COLORADO STATE IMPLEMENTATION 

PLAN FOR REGIONAL HAZE 

TECHNICAL SUPPORT DOCUMENT 

Mandatory Class I Federal Area: 

RAWAH WILDERNESS AREA 

Prepared by 

The Colorado Department of Public Health and Environment 

Air Pollution Control Division 

4300 Cherry Creek Drive South 

Denver, Colorado 80246-1530 

(303) 692-3100 

October 2007 
Table of Contents

SECTION 1: OVERVIEW .........................................................................................................1

A. Description ....................................................................................................................................1

B. Location.........................................................................................................................................2

SECTION 2: VISIBILITY MONITORING...................................................................................3

A. Monitoring Approaches .................................................................................................................3

(1) Scene Monitoring: .....................................................................................................................3

(2) Aerosol Monitoring: ...................................................................................................................3

(3) Optical Monitoring: ....................................................................................................................3

B. IMPROVE Program.......................................................................................................................3

C. Monitor Location............................................................................................................................4

D. Monitoring Strategy .......................................................................................................................4

SECTION 3: VISIBILITY CONDITIONS ...................................................................................5

A. Visibility Metrics.............................................................................................................................5

B. Baseline Visibility...........................................................................................................................6

(1) Baseline Period - Best Days Visibility .......................................................................................6

(2) Baseline Period – Worst Days Visibility ....................................................................................6

C. Natural Visibility.............................................................................................................................7

(1) Natural Conditions - Best Days Visibility...................................................................................7

(2) Natural Conditions – Worst Days Visibility................................................................................7

D. Split-Image of Best and Worst Days .............................................................................................8

E. Uniform Rate of Progress Glide Path............................................................................................9

F. Determination of Uniform Rate of Progress ................................................................................10

SECTION 4: HAZE IMPACTING PARTICLES .......................................................................11

A. Aerosol Composition ...................................................................................................................11

B. Aerosol Composition on Best and Worst Days ...........................................................................11

C. Comparison of Baseline Extinction for Best & Worst Days.........................................................13

D. Visibility Improvement Needed – 20% Worst Days.....................................................................14

E. Aerosol Pollutant Trends – 20% Worst Days..............................................................................15

F. Monthly Distribution - 20% Worst Days ......................................................................................16

G. Monthly Distribution - 20% Best Days.........................................................................................16

SECTION 5: EMISSION SOURCE CHARACTERIZATION ...................................................17

A. State Emission Inventory and Regional Emission Maps ............................................................17

(1) Colorado SO2 Emission Inventory for 2002 & 2018................................................................18

(2) Regional Map of SOx Emissions for 2002 & 2018..................................................................19

(3) Colorado NOx Emission Inventory for 2002 & 2018 ...............................................................20
(4) Regional Map of NOx Emissions for 2002 & 2018..................................................................21

(5) Colorado NH3 Emission Inventory for 2002 & 2018................................................................22

(6) Colorado POA Emission Inventory for 2002 & 2018...............................................................24

(7) Regional Map of POA Emissions for 2002 & 2018 .................................................................25

(8) Colorado VOC Emission Inventory for 2002 & 2018...............................................................26

(9) Colorado EC Emission Inventory for 2002 & 2018 .................................................................27

(10) Regional Map of EC Emissions for 2002 & 2018....................................................................28

(11) Colorado Soil Emission Inventory for 2002 & 2018 ................................................................29

(12) Regional Map of PM2.5 Emissions for 2002 and 2018...........................................................30

(13) Colorado CM Emission Inventory for 2002 & 2018.................................................................31

(14) Regional Map of PMC Emissions for 2002 & 2018.................................................................32

B. Weighted Emissions Potential (WEP).........................................................................................33

(1) Overview .................................................................................................................................33

(2) Sulfur Oxides - Regional WEP Map for 2018 Worst Days......................................................34

(3) Sulfur Oxides - Regional WEP Map for 2018 Best Days ........................................................35

(4) Nitrogen Oxides - Regional WEP Map for 2018 Worst Days..................................................36

(5) Nitrogen Oxides - Regional WEP Map for 2018 Best Days....................................................37

(6) Primary Organic Aerosol - Regional WEP Map for 2018 Worst Days ....................................38

(7) Primary Organic Aerosol - Regional WEP Map for 2018 Best Days ......................................39

(8) Primary Elemental Carbon - Regional WEP Map for 2018 Worst Days .................................40

(9) Primary Elemental Carbon - Regional WEP Map for 2018 Best Days ...................................41

(10) Particulate Matter Fine - Regional WEP Map for 2018 Worst Days .......................................42

(11) Particulate Matter Fine - Regional WEP Map for 2018 Best Days .........................................43

(12) Particulate Matter Coarse - Regional WEP Map for 2018 Worst Days...................................44

(13) Particulate Matter Coarse - Regional WEP Map for 2018 Best Days.....................................45

SECTION 6: REGIONAL VISIBILITY MODELING .................................................................46

A. Overview .....................................................................................................................................46

B. Air Quality Models .......................................................................................................................46

(1) Community Multi-Scale Air Quality Model...............................................................................46

(2) Comprehensive Air Quality Model with Extensions ................................................................47

C. Modeling Performance ................................................................................................................47

(1) Model Performance for 2002 Worst Days...............................................................................48

(2) Model Performance for 2002 Best Days .................................................................................49

D. Modeling Projections...................................................................................................................50

(1) 2018 Worst Days Model Projection using Haze Index Metric.................................................50

(2) 2018 Worst Days Model Projection using Extinction Metric ...................................................50

(3) 2018 Best Days Model Projection using Haze Index Metric ...................................................51
SECTION 7: PM SOURCE APPORTIONMENT TECHNOLOGY (PSAT) MODELING..........52

A. PSAT Overview...........................................................................................................................52

B. Particulate Sulfate PSAT for Worst Days in 2002 and 2018.......................................................52

C. Particulate Sulfate PSAT for Best Days in 2002 and 2018.........................................................53

D. Particulate Nitrate PSAT for Worst Days in 2002 and 2018 .......................................................54

E. Particulate Nitrate PSAT for Best Days in 2002 and 2018 .........................................................55

F. Organic Aerosol PSAT for Worst Days in 2002 and 2018 ..........................................................56

G. Organic Aerosol PSAT for Best Days in 2002 and 2018 ............................................................56

H. Organic Aerosol PSAT for All Days in 2002 and 2018 ...............................................................57

SECTION 8: EMISSIONS TRACE..........................................................................................58

A. ET Overview................................................................................................................................58

B. Sulfate Emissions Trace for 2018 Worst Days ...........................................................................59

C. Nitrate Emissions Trace for 2018 Worst Days ..............................Error! Bookmark not defined.

D. Organic Carbon Emissions Trace for 2018 Worst Days ...............Error! Bookmark not defined.

E. Elemental Carbon Emissions Trace for 2018 Worst Days............Error! Bookmark not defined.

F. Fine Soil Emissions Trace for 2018 Worst Days...........................Error! Bookmark not defined.

G. Coarse Mass Emissions Trace for 2018 Worst Days ...................Error! Bookmark not defined.

Appendix A: IMPROVE Monitoring Program..........................................................................66

Appendix B: Photographic Images of Visibility.......................................................................67

Appendix C: Seasonal Wind Patterns ....................................................................................69

Appendix D: Weighted Emissions Potential ...........................................................................70

Appendix E: Standard Aerosol-Type Equations.....................................................................73

Appendix F: Procedure for Reconstructing Light Extinction...................................................76

Appendix G: Pollutant Statistical Trends ................................................................................83

Appendix H: PM Source Apportionment Technology (PSAT) Modeling.................................87

List of Tables

Table 1- 1: Rawah Wilderness Area - Determination of URP.....................................................................10

Table 4-1 IMPROVE Monitor Aerosol Composition....................................................................................11 

Table of Figures

Figure 1-1: A Mountain Lake in the Rawah (Photo courtesy of Wilderness.net)..........................................1

Figure 1-2: Clark Peak Towers above Hang Lake (Photo courtesy of Jon Bradford at Summitpost.org) ....1

Figure 1-3: Location of the Rawah Wilderness Area ....................................................................................2

Figure 1-4: Map of the Rawah Wilderness Area...........................................................................................2

Figure 2-1: Rawah Wilderness IMPROVE Monitor Location Map ................................................................4
Figure 3-1: Rawah Wilderness - Baseline Best Days ...................................................................................6

Figure 3-2: Rawah Wilderness - Baseline Worst Days.................................................................................6

Figure 3-3: Rawah Wilderness - Natural Best Days .....................................................................................7

Figure 3-4: Rawah Wilderness - Natural Worst Days ...................................................................................7

Figure 3-5: Rawah Wilderness - WinHaze Simulation of the Best and Worst Baseline Conditions.............8

Figure 3-6: URP Glide Path ..........................................................................................................................9

Figure 4-1: Reconstructed Extinction for 20% Worst Days over Baseline Period ......................................11

Figure 4-2: Reconstructed Extinction for 20% Worst Days over Baseline Period ......................................12

Figure 4-3: Reconstructed Extinction for 20% Best Days over Baseline Period ........................................12

Figure 4-4: Reconstructed Extinction for 20% Best Days over Baseline Period ........................................13

Figure 4-5: Comparison of Baseline Extinction for 20% Best & Worst Days..............................................13

Figure 4-6: Average Contributions to Reconstructed Extinction for 20% Worst Days................................14

Figure 4-7: Monthly Distribution of 20% Worst Days ..................................................................................16

Figure 4-8: Monthly Distribution of 20% Best Days ....................................................................................16

Figure 5-1: Colorado SO2 Emissions – 2002 & 2018 Inventory....................Error! Bookmark not defined.

Figure 5-2: Regional SOx Emissions – 2002 & 2018 Inventory..................................................................19

Figure 5-3: Colorado NOx Emissions – 2002 & 2018 Inventory...................Error! Bookmark not defined.

Figure 5-4: Regional NOx Emissions – 2002 & 2018 Inventory .................................................................21

Figure 5-5: Colorado NH3 Emissions – 2002 & 2018 Inventory...................Error! Bookmark not defined.

Figure 5-6: Colorado POA Emissions – 2002 & 2018 Inventory ..................Error! Bookmark not defined.

Figure 5-7: Regional POA Emissions – 2002 & 2018 Inventory.................................................................25

Figure 5-8: Colorado VOC Emissions – 2002 & 2018 Inventory ..................Error! Bookmark not defined.

Figure 5-9: Colorado EC Emissions – 2002 & 2018 Inventory .....................Error! Bookmark not defined.

Figure 5-10: Regional EC Emissions – 2002& 2018 Inventory...................................................................28

Figure 5-11: Colorado Soil Emissions – 2002 & 2018 Inventory ..................Error! Bookmark not defined.

Figure 5-12: Regional PM2.5 Emissions – 2002 & 2018 Inventory ............................................................30

Figure 5-13: Colorado CM Emissions – 2002 & 2018 Inventory...................Error! Bookmark not defined.

Figure 5-14: Regional PMC Emissions – 2002 & 2018 Inventory...............................................................32

Figure 5-15: Regional SOx WEP for 2018 Worst Days ..............................................................................34

Figure 5-16: Regional SOx WEP for 2018 Best Days ................................................................................35

Figure 5-17: Regional NOx WEP for 2018 Worst Days ..............................................................................36

Figure 5-18: Regional NOx WEP for 2018 Best Days ................................................................................37

Figure 5-19: Regional POA WEP for 2018 Worst Days..............................................................................38

Figure 5-20: Regional POA WEP for 2018 Best Days................................................................................39

Figure 5-21: Regional PEC WEP for 2018 Worst Days..............................................................................40

Figure 5-22: Regional PEC WEP for 2018 Best Days ................................................................................41

Figure 5-23: Regional PM Fine WEP for 2018 Worst Days........................................................................42
Figure 5-24: Regional PM Fine WEP for 2018 Best Days ..........................................................................43

Figure 5-25: Regional PMC WEP for 2018 Worst Days .............................................................................44

Figure 5-26: Regional PMC WEP for 2018 Best Days ...............................................................................45

Figure 6-1: CMAQ Model Performance for MOZI 2002 Worst Days ..........................................................48

Figure 6-2: Relative Error: CMAQ Model vs IMPROVE data for MOZI 2002 Worst Days .........................48

Figure 6-3: CMAQ Model Performance for MOZI 2002 Best Days.............................................................49

Figure 6-4: Relative Error: CMAQ Model vs IMPROVE data for MOZI 2002 Best Days............................49

Figure 6-5: CMAQ Model Projections in Haze Index for MOZI 2018 Worst Days ......................................50

Figure 6-6: CMAQ Model Projections in Extinction for MOZI 2018 Worst Days.........................................50

Figure 6-7: Percent Towards Species-Specific UPG for MOZI 2018 Worst Days......................................51

Figure 6-8: CMAQ Model Projections in Haze Index for MOZI 2018 Worst Days ......................................51

Figure 7-1: Sulfate PSAT - Source Region Bar Chart for Worst Days in 2002 and 2018 ..........................52

Figure 7-2: Sulfate PSAT - Regional Pie Chart for Worst Days in 2002 and 2018.....................................53

Figure 7-3: Sulfate PSAT - Source Region Bar Chart for Best Days in 2002 and 2018.............................53

Figure 7-4: Sulfate PSAT - Regional Pie Chart for Best Days in 2002 and 2018.......................................54

Figure 7-5: Nitrate PSAT – Source Region Bar Chart for Worst Days in 2002 and 2018 ..........................54

Figure 7-6: Nitrate PSAT - Regional Pie Chart for Worst Days in 2002 and 2018 .....................................55

Figure 7-7: Nitrate PSAT – Source Region Bar Chart for Best Days in 2002 and 2018 ............................55

Figure 7-8: Nitrate PSAT - Regional Pie Chart for Best Days in 2002 and 2018 .......................................56

Figure 7-9: Organic Carbon PSAT - Bar Chart for Worst Days in 2002 and 2018 .....................................56

Figure 7-10: Organic Carbon PSAT - Bar Chart for Best Days in 2002 and 2018 .....................................57

Figure 7-11: Organic Carbon PSAT - Bar Chart for All Days in 2002 and 2018.........................................57

Figure 8-1: Sulfate Emissions Trace for 2018 Worst Days...........................Error! Bookmark not defined.

Figure 8-2: Nitrate Emissions Trace for 2018 Worst Days ...........................Error! Bookmark not defined.

Figure 8-3: Organic Carbon Emissions Trace for 2018 Worst Days.............Error! Bookmark not defined.

Figure 8-4: Elemental Carbon Emissions Trace for 2018 Worst Days .........Error! Bookmark not defined.

Figure 8-5: Fine Soil Emissions Trace for 2018 Worst Days ........................Error! Bookmark not defined.

Figure 8-6: Coarse Mass Emissions Trace for 2018 Worst Days.................Error! Bookmark not defined.
SECTION 1: OVERVIEW 

A. Description 

Figure 1-1: A Mountain Lake in the Rawah (Photo courtesy of Wilderness.net) 

Located on the southern end of the 

Medicine Bow Mountain Range in 

north central Colorado, the 27,494 

acre Rawah Wilderness was 

designated by the Wilderness Act 

of 1964. "Rawah" is a Native 

American term meaning "wild 

place." The Colorado Wilderness 

Act of 1980 added an additional 

48,800 acres, bringing its total size 

to 76,394 acres. 

Figure 1-2: Clark Peak Towers above Hang Lake (Photo courtesy of Jon Bradford at Summitpost.org) 

Elevations in the 

Rawah 

Wilderness area 

range from 8,400 

to 12,951 feet. 

The high peaks 

were carved by 

glaciers, resulting 

in spectacular 

cirque lakes and 

moraines. There 

are twenty-five 

named lakes, 

ranging in size 

from five to 39 

acres. This high 

alpine area 

contains the 

headwaters of the 

McIntyre, Rawah 

and Fall Creeks, 

as well as the 

Laramie River. 

Colorado State Implementation Plan for Regional Haze - TSD Page 1
B. Location 

The Rawah Wilderness is located in north central Colorado, west of Fort Collins in Larimer 

County. 

Figure 1-3: Location of the Rawah Wilderness Area 

Figure 1-4: Map of the Rawah Wilderness Area 

Colorado State Implementation Plan for Regional Haze - TSD Page 2
SECTION 2: VISIBILITY MONITORING 

A. Monitoring Approaches 

A variety of monitoring techniques exist to document visibility conditions and to make 

quantitative measurements of the atmospheric properties that effect visibility. The IMPROVE 

Program has partitioned visibility-related characteristics and measurements into three groups: 

(1) Scene Monitoring: 

Scene monitoring is defined as the appearance of a scene viewed through the atmosphere. 

Scene characteristics include observer visual range, scene contrast, color, texture, clarity, and 

other descriptive terms. Scene characteristics change with illumination and atmospheric 

composition. Photographs, video images, and digital images are effective ways to document 

scene characteristics. Detailed information on the types of slides, and the selection process can 

be found in Appendix B – Photographic Images of Visibility. 

(2) Aerosol Monitoring: 

Aerosol monitoring is defined as the physical properties of the ambient atmospheric aerosols 

(chemical composition, size, shape, concentration, temporal and spatial distribution, and other 

physical properties) through which a scene is viewed. Fine particle measurements are 

commonly made to quantify aerosol characteristics. Detailed information on the chemical 

species and the assumptions utilized in the determining components of the aerosol sample are 

in Appendix E - Standard Aerosol-Type Equations. The procedures used to calculate the light 

extinction for each aerosol sample are in Appendix F - Procedure for Reconstructing Light 

Extinction from Aerosol Measurements. Reconstructed extinction from aerosol monitoring is the 

primary method identified by EPA for tracking reasonable progress under its Regional Haze 

Rule. 

(3) Optical Monitoring: 

Optical monitoring is defined as the ability of the atmosphere to scatter or absorb light passing 

through it. Extinction, scattering, and absorption coefficients, plus an angular dependence of 

the scattering, known as the scattering phase function, describe the physical properties of the 

atmosphere. Optical characteristics integrate the effects of atmospheric aerosols and gases. 

Commonly applied optical monitoring instruments include transmissometers and 

nephelometers. 

B. IMPROVE Program 

Visibility conditions are measured by the IMPROVE (Interagency Monitoring of Protected Visual 

Environments) monitoring network. More information on the IMPROVE monitoring program can 

be found in Appendix A. 

Colorado State Implementation Plan for Regional Haze - TSD Page 3
C. Monitor Location 

The Mount Zirkel and Rawah Wilderness Areas are represented by the IMPROVE monitor 

(known as MOZI1) that is operated and maintained by the US Forest Service. MOZI1 was 

started in the summer of 1994 and is sited along an unimproved road just south of Buffalo Pass 

near the southern border of the Mount Zirkel Wilderness boundary at an elevation of 10,639 

feet. 

Figure 2-1: Rawah Wilderness IMPROVE Monitor Location Map 

The MOZI1 

IMPROVE 

monitor is 

about 45 

miles to 

the west 

of the 

Rawah 

D. Monitoring Strategy 

The APCD considers the MOZI1 site as adequate for assessing reasonable progress goals of 

both the Mount Zirkel and Rawah Wilderness Areas and no additional monitoring sites or 

equipment are necessary at this time. 

The APCD routinely participates in the IMPROVE monitoring program by attending Steering 

Committee meetings and indirectly via its membership the Western States Air Resources 

Council (WESTAR). 

Colorado State Implementation Plan for Regional Haze - TSD Page 4
SECTION 3: VISIBILITY CONDITIONS 

A. Visibility Metrics 

Each IMPROVE monitor collects particulate concentration data which are converted into 

reconstructed light extinction through a complex calculation using the IMPROVE equation (see 

Appendix F). Reconstructed light extinction (denoted as bext) is expressed in units of inverse 

megameters (1/Mm or Mm-1). The Regional Haze Rule requires the tracking of visibility 

conditions in terms of the Haze Index (HI) metric expressed in the deciview (dv) unit. Generally, 

a one deciview change in the haze index is likely humanly perceptible under ideal conditions 

regardless of background conditions. There is a logarithmic relationship between the haze 

index and reconstructed light extinction expressed by the following conversion equation: 

HI = 10 ln(bext/10) 

Where: HI is the Haze Index 

 ln is the natural log 

 BB

ext is the reconstructed light extinction 

The relationship between extinction (Mm-1), haze index (dv) and visual range (km) are indicated 

by the following scale: 

Under ideal visibility conditions, where only Rayleigh light scatter from air molecules would 

contribute to visibility impairment, the maximum visual range would be about 400 kilometers. 

Colorado State Implementation Plan for Regional Haze - TSD Page 5
B. Baseline Visibility 

Baseline visibility is determined from the MOZI1 IMPROVE monitoring site which is about 45 

miles west of Rawah for the 20% best and the 20% worst days for the years 2001 through 2004 

(2000 data invalid) as specified in the Regional Haze regulations under 40 CFR 

§51.308(d)(2)(i). The baseline visibility for Rawah is calculated at 1.61 deciviews for the 20% 

best days and 10.52 deciviews for the 20% worst days. The photos in Figures 3-1 and 3-2 are 

WinHaze software images modeled to represent the best and worst baseline visibility conditions 

for the Rawah Wilderness Area. The historic visibility photo record is limited and doesn’t include 

the Rawah Wilderness, so the closest base photo (Storm Peak) from WinHaze was selected. 

(1) Baseline Period - Best Days Visibility 

Figure 3-1: Rawah Wilderness - Baseline Best Days 

Reference Vista of Storm Peak 

WinHaze Modeled Image 

Haze Index (HI) = 1.6 deciviews

BB

ext = 11.7 Mm-1

Visual Range = 333 km/207 mi 

(2) Baseline Period – Worst Days Visibility 

Figure 3-2: Rawah Wilderness - Baseline Worst Days 

Reference Vista of Storm Peak 

WinHaze Modeled Image 

Haze Index (HI) = 10.5 deciviews

BB

ext = 28.6 Mm-1

Visual Range = 137 km/85 mi 

Colorado State Implementation Plan for Regional Haze - TSD Page 6
C. Natural Visibility 

Natural visibility represents the visibility condition that would be experienced in the absence of 

human-caused impairment. Based on EPA guidance, the natural visibility for the Rawah 

Wilderness is 0.55 deciviews for the 20% best days and 6.44 deciviews for the 20% worst days. 

Figure 3-3 is a modeled image (in WinHaze software) that is representative of the best natural 

visibility condition. In Figure 3-4, the natural worst days are modeled as a WinHaze image that 

represents the long-term natural visibility goal for year 2064. 

(1) Natural Conditions - Best Days Visibility 

Figure 3-3: Rawah Wilderness - Natural Best Days 

Reference Vista of Storm Peak 

WinHaze Modeled Image 

Haze Index (HI) = 0.55 deciviews

BB

ext = 10.6 Mm-1 

Visual Range = 370 km/230 mi

(2) Natural Conditions – Worst Days Visibility 

Figure 3-4: Rawah Wilderness - Natural Worst Days 

Reference Vista of Storm Peak 

WinHaze Modeled Image 

Haze Index (HI) = 6.44 deciviews

BB

ext = 19.0 Mm-1 

Visual Range = 205 km/127 mi

Colorado State Implementation Plan for Regional Haze - TSD Page 7
D. Split-Image of Best and Worst Days

Figure 3-5 shows a split-image created in WinHaze software to simulate the average 20% best (left – 1.6 decivi

(right – 10.5 deciviews) visibility at the Storm Peak vista based on an average of monitored data for years 2001-

Figure 3-5: Rawah Wilderness - WinHaze Simulation of the Best and Worst Baseline Conditions Colorado State Implementation Plan for Regional Haze - TSD 
E. Uniform Rate of Progress Glide Path 

Figure 3-6 depicts the “glide path” for a uniform rate of progress toward reaching the goal of 

natural conditions. The Regional Haze Regulations (under 40 CFR §51.308) establish that the 

Class I area progress goals must provide for an improvement in visibility for the most impaired 

(i.e., 20% worst) days over the period of the implementation plan, and ensure no degradation in 

visibility for the least impaired (i.e., 20% best) days over the same period. 

The baseline condition (10.5 deciviews) is an average of the haze index (from IMPROVE data 

using the new IMPROVE equation) over the 5-year baseline period of 2001-2004 (2000 data 

invalid). The 1st planning period visibility improvement of 0.95 deciviews is determined by 

multiplying the uniform rate of progress (the amount of visibility improvement needed per year 

over the 60-year period) by the number of years in the initial planning period. 

The best days baseline (1.6 deciviews) is an average of the haze index (from IMPROVE data 

using the new IMPROVE equation) over the 5-year baseline period of 2001-2004 (2000 data 

invalid). 

Only four years of data are available in the baseline period for the Mount Zirkel and Rawah 

Wilderness, 2001-2004. The first year in the period (2000) is incomplete due to missing data 

and may not be used to calculate baseline as per EPA’s “Guidance for Tracking Progress Under 

the Regional Haze Program”, September 2003. 

Figure 3-6: URP Glide Path 

Colorado State Implementation Plan for Regional Haze - TSD Page 9
F. Determination of Uniform Rate of Progress 

Table 1-1 provides the calculations for determining the Uniform Rate of Progress Glide Path and 

Best Days Baseline. 

Table 1- 1: Rawah Wilderness Area - Determination of URP 

Colorado State Implementation Plan for Regional Haze - TSD Page 10
SECTION 4: HAZE IMPACTING PARTICLES 

A. Aerosol Composition 

An aerosol is a gaseous suspension of fine solid or liquid particles. Some of these particles 

absorb light and others reflect or scatter light resulting in light extinction between the viewer and 

the light source. The IMPROVE monitor collects a 24-hour sample these particles onto a filter 

and they are analyzed at a laboratory to determine the components of the aerosol. In order to 

simplify the data analysis, some elemental particles and compounds are grouped together 

(based on scientific principals) into seven standard components. The components’ differing 

effects on visibility are addressed through a complex calculation using the IMPROVE equation. 

The resultant output from the IMPROVE equation is known as reconstructed light extinction. 

Appendix G provides more detailed information on the IMPROVE equation. Table 4-1 lists the 

seven standard aerosol components of light extinction with the abbreviation and the default 

color used in the below listed graphics. 

Table 4-1 IMPROVE Monitor Aerosol Composition 

Aerosol Component Abbreviation (Color) 

1. Ammonium Sulfate SO4 (yellow)

2. Ammonium Nitrate NO3 (red) 

3. Organic Mass Carbon OMC (green) 

4. Elemental Carbon EC (black) 

5. Fine Soil Soil (orange) 

6. Coarse Mass CM (gray) 

7. Sea Salt Sea Salt (light blue) 

B. Aerosol Composition on Best and Worst Days 

The Mount Zirkel Wilderness IMPROVE monitor (MOZI1) is designated as the monitor for the 

Rawah Wilderness. In Figure 4-1, the bar chart denotes the variability in the reconstructed light 

extinction over the baseline period based on IMPROVE data from the MOZI1 monitoring site for 

the 20% worst days. 

Figure 4-1: Reconstructed Extinction for 20% Worst Days over Baseline Period 

Colorado State Implementation Plan for Regional Haze - TSD Page 11
In Figure 4-2, the line graph denotes the variability in the individual components of 

reconstructed light extinction over the baseline period based on IMPROVE data from the MOZI1 

monitoring site for the 20% worst days. The baseline period variation in OMC is over 12 Mm-1, 

which indicates the range of effects produced by wildfire smoke. 

Figure 4-2: Reconstructed Extinction for 20% Worst Days over Baseline Period 

In Figure 4-3, the bar chart denotes the variability in the reconstructed light extinction over the 

baseline period based on IMPROVE data from the MOZI1 monitoring site for the 20% best 

days. Note the range for the best days is around 5.0 Mm-1. 

Figure 4-3: Reconstructed Extinction for 20% Best Days over Baseline Period 

In Figure 4-4, the line graph denotes the variability in the individual components of 

reconstructed light extinction over the baseline period based on IMPROVE data from the MOZI1 

monitoring site for the 20% best days. 

Colorado State Implementation Plan for Regional Haze - TSD Page 12
Figure 4-4: Reconstructed Extinction for 20% Best Days over Baseline Period 

C. Comparison of Baseline Extinction for Best & Worst Days 

In Figure 4-5, the bar graphs compare the baseline average for the 20% best days with the 20% 

worst days based on MOZI1 IMPROVE monitoring data. Although all components of extinction 

are considerably less on the best days, it seems that significant changes (>90%) in OC and soil 

result in visibility improvements on the best days. 

Figure 4-5: Comparison of Baseline Extinction for 20% Best & Worst Days 

Colorado State Implementation Plan for Regional Haze - TSD Page 13
D. Visibility Improvement Needed – 20% Worst Days 

Figure 4-6 displays the relative contributions to visibility extinction averaged over the baseline 

conditions period for the 20% worst days. The significant contributors to visibility degradation, 

as depicted in the stacked bar graph and pie chart, appear to be ammonium sulfate, organic 

carbon and coarse material. In the middle, the light green bar indicates the magnitude of 

improvement (bext = 2.6 Mm-1 or 0.95 deciviews) necessary to achieve the 2018 reasonable 

progress (RP) goal. To the far right, the light blue bar indicates the magnitude of improvement 

(bext = 9.6 Mm-1 or 4.08 deciviews) necessary to achieve the 2064 natural conditions goal. 

Figure 4-6: Average Contributions to Reconstructed Extinction for 20% Worst Days 

Colorado State Implementation Plan for Regional Haze - TSD Page 14
E. Aerosol Pollutant Trends – 20% Worst Days 

The Mount Zirkel Wilderness IMPROVE monitor (MOZI1) is also designated to represent the 

Rawah Wilderness area. The following graphs are a linear regression of the annual data points 

for the six haze causing pollutants from the MOZI1 IMPROVE monitor site for years 1995-2005 

(year 2000 data invalid): 

The graphs utilize valid data from years prior to and after the baseline condition period. None of 

the trends appear to be statistically significant. Appendix G contains detailed statistical 

information on each trend. 

Continued improvements in sulfate levels are expected in the West as further controls are 

realized on major sulfur dioxide sources. Since sulfates have a disproportionately high impact 

on visibility in contrast to coarse mass particulates, we expect the downward trend in sulfates 

will provide continued visibility improvement. However, the trend in organic carbon is not as 

encouraging. The protracted drought throughout the West could be contributing to the recent 

upswing in organic carbon, primarily due to wildfires. 

Colorado State Implementation Plan for Regional Haze - TSD Page 15
F. Monthly Distribution - 20% Worst Days 

The Mount Zirkel Wilderness IMPROVE monitor (MOZI1) is designated as the monitor for the 

Rawah Wilderness. Figure 4-7 displays in reconstructed extinction the monthly distribution of 

20% worst days on 5-year basis over the baseline period for MOZI1. The worst days appear 

scattered throughout the year although very few occur in December when fires, dust events, 

and photochemical processes are at a minimum. The high OMC readings in August 2002 and 

October 2003 are from wildfire smoke. 

Figure 4-7: Monthly Distribution of 20% Worst Days 

G. Monthly Distribution - 20% Best Days 

Figure 4-8 displays in reconstructed extinction the monthly distribution of 20% best days on 5-

year basis over the baseline period for MOZI1. The best days appear scattered throughout the 

year although more occur in the late fall, winter and spring when there is an absence of fires, 

dust events, and photochemical processes that maximize organic and elemental readings as 

well as elevating secondary particulate formation. Very few if any best days occur during the 

summer months. 

Figure 4-8: Monthly Distribution of 20% Best Days 

Colorado State Implementation Plan for Regional Haze - TSD Page 16
SECTION 5: EMISSION SOURCE CHARACTERIZATION 

A. State Emission Inventory and Regional Emission Maps 

The following tables provide summary information for the planning 2002c (Plan 02c) emission 

inventory and the baseline 2018b (Base 18b) emission inventory along with the net change in 

emissions that were used in the regional modeling analysis. 

The Plan 02c emission inventory represents an annual (calendar year) snapshot of emissions 

for ten source categories (point, area, on-road/off-road mobile, oil & gas, road dust, fugitive 

dust, anthropogenic fire, natural fire and biogenic) that were derived from a number of sources 

including Division permits, MOBILE 6 modeling, or other modeled estimates based on activity 

level. 

The State has several existing regulatory programs that mitigate the emissions from point 

sources (Regulations 1, 3, 6 and Common Provisions), mobile sources (Regulations 10, 11 and 

12), area sources (Regulations 4, 7 and 16) and fire (Regulation 9). Future emission reductions 

anticipated from recent changes to Regulation 7 (adopted in Dec. 2006) and Best Available 

Retrofit Technology (BART) under Regulation 3 are partially included in the 2018b emission 

inventory. Further, a revised WRAP region oil and gas inventory (phase II) was completed in 

summer 2007 and is included in this 2018b emission inventory and modeling analyses. The 

phase II oil & gas update builds upon the phase I work and provides more basin specific 

emission data from local producers and a more comprehensive inventory of area sources. 

The WRAP developed a revised preliminary reasonable progress emission inventory for 2018, 

known as the 2018PRP, that reflects not only recent changes to Reg 7 and all Colorado BART 

but projected BART emission reductions across the WRAP region. The WRAP released the 

2018PRP emission inventory in the fall of 2007 along with updated regional modeling. 

The WRAP anticipates that a final emission inventory update, known as 2018FRP, along with 

final regional modeling demonstration would be completed near the end of 2008 to early 2009 

timeframe. The 2018FRP emission inventory would include the final BART determinations for 

the entire WRAP region along with other on-the books (OTB) and on-the-way (OTW) controls. 

These future updates to the 2018 emission estimates and visibility forecasts will be included in 

future presentations of emissions and modeling and will be reflected in future editions of this 

TSD. 

The emission inventories are based on statewide data that include tables for sulfur dioxide 

(SO2), oxides of nitrogen (NOx), ammonia (NH3), primary organic aerosol (POA), volatile 

organic compounds (VOCs), elemental carbon (EC), fine soil (PM2.5) and coarse mass (CM) that 

list as many as ten different source categories. 

The following maps of the regional modeling domain depict 36x36 km grid cells that include 

color-coded emission rates for selected air pollutants. Emissions for each grid cell represent 

summations from point, area, mobile, dust, fire and biogenic sources in tons for year 2002 and 

2018. 

Colorado State Implementation Plan for Regional Haze - TSD Page 17
(1) Colorado SO2 Emission Inventory for 2002 & 2018 

Sulfur dioxide gases (SO2) are formed when sulfur-containing fuels, such as diesel or coal, are 

burned, when gasoline is extracted from oil or when metals are extracted from ore. SO2

dissolves in water vapor to form acid, and contributes to the formation of sulfate compounds 

[e.g. (NH4)2SO4 ] when ammonia is available. These compounds can scatter the transmission 

of light, contributing to visibility reduction on a regional scale at our Class I Areas. 

In Figure 5-1, the projected statewide SO2 emission reduction is about 29%. The anticipated 

29% reduction in point source emissions statewide are largely due to the early implementation 

of BART level emission controls on several large power plants prior to the 2002 baseline period. 

The 18% increase in area sources is largely due to forecast increases in activity levels based on 

population growth. The substantial reduction in on-road and off-road mobile sources are 

primarily due to new federal fuel standards that require lower sulfur content. 

Figure 5-1: Colorado SO2 Emissions – 2002 & 2018 Inventory 

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(2) Regional Map of SOx Emissions for 2002 & 2018 

Figure 5-2, provides the plan02c and base18b regional SOx emission maps with the location of 

the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, identified with a green star 

surrounded by 100 km and 200 km radius concentric circles. The MOZI1 IMPROVE monitor is 

designated to represent the Rawah Wilderness 

Figure 5-2: Regional SOx Emissions – 2002 & 2018 Inventory 

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(3) Colorado NOx Emission Inventory for 2002 & 2018 

Nitrogen oxides (NOx) form when fuel is burned at high temperatures. NOx emissions are highly 

reactive and can form nitrate compounds [e.g. NH4NO3] when ammonia is available. These 

compounds can scatter the transmission of light, contributing to visibility reduction on a regional 

scale at our Class I Areas. 

In Figure 5-3, the projected statewide NOx emission reduction is about 28%. The anticipated 

5% point source reduction statewide is due to BART emission controls on large industrial boilers 

and cleaner engine standards. The 40% increase in area sources is largely due to forecast 

increases in activity levels based on population growth. The substantial reduction in on-road 

and off-road mobile sources are primarily due to new federal vehicle emission standards (Tier 

2). The 20% increase in O&G is attributed to the recent growth surge in this sector. 

Figure 5-3: Colorado NOx Emissions – 2002 & 2018 Inventory 

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(4) Regional Map of NOx Emissions for 2002 & 2018 

Figure 5-4, provides the plan02c and base18b regional NOx emission maps with the location of 

the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, identified with a green star 

surrounded by 100 km and 200 km radius concentric circles. The MOZI1 IMPROVE monitor is 

designated to represent the Rawah Wilderness 

Figure 5-4: Regional NOx Emissions – 2002 & 2018 Inventory 

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(5) Colorado NH3 Emission Inventory for 2002 & 2018 

Ammonia (NH3) is a compound that is normally encountered as a gas with a characteristic 

pungent odor. Ammonia occurs both naturally and is produced by human activity that is an 

important source of nitrogen, which is needed by plants and animals. 

Recent advances in the understanding of the health impacts of particulate pollution and the 

important role ammonia (NH3) emissions play in the formation of secondary particulate matter 

(PM) has spawned a great deal of new research into ammonia emissions. Major sources of 

NH3 emissions include livestock operations, fertilizer use, waste management, mobile sources, 

industrial point sources, and various biological sources including human respiration, wild 

animals, and soil microbial processes. For each of these source categories there remain large 

uncertainties in the magnitude of emissions, the diurnal and seasonal variation, and the spatial 

distribution. Uncertainty in NH3 emissions is a key source of uncertainty in the formation of 

sulfate and nitrate aerosols. Thus, development of improved NH3 emissions inventories is 

essential for modeling the formation of fine PM, regional haze, and for developing effective 

plans to mitigate visibility impairment at National Parks, Forests and Wilderness Areas. 

Significant improvements have been made in the understanding of ammonia emissions since 

the development of the current 1996 National Emissions Inventory (NEI) that was used in the 

Western Regional Air Partnership (WRAP) visibility modeling to meet Clean Air Act Section 309 

requirements. Particularly, the temporal dependence of ammonia emissions on environmental 

parameters has been the focus of several recent research efforts. The WRAP has provided 

funding to the Regional Modeling Center (RMC) to develop an improved NH3 emissions 

inventory for the WRAP States and tribes to use in Clean Air Act Section 308 modeling. 

In Figure 5-5, the projected statewide NH3 emission increase is about 1%. The anticipated 16% 

increase in statewide point sources is attributed to population growth. The 37-38% increase in 

on-road mobile and off-road sources is largely due to forecast increases in vehicle traffic 

associated with population growth. 

Figure 5-5: Colorado NH3 Emissions – 2002 & 2018 Inventory 

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The WRAP has not produced regional maps comparing 2002 and 2018 ammonia emissions. 

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(6) Colorado POA Emission Inventory for 2002 & 2018 

Primary Organic Aerosol (POA) is the directly emitted particulate form of organic carbon that is 

created through the combustion of fossil fuels or organic matter and meat cooking. POA 

particulates scatter the transmission of light that contributes to visibility reduction on a regional 

scale at our Class I Areas. 

In Figure 5-6, the projected statewide POA emission reduction is about 2%. Most of the 

anticipated reduction comes from new federal vehicle emission standards applicable to on-road 

and off-road mobile sources. 

Figure 5-6: Colorado POA Emissions – 2002 & 2018 Inventory 

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(7) Regional Map of POA Emissions for 2002 & 2018 

Figure 5-7, provides the plan02c and base18b regional POA emissions maps with the location 

of the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, identified with a green star 

surrounded by 100 km and 200 km radius concentric circles. The MOZI1 IMPROVE monitor is 

designated to represent the Rawah Wilderness. 

Figure 5-7: Regional POA Emissions – 2002 & 2018 Inventory 

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(8) Colorado VOC Emission Inventory for 2002 & 2018 

Organic carbon particulates can be emitted as a primary organic aerosol (POA) or they can be 

formed as a secondary organic aerosol (SOA). Secondary organics are formed when volatile 

organic carbon (VOC) emissions react with oxides of nitrogen and sunlight to form ozone, which 

acts as a catalyst for particulate formation. VOCs may also condense to form particulate 

organic carbon (OC). The WRAP did not create regional VOC emission maps denoting the 

origin of these emissions. Some example sources of volatile organic compounds include trees, 

fires, vehicle refueling, industrial processes and application of architectural coatings. 

In Figure 5-8, the projected statewide VOC emission reduction is about 1%. Most of the 

anticipated reduction comes from new federal fuel standards applicable to on-road and off-road 

mobile sources. 

Figure 5-8: Colorado VOC Emissions – 2002 & 2018 Inventory 

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(9) Colorado EC Emission Inventory for 2002 & 2018 

Elemental carbon particulates are directly emitted as a primary aerosol. Some example sources 

of elemental carbon include fossil fuel combustion (vehicles, boilers & other industrial 

processes), wildfires and all other types of burning. EC particulates absorb the transmission of 

light that contributes to visibility reduction on a regional scale at our Class I Areas. 

In Figure 5-9, the projected statewide EC emission reduction is about 27%. Most of the 

anticipated reduction comes from new federal standards on vehicle emissions and fuels that are 

applicable to on-road and off-road mobile sources. 

Figure 5-9: Colorado EC Emissions – 2002 & 2018 Inventory 

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(10) Regional Map of EC Emissions for 2002 & 2018 

Figure 5-10, provides the plan02c and base18b regional elemental carbon emissions maps with 

the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, identified with a 

green star surrounded by 100 km and 200 km radius concentric circles. The MOZI1 IMPROVE 

monitor is designated to represent the Rawah Wilderness. 

Figure 5-10: Regional EC Emissions – 2002& 2018 Inventory 

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(11) Colorado Soil Emission Inventory for 2002 & 2018 

Soil emissions are comprised of fine particulates under 2.5 microns that are generated mostly 

by area sources, fugitive dust and windblown dust. Fine particulate matter can remain 

suspended in the atmosphere for long periods of time and travel long distances. Fine 

particulates can efficiently scatter the transmission of light that contributes to visibility reduction 

on a regional scale at our Class I Areas. 

In Figure 5-11, the projected statewide soil emission increase is about 8%. Most of the 

anticipated increase is associated with fugitive dust. 

Figure 5-11: Colorado Soil Emissions – 2002 & 2018 Inventory 

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(12) Regional Map of PM2.5 Emissions for 2002 and 2018 

Figure 5-12, provides the plan02c and base18b regional fine soil (fine particulate -PM2.5) 

emissions maps with the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel 

Wilderness, identified with a green star surrounded by 100 km and 200 km radius concentric 

circles. The MOZI1 IMPROVE monitor is designated to represent the Rawah Wilderness. 

Figure 5-12: Regional PM2.5 Emissions – 2002 & 2018 Inventory 

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(13) Colorado CM Emission Inventory for 2002 & 2018 

Coarse mass (CM) emissions are comprised of particulates with an aerodynamic diameter 

between 10 and 2.5 microns that are generated mostly by point sources, fugitive dust and 

windblown dust. Coarse particulate matter can remain suspended in the atmosphere for long 

periods of time and travel long distances. Coarse particulates scatter the transmission of light 

that contributes to visibility reduction on a regional scale at our Class I Areas. Sources of 

coarse particles include construction sites, tilled fields, windblown dust, vehicle traffic, mineral 

processing facilities, mining and wood burning. 

In Figure 5-13, the projected statewide CM emission increase is about 19%. Most of the 

anticipated increase is associated with fugitive dust. 

Figure 5-13: Colorado Coarse Mass Emissions – 2002 & 2018 Inventory 

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(14) Regional Map of PMC Emissions for 2002 & 2018 

In Figure 5-14, provides the plan02c and base18b regional coarse particulate matter (PMC) 

emissions maps with the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel 

Wilderness, identified with a green star surrounded by 100 km and 200 km radius concentric 

circles. The MOZI1 IMPROVE monitor is designated to represent the Rawah Wilderness. 

Figure 5-14: Regional PMC Emissions – 2002 & 2018 Inventory 

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B. Weighted Emissions Potential (WEP) 

(1) Overview 

The Weighted Emissions Potential (WEP) tool is an analysis technique that identifies the 

predominant emission source regions contributing haze-forming pollutants at each Class I area 

(CIA) based on 5-years of historical meteorology for both the 20% best and worst days. A map 

of the seasonal statewide wind patterns is included in Appendix C that provides a general sense 

of air movement across the state. The CIA specific WEP map for each haze pollutant is 

determined by multiplying the annual emission inventory (in 36 by 36 km grid cells) for all source 

categories by the Air Mass Residence Time (2000-04) values. The resultant map provides the 

distance weighted emissions potential of each grid cell relative to the CIA receptor. Two 

different modeling simulation emission scenarios (2000-04 Baseline and 2018 Base Case) were 

used to produce the WEP maps. 

It is important to note that this technique does not address secondary particulate formation (e.g. 

no complex chemistry) or deposition at the CIA receptor. More information on WEP tool can be 

found in Appendix D. 

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(2) Sulfur Oxides - Regional WEP Map for 2018 Worst Days 

In Figure 5-15, the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, 

is identified with a green star surrounded by 100 km and 200 km radius concentric circles. The 

MOZI1 IMPROVE monitor is the designated to represent the Rawah Wilderness. The areas 

shaded in different colors identify those grid cells with the potential of contributing SOx 

emissions at MOZI1 for the worst days in 2018. The areas shaded in darker colors identify the 

36 km grid cells that are likely dominant contributors of SOx emissions at the class I area 

whereas the white areas denote those grid cells with negligible emission potential. This 

analysis provides information on relevant source areas with the potential to contribute SOx 

emissions but the SOx WEP doesn’t consider particulate deposition or the complex chemical 

conversion of SOx emissions to particulate sulfate. 

Figure 5-15: Regional SOx WEP for 2018 Worst Days 

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(3) Sulfur Oxides - Regional WEP Map for 2018 Best Days 

In Figure 5-16, the SOx WEP max value for the best days (25%) is slightly higher than the worst 

days (22%) and the distribution of larger emission sources impacting the best days appears to 

be more from the west. 

Figure 5-16: Regional SOx WEP for 2018 Best Days 

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(4) Nitrogen Oxides - Regional WEP Map for 2018 Worst Days 

In Figure 5-17, the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, 

is identified with a green star surrounded by 100 km and 200 km radius concentric circles. The 

MOZI1 IMPROVE monitor is the designated to represent the Rawah Wilderness. The areas 

shaded in different colors identify those grid cells with the potential of contributing NOx 

emissions at MOZI1 for the worst days in 2018. The areas shaded in darker colors identify the 

36 km grid cells that are likely dominant contributors of NOx emissions at the class I area 

whereas the white areas denote those grid cells with negligible emission potential. This 

analysis provides information on relevant source areas with the potential to contribute NOx 

emissions but the NOx WEP doesn’t consider particulate deposition or the complex chemical 

conversion of NOx emissions to particulate nitrate. 

Figure 5-17: Regional NOx WEP for 2018 Worst Days 

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(5) Nitrogen Oxides - Regional WEP Map for 2018 Best Days 

In Figure 5-18, the NOx WEP max value for the best days (22%) is slightly higher than the worst 

days (20%) with a pretty similar distribution of larger emission sources impacting the best days. 

Figure 5-18: Regional NOx WEP for 2018 Best Days 

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(6) Primary Organic Aerosol - Regional WEP Map for 2018 Worst Days 

In Figure 5-19, the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, 

is identified with a green star surrounded by 100 km and 200 km radius concentric circles. The 

MOZI1 IMPROVE monitor is the designated to represent the Rawah Wilderness. The areas 

shaded in different colors identify those grid cells with the potential of contributing Primary 

Organic Aerosol (POA) emissions at MOZI1 for the worst days in 2018. The areas shaded in 

darker colors identify the 36 km grid cells that are likely dominant contributors of POA emissions 

at the class I area whereas the white areas denote those grid cells with negligible emission 

potential. This analysis provides information on relevant source areas with the potential to 

contribute POA emissions but the POA WEP doesn’t consider particulate deposition. Since 

POA emissions are emitted as a primary particulate organic carbon, the POA WEP doesn’t have 

the limitations of associated with complex chemical particle conversion. 

Figure 5-19: Regional POA WEP for 2018 Worst Days 

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(7) Primary Organic Aerosol - Regional WEP Map for 2018 Best Days 

In Figure 5-20, the POA WEP max values for the best (9%) and worst (8%) days are pretty 

similar which may indicate a fairly constant signal of POA emissions although there are some 

differences in the distribution of the emissions impacting the best and worst days. 

Figure 5-20: Regional POA WEP for 2018 Best Days 

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(8) Primary Elemental Carbon - Regional WEP Map for 2018 Worst Days 

In Figure 5-21, the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, 

is identified with a green star surrounded by 100 km and 200 km radius concentric circles. The 

MOZI1 IMPROVE monitor is the designated to represent the Rawah Wilderness. The areas 

shaded in different colors identify those grid cells with the potential of contributing Primary 

Elemental Carbon (PEC) emissions at MOZI1 for the worst days in 2018. The areas shaded in 

darker colors identify the 36 km grid cells that are likely dominant contributors of PEC emissions 

at the class I area whereas the white areas denote those grid cells with negligible emission 

potential. This analysis provides information on relevant source areas with the potential to 

contribute PEC emissions but the PEC WEP doesn’t consider particulate deposition. Since 

PEC emissions are emitted as a primary particulate elemental carbon, the PEC WEP doesn’t 

have the limitations of associated with complex chemical particle conversion. 

Figure 5-21: Regional PEC WEP for 2018 Worst Days 

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(9) Primary Elemental Carbon - Regional WEP Map for 2018 Best Days 

In Figure 5-22, the PEC WEP max values for the best (10%) and worst (9%) days are pretty 

similar which may indicate a fairly constant signal of PEC emissions although there are some 

differences in the distribution of the emissions impacting the best and worst days. 

Figure 5-22: Regional PEC WEP for 2018 Best Days 

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(10) Particulate Matter Fine - Regional WEP Map for 2018 Worst Days 

In Figure 5-23, the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, 

is identified with a green star surrounded by 100 km and 200 km radius concentric circles. The 

MOZI1 IMPROVE monitor is the designated to represent the Rawah Wilderness. The areas 

shaded in different colors identify those grid cells with the potential of contributing fine 

Particulate Matter (PM2.5) emissions at MOZI1 for the worst days in 2018. The areas shaded in 

darker colors identify the 36 km grid cells that are likely dominant contributors of PM2.5 

emissions at the class I area whereas the white areas denote those grid cells with negligible 

emission potential. This analysis provides information on relevant source areas with the 

potential to contribute PM2.5 emissions but the PM2.5 WEP doesn’t consider particulate 

deposition. Since PM2.5 emissions are emitted as primary fine particulates, the PM2.5 WEP 

doesn’t have the limitations of associated with complex chemical particle conversion. 

Figure 5-23: Regional PM Fine WEP for 2018 Worst Days 

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(11) Particulate Matter Fine - Regional WEP Map for 2018 Best Days 

In Figure 5-24, the PM Fine WEP max value for the best days (4%) is pretty close to the worst 

days (3%) with a lesser distribution of larger emission sources impacting the best days. 

Figure 5-24: Regional PM Fine WEP for 2018 Best Days 

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(12) Particulate Matter Coarse - Regional WEP Map for 2018 Worst Days 

In Figure 5-25, the location of the MOZI1 IMPROVE monitor, near the Mount Zirkel Wilderness, 

is identified with a green star surrounded by 100 km and 200 km radius concentric circles. The 

MOZI1 IMPROVE monitor is the designated to represent the Rawah Wilderness. The areas 

shaded in different colors identify those grid cells with the potential of contributing coarse 

Particulate Matter (PMC) emissions at MOZI1 for the worst days in 2018. The areas shaded in 

darker colors identify the 36 km grid cells that are likely dominant contributors of PMC emissions 

at the class I area whereas the white areas denote those grid cells with negligible emission 

potential. This analysis provides information on relevant source areas with the potential to 

contribute PMC emissions but the PMC WEP doesn’t consider particulate deposition. Since 

PMC emissions are emitted as primary particulates, the PMC WEP doesn’t have the limitations 

of associated with complex chemical particle conversion. 

Figure 5-25: Regional PMC WEP for 2018 Worst Days 

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(13) Particulate Matter Coarse - Regional WEP Map for 2018 Best Days 

In Figure 5-26, the PMC WEP max value for the best days (9%) is less than half of the worst 

days (7%) with a lesser distribution of larger emission sources impacting the best days. 

Figure 5-26: Regional PMC WEP for 2018 Best Days 

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SECTION 6: REGIONAL VISIBILITY MODELING 

A. Overview 

Visibility impairment occurs when fine particulate matter (PM2.5) in the atmosphere scatters and 

absorbs light, thereby creating haze. PM2.5 can be emitted into the atmosphere directly as 

primary particulates, or it can be produced in the atmosphere from photochemical reactions of 

gas-phase precursors and subsequent condensation to form secondary particulates. Examples 

of primary PM2.5 include crustal materials and elemental carbon; examples of secondary PM 

include ammonium nitrate, ammonium sulfates, and secondary organic aerosols (SOA). 

Secondary PM2.5 is generally smaller than primary PM2.5, and because the ability of PM2.5 to 

scatter light depends on particle size, with light scattering for fine particles being greater than for 

coarse particles, secondary PM2.5 plays an especially important role in visibility impairment. 

Moreover, the smaller secondary PM2.5 can remain suspended in the atmosphere for longer 

periods and is transported long distances, thereby contributing to regional-scale impacts of 

pollutant emissions on visibility. 

The sources of PM2.5 are difficult to quantify because of the complex nature of their formation, 

transport, and removal from the atmosphere. This makes it difficult to simply use emissions data 

to determine which pollutants should be controlled to most effectively improve visibility. 

Photochemical air quality models offer opportunity to better understand the sources of PM2.5 by 

simulating the emissions of pollutants and the formation, transport, and deposition of PM2.5. If 

an air quality model performs well for a historical episode, the model may then be useful for 

identifying the sources of PM2.5 and helping to select the most effective emissions reduction 

strategies for attaining visibility goals. Although several types of air quality modeling systems 

are available, the gridded, three-dimensional, Eulerian models provide the most complete 

spatial representation and the most comprehensive representation of processes affecting 

PM2.5, especially for situations in which multiple pollutant sources interact to form PM2.5. For 

less complex situations in which a few large point sources of emissions are the dominant source 

of PM2.5, trajectory models (such as the California Puff Model [CALPUFF]) may also be useful 

for simulating PM2.5. 

B. Air Quality Models 

The WRAP RMC utilized two regulatory air quality modeling systems to conduct all regional 

haze modeling. A brief discussion of each of these models is provided below. 

(1) Community Multi-Scale Air Quality Model 

EPA initially developed the Community Multi-Scale Air Quality (CMAQ) modeling system in the 

late 1990s. The model source code and supporting data can be downloaded from the 

Community Modeling and Analysis System (CMAS) Center (http://www.cmascenter.org/), which 

is funded by EPA to distribute and provide limited support for CMAQ users. CMAQ was 

designed as a “one atmosphere” modeling system to encompass modeling of multiple pollutants 

and issues, including ozone, PM, visibility, and air toxics. This is in contrast to many earlier air 

quality models that focused on single-pollutant issues (e.g., ozone modeling by the Urban 

Airshed Model). CMAQ is an Eulerian model - that is, it is a grid-based model in which the frame 

of reference is a fixed, three-dimensional (3-D) grid with uniformly sized horizontal grid cells and 

variable vertical layer thicknesses. The number and size of grid cells and the number and 

thicknesses of layers are defined by the user, based in part on the size of the modeling domain 

to be used for each modeling project. The key science processes included in CMAQ are 

emissions, advection and dispersion, photochemical transformation, aerosol thermodynamics 

Colorado State Implementation Plan for Regional Haze - TSD Page 46
and phase transfer, aqueous chemistry, and wet and dry deposition of trace species. CMAQ 

offers a variety of choices in the numerical algorithms for treating many of these processes, and 

it is designed so that new algorithms can be included in the model. CMAQ offers a choice of 

three photochemical mechanisms for solving gas-phase chemistry: the Regional Acid 

Deposition Mechanism version 2 (RADM2), a fixed coefficient version of the SAPRC90 

mechanism, and the Carbon Bond IV mechanism (CB-IV). 

(2) Comprehensive Air Quality Model with Extensions 

The Comprehensive Air Quality Model with extensions (CAMx) model was initially developed by 

ENVIRON in the late 1990s as a nested-grid, gas-phase, Eulerian photochemical grid model. 

ENVIRON later revised CAMx to treat PM, visibility, and air toxics. While there are many 

similarities between the CMAQ and CAMx systems, there are also some significant differences 

in their treatment of advection, dispersion, aerosol formation, and dry and wet deposition. 

C. Modeling Performance 

The objective of a model performance evaluation (MPE) is to compare model-simulated 

concentrations with observed data to determine whether the model’s performance is sufficiently 

accurate to justify using the model for simulating future conditions. There are a number of 

challenges in completing an annual MPE for regional haze. The model must be compared to 

ambient data from several different monitoring networks for both PM and gaseous species, for 

an annual time period, and for a large number of sites. The model must be evaluated for both 

the worst visibility conditions and for very clean conditions. Finally, final guidance on how to 

perform an MPE for fine-particulate models is not yet available from EPA. Therefore, the RMC 

experimented with many different approaches for showing model performance results. The plot 

types that were found to be the most useful are the following: 

• Time-series plots comparing the measured and model-predicted species concentrations 

• Scatter plots showing model predictions on the y-axis and ambient data on the x-axis 

• Spatial analysis plots with ambient data overlaid on model predictions 

• Bar plots comparing the mean fractional bias (MFB) or mean fractional error (MFE) 

performance metrics 

• “Bugle plots” showing how model performance varies as a function of the PM species 

concentration 

• Stacked-bar plots of contributions to light extinction for the average of the best-20% 

visibility days or the worst-20% visibility days at each site; the higher the light extinction, 

the lower the visibility 

Examples of each of these MPE metrics and analysis products can be found in Tonnesen, G. et 

al., 2006. The results of the MPE are available from the WRAP RMC website 

(http://pah.cert.ucr.edu/aqm/308/eval.shtml) 

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(1) Model Performance for 2002 Worst Days 

The CMAQ modeling for Mount Zirkel (MOZI) Wilderness also represents the Rawah 

Wilderness. Figure 6-1, the model performance can be roughly judged by comparing the 

modeled output (on left) against the monitored IMPROVE data (on right) for the worst days in 

2002. As indicated, the model greatly under predicts coarse mass (CM – gray color). Across 

the WRAP region a similar model under prediction is seen, thus the RMC has concluded that 

the CMAQ model cannot be used to forecast future CM concentrations. 

Figure 6-1: CMAQ Model Performance for MOZI 2002 Worst Days 

Figure 6-2, indicates that the model under predicts sulfate, nitrate and soil but over predicts 

OMC and EC for the worst days at MOZI. The Division has determined that the model 

performance for worst days is acceptable for sulfate (-21%), nitrate (-50%), OMC (+10%), EC 

(+35%) and soil (-15%). 

Figure 6-2: Relative Error: CMAQ Model vs IMPROVE data for MOZI 2002 Worst Days 

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(2) Model Performance for 2002 Best Days 

The CMAQ modeling for Mount Zirkel (MOZI) Wilderness also represents the Rawah 

Wilderness. Figure 6-3, the model performance can be roughly judged by comparing the 

modeled output (on left) against the monitored IMPROVE data (on right) for the best days in 

2002. As indicated, the model greatly under predicts coarse mass (CM – gray color). Across 

the WRAP region a similar model under prediction is seen, thus the RMC has concluded that 

the CMAQ model cannot be used to forecast future CM concentrations. 

Figure 6-3: CMAQ Model Performance for MOZI 2002 Best Days 

Figure 6-4, indicates that the model produces mixed predictions for the best days at MOZI. The 

Division has determined that the model performance for the best days is acceptable for EC 

(+25%) and soil (-25%) but is marginally acceptable for sulfate (-73%) and OMC (+80%). Best 

days model performance for nitrate (+322%) is unacceptable. 

Figure 6-4: Relative Error: CMAQ Model vs IMPROVE data for MOZI 2002 Best Days 

Colorado State Implementation Plan for Regional Haze - TSD Page 49
D. Modeling Projections 

(1) 2018 Worst Days Model Projection using Haze Index Metric 

The CMAQ modeling for Mount Zirkel (MOZI) Wilderness also represents the Rawah 

Wilderness. Figure 6-5 indicates the 2018 model projection is estimated at 10.1 deciviews 

(about 42.7% of the 2018 UPG of 9.56 dv) for the worst days using the EPA specific days 

method. The model projections are based on the 2018(b) emissions inventory, which does 

include some of Colorado’s Best Available Retrofit Technology (BART) emission controls. 

Figure 6-5: CMAQ Model Projections in Haze Index for MOZI 2018 Worst Days 

(2) 2018 Worst Days Model Projection using Extinction Metric 

Figure 6-6, provides the species-specific glide slopes with the 2018 model projections for the 

worst days at MOZI using the EPA specific days method. Please note that coarse mass is not 

included in the figure since model performance is unacceptable. 

Figure 6-6: CMAQ Model Projections in Extinction for MOZI 2018 Worst Days 

Colorado State Implementation Plan for Regional Haze - TSD Page 50
Figure 6-7, indicates the percentage of progress toward the Uniform Progress Goal (UPG) for 

each species. The 2018 modeling indicates modest improvements for sulfate and nitrate but all 

extinction components fall short of the UPG. The glide slope for soil is almost flat thus any 

small change is exaggerated when compared to the UPG. 

Figure 6-7: Percent Towards Species-Specific UPG for MOZI 2018 Worst Days 

(3) 2018 Best Days Model Projection using Haze Index Metric 

Figure 6-8 indicates the 2018 model projection for the best days at MOZI using the EPA specific 

days method is estimated at 1.38 deciviews, which is below best days baseline average of 1.61 

deciviews. Thus maintaining the best days is forecast for 2018. 

Figure 6-8: CMAQ Model Projections in Haze Index for MOZI 2018 Worst Days 

Colorado State Implementation Plan for Regional Haze - TSD Page 51
SECTION 7: PM SOURCE APPORTIONMENT TECHNOLOGY 

(PSAT) MODELING 

A. PSAT Overview 

The Regional Modeling Center (RMC) at the University of California – Riverside developed the 

PSAT algorithm in the Comprehensive Air quality Model with extensions (CAMx) model to 

assess source attribution. The PSAT analysis is used to attribute particle species, particularly 

sulfate and nitrate from a specific location within the Western Regional Air Partnership (WRAP) 

modeling domain. The PSAT algorithm applies nitrate-sulfate-ammonia chemistry to a system 

of tracers or “tags” to track the chemical transformations, transport and removal of emissions. 

Each state or region (i.e. Mexico, Canada) is assigned a unique number that is used to tag the 

emissions from each 36-kilometer grid cell within the WRAP modeling domain. Due to time and 

computational limitations, only point, mobile, area and fire emissions were tagged. 

The PSAT algorithm was also used, in a limited application (e.g. no state or regional attribution), 

to track natural and anthropogenic species of organic aerosols at each CIA. The organic 

aerosol tracer tracked both primary and secondary organic aerosols (POA & SOA). Appendix H 

includes more information on PSAT methodology. 

B. Particulate Sulfate PSAT for Worst Days in 2002 and 2018 

The PSAT modeling for Mount Zirkel Wilderness also represents the Rawah Wilderness. Figure 

7-1 displays the particulate sulfate concentrations for 2002 and 2018 worst days (in pairs) for 

each source area impacting Mount Zirkel and Rawah. The chart provides details on the relative 

source type contribution for a particular source area and the future trend (upward or downward) 

of each source area. As indicated, the dominant WRAP sulfate contributors for the worst days 

appear to be Colorado, Wyoming, and Utah. The future 2018 sulfate trends for the worst days 

are downward for Colorado but increasing for Utah and Wyoming. Point sources (denoted “PT” 

in blue) appear to be the dominant source of sulfate. 

Figure 7-1: Sulfate PSAT - Source Region Bar Chart for Worst Days in 2002 and 2018 

Figure 7-2 displays the regional sulfate contributions for 2002 and 2018 worst days at Mount 

Zirkel and Rawah. As indicated, the WRAP states contribute about half the sulfate impact 

Colorado State Implementation Plan for Regional Haze - TSD Page 52
followed by about 30% contribution from sources outside the modeling domain. By comparing 

both pie charts, the future trend indicates that the WRAP states relative contribution stays the 

same whereas the Outside Domain sources appear to be slightly increasing. 

Figure 7-2: Sulfate PSAT - Regional Pie Chart for Worst Days in 2002 and 2018 

C. Particulate Sulfate PSAT for Best Days in 2002 and 2018 

Figure 7-3 displays the particulate sulfate concentrations for 2002 and 2018 best days (in pairs) 

for each source area impacting Mount Zirkel and Rawah. The chart provides details on the 

relative source type contribution for a particular source area and the future trend (upward or 

downward) of each source area. As indicated, the dominant WRAP sulfate contributors for the 

best days appear to be Colorado, Wyoming, and Utah. The future 2018 sulfate trends for the 

best days are downward for Colorado but increasing for Utah and Wyoming. Point sources 

(denoted “PT” in blue) appear to be the dominant source of sulfate. 

Figure 7-3: Sulfate PSAT - Source Region Bar Chart for Best Days in 2002 and 2018 

Figure 7-4 displays the regional sulfate contributions for 2002 and 2018 best days at Mount 

Zirkel and Rawah. As indicated, the WRAP states contribute over half the sulfate impacts 

followed by over 30% contribution from sources outside the modeling domain. By comparing 

both pie charts, the future trend indicates that the WRAP states relative contribution is 

decreasing whereas the Outside Domain sources are slightly increasing. 

Colorado State Implementation Plan for Regional Haze - TSD Page 53
Figure 7-4: Sulfate PSAT - Regional Pie Chart for Best Days in 2002 and 2018 

D. Particulate Nitrate PSAT for Worst Days in 2002 and 2018 

Figure 7-5 displays the particulate nitrate concentrations for 2002 and 2018 worst days (in pairs) 

for each source area impacting Mount Zirkel and Rawah. The chart provides details on the 

relative source type contribution for a particular source area and the future trend (upward or 

downward) of each source area. As indicated, the dominant WRAP nitrate contributors for the 

worst days appear to be Colorado, Utah and Wyoming. The future 2018 nitrate trends for the 

worst days are downward for Colorado, Utah and Wyoming. Point sources (denoted “PT” in 

blue) along with area (orange color) and mobile sources (denoted “MV” in red) appear to be the 

dominant sources of nitrate. 

Figure 7-5: Nitrate PSAT – Source Region Bar Chart for Worst Days in 2002 and 2018 

Figure 7-6 displays the regional nitrate contributions for 2002 and 2018 worst days at Mount 

Zirkel and Rawah. As indicated, the WRAP states are the dominant contributor of nitrate (83%) 

followed by a 14% contribution from Outside Domain sources. By comparing both pie charts, 

the future trend indicates that the WRAP states relative contribution is decreasing whereas the 

Outside Domain sources are increasing by about 3%. 

Colorado State Implementation Plan for Regional Haze - TSD Page 54
Figure 7-6: Nitrate PSAT - Regional Pie Chart for Worst Days in 2002 and 2018 

E. Particulate Nitrate PSAT for Best Days in 2002 and 2018 

Figure 7-7 displays the particulate nitrate concentrations for 2002 and 2018 best days (in pairs) 

for each source area impacting Mount Zirkel and Rawah. The chart provides details on the 

relative source type contribution for a particular source area and the future trend (upward or 

downward) of each source area. As indicated, the dominant WRAP nitrate contributors for the 

best days appear to be Colorado, Utah, and Wyoming. The future 2018 nitrate trends for the 

best days are downward for Colorado and Utah but increasing for Wyoming. Point sources 

(denoted “PT” in blue) along with area (orange color) and mobile sources (denoted “MV” in red) 

appear to be the dominant sources of nitrate. 

Figure 7-7: Nitrate PSAT – Source Region Bar Chart for Best Days in 2002 and 2018 

Figure 7-8 displays the regional nitrate contributions for 2002 and 2018 best days at Mount 

Zirkel and Rawah. As indicated, the WRAP states contribute about three-fourths of the nitrate 

impact followed by about 25% contribution from sources outside the modeling domain. By 

comparing both pie charts, the future trend indicates that the WRAP states relative contribution 

is decreasing whereas the Outside Domain sources are increasing. 

Colorado State Implementation Plan for Regional Haze - TSD Page 55
Figure 7-8: Nitrate PSAT - Regional Pie Chart for Best Days in 2002 and 2018 

F. Organic Aerosol PSAT for Worst Days in 2002 and 2018 

Figure 7-9 displays the monthly organic aerosol concentrations for the baseline (2000-04) and 

2018 (in pairs) for the worst days at Mount Zirkel and Rawah. The organic aerosol PSAT tracks 

the following three components: anthropogenic secondary organic aerosol (SOA - red color), 

biogenic SOA (green color) and anthropogenic/biogenic primary organic aerosol (POA -blue 

color). The chart provides details on the relative anthropogenic and biogenic contribution each 

month and the future trend (upward or downward) in 2018. During the baseline period, it is 

interesting to note that the worst days never occur from September, December and March. 

Figure 7-9: Organic Carbon PSAT - Bar Chart for Worst Days in 2002 and 2018 

G. Organic Aerosol PSAT for Best Days in 2002 and 2018 

Figure 7-10 displays the monthly organic aerosol concentrations for the baseline (2000-04) and 

2018 (in pairs) for the best days at Mount Zirkel and Rawah. The chart provides details on the 

relative anthropogenic and biogenic contribution each month and the future trend (upward or 

Colorado State Implementation Plan for Regional Haze - TSD Page 56
downward) in 2018. During the baseline period, it is interesting to note that the best days never 

occur from March through August. 

Figure 7-10: Organic Carbon PSAT - Bar Chart for Best Days in 2002 and 2018 

H. Organic Aerosol PSAT for All Days in 2002 and 2018 

Figure 7-11 displays the monthly organic aerosol concentrations for the baseline (2000-04) and 

2018 (in pairs) for the all days at Mount Zirkel and Rawah. This chart indicates that 

anthropogenic/biogenic POA (blue color) is the dominant source of organic carbon, which 

occurs at much higher concentrations during the warmer months, which likely corresponds to 

the peak season when fires typically occur. 

Figure 7-11: Organic Carbon PSAT - Bar Chart for All Days in 2002 and 2018 

Colorado State Implementation Plan for Regional Haze - TSD Page 57
SECTION 8: EMISSIONS TRACE 

A. ET Overview 

The emissions trace is a tool that graphically combines the information from the PSAT, WEP, 

and emissions inventory with the statewide stationary source and area source pivot tables. The 

emissions trace is specific to each class I area for each pollutant (e.g. sulfate, nitrate, organic 

carbon, elemental carbon, fine soil and coarse mass). The emissions trace focuses on the 

worst days to allow for easy identification of Colorado sources and the percentage contribution 

of each category of emissions. 

The gray bars identify the source of information displayed in each column. For example “2018 

EC WEP” is shorthand for the elemental carbon weighted emissions potential for 2018 or the 

“2018 POA EI” means the primary organic aerosol emissions inventory for 2018, both of these 

are discussed in Section 5. All the information from the emissions trace is available to the 

public and can be found on the WRAP TSS website. 

The light green colored bars denote natural sources of secondary particulates, which are fairly 

common for organic carbon. The light yellow bars denote natural and anthropogenic sources of 

primary particulates, which are common for elemental carbon, soil and coarse mass. The light 

blue bars denote anthropogenic sources of secondary particulates, which are common for 

sulfate and nitrate. 

For some particulates such as sulfate, nitrate and organic carbon, the WRAP performed two 

different analysis techniques, the PSAT and WEP. The PSAT analysis better characterizes 

sulfate, nitrate and OC, since secondary particle formation dominates these components of 

extinction. As discussed earlier, the PSAT module of the CAMx model addresses complex 

chemistry and particle deposition, which is an important for estimating secondary particulates, 

particularly for sulfate and nitrate. The WEP analysis for sulfate, nitrate and OC are included on 

the emissions trace for purposes of comparison and are indicated by the dashed lines. It is 

important to note that the WEP does not address complex chemistry or particle deposition and 

does not consider intercontinental particle transport, which helps explain the differences 

between the PSAT and WEP apportionment of contributing sources. 

Colorado State Implementation Plan for Regional Haze - TSD Page 58
B. Sulfate Emissions Trace for 2018 Worst Days 

The Emissions Trace analysis for Mount Zirkel (MOZI) Wilderness also represents the Rawah 

Wilderness. In Figure 8-1, the sulfate emissions trace for Mount Zirkel and Rawah indicates 

that sulfate comprises 22.6% of visibility extinction with about 88.7% human-caused through 

secondary particulate formation. The majority of sulfate (41%) comes from outside the modeling 

domain and from international sources along with other states (38.1%). Colorado’s contribution 

is about 20.9%, which is mainly from point sources (54.6%) and natural fire (37.7%). The 

regional 2018 SOx WEP map indicates that SOx emissions from northwest Colorado, Front 

Range, southwest Wyoming and Four Corners are the predominant source regions contributing 

sulfate at MOZI. From the 2002 baseline, Colorado point sources are expected to reduce SO2

emissions by 29% in 2018. These reductions are from the implementation of Best Available 

Retrofit Technology (BART) on specific point sources. In 2018, area source emissions are 

expected to increase by 20%, which is due to increases in population and growth in the oil & 

gas industry. 

The modeled sulfate projection (see Section 6 D.2) indicates good progress (60%) towards the 

2018 UPG. The MOZI sulfate emissions trace indicates that Colorado is a relatively moderate 

contributor (18%) and the Division may conduct additional future analysis to determine if 

additional Colorado SO2 controls are practical. 

Figure 8-1: Sulfate Emissions Trace for 2018 Worst Days 

Colorado State Implementation Plan for Regional Haze - TSD Page 59
C. Nitrate Emissions Trace for 2018 Worst Days 

In Figure 8-2, the nitrate emissions trace for Mount Zirkel (MOZI) and Rawah indicates that 

nitrate comprises 9.3% of visibility extinction with about 97.8% of nitrate is human-caused with 

most coming from Colorado (41.6%) and other states (39.5%). Colorado’s contribution is mainly 

from point sources (79.2%) with lesser contributions from mobile (10.4%) and area (7.8%) 

sources. The regional 2018 NOx WEP map indicates that NOx emissions from the 

northwestern Colorado are the predominant source region that contributes nitrate at MOZI. 

From the 2002 baseline, Colorado point sources are expected to reduce NOx emissions by 5% 

in 2018. These reductions are from the implementation of Best Available Retrofit Technology 

(BART) on specific point sources. In 2018, area source emissions are expected to increase by 

40%, which is due to increases in population and growth in the oil & gas industry. The 68% 

reduction in mobile sources is due to federal Tier 2 emission standards on motor vehicles. 

The modeled nitrate projection (see Section 6 D.2) indicates modest progress (52%) towards 

the 2018 UPG. The MOZI nitrate emissions trace indicates that Colorado is a major contributor 

(41.6%), thus the Division may conduct additional future analysis to determine if additional 

Colorado NOx controls are practical. 

Figure 8-2: Nitrate Emissions Trace for 2018 Worst Days 

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D. Organic Carbon Emissions Trace for 2018 Worst Days 

In Figure 8-3, the organic carbon (OC) emissions trace for Mount Zirkel (MOZI) and Rawah 

indicates that OC comprises 42.8% of visibility extinction with the majority of OC (about 55.7%) 

from primary organic aerosol (POA) originating principally from natural fire. About 42.4% of OC 

is attributed to natural secondary organic aerosol (SOA) and the remaining 1.9% is directly 

attributable to human-caused SOA. Colorado contributes about 90.5% of the POA with the 

majority originating from natural fire (96.2%). The regional 2018 POA WEP map indicates a 

wide distribution of POA emissions with higher contributions from developed areas. 

The VOC WEP indicates that the majority of the MOZI SOA is natural (84.2%) %. This 

discrepancy is likely due to the chemical conversion for VOC to SOA and the deposition of SOA 

that is only addressed in the PSAT and not the WEP. Thus it appears that a large portion of 

biogenic VOC emissions fail to form SOA at the MOZI receptor. 

The anthropogenic portion of VOC emissions are tracked under the anthro SOA trace, which 

contributes about 1.9% to organic carbon particulates. Colorado appears to be the dominant 

contributor of anthro SOA at 7% with the most originating from area sources (39.7%). 

Unfortunately, the WRAP did not produce VOC WEP maps for organic carbon. 

The modeled OC projection (see Section 6 D.2) indicates poor progress (19%) towards the 

2018 UPG. The MOZI OC emissions trace indicates that the majority of OC appears to be POA 

(≈56%) with Colorado POA contributions mostly natural fire (48.5%). The other significant 

contributor is natural SOA (42.4%) which is mostly from wildfires. Although Colorado sources 

appear to contribute significantly to POA, the dominant contributor is natural fire which is largely 

not controllable, thus the Division has determined that no further analysis of potential statewide 

POA emission controls are practical at this time. 

Figure 8-3: Organic Carbon Emissions Trace for 2018 Worst Days 

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E. Elemental Carbon Emissions Trace for 2018 Worst Days 

In Figure 8-4, the elemental carbon (EC) emissions trace for Mount Zirkel (MOZI) and Rawah 

indicates that EC comprises 7.6% of visibility extinction with all EC in the form of primary 

particulates. Colorado’s contribution is about 89.6%, which is mainly from natural fire (94.9%). 

Other WRAP states contribute about 9.7%, CENRAP 0.4% and Pacific Offshore, Mexico & 

Canada contributing about 0.3%. The regional 2018 EC WEP map indicates concentrations of 

EC emissions from the Front Range, northwest Colorado and Salt Lake are contributing EC at 

MOZI. The reductions in off-road and on-road mobile, 55% and 75% respectively, are due to 

federal clean fuel standards and Tier 2 vehicle emission standards. 

The modeled EC projection (see Section 6 D.2) indicates moderate progress (45%) towards the 

2018 UPG. Since elemental carbon emissions are not currently inventoried by the Division but 

based on modeled estimates for area, mobile and fire source categories, the Division has 

determined that no further analysis of potential emission controls on EC sources are practical at 

this time. 

Figure 8-4: Elemental Carbon Emissions Trace for 2018 Worst Days 

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F. Fine Soil Emissions Trace for 2018 Worst Days 

In Figure 8-5, the fine soil emissions trace for Mount Zirkel (MOZI) and Rawah indicates that soil 

comprises 4.2% of visibility extinction with all soil in the form of primary particulates. Colorado’s 

contribution is about 47.1%, which is mostly from natural fire (68.3%), area sources (13.9%) and 

fugitive dust (12.8%). Other WRAP states contribute about 24.8%, CENRAP 5.3% and Pacific 

Offshore, Mexico & Canada contributing about 2.9%. The regional 2018 soil WEP map 

indicates a wide distribution of fine soil emissions across the WRAP region are contributing to 

fine soil impacts at MOZI. 

The modeled soil projection (see Section 6 D.2) indicates significant degradation (-700%) in 

2018, although, the soil glide slope is almost flat thus any small increase is greatly amplified. 

The Division has determined that no further analysis of potential emission controls on sources of 

soil (PM Fine) are practical at this time because most of Colorado’s soil emissions are from 

natural fire which is largely not controllable and other significant sources (area & fugitive dust) 

have very uncertain emission inventories. 

Figure 8-5: Fine Soil Emissions Trace for 2018 Worst Days 

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G. Coarse Mass Emissions Trace for 2018 Worst Days 

In Figure 8-6, the Coarse Mass (CM) emissions trace for Mount Zirkel (MOZI) and Rawah 

indicates that CM comprises 13.5% of visibility extinction with all CM in the form of primary 

coarse particulates. Colorado’s contribution is about 41.4%, which is mostly from natural fire 

(43%), point sources (36.5%) and fugitive dust (15.8%). Other WRAP states contribute about 

22.3%, CENRAP 4% and Pacific Offshore, Mexico & Canada contributing about 1.8%. The 

regional 2018 CM WEP map indicates concentrations of CM emissions from the Front Range, 

West Slope, and southern Wyoming are contributing to CM impacts at MOZI. 

The Division has determined that coarse mass emissions from construction activities and 

controlled burning will be evaluated under the long-term strategy (LTS). As discussed in 

Section 6, the model performance for CM across the WRAP region is unacceptable, thus no 

forecast of CM extinction can be made for 2018. Without model projections, the Division is 

unable to evaluate the effectiveness of potential emission controls on contributing source 

categories. Thus, it is impossible to know what is “reasonable” if the benefit (degree of deciview 

improvement) of such controls cannot be determined at a particular CIA. Under the Regional 

Haze Rule, the Division is required to develop a long-term strategy (LTS) that must consider 

measures to mitigate the impacts of construction activities and smoke management techniques 

for agricultural and forestry management purposes. 

Figure 8-6: Coarse Mass Emissions Trace for 2018 Worst Days 

Colorado State Implementation Plan for Regional Haze - TSD Page 64
 

APPENDICES 

Colorado State Implementation Plan for Regional Haze - TSD Page 65
 

Appendix A: IMPROVE Monitoring Program

In the mid-1980’s, the IMPROVE program (Interagency Monitoring of Protected Visual 

Environments) was established to measure visibility impairment in mandatory Class I areas 

throughout the United States. The monitoring sites are operated and maintained through a 

formal cooperative relationship between the EPA, National Park Service, U.S. Fish and Wildlife 

Service, Bureau of Land Management, and U.S. Forest Service. In 1991, several additional 

organizations joined the effort: State and Territorial Air Pollution Program Administrators and the 

Association of Local Air Pollution Control Officials, Western States Air Resources Council, MidAtlantic Regional Air Management Association, and Northeast States for Coordinated Air Use 

Management. 

IMPROVE Program Objectives 

 Establish current visibility and aerosol conditions in mandatory Class I areas, 

 Identify chemical species and emission sources responsible for existing human-made 

visibility impairment, 

 Document long-term trends for assessing progress towards the national visibility goals, 

 With the enactment of the Regional Haze Rule, to provide regional haze monitoring 

representing all visibility-protected federal Class I areas where practical. 

The data collected at these sites are used by land managers, industry planners, scientists, 

public interest groups, and air quality regulators to better understand and protect the visual air 

quality resource in Class I areas. Most importantly, the IMPROVE Program scientifically 

documents the visual air quality of their wilderness areas and national parks. 

Colorado State Implementation Plan for Regional Haze - TSD Page 66
Appendix B: Photographic Images of Visibility 

Overview

In 1995, the IMPROVE (Interagency Monitoring of Protected Visual Environment) Steering 

Committee formed a consensus, that five years of scene monitoring yields sufficient examples 

of most visual air quality conditions. To secure a representative set of observed air quality 

conditions for each scene-monitoring site, a series of slides is selected from the period of record 

and archived on CD-ROM. This series of slides makes up the historical photographic archive 

and consists of: 

• A spectrum series of regional haze visibility conditions observed at the site for each 

monitored time of day. 

• Selected visibility and meteorological episodes or events observed during the period 

of record (including wildfire, winter inversions, and/or regional haze impacts). 

• Selected layered haze events observed during the period of record. 

• Specific slides that show scenic views of the vista and observations of 

meteorological interest during the period of record. 

• Historical slide selections requested by the IMPROVE Steering Committee and/or 

other air quality managers that depict visibility conditions for project-specific reports or public 

presentations. 

Image Selection Process

The total number of slides selected for each historical archive depends on the vista, the 

variability in visual air quality, the period of record, and completeness of the slide database. 

Final sets can vary from 50 to 150 slides. Final images are selected and assigned to gallery 

archive types (accessed at the left) as follows: 

• Spectrum series slides consist of selected clear sky days that represent a range of 

visibility conditions from good to bad for selected monitoring periods. Slides with fog and 

other weather-related occurrences or evident layered hazes are not included. Impaired 

visibility is most often apparent by the loss of color, texture or contrast of scenic features. 

Each series also contains slides showing a uniform haze, which degrades visibility evenly 

across the scene. 

Using slide densitometry measurements, known target distances, and estimated inherent 

contrast measurements; the visual range (km) is estimated for each slide of the series. All 

values are rounded for precision. Associated extinction (Mm-1) and haziness (dv) values are 

calculated. Representative cumulative frequency summaries for each image are provided 

for sites that also have IMPROVE aerosol-monitoring instrumentation. 

• Episode series slides are chosen to represent regional or layered haze conditions 

that continue for a period greater than an isolated event (2 or more days). These can often 

be attributed to wildfires, long-term periods of air stagnation, plumes, or winter inversions in 

the region. 

• Layered haze slides depict ground-based or elevated layered haze, or some 

combination thereof described as multiple haze layers. Layered hazes occur when 

pollutants are released into a stable atmosphere with little or no vertical mixing. The 

pollutants form a layer of haze that continues to develop as long as the air mass above 

remains stagnant. Layers can form near the ground and are known as ground-based 

layered hazes. Layers that are not in contact with the ground are described as elevated 

Colorado State Implementation Plan for Regional Haze - TSD Page 67
Appendix B: Photographic Images of Visibility 

layered hazes. Layered hazes are usually associated with emissions that are local in nature 

as opposed to pollutants that are transported over hundreds of kilometers. 

• Scenic slides represent special scenic qualities such as interesting cloud formations, 

pristine conditions, snow-covered scenes or meteorological observations. 

• Historical selections include any slide data that has been used to depict visibility 

conditions in project-specific reports or public presentations. Base slides used for the 

modeling application WinHaze Visual Air Quality Modeler, and the header image used for 

HTML-formatted Web pages of this historical archive are also included in historical 

selections. 

Historical slide archives for each site are reproduced digitally to Kodak Photo Cd (PCD) format 

and transferred to a Kodak Gold CD. Master historical photographic archives produced for each 

site contain 4 JPEG resolutions of each selected image (Image Gallery); graphic images of the 

monitoring location and site vistas (Site Specifications); as well as associated cumulative 

frequency summaries, tables, and/or data listings (Spectrum Frequencies). 

Colorado State Implementation Plan for Regional Haze - TSD Page 68
Appendix C: Seasonal Wind Patterns 

While all winds are the movement of air mostly parallel to the Earth's surface, they come in a 

variety of forms including global, synoptic, mesoscale and microscale. Global winds are the 

result of global circulation patterns that include trade winds and the jet stream. Synoptic winds 

are regional winds that are associated with large-scale events such as warm and cold fronts, 

and are part of what makes up everyday weather. Mesoscale winds typically arise and fade 

over time-periods too short and over geographic regions too narrow to predict with any longrange accuracy. Microscale winds take place over very short durations of time (seconds – 

minutes) in areas comprising tens of acres. 

Mountain and valley breezes are mesoscale winds resulting from heating (due to solar radiation) 

and cooling of the land surface. In the case of a valley wind, the radiated ground heats air next 

to a mountain slope in the daytime while colder, denser air farther away from the mountain 

slope settles down upon the warmer air forcing it to move up the mountain slope. At night, the 

opposite movement occurs. The air on the mountain slope gradually cools and becomes 

heavier than the surrounding air and drains down into the valley. Mountain winds are usually 

stronger than valley winds. 

Figure C -1 depicts the synoptic (regional) wind patterns for the various seasons in Colorado. 

The influence of mountain and valley winds for a particular Class I area are not addressed in the 

below map. 

Figure C-1: Airflow into Colorado 

Source: Nolan Doesken, state climatologist 

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Appendix D: Weighted Emissions Potential 

Introduction

The Weighted Emissions Potential analysis (WEP) was developed as a screening tool for states 

to decide which source regions have the potential to contribute to haze formation at specific 

Class I areas, based on both the 2002 and 2018 emissions inventories. This method does not 

produce highly accurate results because, unlike the air quality model and associated PSAT 

analysis, it does not account for chemistry and removal processes. Instead, it relies on an 

integration of gridded emissions data, back trajectory residence time data, a one-over-distance 

factor to approximate deposition, and a normalization of the final results. Residence time over 

an area is indicative of general flow patterns, but does not necessarily imply the area 

contributed significantly to haze at a given receptor. Therefore, users are cautioned to view the 

WEP as one piece of a larger, more comprehensive weight of evidence analysis. 

Emissions Data Inputs

The emissions data used were the annual, 36km grid SMOKE-processed, model-ready 

emissions inventories provided by the WRAP Regional Modeling Center (RMC). The analysis 

was performed for nine (9) pollutants (maps were generated for all but the last three): 

• Sulfur oxides 

• Nitrogen oxides 

• Organic carbon 

• Elemental carbon 

• Fine particulate matter 

• Coarse particulate matter 

• Ammonia 

• Volatile organic carbon 

• Carbon monoxide. 

The following source categories for each pollutant were identified and preserved through the 

analysis: 

• Biogenic 

• Natural fire 

• Point 

• Area 

• WRAP oil and gas 

• Off-shore 

• On-road mobile 

• Off-road mobile 

• Road dust 

• Fugitive dust 

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Appendix D: Weighted Emissions Potential 

• Windblown dust 

• Anthropogenic fires 

Residence Time Inputs

The back trajectory residence times were provided by the WRAP Causes of Haze Assessment 

(COHA). The COHA project used NOAA’s HYSPLIT model to generate eight (8) back 

trajectories daily for each WRAP Class I area for the entire five-year baseline period (2000-04). 

The major model parameters selected for this analysis are presented in Table 1. From these 

individual trajectories, residence time fields were generated for one-degree latitude by onedegree longitude grid cells. Residence time analysis computes the amount of time (e.g., 

number of hours) or percent of time an air parcel is in a horizontal grid cell. Plotted on a map, 

residence time is shown as percent of total hours in each grid cell across the domain, thus 

allowing an interpretation of general air flow patterns for a given Class I area. The residence 

time fields for the 20% worst and best IMPROVE-monitored extinction days were selected for 

the WEP analysis to highlight the potential emissions sources during those specific periods. 

Table 1 

Back Trajectory Model Parameters Selected for WEP Analysis 

Model Parameter Value 

Trajectory duration 192 hours (8 days) backward in time 

Top of model domain 14,000 meters 

Vertical motion option used model data 

Receptor height 500 meters 

Meteorological Field EDAS and FNL (location dependent) 

Integration of Emissions and Residence Time Data

The WEP analysis consisted of weighting the annual gridded emissions (by pollutant and source 

category) by the worst and best extinction days residence times for the five-year baseline 

period. To account for deposition along the trajectories, the result was further weighted by a 

one-over-distance factor, measured as the distance in km between the centroid of each 

emissions grid cell and the centroid of the grid cell containing the Class I area monitoring site 

under investigation. (The “home” grid cell of the monitoring site was weighted by one fourth of 

the 36km grid cell distance, or one-over-9km, to avoid a large response in that grid cell.) The 

resulting weighted emissions field was normalized by the highest grid cell to ease interpretation. 

An example series of maps illustrating the WEP analysis is presented in Figure 1. This example 

shows the annual emissions for NOx across the domain, the specific residence time pattern for 

the 20% worst monitored days at a Class I area, and the resulting weighted emissions map. 

Both the 2002 and 2018 cases are presented. Interpretation of the results should focus on 

which grid cells (or larger regions) have significant potential to affect the Class I area, and on 

changes between 2002 and 2018. 

An example of associated bar charts showing the estimated contribution by source category and 

region is presented in Figure 2. It is important to note that these charts show normalized values 

with no direct connection to original emissions values. Interpretation of the results should focus 

on the relative contributions by each source category and region, and the changes between 

2002 and 2018. 

Colorado State Implementation Plan for Regional Haze - TSD Page 71
Appendix D: Weighted Emissions Potential 

Caveats

The WEP is not a rigorous, stand-alone analysis, but a simple, straightforward use of existing 

data. As such, there are several caveats to keep in mind when using WEP results as part of a 

comprehensive weight of evidence analysis: 

• This analysis does not take into account any emissions chemistry. 

• While actual emissions may vary considerably throughout the year, this analysis pairs up 

annual emissions data with 20% worst/best extinction days residence times – this is 

likely most problematic for carbon and dust emissions, which can be highly episodic. 

• Coarse particle and some fine particle dust emissions tend not to be transported long 

distances due to their large mass. 

• The WEP results are unitless numbers, normalized to the largest-valued grid cell. 

Effective use of these results requires an understanding of actual emissions values and 

their relative contribution to haze at a given Class I area. 

Colorado State Implementation Plan for Regional Haze - TSD Page 72
Appendix E: Standard Aerosol-Type Equations 

This section addresses the equations for calculating aerosol types, e.g. sulfate, organic, soil, 

from elemental concentrations. 

The following table lists the standard formulae and assumptions applied to IMPROVE sampler 

measurements to derive the principal fine aerosol species, reconstructed fine mass, and coarse 

mass. The brackets indicate the mass concentration of the aerosol species or element. For a 

detailed discussion on these aerosol type equations see Malm et al, 1994, Chapter 2 of the 2000 

IMPROVE report and Chapter 2 of the 1996 IMPROVE report 

SPECIES Abbreviation FORMULA ASSUMPTIONS 

Ammonium 

Sulfate

SULFATE 4.125[S] All elemental S is from 

sulfate. All sulfate is from 

ammonium sulfate. 

Ammonium 

Nitrate

NITRATE 1.29[NO3] Denuder efficiency is 

close to 100%. All nitrate 

is from ammonium nitrate. 

total Organic 

Carbon

OC OC1+OC2+OC3+OC4+OP 

(see definitions below) 

Organic Mass 

by Carbon 

OMC 1.4 * OC Average organic molecule 

is 70% carbon. 

Organic Carbon 

by Hydrogen

OCH (11 * (H - 0.25 * S)) Assumes all sulfur is 

ammonium sulfate and 

there is no hydrogen from 

nitrate. Organic mass is 

equal to 1.4*OCH 

Light Absorbing 

Carbon 

LAC EC1+EC2+EC3-OP 

(see definitions below) 

Fine Soil SOIL 2.2[Al]+2.49[Si]+1.63[Ca] 

+2.42[Fe]+1.94[Ti] 

[Soil K]=0.6[Fe]. FeO and 

Fe2O3 are equally 

abundant. A factor of 1.16 

is used for MgO, Na2O, 

H2O, CO2. 

ReConstructed 

Fine Mass 

RCFM [SULFATE]+[NITRATE] 

+[LAC]+[OMC]+[SOIL] 

Represents dry ambient 

fine aerosol mass for 

continental sites. 

Coarse Mass CM [PM10] - [PM2.5] Consists only of insoluble 

soil particles. 

Colorado State Implementation Plan for Regional Haze - TSD Page 73
Appendix E: Standard Aerosol-Type Equations 

Ammonium Sulfate (NH4SO4): The sulfur on the Teflon filter is always present as sulfate 

(SO4). In most cases the sulfate is fully neutralized ammonium sulfate, which is 4.125 times the 

sulfur concentration. The sulfate at Eastern sites during the summer is not always fully 

neutralized resulting in acidic aerosol. If 100% of the sulfur were sulfuric acid, the correct 

sulfate mass would be 74% of the calculated (NH4)2SO4. 

Organic Carbon: Aerosol samples collected on quartz filters are analyzed at Desert Research 

Institute for carbon using the Thermal Optical Reflectance (TOR) combustion method. The 

sample is heated in steps and the evolved CO2 is measured at each step. The first four steps 

take place in a pure helium atmosphere to prevent combustion. The carbon released in these 

steps (OC1-OC4) is interpreted as being evaporated organics. 2% O2 is introduced at 550 °C 

and more carbon is released. During the pure-helium stage, some of the organic material has 

been charred (pyrolized), darkening the filter (decreasing its reflectivity). The filter starts to 

lighten when oxygen is introduced oxidizing the char. The carbon that has been recorded in the 

oxygen stage when the filter returns to its original reflectivity is interpreted as pyrolized organics, 

(OP). The carbon evolved after the filter has returned to its initial reflectance is interpreted as 

elemental (E1-E3). For a full description, see Chow et al., in Atmospheric Environment. 27A, 

1185-1201, (1993). 

Carbon Components as a Function of Temperature and Added Oxygen.

Fraction Pyrolized 

Fraction 

Temperature Range Atmosphere Reflectance 

vs. Initial 

O1 ambient to 120°C at initial 

O2 120 - 250°C 100% He 

O3 250 - 450°C under initial 

O4 450 - 550°C 

E1 OP remains at 

 550°C 98% He 

E2 550 - 700°C 2% O2 over initial 

E3 700 - 800°C 

Organic Mass by Carbon (OMC): The organic mass is the sum of the low temperature 

organics and pyrolized organics multiplied by a factor of 1.4: OMC=1.4 * 

(OC1+OC2+OC3+OC4+OP) where the factor 1.4 is used to adjust the organic carbon mass 

(OC) for other elements assumed to be associated with the organic carbon molecule. 

Light-Absorbing Carbon (LAC): This is the sum of elemental carbon fractions. The pyrolized 

fraction is subtracted. Preliminary analyses indicate that some of the O4 fraction may absorb 

light, and that OP may overestimate the pyrolytic mass. 

Organic Carbon by Hydrogen (OCH): The hydrogen on the Teflon filter is associated with 

sulfate, organics, nitrate, and water. Since the PIXE analysis is done in vacuum, all water will 

volatilize. Also it is assumed that no significant hydrogen from nitrate remains. If it is further 

assumed that the sulfate is fully neutralized ammonium sulfate, the organic carbon 

concentration can be estimated by subtracting the hydrogen from sulfate and multiplying the 

difference by a constant representing the fraction of hydrogen: 

OCH = 11*(H-0.24*S) 

Colorado State Implementation Plan for Regional Haze - TSD Page 74
Appendix E: Standard Aerosol-Type Equations 

The sulfur factor, H/S ratio, for ammonium sulfate is 8/32 = 0.25. The C/OM ratio is 11 and 

operationally defined by forcing OCH to equal OC. Comparison of OCH to OC is used in data 

validation procedures and OCH is used to estimate organic mass when carbon is not explicitly 

measured. 

The OCH calculation is invalid when (1) there is high nitrate relative to sulfate, as at sites near 

Los Angeles and San Francisco, and (2) the sulfur is not present as ammonium sulfate. This 

latter includes sites with marine sulfur, and sites in the eastern United States with unneutralized 

sulfate. The main advantage of using OCH at valid sites is that its precision is better than that 

for OC during periods of low organic, e.g. winter in the West. At sites in the East, OCH is often 

low because of unneutralized sulfate, and imprecise because of the high sulfate relative to 

organic. 

Light-Absorbing Carbon (LAC): This is the sum of elemental carbon fractions. The pyrolized 

fraction is subtracted. Preliminary analyses indicate that some of the O4 fraction may absorb 

light, and that OP may overestimate the pyrolytic mass. 

Soil (SOIL): This is a sum of the soil-derived elements (Al, Si, K, Ca, Ti, Fe) along with their 

normal oxides (Al2O3, SiO2, CaO, K2O, FeO, Fe2O3, TiO2). The variable does not depend on 

the type of soil, such as sediment, sandstone, or limestone. One fine element, K, however, may 

partly derive from smoke as well as soil. Smoke potassium is eliminated from the calculation 

using Fe as a surrogate. This is discussed in nonsoil potassium below. 

Nonsoil Potassium (KNON): Fine potassium has two major sources, soil and smoke, with the 

smoke potassium on much smaller particles than the soil potassium. The potassium in coarse 

particles will be solely produced from soil. The soil potassium is estimated from the measured 

concentration of Fe and the ratio of K/Fe of 0.6 measured on coarse samples (2.5 to 15 μm) 

collected between 1982 and 1986. This ratio depends on the soil composition and varies 

slightly from site to site. If the ratio were slightly smaller (say 0.5), the KNON values will be 

negative when there is no smoke influence. The residual potassium, K - 0.6*Fe, is then 

assumed to be produced by smoke. The burning of most organic fuels will produce potassium 

vapor. During transport, this vapor will transform into fine particles. The KNON parameter is 

not a quantitative measure of the total smoke mass, since the ratio of nonsoil potassium to total 

smoke mass will vary widely, depending on the fuel type and the transport time. However, the 

KNON parameter can be used as an indicator of a nonsoil contribution for samples with large 

KNON. In some situations, there may be some fine Fe from industrial sources, which could 

cause occasional smoke episodes to be lost. 

Colorado State Implementation Plan for Regional Haze - TSD Page 75
Appendix F: Procedure for Reconstructing Light Extinction 

The light-extinction coefficient, bext (expressed as inverse megameters, 1/Mm), is the sum: 

Equation (F-1): bext = bscat + babs = bsg + bsp + bag +bap

Where bscat is the sum of scattering by gases and scattering by particles, and babs is the sum of 

absorption by gases and particles. Scattering by gases in the atmosphere, bsg, is described by the 

Rayleigh scattering theory [VandeHulst, 1981] and will be referred to as Rayleigh scattering. The 

IMPROVE program assumes a standard value of 10 1/Mm. Scattering by particles, bsp, is caused 

by both fine and coarse aerosol species and is the largest contributor to total light extinction in most 

locations [Malm et al., 1994a]. Absorption due to gases, bag, is primarily due to nitrogen dioxide 

(NO2) and is assumed to be negligible because almost all monitoring sites are in rural locations 

[Trijonis and Pitchford, 1987]. Absorption by particles, bap, is caused primarily by carbon containing 

particles.

A particle in the atmosphere can be a mix (internal mixture) of various aerosol species, or in 

some cases its compositional structure may be restricted to one species (external mixture) such 

as (NH4)2SO4. Furthermore, an internally mixed aerosol such as organic/ammoniated 

sulfate/water particle can be externally mixed from wind-blown dust particles. Whether an 

aerosol is internally or externally mixed, it scatters and/or absorbs a specific fraction of radiant 

energy impinging on it. Following the suggestion of White [1986], an aerosol 

scattering/extinction per unit mass ratio will be referred to as specific scattering/extinction, as in 

specific gravity. 

Most routine aerosol monitoring programs and many special study visibility characterization 

programs were designed to measure bulk aerosol species mass concentrations such as 

sulfates, nitrates, carbonaceous material, and selected elements [Heisler et al., 1980; Malm et 

al., 1994b; Tombach and Thurston, 1994; Watson et al., 1990; Macias et al., 1981]. They were 

not designed to determine the microphysical and chemical characteristics of these species. 

The inherent limitations of estimating aerosol optical properties from bulk aerosol 

measurements have been addressed, at least in part, by a number of authors. For instance, 

Ouimette and Flagan [1982] have shown, from basic theoretical considerations, that if an 

aerosol is mixed externally or if in an internally mixed aerosol the index of refraction is not a 

function of composition or size, and the aerosol density is independent of volume, then: 

Equation (F-2) bext = ∑αi mi 

i

Where αi is the specific scattering or absorption efficiency and mi is the mass of the individual 

species. 

Malm and Kreidenweis [1997] demonstrated from a theoretical perspective, that specific 

scattering of mixtures of organics and ammoniated sulfates were insensitive to the choice of 

internal or external mixtures. Sloane [1983, 1984, 1986], Sloane and Wolff [1985], and more 

recently Lowenthal et al. [1995], Malm [1998], and Malm et al. [1997] have shown that 

differences in estimated specific scattering between external and internal model assumptions 

are usually less than about 10%. In the absence of detailed microphysical and chemical 

structure of ambient aerosols, the above studies demonstrate that a reasonable estimate of 

aerosol extinction can be achieved by assuming each species is externally mixed. 

Colorado State Implementation Plan for Regional Haze - TSD Page 76
Appendix F: Procedure For Reconstructing Light Extinction 

However, the issue of water uptake by hygroscopic species must be addressed. Implicit to the 

use of Equation (F-2) is an assumed linear relationship between aerosol mass and extinction. It 

is well known that sulfates and other hygroscopic species form solution droplets that increase in 

size as a function of relative humidity (RH). Therefore, if scattering is measured at various 

relative humidities the relationship between measured scattering and hygroscopic species mass 

can be quite nonlinear. A number of authors have attempted to linearize the model, in an 

empirical way, by multiplying the hygroscopic species by such a factor as 1/(1-RH) to account 

for the presence of water mass [White and Roberts, 1977; Malm et al., 1986]. However, Malm 

et al. [1989] and Gebhart and Malm [1989] proposed a different approach. They multiplied the 

hygroscopic species by a relative humidity scattering enhancement factor, f(RH), that is 

calculated on a sampling-period-by-sampling-period basis using Mie theory and an assumed 

size distribution and laboratory measured aerosol growth curves. 

Measurements of hygroscopic species growth as a function of relative humidity show that 

species such as ammonium sulfate show zero growth until a relative humidity, referred to as the 

deliquescent relative humidity, is reached where they spontaneously form a solution droplet that 

is in equilibrium with water molecules in the ambient atmosphere. Conversely, when the relative 

humidity is decreased from some value greater than 80% the solution droplet retains water 

below the deliquescent point to a relative humidity where all water is spontaneously given up. 

This point is referred to as the crystallization relative humidity. 

However, because the growth factor and light-scattering efficiency for ambient aerosols has 

previously been observed to be rather smooth, [Sloane 1983, 1984, 1986; Wexler and Seinfeld, 

1991; Waggoner et al., 1981; Day et al., 2000; Malm et al. 2000] a “best estimate" for the 

sulfates and nitrates species growth, the laboratory growth curves, as measured by Tang [1996] 

were smoothed between the deliquescence and crystallization points. Malm [1998] and Malm et 

al., [1997] have demonstrated that in both the East (Great Smoky Mountains National Park) and 

West (Grand Canyon National Park) the best estimate growth model, in combination with 

measured size distributions, yields an fT(RH) function that results in good agreement between 

measured and reconstructed scattering for particles less than 2.5 μm. 

Therefore, the following equation is used to estimate reconstructed particle scattering: 

Equation (F-3): 

bscat =(3)ƒT (RH)[SULFATE] +(3)ƒT (RH)[NITRATE] +(4)ƒorg (RH)[OMC]+(1)[SOIL]+(0.6)[CM]+10 

See Aerosol-Type Equations for definitions of the species in equation F-3 

The brackets indicate the species concentration, 3 m2

/g is the dry specific scattering for 

ammonium sulfate and ammonium nitrate, 4 m2

/g for organic carbon, and 1 m2/g and 0.6 m2/g 

are the respective scattering efficiencies for soil and coarse mass. The efficiencies for fine soil 

and coarse mass are taken from a literature review by Trijonis and Pitchford [1987]. 

A dry scattering efficiency of 3 m2

/g is a nominal scattering efficiency based on a literature 

review by Trijonis et al. [1988, 1990] and a review by White [1990]. Trijonis' best estimate for 

ammonium sulfate and ammonium nitrate is 2.5 m2

/g with an error factor of 2, while for organics 

it is 3.75 m2

/g again with an error factor of 2. White took a somewhat different approach in that 

he reviewed 30 studies in which particle scattering and mass were measured. He then 

estimated a high and low scattering efficiency by using mass measurements to prorate the 

measured extinction. For ammonium sulfate, the low estimate was arrived at by assuming 

sulfates, nitrates, and organics scatter twice as efficiently as all other species, and for the high 

estimate he assumed that only the ammonium sulfate was twice as efficient. His low and high 

ammonium sulfate mass scattering efficiencies for the rural west were 3.0 and 3.7 m2

/g, 

respectively. For organics his low estimate assumes organics and other non-sulfate species 

Colorado State Implementation Plan for Regional Haze - TSD Page 77
Appendix F: Procedure For Reconstructing Light Extinction 

scatter half as efficiently as ammonium sulfate, and for the high estimate he assumes organics 

are three, and ammonium sulfate twice as efficient at scattering light as other species. His low 

and high estimates for organic mass scattering coefficients are 1.8 and 4.1 m2

/g. More recently, 

Malm et al. [1996] demonstrated that an assumption of dry specific scattering values given in 

Equation (F-3) yielded good agreement between measured and reconstructed extinction across 

the entire IMPROVE monitoring network. 

Various functions for the hygroscopicity of organics have been proposed. Assumptions must 

not only be made about the solubility of organics but also on the fraction of organics that are 

soluble. It should be noted, models that treat water uptake for non ideal multicomponent 

solutions using theoretical and semi-theoretical thermodynamic relationships have been 

developed and have been applied to both visibility and climate forcing problems [Saxena and 

Peterson, 1981; Pilinis et al., 1995; Saxena et al., 1986, 1993]. The correct treatment of the 

hygroscopicity of species in multicomponent mixtures—especially organic species—remains 

problematic, not only because of the lack of suitable mixture thermodynamic data but also 

because of the lack of information about other critical mixture properties. Given the variety of 

organic species, it is possible that a geographic variation in organic species exists, with large 

fractions of soluble species occurring in certain parts of the continent and much smaller 

fractions in other areas. However, field experiments and subsequent data analysis at Great 

Smoky Mountains and Grand Canyon National Parks [Malm et al., 1997; Malm and 

Kreidenweis, 1996 Malm et al., 2000] and, more generally, data collected in the IMPROVE 

Network [Malm et al., 1996] show that to within the uncertainty of the measurements and 

modeling assumptions, organics are not or are only weakly hygroscopic. Therefore, forg(RH) for 

organics was set equal to one. 

Equation (F-3) has been shown to give a good estimation of scattering for particles less than 2.5 

μm, however, estimating extinction requires a knowledge of particle absorption. Mass 

absorption efficiencies of carbon vary by more than a factor of two as do direct measurements. 

Horvath [1993] has reviewed the measurement of absorption, while Fuller et al. [1999] has 

theoretically explored the variability of absorption efficiency as a function of carbon morphology. 

Although absorption can be estimated in a variety of ways, there is no one method that is 

generally accepted by the scientific community. For purposes of this report, carbon absorption 

is estimated using: 

Equation (F-4): babs = 10[LAC] 

Where babs is particle absorption, LAC is the concentration of light-absorbing carbon as 

measured using the Thermal Optical Reflectance (TOR) analysis scheme [Chow et al., 1993], 

and 10 is the specific absorption for LAC, which has been used by a number of scientists 

[Horvath, 1993]. 

Because aerosol concentrations are derived from averages over long periods, the light 

scattering due to soluble species is derived using hourly RH values less than or equal to 98%, 

as given by the following equation: 

Equation (F-5): bscat = αFT C 

Where C is the average species concentration, α is the specific scattering, and 

Equation (F-6): FT = ƒT (RH)

Using Equation (F-3), extinction budgets for a time interval may be calculated by replacing 

fT(RH) with FT and by using the average concentration of each species over the same time 

Colorado State Implementation Plan for Regional Haze - TSD Page 78
Appendix F: Procedure For Reconstructing Light Extinction 

interval as the mass concentration. Using the data from sites with collocated optical and RH 

data, a polynomial curve was fitted to the annual and seasonal data as defined by: 

Equation (F-7): F=b0+b1(100/(100-RH))+b2(100/(100-RH))2

Where bo = 0.33713, b1 = 0.58601, and b2 = 0.09164 with an R-square of 0.93 annually. Figure 

F-1 shows the fitted curve plotted against annual average RH for IMPROVE sites with 

collocated RH data. Table F-1 lists the regression results for annual and seasonal averaging 

periods. For those sites without collocated optical and RH data, the annual factors can be 

calculated using Equation (F-7) and estimates of annual average RH. (Five significant figures 

are used in the curve fit program used for this report and therefore are included here for 

reference.) 

Figure F -1: Best-fit relation between a site’s annual average RH and its annual average RH correction factor.

Table F-1: Parameters of the best-fit equation relating the relative humidity light-extinction 

correction factors (FT) to seasonal and annual average site relative humidity (F = bo + b1(1/(1-RH)) + 

b2(1/(1-RH))2

. 

Season b0 b1 b2 R2

Spring -0.01097 0.78095 0.080147 0.93 

Summer -0.18614 0.99211 --- 0.91 

Autumn -0.24812 1.01865 0.01074 0.93 

Winter 0.34603 0.81984 --- 0.77 

ANNUAL 0.33713 0.58601 0.09164 0.93 

Colorado State Implementation Plan for Regional Haze - TSD Page 79
Appendix F: Procedure For Reconstructing Light Extinction 

Figure F-2 is a flowchart, which details the process used to account for the effects of relative 

humidity at those sites with or without relative humidity sensors. 

Figure F -2: The process by which IMPROVE data is used to develop site-specific seasonal and annual RH correction factors. 

The extinction reconstruction process starting with the raw IMPROVE data through to the 

extinction calculation can be summarized: 

1. At those sites with collocated RH sensors and particle monitors, discard hourly RH values 

greater than 98% and discard days with less the 16 RH values. 

2. Convert the hourly RH to f(RH) values using the “smoothed” ammonium sulfate fT(RH) 

versus RH lookup table shown graphically in Figure G-3. 

3. Calculate annual and/or seasonal RH and f(RH) averages (FT) [Equation (G-6)]. 

4. Develop an empirical relationship between average RH and average FT (RH) [Eq. (G-7)]. 

5. For the desired time period (annual or seasonal) find the average of the following species: 

ammonium sulfate, ammonium nitrate, organics, light-absorbing carbon, fine soil, and 

coarse mass. 

Colorado State Implementation Plan for Regional Haze - TSD Page 80
Appendix F: Procedure For Reconstructing Light Extinction 

6. Using these averages calculate average reconstructed aerosol extinction according to the 

following equation (referred to as the old IMPROVE equation): 

Equation (F-8): OLD IMPROVE EQUATION:

bext = (3)FT (RH) [Sulfate]+(3)FT (RH) [Nitrate]+(4)[OMC]+(10)[LAC]+(1)[Soil]+(0.6) [CM]+10 

or 

[ ]

[ ]

[ ]

[ ]

[ ]

[ ]

10

6.0

1

10

4

)(3

)(3

+

×+

×+

×+

×+

××+

××≈

Coarse Mass

SoilFine

Elemental Carbon

Organic Mass

RHf Nitrate

RHfb Sulfate ext

Where the parameters enclosed in the brackets are the average concentrations of each 

species. 

The use of a 98% RH cut point is somewhat arbitrary, but it was chosen to allow for the 

likelihood that above 98%, precipitation would obscure visibility without regard to pollutant 

concentrations, and as an expedient measure because fT(RH) is infinite at 100% RH. The same 

fT(RH) was used in the first and second IMPROVE reports [Sisler et al., 1993; Sisler, 1996]. 

However, the assumptions used for estimating this curve will be investigated in light of more 

recent growth and particle size distribution data. 

There are two ways reconstructed extinction is calculated in this report that are different from the 

1996 IMPROVE report. First, the factor f(RH) that accounts for the relative humidity effects on 

hygroscopic aerosols has been upgraded with new relative humidity data from additional relative 

humidity monitoring sites and second, absorption is estimated from measurements of lightabsorbing carbon rather than from transmission measurements of filter media. Therefore, some 

differences in aerosol extinction between this and the 1996 report are due to changes other than 

levels of aerosol mass concentration. 

Figure F –3: RH factors (fT (RH)) derived from Tang’s ammonium sulfate growth curves smoothed between the crystallization 

and deliquescence points. 

Colorado State Implementation Plan for Regional Haze - TSD Page 81
Appendix F: Procedure For Reconstructing Light Extinction 

In 2005-06, researchers updated the light-extinction equation using with a more robust data set 

resulting in the “New IMPROVE Equation”. 

Equation (F-9): NEW IMPROVE EQUATION:

bext ≈ 2.2 × fS (RH) × [Small Sulfate] + 4.8 × fL (RH) × [Large Sulfate] 

 + 2.4 × fS (RH) × [Small Nitrate] + 5.1 × fL (RH) × [Large Nitrate] 

 + 2.8 × [Small Organic Mass] + 6.1 × [Large Organic Mass] 

 + 10 × [Elemental Carbon] 

 + 1 × [Fine Soil] 

 + 1.7 fSS (RH) × [Sea Salt] 

 + 0.6 × [Coarse Mass] 

 + Rayleigh scattering (Site Specific) 

 + 0.33 [NO2 (ppb)] 

Where 

Nitrate and Organic are split using the same process

lfate, nitrate and organic carbon 

d as reconstructed deciview (dv) can now be calculated. The deciview is a 

dv = 10ln(bext/10)

Where the deciview scale is near zero re (dv = 0 for Rayleigh condition at 

[ ] [ ] [ ][ ] , 20

20 arg = × Total TotalforSulfate Sulfate <

Total Sulfate SulfateeL

[ ][ ] arg SultateeL = Total , [TotalforSulfate Sulfate]≥ 20

[ ][ ] Small Sulfate = Total −[ arg SulfateeLSulfate ]

The primary changes of the new IMPROVE equation are the su

are differentiated into large and small size fractions, organic compound mass to organic carbon 

mass ratio changed from 1.4 to 1.8, sea salt is added as a component of extinction (Sea Salt = 

1.8 x [Chlorine]), Rayleigh scattering is calculated for the monitoring site elevation & annual 

mean temperature (ranges from 8Mm-1 at 10,000’ to 12Mm-1at sea level) and NO2 light 

absorption in the visible is included for sites that have such data (not routinely available at 

IMPROVE sites) 

Visibility expresse

visibility metric based on the light-extinction coefficient that expresses incremental changes in 

perceived visibility [Pitchford and Malm, 1994]. Because the deciview expresses a relationship

between changes in light extinction and perceived visibility, it can be useful in describing 

visibility trends. A 1-dv change is about a 10% change in extinction coefficient, which is a small 

but perceptible scenic change under many circumstances. The deciview is defined by the 

following equation: 

 for pristine atmosphe

about 1.8 km elevation) and increases as visibility is degraded. 

Colorado State Implementation Plan for Regional Haze - TSD Page 82
Appendix G: Pollutant Statistical Trends 

Ammonium Sulfate Trend

Figure G-1 indicates a downward trend in ammonium sulfate and a statistical analysis of the 

data indicates that the trend is not statistically significant. 

Figure G -1: Ammonium Sulfate Trend 

Ammonium Nitrate Trend

Figure G-2 indicates an upward trend in ammonium nitrate and a statistical analysis of the data 

indicates that the trend is not statistically significant. 

Figure G -2: Ammonium Nitrate Trend

Colorado State Implementation Plan for Regional Haze - TSD Page 83
Appendix G: Pollutant Statistical Trends 

Organic Carbon Trend

Figure G-3 indicates an upward trend in organic carbon and a statistical analysis of the data 

indicates that the trend is not statistically significant. 

Figure G -3: Organic Carbon Trend 

Elemental Carbon Trend

Figure G-4 indicates a slight upward trend in elemental carbon and a statistical analysis of the 

data indicates that the trend is not statistically significant. 

Figure G -4: Elemental Carbon Trend

Colorado State Implementation Plan for Regional Haze - TSD Page 84
Appendix G: Pollutant Statistical Trends 

Soil Trend

Figure G-5 indicates an upward trend in soil and a statistical analysis of the data indicates that 

the trend is not statistically significant. 

Figure G -5: Soil Trend

Coarse Mass Trend

Figure G-6 indicates a slight upward trend in coarse mass and a statistical analysis of the data 

indicates that the trend is not statistically significant. 

Figure G -6: Coarse Mass Trend

Colorado State Implementation Plan for Regional Haze - TSD Page 85
Appendix G: Pollutant Statistical Trends 

Sea Salt Trend

Figure G-7 indicates the trend in sea salt concentrations and a statistical analysis of the data 

indicates that the trend is not statistically significant. Sea salt is not typically included in our 

technical visibility analysis since the concentrations are so small in Colorado. 

Figure G -7: Sea Salt Trend

Colorado State Implementation Plan for Regional Haze - TSD Page 86
Appendix H: PM Source Apportionment Technology (PSAT) 

Modeling 

Development History

Impairment of visibility in Class I areas is caused by a combination of local air pollutants and 

regional pollutants that are transported long distances. To develop effective visibility 

improvement strategies, the WRAP member states and tribes need to know the relative 

contributions of local and transported pollutants, and which emissions sources are significant 

contributors to visibility impairment at a given Class I area. 

A variety of modeling and data analysis methods can be used to perform source apportionment 

of the PM observed at a given receptor site. Model sensitivity simulations have been used in 

which a “base case” model simulation is performed and then a particular source is “zeroed out” 

of the emissions. The importance of that source is assessed by evaluating the change in 

pollutants at the receptor site, calculated as pollutant concentration in the sensitivity case minus 

that in the base case. This approach is known as a “brute force” sensitivity because a separate 

model run is required for each sensitivity test. 

An alternative approach is to implement a mass-tracking algorithm in the air quality model to 

explicitly track for a given emissions source the chemical transformations, transport, and 

removal of the PM that was formed from that source. Mass tracking methods have been 

implemented in both the CMAQ and CAMx air quality models. Initial work completed by the 

RMC during 2004 used the CMAQ Tagged Species Source Apportionment (TSSA) method. 

Unfortunately, there were problems with mass conservation in the version of CMAQ used in that 

study, and these affected the TSSA results. A similar algorithm has been implemented in 

CAMx, the PM Source Apportionment Technology (PSAT). Comparisons of TSSA and PSAT 

showed that the results were qualitatively similar, that is, the relative ranking of the most 

significant source contributors were similar for the two methods. However, the total mass 

contributions differed. With separate funding from EPA, UCR has implemented a version of 

TSSA in the new CMAQ release (v4.5) that corrects the mass conservation error, but given the 

uncertainty of the availability of this update, the CAMx/PSAT source apportionment method was 

used for the WRAP modeling analysis. 

The main objective of applying CAMx/PSAT is to evaluate the regional haze air quality for 

conditions typical of the 2000-04 baseline period (Plan02c) and future-year 2018 (Base18b) 

conditions. These results are used 

• to assess the contributions of different geographic source regions (e.g., states) and 

source categories to current (2000-04) and future (2018) visibility impairment at Class I 

areas, to obtain improved understanding of (1) the causes of the impairment and (2) 

which states are included in the area of influence (AOI) of a given Class I area; 

• to determine which source categories contributing to the AOI for each Class I area are 

changing, and by how much, between the 2000-04 and 2018 base case, by varying only 

controllable anthropogenic emissions between the 2 PSAT simulations; and 

• to identify the source regions and emissions categories that, if controlled to lower 

emissions rates than the 2018 base case levels, would produce the greatest visibility 

improvements at a Class I area. 

CAMx/PSAT

The PM Source Apportionment Technology performs source apportionment based on userdefined source groups. A source group is the combination of a geographic source region and an 

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Appendix H: PM Source Apportionment Technology (PSAT) 

emissions source category. Examples of source regions include states, nonattainment areas, 

and counties. Examples of source categories include mobile sources, biogenic sources, and 

elevated point sources; PSAT can even focus on individual sources. The user defines a 

geographic source region map to specify the source regions of interest. He or she then inputs 

each source category as separate, gridded low-level emissions and/or elevated-point-source 

emissions. The model then determines each source group by overlaying the source categories 

on the source region map. For further information, please refer to the white paper on the 

features and capabilities of PSAT 

(http://pah.cert.ucr.edu/aqm/308/reports/PSAT_White_Paper_111405_final_draft1.pdf), with 

additional details available in the CAMx user’s guide (ENVIRON, 2005; http://www.camx.com). 

PM source apportionment modeling was performed for aerosol sulfate (SO4) and aerosol nitrate 

(NO3) and their related species (e.g., SO2, NO, NO2, HNO3, NH3, and NH4). The PSAT 

simulations include 9 tracers, 18 source regions, and 6 source groups. The computational cost 

for each of these species differs because additional tracers must be used to track chemical 

conversions of precursors to the secondary PM species SO4, NO3, NH4, and secondary 

organic aerosols (SOA). Table 1 summarizes the computer run time required for each species. 

The practical implication of this table for WRAP is that it is much more expensive to perform 

PSAT simulations for NO3 and especially for SOA than it is to perform simulations for other 

species. 

Table H-1: Benchmarks for PSAT computational costs* for each PM species 

Species 

No. of 

Species 

Tracers 

RAM 

Memory 

Disk 

Storage 

per Day 

Run Time with

1 CPU 

SO4 2 1.6 GB 1.1 GB 4.7 h/day 

NO3 7 1.7 GB 2.6 GB 13.2 h/day 

SO4 and NO3 combined 9 1.9 GB 3.3 GB 16.8 h/day 

SOA 14 6.8 GB Not tested Not tested 

Primary PM species 6 1.5 GB 3.0 GB 10.8 h/day 

*Run time is for one day (01/02/2002) on the WRAP 36-km domain 

Two annual 36-km CAMx/PSAT model simulations were performed: one with the Plan02c 

typical-year baseline case and the other with the Base18b future-year case. It is expected that 

the states and tribes will use these results to assess the sources that contribute to visibility 

impairment at each Class I Area, and to guide the choice of emission control strategies. The 

RMC web site includes a full set of source apportionment spatial plots and receptor bar plots for 

both Plan02b and Base18b. These graphical displays of the PSAT results, as well as additional 

analyses of these results are available on the TSS under 

http://vista.cira.colostate.edu/tss/Tools/ResultsSA.aspx

CAMx/PSAT Configuration for 2002 and 2018 Modeling

PSAT source apportionment simulations for 2000-04 baseline period and 2018 base case were 

performed using CAMx v4.30. Table 2 lists overall specifications for the PSAT simulations. The 

domain setup was identical to the standard WRAP CMAQ modeling domain. The CAMx/PSAT 

run-time options are shown in Table 3. The CAMx/PSAT computational cost for one simulation 

day with source tracking for sulfate (SO4) and nitrate (NO3) is approximately 14.5 CPU hours 

with an AMD Opteron CPU. The source regions used in the PSAT simulations are shown in 

Figure 1 and Table 4. The six emissions source groups are described in Table 5. The 

development of these emissions data are described in more detail below. 

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Appendix H: PM Source Apportionment Technology (PSAT) 

The annual PSAT run was divided into four seasons for modeling. The initial conditions for the 

first season (January 1 to March 31, 2002) came from a CENRAP annual simulation. For the 

other three seasons, we allowed 15 model spin-up days prior to the beginning of each season. 

Based on the chosen set of source regions and groups, with nine tracers, and with a minimum 

requirement of 87,000 point sources and a horizontal domain of 148 by 112 grid cells with 19 

vertical layers, the run-time memory requirement is 1.9 GB. Total disk storage per day is 

approximately 3.3 GB. Although the RMC’s computation nodes are equipped with dual Opteron 

CPUs with 2 GB of RAM and 1 GB of swap space, the high run-time memory requirements 

prevented running PSAT simulations using the OpenMP shared memory multiprocessing 

capability implemented in CAMx. 

Table H-2: WRAP 2002 CAMx/PSAT specifications 

WRAP PSAT Specs Description 

Model CAMx v4.30 

OS/compiler Linux, pgf90 v.6.0-5 

CPU type AMD Opteron with 2 GB of RAM 

Source region 18 source regions; see Figure 4.1 and Table 4.4 

Emissions source groups Plan02b, 6 source groups; see Table 4.5 

Initial conditions From CENRAP 

(camx.v4.30.cenrap36.omp.2001365.inst.2) 

Boundary conditions 3-h BC from GEOS-Chem v2 

Table H-3: WRAP CAMx/PSAT run-time options 

WRAP PSAT specs Description 

Advection solver PPM 

Chemistry parameters CAMx4.3.chemparam.4_CF 

Chemistry solver CMC 

Plume-in-grid Not used 

Probing tool PSAT 

Dry/wet deposition TRUE (turned on) 

Staggered winds TRUE (turned on) 

Table H-4: WRAP CAMx/PSAT source regions cross-reference table 

Source 

Region ID 

Source Region 

Description1

Source 

Region ID

Source Region 

Description1

1 Arizona (AZ) 10 South Dakota (SD) 

2 California (CA) 11 Utah (UT) 

3 Colorado (CO) 12 Washington (WA) 

4 Idaho (ID) 13 Wyoming (WY) 

5 Montana (MT) 14 Pacific off-shore & Sea of Cortez (OF) 

6 Nevada (NV) 15 CENRAP states (CE) 

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Appendix H: PM Source Apportionment Technology (PSAT) 

Source 

Region ID 

Source Region 

Description1

Source 

Region ID

Source Region 

Description1

7 New Mexico (NM) 16 Eastern U.S., Gulf of Mexico, & Atlantic 

Ocean (EA) 

8 North Dakota (ND) 17 Mexico (MX) 

9 Oregon (OR) 18 Canada (CN) 

1

The abbreviations in parentheses are used to identify source regions in PSAT receptor bar plots. 

Figure H-1: WRAP CAMx/PSAT source region map

-2736 -2412 -2088 -1764 -1440 -1116 -792 -468 -144 180 504 828 1152 1476 1800 2124 2448

-2088

-1872

-1656

-1440

-1224

-1008

-792

-576

-360

-144

72

288

504

720

936

1152

1368

1584

1800

2 6

9

12

4

5 8

13

11

3

1 7

15

15

16

16

16

18 18

14

17

14

10

Table H-5: WRAP CAMx/PSAT emissions source groups 

Emissions 

Source 

Groups 

Low-level Sources Elevated Sources 

1 Low-level point sources (including stationary offshore) Elevated point sources 

(including stationary off-shore) 

2 Anthropogenic wildfires (WRAP only) Anthropogenic wild fires 

(WRAP only) 

3 Total mobile (on-road, off-road, including 

planes, trains, ships in/near port, off-shore 

shipping) 

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Appendix H: PM Source Apportionment Technology (PSAT) 

Emissions 

Source 

Groups 

Low-level Sources Elevated Sources 

4 Natural emissions (natural fire, WRAP only, 

biogenics) 

Natural emissions (natural fire, 

WRAP only, biogenics) 

5 Non-WRAP wildfires (elevated fire sources in 

other RPOs) 

Non-WRAP wild fires (elevated 

fire sources in other RPOs) 

6 Everything else (area sources, all dust, fugitive 

ammonia, non-elevated fire sources in other 

RPOs) 

Preparation of Emissions Data

Emissions datasets for the CAMx/PSAT simulations were prepared directly from the SMOKEprocessed emissions developed for CMAQ. A simple format conversion was used to convert 

the original SMOKE output files from the I/O API format to the CAMx format. For certain source 

categories, SMOKE was configured to output CAMx-formatted files directly. In addition, CMAQ 

species names were changed for several of the emissions species to make them consistent with 

the CAMx species. Additional processing was also required for the PSAT algorithm. For each 

of the emissions categories that were tracked in PSAT, the intermediate SMOKE output files for 

those categories were converted to the CAMx/PSAT formats, and read by the CAMx program to 

distinguish among the emissions source categories. The elevated-point-source CAMx 

emissions files were also processed files to specify the PSAT source region individually for each 

point source. This was necessary in order to assign elevated-point-source emissions to the 

correct PSAT source region (particularly near state boundaries and along coastal regions), due 

to the relatively coarse (36-km) grid cell resolution. 

No additional post-processing QA was performed on the CAMx emissions files, since they were 

prepared directly from the previously quality-assured CMAQ emissions files. However, the 

CAMx/PSAT simulations by comparing the CAMx- and CMAQ-predicted species concentrations 

as part of the overall QA of the model simulations. 

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