in the analysis.

## June 2009

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# 1-1 1.0 INTRODUCTION

1.1 BACKGROUND

In response to concerns regardi ng the air quality imp acts of growth in many types of sources,

especially oil and gas sources and power genera tion, on Class I and surro unding Class II areas in

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

the Colorado Department of Public Health an d 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 w ith this effort, the FCAQTF identifie d a need to model the air quality

impacts of potential alternative mitigation stra tegies being developed by various FCAQTF work

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

[PM]) and air quality related values (visibility an d 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 atmosphe ric 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 sensitiv ity analyses. A Four

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

interested stakeholders from the FCAQTF was fo rmed 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 horizont al grid cell size), re gional scale air quality

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

the direction of the FCMG as de scribed above. Extensive deve lopment of air quality modeling

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

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

development projects and alterna tive emission reduction strategies. 1 

# Regional modeling of the

Four Corners region consiste d of the following tasks:

1. Development of a modeling prot ocol 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 a ny modeling work (See Appendix A);

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

PM modeling with suitable photochemical grid model (CAMx);

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

of growth of emission generating activiti es in the region between 2005 and 2018 and

current “on the books” emission control regulations,

4. Application of CAMx with th e 2005 base case inventory us ing 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

G:\Four Corners Task Force - Modeling\ FinalReport\Rev20090806\Sec_1_Intro.doc 

# 1-2 5. Application of CAMx to a 2018 base case em issions 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 oz one 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 apportionme nt 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 me soscale meteorological model as part of an

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

study are summarized in Secti on 5 along with conclusions a nd recommendations for further

analysis.

## June 2009

G:\Four Corners Task Force - Modeling\ FinalReport\Rev20090806\Sec_1_Intro.doc 

# 1-3 -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

G:\Four Corners Task Force - Modeling\Final Report\Rev20090806\Sec_2_EmisInventory_20090806.doc 

# 2-1 2.0 EMISSIONS INVENTORY

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

photochemical grid model was de veloped 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 mode l predictions with observed air contaminant

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

Western Regional Air Pa rtnership (WRAP).

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

1. A future year base case invent ory that reflects both increa ses and decreases in emissions

projected to occur in the region within the next decade. Given the need to model longterm control strategies and the availab ility 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 invent ories are identical to the 2018 base case

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

expected impact of each mitigation scenari o. The mitigation scenarios are defined in

Section 2.3.

2.1 DATA SOURCES AND MODEL-REA DY 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 inventorie s contain estimates of anthropogenic PM, SOx,

NOx, VOC, CO, NH 3 

# and windblown dust emissions as well as biogenic VOC and NOx

emissions and fire emissions (wildfires and pres cribed burns). Primary emissions data sources

used in developing the inventories included:

WRAP Regional Inventory Development and Modeling : WRAP funded

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

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

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

model-ready inventory for 2018 which include s 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 fo r the western U.S. under contract to

WRAP. ENVIRON recently completed upda ting 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 th e Northern San Juan Coal

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

Southern Ute 2005 Oil & Gas Emissions : An updated emissi ons 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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# 2-2 At the time of this study, the inventories listed above represented the most accurate estimate of

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

Inventory did not consider VOC emissions associ ated with production fac ilities. However, as

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

the region which was used in this analysis. It should al so 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 overlappe d by the 12 km modeling domain but outside the 4

km domain were disaggregated to 12 km resolu tion 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 mode ling domain were also obtained via linear

interpolation between the WRAP 2018 (P RP18) 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 sour ce emissions on the portion of the 36 km grid

that is overlapped by the 12 km modeling do main 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 revise d 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 m odeling domain. Part of this effort included

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

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

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

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

is described in Appendix C. Re vised biogenic emissions estimate s 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, SO 2 

# , 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 pro cessed for use in this study over the 12 km western U.S. domain

(see Appendix E for details).

## June 2009

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# 2-3 2.2 EMISSION SUMMARIES: 2005 AND 2018 BASE CASES

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

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

through 2-8. In these tables, road dust and f ugitive dust emissions are in cluded within the area

source category whereas windblown dust was incl uded 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, trib al sources were distinguished fr om the state sources and hence

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

categories, tribal emissions were combined with state emissions. Point s ources associated with

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

tables below. Spatial distributions of annual em issions for oil and gas a nd other area sources are

shown in Figure 2-1. Spatial distributions of a nnual 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 SO 2 

## 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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# 2-4 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 A rea 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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# 2-5 Table 2-6. 2018 SO 2 

## 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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# 2-6 Figure 2-1. Oil and gas and area source gridded em issions in the 4 km modeling domain



# .

## June 2009

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

## June 2009

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# 2-8 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 CategoriesTPY 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 CategoriesTPY 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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# 2-9 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 CategoriesTPY 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 CategoriesTPY 2005

2018



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

for the 2005 and 2018 base case inventories.

## June 2009

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# 2-10 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 SO 2 

# 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 SO 2 

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

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

and the San Juan Generating Stati on (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 Plan t (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 Generati ng 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 (bas ed on estimate received from PNM).

SO 2 

# reductions were based on the presumptive BA RT limit of 0.15 lb/MMBtu at FCPP. Since

the San Juan Generating Station Consent Decree lim its 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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# 2-11 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 Ta ble 2-10. Note that the 41 tons/year NH 3 

# emissions

included in the 2018 base case represent emissi ons 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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# 2-12 2.3.2 Scenario #2

Mitigation scenario #2 is based on the applicati on 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 fo r San Juan and Rio Arriba counties in

northwestern New Mexico used for this study indi cated 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 pr ocess 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 well 1 

# 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 pe r year). Therefore,

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

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

29,500 tons/year 2 

# 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 f ugitives 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 achieve d 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 contro l for completions (it is unlikely all well

completions can be routed to flares due to lo w 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 emissi ons 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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# 2-13 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 und er the 2018 base case with scenario #3.

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# 2-14 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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# 2-15 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 fr om scenario #3. Table 2-14 compares emissions

under this scenario with emi ssions under the 2018 base case.

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# 2-16 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%

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# 3-1 3.0 AIR QUALITY MODELING

Modeling of regional ozone, PM an d 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 devel oped and maintained by ENVIRON International

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

regulatory agencies for 1-hour an d 8-hour ozone and PM SIP mode ling studies, as well as by the

Western Regional Air Partners hip and other Regiona l 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 annua l simulation of gridded high resolution 3-

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

the Pennsylvania State University/National Ce nter 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 mesoscal e atmospheric circulation. The model is

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

MM5 modeling system. MM5 was applied for th e 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 spaci ng, and the Four Corners Region (New Mexico,

Utah, Arizona, and Colorado) at a 4 km spacing (see Figure 1-1). Additional deta ils 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 m odel performance with respect to the model’s

ability to reproduce observed wi nd, 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 (2 003 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 appli cations, with generally good agreement in wind

and temperature fields. As in other MM5 appl ications, 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 predic ted 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 y ear (2018) inventories. Steps in the modeling

analysis are outlined below:

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# 3-2 Base Case Modeling : A modeling domain comp rised of a series of nested grids with 36, 12, and

4 km grid spacing (resolution) was defined as pr eviously 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 meteorologi cal data fields, deve loped as described

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

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

the national 36 km grid for the entire year; each quarter was r un 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 tw o-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 meas urements for ozone, speciated PM and total

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

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

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

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

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

displays of model performance were generated, including scatter pl ots, 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 describe d in Section 2. Results were compared with

the 2005 base case simulation.

Source Apportionment Modeling : A source apportionment modeli ng 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 esti mated 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 re sults of the model performance evaluation are

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

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

presented in Section 4.

3.2.1 2005 Base Case Modeling

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

Study are as follows:

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

MM5CAMx interface proce ssor (see Section 3);

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# 3-3

• Two-dimensional land use/land cover and topog raphy, as prepared for MM5, and generated

using the MM5CAMx interface processor;

• Two-dimensional low-level (surface layer) em issions and elevated po int source emissions

generated via the SMOKE emissi ons 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 de rived 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 sp ecified for the air quali ty model (CAMx may be

applied on Lambert Conformal, Polar Stereogra phic, or UTM Cartesian projections, or in

geodetic latitude/longitude).

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

input data are derived from the MM5 result s. 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 mappi ng 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 projec t 34 MM5 layers were aggreg ated to 19 CAMx layers

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

3. Applies diagnostic analysis tec hniques to derive key variable s required by CAMx that are

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

information).

Table 3-1. CAMx meteorological input data requirements.

CAMx Input Parameter Description

Layer interface height (m) 3-D grid ded 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 (m 2 

## /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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# 3-4 The MM5CAMx program has been written to carefu lly 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 a nd formats. Layer collapsing was employed to

reduce the number of vertical la yers 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 planetar y boundary layer (PBL) and above. The MM5CAMx

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

depending on the physics options set in MM5. Gi ven 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 modeli ng simulations. The O’ Brien method yields

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

method.

Developmental CAMx runs indicat ed a bias toward under predicti ons for most PM species; this

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

meteorological performance for precipitation an d boundary layer mixing. Early sequential tests

investigated sensitivity to precipitation and boun dary 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 sh owed that PM species concentrations were not

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

emission uncertainties, specifically related to under es timation 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 elimin ate 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 wa s 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 da ta into 12 sets of monthly-averaged

diurnally varying boundary conditions. This appr oach 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 indicat ed very high ozone concentrati ons 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 du ring this period rarely exceeded 65 ppb. This

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# 3-5 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 si gnificant effort to identify

the cause, it was found that the lateral boundary conditions extracted fr om 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 con centrations were being vertically transported

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

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

this problem, the 36 km ozone boundary conditi ons 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 drama tically 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 qua rter 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 da ys prior to the beginning of each quarter.

Ancillary Inputs

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

example, total integrated ozone column data for 2005 were obtai ned from the TOMS satellite

database 1 

# and processed as input into CAMx usin g the AHOMAP preprocessor. Ozone column

data were processed for each month of 2005, acco rding 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 pr epared using the NCAR TUV radi ative transfer pre-processor

according to the range of ozone column and surf ace 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 phot olytic reactions over a range of solar zenith angles, altitudes,

ozone column, surface UV albedo, and haze turbidit y. 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 syst ems (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, ISO RROPIA inorganic aerosol thermodynamics

1 

### http://jwocky.gsfc.nasa.gov/

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# 3-6 (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 addresse d 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 th e 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-wa y 12/4 km model simulations. The strategy

for performing the annual 36 and 12/4 km grid si mulations was to run CAMx separately for each

of four quarters of the y ear (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 specifi c 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 star t 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 m odel at beginning of all four 15-day sp inup periods. Prio r tests of CAMx

on the RPO grid suggest that at least two weeks ar e 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 us ed for each of the four

5-day spinups were extracted from the 36 km grid output to remain consiste nt with the manner in

which 12 km boundary conditions were generated.

As was done in several recent annual modeling studi es, we initially select ed two representative

monthly periods to perform diagnostic and sensi tivity testing with CAMx on the 12/4 km nested

grid system: a summer month characterized by high ozone and anthropogenic PM (e.g., SO 4 

# ) and

a winter month characterized by high NO 3 

# (note that EC and OC occur year round and are

heavily associated with natura l 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 diagnosti c and sensitivity tests designed to identify th e 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 th e model performance evaluation,

where the modeled estimates for the base year ar e 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 m odeling protocol (ENVIR ON, 2007) discussed a

general evaluation approach based upon the met hods, data, and analyses recommended in the

EPA modeling guidance (U.S. EPA, 2007). The protocol also delineated the specific analyses

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# 3-7 and products that were to be generated under th e Four Corners modeling program according to

schedule and available resources.

All mathematical models possess inherent limitati ons owing to the necessary simplifications and

approximations made in formulating the governi ng 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 rest s with the input fields that characterize

emissions, meteorology, and initial/boundary conditi ons. Key science limitations in the model

itself include the nitrate formation chemistry an d the secondary organic aerosol (SOA) module.

Preliminary modeling by the RPOs (e.g., WR AP, VISTAS and CENRAP) found both CAMx

and CMAQ nitrate performance suspect w ith winter overestimations and summer

underestimations (Morris et al., 2004, 2005). Wh ile 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 ha ve been addressed in an improved SOA module

now available in the version of CA Mx used in this analysis (ver sion 4.51); additional research in

this area is ongoing.

Overview and Context

The Four Corners modeling protocol laid out th e “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 availa ble resources, the existing aerometric databases,

and present technology constraint s. Hence, the protocol desc ribes 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 th e CAMx modeling system for the annual 2005 simulation was consistent with

EPA’s modeling guidance, which essentially calls for an operat ional evaluation of the model

focusing on a specific set of gas phas e and aerosol chemical species a nd a suite of stat istical metrics

for quantifying model response over the annual cycle. Em phasis was placed on assessing: (a) how

accurately the model predicts observed concentrat ions; and (b) how accurately the model predicts

responses of predicted air qu ality to changes in inputs. Over the past 20 year s, a substantial body of

information and analytical techniques has been developed to addr ess the first aspect. Unfortunately,

even today there are few rigorous methods available for quantifying the accuracy and precision of a

model’s predicted concentrat ion 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 appr oach, but just as importantly describes the

methodology by which to project base -year pollutant levels to targ et 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 ra tios of future-year to

current-year concentrations are called relative res ponse factors (RRFs). Si nce the model is used

to project future year PM 2.5 

# species components ra ther than total PM 2.5 

# mass, then the model

performance for each of the components is ac tually more important than for total PM 2.5 

# mass for

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# 3-8 which the standard was written. These PM 2.5 

# species components are:

• Sulfate (PSO 4 

# );

• Nitrate (PNO 3 

# );

• Ammonium (PNH 4 

# );

• Organic Carbon (OC);

• Elemental Carbon (EC); and

• Other Inorganic fine Par ticulate (FPRM and FCRS).

Therefore, the model testing concentrated on an operational evaluation of the model predictions

for those PM components listed above. We also ev aluated 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 photoch emistry is a necessary element of reliable

secondary PM predictions. This evaluation was ca rried out across the 4 km grid for the entire

year and also on a month-by-month to daily basi s 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. Unfortunatel y, 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 Mexi co, Colorado, Arizona, and Utah as well as in

surrounding states, these surface m easurements included ozone, NOx, SO 2 

# , total PM mass and

PM species components. Routine gas-phase co ncentration measurements for ozone, NOx and

CO are archived in EPA’s Air Quality Subsystem (AQS) database. Additional ozone

measurements obtained from a Forest Service mon itoring site at the Shamrock Mine site in La

Plata County, Colorado. Other sour ces of information were the various PM monitoring networks

including the: (a) Inte ragency Monitoring of Protected Vi sual Environments (IMPROVE), (b)

Clean Air Status and Trends Network (CASTNET), (c) EPA PM 2.5 

# and PM 10 

# Mass Networks

(EPA-FRM), (d) EPA Speciation Trends Networ k (STN); and (e) National Acid Deposition

Network (NADP). Typically, these networks pr ovide ozone, other gas phase precursors and

product species, PM, and visibility measurements . Figure 3-1 shows loca tions of the standard

monitoring network sites with in the 12 km modeling domain.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 

# 3-9 -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 recommende d statistical performance measures that were

used during this study to evaluate CAMx pe rformance (U.S. EPA, 1991, 2001, 2007). Typically,

the statistical metrics are calculated for all mon itoring sites across the full computational domain

for all simulation days. In this evaluation, we st ratified 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 measur es for the appropriate averaging times: 8 hourly

for ozone, and 24 hour for total PM 2.5 

# , sulfate, nitrate, EC, OC, a nd other aerosol species.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 

# 3-10 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 i i



# O O P P O O P P

1 1 2 2 2

1



# ) ( ) ( ) )( ( P i 

## = prediction at time/location i ;

O i 

## = observation at time/location i ;



# P = arithmetic average of P i 

## ;



# O = arithmetic average of O i



## Normalized Gross

Error ( NGE ) 

# ∑

= 

# − N

i i i i



# O O P

N

1



# 1 Reported as %

Normalized Bias

( NB ) 

# ( ) 

# ∑

= 

# − N

i i i i



# O O P

N

1 

# 1 Reported as %

Fractional Gross

Error ( FGE ) 

# ∑

= 

# + − N

i i i i i



# O P O P

N

1 

# 2 Reported as %

Fractional Bias

( FB ) 

# ∑

= 

# ⎟ ⎟

⎠ ⎞

⎜ ⎜

⎝ ⎛

+ − N

i i i i i



# O P O P

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 re search and debate (Morris et al., 2005). Here, performance



# goals refer to targets that we believe a good pe rforming PM model should achieve, whereas less

stringent performance 

# criteria represent a minimal level of m odel performance that a PM model

should achieve for use in regulatory modeling. Performance goals are necessary in order to

provide consistency in model a pplications and expectations acr oss 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 li kely 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 perf ormance and then rectifying them.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 

# 3-11 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 comp onents, difficulties arose because performance

statistics that divide by low c oncentration observations (such as nitrate, which is often zero)

become practically meaningless. In time, this has led to the in troduction 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 s hould 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 measurem ent 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 tec hnologies found differences of ~20 percent for

sulfate and ~50 percent for elem ental carbon (Morris et al., 2005).

As with ozone in the 1980s, actua l experience with PM models has led to the development of the

current performance expectations for these models. For example, PM 10 

# SIP model performance

goals of 30 percent an d 50 percent for normali zed gross error have b een used for southern

California (SCAQMD, 1997; 2003) and Phoeni x (MAG, 1997), respectively. Boylan and

Russell (2006) have proposed frac tional bias and error goals of ± 30 percent and 50 percent, and

fractional bias and error criteria of ± 60 percent and 75 percent, respec tively. Furthermore, they

proposed that these goals and criteria values vary as a function of concentration, such that below

2 μ g/m 3 

# , 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 in ter-comparing model perfor mance 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 primar ily using monitoring sites located within the

4 km modeling domain (Figure 3-2). Additional evaluation of ozone prediction performance was

conducted using data from CASTN ET sites located throughout th e intermountain western U.S.

within the 12 km domain (Figur e 3-3). Use of only CASTNET sites in the 12 km domain

avoided inclusion of data from urban sites whic h might bias the performance statistics as the

relatively coarse grid 12 km domain simulati on was not designed to re produce the finer scale

features of these distant urban locations. Both se ts of monitoring sites are listed in Table 3-4.

## June 2009

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# 3-12 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. Mont hly 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 c oncentrations, especially in winter months.

Results from two model runs are shown: “origina l” 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 earlie r). The performance

sensitivity from this change is substantial, especial ly in winter months and within the 4 km grid.

The winter months were most im pacted because stratospheric oz one levels (reaching well above

100 ppb) exist much lower in the atmosphere a nd 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 CM AQ 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 concentrat ion, 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 esta blished EPA acceptance criteria, most months

are within the ± 15 percent bias envelope and all months ar e 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 n ear a zero bias relative to the re st of the year. This indicates

that CAMx exhibits the largest under predicti ons for the highest ozone concentrations.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 

# 3-13 Figure 3-7 shows time series of hourly observed oz one 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) s how strong diurnal varia tions 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 al so does not capture the rather large diurnal

ozone variations at the Shamrock and Gothic rural si tes. In the latter case, Gothic is located in

the 12 km grid, so model resolution has an even gr eater 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 meteorologi cal 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 referen ce method (FRM) monitoring of total PM 2.5 

# and PM 10 

# 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 summar ize 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 re presents 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 pl otted to show how the fi eld of bias/error points

fall within these ranges. As no ted earlier, VISTAS proposed that these goals/crite ria vary as a

function of observed concentr ation, such that below 2 μ g/m 3 

# , they expand exponentially to 200

percent (the maximum of fractiona l bias and error) at zero observed concentrations. Hence these

goal/criteria lines take on a “ bugle” appearance at low concentra tions, giving more leeway for a

wider range of acceptable model performance.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 

# 3-14 Figures 3-9 through 3-15 presents monthly fractional error/bias performance for total and

speciated PM 2.5 

# concentrations in the form of bugle plots. Only sites within the 4 km grid are

evaluated. In terms of total PM 2.5 

# , 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 PM 2.5 

# concentrations do not exceed 10 μ g/m 3 

# 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 concentr ations, and CAMx shows a wide

range of over and under predictions but these are n early all at or within the performance criteria

limits. Ammonium is driven primarily by the sulf ate concentrations, and is replicated reasonably

well although with an ov erall under prediction bias. Elemen tal 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 w ith elemental carbon, its performance is entirely

related to inaccuracies in emissi ons characterization and dispersion. Of more significant concern

is the under prediction of organic car bon, a component that dominates the PM 2.5 

# mass budget.

Most months are outside the performance goals, primarily in late spring and early summer.

Under prediction of organic car bon was identified as a concern during initial model simulations

in which fire emissions were not included. Howeve r, adding wildfire emissions did not result in

a significant performance improvement as demonstr ated by the results in Fi gure 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 larg e fraction of the mass budget (on pa r with sulfate), and although it

exhibits an under prediction tendency, soil is generally well replic ated given the obvious

uncertainties in emission estimates.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 

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

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\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\Fi nalReport\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

Janu ar

y Febr ua

r y

M a rc

h Apri l May Ju ne July August September Oct obe r November Dece m

berError (%) Original Run

Final Run 

### Fractional Error

Sites in 4-km Grid

0 10 20 30 40 50 60 70

Janu ar

y Feb r uary

M a r ch Apri l M ay

J u ne

J ul

y Aug u st

Sep t ember Oct o ber

N o vember

Dec em

be

rError (%) Original Run

Final Run

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\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 60JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberBias (%) 

### Original Run

Final Run 

### Fractional Bias

Sites in 4-km Grid

-60 -50 -40 -30 -20 -10

0 10 20 30 40 50 60JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberBias (%)` 

### Original Run

Final Run

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\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 30JanFebMarAprMayJunJulAugSepOctNovDec(%) fc2005

+15%

-15%



## Normalized Error

0 10 20 30 40JanFebMarAprMayJunJulAugSepOctNovDec(%) fc2005

35%

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\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\Fi nalReport\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

G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 

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

.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\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 PM 2.5 

## by site relative to monthlymean observations and to RPO performance goals and criteria.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\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 8 10

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 8 10

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\Fi nalReport\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 8 10

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 8 10

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\Fi nalReport\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 8 10

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 8 10

Average Concentration (ug/m3)Fractional Error (%) Goal

Criteria

CASTNET

IMPROVE



## Figure 3-12. Monthly fractional bias (top) and error (botto m) for ammonium by site relative to

monthly-mean observations and to RPO performance goals and criteria.

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\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 8 10

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 8 10

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\Fi nalReport\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 8 10

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\Fi nalReport\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 8 10

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

G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\Sec_3_AQModeling_20090806.doc 

# 3-30 Conclusions Regarding the CAMx Model Performance Evaluation

As part of the modeling analysis, a compressi ve evaluation of the CAMx model was conducted

which compared model predictions to ambient ai r measurements. In terms of ozone, the model

performed adequately in replica ting rural levels of ozone throughout the year and over the 4 km

resolution grid that covered the Four Corners area . CAMx could not resolv e 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 obs erved 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 rem oved 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 asso ciated ammonium. Nitrate exhibited mixed

performance, but at such low concentrations its contribution to overa ll 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 (a lthough 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

G:\Four Corners Task Force - Modeling\Fina lReport\Rev20090806\Sec_4_(results)_20090806.doc 

# 4-1 4.0 RESULTS

In this section we summarize results of the CA Mx 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 describe d in Section 2 were used in this analysis.

Atmospheric chemistry resulting in ozone and second ary 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 ozon e and PM precursors can result in disproportionate changes in

ozone and secondary PM. Unde r 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 th e 2005 meteorological data as

described in Section 3. Given that emissions changed very l ittle overall between the 2005 and

2018 base case, the ozone results are nearly identical between th ese two base cases. Annual

fourth highest daily maximum 8-hour average ozone values predicte d 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 4 th 

# highest values shown here can vary from one grid cell to the next so

these comparisons are not matched in time. Most lo cations show either no change or a decline of

1 or 2 ppb in the 4 th 

# highest modeled daily maximum 8-hour ozone concentrations, with a few

localized increases.

## June 2009

G:\Four Corners Task Force - Modeling\Fina lReport\Rev20090806\Sec_4_(results)_20090806.doc 

# 4-2 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 appo rtionment modeling was

conducted on the 2018 base case s cenario to assist with unders tanding the cont ributions of

different source groups to ozone and PM impacts. These analys es were prepared using ozone

and PM source apportionment technology (OSAT and PSAT, re spectively). In OSAT/PSAT,

precursor emissions are stratified by source category a nd by source region; tr acers are used to tag

emissions from each category-regio n pair and track transport, che mical evolution, and removal.

In OSAT, NOx and VOC tracers are run to apporti on 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 tr ansport route. In

## June 2009

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# 4-3 PSAT, tracers can be run, separately or in co mbination, for a sulfur group (sulfur dioxide gas

[SO 2 

# ] and particulate sulfate [PSO 4]); a nitrogen gr oup (nitrogen oxide gas [NOx], nitrogen

oxide chemical products [ NOy], nitric acid gas [HNO 3 

# ], ammonia [NH 3 

# ], particulate nitrate

[PNO 3 

# ], 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 im portant to understand that the

apportionment of ozone a nd PM in this manner is defined by the particular emission scenario run

during the OSAT/PSAT simulation (i n this case the 2018 base case sc enario). Use of a different

emission scenario would result in a different set of relative s ource contributions as non-linear

chemistry changes the source apportionment matr ix. 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 ch anges in emissions for a certa in source type/location impact

chemistry and ultimately ozone and PM concentration patt erns. Thus, a source reduction

sensitivity test is not an attrib ution 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 diff erence between emission sensitivity and source

apportionment is the no n-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 type s (e.g., electric generating units)

and source regions (e.g., portion of Arizona contained within th e 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 s ources 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 di vided into two types:

combustion sources such as compressors, etc. wh ich emit primarily NOx along with some other

pollutants and non-combustion sources such as comp letions, etc. which result in VOC emissions.

Electric generating units (EGUs) were grouped se parately from other ( non oil and gas) point

sources. Because of the way emissions from fire s were modeled, the othe r point source category

includes fire emissions. The ot her area source group includes al l 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 gr oups defined in Table 4-1, OS AT and PSAT produce source

contribution estimates for initial conditions and for boundary conditions.

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

apportionment runs 1



## 4 km Modeling Domain

Source Category/Region Colorado New Mexico Arizona & Utah 12 km

Modeling

Domain 2



## Oil & gas combustion sources

(compressors, drill rigs,

heaters, all other engines) Area &

Point 3 

## Area Area & Point 4



## Oil & gas large po int 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 AreaAll 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 Ju ly are illustrated by the time seri es 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 stat e, the oil and gas gr oup includes all point and area oil and gas

related sources in the 4 km domai n 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 tota l “other point source category” fo r 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 sourcesJune 2009G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc4-5Figure 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 contributionsJuly01020304050607080901007/01 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 7/28 7/29 7/30 7/31Date

1-hour Ozone (ppb)12km sourcesbiogenicsother areamobilenonEGUsEGUsO+GBCsICs2018 OSAT Navajo Lake ozone contributionsJuly01020304050607080901007/01 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 7/28 7/29 7/30 7/31Date

1-hour Ozone (ppb)12km sourcesbiogenicsother areamobilenonEGUsEGUsO+GBCsICsJune 2009G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc4-62018 OSAT Mesa Verde ozone contributionsApril01020304050607080904/01 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 4/27 4/28 4/29 4/30Date

1-hour Ozone (ppb)12km sourcesbiogenicsother areamobilenonEGUsEGUsO+GBCsICsFigure 4-4.OSAT source apportionment results at Mesa Verde monitoring location in April.2018 OSAT Mesa Verde ozone contributionsJuly01020304050607080901007/01 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 7/28 7/29 7/30 7/31Date

1-hour Ozone (ppb)12km sources(ppb)biogenics(ppb)other area(ppb)mobile(ppb)nonEGUs(ppb)EGUs(ppb)O+G(ppb)BCs(ppb)ICs(ppb)Figure 4-5.OSAT source apportionment results at Mesa Verde monitoring location in July.

## June 2009

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# 4-7 As expected, boundary conditions (BCs), representi ng the contributions of ozone and precursors

transported into the Four Corners area from sources outside of th e western US (i.e., outside of

the 12 km modeling domain) ar e the single largest contri buting source group. These

contributions can be conceptualized as the “background” conditions to which emissions from

sources in the Four Corners region and other we stern US locations contribute additional ozone

and ozone precursor loadings. Du ring April, nearly all of the pr edicted ozone is attributed to

boundary conditions most of the time with occas ional small contributions from oil and gas

sources. As compared to July, the dominance of boundary condition influe nce in April reflects

the relatively higher April boundary conditions, rapid transp ort from the upwind boundary, and

limited amount of local ozone production due to cooler te mperatures and lower sun angles.

Boundary condition influence wanes in July w ith local sources becoming more important.

Different source groups are found to be the major contributor at different times, depending on

meteorological conditions (and hou rly 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 contributo rs on days with elevated ozone.

Average source contributions to all 8-hour running average peri ods with 8-hour average ozone

levels exceeding selected thre shold levels (50, 60, 65, 70 and 75 ppb) were calculated at key

monitoring site locations. An ex ample 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 sour ces within the 4 km

modeling domain are illustrated in Figure 4-6 which show s 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 w ith oil and gas combustion sources representing

the single biggest contribution. Corresponding results for Mesa Ve rde 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 sa me 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 domin ant contributors at Mesa Verde alth ough their total cont ribution 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 source s, contribute more at Mesa Verde during the

summer (July) but most of the predicted ozon e greater than 70 ppb oc curs during the spring

(April) at this location.June 2009G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc4-8Table 4-2.Average ozone source contributions calculated by OSAT at the Navajo Lake monitoring location for all running 8-hourperiods 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 ppbO3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_totalNew Mexico7.60 1.06 8.67 0.00 2.24 2.24 3.64 0.57 4.21 3.15 0.12 0.32 0.74 4.26 0.0023.71Colorado1.16 0.16 1.32 0.00 0.04 0.04 0.00 0.09 0.20 0.07 2.13 0.003.85AZ & UT0.03 0.01 0.04 0.00 0.00 0.00 0.00 0.09 0.18 0.02 0.68 0.001.0112km0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.45 0.00 0.00 0.00 4.918.36IC0.000.00BC38.3538.35Grand 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 0.83 7.07 4.91 75.28Oil & Gas combustion Oil & Gas non-combustion Oil & Gas NM large pointsRegion TotalsIC BCbiogenic 12km sourcesEGUsother points(incl. fires)mobile other 4km areaNavajo Lake hourly O3 contributions for 8-hr O3 average > 70 ppbO3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_totalNew Mexico7.05 1.34 8.39 0.00 3.02 3.02 2.55 0.62 3.17 1.96 0.16 0.43 0.50 3.54 0.0021.17Colorado0.64 0.13 0.77 0.00 0.03 0.03 0.00 0.06 0.20 0.06 1.24 0.002.36AZ & UT0.06 0.00 0.06 0.00 0.00 0.00 0.01 0.02 0.13 0.02 0.37 0.000.6112km0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.32 0.00 0.00 0.00 3.766.08IC0.050.05BC41.2141.21Grand 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 0.58 5.15 3.76 71.48IC BCbiogenic 12km sourcesEGUsother points(incl. fires)mobile other 4km areaOil & Gas combustion Oil & Gas non-combustion Oil & Gas NM large pointsRegion TotalsNavajo Lake hourly O3 contributions for 8-hr O3 average > 65 ppbO3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_total O3_NOx O3_VOC O3_totalNew Mexico5.15 1.08 6.22 0.00 2.39 2.39 1.93 0.57 2.50 1.57 0.13 0.30 0.42 2.83 0.0016.36Colorado0.54 0.13 0.66 0.00 0.02 0.02 0.00 0.06 0.17 0.06 1.07 0.002.04AZ & UT0.04 0.00 0.04 0.00 0.00 0.00 0.01 0.02 0.10 0.01 0.31 0.000.4912km0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.49 0.00 0.00 0.00 3.906.39IC0.040.04BC42.4142.41Grand 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 0.49 4.21 3.90 67.73Oil & Gas combustion Oil & Gas non-combustion Oil & Gas NM large pointsRegion TotalsIC BCbiogenic 12km sourcesEGUsother points(incl. fires)mobile other 4km area

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# 4-9 Figure 4-6. Average ozone source contributions from sour ces 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 bounda ry 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 sour ces 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 bounda ry 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 noncombustion Oil & Gas NM large

points EGUs Otherppb 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 noncombustion Oil & Gas NM large

points EGUs Otherppb New Mexico

Colorado

AZ & UT

## June 2009

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# 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 bi ogenic sources whenever it is formed from a combination of

biogenic VOC and anthropogenic NOx under VOC limited conditi ons or from biogenic NOx and

anthropogenic VOC under NOx limite d conditions. Sin ce biogenic emissions are not directly

controllable, however, apportioning ozone to biogenics in this ma nner does not give a directly

relevant picture of the potentia l importance of anthropogenic sour ce controls in reducing ozone.

An alternative approach, under which ozone formed from anthropogenic NOx (VOC) and

biogenic VOC (NOx) is always assigned to th e anthropogenic source, even under VOC (NOx)

limited conditions is available to address this situation. This methodology is known as

Anthropogenic Precursor Culpability Assess ment (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 polic y-relevant picture of the culpab ility of anthropogenic sources

in ozone formation. Additional information ab out OSAT and APCA ar e provided by ENVIRON

(2008).

CAMx was rerun for the 2018 base case using APCA for comparison w ith the previously

generated OSAT results. OSAT and APCA resu lts were compared for the summer season when

biogenic emissions are generally highest. A comparison of av erage source contributions to

ozone formation under OSAT and APCA in July is provided for th e 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 do main are included with all ot her 12 km domain sources in the

“12 km sources” group and CAMx tr eated this entire source group as anthropogenic even though

a significant portion of VOC emi ssions in the 12 km domain ar e biogenic. Under OSAT, the

biogenic source contributions ar e on par with those of the ma jor anthropogenic source groups.

Under APCA, however, the 4 km domain biogenic co ntribution is near zero, indicating that most

of the ozone apportioned under OS AT to biogenics is formed fr om a combination of biogenic

emissions (mostly VOCs) with anthropogenic emissions (mostly NOx from EGUs and oil and

gas combustion sources).

## June 2009

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# 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 40ppb 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 45ppb 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

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# 4-12 4.1.2.3 PM Source Apportionment

Apportionment of predicted PM to source groups listed in Tabl e 4-1 was calculated using the

CAMx PSAT probing tool desc ribed by ENVIRON (2008). PSAT was used to apportion each

PM species carried in the mo del to each of the 24 source groups plus initial and boundary

conditions. Although PSAT also tr acks organics, it was not to r un the organic group in PSAT

since this PM component is dom inated by biogenic VOC in the rural west and the core CAMx

PM chemistry already tracks organic aerosols by their anthropogenic and bi ogenic precursors.

For purposes of displaying the PSAT results , quarterly and annual average source

apportionments were calculated fo r four key PM species (sulfate, nitrate, ammonium, elemental

carbon) as well for total PM 2.5 

# mass (within which the separate anthropogenic and biogenic

contributions to PM 2.5 

# are displayed). These results are summarized in Figures 4-10 for Mesa

Verde and in Figure 4-11 for Weminuche. Ini tial/Boundary conditions and 12 km source

influences are greater at Weminuche which is lo cated further away than Mesa Verde from the 4

km domain sources. EGU and oil and gas combustion source s contribute in roughly equal

amounts to nitrate loadings at Me sa 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 sulfat e at Mesa Verde but their contri bution 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

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# 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 μ g/m 3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC Sulfate Four-Corner PSAT Analysis (4km), M esaVerde

0 0.1 0.2 0.3 0.4 0.5 0.6

Q1 Q2 Q3 Q4 ANN μ g/m 3

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 μ g/m 3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC

Ammonium Four-Corner PSAT Analysis (4km), M esaVerde

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

Q1 Q2 Q3 Q4 ANN μ g/m 3

O&G NonComb

O&G Comb

EGU

Other Pt (incl. fires)

Area&Mobile

Biogenics

12km

IC/BC Elemental Carbon

Four-Corner PSAT Analys is (4km), MesaVerde

0 0.5 1 1.5 2 2.5 3

Q1 Q2 Q3 Q4 ANN μ g/m 3

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 PM 2.5 

## mass.

## June 2009

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# 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 μ g/m 3

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 μ g/m 3

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 μ g/m 3

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 μ g/m 3

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 μ g/m 3

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 PM 2.5 

## mass.

## June 2009

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# 4-15 4.2 MITIGATION SCENARIO RESULTS

4.2.1 Mitigation Scenarios

A set of five mitigation scenarios were define d by the FCAQTF for analysis as described in

Section 2.3:

Scenario #1 : An overall 16% reduction in 4 km domain EGU SO 2 

# emissions to presumptive

BART limits and a 70% reductio n of NOx emissions from thes e sources via installation of

selective catalytic reduction (SCR ) at the Four Corners Power Plant and the San Juan Generating

Station. This scenario includ es a 305 tons/year in crease in ammonia emi ssions 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 orga nics (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 oi l and gas NOx emissions resulting from

penetration of NSPS compliant engines into half of the existing engine fleet.

Scenario #4 : EGU SO 2 

# and NOx reductions from scenario #1 plus oil and gas VOC and NOx

reductions from scenario #2 a nd 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 scenar io described

above were compared for two key months: April (when high oz one is predicted at higher

elevations in the San Ju ans and other mountain ranges) and July (when high ozone is predicted at

lower elevations). Time seri es of hourly predicted ozone under each scenario for these two

months/locations are provided in Figures 4-12 th ru 4-16. Ozone reductions under the mitigation

scenarios are generally less than 5 ppb with the excep tion of a few hours und er scenario #4 in

July at Navajo Lake. Some increases in ozon e 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 appo rtionment results presented in Section 4.1.2 which

showed that the higher ozone values observed duri ng the late spring are mo re heavily influenced

by precursor sources and ozone transport from outside of the 4 km dom ain as compared to

summer. The largest ozone reductions during peak ozone hours are pred icted for scenario #4

with hours greater than 65 ppb during July at Na vajo Lake reduced by as much as 8 ppb.

Ozone changes under each mitigation scenario rela tive 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 th e 4 km modeling domain during a typi cal summer afternoon high ozone

event (3:00 pm MST on 18 July which correspo nds 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 s howing the differences in values between the

## June 2009

G:\Four Corners Task Force - Modeling\Fina lReport\Rev20090806\Sec_4_(results)_20090806.doc 

# 4-16 mitigation scenarios and the base case (neg ative values indicate ozone reductions).

Corresponding results for scenario s #3, #4 and #5 are shown in Fi gure 4-19. Ozone reductions

under the oil and gas mitiga tion scenarios (#2, #3 and #5) are pr edicted to be largest in northern

Rio Arriba County. Power plant NOx reductions (included in sc enarios #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 redu ctions are predicted

under Scenario #4, with a maximum reducti on of nearly 9 ppb during this hour.June 2009G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc4-17Figure 4-12. Time series of predicted hourly ozone under the 2018 basecase and mitigation scenario #1 during April at Mesa Verde(top) and July at Navajo Lake (bottom).Navajo Lake-1001020304050607080 7/1 7/10 7/19 7/28Date

O 3 [ppb]baseMitigation Scenario #1Mitigation Scenario #1 - baseMesa Verde: April-10010203040506070804/1 4/10 4/19 4/28Date

1-H r O 3 [ppb]baseMitigation Scenario #1Mitigation Scenario #1 - base

O 3 (pp b ) O 3 (pp b )June 2009G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc4-18Figure 4-13. Time series of predicted hourly ozone under the 2018 basecase and mitigation scenario #2 during April at Mesa Verde(top) and July at Navajo Lake (bottom).Navajo Lake-10010203040506070807/1 7/10 7/19 7/28Date

1-H r O 3 [ppb]baseMitigation Scenario #2Mitigation Scenario #2 - baseMesa Verde: April-10010203040506070804/1 4/10 4/19 4/28Date

1-Hr O3 [ppb]baseMitigation Scenario #2Mitigation Scenario #2 - base

O 3 (pp b ) O 3 (pp b )June 2009G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc4-19Figure 4-14.Time series of predicted hourly ozone under the 2018 basecase and mitigation scenario #3 during April at Mesa Verde(top) and July at Navajo Lake (bottom).Navajo Lake-10010203040506070807/1 7/10 7/19 7/28Date

1-Hr O3 [ppb]baseMitigation Scenario #3Mitigation Scenario #3 - baseMesa Verde-10010203040506070804/1 4/10 4/19 4/28Date

1 -H r O 3 [p p b ]baseMitigation Scenario #3Mitigation Scenario #3 - base

O 3 (pp b ) O 3 (pp b )June 2009G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc4-20Figure 4-15.Time series of predicted hourly ozone under the 2018 basecase and mitigation scenario #4 during April at Mesa Verde(top) and July at Navajo Lake (bottom).-10010203040506070807/1 7/10 7/19 7/28Date

1-Hr O3 [ppb]Mesa Verde: April-10010203040506070804/1 4/10 4/19 4/28Date

1-Hr O3 [ppb]baseMitigation Scenario #4Mitigation Scenario #4 - base

O 3 (pp b ) O 3 (pp b )June 2009G:\Four Corners Task Force - Modeling\FinalReport\Rev20090806\Sec_4_(results)_20090806.doc4-21Figure 4-16. Time series of predicted hourly ozone under the 2018 basecase and mitigation scenario #5 during April at Mesa Verde(top) and July at Navajo Lake (bottom).Navajo Lake-10010203040506070807/1 7/10 7/19 7/28Date

1-Hr O3 [ppb]baseMitigation Scenario #5Mitigation Scenario #5 - baseMesa Verde: April-10010203040506070804/1 4/10 4/19 4/28Date

1-Hr O3 [ppb]baseMitigation Scenario #5Mitigation Scenario #5 - base

O 3 (pp b ) O 3 (pp b )

## June 2009

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# 4-22 Mesa Verde: April 1-Hour O3 (ppb)

-10 -5 0 5 10 15

50 55 60 65 70 75 80

2018 Base CaseMitigation 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 CaseMitigation 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 CaseMitigation 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 CaseMitigation 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

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# 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

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# 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

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# 4-25 Ozone impacts of the five mitigation scenarios we re also analyzed in te rms of changes in the

predicted annual 4 th 

# highest daily maximum 8-hour averag e ozone concentration in each model

grid cell. Color plots of th ese ozone design values are pr ovided 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 scen ario and the base case (negative va lues indicate ozone reductions).

Results for scenarios #3, #4 and #5 are shown in Figure 4-21. No te that these results are not

paired in time as the date of the 4 th 

# highest 8-hour value can chan ge from one grid cell to the

next. Both the overall largest ozone design value reductions (max imum decrease of 5.0 ppb) and

the most wide-spread reductions ar e predicted to occur under scen ario #4. Some of the larger

reductions occur in the area of the local design value peak in Rio Arriba Count y. 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 w ith the highest base case design value locations.

Results for scenario #5 indicate that a combination of VOC an d NOx controls on oil and gas

sources are more effective at reducing ozone than VOC or NOx controls alone.

## June 2009

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# 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

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# 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

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# 4-28 4.2.3 Mitigation Scenario Results: PM and Visibility

Mitigation scenario impacts on visi bility in Class I areas within the 4 km dom ain were calculated

based on differences in predicte d pollutant concentrations betw een the 2018 base case and each

mitigation scenario. For the sake of simplicity, visibility unde r each scenario was calculated

using the original IMPROVE eq uation 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 ma tter concentration (soil),

[PMC] = coarse particulate matter concentr ation (diameter between 2.5 and 10 microns),

Daily average predicted pollutant concentrations averaged over a ll model grid squares wholly or

partially contained within each Class I area were used in the calculati ons. Monthly default

values of f(RH) provided by EPA (E PA, 2003, Appendix A) were used to compute Bext values.

Resulting light extinction coeffici ent, Bext, was then converted to deciview haze index (DHI)

expressed in deciview units (dV):

DHI = 10In [(Bext + 10)/10].

Where:

Bext = light extincti on 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 calculate d 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

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# 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 thes e results are listed in Tables 4-3 and 4-4 (for

Mesa Verde) and 4-5 and 4-6 (fo r Weminuche). Note that visi bility impairment increases on

some days reflecting situations in which NOx emission reductions result in particulate nitrate

concentration increases at certain times and locations. This ef fect is similar to the ozone

disbenefit associated with NOx emissi on 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 2018baseline (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 2018baseline (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 2018baseline (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 2018baseline (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 2018baseline (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

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# 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 8 10 Haze Index 2018 baseline (dv)Haze Index 2018 miti1 - Haze Index 2018baseline (dv) Weminuche WA Mitigation Scenario 2

Worst 20%--> <--Best 20% <--Best 20% Worst 20%-->

-1 -0.5

0 0.5

1 0 2 4 6 8 10 Haze Index 2018 baseline (dv)Haze Index 2018 miti2 - Haze Index 2018baseline (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 8 10 Haze Index 2018 baseline (dv)Haze Index 2018 miti3 - Haze Index 2018baseline (dv) Weminuche WA Mitigation Scenario 4

Worst 20%--> <--Best 20% <--Best 20% Worst 20%-->

-1 -0.5

0 0.5

1 0 2 4 6 8 10 Haze Index 2018 baseline (dv)Haze Index 2018 miti4 - Haze Index 2018baseline (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 8 10 Haze Index 2018 baseline (dv)Haze Index 2018 miti5 - Haze Index 2018baseline (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

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# 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 0.0 053 -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 0.0 022 -0.0103 -0. 0672 -0.0077



# Examination of the visibility re sults shows that both base case visibility impacts and predicted

visibility changes are larger at Mesa Verde as compared to the mo re remote Weminuche

Wilderness. In both locations, the largest visibi lity improvements are pr edicted to occur under

mitigation strategy #4 (whi ch represents the combination of m itigations #1, #2 and #3) but nearly

all of these visibility improvements stem from the EGU emission reducti ons (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 visibili ty in these Class I areas.

## June 2009

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# 4-32 4.3 CALCULATION OF OZONE SE NSITIVITIES VIA HDDM

Results from the mitigation scenario modeling pr esented 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 m odeling domain (EGUs and oil and gas) will re duce ozone design

values by at most 5 to 10 ppb. Achieving additi onal ozone reductions would require reducing

emissions from sources outside the 4 km do main which the source apportionment results

presented in Section 4.1.2 suggest ar e substantial contributors to oz one in the Four Corners area.

However, formulating a realistic mitigation scenario for these s ources 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 sour ces) using the Higher-Order Dire ct Decoupled (HDDM) method

(ENVIRON, 2008). Ozone se nsitivity coefficients calculated with this method represent an

estimate of the change in ozone given a small change in VOC or NO

x 

# emissions individually or a

combined change in both VOC and NOx. Simila r to the source appor tionment analysis, HDDM

sensitivities can be calculated for any source group defined by either s ource category and/or

location. Since HDDM computes first and second -order sensitivities, nonlinearities in ozone

formation chemistry are treat ed to second order and it is thus fe asible to generate diagrams that

show the approximate change in ozone corresponding to differ ent combinations of VOC and

NO

x 

# emission reductions (or incr eases) from each source group.

Results of the CAMx HDDM analysis, while not offi cially a part of this study, will be presented

to the Four Corners Air Quality Group when available.

## June 2009

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# 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 emi ssions estimates for 2005, and a pr ojected inventory for 2018.

The resulting modeling database was used to exam ine 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 activi ties in the Four Corners region.

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

information at the time the analysis wa s conducted, including th e 2002 WRAP Phase II

inventory scaled to 2005 based on activity data , the New Mexico Environmental Department

ozone precursor inventory for San Juan and Ri o Arriba counties and the 2005 updated Southern

Ute inventory. In southwestern Colorado, these ar e essentially the same em issions that will be

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

the WRAP Phase III study include s 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 st udy. Emission estimates used in

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

part of the WRAP Phase III study. Overall, i nventories 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 ev aluate possible air qual ity impacts of future

emissions growth and control.

Analyses of model performance for the 2005 ba se case showed reasonably good performance for

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

under prediction bias during the summe r. While predictions of elev ated 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 th e Four Corners area. Diagnostic analyses showed that these

predicted ozone peaks result from the long rang e 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 no t very sensitive to controls on local sources of

emissions. Efforts are currently underway to improve th e treatment of verti cal circulations over

elevated terrain in the model. Model perfor mance for sulfate PM showed good agreement with

observations, consistent with the expect ed accurate representation of EGU SO 2 

# emissions

provided by the EPA Acid Rain database. Nitrate PM model performance was mixed but

concentrations are low so nitr ate contributions to overall modeled PM wa s not significant.

Although a consistent under predic tion bias was noted for elementa l and organic carbon PM, this

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

reductions in SO 2 

# and NOx emissions which are effec tively 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 me teorological 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\Fi nalReport\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 apportio nment analysis performed on the 2018 base case scenario

showed that local oil & gas and EGU sources are the bi ggest 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 sour ces 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 scenario s focused on EGU and oil and gas sources within

the Four Corners area were examined in th is study. These scenar ios included a 70% NOx

reduction and a 16% SO 2 

# reduction from local (i.e., 4 km do main) EGU sources as well as an

approximate 50% VOC reduction a nd a 16% NOx reduction from lo cal oil and gas sources.

EGU and oil & gas together account for 75% of 4 km domain NOx emissi ons (oil and gas alone

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

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

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

in local VOC emissions. These mitigation scenar ios 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 combinati on 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 c onditions but combining them with the oil and

gas controls appears to put a “f loor” 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 vi sibility 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 relate d value impacts in the Four Corners region.

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

support future air quality manageme nt 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 a nd gas emissions estimates cu rrently being developed via

the WRAP Phase III study.

• Further investigation of the apparent undere stimation of elemental carbon emissions and

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

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

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

e.g., oil & gas, EGU, urban area emissions a nd biogenic and fire em issions. This could

## June 2009

G:\Four Corners Task Force - Modeling\Fi nalReport\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 modeli ng algorithms currently under development

for secondary organic aerosols are able to provide more rea listic predictions of organic

carbon.

• Incorporation of model improvements for se tting stratospheric boundary condition inputs

and reducing vertical circulations over el evated terrain that are currently under

development.

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

impacts for potential improvements to emissi ons 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\Fi nalReport\Rev20090806\Sec_6_References.doc 

# 6-1 6.0 REFERENCES

Abt. 2008. Modeled Attainment Test Software: Us er’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-di mensional 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 Atla ntic 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: Comprehe nsive 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 Mode l (MM5). NCAR Tech. Note, NCAR TN-398-STR, 138 pp.

MAG. 1997. "Technical Support Document fo r 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 App lication of the Urban Airshed Model (UAM), "

EPA-450/4-91-013, Office of Air Quality Planni ng 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. Environm ental 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 Protecti on Agency, Research Triangle Park, NC.

U.S. EPA, 2007. Guidance on the Use of Mode ls and Other Analyses for Demonstrating

Attainment of Air Quality Goals for Oz one, 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 Co rners 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 th e Four Corners Air Quality Study ............................. 1-6

Figure 2-1. 2005 36/12/4 km MM5 mode ling 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 dom ain to be used for the Four Corners

Air Quality Modeling Study ................................................................................ 2-6

Figure 2-3(b). CAMx 12/4 km modeling doma in 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 ENVI RON International Corporation

and Alpine Geophysics, LLC. This particul ar component of the study will apply the

Comprehensive Air Quality Model with exte nsions (CAMx), a photochemical/PM dispersion

grid model, to project and evalua te criteria pollutants and air qual ity related values in the Mesa

Verde, San Pedro Parks, and Weminuche Class I areas and surrounding Cl ass II areas. The air

quality model will be supplied wi th high-resolution meteorological fi elds 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 emissi on 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 genera tion, on Class I and surrounding Class II areas in

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

the Colorado Department of Public Health a nd 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 strategi es for air quality management

in the region. In connection w ith 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 pollu tants (ozone, particulate matter [PM]) and air

quality related values (visibility and deposition) are to be evaluate d. 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, EN VIRON was contracted by BP America Production

Company to develop a gridded, model-ready ba se 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, wo rking 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 quali ty 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 meteorologi cal data we have devel oped as described above.

1.1.1 Purpose of the Modeling Protocol

This Modeling Protocol sets fort h the procedures, data sources a nd 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

## June 2007

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# 1-2 guides the technical deta ils 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 m eans 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 qual ity modeling of the Four Corners area is not currently planned

to be part of a SIP attainment demonstration, mu ch of EPA’s guidance rela ted to the content of

Modeling Protocols is directly relevant and provide a road ma p for our work under this task

(EPA, 1991; 2001; 2006; 2007). Note that because so me aspects of the Four Corners Air Quality

Modeling Study are predetermined (e.g., CAMx has b een previously chosen for these analyses,

and MM5 meteorological model outputs and em ission inputs will be provided from other

projects), this Modeling Prot ocol 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 pr ocedures 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 quali ty modeling to be conducted in this project will provide

estimates of ozone, PM, sulfur and nitrogen de position, 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 Corner s Air Quality Study are as follows:

Modeling Protocol : A Modeling Protocol (this document) w ill 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 ne sted grids with 36, 12, and 4 km grid

spacing (resolution) will be de fined. The 2005 MM5 meteorologi cal data fields will be

processed into CAMx inputs for this domain c onfiguration 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 peri od (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 pe rform 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). Th en 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.

## June 2007

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# 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 re gional modeling conducted for WRAP and other

urban and larger-scale modeling programs. Grap hical displays of model performance will be

generated including scatter plots, ti me series plots, quantile-quantile plots, spatial maps of model

predictions with superimposed observations, “s occer goal” plots, “bugle” plots and other

displays. A Technical Report a nd associated presentation will be prepared that documents the

2005 CAMx Base Case performance evaluation.

Future-Year Modeling : A 2018 Base Case and 5 future-yea r mitigation strategies will be

simulated using the 2005 36/12/4 CAMx modeli ng databases and configurations developed

above. We will employ EPA guidance techniques to project future-year ozone, visibility and

PM 2.5 

# concentrations. These techniques use the m odel in a relative sense to scale the observed

values using relative response f actors (RRFs). Spatial maps, ba r charts and other graphical

displays would be used extensively to convey th e 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 comment s, 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 sy stem that is developed and maintained by

ENVIRON International Corpor ation (ENVIRON, 2006). CAMx was developed with all new

code during the late 1990s using modern and mo dular coding practices. This has made the

model an ideal platform for the extension to trea t a variety of air quality issues including ozone,

particulate matter (PM), visibility, acid de position, 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 sens itivity analysis techniques (ref erred to as “Probing Tools”),

including Process Analysis (IRR, IPR, and CP A), 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 PM 10 

# 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 Imp licit Explicit Hybrid (IEH) solver;

## June 2007

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# 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 th e effects of condensation/evaporation,

coagulation and nucleation upon th e 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 va riety of meteorological models including the MM5, RAMS,

and WRF prognostic hydrostatic meteorologi cal models and the CALMET diagnostic

meteorological model (others also compatible);

CAMx provides two key options to users interest ed 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 si mulations (perhaps for

shorter episodes) may be addressed using the versi on that treats N-size sections (N is typically

10) and the rigorous, but computationally-exten sive MADM multi-section chemistry module.

The databases required to set up and operate CA Mx for the Four Corners Air Quality Modeling

Study are as follows:

• Three-dimensional hourly meteor ological fields generated by MM5 and prepared using

the MM5CAMx interface processor;

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

generated by an emissions processor;

• Initial/boundary (IC/BC) inputs for the coar sest (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 limitati ons owing to the necessa ry simplifications and

approximations made in formulating the governin g 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 limita tion 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 a nd the secondary organic aeroso l (SOA) module. Preliminary

modeling by the RPOs (e.g., WRAP, VISTAS and CENRAP) found both CAMx and CMAQ

nitrate performance suspect with winter overest imations 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

## June 2007

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# 1-5 have been addressed in an improved SOA modul e 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 Geophysic s, with input from FCAQTF participants. Organizational

commitment is an essential element for developing and implementing a successful research

project. The overall project orga nization is shown in Figure 1-1.

The project management organiza tion established for the ENVIRON/ Alpine team is based on the

respective expertise of the indi vidual team members. ENVIRON is the prime contractor, and

Mr. Till Stoeckenius in ENVIRO N’s Novato, CA office will serv e 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 (als o of ENVIRON’s Novato

office) will serve as Principal-in-Charge for the pr oject. Mr. Morris will ensure that staffing and

other resources are made available to the proj ect to meet the needs of NMED. Mr. Dennis

McNally will serve as Alpine Ge ophysics’ subcontractor point of c ontact; he will direct all of

Alpine’s work on preparation of the MM5 m odel output for use in CAMx and CAMx model

evaluation. ENVIRON has a substa ntial track record of successf ully working together with

Alpine Geophysics on complex and high profile air sciences mode ling projects and generating

high-quality deliverables.

1.3.1 Data Gatekeepers

For quality assurance purposes, we have desi gnated certain key indi viduals to serve as

gatekeepers for data flowing into the modeling an alysis. These individuals will be responsible

for ensuring that the data provided to the mode l 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, mete orological and emissions gatekeepers have been

identified whose roles are defined below.

• Meteorological Gatekeeper : As necessary, obtain meteor ological 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 da ta 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.

## June 2007

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 mode l inputs and outputs for annual runs and the

transfer (via USB/firewire porta ble disk or alternative media) of electronic files to NMED

and other interested parties. Till Stoe ckenius will serve as the Air Quality Data

Gatekeeper.

1.3.2 Communications Plan

The ENVIRON team will conduct several activitie s to ensure smooth project management and

communications throughout the project, including:

• Weekly or bi-weekly co nference calls among ENVIRON and Alpine Geophysics

personnel.

• Monthly calls between the ENVIRON team and NMED pe rsonnel; ENVIRON team task

managers will participate as appropriate.

• Monthly progress reports prepared by ENVI RON with input as n eeded from Alpine

Geophysics; these reports will summarize proj ect progress, results to date, problems

encountered and necessary action items, a nd plans for the upcoming reporting period.

• Continuous, regular e-mail and telephone c ontacts as necessary to ensure the

communication of progress, issues and thei r resolution, accuracy of the results, and

timely completion of the tasks.

## June 2007

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# 1-7 The modeling team members and agency representa tives will utilize e-mail as the primary means

of routine correspondence and data exchange. Larg er 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, re ports, 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 projec t is detailed in Table 1-1. A ll work is to be completed by 30

November 2007. While this is a fairly ambitious sc hedule, we will be able to complete all of the

requested work within this timeframe by comb ining resources availabl e 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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# 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 displa ys 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 projec tion 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 nesti ng the modeling domain boundaries of the fine grids must align

with the coarsest grid used. Figure 2-2(a) displays the maximum po ssible CAMx 36/12/4 km

modeling domain that can be se t within the existing 2005 36/12/4 km MM5 configuration. Also

plotted in Figure 2-2(b) are the locations of the IMPROVE and CA STNet monitoring sites in the

Four Corners states.

The CAMx 36 km grid will correspond to th e 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 cons idered whether the 36 km grid should be reduced,

given our focus on the Four Corners area. Howeve r, 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 eas tern 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 maximu m 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 re mains unchanged. However, the southern

boundary will be moved northward somewhat to make it consistent with 12 km emissions

surrogate distributions that E NVIRON 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 s outh 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

## June 2007

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.

## June 2007

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec2.doc 

# 2-3 -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 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.

## June 2007

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec2.doc 

# 2-4 -2280-2160-2040-1920-1800-1680-1560-1440-1320-1200-1080 -960 -840 -720 -600 -480 -360 -960 -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 domai n for the 2005 Four Corners Air Quality

Modeling Study is displayed in Figure 2-3(a). Fi gure 2-3(b) displays th e 12/4 grid domain and

the locations of the IMPROVE, CASTNet and NADP monitoring sites. The 4 km domain

includes 5 IMPROVE, 5 CASTNet a nd 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 me thodology used by WRAP. Note that 19 CAMx

layers will comprise an aggregation of 34 MM5 la yers. This is done to increase computational

efficiency, while maintaining higher resolution in the planetary boundary la yer (lowest ~3 km of

the atmosphere) where the bulk of pollutant chem istry 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 re sults were obtained using no layer collapsing (i.e., 34 vertical layers that

exactly match MM5) and 19 vertical layers. Howe ver, more aggressive layer collapsing (e.g., 12

layers) affected the surface pred icted concentrations. Thus, in Table 2-1 we have proposed a 19

vertical layer structure that is cons istent with WRAP, CENRAP and VISTAS.

## June 2007

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# 2-6 -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 2-3(a). CAMx 36/12/4 km modeling domain to be used for the Four

Corners Air Quality Modeling Study.

## June 2007

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# 2-7 -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 2-3(b). CAMx 12/4 km modeling domain to be used for the Four Corners

Air Quality Modeling Study.

## June 2007

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# 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

## June 2007

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# 3-1 3. BASE YEAR MODELING

3.1 CONTEXT FOR MODELING AN D 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 give n: the many factors associated with evaluating

performance for ozone, multiple PM 2.5 

# species, and wet/dry depositi on; uncertainties associated

with the meteorology and emissions; and variatio ns in the important physio-chemical processes

associated with different locations and times of the year. With the limitations imposed by the

schedule and resource availabilit y, 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 epis odic SIP modeling, will not be possible 1 

# . 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 spec ific and unique conditions of the Four Corners

region. Some elements in our recent past expe rience that we would put to use in the Four

Corners air quality stud y include the following:

• We have amassed a history of model performance expectations for ozone, PM 2.5 

# and

regional haze from which general model pe rformance attributes can be gleaned:

Ozone : Current photochemical grid models ge nerally perform well for ozone in most

urban and rural applications, w ith 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 oz one underestimation tendencies likely include

misrepresented emission rates and VOC sp eciation, limitations in the photochemical

mechanisms themselves, improperly characte rized vertical mixing, and/or inadequate

treatment of other pro cesses, 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 emi ssions 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 tran sforms 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), ve ry large uncertainties in ammonia

emissions, and the strong roll of wet and dry depo sition pathways. It is also very difficult

to measure, given its volatility. This is an ar ea 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 particul ar application, depending upon which acid is

1 

## For example, ENVIRON’s work with the State of Texas for a recent oz one SIP required over 40 base case

simulations in order to achieve an adequate level of model performance.

## June 2007

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# 3-2 dominant. Performance evaluations have been limited by the fact that many PM

monitoring networks do not measure NH4 (s uch as IMPROVE), and complicated by the

fact that ammonia emissions are very uncer tain 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 even ts, performance for EC has been

fairly good. Again, well characterized em issions 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 contributi ng sources. OC is

typically one of the la rgest components of PM 2.5 

# 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 c ontributors, and its vol atile components are

difficult to measure. There is currently an intense area of research investigating the

sources and chemistry of secondary orga nic 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 late st version of CAMx, which incorporates a

new SOA module that includes SOA pathways from biogenic sesquiterpene, isoprene,

and terpenes. However, this would require th at 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 unident ified 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 appropria tely modeled with regional m odels (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 dilute d to grid scale).

• An important issue to consider is that different PM monitoring networks may use

different measurement approaches that “measur e” different amounts of the same species,

which further differ from the form of the mode led species. It is of ten difficult to gauge

model performance against spec ific measurements, since we must emulate “how” the PM

is measured (i.e., the form of sulfate reporte d, loss of carbon and nitrate via volatilization,

etc.). Such measurement issues can introdu ce an apparent model bi as 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 di agnostic 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

## June 2007

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# 3-3 an optimal model configuration for the annual r un. We have used this same approach for

the VISTAS (Morris et al., 2004), and ot her studies (e.g., CENRAP, WRAP and the

ongoing St. Louis 8-hour ozone and PM 2.5 

# modeling study).

The CAMx model evaluation protoc ol concentrates on an operati onal evaluation of 1-hour and 8-

hour ozone concentrations and PM species concentrations that ar e consistent between the model

and measurements (i.e., SO 4, 

# NO 3, 

# NH 4, 

# OC, EC, IP, and CM). Wher e feasible and supported by

sufficient measurement data, we will also eval uate the modeling system for its ability to

accurately estimate gas-phase oxidant and prec ursor/product species since correct, unbiased

simulation of gas-phase photoche mistry is a necessary elemen t of reliable secondary PM

formation. This evaluation will focus on availabl e 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 applicati on and evaluation process.

3.2 2005 BASE CASE MODELING

The modeling team will develop CAMx modeli ng inputs on the 36/12/4-km nested modeling

domain for a 2005 Base Case simulation. Pre liminary model testing and evaluation will be

performed over two months of the year to es tablish the model confi guration 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 struct ure (as described in Section 2). The MM5

simulation has been completed by the ENVIRON/Al pine 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 MM5CA Mx 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 a ny projection to be specified for the air quality

model (CAMx may be applied on Lambert Conforma l, Polar Stereographic, or UTM Cartesian

projections, or in geode tic latitude/longitude).

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

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

## June 2007

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# 3-4 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 (m 2 

## /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 mappi ng 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 dire ctly output by MM5 (e .g., vertical diffusion

coefficients and some cloud information).

The MM5CAMx program has been written to carefu lly 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 pro cess the 2005 MM5 output data fields from each

modeling grid to the CAMx grids, variables a nd 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 di ffusivities (Kv) are an important input to the CAMx simulation since they

determine the rate and depth of mixing in th e planetary boundary layer (PBL) and above. In

general, our experience has been that diffusiv ities 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 diffusiv ities than the meteorological model results. We

will evaluate the CAMx diffusion inputs by compar ing the Kv values from several diagnostic

calculation approaches. Two sets of vertical turbulent diffusiv ity 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 m 2 

# /s and 1.0 m 2 

# /s minimum Kv values, which will be evaluated

in these same sensitivity test s. The choice of minimum Kv value is an area of ongoing

investigation by both the CMAQ and CAMx develope rs. 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

## June 2007

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# 3-5 artificially remove ozone, while a value th at 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 establis hed a Meteorological Gatekeeper to provide

independent review and QA/QC of the meteorologi cal inputs. Mr. Chris Emery will serve as the

Meteorological Gatekeeper for this study. He w ill 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 th e 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. Th e GEOS-CHEM 2002 output was processed as 3-

hourly spatially varying boundary conditions alo ng the edges of the 36 km RPO grid. For

modeling years other than 2002, we have proces sed 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 take n from the MM5 simulation (as processed by

## June 2007

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# 3-6 MM5CAMx). This will include monthly snow cove r. 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, oz one column, surface ultraviolet albedo, and haze

turbidity.

3.2.5 CAMx Model Options

The latest version of CAMx (ver 4.50 or ne wer) will be employed. The CAMx configuration

options for the preliminary model pe rformance will include the following:

• CAMx will be run separately on the 36-km gr id (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 th e 12 and 4 km grids);

• Plume-in-Grid (PiG) subgrid-scale plume ch emistry and dynamics module will be used

for major stationary sources (e.g., Sa n 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, ISORRO PIA 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, pr ocess analysis, and reactive tracers).

3.2.6 Modeling Strategy

An initial CAMx simulation will be performed fo r the entirety of 2005 on the 36 km continental

RPO domain. Output will be processed to gene rate 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 cond itions from such larg e 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 provide d to the model on a daily basis.

## June 2007

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# 3-7 As was done in several recent annual mode ling studies (e.g., VISTAS, CENRAP, WRAP, St.

Louis PM 2.5 

# SIP, etc.), we will initia lly select two representative monthly periods to perform

diagnostic and sensitivity testing with CAMx on th e 12/4-km nested grid sy stem. 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 m odeling periods will be chosen according to an

analysis of the 2005 air quality data in the Four Corners regi on. 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 c hosen summer and winter months of 2005, and

conduct a preliminary model performance evalua tion. We will then pe rform a series of

diagnostic and sensitivity tests designed to identify the optimal model configuration for

simulating ozone and PM air quality in the Four Co rners region. The specific types of tests to be

performed will depend upon the initial model perfor mance and our assessment of the quality and

uncertainties of the model inputs.

The summer and winter diagnosti c 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 configur ation for the summer and winter 2005 periods, we

will perform a full annual CAMx simulation on th e 12/4 km grids to establish the 2005 Base

Case scenario. The 12/4-km annual simulation w ill 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 propertie s over a range of synoptic and geophysical

conditions. To the extent possible given availa ble data, the MPE should provide evidence that

the modeling system is properly simulating the ob served conditions for the correct reasons, and

not through a serendipitous combination of compensa tory errors (i.e., arrivi ng at the right answer

for the wrong reason). Likewise, the MPE proces s should also provide evidence that the model

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# 3-8 properly responds to changes in em issions, allowing us to judge th e suitability of the CAMx air

quality modeling system as an effective tool to evaluate alternative mitigation strategies. When

conducted thoughtfully and thoroughl y, the process focuses and dire cts the continuing cycle of

model development, data collect ion, model testing, diagnostic analysis, refinement, and retesting.

The objective of the model performance evaluati on (MPE) to be conducted for this project will

be to evaluate the ability of CAMx, through the use of common stat istical measures and

graphical procedures, to appropria tely replicate the spatial/tem poral 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 philos ophy 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 evalua tion plan conforms to the final

procedures published by the EPA (2007) for 8-hour ozone, PM 2.5 

# , 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 perf ormance 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 m easurements include ozone, NOx, CO, SO 2 

# , some VOCs,

PM 10 

# , PM 2.5 

# , PM species components, light scattering/ extinction, and deposited mass. The first

is the routine gas-phase concentr ation 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 Ai r Status and Trends

Network (CASTNET), (c) EPA PM 2.5 

# and PM 10 

# Mass Networks (EPA-FRM), (d) EPA

Speciation Trends Network (STN); and (e) Na tional Acid Depositi on 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 pa rticular, 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 ju st the Four Corners area), no CO data were

available and there were only two NO monitors (Bondad and Ignacio). NO 2 

# monitoring data

were available at four monitors (Bondad, Ignaci o, Substation, and Bloomfield). No ambient

monitoring of volatile organic co mpounds (VOCs), either speciated or total mass, was available

within the 4 km modeling domain.

Care must be taken in the PM model performanc e evaluation to assure the PM measurements and

modeled concentrations are consis tent with the measured quanti ties, as discuss previously.

Although the focus of the CAMx PM evaluation will be on PM component performance in the

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# 3-9 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”. Fort unately, gas, PM, and visi bility data are available

within the 12 km grid domain and these will likely serve as the prin cipal basis upon which the

model’s performance for secondary aeroso l and visibility impacts will be based.

As described in Section 1, we have establishe d 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 dia gnostic 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 concen tration fields as well as concentrations

of gas-phase and particulate precursor a nd 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 simula ting gas-phase (e.g., ozone) and particulate

precursor/product species using a two-step proc ess consisting of: (a) an initial “screening

model performance evaluation” (SMPE) proces s; and if the modeling results pass the

screening analysis, (b) a “refined model pe rformance evaluation” (RMPE) consisting of

progressively more stressful testing proce dure involving multi-species, multi-scale (e.g.,

36/12/4 km grids) surface model evaluations;

• Identify sources of error and/or comp ensating biases, through examination of

preprocessor models (e.g., MM5), air qua lity model inputs, mass budgets and

conservation checks, process analysis , etc. (as time and budget permit);

• Through a documented process of diagnostic an d sensitivity investigation, pinpoint and

correct specific performance problems th rough 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 proce ss outlined above should avoid a model “tuning” exercise, wherein

specific non-physical, unrealistic, or uncorroborat ed adjustments are made simply to obtain a

better answer.

## June 2007

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec3.doc 

# 3-10 In addition to 1-hour and 8-hour ozone performa nce, the performance evaluation will focus on

the evaluation of CAMx to estimate PM con centrations 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 depositi on of sulfur and nitroge n 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 reco nstructed 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 pub lished draft guidance for fine particulate and

regional haze modeling (EPA, 2001), utilizing a Fine Particulate Guidance Workgroup to

provide technical input in its development 2 

# . More recently, EPA provided an informal update on

the PM/regional haze modeling guidance (Timi n, 2002) and conducted a PM model evaluation

workshop (see, for example, Timin, 2004; Boyla n, 2004) shedding additional light on what the

final guidance document would contain (final gu idance was released by EPA in April 2007). A

key concept in the guidance for a ddressing annual PM issues is th at 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] + [E C] + [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 car bon concentration (=POA+SOA);

[EC] is the elemental carbon concentration;

[IP] is the inorganic prim ary 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 corre ctions for the measurement.

CAMx does not directly estimate visibility, instead it estimates PM and gaseous species

concentrations from which visibility can be es timated, usually in a post-processing step. The

most frequent equation to convert PM species con centrations to light exti nction 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.

## June 2007

G:\Four Corners Task Force - Modeling\Protocol\Draft\Sec3.doc 

# 3-11 where:

b

ext 

# is the estimated total light extinction coefficient (Mm -1 

# );

[( NH 4 

# ) 2 

# SO 4 

# ] is the sulfate concentration assumed to be ammonium sulfate;

[ NH 4 

# NO 3 

# ] is the particulate nitrate concentra tion assumed to be ammonium nitrate;

[ OC ] is the total organic mass concentration (=POA+SOA);

[ EC ] is the elemental carbon concentration;

[ IP ] is the inorganic primar y fine particulate (< 2.5 μ m) concentration excluding primary

sulfates and nitrates;

[ CM ] is the primary coarse particulate (> 2.5 μ m and <10 μ m) concentration;

b rayleigh 

# is the natural atmospheric Rayl eigh scattering (assumed to be 10 Mm -1 

# );

f

( RH ) is a relative humidity adjustment f actor for the sulfat e and nitrates; and

f ′

( RH ) is a relative humidity adjustment fact or 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 s cattering components (SO4, NO3, OC, IP, CM, and

natural atmospheric Rayleigh scattering) and abso rbing components (EC). The relative humidity

adjustment factor f ( RH ) accounts for the growth of sulfate an d 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. Th e humidity adjustment

function defined by IMPROVE is shown in Figure 3-1.

3.3.3 Statistical Performance Metrics

The current PM 2.5 

# guidance document (EPA, 2007) focuses more on a holistic model evaluation

approach compared to the original 1-hour oz one and draft PM guidance (EPA, 1991; 2001). Not

only should we assess how well the model matc hes the observation, but we also need to

determine whether the model is correctly simula ting the processes that produce the elevated

concentrations, which includes comparing against a conceptual model. Tabl e 3-2 lists a standard

set of statistical pe rformance measures that can be used to evaluate fine particulate models.

These performance measures will be calculate d 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 sta tistical 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



# b CM IP EC OC RH f NO NH RH f SO NH RH f b

+ × + × + × + ′ × + × + × =

6

. 0 1 10 ) ( 4 ) ( 3 ) ( 3

3 4 4 2 4

## 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 sulf ate 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 pl ots, time series plots and spat ial comparisons of predictions

and observations. MAPS also produces spatia lly averaged performance summaries (e.g.,

time series of bias and error) that are us eful for synthesizing model performance.

PAVE by MCNC : Used on a Linux platform to gene rate spatial maps (tile plots) of

model predictions with super imposed observations as colored symbols.

ENVIRON Performance Software : Calculates performance st atistics 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 graphica l displays of model

performance that can be customized by the us er 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 ( A

p 

## ) Paired_Peak

peak peak



# O O P

− P peak 

## = 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



# O O P P O O P P

1 1 2 2 2

1



# ) ( ) ( ) )( ( P i 

## = prediction at

time and location

i ;

O i 

## = observation

at time and

location i ;



# P = arithmetic

average of P i 

## , i=1,2,..., N;



# O = arithmetic

average of O i 

## , i=1,2,...,N

Normalized Mean

Error ( NME ) Norm_Mean_Err



# ∑ ∑

=

= 

# −

N

i i N

i i i



# O O P

1 1 

## Reported as %

Root Mean

Square Error

( RMSE ) Rt_Mean_Sqr_Err 

# ( ) 2 1

1 2 

# 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− 

# ∑

= N

i i i 

# O P

N Reported as %

Fractional Gross

Error ( F E



## ) Frac_Gross_Err 

# ∑

= 

# + − N

i i i i i



# O P O P

N

1 

# 2 Reported as %

Mean Absolute

Gross Error

( MAGE ) Mean_Abs_G_Err 

# ∑

= 

# − N

i i i 

# O P

N

1 

# 1

Mean Normalized

Gross Error

( MNGE ) Mean_Norm_G_Err 

# ∑

= 

# − N

i i i i



# O O P

N

1



# 1 Reported as %

Mean Bias ( MB ) Mean_Bias 

# ( ) 

# ∑

= 

# − N

i i i 

# O P

N

1 

# 1 Reported as

concentration

(e.g., μ g/m 3 

## )

## 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 i i



# O O P

N

1 

# 1 Reported as %

Mean

Fractionalized

Bias (Fractional

Bias, MFB ) Mean_Fract_Bias 

# ∑

= 

# ⎟ ⎟

⎠ ⎞

⎜ ⎜

⎝ ⎛

+ − N

i i i i i



# O P O P

N

1 

# 2 Reported as %

Normalized Mean

Bias ( NMB ) Norm_Mean_Bias



# ∑ ∑

=

= 

# −

N

i i N

i i i



# O O P

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 Perf ormance for the 2005 Base Case

The CAMx performance evaluation will be carrie d 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, comp arisons of pollutant ratios and groupings).

The formal procedures outlined in EPA’s final ozone, PM 2.5 

# , 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 ex tent 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 precurs or emissions or species concentrations;

• Comparison of observed and predicte d ratios of indicator species;

• Comparison of predicted source category cont ribution factors with estimates obtained

using observational models; and

• Use of retrospective analyses in which air qu ality differences predicted by the model are

compared with observed trends.

Obviously, a comprehensive measurement databa se for gas phase species including ozone and

particulate aerosol precursors re quires 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 clear ly not possible with the curren t 2005 Four Corners data base,

particularly in regards to precu rsor measurements, since no inte nsive field measurements were

conducted in this area during the 20 05 year. Therefore, the evalua tion approach will consist of a

blend of those points above and th e steps outlined below. To th e 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 co mponents 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 performa nce 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 concentr ation scat terplots;

• Bias and error stratified by concentration; and

• Bias and error stratified by time.

This screening is intended to identify obvi ously flawed simulations. Experience in

photochemical modeling is the be st basis upon which to identif y obviously flawed simulation

results. Efforts to improve photochemical mode l 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-ana lysis 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 ad visory group(s); and

• Changes to the modeling approach need to be documented in the Modeling Protocol.

If the initial scr eening of the CAMx ozone and particulat e aerosol results doe s 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 diag nosis, 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 obvious ly flawed model simulation as it would be

wasteful of project re sources 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 fo r 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 th ere is a limited quantity of relevant ambient

measurements collected in the Four Corners regi on. 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 ma y 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 reas onableness. 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 inpu ts. 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. Sens itivity experiments may be conducted as part of

the CAMx model performance evaluation analysis as appropriate. However, much of these

diagnostic tests will have been performe d using the Jan/Jul 2 005 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 deba te (Morris et al., 2005). Here, performance



# goals refer to targets that we believe a good pe rforming model should achieve, whereas less

stringent performance 

# criteria represent a minimal level of model performance that a model

should achieve for use in regulat ory 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 mode ling 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 perf ormance and then rectifying them.

Decades ago EPA established performance goa ls for 1-hour ozone centered on the use of

normalized bias (<15%) and error (<35%). Howe ver, when these evaluation metrics were later

adapted to PM and its components, difficulties aros e because performance statistics that divide

by low concentration observations (such as nitr ate, which is often zero) become practically

meaningless. In time, this has led to the intr oduction of the fractional bi as 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 measurem ent 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 diffe rences of ~20% for sulfate

and ~50% for elemental carbon (Morris et al., 2005).

As with ozone in the 1980s, actua l experience with PM models has led to the development of the

current performance expectations for these models. For example, PM 10 

# 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 fractiona l bias and error goals

of 30% and 50%, and fractional bias and erro r criteria of 60% and 75%, respectively.

Furthermore, they proposed that these goals and criteria values va ry as a function of

concentration, such that below 2 μ g/m 3 

# , they expand exponentially to 200% (the maximum of

fractional bias and error) at zero observed concentrations.

The following levels of model performance crite ria 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 pr oblems 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 inpu ts 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 CA Mx simulations. To maximize efficiency, our

approach will be to run the 2018 Base Case sim ilarly 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. Th e 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 th e Speciated Modeled Attainment Test (SMAT) for PM 2.5 

# and

regional haze (visibility). MATS will be used with the 2018 modeling results. These approaches

use the relative changes in the modeling resu lts 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 obser ved concentrations to make projections for

unmonitored areas (which are important for Class II assessments).

We will also analyze the absolute modeling re sults for the 2018 Base Case and mitigation

scenarios. When looking at changes in emission s from specific sources, the absolute modeled

incremental impact may be enlightening in additi on 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 m odeled values for the different scenarios;

• Time series of species concentrations and chan ges in visibility at specific monitoring sites

and locations;

• Comparison of changes in concentrations, visi bility and deposition against thresholds that

are used to judge significance;

• Development of visibility trend lines in te rms of total light extinction and deciview;

• Other displays to be determined.

4.1 PM SPECIATED MODEL ATTAINMENT TEST

EPA guidance for projecting PM 2.5 

# and regional haze to future year conditions relies on the

SMAT technique, which applies model result s in a relative sense to scale observed PM 2.5 

# 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 resu lts. The SMAT procedures consists of two

components: (1) the combination of total PM 2.5 

# mass measurements from the FRM network with

speciated PM 2.5 

# measurements such as those from th e STN or IMPROVE networks; and (2) the

use of modeling results to scale the speciated FRM PM 2.5 

# observations to obtain future-year

projected PM 2.5 

# levels.

Speciated PM 2.5 

# measurements are routinely collected on the same 1-in-3 day sampling schedule

as used by the FRM network. The FRM, ST N, 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 PM 2.5 

# mass, and includes water in the measurement

(after equilibration at ~35% rela tive humidity). Part iculate 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 whic h results in inaccurate OC meas urements. IMPROVE also uses

multiple filters and does not include ammonium in its measurements.

As the FRM is the de-facto regulatory definition of PM 2.5 

# , EPA has developed procedures for

adjusting the STN and IMPROVE speciated PM 2.5 

# measurements to account for the

measurement artifacts of the diffe rent networks and to make th e speciated PM measurements

consistent with the FRM PM 2.5 

# mass measurements. These adju stments 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 proce dure involves the following steps:

1. Use the ratio of base to futu re year model-estimated PM components at each monitor and

for each of the four annual quarters to devel op monitor-, quarter- and PM species-specific

Relative Response Factors (RRFs).

2. Apply the monitor-, quarter- and species-sp ecific RRF to each observed quarterly PM

species concentrations to obtai n quarterly PM species concentr ations 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 PM 2.5 

# concentrations against the NAAQS PM 2.5 

# 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 tec hniques 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 Corn ers Cumulative Prevention of

Significant Deterioration (PSD) Increment C onsumption Modeling Study.” Prepared by

ENVIRON International Corpor ation, Novato, CA and Alpi ne 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 Co rporation, Novato, CA (September 2006).

EPA. 1991. "Guidance for Regulatory Applic ation of the Urban Airshed Model (UAM).”

Prepared by the Office of Ai r Quality Planning and Standa rds, U.S. Environmental

Protection Agency, Research Triangle Park, N.C.

EPA. 2001. “Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 

# and

Regional Haze,” Draft Report, U.S. Envi ronmental Protection Agency, Research

Triangle Park, NC.

EPA. 2006. “Guidance on the Use of Mode ls and Other Analyses for Demonstrating

Attainment of Air Quality Goals for Oz one, 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 a nd 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 Carolin a. (EPA-454/B-07-002; April 2007).

NcNally, D., and George Schewe. 2006. “Evalu ation of 36/12/4 km MM5 for Calendar Year

2005.” Prepared for the New Mexico Envir onment Department, Santa Fe, NM, prepared

by Alpine Geophysics, Arva da, CO (19 October 2006).

Morris, R.E., B. Koo, S. Lau, T.W. Tesche, D. Mc Nally, 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 Mo del Sensitivity Tests for Three Phase I

Episodes.” Prepared for the VISTAS T echnical Analysis Committee, prepared by

ENVIRON International Corpor ation, 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 Count y Early Action Compact: Ozone Modeling

Protocol. Prepared by Alpine Geophysics a nd ENVIRON. Prepared for the New Mexico

Environmental Department (June 16, 2003).

Timin, B. 2002. “PM 2.5 

# and Regional Haze Modeling Guidance.” Prepared by the U.S.

EPA/OAQPS (April 2002).

Timin, B. 2004. “PM 2.5 

# Model Performance Evaluation: Pur pose and Goals.” Presented at PM

Model Evaluation Workshop, Chapel Hill, NC (February 2004).

# APPENDIX B

Oil & Gas Emissions Inventory DevelopmentG:\Four Corners Task Force - Modeling\Fi nalReport\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 includ ed 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, th e Phase II inventory incorporated the oil and gas emissions for

San Juan and Rio Arriba Counties from the Ne w 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 em issions inventory for year 2005 were developed

by applying scaling factors, derived on the basi s 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 pr oducers provided activity data and emissions

factors that were used to generate more refine d emissions estimates in San Juan and Rio Arriba

counties, New Mexico. Oil and gas source categori es 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 sa ltwater disposal engines.

After the WRAP Phase II inventory project was completed, an error was identified in the heater

emissions calculations for Colorado. For the invent ory used in this project, the Colorado heater

emissions were corrected.

SUIT 2005 Inventory

An inventory of oil and gas emissions was comp iled by the Southern Ute Indian Tribe (SUIT,

2005) for the calendar year 2005. Oil and gas sour ce categories in the Southern Ute inventory

include drill rigs, compressors, heaters, other e ngines, venting, flaring, and process fugitives.

ENVIRON reviewed that inventory and incorporat ed 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 previ ous WRAP Phase II inventory used broader

assumptions for estimating the emissions in the coun ties that contain the SUIT lands. In order to

incorporate these updated and more detailed 200 5 SUIT emissions into the modeling inventory,

the 2002 SUIT emissions in the WRAP Phase II i nventory were removed using GIS analysis.

Table B-1 summarizes 2005 SUIT em issions by county and SCC tpd).G:\Four Corners Task Force - Modeling\Fi nalReport\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 Emissi ons from the WRAP Phase II Inventory

The overall change in the oil and gas NOx emi ssions 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 ca tegory. Although the

emissions reported in the table are for the entire 4 km domain, most of the changes are a result ofG:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\App_B_O&G Emis_20090806.doc 

# B-3 changes made to emissions estimates for source s on SUIT lands, except that heater emissions

were revised for all of Colora do. For the heaters on SUIT lands , the previous inventory had

about 1,000 tpy NOx, and the revised i nventory 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 know n as VOC speciation. DifferentVOC speciation profiles were

used for each of the various oil and gas source ca tegories as described belo w. 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 al l use large diesel-powered internal

combustion engines. For drilling rigs, ENVI RON 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 spar k-ignition engines.

ENVIRON used an EPA speciation profile fo r a natural gas-fired internal combustion

engine (see Table B-3 - SPECIATE4 profile#1001).

3. Heaters – these were assumed to be natural-ga s 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, dehy drator 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\Fi nalReport\Rev20090806\App_B_O&G Emis_20090806.doc 

# B-4 VOC speciation was handled differently fo r CBM versus conventional (non-CBM) gas

wells. There are few true oil wells in the modeling domain, theref ore 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 ther e 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, de hydrators, and truck loading, as there is

no condensate production at these wells (S ee Table B-4 – VOC speciation for CBM

wells in Colorado).

• NEW MEXICO – In New Mexico it was assu med that all wells in the 4 km modeling

domain are conventional gas wells. A c onventional gas well VOC speciation profile

was developed based on averaging gas co mposition analyses provided by BP for

several formations in San Juan and Rio Arriba countie s, 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 0.02G:\Four Corners Task Force - Modeling\Fi nalReport\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 100.00 100.00 100.00G:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\App_B_O&G Emis_20090806.doc 

# B-6 Table B-4. VOC Speciation for venting, flaring, pneum atic 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\Fi nalReport\Rev20090806\App_B_O&G Emis_20090806.doc 

# B-7 Oil & Gas Emissions – 2018

To project 2005 oil and gas emissions to 2018, diffe rent 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 doma in 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 a ssumed 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 sour ces were held constant at 2005 levels.

• Compressors (with NSPS incorporated), se parators and dehydrat ors were added and

modeled as point sources (with unique coordi nates and appropriate stack parameters).

• For drill rigs, WRAP 2005 emissions were gr own 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 En ergy Information Agency Annual Energy

Outlook (http://www.eia.doe.gov/oiaf/forecasting.ht ml) gas production growth factor of

1.21.

New Mexico portion of the 4 km doma in 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 Outl ook (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 fact or 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 th en reduced to account fo r 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 requ ired 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 ArribaG:\Four Corners Task Force - Modeling\Fi nalReport\Rev20090806\App_B_O&G Emis_20090806.doc 

# B-8 Counties, grown by the EIA gas production fact or (1.21), and calculati ng 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 sour ces, except drilling, reflecting growth were

provided and modeled as individual point s ources in a spreadsheet from the NSJB EIS

development (BLM, 2004) as points except drilli ng. 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 ther efore 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 turn over of engines was assumed, and so no NSPS

reduction was applied.

Utah and Arizona

Oil and gas emissions estimates in the 2005 invent ory are very small in the Utah and Arizona

portion of the 4 km modeling domain and were ke pt constant at WRAP Phase II 2005 levels.G:\Four Corners Task Force - Modeling\Fi nalReport\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 DevelopmentG:\Four Corners Task Force - Modeling\FinalReport\R ev20090806\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) data base of Continuous Emissions

Monitoring (CEM) data (http:// camddataandmaps.epa.gov/gdm/). The CEM database provides

hourly values of NO

x 

# and SO 2 

# emissions as measured by in-stack monitoring equipment. Table

C-1 lists the resulting total annual emissions fr om all EGUs located within the 4 km modeling

domain. EGU emissions of othe r pollutants not reported in the CAMD database were estimated

by linearly interpolating between the WRAP 2002 (Base02b 1 

# ) and 2018 (PRP18 2 

# ) emissions for

these sources to 2005 and temporally allocating th e 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 Ne w Mexico Environmental Department (Jones,

2008). Future year 2018 base case emissions inve ntory for the largest sources (i.e. coal-fired

power plants) was constructed by scaling up 2005 estimates accord ing 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. Si nce the San Juan Generating Stat ion (SJGS) was in process of

construction on emissions contro l technology under the Consent D ecree requirements that would

result in emissions reductions close to pres umptive BART, the Consent Decree enforceable

condition of 90% SO2 reduction and the presump tive 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 St eve 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 al ready. Steve Frey estimated that existing rules

would reasonably limit FCPP to a cons ervative estimate of 0.216 lb/MMBtu SO 2 

# 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 processi ng 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 inventor y 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\R ev20090806\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 Ener gy Information Administration (EIA) projections released in

February 2007

( http://www.wrapair.o rg/forums/ssjf/documents/eictts/ Projections/PRP18_EI_tech%20memo_06

1607.pdf ). WRAP assumed that a typical future co al-fired EGU has a name plate 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 curren t 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 existin g 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 41,743 1a 

## 12,653 1a 

## 50,981 2a 

## 17,935 2a



## San Juan 26,809 1a 

## 16,569 1a 

## 16,546 2b** 

## 9,352 2b**



## Prewitt Escalante Generating Station 3,797 1a 

## 1,293 1a 

## 3,729 2c** 

## 1,796 2c**



## Reeves Generating Station 151 1a 

## 0 1a* 

## 151 2d 

## 0 2d



## Milagro 110 1a 

## 3 1a 

## 110 2d 

## 3 2d



## Animas 54 1b 

## 0 1b 

## 54 2d 

## 0 2d



## Person Generating Project 4 1a 

## 0 1a* 

## 4 2d 

## 0 2d



## Desert Rock n/a n/a 3,325 2e 

## 3,319 2e



## Future Coal Units n/a n/a 2,680 2f 

## 2,904 2f



## 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 esti mate from FIP with 88% contro l; (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 fo r 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 late st 2018 (PRP18) point source

inventories as described for EGUs above. Emission source r ecords 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 us ing the same SMOKE settings used in WRAP

regional modeling (Tonnesen et al., 2005). Point s ources associated with the oil and gas sector

were extracted and processed se parately so that a ppropriate basin-specific VOC speciation

profiles could be applied.G:\Four Corners Task Force - Modeling\FinalReport\R ev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc 

# C-3 MOBILE SOURCES

Mobile sources include on-road and off-road vehicles and engi nes. 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 fi eld equipment, such as mechanical drilling engines.

Mobile source emissions were taken from the 2005 and 2018 mobile source inventories

originally developed for WRAP regional modeli ng (Pollack et al., 2 006). 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: Sp ring (March–May), Summer (June-August), Fall

(September-November), and Winter (December-Ja nuary). Emissions were estimated for PM 10 

# , PM 2.5 

# , NO

x 

# , SO

x 

# , VOCs, carbon monoxide (CO), NH 3 

# , 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 emi ssion 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 Sa n Miguel. For these

three counties, for both the 2002 and 2018 on-road emissions, the vehicle miles traveled (VMT)

data were applied incorrectly in generati ng the emissions. Specifically, Sandoval County VMT

was applied to generate San Miguel County em issions, San Juan County VMT was applied to

generate Sandoval County emissions, and San Migue l County VMT was applied to generate San

Juan County emissions. These errors were fixed and the emissions recalcul ated for use in this

modeling project.

The 2018 WRAP regional modeling (PRP18) invent ories 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 locomo tive and marine diesel emissions (finalized in

March, 2008), based on EPA’s estimate of emi ssions reductions in th eir Regulatory Impact

Analysis (EPA, 2008).

All of the mobile source seasonal county-leve l emissions were processed using SMOKE to

generate gridded model-ready emissions.G:\Four Corners Task Force - Modeling\FinalReport\R ev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc 

# C-4 AREA SOURCES

Area source emissions (aside from oil and gas sour ces 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 doma in were estimated using a GIS-based ammonia

emissions modeling system developed for the We stern Regional Air Part nership (WRAP). The

development of the model, including data sour ces and estimation methodology, is documented in

Chitjian and Mansell (2004). The model treats th e source categories of primary significance in

the overall emission inventory (excluding the mobile, industrial point and fi re source categories)

as described below. Ammonia emission s ource categories include livestock, fertilizer

application, natural soils and domestic s ources. Where possible, the model considers

environmental conditions (wind speed, temperatur e, soil moisture and pH) in developing the

emission factors as well as the temporal allo cation of the ammonia emissions. Meteorological

data was obtained from the 2005 MM5 output. Sp atial allocation was ba sed on application of

EPA gridding surrogates (EPA, 2006).

Livestock : Ammonia emissions from livestock we re developed using county-level head

counts and emission factors based on a literatu re 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 ba sed on the National Agricultural Statistics

Service (NASS) county livest ock 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 NH 3 

# model.

Ammonia emissions from fertilizer appli cation 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 sour ce and a sink of ammonia emissions

depending on the ambient NH 3 

# concentrations, climatic c onditions and the conditions of

the soils. While there are a number of re searchers considering this issue, ammonia

emission from natural soils remains highl y 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 inventor y 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 pers piration, disposable and cloth diapers and

domestic pets (cats and dogs). The emission factors are fr om the report by Chitjian and

Mansell (2004). Activity data for domes tic sources are based on the most recent US

Census (2000), and pet ratios based on reco mmendations of Dickson et al. (1991).G:\Four Corners Task Force - Modeling\FinalReport\R ev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc 

# C-5 Windblown Dust Emissions

The windblown fugitive dust PM emission invent ory 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 ENVIR ON 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 partic le 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 m odel simulations. Friction velocity, u

* 

# , is related to

the slope of the velocity vers us the natural logarithm of he ight through the relationship:

o z



# z z

u u ln 1

* 

# κ

=



# where u

z 

# = wind velocity at height z (m/s)

u * 

# = friction velocity (m/s)

κ = von Karman's constant (0.4)

z 0 

# = 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, z 0 

# . Surface

friction velocities, including the threshold fric tion velocity, are a function of the aerodynamic

surface roughness lengths. The surface friction ve locities 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 surfa ce friction velocity and

soil texture. The relatio nships that Chatenet et al, (1996) es tablished 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 stab le 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 andG:\Four Corners Task Force - Modeling\FinalReport\R ev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc 

# C-6 meteorological parameters. Finally, the time re quired for a reservoir to recharge following a

wind event is influenced by a number of factors including precip itation and snow events and

freezing conditions of the soils. A recharge tim e of 24 hours is assigned to all surfaces. In

addition, it is assumed that no su rface will generate emissions fo r more than 10 hours in any 24-

hour period.

The duration and amount of precipitation and s now 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 wi nd 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 fo r agricultural lands, which are tr eated separately in the model

as described below, vacant land parcels are typically undisturbed un less some activity is present

such as to cause a disturbance (e.g., off-road vehicl e activity in desert land s, or animal grazing on

rangelands). It is recommended that all non-agricultu ral land types be c onsidered undisturbed,

since there is no a priori information to indicate otherw ise for the regional scale modeling

domain to be considered.

Soil Characteristics

Application of the emission factor relations desc ribed 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 St ate 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 invent ory is developed. The classification of soil

textures and soil group codes is based on the standard soil triangle that clas sifies 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 th e standard soil triangle provides the mapping of

the 12 soil textures to the four soil groups cons idered 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 2G:\Four Corners Task Force - Modeling\FinalReport\R ev20090806\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 surf aces has the effect of alteri ng 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 th e threshold surface friction velocity and the

aerodynamic surface roughness length, reveals that for surface r oughness lengths larger than

approximately 0.1 cm, the threshold friction velociti es increase rapidly above values that can be

realistically expected to occur in the meteorological data used in the model implementation.

Therefore to simplify the m odel implementation, only those land types with roughness length

less than or equal to 0.1 cm are consid ered 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 res ource 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/wetland s 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, Z 0 (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 th e dust estimation methodology is

based on MM5 model simulation results. Data fi elds 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, includi ng irrigation and seasonal crop growth. As a

result, several non-climatic correction or adjustme nt factors were develope d for applicability to

the agricultural wind erosion emi ssions. These factors included:

• Long-term effects of irriga tion (i.e., soil “clodiness”)

• Crop canopy cover

• Post-harvest vegetative cover (i.e., residue)

• Bare soil (i.e., barren areas within an agricu lture field that do not develop crop canopy for

various reasons, etc.)

• Field borders (i.e., bare areas surroundi ng and adjacent to agricultural fields)G:\Four Corners Task Force - Modeling\FinalReport\R ev20090806\App_C_ei4km(4km-pt-area-mobile)_20090806.doc 

# C-8 The methodology used to devel op individual non-climatic co rrection factors was based upon

previous work performed by the California Ai r Resources Board in their development of

California-specific adjustment factors for the USDA’s Wind Erosi on Equation (CARB, 1997)

Other Adjustments

Two other adjustments to modeled air quality impact s 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 PM 10 

# dust emissions. The fine

fraction of dust is obtai ned by using a nominal PM 2.5 

# of 0.10, as used in the implementation of

the model for the WRAP.

Other Area Source Emissions

Emissions from numerous small sources treate d as area sources such as commercial and

residential fuel combustion, architectural coatings, etc. that are not includ ed in the other source

categories described above, were obtained from the WRAP invent ories. This category of

emissions includes road dust and fugitive dust but not wind blown dust. Area source emissions

for 2005 were estimated via linear interpolati on between the WRAP 2002 and latest WRAP 2018

(PRP18) emission inventories at the county leve l. The WRAP 2018 (PRP18) inventory was used

to represent 2018 area source emissions.

Spatial allocation of area source emissions to mode l grid cells requires the use of spatial gridding

surrogates. Within the 4 km domain, a new set of gridding surrogates we re developed from the

EPA population and landuse/landcover distributi ons (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-leve l WRAP area source inventory. Temporal allocations were

then applied as in the WRAP m odeling to obtain hourly gridded emissions for input to CAMx.

All emissions processing was done using SMOKE.

# APPENDIX D

Biogenic EmissionsG:\Four Corners Task Force - Modeling\FinalRe port\Rev20090806\App_D_eibio(Biogenic Emissions).doc 

# D-1 (Appendix D)

BIOGENIC EMISSIONS

Gridded hourly biogenic emission inventories suita ble 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 ENVI RON (Guenther et al , 2006; Guenther and

Wiedinmyer, 2007; Mansell et al, 2007). MEGAN accounts for spatial vari ability by using high

resolution estimates of vegetation type and quantity. Key MEGAN vari ables include weather

data, Leaf Area Index (LAI), pl ant functional type (PFT) cove r, and compound specific emission

factors that are based on plant species compositio n. 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 temp erature fields were extracted from the

2005 MM5 predictions for each day for each grid resolution.

• Photosynthetically active radiation (PAR): The PAR data re presents the spectral range of

solar radiation that is used by plants for the photosynthesi s process. The data were

downloaded from the University of Mary land (UMD; 2006) and a FORTRAN program

was used to reformat the data. Some of th e 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 generate d as described above for all three modeling domains. Spatial

distributions of the annual total organic comp ounds (TOG) and NOx in the 4 km domain are

shown in Figures D-1 and D-2, respectively. Bi ogenic emissions are gene rally highest in the

higher elevation areas, including the San Juan Mount ains of southwestern Colorado, and lowest

in the arid lower elevat ion mesas and plains, including much of San Juan County in northwestern

New Mexico. Annual biogenic emissi ons 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 1,834G:\Four Corners Task Force - Modeling\FinalRe port\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 InventoryG:\Four Corners Task Force - Modeling\FinalRe port\Rev20090806\App_E_eifire(fire emissions).doc 

# E-1 (Appendix E)

DEVELOPMENT OF WESTERN U.S. FIRE EMISSIONS INVENTORY

The National Center for Atmospheri c Research (NCAR) generates a nnual fire databases that are

derived from MODIS satellite data. The MODIS pl atform 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 em ission factors, daily fire emission estimates are

estimated for criteria pollutants (CO, NOx, VOC, SO 2 

# , 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 diurna l 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 th e TROFEE study (Karl et al., 2007). Resulting

hourly point source emissions for speciated SO 2 

# , NOx, VOC, CO, and PM (primary EC and OC)

were compiled into a CAMx point source file fo rmat and merged with the anthropogenic point

source inventory.

Fire emissions vary widely from day to day an d month to month. Duri ng 2005, fires were most

prevalent in the general vicinity of the Four Corners region duri ng June and July. Emissions of

NOx and PM for these months are shown in Figure E-1.G:\Four Corners Task Force - Modeling\FinalRe port\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 M ODEL 

## S ELECTION AND 

## A PPLICATION 

## ..................................................................................................................... 2-1

2.2 E VALUATION 

## A PPROACH



## ............................................................................................................................... ........... 2-2



# 3 MM5 PERFORMANCE EVALUATION RESULTS................................................................... 3-6

3.1 Q UANTITATIVE 

## M ODEL 

## E VALUATION 

## R ESULTS 

## ........................................................................................................ 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 M ONTHLY 

## P RECIPITATION 

## A NALYSIS 

## ....................................................................................................................... 3-7



# 4 COMPARISON WITH OTHER ANNUAL MM5 SIMULATIONS ........................................... 4-1

4.1 C OMPARISON TO 

## O THER 

## A NNUAL 

## 36 KM 

## S IMULATIONS 

## ............................................................................................. 4-1

4.2 C OMPARISON TO 

## O THER 

## A NNUAL 

## 12 KM 

## S IMULATIONS 

## ............................................................................................. 4-3

4.3 C OMPARISON TO 

## O THER 

## A NNUAL 

## 4 KM 

## S IMULATIONS 

## ............................................................................................... 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 (R PO) Boundaries .................................................................... ......................... 3-24

Figure 3-2: CPC Analyzed Precipitation fo r 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 fo r February 2005 over the 36km Domain.................................................. .............. 3-26

Figure 3-5: MM5 Estimated Precipitation for February 2005 ov er 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 fo r 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 Precipitati on for May 2005 over th e 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 fo r 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 fo r September 2005 over the 36km Domain ................................................ ........... 3-33

Figure 3-19: MM5 Estimated Precipitation fo r 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 fo r 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 fo r December 2005 over the 36km Domain ................................................ ........ 3-36

Figure 3-26: CPC Analyzed Precipitation fo r 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 fo r February 2005 over the 12km Domain................................................. ............. 3-38

Figure 3-29: MM5 Estimated Precipitation for February 2005 ov er 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 fo r 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 Precipitati on for May 2005 over th e 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 fo r 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 fo r September 2005 over the 12km Domain ................................................ ........... 3-45

Figure 3-43: MM5 Estimated Precipitation fo r 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 fo r 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 fo r December 2005 over the 12km Domain ................................................ ........ 3-48

Figure 3-50: CPC Analyzed Precipitation fo r January 2005 over the 4km Domain................................................... ............... 3-49

Figure 3-51: MM5 Estimated Precipitatio n for January 2005 ove r the 4km Domain .................................................. ............ 3-49

Figure 3-52: CPC Analyzed Precipitation for February 2005 ove r the 4km Domain.................................................. .............. 3-50

Figure 3-53: MM5 Estimated Precipitation for February 2005 ov er the 4km Domain ................................................. ........... 3-50

Figure 3-54: CPC Analyzed Precipitation for March 2005 ove r the 4km Domain..................................................... ............... 3-51

Figure 3-55: MM5 Estimated Precipitatio n 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 Precipitatio n for April 2005 over the 4km Domain .................................................... .............. 3-52

Figure 3-58: CPC Analyzed Precipita tion for May 2005 over the 4km Domain....................................................... ................ 3-53

Figure 3-59: MM5 Estimated Precipitatio n for May 2005 over the 4km Domain ...................................................... ............. 3-53

Figure 3-60: CPC Analyzed Precipitati on for June 2005 ove r 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 fo r 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 fo r September 2005 over the 4km Domain ................................................. ............ 3-57

Figure 3-67: MM5 Estimated Precipitation fo r September 2005 ov er the 4km Domain................................................ .......... 3-57

Figure 3-68: CPC Analyzed Precipitation for October 2005 ove r 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 fo r November 2005 ov er the 4km Domain .................................................. ........... 3-59

Figure 3-71: MM5 Estimated Precipitation fo r November 2005 ov er the 4km Domain................................................. ......... 3-59

Figure 3-72: CPC Analyzed Precipitation fo r December 2005 over the 4km Domain.................................................. ............ 3-60

Figure 3-73: MM5 Estimated Precipitation for December 2005 ov er 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 a nd by State and Region in the 36km Domain............................................ ........... 3-9

Table 3-2: Temperature Bias (K) by Month a nd 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 a nd by State and Region in the 36km Domain ........................................... ........ 3-12

Table 3-5: Temperature Error (K) by Month an d 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/k g) 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/k g) by Month for the 4km Domain ............................................................... ...................... 3-20

Table 3-13: Wind Index of Agreement by Month an d 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 Agreem ent 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 Simu lations ............................................................ ..................... 4-6

Table 4-4: Mixing Ratio Error (g/kg) for 36km Annual MM5 Simu lations........................................................... ..................... 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 th e 12km MM5 Domain a nd Four-Corne r States............................................ .......... 4-8

iii

## Table 4-7: Mixing Ratio Error (g/kg) over th e 12km MM5 Domain a nd Four-Corne r 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 th e 12km MM5 Domain and Four-Corner States............................................ ... 4-12

Table 4-11: Mixing Ratio Bias (g/kg) over 4k m 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 4k m MM5 Domain....................................................................... ........................ 4-13

Table 4-14: Temperature Error (K) over the 4km MM5 Domain ...................................................................... ....................... 4-13

Table 4-15: Wind Index of Agre ement over the 4k m MM5 Domain.................................................................... .................... 4-14

iv

# 1 INTRODUCTION

Over the past half decade, emergent requir ements for direct num erical simulation of

urban and regional sc ale photochemical and secondary aerosol air quality—spawned

largely by the new particulate matter (PM2.5) and regional haze re gulations—have led to

intensified efforts to construct high-resolu tion emissions, meteorol ogical and air quality

data sets. The concomitant increase in co mputational throughput of low-cost modern

scientific workstations has ushered in a new era of regional air qua lity modeling. It is

now possible, for example, to exercise sophisticated mesoscale prognostic meteorological

models and Eulerian and Lagrangian photoc hemical/aerosol models for the full annual

period, simulating ozone, sulfate and nitrat e deposition, and secondary organic aerosols

(SOA) across the entire United States (U .S.) or over discrete subregions.

One such model is the Pennsylvania State Un iversity/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-h ydrostatic, terrain-following

model designed to simulate mesoscale atmospheric circulation. The model is supported

by several pre- and po st-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 fo r 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 Co rners region (New Mexico, Utah, Arizona, and

Colorado) at a 4km spacing.

1- 1

# 2 METHODOLOGY

The methodology for this approach is very straightforward. The ba sic methodology was

to apply the MM5 model for the annual period (2005 in this case) and the model results

(wind speeds, wind directions, temperatures, et c.) were compared with available surface

meteorological observations.

2.1 Model Select ion and Application

Below we give a brief summary of the MM5 input data preparati on procedure used for

this annual modeling exercise.

Model Selection : The publicly available non-hydrosta tic version of MM5 (version 3.7.2)

was used for this modeling study. Preproce ssor programs of the MM5 modeling system

including TERRAIN, REGRID, LITTLE_R, and IN TERPF 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) ha s 172 x 169 grid cells. The projection is

Lambert Conformal with the "national RPO" gr id 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 doma in is presented in

both sigma and height co ordinates in Table 2-1.

Topographic Inputs: Topographic information for th e 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 Da ta Center global data . Terrain data was

interpolated to the model grid using a Cr essman-type objective analysis scheme. To

avoid interpolating elevated terrain over water bodies, after the terrain databases were

interpolated onto the MM5 gr id, 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 re cently released PSU/NCAR databases provided with the

MM5 distribution. Standard MM5 surface charac teristics 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 us ed in the objective analyses, following the

procedures outlined by Stauffer and Seaman at PSU, were quality-inspected by MM5

2- 1

# pre-processors using automa ted 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 th e analysis nudging disc ussed below) were

obtained from the conven tional National Weather Se rvice (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 temperat ure 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 Co rners area, this issue is likely not important

and the skin temperatures were used.

FDDA Data Assimilation: This simulation used a co mbination 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 te mperature 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 (NCA R DS472.0) was used for winds with a

nudging coefficient of 4x10- 4 

# . No observation nudging wa s 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 Surf ace 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 in tervals. 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 base d on a combination of qualitative and

quantitative analyses. The qualitative appr oach was to compare the model estimated

monthly total precipitation with the monthl y Center for Prediction of Climate (CPC)

precipitation analysis. The statistical approa ch 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 statisti cs over a continental scale dom ain is problematic. To detect

if the model is missing important sub-regional features is diffi cult. For this analysis the

2- 2

# statistics are performed on a state by state ba sis, a Regional Planning Organization (RPO)

basis for the continental 36km do main, and on a domain-wide basis.

The observed database for wi nds, temperature, and water mixing ratio used in this

analysis was the NOAA Te chniques 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 fo r this analysis. The ra in observations are

taken from the CPC retrospective rainfall archives available at:

http://www.cpc.ncep.noaa.gov/products/precip/realtime/retro.shtm l.

- 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 

# 7 7 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 re sults are presented in this section. A full annual model

evaluation is very difficult to summarize in a single docume nt, 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 fo r each state, for each Regional Planning

Organization (RPO), and for the United St ates portion of the modeling domain. A

graphic of RPO boundaries is presented if Fi gure 3-1. In this comparison the vertical

level 1 (~19m) model estimates are compared directly with the no minal ~2m temperature

and moisture and ~10m wind measurements.

3.1.1 Temperature Bias and Error

Temperature bias statistics are presented in Ta bles 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 mode ling domain (ALL), the model

has a bias of 0.52 °C for the 36km domain and 0.14 °C for the 12k m 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 overes timated the remainder of th e year on the 12km domain

for the NMED4KM region. Table 3-3 show s that temperatures are generally

overestimated for the 4KM grid for all months except March.

Temperature error data are presented in Ta bles 3-4 through 3-6 for the 36km, 12km, and

4km grids, respectively. Th e overall temperature error (A LL category) is 2.28°C on the

36km domain, 2.72°C on the 12k m domain, and 3.34°C on th e 4km domain. The mean

error of 3.34°C for the 4km grid was somewh at consistent across all 12 months with

February being the lowest temperature erro r at 2.59°C. All temperature errors were

typically greater than 3.0°C for the 4km do main, 2.0°C for the 12km domain, and greater

than about 1.5°C fo r the 36km domain.

3.1.2 Mixing Ratio Bias and Error

Mixing ratio bias data are pr esented 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 domain s, 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) a nd Central States (CEN RAP) 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 resu lts 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 dom ain, 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 36k m and 12km domains

and 0.8 for the 4km domain. No significant mont hly trends were discerned in any of the

month to month variations or by State, Re gion, or area review ed. 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 co mparing the CPC and MM5 precipitation data,

note should be taken that the CPC analysis c overs only the Continental U.S. and does not

extend offshore or into Canada or Mexi co. 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 comp arisons for the 36km domain are presented in Figures 3-

2 through 3-25. For each month, th e 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 prec ipitation, MM5 does a

reasonably good job representing both the spatial coverage and magnitude of the

precipitation in the Western U.S. through out 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 a nd Eastern U.S., MM5 performs well during the

fall, winter and spring wh ich are the cooler months (Sept. through May), but

overestimates precipitation from June thr ough August especially in the southeast U.S.

Monthly total precipitation comparisons for th e 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 ye ar. The refinement of the 12km grid size is obvious when comparing

3- 7

# the CPC precipitation to that of MM5. The te ndency 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 mo re 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 re gion. 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 Figu res 3-38 and 3-40 for CP C 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 mont hly total precipitation comparisons for the

4km domain. While the general pa tterns of precipitation over the Four Corners region

are similar between the CPC and MM5 data, th e magnitude of the precipitation is highly

variable between the two da ta 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 CP C data. Even the late fall and winter

months show more precipitation although th e 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 anal ysis, 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 othe r meteorological data sets (other years and

at all spatial grid sizes) shou ld be considered prior to the decision to use or not use the

4km data in modeling studies.

3- 8Table 3-1: Temperature Bias(K)byMonthand byState and Region in the36km Domain.RegionJan '05Feb '05Mar '05Apr '05May '05Jun '05Jul '05Aug '05Sep '05Oct '05Nov '05MeanAK-0.460.73-0.36-0.09-1.03-1.22-0.740.28-0.070.430.34-0.20AL0.841.311.021.021.511.211.451.111.121.230.991.16ALL0.300.540.190.500.800.400.450.650.540.730.650.52AR0.661.651.120.951.451.191.531.221.111.300.591.16AZ0.980.28-0.04-0.82-1.36-1.57-1.20-0.33-0.34-0.341.08-0.33CA1.760.420.29-0.46-1.13-1.73-0.85-0.30-0.070.361.65-0.01CENRAP0.270.950.641.101.310.920.981.080.800.820.550.86CO0.33-0.46-1.67-0.98-0.28-0.21-0.460.070.020.14-0.08-0.33CT-0.20-0.77-1.260.601.080.540.360.250.240.520.640.18DC-0.25-0.06-0.220.480.740.280.270.07-0.68-0.220.230.06DE-1.000.050.200.061.260.24-0.230.05-0.050.380.540.14FL0.850.740.440.09-0.080.51-0.040.400.260.621.000.44GA0.861.270.960.781.241.001.151.050.761.081.251.04IA-1.010.200.901.721.811.101.401.701.130.890.540.94ID0.920.52-0.31-0.450.460.590.440.781.341.271.260.62IL-0.190.720.961.471.671.161.341.831.491.180.131.07IN-1.060.410.871.331.650.881.031.391.081.070.300.81KS0.881.901.711.161.020.680.450.690.260.390.370.86KY-0.381.281.110.991.080.730.781.050.881.140.430.83LA0.581.040.840.220.490.520.560.900.461.060.850.68MA-0.52-0.75-1.660.460.990.320.030.120.560.590.550.06MANE VU-0.28-0.46-0.980.161.020.480.280.340.310.500.270.15MD-0.050.480.160.441.100.340.250.460.070.450.670.40ME-0.43-0.64-1.61-0.451.150.870.530.570.700.400.300.13MI-0.09-0.48-0.320.451.220.450.500.550.581.080.410.40MN-1.36-1.03-1.461.281.921.400.961.031.000.810.470.46MO0.421.281.150.970.960.910.641.181.000.86-0.060.85MS1.231.701.531.141.591.351.641.411.171.491.301.41MT1.130.39-0.14-0.500.380.37-0.050.380.180.77-0.240.24MW-0.61-0.28-0.051.011.480.790.871.030.921.040.220.583- 9NC0.560.980.750.350.970.450.590.550.460.821.300.71ND-0.730.640.741.861.741.411.281.061.131.350.811.03NE0.411.911.721.951.330.720.581.150.490.790.971.09NH-0.72-0.41-1.620.891.771.290.761.010.900.450.650.45NJ-0.20-0.58-0.510.120.750.100.180.240.080.420.340.09NM1.100.810.230.140.30-0.30-0.430.320.250.571.010.36NMED4KM0.16-0.47-1.19-0.96-0.42-0.52-0.710.07-0.020.220.59-0.30NV0.64-1.15-1.68-2.19-1.83-2.47-2.19-1.60-1.28-0.650.25-1.29NY0.06-0.48-1.27-0.100.630.19-0.020.070.060.48-0.15-0.05OH-0.780.160.101.001.620.710.761.040.971.370.340.66OK1.551.861.140.731.341.151.071.180.850.56-0.041.04OR0.621.350.50-0.48-0.54-1.23-1.31-1.12-0.160.601.43-0.03PA-0.15-0.25-0.040.031.110.600.560.460.150.630.310.31RI-0.39-0.85-0.950.791.240.360.430.350.670.810.320.25SC1.081.590.860.521.170.880.870.820.410.911.530.97SD0.041.891.131.931.801.331.071.290.871.181.041.23TN0.131.290.430.740.970.760.970.950.880.860.500.77TX1.281.721.120.770.860.541.020.890.670.860.760.95UT0.740.27-0.76-1.10-0.42-0.47-0.320.210.330.611.310.04VA-0.050.550.320.360.890.390.570.640.390.480.620.47VISTAS0.561.010.710.510.890.690.710.740.570.841.010.75VT-0.79-0.84-2.220.051.000.780.180.490.390.08-0.89-0.16WA0.701.570.570.170.01-0.59-0.44-0.030.170.741.050.36WI-1.48-1.69-1.321.091.450.830.780.590.620.62-0.030.13WRAP0.920.64-0.04-0.31-0.30-0.65-0.480.040.160.510.970.13WV-0.250.340.260.381.360.740.670.600.530.490.280.49WY0.670.70-1.54-1.48-0.24-0.14-0.650.42-0.070.25-0.68-0.253-10Table 3-2:Temperature Bias (K) by Month and byState andRegion inthe 12kmDomain.RegionJan '05Feb '05Mar '05Apr '05May '05Jun '05Jul '05Aug '05Sep '05Oct '05Nov '05MeanALL0.940.41-0.16-0.42-0.41-0.53-0.210.320.260.390.900.14AZ0.59-0.30-0.36-0.97-1.43-1.70-1.44-0.48-0.44-0.520.77-0.57CA1.360.160.20-0.34-0.83-1.15-0.030.400.150.441.380.16CO0.68-0.06-1.42-0.94-0.30-0.01-0.140.450.380.320.46-0.05ID1.370.32-0.92-1.14-0.050.290.580.701.641.201.390.49NM0.640.08-0.15-0.06-0.07-0.40-0.660.120.030.250.890.06NMED4KM0.25-0.43-1.19-0.90-0.39-0.33-0.480.270.210.310.86-0.17NV0.72-1.09-1.55-2.01-1.75-2.20-1.61-1.19-0.79-0.390.27-1.05UT0.760.34-0.63-0.97-0.25-0.320.070.450.871.001.400.25WY0.731.56-0.80-0.930.090.450.171.050.680.780.200.36Table 3-3TemperatureBias(K) byMonthforthe 4km Domain.RegionJan '05Feb'05Mar '05Apr'05May '05Jun05Jul'05Aug'05Sep'05Oct'05Nov'05MeanALL1.450.95-0.030.721.190.940.581.221.271.541.911.073-11Table 3-4:Temperature Error(K) by Month and by State and Regionin the 36kmDomain.•RegionJan '05Feb '05Mar '05Apr '05May '05Jun '05Jul '05Aug '05Sep '05Oct '05Nov '05MeanAK1.941.521.351.741.881.861.291.651.141.241.621.57AL2.172.112.352.272.411.952.082.072.232.442.332.22ALL2.362.322.412.372.262.102.172.202.262.262.362.28AR1.992.352.142.032.181.992.192.142.022.192.032.11AZ2.271.982.502.973.123.263.282.812.882.823.102.82CA2.942.282.492.632.662.862.942.892.962.863.302.80CENRAP2.352.402.402.272.181.901.992.092.082.252.292.20CO3.443.293.583.433.092.903.232.883.022.913.333.19CT1.822.132.371.971.711.751.511.551.881.762.171.87DC2.261.231.361.431.481.151.211.221.331.411.431.41DE2.592.401.772.222.251.651.811.561.721.611.851.95FL2.072.052.111.941.871.751.721.911.741.942.061.92GA2.412.282.512.392.441.912.081.922.102.312.502.26IA2.061.942.172.482.491.982.032.252.272.262.102.18ID2.922.872.992.972.732.803.443.423.222.902.913.02IL1.861.731.952.232.242.011.892.272.262.111.692.02IN2.071.671.852.282.221.801.751.911.971.971.591.92KS2.522.622.562.162.051.861.901.931.892.242.472.20KY1.731.871.851.941.791.691.491.961.832.021.961.83LA2.262.142.142.152.031.992.012.202.002.522.462.17MA2.112.422.532.231.882.111.811.852.131.852.062.09MANE_VU2.112.252.302.321.971.941.781.882.081.872.082.05MD2.271.961.802.062.111.661.581.792.162.032.251.97ME2.362.602.472.641.882.131.941.891.981.771.872.14MI1.961.832.192.422.292.072.162.002.142.011.802.08MN2.492.562.862.622.502.161.872.002.182.252.052.32MO2.002.112.232.021.951.831.822.051.872.021.901.98MS2.142.292.372.212.402.062.232.252.182.632.442.293-12MT4.073.403.402.972.502.162.952.752.702.792.672.94MW2.031.902.152.322.252.001.942.052.132.051.732.05NC2.212.212.382.182.171.891.901.791.902.132.712.13ND2.592.402.612.762.461.972.092.212.402.502.072.37NE2.712.842.702.612.381.832.062.232.322.532.702.45NH2.803.233.573.312.682.802.542.592.842.362.672.85NJ1.822.021.942.131.861.651.511.742.161.852.161.89NM2.792.212.612.862.642.672.582.412.572.473.132.63NMED4KM3.022.673.243.323.083.053.172.772.912.763.343.03NV2.952.683.133.393.373.924.293.924.203.563.263.52NY1.962.222.282.291.841.881.751.922.021.882.112.01OH1.951.731.982.172.261.951.711.952.002.041.701.95OK2.682.572.201.962.051.992.042.182.152.202.432.22OR2.592.962.672.082.112.543.113.142.982.462.822.68PA1.941.831.922.202.001.761.671.811.961.791.901.89RI1.851.961.951.881.911.841.751.851.821.721.571.83SC2.282.352.362.102.091.741.851.741.732.122.652.09SD2.412.872.672.692.462.042.212.282.362.532.512.46TN1.802.112.022.072.091.771.682.032.132.132.292.01TX2.282.382.242.061.931.682.042.021.932.202.422.11UT2.722.722.952.892.572.783.072.923.072.773.002.86VA2.141.972.172.202.111.891.821.852.182.082.462.08VISTAS2.152.132.272.162.151.861.881.911.982.152.412.10VT2.512.613.012.632.022.161.832.012.262.052.512.33WA2.202.752.251.941.972.032.402.652.371.891.922.22WI2.342.402.542.412.232.011.941.962.092.061.752.16WRAP2.862.672.732.712.572.622.872.792.802.642.892.74WV2.111.922.212.302.351.981.871.942.222.012.332.11WY3.553.593.203.192.402.533.042.923.002.752.923.013-13Table 3-5:TemperatureError(K) by Month and byState and Regionin the 12km Domain.RegionJan '05Feb '05Mar '05Apr '05May '05Jun '05Jul '05Aug '05Sep '05Oct '05Nov '05MeanALL2.832.562.702.692.472.522.772.642.812.783.122.72AZ2.211.852.482.883.043.193.162.572.772.773.082.73CA2.732.202.382.432.332.352.562.562.732.723.252.57CO3.263.063.293.172.932.803.112.692.932.903.323.04ID3.063.093.053.062.582.643.383.453.353.023.113.07NM2.752.112.582.862.542.662.552.372.652.553.302.63NMED4KM3.012.533.123.283.023.023.042.632.902.853.512.99NV3.102.733.143.233.063.583.893.614.003.633.673.42UT2.742.702.882.752.462.683.152.903.192.963.112.87WY3.303.572.812.752.172.362.862.922.902.722.722.83Table 3-6TemperatureError(K)byMonthfor the 4km Domain.RegionJan '05Feb '05Mar'05Apr05May '05Jun '05Jul'05Aug'05Sep'05Oct'05Nov'05MeanALL3.092.593.033.263.323.513.713.253.553.503.983.343-14Table 3-7: Mixing Ratio Bias (g/kg)by Month and by State and Regionin the 36kmDomain.RegionJan '05Feb '05Mar '05Apr '05May '05Jun '05Jul '05Aug '05Sep '05Oct '05Nov '05MeanAK0.050.150.290.190.550.530.170.370.420.540.200.31AL1.291.161.121.160.83-0.070.03-0.150.390.260.390.58ALL0.390.390.350.440.050.06-0.17-0.240.190.110.250.17AR0.860.760.410.590.170.00-0.32-0.59-0.04-0.240.100.15AZ-0.31-0.34-0.340.401.041.411.370.221.070.540.610.52CA0.02-0.21-0.40-0.39-0.32-0.48-0.34-0.03-0.48-0.48-0.33-0.31CENRAP0.360.360.150.19-0.08-0.09-0.51-0.550.06-0.09-0.02-0.02CO0.150.060.100.13-0.33-0.89-0.25-0.85-0.34-0.57-0.15-0.27CT0.370.440.540.590.270.600.100.220.560.690.940.48DC0.980.860.810.450.160.13-0.49-0.350.240.550.490.35DE0.430.530.550.480.22-0.11-0.49-0.170.290.410.610.25FL1.070.930.880.840.680.01-0.100.190.550.310.780.56GA1.161.051.230.990.62-0.25-0.16-0.330.210.090.340.45IA0.090.250.03-0.16-0.330.26-0.69-0.850.04-0.06-0.01-0.13ID0.230.110.47-0.01-0.60-0.69-0.38-0.06-0.46-0.210.16-0.13IL0.310.430.210.38-0.080.51-0.60-1.08-0.32-0.040.15-0.01IN0.000.500.440.65-0.140.46-0.15-0.660.290.160.270.17KS0.350.28-0.16-0.030.170.27-0.190.110.340.180.120.13KY0.460.700.461.100.230.480.370.080.440.180.500.45LA0.960.960.850.720.430.410.14-0.040.770.200.180.51MA0.190.240.350.640.300.730.480.800.960.870.940.59MANE VU0.350.390.510.820.240.660.220.490.730.720.770.54MD0.650.750.640.38-0.170.09-0.55-0.410.440.580.630.28ME0.110.150.390.920.561.220.700.620.850.790.660.63MI0.120.200.451.040.100.710.220.040.450.460.300.37MN-0.070.020.020.67-0.050.410.22-0.170.320.140.020.14MO0.620.660.150.220.270.640.06-0.170.10-0.060.320.263-15MS1.060.960.900.830.450.06-0.14-0.090.370.130.280.44MT0.290.340.450.03-0.42-0.90-0.73-0.10-0.09-0.070.33-0.08MW0.140.310.380.760.040.730.00-0.350.150.220.220.24NC1.120.891.101.200.830.130.10-0.210.540.520.730.63ND-0.040.140.050.05-0.35-0.31-0.160.060.450.140.170.02NE0.230.290.010.050.080.360.220.050.690.150.030.20NH0.130.160.320.960.261.080.560.560.730.790.860.58NJ0.590.620.761.120.230.30-0.100.440.770.760.940.58NM0.08-0.07-0.170.05-0.040.380.11-1.020.34-0.160.42-0.01NMED4KM0.230.090.170.22-0.08-0.080.10-0.760.21-0.240.110.00NV0.170.100.39-0.03-0.230.160.880.510.850.130.340.30NY0.270.300.370.850.070.410.050.360.640.690.720.43OH0.130.520.500.66-0.010.800.270.180.540.420.470.41OK0.570.19-0.24-0.33-0.67-1.34-2.22-1.19-0.68-0.61-0.10-0.60OR0.200.390.25-0.10-0.47-0.480.270.340.080.000.400.08PA0.580.630.750.960.300.740.170.660.770.660.740.63RI0.250.300.460.520.411.041.000.900.820.840.790.67SC1.131.001.351.280.780.150.12-0.240.630.350.650.65SD0.120.220.03-0.01-0.38-0.080.01-0.180.290.020.060.01TN0.850.840.640.910.290.12-0.04-0.370.320.210.310.37TX0.490.500.360.04-0.13-0.61-0.96-0.97-0.20-0.23-0.17-0.17UT0.210.180.37-0.05-0.62-0.540.27-0.19-0.02-0.32-0.25-0.09VA0.710.700.620.52-0.17-0.51-0.86-1.12-0.040.250.430.05VISTAS1.010.910.950.960.53-0.03-0.14-0.270.390.300.560.47VT0.110.170.300.880.190.960.580.600.700.700.620.53WA0.160.190.330.04-0.31-0.52-0.11-0.03-0.100.020.310.00WI0.030.110.390.970.201.070.30-0.190.010.140.030.28WRAP0.100.040.01-0.05-0.26-0.37-0.07-0.15-0.06-0.180.07-0.08WV0.770.800.751.190.440.31-0.08-0.170.640.540.750.54WY0.310.250.160.09-0.74-1.33-0.42-0.25-0.07-0.360.03-0.213-16Table 3-8: Mixing Ratio Bias (g/kg)by Month and by State and Regionin the 12kmDomain.RegionJan '05Feb '05Mar '05Apr '05May '05Jun '05Jul '05Aug '05Sep '05Oct '05Nov '05MeanALL0.110.020.010.01-0.07-0.100.290.030.13-0.100.020.03AZ-0.24-0.16-0.130.541.171.551.750.321.070.700.710.66CA0.02-0.19-0.29-0.35-0.20-0.36-0.030.23-0.26-0.36-0.45-0.20CO0.150.080.100.23-0.12-0.460.24-0.57-0.10-0.37-0.08-0.08ID0.25-0.010.490.20-0.60-0.47-0.220.29-0.32-0.150.15-0.04NM0.10-0.08-0.070.220.220.600.60-0.810.43-0.080.470.15NMED4KM0.210.080.200.340.170.270.69-0.420.41-0.050.180.19NV0.07-0.060.340.12-0.090.450.870.690.960.230.280.35UT0.100.040.370.11-0.35-0.130.680.250.22-0.07-0.140.10WY0.230.220.190.13-0.59-0.89-0.100.000.11-0.230.06-0.08Table 3-9Mixing RatioBias(g/kg)byMonthforthe 4kmDomain.RegionJan '05Feb'05Mar '05Apr '05May '05Jun '05Jul'0Aug'05Sep'05Oct'05Nov'05MeanALL-0.10-0.29-0.28-0.200.050.521370.331.080.000.140.243-17Table 3-10:Mixing Ratio Error(g/kg) byMonth andbStatandRegionithe 36kmDomain.RegionJan '05Feb '05Mar '05Apr '05May'05Jun'05Jul '05Aug'05Sep'05Oct '05Nov '05MeanAK0.450.390.460.541.050.980.600.700.640.680.440.63AL1.351.261.271.361.391.451.591.601.370.961.021.33ALL0.710.740.811.111.161.491.591.521.331.000.861.12AR0.971.020.911.161.281.531.811.861.430.960.931.26AZ0.950.940.940.881.431.832.542.002.061.290.951.44CA0.790.851.071.031.011.121.441.481.411.181.241.15CENRAP0.670.770.761.151.331.701.841.691.431.020.821.20CO0.550.590.590.831.051.531.531.531.241.010.651.01CT0.460.540.610.840.871.141.241.141.120.881.020.90DC1.030.900.881.010.991.361.501.341.100.960.941.09DE0.540.620.620.830.871.061.261.171.100.740.800.87FL1.381.301.341.281.491.411.521.441.551.211.401.39GA1.311.231.411.341.321.571.721.561.611.161.151.40IA0.350.570.651.161.371.841.901.911.350.890.641.15ID0.550.520.810.791.161.281.311.341.020.840.690.94IL0.550.580.611.111.121.521.701.821.380.960.691.09IN0.580.610.661.201.041.441.351.511.210.900.721.02KS0.600.710.620.991.321.511.481.411.250.920.651.04KY0.800.810.821.441.111.451.461.511.210.910.871.13LA1.231.231.231.231.421.631.681.541.601.041.301.38MA0.420.410.490.890.781.281.211.341.260.981.020.92MANE_VU0.500.520.611.030.881.321.291.291.230.930.920.96MD0.790.830.790.981.031.421.471.521.441.000.961.11ME0.310.330.481.030.841.531.271.181.170.890.760.89MI0.350.350.551.200.791.381.291.181.090.830.630.88MN0.230.360.421.231.071.521.471.281.270.880.510.93MO0.730.800.641.071.241.571.481.591.290.940.781.10MS1.221.181.201.241.331.541.721.641.551.031.091.343-18MT0.470.510.730.750.981.481.361.220.920.860.630.90MW0.430.460.581.160.941.481.441.411.210.890.640.97NC1.190.991.231.531.371.561.691.571.411.141.151.35ND0.250.360.450.861.031.281.391.201.130.820.520.84NE0.490.590.561.021.221.471.661.601.320.880.581.04NH0.370.380.481.120.941.611.291.261.220.890.990.96NJ0.650.710.791.220.851.151.191.211.321.001.021.01NM0.770.780.800.791.151.571.781.841.651.010.681.17NMED4KM0.600.590.660.801.001.451.561.521.410.990.621.02NV0.650.610.970.701.221.101.731.461.250.880.801.03NY0.420.430.511.010.841.191.301.241.170.900.850.90OH0.550.640.651.030.951.411.281.341.170.910.750.97OK0.880.850.791.091.512.172.611.961.561.120.851.40OR0.660.680.800.770.950.901.161.160.910.930.710.88PA0.670.700.821.140.911.341.291.391.260.940.901.03RI0.410.460.550.780.931.321.571.511.291.060.930.98SC1.211.101.431.461.291.361.431.411.351.121.141.30SD0.330.520.520.891.071.541.581.441.150.810.530.94TN0.960.960.931.331.091.331.611.681.300.990.881.19TX0.941.051.051.171.511.802.061.901.631.211.121.40UT0.560.540.740.751.221.481.581.411.100.920.771.01VA0.880.830.841.201.251.641.861.871.431.030.911.25VISTAS1.171.081.181.361.331.501.651.581.441.091.121.32VT0.330.330.461.180.961.551.241.231.170.900.920.93WA0.560.560.720.720.790.891.021.040.890.810.610.78WI0.270.300.511.260.901.641.441.221.190.870.520.92WRAP0.640.670.800.851.051.311.471.421.251.000.821.03WV0.850.880.841.481.091.431.531.521.130.940.941.15WY0.540.510.550.751.141.741.411.311.020.820.600.943-19Table 3-11 Mixing Ratio Error (g/kg) by Monthand by State and Regionin the 12km Domain.RegionJan '05Feb '05Mar '05Apr '05May '05Jun '05Jul '05Aug '05Sep '05Oct '05Nov '05MeanALL0.660.680.770.861.091.381.671.521.340.990.851.07AZ0.880.790.950.891.461.882.691.842.021.301.001.43CA0.790.830.971.010.981.061.481.491.351.141.221.12CO0.510.580.560.800.951.391.571.461.190.880.620.96ID0.480.450.740.691.091.221.261.380.950.710.610.87NM0.740.700.750.791.121.591.911.891.671.010.681.17NMED4KM0.570.560.640.800.931.421.711.531.420.890.621.01NV0.600.570.950.681.121.221.771.561.290.950.771.04UT0.550.520.710.721.121.381.741.541.160.870.691.00WY0.460.510.550.731.011.521.391.281.030.750.550.89Table 3-12 MixingRatioError(g/kg)byrMonth forthe4kmi Domain.RegionJan '05Feb'05Mar'05Apr '05May05Jun '05Jul'05Aug'05Sep'05Oct'05Nov'05MeanALL0.590.700.680.821.011.491.911.491.701.010.691.103-20Table 3-13:Wind Index of Agreement byMonth andbStatandRegionithe 36kmDomain.RegionJan '05Feb'05Mar '05Apr '05May'05Jun'05Jul '05Aug'05Sep'05Oct '05Nov '05MeanAK0.550.590.550.600.500.470.530.520.520.480.500.53ALL0.850.870.870.870.860.880.880.900.870.890.870.87AL0.640.640.640.630.650.610.640.630.650.670.650.64AR0.660.680.680.680.670.670.710.720.700.700.730.69AZ0.720.670.720.720.730.710.680.750.730.740.740.72CA0.730.750.760.730.760.760.770.780.780.790.800.76CENRAP0.810.830.840.860.820.850.850.870.830.840.850.84CO0.790.750.760.790.780.770.780.780.760.730.760.77CT0.550.550.540.560.520.560.570.570.520.510.500.54DE0.740.790.760.730.710.740.790.790.730.740.750.75FL0.710.770.710.720.710.730.690.700.680.700.730.71GA0.610.580.610.620.580.570.530.590.560.590.600.59IA0.610.660.660.610.680.670.620.680.690.690.680.66ID0.740.770.650.720.670.770.760.750.670.780.730.73IL0.630.640.630.650.590.680.670.670.660.690.670.65IN0.590.580.620.550.600.630.620.620.660.650.600.61KS0.730.680.700.730.730.750.750.760.740.720.750.73KY0.520.560.520.570.560.560.580.590.620.530.570.56LA0.660.660.670.670.630.650.660.660.670.660.660.66MA0.610.690.580.620.680.600.590.650.570.610.560.61MANE VU0.700.760.720.660.770.700.760.690.700.670.690.71MD0.580.600.590.590.610.560.560.520.580.610.540.58ME0.550.570.600.530.530.540.590.530.560.560.520.55MI0.660.630.670.630.650.680.590.660.700.650.630.65MN0.660.670.710.680.660.700.690.640.700.680.690.68MO0.670.670.670.680.620.700.680.670.680.650.710.67MS0.620.590.600.610.600.540.560.630.650.610.590.60MT0.770.790.800.770.730.760.770.790.760.780.750.773-21MW0.700.720.700.720.670.750.720.790.740.760.730.73NC0.560.600.590.580.540.630.610.590.580.620.620.59ND0.720.720.690.720.730.750.670.700.720.720.730.72NE0.730.740.750.760.760.750.770.780.760.750.790.76NH0.560.480.430.510.420.340.360.280.390.350.390.41NJ0.560.580.540.610.590.520.520.540.530.610.570.56NMED4KM0.790.770.770.790.790.780.780.790.780.790.770.78NM0.770.790.770.770.780.780.780.770.780.760.750.77NV0.720.740.770.720.740.710.740.740.740.740.780.74NY0.710.710.730.690.750.720.740.690.710.690.690.71OH0.620.600.610.620.620.640.600.660.630.650.640.63OK0.680.670.710.660.650.670.690.720.710.670.700.68OR0.750.720.740.730.720.750.770.770.730.770.770.75PA0.620.640.620.640.650.650.660.680.660.660.660.65RI0.680.680.670.670.690.710.710.760.720.700.700.70SC0.580.610.610.640.550.570.610.590.580.570.570.59SD0.760.750.760.770.770.770.770.760.730.750.770.76TN0.570.570.610.600.610.550.660.640.600.630.670.61TX0.730.760.770.760.790.760.750.770.810.750.780.77UT0.660.660.730.750.740.690.750.740.730.740.750.72VA0.580.610.630.610.650.620.650.650.580.660.640.63VISTAS0.710.760.770.730.750.730.740.760.790.760.770.75VT0.490.510.500.500.490.500.500.510.510.510.500.50WA0.710.780.740.800.720.720.790.760.800.790.750.76WI0.610.620.600.590.640.620.630.610.650.590.620.62WRAP0.850.840.860.850.860.850.840.870.860.860.870.86WV0.530.490.550.520.540.550.570.580.580.560.540.55WY0.770.720.760.750.690.740.760.760.710.740.720.743-22Table 3-14:Wind Index of Agreement byMonth and bySt tand Region in the in 12km Domain.RegionJan '05Feb '05Mar '05Apr '05May '05Jun '05Jul '05Aug '05Sep '05Oct '05Nov '05MeanALL0.870.860.880.870.850.870.890.850.880.870.880.87AZ0.730.670.730.720.720.710.680.740.720.730.740.72CA0.770.790.780.760.790.800.800.810.810.830.830.80CO0.830.820.810.830.810.810.800.810.790.790.790.81ID0.730.650.710.760.610.780.660.760.750.750.690.71NMED4KM0.810.800.810.810.820.800.830.820.800.810.800.81NM0.770.810.780.800.790.810.810.810.800.790.780.80NV0.710.710.760.710.750.710.760.740.740.750.780.74UT0.710.720.770.770.740.760.780.760.740.760.780.75WY0.780.790.790.780.780.790.800.780.750.770.800.78Table 3-15: WindIndeof Agreement by Monthin the 4km Domain.RegionJan '05Feb'05Mar '05Apr '05May '05Jun '05Jul'05Aug'05Sep'05Oct'05Nov'05MeanALL0.830.820.790.780.800.800.800.810.800.810.790.803-23

# 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 Precipitati on 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 fo r 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 20 05 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 20 05 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 Precipitati on 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 Estimate d Precipitation for December 2005 over the 4km Domain.



# 3-60

# 4 Comparison with Other Annual MM5 Simulations

This section presents a comp arison of this 36 km MM5 si mulation with other 36km

annual meteorological simulations that have b een completed during the past several years

by Alpine Geophysics and other researchers (T ables 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, a nd 4km grid domains (Tables

4-1 through 4-10).

4.1 Comparison to Ot her Annual 36km Simulations

Comparisons between the Alpine MM5 simu lations 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 gr id resolution using the same horizontal and

vertical grid definitio ns as the 36km grid simulations presented in this report. The

simulations compared include the 2001 EP A (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) studie s. The current study will be referred

herein as the NMED 2005 study because the MM5 ap plication was pe rformed under

contract to the New Mexico Environmen t Department (NMED) (and funded by GIANT

Refining) and was perfor med 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 m odel 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 wa s not to give a passing or failing grade to

any one particular meteorological model applica tion, but rather to put its results into the

proper context of other models and meteorological data se ts. The key to the benchmarks

is to understand how good or poor the results ar e 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 Agreemen t - 0 = worst, 1 = best

4- 1

# Temperature bias for both the entire domain a nd 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 (A LL 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 wh ich is important in this evaluation to

determine viability of the data for use in the companion dispersion modeling that will

ensue. This NMED 2005 simu lation 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 regi ons at 0.86, 0.58 and 0.75 K, respectively.

Temperature error is presen ted 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 thr ee years of simulation produced

within this NMED study. As with the other si mulations the MM5 results for this analysis

are somewhat greater than the benchmark of 2.0 K. Table 4-2 s hows the temperature

error for the NMED 2005 MM5 simulati on was 2.28 K over ALL study areas. The

2.74K for the WRAP RPO was co mparable to other simulations. As with the other

studies, the temperatur e error in this NMED 2005 study is rather c onsistent 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 th e 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 resu lted in mixing ratio errors that were

comparable with the other annual MM5 applicat ions by other researchers and to the other

NMED simulation years.

Wind Speed Index of Agreement (IA) is presen ted 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 Ot her Annual 12km Simulations

No other consistent model evaluations for 12km scale grid domain simulations over this

domain were available for comparison to thos e 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 compar isons 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. Th e mean mixing ratio bias was within the range of the

benchmark for of four of th e 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 we ll as each state.

Mixing ratio error over the 12km simulation domain is pr esented 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 sligh tly greater than the

benchmark for this NMED 2005 da ta set in July and Septem ber. All other months for

Arizona are less than the benc hmark 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 benc hmark of +/- 0.5 K. Month- to-month variability of the

temperature bias was within the benchm ark with January and November being

overestimated and June underes timated. Review of the state temperature bias indicated

that the summer months were underestimated and the winter months overestimated for

the four states. When comparing temperatur e bias for the NMED 2005 simulations to

other years, the NMED 2005 simu lations generally were closer to the temperatures than

the other years. On a month-to-month comp arison between the years of simulation, the

NMED 2005 results were generally comparable.

Temperature error over the 12km domain is pres ented 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 pres ented in Table 4-10 over the 12km domain.

The domain-wide IA for the NMED 2005 12k m 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 acr oss all states for th e NMED 2005 and also

comparable to the NMED 2003 and NMED 2004 data sets.

4.3 Comparison to Ot her Annual 4km Simulations

The 4km domain covered only por tions of each of the Four Co rners states. Thus, no state

temperature, mixing ratio, or winds were av ailable for an individual state. Rather

comparisons were made for the overall 4k m 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 th e three years of si mulation. On a monthly basis the mixing

ratio bias is just greater than the benchmar ks 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 benchm ark 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 temperatur e bias on an annual average of 1.07 K as

shown in Table 4-13, greater than the guide line 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 pr esented 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 th e means for the three years s hows comparable results as

does the month-to-m onth variation. In all ca ses the temperature er ror was greater than

the benchmark of 2.0 K.

4- 4

# Wind Speed Index of Agreement (IA) is pres ented in Table 4-15 over the 4km domain.

The domain-wide IA mean for the NMED 200 5 4km simulations was 0.80 as it also was

for the NMED 2003 and NMED 2004 simulation s which compares fa vorably with the

best score of 1.0 and is hi gher 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- 7Table 4-6: Mixing Ratio Bias (g/kg) overthe 12km MM5 Domain and Four-Corner States.RegionJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 2003ALL0.09-0.04-0.010.08-0.14-0.180.08-0.02-0.34-0.12-0.190.02-0.06AZ-0.12-0.15-0.260.260.530.570.540.260.180.48-0.200.030.18CO0.260.040.100.15-0.09-0.140.63-0.05-0.360.04-0.150.010.04NM0.480.290.260.410.570.850.960.340.120.390.100.250.42UT0.150.040.270.250.060.471.240.460.460.42-0.060.130.32NMED 2004ALL-0.07-0.080.03-0.12-0.12-0.16-0.23-0.38-0.33-0.08-0.15-0.06-0.15AZ-0.29-0.14-0.060.080.861.040.770.390.130.20-0.21-0.370.20CO-0.09-0.090.26-0.05-0.42-0.13-0.54-0.76-0.60-0.20-0.100.01-0.23NM0.030.080.20-0.030.520.930.22-0.09-0.13-0.06-0.04-0.090.13UT0.240.100.770.27-0.050.480.840.500.470.26-0.130.080.32NMED 2005ALL0.110.020.010.01-0.07-0.100.290.030.13-0.100.020.03AZ-0.24-0.16-0.130.541.171.551.750.321.070.700.710.66CO0.150.080.100.23-0.12-0.460.24-0.57-0.10-0.37-0.08-0.08NM0.10-0.08-0.070.220.220.600.60-0.810.43-0.080.470.15UT0.100.040.370.11-0.35-0.130.680.250.22-0.07-0.140.104- 8Table 4-7: Mixing Ratio Error(g/kg) over the 12km MM5 Domain andFour-Corner States.RegionJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 2003ALL0.680.630.750.791.071.301.731.531.311.150.760.651.03AZ0.740.760.840.751.171.381.992.041.731.440.800.661.19CO0.550.470.610.851.141.231.731.461.050.790.560.460.91NM0.720.660.730.861.191.591.941.511.341.180.850.511.09UT0.570.510.620.660.991.252.131.641.160.930.610.500.96NMED 2004ALL0.630.610.970.921.081.361.611.451.310.940.720.641.02AZ0.740.641.000.911.191.491.811.801.591.020.760.711.14CO0.460.480.770.781.051.271.641.471.150.790.560.450.91NM0.530.610.860.951.301.751.791.531.341.100.660.601.09UT0.450.401.110.780.921.441.741.421.200.770.590.460.94NMED 2005ALL0.660.680.770.861.091.381.671.521.340.990.851.07AZ0.880.790.950.891.461.882.691.842.021.301.001.43CO0.510.580.560.800.951.391.571.461.190.880.620.96NM0.740.700.750.791.121.591.911.891.671.010.681.17UT0.550.520.710.721.121.381.741.541.160.870.691.004- 9Table 4-8: Temperature Bias (K) overthe 12km MM5Domaiin and Four-Corner States.RegionJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 2003ALL0.66-0.10-0.68-1.15-0.71-0.88-0.91-0.850.170.870.190.83-0.21AZ0.65-0.12-0.25-1.49-1.20-1.59-1.66-1.36-0.360.290.150.96-0.50CO0.55-0.68-2.11-2.20-0.75-0.59-0.75-0.630.330.80-0.140.99-0.43NM0.32-0.13-0.53-1.06-0.81-1.05-1.32-0.96-0.450.520.111.08-0.36UT0.91-1.04-1.78-1.35-0.61-1.20-1.27-0.700.541.51-0.020.93-0.34NMED 2004ALL1.020.20-0.08-0.34-0.66-0.59-0.27-0.020.110.220.720.840.10AZ0.730.14-0.03-0.98-1.63-1.32-0.87-0.52-0.78-0.41-0.070.61-0.43CO1.47-0.04-1.04-1.08-0.170.000.250.220.07-0.170.260.580.03NM0.700.480.17-0.03-0.24-0.68-0.110.19-0.090.020.341.010.15UT1.900.14-0.72-0.45-0.53-0.45-0.44-0.160.180.331.031.090.16NMED 2005ALL0.940.41-0.16-0.42-0.41-0.53-0.210.320.260.390.900.14AZ0.59-0.30-0.36-0.97-1.43-1.70-1.44-0.48-0.44-0.520.77-0.57CO0.68-0.06-1.42-0.94-0.30-0.01-0.140.450.380.320.46-0.05NM0.640.08-0.15-0.06-0.07-0.40-0.660.120.030.250.890.06UT0.760.34-0.63-0.97-0.25-0.320.070.450.871.001.400.254-10Table 4-9: Temperature Error(K) over the 12km MM5 Domainand Four-Corner States.RegionJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 2003ALL2.982.482.682.672.522.572.842.642.843.272.622.792.74AZ3.002.202.392.712.752.752.832.772.883.062.622.942.74CO3.692.793.503.642.822.733.232.622.863.502.943.533.15NM3.142.402.702.942.762.732.702.462.492.892.783.102.76UT2.932.462.872.852.813.063.522.853.313.622.172.792.94NMED 2004ALL2.992.522.932.572.692.622.612.602.742.552.613.022.70AZ2.362.262.642.432.982.792.592.562.612.532.242.692.56CO3.712.923.332.913.042.882.852.782.752.822.733.483.02NM2.742.512.522.242.792.782.622.382.462.512.363.122.59UT3.672.583.422.422.783.023.052.953.042.552.533.042.92NMED 2005ALL2.832.562.702.692.472.522.772.642.812.783.122.72AZ2.211.852.482.883.043.193.162.572.772.773.082.73CO3.263.063.293.172.932.803.112.692.932.903.323.04NM2.752.112.582.862.542.662.552.372.652.553.302.63UT2.742.702.882.752.462.683.152.903.192.963.112.874-11Table 4-10: Wind Index of Agreementoverth; 12km MM5 Domainand Four-CornerStates.RegionJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 2003ALL0.880.890.880.890.900.900.900.900.890.890.880.880.89AZ0.730.770.770.780.740.760.740.720.730.740.750.750.75CO0.840.830.830.800.830.840.850.840.850.870.840.850.84NM0.860.830.830.800.800.810.800.800.840.840.820.830.82UT0.790.760.770.800.790.810.780.800.810.810.780.800.79NMED 2004ALL0.880.890.890.90.90.910.890.890.890.880.890.880.89AZ0.750.770.760.770.760.720.710.70.730.750.750.780.75CO0.840.840.820.80.840.850.830.820.820.820.840.850.83NM0.850.830.850.820.820.820.80.80.820.820.860.860.83UT0.720.780.780.810.810.790.770.790.790.790.780.770.78NMED 2005ALL0.870.860.880.870.850.870.890.850.880.870.880.87AZ0.730.670.730.720.720.710.680.740.720.730.740.72CO0.830.820.810.830.810.810.800.810.790.790.790.81NM0.770.810.780.800.790.810.810.810.800.790.780.80UT0.710.720.770.770.740.760.780.760.740.760.780.754-12Table 4-11: Mixing Ratio Bias(g/kg) over 4km MM5 Domain.SimulationJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 20030.0-0.09-0.200.00.430.511.240.760.320.49-0.21-0.00.27NMED 2004-0.1-0.12-0.18-0.2-0.070.720.620.150.13-0.05-0.25-0.10.03NMED 2005-0.1-0.29-0.28-0.20.050.521.370.331.080.000.140.24Table 4-12:MixinRatio Error (g/kg)overthe4km MM5 Domain.SimulationJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 20030.50.550.670.71.181.371.881.561.181.050720.50.99NMED 20040.50.550.780.80.991.501.731.481.240.840660.50.98NMED 20050.50.700.680.81.011.491.911.491.701.010691.10Table 4-13: Temperature Bias(K) over the 4km MM5 Domain.SimulationJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 20030.90.73-0.24-0.480.580.49-0.110.271.492.061272.00.75NMED 20041.80.890.500.831.260.870.971.311.261.391401.41.16NMED 20051.40.95-0.030.721.190.940.581.221.271.541911.07Table 4-14: Temperature Error (K) over the 4km MM5 Domain.SimulationJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 20033.822.703.233.503.363.363.703.063.694.203.223.713.46NMED 20043.582.803.373.013.543.513.283.233.383.132.873.493.27NMED 20053.092.593.033.263.323.513.713.253.553.503.983.344-13Table 4-15: Wind Index of Agreement over the 4km MM5 Domain.SimulationJanFebMarAprMayJunJulAugSepOctNovDecMeanNMED 20030.80.810.790.70.790.800.800.800.820.820790.80.80NMED 20040.80.820.800.70.800.810.810.770.750.790810.80.80NMED 20050.80.820.790.70.800.800.800.810.800.810790.804-14

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5- 1