MEASUREMENTS OF AMBIENT BACKGROUND

AMMONIA ON THE COLORADO PLATEAU AND

VISIBILITY MODELING IMPLICATIONS

Contributors:

Dr. Ivar Tombach, Consultant to SRP

Robert Paine, AECOM Environment

Salt River Project

P.O. Box 52025 PAB352

Phoenix, Arizona 85072‐2025

September 2010
TABLE OF CONTENTS

EXECUTIVE SUMMARY............................................................................................................. 1

INTRODUCTION....................................................................................................................... 4

PART A.  MEASUREMENTS OF AMBIENT BACKGROUND AMMONIA

A‐1. BACKGROUND.................................................................................................................. 9

The Role of Ammonia in Nitrate Particle Formation............................................................... 9

Previous Ammonia Measurements....................................................................................... 11

A‐2. THE SRP NH3 MONITORING PROGRAM........................................................................... 14

Management of the SRPNH3 Monitoring Program............................................................... 14

Monitoring Program Design Considerations......................................................................... 14

Site Selection Rationale ......................................................................................................... 15

Monitoring Program Description........................................................................................... 16

Quality Assurance .................................................................................................................. 25

A‐3. MEASUREMENT RESULTS ............................................................................................... 31

URG Denuder System Measurements at the Primary Sites.................................................. 31

Radiello Passive Sampler Measurements.............................................................................. 39

Meteorological Measurements............................................................................................. 40

A‐4. ANALYSIS OF THE MEASUREMENT RESULTS ................................................................... 46

Seasonal Variation – Two Measurement Periods.................................................................. 47

Spatial Relationships.............................................................................................................. 56

Chemistry of the Aerosol....................................................................................................... 62

A‐5. CONCLUSIONS AND POTENTIAL FUTURE WORK ............................................................. 71

PART B.  VISIBILITY MODELING IMPLICATIONS

B‐1. BACKGROUND................................................................................................................ 74

Role of Background Ammonia in CALPUFF............................................................................ 74

Sensitivity of CALPUFF Predictions to Ammonia Concentration Input ................................. 76

B‐2. AMMONIA ASSUMPTIONS USED IN BART MODELING.................................................... 78

SRP Ammonia Inputs ............................................................................................................. 78

EPA Back‐Calculated Ammonia Inputs .................................................................................. 78

Importance of Ammonia Inputs ............................................................................................ 79

B‐3. MODELING OF SRP AND EPA AMMONIA INPUTS............................................................ 80

Review of Previous Modeling ................................................................................................ 80

Additional Modeling for the Indian Garden Monitoring Site ................................................ 82

Comparison of Model Performance at Hance Camp and Indian Garden Sites..................... 82

B‐4. EFFECT OF MODEL CHEMISTRY ON NITRATE PREDICTIONS............................................. 84

B‐5. CONCLUSIONS AND POTENTIAL FUTURE WORK ............................................................. 89
1 

EXECUTIVE SUMMARY

The United States Environmental Protection Agency (EPA) is currently in the process of 

performing a Best Available Retrofit Technology (BART) determination for the Navajo 

Generating Station (NGS). One of the statutory factors that must be considered in 

performing the BART analysis is the degree of visibility improvement that can be achieved 

by each BART control option, which is estimated using the CALPUFF model. Previous 

modeling performed by Salt River Project (SRP) indicated that wintertime background 

ammonia is the most important parameter affecting the degree of visibility improvement 

predicted by the model for a given BART control option for nitrogen oxides (NOx).   

In its BART proposal submitted to EPA in December 2008, SRP used background ammonia 

concentrations that were the same as those used in previous Prevention of Significant 

Deterioration (PSD) permitting proceedings in the Southwest (“SRP’s ammonia inputs”), in 

accordance with a modeling protocol that was approved by EPA. EPA subsequently issued 

an Advanced Notice of Proposed Rulemaking (ANPR) in which the agency used a back‐

calculation method to establish higher background ammonia concentrations for use in 

BART modeling for NGS.   

SRP submitted comments on the ANPR that included a modeling analysis demonstrating 

that EPA’s back‐calculation method overestimates the background ammonia concentration 

values that would be appropriate for use in CALPUFF modeling of NGS emissions. In this 

analysis, actual emissions from NGS were modeled using CALPUFF with both SRP’s 

ammonia inputs and EPA’s higher back‐calculated values. The ammonium nitrate 

concentrations predicted by the model using both sets of ammonia inputs were compared 

to ammonium nitrate concentrations measured at monitoring sites (Hance Camp and Bryce 

Canyon) that are part of the Interagency Monitoring of Protected Visual Environments 

(IMPROVE) network. The results show that the use of SRP’s ammonia inputs produces the 

most reasonable agreement between modeled and measured ammonium nitrate 

concentrations. The use of EPA’s back‐calculated ammonia concentrations produces order‐

of‐magnitude overpredictions (i.e., by a factor of 10 or more in many cases) of ammonium 

nitrate concentrations relative to measured values.  

In the ANPR, EPA cited a lack of available background ammonia measurements in the 

Colorado Plateau as justification for the use of its back‐calculation method. To provide 

additional data to address this issue, SRP conducted a monitoring program to measure 

background ammonia in the vicinity of NGS between December 2009 and April 2010. 

Monitoring sites were selected based on a number of criteria, including accessibility, basic 

infrastructure, and their locations relative to possible transport paths from NGS to nearby 

Class I areas. Based on these criteria, a monitoring site was established at Hall’s Crossing, 

Utah, and several others were established near Cameron, Arizona. 
2 

The results obtained in SRP’s monitoring study showed consistently lower background 

ammonia concentrations at the Hall’s Crossing site compared to those measured at the 

Cameron area sites. The highest measured ammonia values were close to EPA’s back‐

calculated values, and the lowest measured values were close to SRP’s ammonia inputs. 

While it was not initially expected, this spatial variability is comparable to the variability 

observed in other monitoring studies, such as the Four Corners area study performed in 

2007. There are several possible contributors to the higher ammonia concentrations 

observed in the Cameron area, including animal grazing, highway vehicles, septic tanks, 

and nearby sewer lagoons.  

Without additional measurements at other locations, it is not possible to determine 

whether the ammonia concentrations at other areas within the modeling domain would be 

more accurately represented by values measured at the Cameron area sites or the Hall’s 

Crossing site. It is also unclear whether the higher measured concentrations at the 

Cameron area sites are indicative of a local ammonia source and as such, may only be 

representative over a small area. Even within the two Cameron area sites, despite the 

relatively small distance that separates them, a consistent difference in measured ammonia 

values was observed, which suggests that a local source could be affecting the measured 

values.   

The range of ammonia concentrations measured, and the uncertainty regarding the spatial 

extent to which the measured values are representative, does not provide a clear picture of 

which ammonia values are appropriate for input to CALPUFF, particularly when modeling 

areas where monitoring data is not available. In addition, the results obtained in SRP’s 

monitoring study do not explain why SRP’s ammonia inputs produce the best agreement 

between model‐predicted and measured ammonium nitrate concentrations. 

To further investigate this issue, SRP conducted an additional ammonia background 

sensitivity modeling analysis using the same approach that was used in the analysis that 

SRP included in its comments on the ANPR. As in the previous analysis, ammonium nitrate 

concentrations were predicted using CALPUFF with both SRP’s and EPA’s background 

ammonia inputs and compared to measured values. This new analysis was performed for 

the Indian Garden IMPROVE site. The Indian Garden site is located part way between the 

Grand Canyon floor and the rim, whereas the Hance Camp site, which was included in the 

previous modeling analysis presented in SRP’s ANPR comments, is located on the rim of the 

canyon. The same conclusions were obtained in this new analysis as the previous one – the 

use of SRP’s ammonia inputs produced the most reasonable agreement between modeled 

and measured ammonium nitrate concentrations. When considered with the previous 

modeling analysis, these results suggest a general tendency of the CALPUFF model to 

overpredict nitrate concentrations in the winter. This is significant because it suggests that 

CALPUFF also overpredicts the degree of visibility improvement that would be expected 

from implementing a given NOx BART control option.   

Several independent evaluations confirm this finding. The most relevant investigation is a 

recent study performed by Atmospheric and Environmental Research, Inc. (AER), which 

suggested several potential improvements to the nitrate chemistry algorithms in CALPUFF. 
3 

The improvements were implemented and an analysis was conducted in which the 

concentrations predicted by the improved model were compared with measured values. 

The results obtained using the improved version of the model showed significantly less 

overprediction of nitrate concentrations.  

The modeling performed by SRP, and the other studies and evaluation work presented in 

this report, compel the following conclusions:  

• The EPA‐approved version of CALPUFF has a general tendency to overpredict 

ammonium nitrate concentrations during the winter. To compensate for this 

tendency, SRP’s ammonia inputs should be used in the visibility benefits modeling 

for the pending NGS BART determination. 

• The incremental visibility improvement predicted for each BART control option is 

critically dependent on the background ammonia input to the model. The use of the 

SRP’s ammonia inputs yields sufficiently conservative visibility improvement 

estimates for the purpose of determining BART for NGS. 

• A sensitivity modeling analysis that uses EPA’s back‐calculated ammonia inputs 

would overestimate the degree of visibility improvement predicted for more 

stringent BART control options. It has been shown that the use of EPA’s back‐

calculated ammonia inputs produces order‐of‐magnitude (i.e., by a factor of 10 or 

more in some cases) overestimates compared to measured values. 

• The degree of overprediction in CALPUFF has significant implications on many 

pending BART determinations. Therefore, the nitrate formation modules in 

CALPUFF should be updated to ensure that BART determinations are not based on a 

model with a known technical flaw. 

• In order to determine regionally representative background ammonia input values 

for use with an improved CALPUFF model, SRP recommends the initiation of a 

collaborative monitoring program using a network of passive samplers. The passive 

samplers can be deployed at remote locations at a relatively low cost. 

To facilitate the model improvement effort, SRP has joined with several other western 

utilities to sponsor the incorporation of the nitrate chemistry improvements into the 

CALPUFF model. Although the process of reviewing and approving the improvements may 

require considerable time, SRP intends to run the updated version of the model to provide 

additional information to EPA for use in the BART determination for NGS. In the meantime, 

given the tendency of the currently approved version of the CALPUFF model to overpredict 

nitrate concentrations and the degree of visibility improvement associated with more 

stringent BART control options, SRP urges EPA to proceed with a BART determination that is 

based on SRP’s ammonia inputs. 
4 

INTRODUCTION

Gaseous ammonia (NH3) in the atmosphere (“background ammonia”) combines with 

nitrogen oxides (NOx) and sulfur dioxide (SO2) gases emitted from power plants and other 

emission sources (such as motor vehicles and oil and gas exploration facilities) to form 

ammonium nitrate and ammoniated sulfate particles, both of which impair visibility.  

Ammonium nitrate concentrations from stationary sources predicted using the CALPUFF1

model are highly sensitive to the concentration of background ammonia that is input to the 

model, particularly during the winter months.   

The Salt River Project (SRP) submitted a Best Available Retrofit Technology (BART) 

analysis for the Navajo Generating Station (NGS) in December 2008.  In this analysis, SRP 

utilized the CALPUFF model to assess the degree of visibility improvement associated with 

each BART control option under consideration, in accordance with a modeling protocol 

approved by the U.S. Environmental protection Agency (EPA).2  For these simulations, SRP 

used the same background ammonia concentrations as those that had been used in the 

previous PSD permit application for the Desert Rock Energy Facility (DREF) in 

northwestern New Mexico (“SRP’s ammonia inputs”).3  More recently, the New Mexico 

Environmental Department used the same background ammonia concentrations in its draft 

BART determination for the San Juan Generating Station in New Mexico.4

In August 2009, EPA issued an Advanced Notice of Proposed Rulemaking (ANPR) in which 

the agency used an indirect “back‐calculation” method to infer higher background 

ammonia concentration estimates than those used by SRP.5,

6

 EPA’s method involves a 

determination of the amount of ammonia that was involved in the formation of 

ammoniated sulfate‐ and nitrate‐containing particles measured at sites that are part of the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) program.  The EPA 

                                                       

1 The U.S. Environmental Protection Agency (EPA) has specified that CALPUFF be used for modeling in 

connection with determining Best Available Retrofit Technology (BART). 

2 SRP submitted a BART modeling protocol to EPA in September 2007.  In a letter dated July 1, 2008, EPA 

recommended several changes to the modeling analysis, and noted that the protocol was approvable once 

these changes were addressed.  In separate e‐mail correspondence, the National Park Service also approved 

the proposed monthly ammonia background values. 

3 Application for Prevention of Significant Deterioration Permit for the Desert Rock Energy Facility, by ENSR 

Corporation, May 2004. EPA‐R09‐OAR‐2007‐1110‐0012.  These ammonia concentrations values had been 

approved by the Federal Land Managers (FLMs) for use in the DREF modeling. 

4 New Mexico Environment Department Air Quality Bureau BART Determination, Public Service Company of 

New Mexico San Juan Generating Station, Units 1‐4, New Mexico Environment Department Air Quality 

Bureau, 21 June 2010. 

5 Assessment of Anticipated Visibility Improvements at Surrounding Class I Areas and Cost Effectiveness of Best

Available Retrofit Technology for Four Corners Power Plant and Navajo Generating Station: Advanced Notice of

Proposed Rulemaking 74FR44313‐44334, 26 August 2009.

6 This was an unexpected change in approach by the EPA, since modeling protocol approval letters sent by the 

EPA on 1 July 2008 stated that the background ammonia values used in the NGS BART modeling were 

“appropriate.”
5 

asserts that this back‐calculated concentration represents the amount of ammonia that 

would be available to interact with emissions from NGS to form ammonium nitrate. 

SRP submitted comments to the ANPR in October 2009.  In the submitted comments, SRP 

included a technical paper7 that demonstrated that EPA’s back‐calculation method 

overestimates the background ammonia concentration values that would be appropriate 

for use in CALPUFF modeling of NGS emissions.  Those overestimates occur because some 

of sulfates and nitrates measured at the IMPROVE sites are due to sources other than NGS, 

many of which may be outside of the region.  The ammonia that was involved in their 

formation would never have been available to interact with emissions from NGS.  

Therefore, for these situations, the back‐calculation method overestimates the amount of 

ammonia that is available to combine with emissions from NGS to produce ammonium 

sulfates and nitrates. 

To evaluate the extent to which the back‐calculation method overestimates the amount of 

ammonia available to interact with NGS emissions, the technical paper included an analysis 

performed using the CALPUFF model.  In this analysis, the model was used to estimate 

ammonium nitrate concentrations at several IMPROVE sites using both SRP’s ammonia 

inputs and EPA’s back‐calculated ammonia inputs.  The results of the analysis indicate that 

if EPA’s back‐calculated ammonia inputs were used, the model‐predicted ammonium 

nitrate concentrations far exceed the measured values at each IMPROVE site.  The analysis 

further indicates that the use of SRP’s ammonia inputs produces the most reasonable 

agreement between modeled and measured ammonium nitrate concentrations.  

In the ANPR, EPA cited a lack of available monitoring data for background ammonia in the 

Colorado Plateau as justification for the use of its back‐calculation method.  Indeed, 

previous ammonia measurements provide only limited insight into the background 

ammonia concentrations on the Colorado Plateau in southern Utah and northern Arizona – 

the area shown in Figure 1.  However, the technical paper submitted with SRP’s comments 

on the ANPR demonstrates that there is justifiable concern about the correctness of 

ammonia concentrations derived using EPA’s back‐calculation method.   

In order to provide a firmer basis for estimating ambient ammonia concentrations in this 

area, SRP offered to perform monitoring of ammonia in the vicinity of NGS.  SRP submitted 

a conceptual monitoring plan to EPA in June 2009.  Despite limited feedback, SRP decided 

to pursue the monitoring and provided a final plan to EPA and the National Park Service 

(NPS) in October 2009.  NPS provided feedback on the plan in a conference call in 

November 2009.  The monitoring program was initiated in early December 2009 and ran 

until the end of April 2010.   

                                                       

7 Analysis of the U.S. EPA’s Back­Calculation Approach for Estimating Ammonia Concentrations, by Ivar 

Tombach and Robert Paine, 26 October 2009.  Report submitted to the US EPA Region 9 as Attachment 2 to 

comments provided by the Salt River Project in connection with Docket No. EPA‐R09‐OAR‐2009‐0598. 
6 

Figure 1. The Colorado Plateau and Class I areas within 300 km of NGS.
7 

Contents of This Report

The remainder of this report is organized in two parts. Part A describes the SRP NH3

monitoring program and its findings, Part B addresses the role of NH3 in CALPUFF visibility 

modeling, and a final part summarizes the conclusions of the entire report.  

In Part A, Section A‐2 describes the monitoring program in detail; Section A‐3 presents the 

results of the measurements; Section A‐4 interprets those measured data; and Section A‐5 

contains conclusions and a brief discussion of areas of potential future work. In Part B, 

Section B‐1 reviews the role of NH3 background in CALPUFF modeling of visibility; Section 

B‐2 addresses various assumptions about NH3 concentrations; and Section B‐3 presents 

modeling performed by SRP using different ammonia inputs; Section B‐4 discusses results 

of recent evaluations of nitrate predictions by CALPUFF; and Section B‐5 contains 

conclusions and a brief discussion of areas of potential future work.  
8 

PART A

MEASUREMENTS OF AMBIENT BACKGROUND AMMONIA
9 

A‐1. BACKGROUND

The Role of Ammonia in Nitrate Particle Formation

A brief introduction into the role of background ammonia (NH3) in the formation of nitrate 

particles will illuminate some of the technical issues that were considered in the design of SRP’s 

monitoring program.  Background NH3 reacts preferentially with SO2 to form ammoniated 

sulfate particles.  If any NH3 is left over, it may be involved in reactions with NOx that form nitrate 

particles and gases.  Specifically, the chemical species that are produced in these latter reactions 

are a mixture of gaseous nitric acid (HNO3) and particulate ammonium nitrate (NH4NO3), the 

sum of which we label as total nitrate.  It is the particulate form of ammonium nitrate that 

impairs visibility.  

The results of these reactions depend on the relative availabilities of SO2, NOx, and NH3.

 8  

Specifically, three scenarios describe the possible outcomes for nitrate production.  The 

scenarios are presented here in order of decreasing concentrations of background NH3: 

• NOx­Limited.  If the concentration of background NH3 is sufficiently large, the NH3 first 

reacts with the SO2 to form fully neutralized (i.e., non‐acidic) ammonium sulfate particles.  

The NH3 left over after the sulfate‐producing reactions are complete is then available to 

react with NOx to form ammonium nitrate.  If some NH3 still remains after the nitrate‐

producing reactions, then the amount of nitrates formed is independent of the NH3

concentration.  Rather, it depends only on the NOx concentration, i.e., the formation of 

nitrates is said to be “NOx‐Limited”. 

• NH3­Limited.  If the background NH3 concentration is large enough to produce 

neutralized ammonium sulfate particles with some NH3 left over, but not enough NH3

remains to combine with all of the available NOx, then the amount of nitrates produced 

depends on the NH3 concentration, i.e., the formation of nitrates is said to be “NH3

limited”.  In this case, nitrates will be formed only to the extent that sufficient NH3 is 

available. 

• No Nitrate Production.  If the background NH3 concentration is not even sufficient to 

produce fully neutralized sulfate particles, then the resulting sulfate particles will be 

acidic.  In this scenario, all of the background NH3 will be consumed in the process of 

forming the sulfate particles.  No NH3 will remain to react with NOx and there is no 

production of nitrate particles.  (This situation occurs at times in the Southeast in the 

summer.) 

The equilibrium between the gaseous and particle forms of nitrates produced in these reactions 

depends on the ambient temperature and relative humidity.  The reactions are reversible and 

occur very rapidly (typically within a few minutes), so the fraction of total nitrate that is in 

                                                       

8 For the sake of brevity and clarity, this presentation simplifies the chemical processes that are involved. 
10 

particulate form depends on the conditions at or near the area of concern (e.g., the Class I area).  

Cooler temperatures and higher relative humidity favor the formation of particulate ammonium 

nitrate.   

The relationship is illustrated conceptually in Figure 2 (and is also discussed further in the Part B 

report), which shows an example of how the ratio of (particulate) ammonium nitrate to total 

nitrate (nitric acid gas + particulate ammonium nitrate) concentration varies as a function of 

temperature and relative humidity (RH), based on the parameterization of the nitrate 

equilibrium process that is contained in the CALPUFF model.  The example in Figure 2 is for a 

total nitrate concentration (expressed in terms of nitrate ion, NO3‐

) of 1.625 parts per billion by 

volume (ppbv) and a NH3 concentration of 1 ppbv.  The curves show the partitioning of total 

nitrate, so the numbers on the curves indicate the percentage of the total nitrate (again in terms 

of NO3‐

) that is in particulate form.  

Figure 2. Example of how the percentage of total nitrate that is in particulate form varies with

temperature and relative humidity.  This example is for the specific case of total nitrate

concentration (as NO3

‐

) of 1.625 ppbv and NH3 concentration of 1.0 ppbv.9

According to Figure 2, at a warm ambient temperature of 298 K (i.e., 25° C or 77° F), the 

particulate nitrate concentration would be zero at RH less than about 85% ‐‐ i.e., all of the nitrate 

would be in gaseous form – for the specific total nitrate and NH3 conditions represented in the 

figure. At a colder temperature of about 277 K (4° C or 40° F), Figure 2 shows a 50‐50 split 

                                                       

9 CALMET Overview and Unresolved Issues for VISTAS BART Protocol. PowerPoint presentation prepared by Joseph 

Scire. Presented by Chris Arrington at the VISTAS BART Protocol Meeting, Research Triangle Park, NC. 21 September 

2005. http://www.vistas-sesarm.org/documents/index.asp
11 

between particle and gaseous nitrate whenever the RH is less than about 60%. At all 

temperatures, the fraction of particulate nitrate increases as the RH approaches 100%.  

This example illustrates that nitrate particles could volatilize and become HNO3 gas if the 

temperature increases or the relative humidity decreases.  Conversely, the gaseous HNO3 could 

convert into particulate nitrates if the temperature decreases.  The behavior of nitrates is 

different from sulfates: the sulfate‐forming process is not reversible and therefore the amount of 

sulfate particles at a receptor is determined by the history of the amount of SO2 occurring in an 

air mass throughout its travel to the receptor. 

In summary, unless the amount of ammonia is so abundant that the formation of nitrates is 

limited by the amount ofNOx (Scenario #1, above), we have the following situation: 

• The ammonia concentration is the most important determinant of how much of the NOx in 

the atmosphere is converted into nitrate, and  

• The fraction of that nitrate that is in particulate form increases with decreasing 

temperature.  

A strong correlation has been found between background NH3 concentration and ambient 

temperature during the course of a day.10 This temperature dependence is consistent with 

observations that background ammonia concentrations tend to be greater in summer than in 

winter, especially because the organic sources of ammonia are more active in warmer 

temperatures. Since the nitrates produced in summertime are mostly in the gaseous form that 

does not affect visibility, the higher summertime NH3 concentrations have a negligible effect on 

visibility.   

Colder temperatures are needed to form nitrate particles, and IMPROVE measurements show 

that particulate nitrate concentrations in rural areas are, indeed, generally greatest in the winter, 

despite the lower ammonia concentrations at that time. This means that nitrates are of greatest 

importance to visibility during the cooler months of the year, hence the background ammonia 

concentrations during those months are a critical input to the visibility modeling that is 

performed as part of the BART analysis. For this reason, the SRP NH3 measurements described in 

this report took place during the relatively cooler months of December through April. 

Previous Ammonia Measurements

Very few measurements have been made of the actual concentrations of background ammonia in 

the area shown in Figure 1, so background ammonia concentrations inputs to the model must 

typically be estimated from other information.  Several studies have been performed that could 

provide some insight into ammonia concentrations that might interact with emissions from NGS. 

Two sets of NH3 measurements on the Colorado Plateau are known to us: 

                                                       

10 Gaseous Ammonia Fluxes and Background Concentrations in Terrestrial Ecosystems of the United States, by A.O. 

Langford, F.C. Fehsenfeld, J. Zachariassen, and D.S. Schimel. Global Biochemical Cycles, Vol. 6 (1992), pp. 459‐483. 
12 

• Grand Canyon National Park. Ambient ammonia was measured daily at Grand Canyon 

National Park by Colorado State University during the month of May 2003.11  The 

ammonia concentration measured during this late spring month averaged 0.56 ppbv over 

the month. 

• Four Corners Area.  A complete year of ammonia measurements was made during 2007 

at five locations in the Four Corners area, using passive samplers that provided aggregate 

values for 3‐week periods.12  One measurement site was Mesa Verde National Park (about 

260 km from NGS), a site that investigators considered to be most representative of the 

regional background ammonia concentrations.  

The annual mean ammonia concentration at Mesa Verde was 0.2 ppbv, with a standard 

deviation of 0.2 ppbv.  The 3‐week average concentrations at Mesa Verde were 0.2 ppbv 

or less in all but one of the sampling periods ending in January through April, and in all 

periods ending in September through December.  In fact, all sampling periods ending in 

January and all those ending in November and December had concentrations of 0.1 ppbv 

or less.  The sampled concentrations at Mesa Verde were greater than 0.2 ppbv only 

during May through August, with a peak of 0.6 ppbv for one sample in July.  

Interestingly, the ammonia concentrations at another site (Navajo Lake), which the 

investigators considered to be impacted by emissions from oil and gas development, 

closely tracked the concentrations at Mesa Verde even though the two sites were roughly 

100 km (60 miles) apart. 

Ammonia concentrations at the other three sites, all of which were considered by the 

investigators to be impacted by emissions from power plants, oil and gas development, 

agriculture, mobile sources, and/or urban activities, were always greater than the 

concentrations at Mesa Verde and Navajo Lake.  

Background ammonia concentrations have also been measured at four Western locations outside 

the Colorado Plateau: 

• Niwot Ridge, Colorado.  Two‐hour averages of background ammonia concentrations 

were measured throughout the day in several seasons of the year at this forested location 

in the Rocky Mountains, northwest of Boulder, Colorado.13  The average wintertime NH3

concentration was 0.2 ppbv and it exhibited almost no diurnal variation.  In contrast, in 

summer the average daytime peak 2‐hr NH3 concentration of about 0.6 ppbv was more 

than double the average nighttime 2‐hr concentration of less than 0.3 ppbv, giving a daily 

average of 0.42 ppbv for the summer.  

                                                       

11 Ammonia measurement data provided in 2005 to Robert Paine of ENSR by Dr. Jeff Collett of Colorado State 

University. 

12 Baseline ambient gaseous ammonia concentrations in the Four Corners area and eastern Oklahoma, USA, by Mark 

E. Sather, Johnson Mathew, Nghia Nguyen, et al., Journal of Environmental Monitoring, Vol. 10 (2008), pp. 1319‐1325.  13 Langford et al., 1992. See footnote 10.
13 

• Rocky Mountain National Park. The RoMANS (Rocky Mountain Atmospheric Nitrogen 

and Sulfur Study) measured atmospheric chemistry and deposition in Rocky Mountain 

National Park during two 5‐week periods in 2006 (25 March through 29 April in spring 

and 6 July through 11 August in summer).14  24‐hr NH3 measurements were made daily at 

one location. During the 35‐day spring study, about half of the 24‐hr NH3 concentrations 

there were less than 0.20 ppbv.15  In contrast, during the 37‐day summer study, about half 

of the 24‐hr NH3 concentrations were less than 0.50 ppbv. 

• Upper Green River Basin, Wyoming. Ammonia measurements were carried out for 14 

months at a location in the Upper Green River Basin of central Wyoming.16  Two samples 

(one each of 3 days and 4 days duration) were taken each week from December 2006 

through January 2008.  Monthly‐average NH3 concentrations were well under 0.1 ppbv 

during the three months from December 2006 through February 2007, and again during 

the five months from November 2007 through March 2008.  Concentrations of all 3‐ or 4‐

day samples during those months were also less than 0.1 ppbv, except for one sampling 

period in the first half of November 2007. 

• Big Bend National Park, Texas.  The BRAVO Study (Big Bend Regional Aerosol and 

Visibility Observational Study) included daily measurements of NH3 at Big Bend National 

Park (in Texas on the US‐Mexico border) during July through October 1999.17  The park 

has an arid, desert‐like environment.  The average daily NH3 concentration during this 

summer and early‐fall study was 0.156 μg/m3 (about 0.22 ppbv) and values ranged from 

a low of 0.003 μg/m3 to a high of 0.624 μg/m3 (about 0.004 to 0.89 ppbv).18

Of the six studies described above, only three (Four Corners, Niwot Ridge, and Upper Green River 

Basin) included NH3 measurements in the cooler months of late fall, winter, and early spring.  

The average wintertime concentrations measured in those three studies ranged from less than 

0.1 ppbv to less than 0.2 ppbv. These concentrations are consistent with the monthly values 

estimated for the colder months by SRP in its December 2008 NGS BART modeling.  

Interestingly, even the average concentrations over periods that included the summer were 

about 0.2 ppbv in the Four Corners and Big Bend studies.  

                                                       

14 RoMANS: Rocky Mountain Atmospheric Nitrogen and Sulfur Study Report, by William C. Malm, Jeffrey L. Collett, Jr., 

et al., October 2009. ISSN 0737‐5352‐84. 

15 Ammonia concentrations are reported in the RoMANS report in terms of micrograms of nitrogen per cubic meter. 

These values were converted here to micrograms of NH3 by multiplying by 17/14, the ratio of formula weights for 

NH3 and N, respectively. Conversion to units of ppbv depends on the atmospheric pressure and temperature, both of 

which vary from hour to hour. At standard atmospheric temperature and pressure, 1 ppbv NH3 = 0.7 μg/m3, so 

division by 0.7 provides the approximate concentration in ppbv.   16 NH3 Monitoring in the Upper Green River Basin, Wyoming, by John V. Molenar, H. James Newell, Jeffrey Collett, et al. 

Extended Abstract #70, A&WMA Specialty Conference “Aerosol & Atmospheric Optics: Visual Air Quality and 

Radiation”, Moab, Utah, 28 April ‐ 2 May 2008.  17 Aerosol ion characteristics during the Big Bend Regional Aerosol and Visibility Observational Study, by Taehyoung 

Lee, Sonia M. Kreidenweis and Jeffrey L. Collett, Jr., Journal of the Air & Waste Management Assoc., Vol. 54 (2004), pp. 

585‐592. 

18 The NH3 concentration information published by the investigators is in units of micrograms per cubic meter. As 

noted in Footnote 14, conversion to units of ppbv depends on the atmospheric pressure and temperature, both of 

which vary from hour to hour. At standard atmospheric temperature and pressure, 1 ppbv NH3 = 0.7 μg/m3, which is 

the conversion used here. 
14 

A‐2. THE SRP NH3 MONITORING PROGRAM

As discussed in Section A‐1, previous ammonia measurements provide only limited insight into 

the background ammonia concentrations on the Colorado Plateau in the vicinity of NGS.  In 

particular, those measurements do not support the ammonia concentrations derived by the EPA 

using its back‐calculation method.  In order to provide a firmer basis for estimating ambient 

ammonia concentrations in this area, SRP initiated a program to measure wintertime 

background ammonia concentrations in December 2009.  This section describes the conceptual 

design of the program, as well as the equipment and procedures used. 

Management of the SRPNH3 Monitoring Program

The monitoring program that is the subject of this report was the joint effort of several 

organizations.  Their roles in the program are listed below: 

• Project Management ­­ Salt River Project

o Grant Smedley, Project Manager 

o L Dee Shakespear, Field Manager 

o Paul Ostapuk, Meteorologist 

• Advisors to SRP

o Ivar Tombach, Ph.D., Environmental Consultant 

o Robert Paine, AECOM Environment 

• Field Program and Data Processing– Air Resource Specialists, Inc. (ARS)

o Mark Tigges, Project Manager 

o Stephen Holcomb, Monitoring Coordinator 

o Gloria Mercer, Quality Assurance Manager 

• Laboratory Analyses – Atmospheric Science Laboratory, Colorado State University

(CSU)

o Jeffrey Collett, Ph.D., Data Analysis Supervisor 

o Taehyoung Lee, Project Scientist 

o Yi Li, Project Scientist 

Monitoring Program Design Considerations

Several assumptions and premises influenced the design of SRP’s NH3 monitoring program: 

• The ammonia concentrations of interest are the ones that are most appropriate to use in 

the CALPOST processor of the CALPUFF model.  CALPUFF and the associated POSTUTIL 

post‐processor provide for input of monthly average ammonia concentrations, which is 

the convention the EPA used in its CALPUFF modeling of NGS emissions.  Since SRP and 

EPA Region 9 with its BART modeling have identified the 8th highest day as the 

appropriate metric for identifying the visibility impacts at Class I areas, it is not 
15 

productive to target measurement of conditions that are associated with rare outlier 

events. 

• The relevant background ammonia concentrations for modeling the effects of current and 

future NGS emissions are those in the air mass into which the NGS emissions are emitted 

and within which they are transported.  Those concentrations have to be measured 

outside of the portion of the atmosphere where the actual NGS emissions are present 

today, because the current emissions could react with the background ammonia and 

reduce its concentration.  The ammonia concentration upwind of NGS or immediately 

outside the region of influence of current NGS emissions may best represent the 

concentration that is available to react with the NOx emissions from NGS in the model. 

• Measured ammonium nitrate concentrations in the area are greatest during the winter 

months and CALPUFF modeling indicates that particulate nitrate concentrations are most 

sensitive to the background ammonia concentrations during the cooler months.  

Therefore, the period where background ammonia concentrations are most important to 

visibility spans from the late fall through winter into early spring.  

• Much of the topography around NGS is very complex, including near‐vertical cliffs and 

deep canyons with steep walls, so CALPUFF is not able to simulate the details of the local 

flows in and around such topographic features.19  In order to be consistent with the 

spatial resolution used in practice with CALPUFF, the background ammonia 

concentrations should be measured at locations that are representative of the region in 

general, and are not characteristic of conditions at geographic features, such as the 

bottoms of canyons or the tops of buttes, whose effects on transport and dispersion may 

not be represented in CALPUFF. 

  

• The region between NGS and nearby Class I areas is free of major ammonia emission 

sources, such as agricultural feedlots.  Consequently, it was reasonable to assume that the 

ambient ammonia concentrations in this region would be relatively uniform spatially, and 

therefore measurements at a point would likely reflect ammonia concentrations over a 

large area. 

• While the key variable of interest is the background NH3 concentration, other 

measurements would to assist with interpreting the NH3 results.  Specifically, the SO2 gas 

concentration would provide an indication of the presence of emissions from coal 

combustion that could react with the NH3.  Knowledge of local wind speed and direction 

could provide insight into the general direction of airflow to the site.  

Site Selection Rationale

In light of the above considerations, the SRP NH3 measurement program was designed to address 

the background NH3 ammonia concentrations occurring along the transport routes of NGS 

                                                       

19 In fact, to the knowledge of the authors, no one has demonstrated any model that is able to produce a reasonable 

simulation of the finer‐scale details of transport and dispersion in much of the topographically complex region 

around NGS. 
16 

emissions to the three class I areas for which CALPUFF modeling has predicted higher visibility 

impairment, i.e., Grand Canyon, Capitol Reef, and Canyonlands National Parks.  

Since the first part of the transport route to Canyonlands is almost the same as that to the 

southern portion of Capitol Reef, and hence the ammonia concentrations along much of the route 

to Canyonlands are likely to be the same as those on the way to Capitol Reef, the design of the 

monitoring program focused on measuring background ammonia concentrations representative 

of the transport routes to Grand Canyon and Capitol Reef, as symbolized by the two arrows in 

Figure 3. Desirable ammonia monitoring site locations are expected to lie in the general vicinity 

of these two arrows, as suggested by the red circles in Figure 3. The paucity of known ammonia 

emission sources in the area implied considerable freedom in placing the monitoring sites, since 

the background NH3 concentrations were expected to be quite uniform spatially. 

Background NH3 measurements will represent the regional concentrations of available ammonia 

whenever the NGS emissions are not directly affecting the air mass that is being measured.  

Because of opposite directions of the two arrows in Figure 3, at any moment in time at least one 

of the two monitoring sites implied by the red circles is likely to measure ammonia 

concentrations that are regionally representative, either because they are upwind of NGS or are 

near (but not on) the actual transport route of NGS emissions to a Class I area.   

Monitoring Program Description

Taking into account the design factors mentioned earlier, SRP carried out measurements of 

atmospheric concentrations of ammonia gas and other gaseous and particulate species at several 

locations in the region around NGS from the beginning of December 2009 to the end of April 

2010.  These are the coldest months of the year, when measured particulate ammonium nitrate 

concentrations tend to be highest.  

Monitoring Site Locations. Four criteria were used to select the locations of the monitoring sites: 

• The Monitoring Program Design Considerations described above 

• The Site Selection Rationale and the map in Figure 3, also above 

• Site exposure to background NH3 and avoidance of potential local sources 

• Logistical considerations – electric power, access for servicing, and security.   

Typically, all four of the criteria could not be met simultaneously at any particular location, so 

the site selection process involved balancing of sometimes‐contradictory criteria.  Ultimately, 

two locations were selected to start the program – a northern site adjacent to the little‐used Cal 

Black airport at Hall’s Crossing (Utah), along the Colorado River some 90 km (60 mi) northeast of 

NGS, and southern site near a former coal‐slurry pipeline pumping station at Gray Mountain, a 

location south of Cameron (Arizona), about 120 km (80 mi) south of NGS.  Monitoring with 

diffusion denuders (described below) began at these two sites on 5 December 2009. 
17 

Figure 3. Locations of Class I areas and IMPROVE monitoring sites (blue triangles) relative to NGS

(red star).  The arrows indicate approximate transport directions from NGS to Grand Canyon and

Capitol Reef National Parks, and the red circles suggest general areas of potential sites for

monitoring background NH3 in the vicinity of these paths.
18 

Initial results from the first few weeks of measurements showed ammonia concentrations at the 

Gray Mountain site to be substantially greater than those at the Hall’s Crossing site, a difference 

that was larger than expected.  To determine if the Gray Mountain ammonia concentrations were 

affected by localized sources of NH3, SRP added a third monitoring site, comparably equipped to 

the original two sites, at the Arizona Public Service Moenkopi Switchyard, approximately 8 km (5 

miles) northeast of the Gray Mountain site.  At the same time, to provide further information 

concerning the spatial distribution of ammonia concentrations, SRP also installed passive 

ammonia samplers, which do not need electric power, at six locations – at all three of the 

instrumented monitoring sites plus at three remote locations in the region around the two 

southern monitoring sites.  (The three sites with both diffusion denuders and passive samplers 

are hereafter referred to as “primary” sites, to distinguish them from the three sites with only 

passive samplers.)   

The locations of all of the monitoring sites are shown on the map in Figure 4 and a finer scale 

view of the locations of the five southern sites appears in Figure 5. Geographic coordinates, aerial 

views, and photographs of the equipment installations at all sites are provided in the monitoring 

study report prepared by ARS.20

Variables Measured. The monitoring program collected air samples that were analyzed to 

determine concentrations of gases and chemical components of fine particulate matter,21 and 

measured meteorological variables, as summarized in Table 1. 

                                                       

20  Ambient Ammonia Monitoring Near the Navajo Monitoring Station: Southern Utah/Northern Arizona: December 5,

2009 – May 4, 2010; Operational Summaries, Data Summaries and Plots. Report to the Salt River Project by Air 

Resource Specialists, Ft. Collins, CO. Revised August 25, 2010.

21 More succinctly, this is a description of an aerosol measurement program. An aerosol is defined as a suspension of 

particles in a gas, i.e., it includes both the particles and the gas. 
19 

Figure 4. Locations of NH3 monitoring sites (circles) relative to NGS and the Grand Canyon.
20 

Figure 5. Detail of locations of the NH3 monitoring sites south of NGS.
21 

Table 1. Summary of Variables Measured and Instrumentation Used.

Component Parameter Instrumentation Sample Period

PRIMARY SITES

Gases Ammonia (NH3) URG Denuders 6‐day integrated

samples

Nitric Acid (HNO3) URG Denuders 6‐day integrated

samples

Sulfur Dioxide (SO2) URG Denuders 6‐day integrated

samples

Fine Particulate Matter Ammonium ion (NH4

+

) URG Stacked Filter 6‐day integrated

samples

Nitrate ion (NO3

‐

) URG Stacked Filter 6‐day integrated

samples

Sulfate ion (SO4

2‐

) URG Stacked Filter 6‐day integrated

samples

Soil Constituents (K+

, Mg2+,

Ca2+)

URG Stacked Filter 6‐day integrated

samples

Wind Wind Speed and Direction R.M. Young

Anemometer

Continuous

Relative Humidity Relative Humidity Rotronic Instrument Continuous

Sample Flow Temperature, Barometric

Pressure, Flow Rate

URG Pump Output Continuous

ADDITIONAL MEASUREMENTS AT PRIMARY AND PASSIVE SITES after 15 Feb

Gases Ammonia (NH3) Radiello Passive

Samplers

18‐day integrated

samples

Primary Sites. The principal measurements for the project were made on samples collected by 

continuously operated University Research Glassworks (URG) samplers at the three primary 

sites. A typical sampling configuration is sketched in Figure 6 and photographs in Figure 7 show 

the specific configuration used in this monitoring program. Two samplers in parallel each drew 

metered air through a PM2.5 size‐selective cyclone inlet at 1.5 m (5 ft) above the ground, then 

through a series of two coated annular denuders, a Nylon filter, and another denuder.22  The first 

denuder was coated with sodium carbonate to collect gaseous nitric acid (HNO3), while the 

second denuder was coated with phosphorous acid to collect ammonia gas (NH3).  After these 

denuders, a 37 mm nylon filter collected particles, including those containing sulfate (SO42‐ or 

SO=

4 ), ammonium (NH4+), and nitrate (NO3‐

) ions.  Lastly, a “backup” denuder collected any NH3

volatilized from ammonium nitrate particles on the Nylon filter. System operations and analysis 

                                                       

22 The typical sampling configuration sketched in Figure 6 does not show this final denuder.  The drawing illustrates 

a configuration used in previous studies, where a backup filter in the filter holder was used instead of the backup 

denuder that was used in the SRP study.  The photograph in Figure 7 shows the configuration used in this study, 

with the backup denuder on top. 
22 

of collected samples were carried out in accordance with EPA protocols for Compendium Method 

IO‐4.2. 

Figure 6. Typical configuration of the two‐channel URG sampler.  The two denuders in each channel

are above the cyclone inlet and are followed by the filter holder.  The backup denuders used in the

SRP study, which go above the filter holder, are not shown in this sketch.

A six‐day sampling period was dictated by SRP’s desire to quantify background ammonia 

concentrations down to 0.1 ppbv. Consequently, the denuders were changed every 6 days, 

resulting in an integrated 6‐day sample in each sample set. For each period, sampling began in 

the morning (about 8 a.m.) of the first day and the denuder train was changed in the morning of 

the 7th day. In order to facilitate comparisons with concentrations measured at IMPROVE sites on 

every third day, the 6‐day periods used included an IMPROVE sampling day on the 2nd and 5th

days. 
23 

Figure 7. The sampler installation at the Hall’s Crossing site.  Left: Pump, dry gas meter, and data

logger in cabinet, with URG sampler case, relative humidity and temperature sensors, and

anemometer on post.  Right: Inside of sampler case, with only the right sampling train installed.  

The filter holder is the large white cylindrical assembly; the two denuders are below it and the

backup denuder is the short tube above it.

At each primary site, a wind vane and anemometer at 3 m (10 ft) above the ground measured 

wind direction and speed.  Relative humidity was measured just below the anemometer.  The 

URG sampling system included measurements of ambient temperature, barometric pressure, and 

sample airflow rate.  All of this continuous measurement data was recorded digitally on‐site and 

was telemetered daily via the cellular network to ARS.  

Passive Sampling.  To provide further insight into the representativeness of the Gray Mountain 

and Moenkopi site measurements, Radiello passive NH3 samplers were installed at the three 

primary sites and at the three remote locations in the vicinity of the two southern primary sites.  

The Radiello passive samplers consist of adsorbent cartridges housed within “diffusive bodies”, 

which were placed 1.5 m (5 ft) above the ground.  The adsorbent cartridges were impregnated 

with phosphoric acid to collect ambient atmospheric ammonia.  For quality assurance purposes, 

each site had duplicate passive samplers, as shown in Figure 8.
Figure 8. Du

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25 

Quality Assurance

Quality Assurance Program. Key elements of the quality assurance program for the study 

included the following: 

• Written procedures for field handling of samples, sample analysis in the laboratory, and 

data processing, in the QAPP 

• Collection and analysis of field and laboratory blanks 

• Audit and calibration of the URG samplers and meteorological equipment, jointly 

performed by ARS and CSU personnel, on 27‐29 January and 5 March 

• Visits to all primary and passive sampler sites by the SRP project team and Dr. Ivar 

Tombach on 12 February 

• Collocated measurements of NH3 at (1) the primary sites, by URG samplers and Radiello 

passive samplers, and (2) all passive sites, by collocated Radiello samplers 

• Replicate analyses of samples in the laboratory (to determine analytical precision) 

• Analysis of standards in the laboratory (to determine analytical accuracy) 

Details of these aspects of quality assurance are provided in the project QAPP and in laboratory 

SOPs. 

NH3 Sampler Comparison. One means of data quality evaluation was a consequence of the 

collocation of multiple samplers at every site.  Duplicate Radiello passive samplers were installed 

at all sites and URG denuder samplers were collocated with the passive samplers at the primary 

sites.  This collocation allowed evaluation of the internal consistency of the two forms of NH3

measurements as well as determination of the precision of the passive NH3 measurements. 

Figure 9 is a plot of the blank‐corrected NH3 concentrations determined from one passive 

sampler (Sampler A) vs. the other passive sampler (Sampler B) at the same location.  The 

diagonal line is the 1:1 line.  All 18‐day samples at all six sites collected from the commencement 

of passive monitoring on 15 February through the termination of sampling on 28 April (four 

samples per site) are represented, with one exception.  Data for both Hall’s Crossing passive 

samples for the period beginning 23 March were discarded because the blank‐corrected 

concentrations were negative and differed greatly from the reasonable concentration 

determined by the URG sampler there.   
26 

Table 2. Monitoring program sampling schedule, with colored cells indicating when valid samples were obtained. Sa

identified by the date on which the sampling began. Double asterisks indicate when the concentrations of one or tw

were below the minimum detection limit (see details in Table 4).

Sampler Site December January February March 

5 11 17 23 29 4 10 16 22 28 4* 9 15 21 27 5 11 17 23 29 

URG Hall’s Crossing **   **  **   ** 

Moenkopi **Gray Mountain **

Passive Hall’s Crossing

MoenkopiGray MountainWaunetaBabbitt RanchBlack Point

Note: * The sampler exchange that was supposed to take place on February 3rd actually took place on February 4th, resulting in a 7‐day sa

sample.
27 

Figure 9. Comparison of NH3 concentrations determined from collocated passive

samplers at all six sites, for 18‐day samples with start dates of 15 February through 10

April 2010.  The line is the 1:1 line.

As Figure 9 shows, agreement between the two passive samplers in each pair was 

generally excellent; the measurements were very well correlated (R2 = 0.98) and the 

Root Mean Square (RMS) discrepancy was 0.023 ppbv.  The largest magnitude of 

discrepancy was ‐0.044 ppbv, which represented a fractional error of ‐11%.  The 

largest fractional error was ‐13%.  Based on the same data set, the relative 

measurement precision of the passive measurements of NH3 was determined to be 

3.2%. 25 26

                                                       

25 The measurement precision indicates how well the complete measurement program would 

reproduce the same results if it were to be presented with the same atmospheric composition 

multiple times.  The relative precision expresses that reproducibility as a fraction of the value being 

measured – the NH3 concentration in this instance.  It is usually straightforward to determine 

laboratory precision by repeated analysis of aliquots of the same sample, but it is difficult to quantify 

the precision of the field portion of the monitoring program – the sampling and the sample handling 

– because such repeated measurements of the same atmosphere are typically not possible.  The 

operation of the passive samplers in this SRP NH3 monitoring program was a refreshing exception to 

this dilemma, because it was economically feasible to operate multiple samplers in the same 

atmosphere and to then analyze the samples in the same laboratory.  Therefore it was possible to 

calculate the precision of the entire measurement process (field sampling and laboratory 

components) when using the passive samplers.   26 Accuracy, Precision, and Minimum Detection Limit (MDL). Memorandum prepared by the CSU 

Atmospheric Science Laboratory for the SRP NH3 monitoring program, 4 June 2010.  
28 

Figure 10 is similar to Figure 9, but now the NH3 concentration that is the average of 

a pair of 18‐day measurements by the two passive samplers at a site is compared to 

the temporal average of the concurrent three consecutive 6‐day NH3 concentrations 

from the URG sampler at the same site.  The data sources represented in this plot 

include only the three primary sites and the duration of the data is the same as in 

Figure 9 (taking into consideration the discarding of the Hall’s Crossing passive data 

for the 18‐day period starting on 23 March).  

Figure 10. Comparison of NH3 concentrations determined from collocated passive and

URG samplers at the 3 primary sites, for 18‐day samples with start dates of 15

February through 10 April 2010. The line is the 1:1 line.

Not surprisingly, the agreement between the differing technologies of the URG and 

passive samplers (R2 = 0.89) is not as strong as that between two collocated 

identical passive samplers.  The RMS difference is 0.078 ppbv, about three times as 

large as the corresponding value for the collocated passive samplers.  The largest 

difference was 0.225 ppbv, which corresponded to the largest fractional error of 

28%.  Nevertheless, this degree of agreement is good enough to allow mingling of 

NH3 concentration data derived by the URG and passive samplers, and this has been 

done in this report. 
29 

The very few points plotted in Figure 10 could lead one to infer that the passive 

measurements tend to understate the URG measurements at the lower 

concentrations on the plot and overstate the URG values at the highest 

concentration.  However, this same stratification is also by site, with the lower 

values occurring at Hall’s Crossing and the highest values at Gray Mountain, so the 

discrepancy with the passive measurements may just reflect slightly different 

calibrations of the URG sampling systems (or other systematic or operational 

differences) at different sites.  Therefore, there is no evidence that there is a 

consistent bias between the two types of measurements and they do agree quite 

well most of the time. 

Laboratory Accuracy, Precision, and Minimum Detection Limit. Table 3 summarizes 

the results of determinations of analytical minimum detection limit (MDL), 

analytical precision, and analytical accuracy for the ion chromatographic analyses in 

the CSU laboratory.27  The MDL and analytical precision were calculated specifically

for this monitoring program.  The accuracy was calculated by analyzing standards.  

Since the accuracy can vary with concentration, a range of accuracies is given in the 

table.  Empty spaces in the accuracy column indicate that the accuracy was not 

determined.  Dashes appear in the MDL (ppbv) column when the species is solid and 

therefore ppbv is meaningless. 

In general, the MDLs are well below the concentrations encountered during the 

monitoring program.  The minor exceptions are listed in Table 4. 

Note particularly that the MDL of NH3 is 0.016 ppbv for the URG samplers, so the 

goal of quantifying the NH3 concentration down to 0.1 ppbv was met.  In fact, even 

3‐day samples would have been sufficient to meet that criterion.  The MDL of 0.042 

ppbv for the passive samplers is also sufficient to meet the 0.1 ppb goal. 

                                                       

27 This information is based on the CSU laboratory memo referenced in Footnote 26. 
30 

Table 3. Minimum detection limits, precisions, and accuracies of laboratory analyses

Species MDL (ng/m3) MDL (ppbv) Relative

Precision (%)

Accuracy  

Range (%)

GASES

NH3 10.36 0.016 4.5   

NH3 (Passive) 26.08 0.042    3.2*   

HNO3 3.25 0.002 1.1   

SO2 3.49 0.002 1.7   

SOLIDS AND LIQUIDS

SO4

2‐ 1.08  ‐‐  1.5 0.1 to 1.8

NO3

‐

1.76  ‐‐  3.5  ‐7.0 to +2.6

NH4

+

0.16  ‐‐  3.5 3.2 to 9.7

Cl‐ 3.12  ‐‐  2.5  ‐0.6 to ‐0.3

Na+ 1.51  ‐‐  4.9  ‐5.4 to +3.2

K= 0.65  ‐‐  8.2  ‐7.7 to +3.6

Mg2+ 0.51  ‐‐  3.8  ‐8.2 to 5.6

Ca2+ 1.07  ‐‐  4.5  ‐7.1 to +3.6

*  Measurement precision; all other values are laboratory precision

Table 4. Dates and locations of URG samples with a measured species concentration

below the MDL.

Site Start of sampling period Species below MDL

Hall’s Crossing 12/17/09 Na+

Hall’s Crossing 12/23 Na+ and Mg2+

Hall’s Crossing 2/9, 2/21, 2/27, 3/5, and 3/11 Cl‐

Gray Mountain 12/23 Mg2+

Moenkopi 2/27 Cl‐
31 

A‐3. MEASUREMENT RESULTS

The measurement systems of this program provided raw data in many forms and 

from many sources, including the following: 

• Results of chemical analyses of denuders, filters, adsorbents and 

corresponding field and laboratory blanks from all six sites 

• Digital records of sampler flow rate, ambient temperature and relative 

humidity, and wind speed and direction from the three primary sites 

• Station operator and laboratory logs 

• Results of QA activities such as calibrations and audits 

This information was processed, checked, and compiled into data records usable for 

providing credible insights into the atmospheric chemistry in the study area, 

including the amounts of ammonia that are present and how they are involved in 

chemical reactions that produce visibility‐impairing particles.

This section describes the results of the measurements made during this program. 

Details of the calculations and the resulting values are contained in the field 

program report by Air Resource Specialists.28

URG Denuder System Measurements at the Primary Sites

As described in Section A‐2, URG denuder‐plus‐filter‐pack samplers were used at 

the three primary sites to collect samples of gases (NH3, HNO3, and SO2), and fine 

particulate matter components (SO42‐, NO3‐, and NH4+ and several elements) over 6‐

day periods.  To enable sample concentration calculations, temperature, barometric 

pressure, and sample flow rate were recorded continuously.  Wind speed and 

direction were also recorded continuously. 

As an overview of the results of these measurements, Table 5 shows the study‐

period averages and standard deviations of the quantities measured using the URG 

samplers. The concentrations in that table, as well as all concentrations presented in 

the rest of this report, are all blank corrected. Note that the sites are listed in order 

from north to south, but any pattern that arrangement suggests should be analyzed 

carefully because the Moenkopi site only operated for the last half of the monitoring 

program.  

                                                       

28 See citation in Footnote 20. 
32 

Table 5. Averages and standard deviations of six‐day gas (g) and particulate (p) component concentrations m

5‐month monitoring period at the three primary sites.

HNO3

(g)

SO2

(g)

NH3

(g)

NO3‐

(p)

SO4=

(p)

NH4+

(p)

Cl‐

(p)

Na+

(p)

K+

(p)

Mg2+

(p)

ppbV ppbV ppbV μg/m3 μg/m3 μg/m3 μg/m3 μg/m3 μg/m3 μg/m3 

HALL'S CROSSING

Average 0.21 0.08 0.22 0.25 0.55 0.27 0.01 0.01 0.01 0.01 

Std. Dev. 0.15 0.06 0.08 0.23 0.23 0.12 0.00 0.01 0.01 0.01 

MOENKOPI (15 February onward)*

Average 0.11 0.06 0.45 0.15 0.53 0.21 0.01 0.03 0.01 0.01 

Std. Dev. 0.05 0.02 0.12 0.07 0.14 0.06 0.01 0.02 0.00 0.01 

GRAY MOUNTAIN

Average 0.17 0.06 0.63 0.34 0.51 0.29 0.01 0.02 0.01 0.01 

Std. Dev. 0.05 0.03 0.25 0.26 0.21 0.12 0.01 0.02 0.01 0.01 

* Note: The averages for Moenkopi are only for the 13 sampling periods starting 15 February and so are not directly comp

25 sampling periods starting 5 December that are reflected in the Hall’s Crossing and Gray Mountain averages.
More

three

mont

durin

conce

analy

Note

perio

in Ta

Table

samp

Dece

Janu

Febr

Mar

Apri

Aver

Figur

three

e details abo

e primary si

thly average

ng a given m

entration du

ysts. The da

 that there a

ods starting

able 2. 

e 6. Monthly

plers at the t

ember 2009

uary 2010

ruary 2010

ch 2010

l 2010

rage

Notes:    *

**

re 11. Conce

e primary sit

out the NH3

ites are prov

e NH3 conce

month. Figur

uring the 5‐

ates on Figur

are gaps in 

g on 12/29 a

y averages o

three prima

Ha

= Based on 4

* = Only the la

ntrations of

tes. The date

concentrat

vided in Tab

entrations fo

re 11 displa

‐month stud

re 11 indica

the Gray Mo

and 1/16 wa

of NH3 gas co

ry sites.

all’s Crossing

0.19

0.14

0.23

0.27

0.28

0.22

4 samples; on

ast 3 sampling

f NH3 gas (in

es represen

33 

tions measu

ble 6 and Fi

for all sampl

ays the samp

dy period, a

ate the first 

ountain dat

as not succe

oncentration

M

  

ne sample per

g periods of t

n ppbv) in in

t the first da

ured by the U

igure 11. Ta

les whose s

ple‐to‐samp

s provided 

day of each

ta because th

essful, as wa

ns (in ppbv)

Moenkopi

–

–

  0.58**

0.41

0.41

0.45

riod missing d

the month, st

dividual 6‐d

ay of each sa

URG sample

able 6 shows

ampling per

ple values of

by the CSU 

h 6‐day samp

he sampling

as indicated

measured b

Gray

  

  

during each m

tarting 2/15/2

day URG sam

ampling per

ers at the 

s the 

riods began

f NH3

and ARS 

pling cycle.

g for the 

d previously

by the URG

Mountain

0.89*

0.65*

0.71

0.48

0.48

0.63

month

2010.

mples at the

riod.

n

y
34 

Prominent features of the measured NH3 concentrations that are displayed in Table 

6 and Figure 11 include the following: 

• At the beginning of the study, the NH3 concentrations at Gray Mountain were 

considerably greater than those at Hall’s Crossing. This difference became 

smaller during the course of the study, as the Gray Mountain concentrations 

decreased and those at Hall’s Crossing increased; the two concentrations 

were almost identical in the last (28 April) sample 

• The concentrations measured at Moenkopi, which were only taken during 

the second half of the study, were comparable to, but just a bit smaller, than 

those at Gray Mountain. 

Figure 12 and Figure 13 similarly display the concentrations of other key species at 

all three primary sites during the 5‐month study period, as measured by the URG 

samplers.  Concentrations of the gases HNO3 (nitric acid) and SO2 are displayed in 

Figure 12; the inorganic ions NH+

4, (ammonium), NO3‐

, and SO42‐ are displayed in 

Figure 13; and the sum of two crustal cations (Mg2+ + Ca2+) is plotted similarly in 

Figure 14. Note that the concentration scales are not the same on all plots.  

Several features stand out in these plots. Many species demonstrated expected 

seasonal behavior, with concentrations measured toward the end of the 5 months of 

monitoring differing from those measured in the first months. There are several 

strong seasonal effects, including the difference between NH3 concentrations at 

Hall’s Crossing and Gray Mountain in the first months that has already been noted. 

For the crustal species plotted in Figure 14, the difference between concentrations 

in the first months and the last months is especially prominent.   

The plots in Figure 12, Figure 13, and Figure 14 also illuminate several short‐term 

events that are reflected in one or two 6‐day samples. For instance, in the sample 

taken starting 10 January and, to a lesser degree, in the 4 January sample, Hall’s 

Crossing concentrations of all species except ammonia were greater than those at 

Gray Mountain, opposite to the situation at most other times. For the 10 January 

sample, the concentrations of nitric acid, SO2, ammonium and nitrate were 

unusually high at Hall’s Crossing and concentrations of the same constituents were 

also elevated at Gray Mountain (the Moenkopi site was not yet operational then). 

Also, in the 28 January sample, unusually high sulfate concentrations were observed 

at both the Hall’s Crossing and Gray Mountain sites and ammonium concentrations 

were elevated at both sites in that period.  
Figur

prima

e 12. Six‐da

ary sites.

y sample concentration

35 

s of the gases HNO3 and SO2 at the three
Figur

sulfat

e 13. Six‐da

te ions at th

y sample co

he three prim

oncentration

ary sites.

36 

ns of particulate nitrate, ammonium, and
Figur

calciu

To pr

plott

parti

the e

cubic

mass

          

29 An 

weigh

availa

reacti

ion is

g) – a

weigh

ions (

of am

weigh

ammo

react 

norm

have 

48 na

re 14. Six‐da

um ions, Mg

rovide a diff

ted in Figure

iculate spec

earlier plots 

c meter, a qu

s balances in

                     

ultra‐simpli

ht in grams o

able or will a

ion that form

s the sum of t

a total of 96 g

ht of 48 g. On

Now, whe

(NH+

4) becaus

mmonium is t

ht of hydroge

onium ion ac

The usefu

with one eq

mally react wi

already) rea

And, as fo

anograms pe

y average co

g2+ + Ca2+, at

fferent view

e 13 and Fig

ies are inclu

is that the c

uantity that 

n chemical r

                      

fied explanat

of a chemical

accept in a re

med the comp

the atomic w

g – divided by

ne equivalent

en ammonium

se each amm

the sum of th

en (4x1 g), an

ccepts one el

lness of this 

uivalent of a

ith each othe

acted comple

r the magnit

r cubic mete

oncentratio

the three p

wpoint, Figur

gure 14, exc

uded in that

concentrati

may be unf

reactions.29

  

tion of neq/m

 species, divi

eaction, or (fo

pound. For e

weights of one

y 2 because i

t weight of su

m sulfate is fo

monium ion is

he atomic we

nd the sum (

lectron. So, o

concept is th

 positively ch

er have the sa

tely with eac

tude of one n

r of sulfate io

37 

ns of the su

primary sites

re 15 displa

cept that the

t plot. An im

on units in 

familiar but

m3: An “equiv

ided by (for i

or compound

xample, the f

e sulfur atom

it has 2 elect

ulfate thus e

ormed, the s

s able to acce

ight of nitrog

(18 g) is then

ne equivalen

hat one equiv

harged ion. S

ame concent

ch other. 

neq/m3, that a

on, or roughl

m of crustal

s.

ays the same

ere is one pl

mportant dif

Figure 15 a

t is very use

valent weigh

ions) the num

ds) how man

formula weig

m (32 g) and f

rons to share

quals 48 g. 

ulfate ion rea

ept only one 

gen (14 g) pl

n divided by 1

nt for ammon

valent of a ne

So, in Figure 

tration in neq

amount corr

ly 0.05 μg/m

l magnesium

e informatio

lot per site a

fference com

are nano equ

eful when ex

ht” is just the

mber of elect

ny were enga

ght of the sul

four oxygen 

e, giving an e

acts with two

electron. One

us 4 times th

1 because ea

nium is 18 g. 

egatively cha

3‐4, if two io

q/m3, then th

responds to (

m3, a small am

m and

on as that 

and several

mpared to 

uivalents per

xploring 

 formula 

trons it has 

ged in the 

lfate (SO=

4 ) 

atoms (4x16

equivalent 

o ammonium

e equivalent 

he atomic 

ach 

arged ion wil

ons that 

hey can (or 

(for example)

mount.  

l

r

6

m

l 

), 
Figur

prima

e 15. Conce

ary site, in u

ntrations of

nits of neq/

f potentially

m3.

38 

y‐reacting inorganic ions in fine particles at each
39 

Figure 15 shows, for example, that the atmospheric chemistry associated with the 

ammonium peaks on the 1/10 sample at Hall’s Crossing is very different from that 

associated with the1/24 sample there. In the sample taken starting 1/10, there are 

nearly equal numbers of equivalents of sulfate and nitrate, and their sum equals the 

number of equivalents of ammonium, so the ammonium is about equally divided 

between sulfate and nitrate. In the sample taken starting 1/28, on the other hand, 

essentially all of the ammonium is tied up in ammoniated sulfate particles and there 

is virtually no ammonium nitrate. The relationships between the chemical 

components plotted in Figure 15 are analyzed at greater length in Section A‐4. 

Radiello Passive Sampler Measurements

As described in Section A‐2, tandem Radiello samplers measured 18‐day NH3

concentrations at six sites during the last half of the monitoring program. The 

adsorbent material in the samplers was extracted and analyzed using the same ion‐

chromatographic analysis method that was used to determine NH3 from the URG 

denuders.  The sampling protocol included dynamic field blanks.

Figure 16 is a plot of all of the passive sampler data – four 18‐day pairs of samples 

per site.  The values shown are averages of the two passive sampler readings.  As 

Figure 9 showed, the differences between the readings of the two samplers were 

very small.  The plot also includes 18‐day average NH3 measurements by the URG 

denuder, for comparison with the passive data. 

The bars in Figure 16 are arranged in four clusters, one for each of the 18‐day 

sampling periods that began on the dates shown at the bottom of the graph.  In each 

cluster, the bars are in order from the northernmost sampler at Hall’s Crossing to 

the southernmost samplers.  The order is as follows:  HC (Hall’s Crossing), MS 

(Moenkopi Switchyard), GM (Gray Mountain), WA (Wauneta), BR (Babbitt Ranch), 

and BP (Black Point).  Although the order is based on location, it happens that the 

first three sites on this list are the primary ones. For comparison, the maroon bars 

indicate the 18‐day average concentrations determined by the URG samplers during 

the same period as the passive sampling. 

There are two prominent features in Figure 16: (1) NH3 concentrations obtained 

from the passive samplers agree quite well with those from the URG denuder 

samplers (a point that was also made in Figure 10); and (2) There is a consistent 

geographic pattern over the four measurement periods shown, with the Gray 

Mountain site always having the highest NH3 concentration, as derived by both the 

passive sampler and the URG sampler, and the Hall’s Crossing site showing the 

lowest concentration in every set.  The passive sampling period of 2 1⁄2 months in 

late winter and early spring was too short to permit drawing any general 

conclusions for the winter or the year, however. 
Figur

meas

(gree

conce

site n

Met

Meas

made

from

chrom

Anem

at the

surfa

from

and s

re 16. The sp

sured by pas

en bars).  The

entrations b

names are d

eorologica

surements o

e during the

m the passive

matographi

mometers m

e three prim

ace winds an

m the anemo

synoptic‐sca

patial and te

ssive sample

e maroon ba

by the URG s

efined in th

al Measure

of temperat

e study, prin

e samplers a

ic analyses i

mounted at a

mary sites. T

nd their pos

meters are 

ale weather

emporal dist

ers during th

ars represen

samplers at

e text.

ements

ure, relative

ncipally for 

and for conv

into concen

a height of a

Their purpo

ssible effect

also useful 

r systems. 

40 

tribution of

he second h

nt concurren

the primary

e humidity, 

the purpose

verting the 

ntrations exp

about 3 m ab

ose was to p

ts on the che

to evaluate 

NH3 concen

alf of the m

nt measurem

y sites.  The

and barom

e of calculat

mass deter

pressed in v

bove groun

rovide infor

emical meas

response to

trations (in

onitoring pr

ments of NH

two‐letter c

etric pressu

ting concen

minations f

various unit

d level wer

rmation abo

surements. 

o passing m

ppbv)

rogram

H3

codes for the

ure were 

trations 

from the ion

ts.  

e operated 

out localized

The data 

mesoscale 

e

n

d
41 

Table 7 tabulates some of the surface meteorological information for the study 

period for Hall’s Crossing and Gray Mountain.30 The temperature at Hall’s Crossing 

was slightly warmer than that at Gray Mountain, which is consistent with its lower 

elevation, and the Hall’s Crossing winds were lighter than those at Gray Mountain, 

both in terms of frequency of calm conditions and highest wind speed. The average 

relative humidities at both locations were similar. 

Table 7. Summary of measured meteorological parameters at the Hall’s Crossing and

Gray Mountain sites over the 5‐month monitoring program. Wind percentages

represent percent of all wind observations (calm plus all speeds and directions)

Hall’s Crossing Gray Mountain

Average temperature 6.1 °C 4.7 °C

Average relative humidity 56% 53%

Frequency of calm conditions  

(WS < 0.5 m/s)

5.9% 1.1%

Highest wind speed 11.6 m/s 18.1 m/s

Most frequent wind directions SSE (10%) and SE (9%) SW (18%) and SSW (15%)

Least frequent wind directions   ENE (2%) and NE (2%) NW (2%) and SE (2%)

Predominant wind directions for wind

speed ≥ 0.5 m/s and < 1 m/s

SE (5%) and SSE (5%) E (1%) and all other

directions (each <1%)

Using a display format similar to that of Figure 11 through Figure 15, Figure 17 

shows the average wind speed at each site over each 6‐day sampling period.31

Although the wind speed and direction can be expected to vary widely over six days, 

the average wind speed is nevertheless an indicator of mesoscale atmospheric 

activity. Figure 18 displays another measure of atmospheric motion, the 6‐day 

average of the hourly standard deviations of wind direction (often denoted sigma

theta), which is an indicator of horizontal turbulent mixing near the surface. 

                                                       

30 Since Table 7 addresses results over the 5‐month monitoring period, data for the 21⁄2 months of 

Moenkopi operation are not included. 

31 Because of a data logger malfunction, meteorological measurements at Moenkopi began on 5 

March 2010. 
42 

Figure 17. Six‐day averages of surface wind speed at the sampling sites.

Figure 18. Six‐day averages of hourly standard deviations of wind direction (sigma

theta).  
43 

Figure 17 shows that the 6‐day average wind at Gray Mountain was appreciably 

greater than that at Hall’s Crossing during all but one sampling period. The Gray 

Mountain wind speed was often twice as great as that at Hall’s Crossing, beginning 

with the very first sampling period of 5 December. The one exception to this pattern 

was the period that began on 16 January, when the average Hall’s Crossing wind 

speed slightly exceeded that at Gray Mountain. At both sites, the average wind speed 

tended to increase during the course of the 5‐month study. Although the Moenkopi 

site was much closer to Gray Mountain than to Hall’s Crossing, the wind speed at 

Moenkopi (which was only measured starting on 5 March 2010), tended to be 

similar to or a bit greater than that at Hall’s Crossing, and it was always well under 

the wind speed at Gray Mountain. 

In contrast to the rather large differences between wind speeds at the two sites in 

Figure 17, the inter‐site differences in the standard deviation of the wind direction 

in Figure 18 were much smaller, with the values of this turbulence indicator being 

greater at Hall’s Crossing than at Gray Mountain most of the time. A notable 

exception was the period that began on 16 January, when the Gray Mountain 

turbulence was considerably greater than that at Hall’s Crossing. During the time 

that wind measurements were made at Moenkopi (the last 10 sampling periods of 

the monitoring program), the standard deviation of the wind direction there was 

greater than at the other two sites. There was no obvious temporal trend in this 

turbulence indicator over the course of the monitoring program at any of the sites. 

Insight into the wind directions during the course of the monitoring program is 

provided by the wind roses in Figure 19 and Figure 20. These wind roses show that, 

in general, the measured surface winds at Halls Crossing were weaker than those at 

Gray Mountain, and calm conditions (WS < 0.5 m/s) were much more frequent at 

Hall’s Crossing (5.9% of the time) than at Gray Mountain (1.1% of the time).  At 

Hall’s crossing, calm winds and very light winds (with wind speeds < 1 m/s or 2.24 

mph), occurred 31% of the time during the 5‐month period, while at Gray Mountain 

such low wind conditions occurred only 8% of the time. Considering all wind 

speeds, the wind at Hall’s Crossing was most often from a generally southeasterly 

direction, which was also the general direction for winds with speeds < 1m/s (but 

greater than the calm threshold of 0.5 m/s). At Gray Mountain, on the other hand, 

the winds came most of the time from a generally southwestern direction, although 

the preferred direction for the light wind speeds between 0.5 and 1 m/s tended to 

be more from a generally eastern direction. 

The next section of this report, which is devoted to analyzing and interpreting the 

monitoring program results, will also provide further examples of the data. 
Figur

the m

e 19. Surfac

monitoring p

e wind rose

program.  

for the Hal

44 

l’s Crossing site over the 5‐month duration of
Figur

the m

re 20. Surfac

monitoring p

e wind rose

program.  

for the Gra

45 

y Mountain site over the 5‐month duration of
46 

A‐4. ANALYSIS OF THE MEASUREMENT RESULTS

The data acquired by the monitoring network described in Section A‐3 was analyzed 

in various ways in order to arrive at a description of the atmospheric conditions 

and, in particular, background ammonia concentrations, in the region around NGS.  

For these analyses, the concentration and meteorology data from the SRP 

monitoring network was supplemented by data from other sources during the 

December 2009 through April 2010 period. This additional data included the 

following. 

• Particulate matter data from IMPROVE monitoring stations in the area.32 This 

data includes 24‐hr concentration data for ions (SO4= and NO3‐

) and a variety 

of elements contained in fine particulate matter at the IMPROVE monitoring 

stations at Bryce Canyon National Park (IMPROVE site identifier BRCA1), 

Canyonlands NP (CANY1), Capitol Reef NP (CAPI1), Grand Canyon NP (at 

Hance Camp on the rim, GRCA2, and at Indian Garden within the canyon, 

INGA1), and Petrified Forest NP (PEFO1). The data were of normal IMPROVE 

quality, except that they had not undergone a final review.  

• Calculations of atmospheric stability indices using meteorological data inputs 

from the EDAS data archive33 of the National Oceanic and Atmospheric 

Administration (NOAA). Calculations were carried out using the web‐based 

Real‐time Environmental Applications and Display sYstem (READY) of the 

NOAA Air Resources Laboratory (ARL). 

In the analyses in this section the following questions are addressed: 

1. How do the measured concentrations reflect changing atmospheric

conditions during the course of the study? 

2. What geographic areas do the results of the SRP monitoring program 

represent? 

3. What do the measurements imply about the abundance of background 

NH3 and its involvement in atmospheric chemical reactions at the 

monitoring sites, both on average and episodically? 

                                                       

32  Courtesy of the IMPROVE particulate monitoring team at the University of California at Davis. 

33 EDAS (Eta Data Assimilation System) creates representations of meteorological conditions 

throughout the country by “Optimum Interpolation” analyses of successive 3‐hr forecasts produced 

by the NOAA Eta meteorological forecast model. 
47

Seasonal Variation – Two Measurement Periods

The timelines of measured concentrations in Figure 11 through Figure 14 reveal 

that most of the concentrations show two patterns over the 5‐month monitoring 

period:

  

• A long term pattern in which averages over many 6‐day sampling periods 

tend to be stable or vary slowly from month to month; and 

• Short episodes of one or two sampling periods duration that are associated 

with simultaneous high concentrations of two or more species. Two notable 

examples are the sampling periods beginning on 10 January and 28 

January.34

Similar behavior is also apparent in the wind speed plot in Figure 17. 

The long‐term pattern shows some important changes during the course of the 5‐

month monitoring. The concentrations of some chemical species, their relationships 

to the concentrations of other species, and differences in some relative 

concentrations between sampling sites tend to be different in the early months of 

the study than in the final months. The change in long‐term behavior appears to be 

most rapid about the middle of February, which suggests a transition in atmospheric 

conditions occurring around that time.  

Specifically, beginning around mid‐February, fine particulate ammonium (NH4+) and 

nitrate (NO3‐) ion concentrations decrease appreciably and vary less from sampling 

period to sampling period and from site to site than they did earlier. Other species 

(gaseous nitric acid, HNO3; sulfur dioxide gas, SO2; and fine particle sulfate, SO=

4 ) 

also show more consistency from site to site after this time. The only species that 

does not display as clear a dichotomy is ammonia gas (NH3), for which the Gray 

Mountain and Moenkopi concentrations are always considerably greater than those 

at Hall’s Crossing, although the difference decreases toward the end of the study. 

Coincidentally, sampling began at Moenkopi in mid February (on the 15th), at the 

midpoint of the monitoring and at about the middle of these behavioral changes, so 

it is useful to divide the duration of the monitoring program into two periods and 

investigate them separately: 

• Period 1 – sampling at two sites (Hall’s Crossing and Gray Mountain) from the 

beginning of monitoring on 5 December through the sample beginning on 9 

February 

                                                       

34 Sampling dates refer to the first day of a six‐day (or, for the passive samplers, 18‐day) sampling 

period. 
48

• Period 2 – sampling at three sites (Hall’s Crossing, Gray Mountain, and

Moenkopi) from 15 February through the end of monitoring on 28 April, plus 

passive sampling of NH3 at these sites and three others. 

The meteorology differed between these two periods. As Table 8 shows, in addition 

to the expected warmer temperatures during Period 2 than Period 1, the air became 

drier (the RH decreased substantially) and the average wind speed measured by the 

monitoring site anemometers roughly doubled. 

Table 8. Averages and standard deviations (in parentheses) of hourly measurements

of surface meteorological variables for the full 5‐month duration of the monitoring

program and for Periods 1 and 2 individually.  

Variable Units

Hall’s Crossing Gray Mountain Moenkopi

5 mos Pd 1   Pd 2 5 mos Pd 1 Pd 2 Pd 2*

Temperature °C 6.1 1.3 10.6 4.7 0.2 8.7 11.8

    (6.8) (3.4) (6.1) (6.8) (3.9) (6.3) (6.2)

RH % 56 71 42 53 69 39 33

(24) (15) (23) (23) (14) (20) (19)

WS m/s 2.2 1.6 2.8 3.5 2.6 4.3 3.3

(1.8) (1.3) (2.1) (2.6) (1.9) (3.0) (2.7)

* Starting 5 March 2010

As Figure 21 shows, the surface wind direction patterns differed between Periods 1 

and 2. At Hall’s Crossing, the Period 1 wintertime propensity for winds from the 

southeast shifted to a less focused, more southwesterly tendency in Period 2. At 

Gray Mountain, similarly, a strong tendency for winds from the southwest during 

Period 1 became more diffuse, although the change in wind direction was slight. 
Figur

Hall’

Gray

e 21. Wind

s Crossing, P

y Mountain,

roses for Pe

Period 1

Period 1

eriods 1 and

49

2 at Hall’s C

Gray

Hall’

rossing and

y Mountain,

’s Crossing,

d Gray Moun

, Period 2

Period 2

ntain.
50

The depth of the atmospheric boundary layer is an indicator of the coupling 

between local conditions at the surface and the large‐scale atmospheric conditions 

aloft.  Figure 22 shows values of the daily maximum depth of the boundary layer 

(Zi), in meters, at the location of NGS, over the duration of the monitoring program, 

as estimated from EDAS simulations by the web‐based NOAA‐ARL READY system. 

Vertical lines indicate the beginning of each 6‐day sampling period; a starting date is 

given for every other sampling period. The red line marks the dividing point 

between the two halves of the monitoring program, Period 1 and Period 2.  

Figure 22. Daily maximum boundary layer depths (Zi, in meters) at NGS during the

monitoring program. The red line separates Period 1 and Period 2

Generally, a shallow boundary layer depth corresponds to stability at the surface 

and little mixing between the surface layer and the atmosphere aloft, while a deep 

layer reflects extensive vertical mixing with coupling to the mesoscale atmosphere 

aloft. Figure 22 shows a clear difference in boundary layer depths during the 

monitoring program. Period 1, the colder period, is characterized by daily‐maximum 

Zi values that are mostly relatively uniform with most values in the range of 300 ‐ 

800 m. In contrast, during Period 2, the increasing solar heating of the ground 

results in growing values of Zi,, from around 500 m in mid‐February to the vicinity of 

2500‐3000 m at the end of the monitoring program.  

In light of the atmospheric mixing implications of Figure 22, it is expected that the 

concentrations measured during Period 1 might represent relatively local 

conditions for an area with relatively flat topography, while those during Period 2 
51

would be indicative of more regional conditions. As discussed later in this section, 

although this is often true in the area of the SRP sites, the complex topography of the 

region makes the actual picture more complex than this simple representation, 

especially when the region of interest includes the surrounding Class I areas. 

   

Turning now to the concentration measurements, Table 9 summarizes some of the 

concentration measurements over the entire 5‐month measurement period and 

during Periods 1 and 2 separately. Both the mean measured concentrations and 

standard deviations of the six sampled species are shown.  Those data support the 

qualitative observations noted above about different conditions before and after 

mid‐February.  

Specifically, note the following in Table 9: 

• At both Halls Crossing and Gray Mountain, the average concentrations of SO2, 

NO3‐ and NH4+ were all less in Period 2 than in Period 1 and their standard 

deviations (which indicate sample to sample variability) were substantially 

less in Period 2 than in Period 1.  These are likely consequences of the 

increased atmospheric mixing, and consequent increasing atmospheric 

homogeneity, as the solar radiation increased from winter to spring.  

• At both locations, the average sulfate (SO=

4 ) concentration increased slightly 

from Period 1 to Period 2, accompanied with a substantial reduction in its 

standard deviation.  

• The average ammonia (NH3) concentration increased from Period 1 to Period 

2 at Hall’s Crossing, but decreased at Gray Mountain. In both cases, the 

standard deviations during Period 2 were substantially less than those in 

Period 1.  

• The average nitric acid (HNO3) concentration was the same during both 

periods at Gray Mountain (and the standard deviation remained essentially 

the same), while both the concentration and the standard deviation at Hall’s 

Crossing were less during Period 2 than during Period 1. 

Table 9 also reveals some of the differences in average concentrations between the 

three sampling sites. In general, NH3 and NH4+ concentrations at Gray Mountain and 

Moenkopi were greater than those at Hall’s Crossing, while the HNO3 concentration 

was greater at Hall’s Crossing than at the other two sites.  For all species, average 

inter‐site differences were smaller (usually much smaller) during Period 2 than 

during Period 1, which is consistent with the inference from Figure 22 that, for the 

geographic area monitored by the SRP sites, the concentration measurements in 

Period 2 would be more representative of regional conditions than those of Period 

1.  
52 

Table 9. Averages of 6‐day measurements of concentrations and their standard deviations (in parentheses), f

duration of the monitoring program and for Periods 1 and 2 individually.

Species Units

Hall’s Crossing Gray Mountain 

5 Months Period 1 Period 2 5 Months Period 1 Period 2 

HNO3(g) ppbV 0.21 0.25 0.18 0.17 0.17 0.17 

(0.15) (0.19) (0.09) (0.05) (0.05) (0.06) 

SO2(g) ppbV 0.08 0.11 0.06 0.06 0.08 0.05 

(0.06) (0.08) (0.02) (0.03) (0.04) (0.02) 

NH3(g) ppbV 0.22 0.17 0.27 0.63 0.76 0.53 

(0.08) (0.08) (0.04) (0.25) (0.31) (0.13) 

NO3‐ (p) μg/m3 0.25 0.39 0.12 0.34 0.58 0.16 

(0.23) (0.27) (0.04) (0.26) (0.24) (0.06) 

SO=4 (p) μg/m3 0.55 0.53 0.57 0.51 0.46 0.54 

(0.23) (0.30) (0.15) (0.21) (0.28) (0.15) 

NH4+(p) μg/m3 0.27 0.33 0.21 0.29 0.38 0.22 

(0.12) (0.14) (0.06) (0.12) (0.13) (0.06) 
53 

The major aspects of all of these spatial and temporal patterns are portrayed 

symbolically in the matrices in Table 10, which summarize the general inter‐site 

relationships of concentrations of the measured species during the two periods.  

Table 10. Spatial patterns of concentrations during the two halves of the NH3

monitoring program. Yellow cells indicate the predominant relationships.

a) Period 1 ‐‐ 5 December through 9 February

Notes:  1 Except Hall’s Crossing NO3‐ as much greater for 1/4 and 1/10 periods 

2 Hall’s Crossing HNO3 was much more than twice Gray Mtn. HNO3 for 1/4 and 1/10 

3 Gray Mountain NH4+ was more than twice Hall’s Crossing NH4+ for 1/22 

4 Except Gray Mountain SO2 was more than twice Hall’s Crossing SO2 for 1/28 

b) Period 2 ‐‐ 15 February through 28 April

Notes:  1 Gray Mountain HNO3 vs. Hall’s Crossing HNO3

2 Moenkopi HNO3 vs. Hall’s Crossing HNO3

3 Hall’s Crossing SO2 was greater than Gray Mountain SO2 more often than it was 

greater than Moenkopi SO2

In Table 10, the yellow cells marked “XX” indicate the predominant relationships 

between concentrations listed vertically along the left and sites listed across the top. 

Footnotes point out some notable exceptions to the general pattern. The following 

terminology is used to provide additional discrimination: 

Species Gray Mtn.

is Greater

Gray Mountain

Usually Greater

Variable or

Similar

Hall’s Crossing

Usually Greater

Hall’s Crossing

is Greater

NH3 (g) XX        

NO3

‐ (p)    XX1

      

HNO3 (g)     Similar2    

NH4

+ (p)     Variable3

    

SO=

4 (p)       XX   

SO2 (g)       XX4   

Species Gray Mtn. &

Moenkopi

are Greater

Gray Mountain

& Moenkopi

Usually Greater

Variable or

Similar

Hall’s Crossing

Usually Greater

Hall’s Crossing

is Greater

NH3 (g) XX       

NO3

‐ (p)    XX      

HNO3 (g)     Variable1 XX2   

NH4

+ (p)     Similar    

SO=

4 (p)     Similar    

SO2 (g)       XX3   
54

• “Greater” means that all samples during the period, with no more than one 

exception, had the indicated relationship; 

• “Usually Greater” means than more than 50% of the samples behaved as 

indicated; 

• “Variable or Similar” means that the sample‐by‐sample relative 

concentrations were not consistently greater at any one site, or that the 

concentrations at all the sites were similar most of the time. 

Broadly, the matrices in Table 10 show that, despite the different concentration 

levels in Periods 1 and 2 shown in Table 9, some general spatial relationships held 

throughout the monitoring program: 

• Nitrogen compounds tended to have higher concentrations at Gray Mountain 

and Moenkopi than at Hall’s Crossing, while sulfur compounds tended to 

have higher concentrations at Hall’s Crossing. This pattern is stronger during 

Period 1 than during Period 2; in fact, Table 9 showed that average 

concentrations for some species were very nearly the same at all three sites 

during Period 2.  

• None of the measured species consistently had highest concentrations at 

Hall’s Crossing during either period. Figure 11 through Figure 13 showed 

that Hall’s Crossing had the highest concentrations of all measured non‐

crustal species except NH3 in the 4 January and 10 January samples, though.  

• NH3 concentrations were consistently greatest at Gray Mountain. 

Additional general observations, by species, of temporal and spatial patterns over 

the two periods include the following 

• NH3: During both periods, ammonia gas at Hall’s Crossing was almost always 

less than at Gray Mountain and Moenkopi 

• NO3­

: More than half the time during each period, the nitrate ion 

concentration in particulate matter at Hall’s Crossing was less than that at 

Gray Mountain, and also at Moenkopi during its operation in Period 2. 

Notable exceptions occurred on 4 and 10 January, when Hall’s crossing NO3‐

was much greater than at Gray Mountain.
55

• HNO3: Except for the episodes that were reflected in the 4 January and 10 

January samples, the sample‐by‐sample nitric acid gas concentrations at 

Hall’s crossing and Gray Mountain were relatively similar throughout the 

study. (Because of the effect of the January episode(s), the average HNO3

concentrations for Period 1 in Table 8 are quite different for the two sites, 

however.) Moenkopi tended to have lower HNO3 concentrations than Gray 

Mountain, so their comparisons with Hall’s Crossing concentrations differed, 

in that more than half of the Period 2 samples showed Moenkopi HNO3

concentrations to be less than those at Hall’s Crossing. 

• NH4+: Ammonium ion concentrations during Period 1 were quite variable

and there is no obvious indication that one site had more ammonium than 

the other. The variability diminished greatly in Period 2, so that the 

comparability in ammonium concentrations (now among three sites) was 

now more clearly apparent.

• SO=

4 : Sulfate ion concentrations varied widely during Period 1 and the Halls 

Crossing concentrations were more than those at Gray Mountain in more 

than half of the samples. During Period 2 the sulfate concentrations varied 

less from sample to sample and tended to be quite similar at all three sites. 

• SO2: During Period 1, sulfur dioxide gas concentrations tended to be greater 

at Hall’s Crossing than at Gray Mountain, with the difference being 

particularly large in the 4 January and 10 January samples. These episodic 

events amplify the differences in the averages in Table 8. SO2 concentrations 

at all three sites were more similar during Period 2. In more than half of the 

samples the Hall’s Crossing concentrations of SO2 were slightly greater than 

those at Gray Mountain. Moenkopi SO2 concentrations often slightly 

exceeded those at Gray Mountain.

At both Hall’s Crossing and Gray Mountain, the average ambient temperature during 

Period 2 was about 9 °C warmer than during Period 1. Although an increase in 

temperature increases biological emission of ammonia, it also reduces the fraction 

of total nitrates that is in the form of particulate ammonium nitrate. The expected 

particulate NO3‐ behavior is shown in Table 8, where the NO3‐ concentrations are 

smaller during Period 2 than in Period 1. The picture with the ammonia 

measurements is not as clear cut; the ammonia concentrations at Hall’s Crossing do 

indeed increase as expected from Period 1 to Period 2 ‐‐ but the Gray Mountain 

ammonia concentrations decrease relative to Period 1, perhaps because increased 

ventilation and mixing offsets the natural tendency for greater emissions.

In summary, the following observations can be made concerning the seasonality of 

the measurements: 
56

• The duration of the monitoring program encompassed two distinct

meteorological and air quality regimes, with a transition around mid‐

February: Period 1, a typically colder period with little systematic change in 

conditions, and Period 2, a period of increasing warming and atmospheric 

activity. 

• During Period 1, the concentrations of several chemical components were 

substantially different at Hall’s Crossing than at Cameron and Moenkopi. The 

differences were much smaller during Period 2.  

• The relative mix of the measured chemical components of the aerosol 

differed considerably between the first and second halves of the duration of 

the monitoring. 

Period 1, with its lower temperatures and much higher nitrate concentrations, is the 

more typical of the winter atmospheric conditions that are most relevant to the 

monitoring program objectives expressed in Section A‐2.  

Spatial Relationships

One of the considerations that influenced the design for the SRP monitoring 

program was the assumption that, because of the assumed absence of major NH3

sources, the ambient concentration of NH3 would be relatively uniform spatially. 

Since the measurements showed that there were some differences in concentrations 

between the Gray Mountain and Hall’s Crossing sites, this subsection is devoted to 

evaluating the extent of the geographic area represented by the measurements at 

each sampling site location. 

In the idealized case of an absence of sources and sinks, and with meteorology that 

is uniform spatially, one would expect the concentration of an inert atmospheric 

constituent would be relatively uniform and independent of location in the study 

area, although there could be some differences resulting from altitude. Deviations 

from these assumptions would result in different concentrations at different 

locations.  

Figure 23 explores this concept for the period and locations of the SRP monitoring 

program, using total oxidized sulfur (the sulfur in SO2 + SO=

4 ) as an inert marker of 

atmospheric concentrations. Figure 23 shows that the total S concentrations were 

indeed the same, or nearly so, at all of the monitoring locations for more than half of 

the sampling periods. This suggests that it was likely that other air pollutant 

concentrations would also be relatively uniform over the area at those times, a 

postulate that can be readily checked against the concentrations that were plotted in 

Figure 11 and Figure 13.  This check reveals that the sample periods that had similar 

Total S concentrations at all sites in Figure 23 also generally showed similar inter‐

site agreement for HNO3, SO2, NH4+ , and SO=

4 . The NH3 and NO3‐

 concentrations did 
57

not display the same level of agreement, though, which suggests that local emissions 

may be playing a role.   

Figure 23. Comparison of “total” sulfur (SO2 + SO=

4 ) concentrations, in units of μg/m3

of S, at the 3 sampling sites.

A larger geographic region can be considered by including the IMPROVE 

measurements in this analysis. As mentioned at the beginning of this section, PM2.5

chemical composition data was obtained for six IMPROVE sites in the area 

surrounding NGS. IMPROVE does not measure SO2, so the same Total S indicator 

cannot be used.  Instead, the SO=

4 concentration alone has to be relied upon as an 

indicator, despite that it is somewhat less stable because it can reflect varying 

degrees of conversion of SO2. As the analysis below shows, for the geographic area 

of the SRP monitoring study, the information that can be deduced about inter‐site 

differences from a plot of SO=

4 is virtually identical with what can be learned from 

the plot of Total S in Figure 23, so the SO=

4 concentration is likely to be adequate for 

the purposes of this analysis. 

Some adjustments are needed in order to include both the SRP monitoring and 

IMPROVE sites in the same analysis. Since IMPROVE makes 24‐hr measurements 

every 3rd day while the SRP study collected 6‐day samples, comparison of SO=

4  

measurements by the two network will be somewhat approximate, with the average 

of the two IMPROVE measurements (on the 2nd and 5th days of the SRP sampling 

cycle) compared against the 6‐day average of the SRP measurements. After these 

adjustments, Figure 24 and Figure 25 show the results for SO=

4  at the SRP sites and 

three nearby IMPROVE sites respectively. 
58

Figure 24, which is indeed very similar to the plot of total sulfur in Figure 23, shows 

that the SO=

4 concentrations at the three SRP sites were well correlated. Figure 25 

leads to the same conclusion for three IMPROVE sites in the vicinity of the SRP sites. 

Furthermore, the similarity of Figure 24 and Figure 25 suggests a correlation 

between the SO=

4 concentrations at SRP sites and those at IMPROVE sites.35

The degree of such correlation was investigated, taking Period 1 and Period 2 

separately. First the relationships between the IMPROVE sites was considered. 

During Period 1, there was a pattern of high inter‐site correlations of SO=

4  

concentration (r > 0.81 over the duration of the period) at the IMPROVE sites 

extending from Bryce Canyon (BRCA1) in the northwest to Indian Garden (INGA1) 

and Hance Camp (GRCA2) at the Grand Canyon36 and on to Petrified Forest (PEFO1) 

in the southeast (see Figure 2 for IMPROVE site locations). Even the correlation 

between the two ends of this string of sites, BRCA1 and PEFO1, was 0.82 (although 

the SO=

4 concentrations at BRCA1 averaged 64% of the PEFO1 values). These high 

correlations suggest a consistency in the general pattern of variation of SO=

4  

concentrations in the region from BRCA to PEFO1, a region that encompasses the 

area of SRP’s Gray Mountain monitoring site. 

There was also a separate region of very high correlation (r = 0.91) between two 

sites to the north ‐‐ Capital Reef (CAPI1) and Canyonlands (CANY1) ‐‐ well removed 

from the locations of SRP’s measurements. 

The pattern differed in Period 2, when the highest SO=

4  correlations were in two 

separated clusters of sites: BRCA1 and Capitol Reef (CAPI1) with r = 0.88, and 

INGA1 and GRCA2 at the Grand Canyon correlated with PEFO1 with r = 0.78 and 

0.83, respectively. There was no longer any correlation between BRCA1 and the two 

Grand Canyon sites, with correlation coefficients of 0.22 and under – even though 

the average concentrations at all three locations were quite similar, in the relatively 

narrow range from 0.45 to 0.54 μg/m3.  The three‐site concentration patterns 

during Period 2 in Figure 25 aren’t as congruent as those of the more compact SRP 

network were in Figure 24, most likely because of the long inter‐site distance 

between PEFO1 (Petrified Forest) and the Grand Canyon sites, and the very large 

elevation difference between GRCA2 (Hance Camp) and INGA1 (Indian Garden).  

                                                       

35 Note that the vertical scales are the same in Figure 23 and Figure 24, so the difference in 

magnitude between Total S and SO=

4  is readily apparent. The scale is also the same in Figure 25,

enabling comparison of the SRP and IMPROVE site SO=

4  data 

36 The INGA1 and GRCA2 sites are close together on the map, but at very different elevations. INGA1 

is located within the Grand Canyon at an elevation of 1166 m and GRCA2 is located on the rim at an 

elevation of 2267 m – an elevation difference of 1101 m (3611 ft). During Period 1, the correlations 

between the SO=

4 concentrations at the two sites were 0.87, while during the more strongly mixed 

Period 2 they were 0.98. 
59

Figure 24. Comparison of 6‐day average particulate sulfate ion (SO=

4 ) concentrations,

in units of μg/m3 of S, at the 3 SRP sampling sites.  

Figure 25. Comparison of particulate sulfate ion (SO=

4 ) concentrations of consecutive

sample pairs, in units of μg/m3 of S, at 3 IMPROVE sampling sites near the SRP

network.  
60

Adding the SRP sites to this discussion, during Period 1, the Hall’s Crossing and Gray 

Mountain SO=

4  concentrations were highly correlated, r = 0.94. The high correlation 

does not necessarily indicate the concentrations at all locations were identical, but 

rather that they tended to vary proportionally. For example, the average Period 1 

SO=

4  concentration of 0.59 μg/m3 at Hall’s Crossing differed from the 0.46 μg/m3 at 

Gray Mountain. During Period 1 both the Hall’s Crossing and Gray Mountain SO=

4  

measurements also correlated very well with the average concentrations from 

IMPROVE samples at INGA1 (r = 0.88 for Hall’s Crossing and r = 0.93 for Gray 

Mountain). Measurements at both SRP sites also correlated well with measurements 

at PEFO1 (r = 0.82 for Hall’s Crossing and r = 0.80 for Gray Mountain).  

During Period 2, the correlation between Hall’s Crossing and Gray Mountain SO=

4  

concentrations was 0.84 vs. a correlation of 0.94 in Period 1. The average Period 2 

SO=

4  concentrations at Hall’s Crossing, Moenkopi, and Gray Mountain were within 

about 5% of each other, at 0.59, 0.55, and 0.56 μg/m3, respectively. Noting that 

concentrations at all three sites are nearly identical throughout Period 2, it appears 

that the smaller correlations in Period 2 than in Period 1 are a consequence of the 

smaller dynamic range of concentrations during Period 2. In particular, those 

smaller correlations do not imply larger differences in concentrations during Period 

2.  

As for the relationship between SRP and IMPROVE SO=

4  measurements during 

Period 2, the correlations between Gray Mountain and PEFO1, GRCA2, and INGA1 

SO=

4  measurements were all between 0.71 and 0.73. As with the SRP inter‐site 

correlations, this range is less than the one for Period 1; the apparent looser linking 

of conditions that was observed between IMPROVE sites also manifested itself at the 

SRP sites. The Moenkopi correlations were similar to those of Gray Mountain.37 The 

correlations between Hall’s Crossing SO=

4 measurements and those at the same 

IMPROVE sites were low, however, in the range of 0.36 to 0.40. 

From this discussion about SO=

4 concentrations, as well as data presented earlier, it 

appears that the distribution of air pollutants over the region differed during 

Periods 1 and 2. Using SO=

4 concentrations as a surrogate for a regional air mass 

marker, Period 1, which was characterized by large differences in concentrations of 

SO=

4  from SRP sample period to sample period, nevertheless showed a high degree 

of regional consistency in behavior, exemplified by high correlation of 

concentrations over a large geographic band from Bryce Canyon to Grand Canyon to 

Petrified Forest, including the SRP Hall’s Crossing and Gray Mountain sites. Period 2 

showed a high degree of regional uniformity, with nearly identical SO=

4  

concentrations at the three SRP sites and nearly the same concentrations at the 

Grand Canyon and PEFO1 IMPROVE sites. 

As a final indication of the close relationships between the IMPROVE and SRP 

measurements of SO=

4 , Table 11 displays multivariate regression equations for the 

                                                       

37 The Moenkopi vs. Gray Mountain correlation during Period 2 was r = 0.996! 
61

SRP site concentrations, as expressed in terms of IMPROVE concentrations. For 

simplicity, each site name abbreviation represents the SO=

4 concentration at that site. 

These equations were derived by stepwise regression with the SO=

4 concentrations 

at all 6 IMPROVE sites considered as independent variables. The process was 

terminated when adding another site did not offer an incremental improvement in 

fit above a selected threshold or when an IMPROVE concentration appeared with a 

negative coefficient. Interestingly, the stepwise regression process in each of the 

four cases in Table 11 ended with concentrations from only a single site. (A fit for 

the entire study period at either site involved multiple sites, however.) In Table 11, 

R2 is the coefficient of determination, which represents the fraction of the variability 

in the SRP site concentration that is explained by the formula,38 and RMSE stands for 

the RMS error. As has been the practice for this discussion, all SO=

4 concentrations 

are expressed in terms of μg/m3 of sulfur. 

Table 11. Results of stepwise regression modeling of SRP site SO=

4 concentrations in

terms of IMPROVE site concentrations

Stepwise Regression Formula for SO=

4   R2

RMSE,

μg/m3 S

Hall’s Crossing      

   Period 1 HC = ‐0.04 + 1.11 INGA1 0.77 0.16

   Period 2 HC = 0.14 + 0.28 CANY1 0.29 0.12

Gray Mountain     

   Period 1 GM = ‐0.03 + 1.25 INGA1 0.85 0.13

   Period 2 GM = 0.11 + 0.43 INGA1 0.37 0.09

    

The relationships in Table 11 are surprisingly simple and, for Period 1, surprisingly 

strong. For both Hall’s Crossing and Gray Mountain in Period 1, there are strong 

linear relations with the SO=

4  concentration at INGA1, which explain, respectively, 

77% and 85% of the concentration variation at these SRP sites. The coefficients are 

1.11 and 1.25, so the concentrations at the SRP sites are a little greater than those at 

INGA1. Note, though, that the RMSE is comparable to the concentration values 

themselves. 

                                                       

38 The interpretation of R2 and r can be confusing. The quantity R2 represents the coefficient of 

determination, which indicates what fraction of the variation in the dependent variable (the SRP site 

concentration) is accounted for by the regression model. In the case of a linear regression with only 

one independent variable, the R2 for the linear regression line is numerically equal to the square of 

the linear correlation coefficient (which we have written in this section with a lower case r) between 

the variable being compared. In Table 11, since all of the equations are linear, the R2 there equals r2, 

where the values of r have been discussed previously in this section. 
62

The relationships are much weaker in Period 2, explaining only 29% or 37% of the 

SRP site concentration.39 Note that the primary IMPROVE site that relates to Hall’s 

Crossing during Period 2 is CANY1, which is geographically in the opposite direction 

from the INGA1 site of Period 1, a change that is consistent with the differing winds 

in the two periods along the Colorado River channel. The average Period 2 

concentrations are identical at Hall’s Crossing and CANY1, however, at 0.20 μg/m3

of S. At the Gray Mountain site, which is distant from the Colorado River channel, 

INGA1 remains the primary IMPROVE site related to it in Period 2, but it only 

explains 37% of the variation in the SO=

4  concentration at Gray Mountain. Here again 

the average SO=

4  concentrations at the two locations were nearly the same; 

0.19 μg/m3 of S at Gray Mountain vs. 0.18 at INGA1.  

In summary, this multifaceted examination of relationships between SRP and 

IMPROVE sites, using the relatively stable SO=

4  as a surrogate for a inert marker, 

yields the conclusions that the 6‐day air samples taken at the Hall’s Crossing and 

Gray Mountain sites (as well as Moenkopi) are closely linked to the atmosphere at 

nearby Class I areas, especially PEFO1 and INGA1, respectively. The linkage is 

stronger during Period 1, when conditions often changed dramatically from one 6‐

day sampling period to the next as a result of weather systems. These strong 

meteorological forcing functions play a much smaller role in Period 2, as solar 

insolation increases vertical mixing. As a result, the concentrations tend to change 

less from one sample to the next and correlations between changes at the sites are 

weaker, while the concentrations are more similar over all these sites.  

Chemistry of the Aerosol

The measured concentrations of the various ions can provide insight into the nature 

of the atmospheric aerosol at the time of sampling. For this purpose, concentrations 

will be expressed in terms of equivalents, i.e., the formula weight associated with a 

single charge, and consider the concept that one equivalent of a cation (positive ion) 

combines with one equivalent of an anion (negative ion).40 The analyses will be 

limited to the SRP sites because the IMPROVE program does not measure all the 

ions of relevance. 

Charge Balance. First, consider the charge balance of the ions that were sampled 

during the SRP program. For an electrically neutral aerosol, the sum of the cations 

(measured in equivalents) should equal the sum of the anions. The measured 

cations were Na+, NH4+, K+, Mg2+, and Ca2+. The measured anions were Cl‐

, NO3‐, and 

SO42‐. Figure 26 shows that the sum of cations equaled or exceeded the sum of 

anions for every sample taken during the study. In other words, the combination of 

the sampled ions is neutral at times and positively charged most of the time. 

                                                       

39 The slightly different Multiple Least Squares regression process produces an equation that 

includes small contributions from other sites, which results in slightly greater R2 for both sites.  40 See Footnote 26 for an explanation of equivalents. 
63

Although Figure 26 is for the entire study period, separate plots for Period 1 and 

Period 2 show similar behavior.  

The charge imbalance shown in Figure 26 is not surprising, since several crustal 

cations were measured, but Cl‐

 was the only measured crustal anion. If additional 

negative ions were to be measured, it is likely that Figure 26 would show charge 

neutrality. 

Figure 26. Charge balance of the measured ions.

If the focus of the charge balance evaluation is narrowed to only the non‐crustal 

components NH4+, NO3‐

, and SO=

4  that make up the ammoniated sulfate and 

ammonium nitrate of interest here, the charge balance can be represented by the 

ratio of the sum of sulfate and nitrate anion equivalents to ammonium cation 

equivalents, i.e.,  

Ion Ratio = {[SO=

4 ] + [NO3­

]}/[NH4+],  (1) 

where the square brackets denote concentration in equivalents. 

 If the Ion Ratio is exactly unity, this suggests that the combination of sulfates and 

nitrates is fully neutralized by NH4+, implying that their chemical forms are 

ammonium sulfate, (NH4)2SO4, and ammonium nitrate, NH4NO3.  
64

Figure 27 shows the variation of the Ion Ratio with time over the five months of the 

SRP study. The Ion Ratio is generally less than unity until about the end of February 

and then increases rapidly in March to a value of about 1.3, where it remains for the 

last month of the study. The behavior at all sites followed this pattern, although the 

Ion Ratio at Hall’s Crossing was often a bit higher than the Gray Mountain and 

Moenkopi values. 

Figure 27. Temporal variation in the Ion Ratio = {[SO=

4 ] + [NO3

‐

]}/[NH4

+

] over the study

period.

To interpret this behavior, a value of [Ion Ratio] < 1, suggests that NH4+ is paired 

with the sulfates and nitrates, but there is still some NH4+ remaining, presumably in 

some other compound, perhaps a salt of an organic acid. On the other hand, a value 

of [Ion Ratio] > 1 means that there is insufficient NH4+ to pair with the total of the 

sulfates and nitrates that are actually present, so some other cation besides NH4+

has reacted with some of the remaining nitrates. Since these opposing situations of 

“remaining NH4+” and “excess NO3‐

” cause the NH4+, NO3‐, and SO42‐ system to 

respond differently to changes in NOx or NH3 concentrations in winter and in spring, 

they warrant further discussion. 

Excess NO3

‐

. When both sulfate and nitrate ions are present in the atmosphere, the 

ammonium ions react preferentially with sulfate; and then any ammonium left over 

is available to react with the nitrate. Mathematically, that left over ammonium is 

represented by the following formula” 
65

[Leftover NH4+] =[NH4+] – [SO4=],  (2) 

Ambient ammonium and sulfate concentrations were measured during the 

monitoring program, so the leftover ammonium can be calculated for each sampling 

period by using Equation 2. 

This leftover ammonium is available to react with nitrate to form ammonium 

nitrate. By reasoning analogous to that used above, the amount of any of the 

measured nitrate that is left over after reactions between the nitrate ions and 

ammonium ions have gone to completion can be calculated. This left over or 

“excess” nitrate is then 

[Excess NO3­

] = [NO3­] – [Leftover NH4+].  (3) 

The Excess NO3­

 concentration is the difference (in equivalents) between the 

measured NO3‐ concentration and the amount of NO3‐ that could have been 

produced with the ammonium left over after the formation of sulfates was 

completed. The above two formulas can be combined to give a formula for 

calculating Excess NO3­ from the measured concentrations, 

[Excess NO3­

]   = [NO3­] ­ {[NH4+] ­ [SO=

4 ]} (4)

    = [NO3­] + [SO=

4 ] ­ [NH4+].

Note that the right hand side of Equation 4 contains all the terms of both the 

numerator and denominator of the Ion Ratio. Consequently, if Ion Ratio > 1 then 

[Excess NO3­] > 0, and if Ion Ratio < 1 then [Excess NO3­] < 0. Hence Ion Ratio and 

[Excess NO3­

] can be considered two sides of the same coin. 

Both the average Ion Ratio and the average amount of Excess NO3­ are tabulated in 

Table 12 for the full 5‐month study period and for Periods 1 and 2 separately. The 

standard deviation values noted in brackets are a measure of the sample‐to‐sample 

variability of these variables during each period, and not a measure of the 

uncertainty of a given measurement or set of measurements. Note that, during 

Period 1 the average Ion Ratio was less than unity, as plotted in Figure 27 and the 

average [Excess NO3­

] was negative at the two sites that were operational then. In 

contrast, during Period 2 the average Ion Ratio was greater than unity and [Excess

NO3­] was positive (actually greater than +1) at all three sites.  
66

Table 12. Values of Ion Ratio and [Excess NO3

‐

] and their standard deviations (in

parentheses) during the two seasonal periods.

Variable

Hall’s Crossing Gray Mountain Moenkopi

5 mos Pd 1 Pd 2 5 Mos Pd 1 Pd 2 Pd 2

Ion Ratio 1.08 0.94 1.20 1.05 0.90 1.16 1.16

(0.18) (0.05) (0.18) (0.19) (0.05) (0.18) (0.19)

[Excess NO3

‐

],   0.24  ‐0.95 1.33  ‐0.15  ‐2.01 1.28 1.62

neq/m3 (1.70) (1.29) (1.25) (2.30) (1.16) (1.89) (2.24)

Figure 28 shows the temporal variation of [Excess NO3­] and provides more detail 

concerning the sample‐to‐sample variation at each site. As with almost all other 

variables measured in this study, the graph lines oscillate extensively and somewhat 

independently during the first part of the study and then become smoother and 

more consistent toward the end of the study. In Figure 28, all but two 6‐day values 

of excess NO3‐

 are negative from the initiation of monitoring up to the March 5th

sampling period. After that time, the 6‐day excess NO3­

 values are all consistently 

positive. 

Figure 28. Values of [Excess NO3

‐

] for each sample during the monitoring program.

For most of the samples in Period 1, [Excess NO3­

] was negative (and equivalently, 

Ion Ratio < 1). During those times there was sufficient NH4+ in the atmosphere to 
67

fully react with all the available NO3‐ and SO=

4 , thereby forming ammonium nitrate 

and ammonium sulfate, and yet still have a surplus of NH4+ left over. The chemical 

form of the compound that contains the surplus NH4+ is unknown. Organic acid (e.g., 

oxalate) ammonium salts are a possibility, since organic acids are widely emitted by 

vegetation and can be in condensed form in cold weather, but their presence was 

not measured in this monitoring program.   

A positive value of [Excess NO3­

], which is the case for all of Period 2 except the 

beginning (and for the Hall’s Crossing sample of 1/28 in Period 1) means that there 

was insufficient NH4+ to react with all of the measured sulfates and nitrates and so

some of the measured NO3‐ must have been in some other form than ammonium 

nitrate. An analysis below will suggest that crustal nitrates are the likely form of the 

additional nitrates.  

In summary, the results in Table 12 imply that, on average,41 the formation of 

nitrates during Period 1 was not limited by the availability of ammonia – typically 

there appears to be sufficient ammonia/ammonium to fully account for the 

measured particulate nitrate.42 In contrast, on average, during Period 2 the nitrate 

formation would have been limited by the availability of ammonium, except that (as 

discussed below) crustal cations appear to have stepped in to enable further 

production of nitrate compounds. 

The nitrate compounds formed in reactions with crustal material have a much 

smaller effect on visibility that does ammonium nitrate. First, much of the crustal 

material with which gaseous HNO3 reacts is sitting on the ground (in fact, it is the 

ground) and has no effect on visibility at all. Furthermore, the small amount of 

crustal material in the air, picked up by the wind, is composed of coarse particles 

that are nearly all larger than the 2.5 μm upper cut point of the fine particle 

samplers used in this study. The “tail” of their size distribution extends into sizes 

smaller than 2.5 μm, though, and the concentration in that tail is what was sampled 

during the SRP study.   

The IMPROVE algorithm, which is used by the EPA and NPS to determine light 

extinction from particle component measurements, calculates the total light 

extinction by adding together the contributions of ammonium sulfate, ammonium 

nitrate, organic matter, light‐absorbing carbon, and soil, all of which are in the fine 

                                                       

41 Note that the standard deviations of the [Excess NO3

­

] averages in Table 12 are quite large during 

both of the two periods, so not every sample will conform to the average pattern. 

42 The reactions that form ammonium from ammonia are reversible, so often the terms can be used 

interchangeably in the discussion. 
68

size fraction, plus the contribution of coarse matter.43 Each component contribution 

to extinction is calculated by multiplying its concentration by a factor. For 

ammonium nitrate the factor depends on relative humidity, but in practice ranges 

from 3.0 to over 10, while the factor for fine soil (i.e., fine crustal matter) is 1.0. This 

means that fine crustal matter produces light extinction that is mostly in the range 

of 10% to 33% of the extinction caused by an equal mass of ammonium nitrate.  

That contribution is further tempered when considering an increase in light 

extinction due to the formation of crustal nitrates because the mineral portion of the 

crustal mass (silicon, aluminum, iron, potassium, etc.) is naturally present and the 

additional light extinction contribution due to the formation of crustal nitrates is no 

more than the portion attributable to the nitrate that was added to the crust. 

Although coarse matter was not measured in this study, the effect on extinction by 

the coarse crustal nitrates is even smaller, because the coarse matter factor in the 

IMPROVE algorithm is 0.6. 

Therefore, any crustal nitrates will have a minute effect on visibility compared to 

ammonium nitrate because (1) the crustal nitrates are mostly on the ground; (2) 

those crustal nitrates that do become airborne contribute much less to light 

extinction than an equal mass of ammonium nitrate; and (3) the contribution of the 

crustal nitrates to increasing the light extinction is only due to the portion of the 

crustal mass that is nitrate. 

Effect of Different NH3 Concentrations. Now, how would the situation differ if the 

ambient ammonia concentration differed? The result will differ depending on 

whether the new NH3 concentration is less or more than the original 

concentration44 and whether the new [Excess NO3­] is positive or negative.45

In the case of negative [Excess NO3­

], where there is a surplus of NH4+, if the new 

condition involves an increase in NH3, this means that there will be even more NH4+

left over after completing the formation of ammonium sulfate and ammonium 

nitrate, so there will be no further change in the concentrations of ammonium 

sulfate and ammonium nitrate. If the new condition has less NH3, the result will 

depend on the size of the NH3 concentration difference relative to the amount of 

                                                       

43 For simplicity, the discussion here is based on the original IMPROVE algorithm. A new algorithm 

developed a few years ago is based on the same principles, but the multipliers for the concentrations 

are more complex. It includes terms for light scattering by sea salt and light absorption by NO2 gas, 

neither of which is an important contributor to light extinction in the geographic area studied here. 

For purposes of this discussion, there are no material differences between the original and new 

algorithms. 

44 The discussion here will be limited to ammonia concentration differences that are small enough 

that they don’t change the sign of [Excess NO3

­

]. Also, large differences in NH3 concentration may 

bring other factors into play that could limit the resulting difference in the ammonium nitrate 

particle concentration. 

45 The discussion here assumes a steady state situation.  
69

surplus NH4+. If the ammonia concentration reduction is small enough that some 

surplus NH4+ still remains,46 then the same situation of unchanged ammonium 

sulfate and nitrate will still exist. An NH3 concentration that is so low that there is no 

surplus NH4+ changes the state of the system to one where [Excess NO3­] has changed 

sign and is now positive. 

In the case of positive [Excess NO3­

], where there are some crustal nitrates, an NH3

concentration that is less will result in a reduced NH4+ concentration, which in turn 

will result in a smaller amount of ammonium nitrate particles. As a consequence, the 

[Excess NO3­

] will be greater and there may be more crustal nitrate particles. On the 

other hand, a larger concentration of NH3 will mean that more ammonium nitrate 

particles are formed, which means there will be less nitrate available to pair up with 

crustal matter and so the amount of crustal nitrates will be less. Note that this case 

of positive [Excess NO3­] is the only situation where different NH3 concentrations 

could result in parallel changed ammonium nitrate particle concentrations. 

Let us look at the implications of the above discussion on a site‐by‐site basis. At 

Hall’s Crossing during Period 1, Table 9 showed slightly greater average measured 

sulfate concentrations and noticeably lower nitrate concentrations there than at 

Gray Mountain, along with much lower ammonia gas concentrations and somewhat 

greater nitric acid gas concentrations. The Ion Ratio < 1 and negative [Excess NO3 ]

values in Table 12 suggest that, on average, there was sufficient ammonium in the 

atmosphere at Hall’s Crossing to form ammonium nitrate from all of the available 

oxidized nitrogen during Period 1. If more ammonia were to be present there, it is 

unlikely that more ammonium nitrate than the amount currently measured would 

be formed. Reducing the ambient ammonia concentration will also have no effect on 

current nitrate concentrations until [NH4+] is reduced sufficiently to eliminate the 

negative [Excess NO3­

].   

The average [Excess NO3­] value at Gray Mountain is even more negative than that at 

Hall’s Crossing, which implies that the availability of ammonia there is even greater 

relative to the sulfate and nitrate concentrations, and therefore the current nitrate 

concentration there is even more immune to any differences in ammonia 

concentration. (This is another indication that the Gray Mountain ammonia 

concentrations may include a contribution from local sources.) 

During Period 2, on average, [Excess NO3­] is greater than unity at all three sites. In 

this situation the ammonia that was present in the atmosphere would have been 

insufficient to pair with all the nitrate and so some other cations were stepping in to 

partially fill the void. For this case, the excess NO3 in Table 12 is the amount of 

particulate nitrate (in neq/m3) that was formed by other means than reaction with 

ammonia. In this situation, when the formation of ammonium nitrate is limited by 

the availability of ammonia, a change in the ammonia concentration is likely to 

result in a similar change in the ammonium nitrate concentration.  

                                                       

46 NH3 gas and the NH4+ ion are in equilibrium in a reversible relationship. 
70

Contribution of Crustal Ions. The inference that the cations that contribute to [Excess

NO3­

] in the latter portion of the monitoring period are crustal in origin is based on a 

strong correlation between [Excess NO3­

] and the sum of the two crustal cations, 

Mg2+ and Ca2+, as shown in Figure 29. Negative values of [Excess NO3­

] are associated 

with low concentrations of the crustal cations while positive values of [Excess NO3­

]

are mostly associated with larger concentrations of the crustal cations. Since Figure 

14 showed that the crustal cation concentration is small during Period 1 and 

becomes large during the course of Period 2, Figure 29 leads again to the conclusion 

[Excess NO3 ] was negative early in the study and was positive late in the study. 

Figure 29. Demonstration of correlation between [Excess NO3

‐

] and crustal ion

concentrations (in neq/m3

). The solid lines are linear regression lines; the

corresponding R2 values are noted next to the site names.
71 

A‐5. CONCLUSIONS AND POTENTIAL FUTURE WORK

The following points summarize the key findings from SRP’s ammonia monitoring 

program: 

• The average ammonia concentrations measured at the SRP sites during the 

monitoring program from December through April were 0.22 ppb at Hall’s 

Crossing and 0.63 ppb at Gray Mountain. The average at Moenkopi was 0.45 ppb 

during its shorter period of operation from mid‐February to the end of April. 

• The SRP monitoring program spanned two distinct meteorological and air 

quality regimes: 

o Period 1. A winter period (from the beginning of December to February 

15th) with cold temperatures, shallow boundary layer depth, and large 

changes in conditions from one sampling period to the next driven by 

changing weather systems; and 

  

o Period 2.  A transitional spring period (from February 15th to the end of 

April) with increasing temperatures and boundary layer depths, more 

vigorous atmospheric mixing, and less variation from one sampling 

period to the next.  

• Consistently lower background ammonia concentrations were measured at the 

Hall’s Crossing site than at the Cameron area sites (Gray Mountain and 

Moenkopi). While it was not initially expected, this spatial variability is 

comparable to that observed in other monitoring studies, such as the Four 

Corners Area study.  

• Although the two Cameron area sites are separated by less than 5 miles, a 

consistent difference in measured ammonia values was observed between the 

two sites. This suggests that local sources could be affecting the measured 

values. Possible local sources could include animal grazing, highway vehicles, 

sewer lagoons, and septic tanks. 

• The sulfate concentrations measured at the SRP sites demonstrated a systematic 

relationship with those measured at the IMPROVE sites. This degree of 

consistent regional behavior was not observed in the ammonia measurements, 

suggesting the possible influence of local sources affecting the measured 

ammonia values.   

• On average, during Period 1, there appears to be more ammonia available than 

what would be needed to fully account for the measured particulate nitrate. On 
72

average, during Period 2, the nitrate formation would have been limited by the 

availability of ammonia, but crustal cations appear to have stepped in to enable 

production of additional nitrate compounds. Crustal nitrates have a minute 

effect on visibility compared to ammonium nitrate.  

• The sensitivity of the ammonium nitrate concentrations to differences in the 

ambient ammonia concentrations differs during the two periods. 

o During Period 1, an increased ammonia concentration is unlikely to result 

in any difference in the concentration of ammonium nitrate particles.  

o During Period 2, a higher ammonia concentration could result in a 

parallel increase in the ammonium nitrate particle concentration during 

the colder part of the period.  The magnitude of this increase will 

diminish as the ambient temperature increases and the formation of 

particulate ammonium nitrate is no longer favored. 

• The situations of excess ammonia in much of Period 1 and excess nitrate in much 

of Period 2 represent chemistry that is not reflected in the simplified nitrate‐

formation modules of the CALPUFF system.  Therefore, the model cannot 

simulate similar conditions. More advanced representation of ammonia‐nitrate 

chemistry, such as that described in Section B‐4 in Part B of this report, has the 

potential of producing more realistic nitrate estimates. 

The findings described above compel the following conclusions: 

• The higher ammonia concentrations measured at the Cameron area sites may be 

affected by local sources, and therefore may not be representative of background 

ammonia concentrations on the Colorado Plateau.   

• The balance of ammonium and nitrate concentrations observed in the 

monitoring program cannot be simulated by the simplified nitrate‐formation 

modules in CALPUFF. Improvements to these modules could result in more 

realistic nitrate estimates. 

• In order to determine regionally representative background ammonia input 

values for use with an improved CALPUFF model, SRP recommends the initiation 

of a collaborative monitoring program using a network of passive samplers. The 

passive samplers were shown to correlate well with the URG samplers, and can 

be deployed at remote locations at a relatively low cost. 
73 

PART B 

VISIBILITY MODELING IMPLICATIONS 
74

B‐1. BACKGROUND

Role of Background Ammonia in CALPUFF

Secondary pollutants such as nitrates and sulfates contribute to light extinction in Class I areas. 

The CALPUFF model was approved by EPA for use in BART determinations to evaluate the effect 

of these pollutants on visibility in Class I areas. CALPUFF uses the EPA‐approved MESOPUFF II 

chemical reaction mechanism to convert SO2 and NOx emissions to secondary sulfates and 

nitrates. This section describes how secondary pollutants, specifically nitrates, are formed and 

the factors affecting their formation, especially as formulated in CALPUFF. 

In the CALPUFF model, the oxidation of NOx to nitric acid (HNO3) depends on the NOx

concentration, ambient ozone concentration, and atmospheric stability. Some of the nitric acid is 

then combined with available ammonia in the atmosphere to form ammonium nitrate aerosol in 

an equilibrium state that is a function of temperature, relative humidity, and ambient ammonia 

concentration, as shown in Figure 30 (taken from the CALPUFF user’s guide).   

Figure 30. MESOPUFF II NOx Oxidation.

In CALPUFF, total nitrate (TNO3 =HNO3 + NO3) is partitioned into gaseous HNO3 and NO3

particles according to the equilibrium relationship between the two species. This equilibrium is a 

function of ambient temperature and relative humidity. Moreover, the formation of nitrate 

particles strongly depends on availability of NH3 to form ammonium nitrate, as shown in Figure 

31 (taken from CALPUFF courses given by TRC). The figure on the left47 shows that with 1 ppb of 

available ammonia and fixed temperature and humidity (for example, 275 K and 80% humidity), 

only 50% of the total nitrate forms particulate matter. When the available ammonia is increased 

to 2 ppb, as shown in the figure on the right, as much as 80% of the total nitrate is in the 

                                                       

47 A larger image of the left panel appears in Figure 2. 
75

particulate form. Figure 31 also shows that colder temperatures and higher relative humidity 

significantly favor nitrate formation and vice versa. A summary of the conditions affecting nitrate 

formation are listed below: 

• Colder temperature and higher relative humidity create favorable conditions to form 

nitrate particulate matter, and therefore more ammonium nitrate is formed; 

• Warm temperatures and lower relative humidity create less favorable conditions to form 

nitrate particulate matter, and therefore less ammonium nitrate is formed; 

• Sulfate preferentially scavenges ammonia over nitrates.  In areas where sulfate 

concentrations are high and ambient ammonia concentrations are low, there is less 

ammonia available to react with nitrate, and therefore less ammonium nitrate is formed. 

For the NGS BART analysis, the effects of temperature and background ammonia concentrations 

on the nitrate formation are the key to understanding the effects of various NOx control options. 

For the reasons discussed above, the periods of low temperatures are the most likely to be 

sensitive to ammonium nitrate formation.   

Figure 31. NO3/HNO3 Equilibrium Dependency on Temperature and Humidity.
76

Sensitivity of CALPUFF Predictions to Ammonia Concentration Input

Previous modeling performed by SRP indicated that wintertime background ammonia is the 

most important parameter affecting the degree of visibility improvement predicted by CALPUFF 

for a given BART control option. A more detailed discussion of this modeling analysis was 

included in SRP’s comments submitted on the EPA’s ANPR.48

In an independent analysis, the Colorado Department of Public Health and Environment 

(CDPHE) performed a sensitivity modeling analysis to explore the effect of the ammonia 

concentration input to CALPUFF on the predicted visibility impacts for a source with high NOx

emissions relative to SO2 emissions such as NGS.49 The results of the sensitivity modeling are 

shown in Figure 32. It is noteworthy that the largest sensitivity occurs for ammonia input values 

between 1 and 0.1 ppb. In that range, the difference in the peak visibility impacts predicted by 

CALPUFF is slightly more than a factor of 3 between ammonia concentration input values of 1 

and 0.1 ppb. This sensitivity analysis shows that the choice of background ammonia is very 

important in terms of the magnitude of visibility impacts predicted by CALPUFF. 

                                                       

48  Comments submitted by SRP to EPA on ANPR, Section 6.3, Pages 46‐49. 

49 Supplemental BART Analysis: CALPUFF Protocol for Class I Federal Area Visibility Improvement Modeling 

Analysis (DRAFT), revised June 25, 2010, available at http://www.colorado.gov/airquality/documents/Draft‐

ColoradoSupplementalBARTAnalysisCALPUFFProtocol‐25June2010.pdf.
77

Figure 32. CDPHE Plot of Sensitivity of Visibility Impacts Modeled by CALPUFF for Different

Ammonia Backgrounds.
78

B‐2. AMMONIA ASSUMPTIONS USED IN BART MODELING

SRP Ammonia Inputs

SRP submitted a BART analysis for NGS in December 2008. In this analysis, SRP used the 

CALPUFF model to assess the visibility improvement associated with each BART control option 

under consideration, in accordance with a modeling protocol approved by the EPA.50 For these 

simulations, SRP used the same background ammonia concentrations as those used in the 

previous PSD permit application for the Desert Rock Energy Facility (DREF) in northwestern 

New Mexico (“SRP’s ammonia inputs”).51 Recently, the New Mexico Environmental Department 

used the same ammonia concentrations in its BART determination for the San Juan Generating 

Station.52 SRP’s ammonia inputs are shown for reference in Table 13.  The average ammonia 

values measured in SRP’s monitoring program described in Part A of this report are also 

included in this table for reference. 

EPA Back‐Calculated Ammonia Inputs

In August 2009, EPA issued an ANPR in which the agency used an indirect “back‐calculation” 

method to infer higher background ammonia concentration estimates than those used by SRP.53 

EPA’s method involves a determination of the amount of ammonia that was involved in the 

formation of ammoniated sulfate‐ and nitrate‐containing particles measured at sites that are part 

of the Interagency Monitoring of Protected Visual Environments (IMPROVE) program. The EPA’s 

back‐calculated ammonia inputs are shown for reference in Table 13, along with the values SRP 

used in its modeling and the values measured during the SRP NH3 monitoring program. 

                                                       

50 SRP submitted a BART modeling protocol to EPA in September 2007. In a letter dated July 1, 2008, EPA 

recommended several changes to the modeling analysis, and noted that the protocol was approvable once these 

changes were addressed. In separate e‐mail correspondence, the National Park Service also approved the proposed 

monthly ammonia background values. 

51 Application for Prevention of Significant Deterioration Permit for the Desert Rock Energy Facility, by ENSR 

Corporation, May 2004. EPA‐R09‐OAR‐2007‐1110‐0012. These ammonia concentration values had been approved 

by the Federal Land Managers (FLMs) for use in the DREF modeling. 

52  New Mexico Environment Department Air Quality Bureau BART Determination, Public Service Company of New 

Mexico San Juan Generating Station, Units 1‐4, New Mexico Environment Department Air Quality Bureau, 21 June 

2010. 

53 Assessment of Anticipated Visibility Improvements at Surrounding Class I Areas and Cost Effectiveness of Best

Available Retrofit Technology for Four Corners Power Plant and Navajo Generating Station: Advanced Notice of

Proposed Rulemaking 74FR44313‐44334, 26 August 2009.
79

Table 13. Ammonia Monthly Background Concentrations (in ppbv) Used by EPA and SRP for NGS

BART CALPUFF Modeling. 54

* The February average for Moenkopi is for only the last half of the month. 

Importance of Ammonia Inputs

As discussed in Section B‐1, both previous modeling performed by SRP and the independent 

evaluation performed by CDPHE indicated that the background ammonia concentrations input to 

CALPUFF has a significant impact on the degree of visibility improvement predicted by the model 

for different NOx BART control options. The ammonia concentrations assumed by EPA for the 

winter months range from 0.8 to 1 ppb, which are much higher than the value of 0.2 ppb that was 

used in the modeling performed by SRP. This is precisely the range of ammonia input values for 

which the CDPHE sensitivity analysis showed the largest differences in visibility impacts. 

Therefore, it is clear that the differences in the ammonia concentration input to CALPUFF 

between SRP and EPA have very important implications for the modeled effectiveness of BART 

NOx emission controls.  

Since the observed ammonia concentrations vary in time and space over this critical input range, 

the best way to determine the appropriate input values to CALPUFF is to compare model 

predictions to measured values for the different background ammonia inputs and select the 

inputs that yield the best agreement between the modeled results and the measured values. The 

next two sections address the question of which ammonia input values provide the best 

agreement between modeled and measured data.   

                                                       

54 From Table 22 of EPA’s Advance Notice of Proposed Rulemaking, 74 Federal Register, pages 44326‐44327.

Previously Modeled Values Measured Values in SRP's 2009/2010 Monitoring Program

SRP NH3

(ppb)

EPA NH3

(Mesa Verde)

(ppb)

EPA NH3

(all other areas)

(ppb)

Hall's Crossing

NH3

(ppb)

Moenkopi *

NH3

(ppb)

Gray Mountain

NH3

(ppb)

January 0.2 1 0.8 0.1 - 0.7

February 0.2 1 0.7 0.2 0.6 0.7

March 0.5 1.3 0.7 0.3 0.4 0.5

April 0.5 1.3 1 0.3 0.4 0.5

May 1 1.3 1 - - -

June 1 1.3 1.3 - - -

July 1 1.5 1 - - -

August 1 1.5 1 - - -

September 1 1.5 1 - - -

October 0.5 1.5 1 - - -

November 0.5 1.3 1 - - -

December 0.2 1 0.9 0.2 - 0.9

Month
80

B‐3. MODELING OF SRP AND EPA AMMONIA INPUTS

Review of Previous Modeling

In the SRP comments on the EPA’s ANPR, CALPUFF modeling analyses were performed to 

compare the ammonium nitrate concentrations predicted by the model with measured values for 

different background ammonia inputs. In these analyses, each day within the BART 

meteorological modeling period of 2001‐2003 was modeled using daily meteorological data and 

actual daily emissions of SO2 and NOx for each NGS unit. Three runs were made, one for each of 

the background ammonia concentrations sets given in Table 14. The “SRP” values are those that 

were used in the December 2008 BART analysis submitted to EPA. The “EPA” values are the 

back‐calculated ammonia concentrations that were used by EPA in the modeling presented in the 

ANPR. An alternative set of “Low” values presents a case with lower ammonia concentrations 

than the SRP values to provide additional information on the change in CALPUFF prediction 

performance with changes in the ammonia input values.  The resulting sulfate and nitrate 

concentrations were predicted at receptors placed at the locations of nearby IMPROVE monitors. 

The modeling was performed at Grand Canyon and Bryce Canyon, since these are the closest 

Class I area IMPROVE monitors to NGS. The Grand Canyon modeling was performed at the Hance 

Camp IMPROVE monitor located on the rim of the canyon. 

This modeling analysis targeted days on which CALPUFF predicted that NGS emissions could 

potentially reach the monitor of interest and there was an elevated measurement of sulfate 

concentrations. Such days were ones where modeled ammonium sulfate concentrations, which 

are not sensitive to the background ammonia concentration, were similar to the measured ones 

and the likelihood of NGS emissions reaching the monitor of interest was corroborated by back 

trajectories. On such days, it is expected for a well‐performing model that predicted ammonium

nitrate concentrations should be comparable to, but somewhat less than, the measured values. 

The possibility of lesser concentrations would allow for the contributions of other anthropogenic 

and natural sources that were not included in the CALPUFF modeling. 

Eleven days in 2001 through 2003 met the criteria identified above at Hance Camp, so the 

measured and modeled ammonium nitrate concentrations were compared on those days.55  

                                                       

55 Interestingly, those days with the highest possibilities of NGS emissions reaching the Hance Camp (and Indian 

Garden, discussed later) IMPROVE sites tended to be among the cleanest days monitored during the 3‐year period. 

Seven of the 11 days at Hance Camp were among the 20% of the days with best visibility during their respective 

years, and 5 of the 7 at Indian Garden were in the 20% best category. 
81

Table 14. Ambient NH3 Concentrations (ppb) Used in CALPUFF Simulations of NGS Emissions.

 Low SRP EPA 

Jan 0.1 0.2 0.8

Feb 0.1 0.2 0.7

Mar 0.2 0.5 0.7

Apr 0.5 0.5 1.0

May 1.0 1.0 1.0

Jun 1.0 1.0 1.3

Jul 1.0 1.0 1.0

Aug 1.0 1.0 1.0

Sept 0.5 1.0 1.0

Oct 0.2 0.5 1.0

Nov 0.2 0.5 1.0

Dec 0.1 0.2 0.9

On some of the days selected for the case study at Hance Camp, the predicted ammonium sulfate

concentration exceeded the measured value. This could occur if the CALPUFF model 

overestimated the sulfate concentrations, for which there could be several causes. In such a case, 

the bias in the CALPUFF prediction of sulfate concentrations was likely due to transport and 

dispersion issues with the NGS plumes as a whole, so that bias was also likely to be reflected in 

the nitrate concentrations. To remove this potential bias on the case study days, whenever the 

modeled 24‐hr ammonium sulfate concentration exceeded the measured sulfate concentration, 

the modeled ammonium nitrate concentrations were scaled downward by the factor that would 

be required to reduce the modeled ammonium sulfate to its measured concentration. Such 

adjustment took place on three of the 11 days at Hance Camp.  

The lack of scaling upward resulted in reducing the possible overprediction tendency of 

CALPUFF for the predicted vs. observed nitrate comparison. In addition, an upward adjustment 

was not made in predicted nitrates for days when CALPUFF underestimated the measured 

sulfate concentration because, even if the modeled prediction was perfect, some of the measured 

sulfates are likely to come from other sources and regional background. Consequently, some 

underestimation of total ambient sulfates should be expected from CALPUFF if it were providing 

unbiased model predictions.   
82

Additional Modeling for the Indian Garden Monitoring Site

In this report, to supplement the previous modeling performed for the Grand Canyon Hance 

Camp and Bryce Canyon IMPROVE sites, the same model comparison analysis was performed for 

the Indian Garden IMPROVE site. The Indian Garden site is located part way between the Grand 

Canyon floor and the rim, whereas the Hance Camp site, which was included in the previous 

modeling analysis, is located on the rim of the canyon.  The following section summarizes the 

results of the analysis for the Indian Garden site and compares them with the results obtained 

previously for the Hance Camp site. While eleven days in 2001 through 2003 met the criteria 

identified above at Hance Camp, a total of seven days met these criteria at Indian Garden, so the 

measured and modeled ammonium nitrate concentrations were compared on those days. 

Consideration of both Grand Canyon IMPROVE sites provides a more extensive test of the 

appropriate selection of the ammonia concentration input to CALPUFF. 

Comparison of Model Performance at Hance Camp and Indian Garden Sites

Figure 33 compares the measured and modeled ammonium nitrate concentrations on the 11 

case study days at Hance Camp and the 7 case study days at Indian Garden. (The results for Bryce 

Canyon, involving fewer days, are not repeated here, and can be found in Attachment 2 of SRP’s 

ANPR comments.) Asterisks mark the modeled values that were adjusted for Hance Camp 

according to the procedure described in the previous section to remove the potential 

overprediction bias. Even after such rolling back of the modeled ammonium nitrate 

concentrations on some days, the nitrate concentrations calculated using EPA’s background 

ammonia concentrations (the blue, rightmost bars) still greatly exceed the measured 

concentrations (orange, leftmost bars) on every one of the case study days for both sites. (For 

Indian Garden, the predicted ammonium sulfate concentration never exceeded the measured 

value, so no scaling was required.) 

The ammonium nitrate concentrations predicted by CALPUFF using the SRP concentrations of 

ammonia (green bars) also exceed the measured concentrations on most days, but by a 

considerably smaller margin. The ammonium nitrate concentrations calculated with the Low and 

SRP ammonia background (magenta bars) tend to be closest to the measured values.  

It should be noted that two of the days analyzed at Indian Garden occur during the summer. For 

these two days, the predicted ammonium nitrate concentrations for the Low, SRP, and EPA 

inputs are the same (insensitive to the ammonia concentration), so they do not follow the same 

pattern as the days that occur during the winter months.   

Both the previously conducted modeling for Hance Camp and the new analysis presented here 

for Indian Garden confirm that the use of EPA’s background ammonia inputs produce significant 

overpredictions of nitrate concentrations relative to actual measured values. SRP’s ammonia 

inputs produce some overprediction, but to a significantly lesser degree.   
83

Figure 33. Comparisons of measured and modeled estimates of ammonium nitrate concentrations

at Hance Camp and Indian Garden.  
84

B‐4. EFFECT OF MODEL CHEMISTRY ON NITRATE PREDICTIONS

The results of the CALPUFF performance evaluation described in Section B‐3 of this 

report indicate that SRP’s ammonia inputs produce the best agreement between 

modeled and measured ammonium nitrate concentrations.  However, the monitoring 

study described in Part A of this report, and the study conducted in the Four Corners 

area, found that actual ammonia measurements in winter can vary between 0.1‐0.2 

ppb and 1 ppb.  This raises the question of why CALPUFF appears to yield the most 

reasonable results with lower ammonia values, even though actual ammonia 

concentrations may not be consistently that low throughout the entire modeling 

domain.  To further investigate this question, a search of independent evaluations of 

the CALPUFF model was conducted, with a focus on identifying studies that address 

the nitrate chemistry used in the model.   

Morris et al.56 reported that the CALPUFF MESOPUFF II transformation rates were 

developed using temperatures of 86, 68 and 50°F. Therefore, the 50°F minimum 

temperature used in development of the model could result in overestimating sulfate 

and nitrate formation in colder conditions. These investigators found that CALPUFF 

tended to overpredict nitrate concentrations during winter by a factor of about 3. 

A recent independent study that is relevant to the CALPUFF performance for nitrate 

prediction was performed by Atmospheric and Environmental Research, Inc. (AER) 

and presented at the October 2009 Air & Waste Management Association Specialty 

Conference in Raleigh, North Carolina, by Karamchandani et al.57 (“the KCBB study”). 

This study presented several improvements to the RIVAD chemistry option in 

CALPUFF, an alternative treatment that was more amenable to an upgrade than the 

MESOPUFF II chemistry option. Among other items, the improvements included the 

replacement of the original CALPUFF secondary particulate matter (PM) modules by 

newer algorithms that are used in current state‐of‐the‐art regional air quality models 

such as CMAQ, CMAQ‐MADRID, CAMx and REMSAD, and in advanced puff models 

such as SCICHEM. In addition, the improvements included the incorporation of an 

aqueous‐phase chemistry module based on the treatment in CMAQ. Excerpts from the 

study papers describing each of the improvements made to CALPUFF in the KCBB 

study are repeated below. 

                                                       

56 Morris, R., Steven Lau and Bonyoung Koo, 2005. Evaluation of the CALPUFF Chemistry Algorithms. 

Presented at A&WMA 98th Annual Conference and Exhibition, June 21‐25, 2005 Minneapolis, 

Minnesota. 

57 Karamchandani, P., S. Chen, R. Bronson, and D. Blewitt, 2009.  Development of an Improved 

Chemistry Version of CALPUFF and Evaluation Using the 1995 SWWYTAF Data Base.   Presented at the 

Air & Waste Management Association Specialty Conference on Guideline on Air Quality Models: Next 

Generation of Models, October 28‐30, 2009, Raleigh, NC.  
85

Gas‐Phase Chemistry Improvements

The KCBB study applied a correction to CALPUFF in that the upgraded model was 

modified to keep track of the puff ozone concentrations between time steps. The 

authors also updated the oxidation rates of SO2 and nitrogen dioxide (NO2) by the 

hydroxide ion (OH‐

) to the rates employed in contemporary photochemical and 

regional PM models. 

Treatment of Inorganic Particulate Matter

The KCBB study scientists noted that the EPA‐approved version of CALPUFF 

currently uses a simple approach to simulate the partitioning of nitrate and sulfate 

between the gas and particulate phases. In this approach, sulfate is appropriately 

assumed to be entirely present in the particulate phase, while nitrate is assumed to 

be formed by the reaction between nitric acid and ammonia, as noted in Section B‐1.   

The KCBB study implemented an additional treatment for inorganic gas‐particle 

equilibrium, based upon an advanced aerosol thermodynamic model referred to as 

the ISORROPIA model. 58 This model is currently used in several state‐of‐the‐art 

regional air quality models. With this new module, the improved CALPUFF model 

developed in the KCBB study includes a treatment of inorganic PM formation that is 

consistent with the state of the science in air quality modeling, and is critical for the 

prediction of regional haze due to secondary nitrate formation from NOx emissions.  

Treatment of Organic Particulate Matter

The KCBB study added a treatment for secondary organic aerosols (SOA) that is 

coupled with the corrected RIVAD scheme described above. The treatment is based 

on the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution 

(MADRID)59,60, which treats SOA formation from both anthropogenic and biogenic 

volatile organic compound emissions.   

                                                       

58Nenes A., Pilinis C., and Pandis S.N. (1998) Continued Development and Testing of a New 

Thermodynamic Aerosol Module for Urban and Regional Air Quality Models, Atmos. Env., 33, 1553‐

1560. 

59 Zhang, Y., B. Pun, K. Vijayaraghavan, S.‐Y. Wu, C. Seigneur, S. Pandis, M. Jacobson, A. Nenes and J.H. 

Seinfeld, 2004. Development and Application of the Model of Aerosol Dynamics, Reaction, Ionization 

and Dissolution (MADRID), J. Geophys. Res., 109, D01202, doi:10.1029/2003JD003501.  60 Pun, B., C. Seigneur, J. Pankow, R. Griffin, and E. Knipping, 2005.  An upgraded absorptive secondary 

organic aerosol partitioning module for three‐dimensional air quality applications, 24th Annual 

American Association for Aerosol Research Conference, Austin, TX, October 17‐21, 2005.
86

Aqueous‐Phase Chemistry

The current aqueous‐phase formation of sulfate in both CALPUFF’s RIVAD and 

MESOPUFFII schemes is currently approximated with a simplistic treatment that uses 

an arbitrary pseudo‐first order rate in the presence of clouds (0.2% per hour), which 

is added to the gas‐phase rate. There is no explicit treatment of aqueous‐phase SO2

oxidation chemistry. The KCBB study incorporated into CALPUFF a treatment of 

sulfate formation in clouds that is based on the treatment that is used in EPA’s CMAQ 

model.   

CALPUFF Model Evaluation and Sensitivity Tests

The EPA‐approved version of CALPUFF and the version with the improved chemistry 

options were evaluated using the 1995 Southwest Wyoming Technical Air Forum 

(SWWYTAF) database61, available from the Wyoming Department of Environmental 

Quality. The database includes MM5 output for 1995, CALMET and CALPUFF codes 

and control files, emissions for the Southwest Wyoming Regional modeling domain, 

and selected outputs from the CALPUFF simulations. Several sensitivity studies were 

also conducted to investigate the effect of background NH3 concentrations on model 

predictions of PM nitrate. 

Twice‐weekly background NH3 concentrations were provided from monitoring 

station observations for the Pinedale, Wyoming area. These data were processed to 

calculate seasonally averaged background NH3 concentrations for CALPUFF.  

Two versions of CALPUFF with different chemistry modules were evaluated with this 

database: 

1. MESOPUFF II chemistry using the Federal Land Managers' Air Quality Related 

Values Work Group (FLAG) recommended background NH3 concentration of 1 

ppb for arid land. As discussed previously, the MESOPUFF II algorithm is the 

basis for the currently approved version of CALPUFF that is being used in the 

BART determination for NGS.  

2. Improved CALPUFF RIVAD/ARM3 chemistry using background values of NH3

concentrations based on measurements in the Pinedale, Wyoming area, as 

described above. 

PM sulfate and nitrate were predicted by the two models and compared with actual 

measured values obtained at the Bridger Wilderness Area site from the IMPROVE 

                                                       

61 Background and database description are available at 

http://deq.state.wy.us/aqd/prop/2003AppF.pdf. 
87

network and the Pinedale site from the Clean Air Status and Trends Network 

(CASTNET). For the two model configurations evaluated in this study, the results for 

PM sulfate were very similar, which was expected since the improvements to the 

CALPUFF chemistry were anticipated to have the most impact on PM nitrate 

predictions. Therefore, the remaining discussion focuses on the performance of each 

model with respect to PM nitrate. 

The EPA‐approved CALPUFF model was found to significantly overpredict PM nitrate 

concentrations at the two monitoring locations, by a factor of 2 to 3. The performance 

of the version of CALPUFF with the improved RIVAD chemistry was much better, 

with an overprediction of about 4% at the Pinedale CASTNET site and of about 28% 

at the Bridger IMPROVE site. 

   

In an important sensitivity analysis conducted within the KCBB study, both the EPA‐

approved version of CALPUFF and the improved version were run with a constant 

ammonia background of 1 ppb. 62 The results were similar to those noted above:  the 

improved CALPUFF predictions were about 2‐3 times lower than those from the EPA‐

approved version of CALPUFF. This result is similar to the results using the seasonal 

observed values of ammonia, and indicates that the sensitivity of the improved 

CALPUFF model to the ammonia input value is potentially much less than that of the 

current EPA‐approved model. 

Similar sensitivity was noted by Scire et al. in their original work in the SWWYATF 

study63, in which they tested seasonally varying levels of background ammonia in 

CALPUFF (using 0.23 ppb in winter, for example; see Figure 34). The sensitivity 

modeling for predicting levels of nitrate formation shows very similar results to those 

reported in the KCBB study. 

                                                       

62 This is a recommendation from the Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 

2 Summary Report and Recommendations for Long‐Range Transport Modeling, EPA‐454/R‐98‐019, 

1998.  

63 Scire, J.S., Z‐X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional CALPUFF 

Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of Environmental Quality.
88

Figure 34. Sensitivity Study of Nitrate Predictions at Bridger Wilderness Area as a

Function of Input Ammonia Concentrations to CALPUFF (0.23, 0.5, and 1.0 ppb).
89

B‐5. CONCLUSIONS AND POTENTIAL FUTURE WORK

The EPA‐approved version of CALPUFF is highly sensitive to the background 

ammonia value used as input, particularly as the input ranges from 0.1 ppb to 1 ppb.  

Case studies conducted by modeling NGS emissions using CALPUFF showed that the 

EPA’s back‐calculated ammonia input values resulted in order‐of‐magnitude (i.e., by a 

factor of 10 in some cases) overpredictions of ammonium nitrate concentrations.  

The ammonia inputs used by SRP in the December 2008 BART analysis produce the 

best agreement between modeled and measured ammonium nitrate concentrations. 

In monitoring studies of ammonia concentrations such as the one conducted by SRP 

as described in Part A of this report, spatial gradients in ammonia concentrations 

have been observed during winter. However, as discussed in detail in Part A of this 

report, further study would be required to determine whether the measured 

concentrations are representative over a larger area, or just at certain locations.   

The sensitivity modeling performed by SRP, and the other studies and evaluation 

work presented in this report, compel the following conclusions:  

• To compensate for the general tendency of the current EPA‐approved version 

of CALPUFF to overpredict nitrates, the background ammonia values that 

should be used as input in the modeling of NGS emissions should be much 

lower than the EPA back‐calculated values.  If EPA intends to use the currently 

approved version of CALPUFF in the BART determination for NGS, the 

visibility benefits modeling should be conducted using the background 

ammonia values that were used by SRP in the December 2008 BART analysis. 

• A sensitivity modeling analysis that uses EPA’s back‐calculated ammonia 

inputs would overestimate the degree of visibility improvement predicted for 

more stringent BART control options. It has been shown that the use of EPA’s 

back‐calculated ammonia inputs produces order‐of‐magnitude (i.e., by a factor 

of 10 or more in some cases) overestimates compared to measured values.  

• The nitrate formation modules in CALPUFF should be updated in order to 

correct the general tendency of the model to overpredict nitrate 

concentrations in the winter. The degree of overprediction in CALPUFF has 

significant implications on many pending BART determinations. A

requirement to install costly emission controls should not be based on results 

obtained using a model with a technical flaw, unless the measures described 

above (use of lower ammonia background concentration input values) are 

taken to compensate for the known overprediction. 
90

To facilitate the model improvement effort, SRP has joined with several other western 

utilities to sponsor the implementation of the nitrate chemistry improvements that 

were developed in the KCBB study into the CALPUFF model. This work is being 

undertaken by TRC, the CALPUFF model developer. Upon completing the 

improvements, TRC will submit the revised model algorithms to EPA for review and 

approval. Although the review process may require considerable time and effort, SRP 

intends to run the updated version of the model to provide additional information to 

EPA for use in the BART determination for NGS. In the meantime, given the tendency of 

the EPA‐approved version of the CALPUFF model to overpredict nitrate concentrations 

and the degree of visibility improvement associated with more stringent BART control 

options, SRP urges EPA to proceed with a BART determination that is based on the 

ammonia input values used by SRP in the December 2008 BART analysis. 
Measurements of Ambient Backg

Ammonia on the Colorado Platea

Visibility Modeling Implicatio

September 28, 2010
Outline

1. Background2. SRP’s Ammonia Monitoring Program

3. Visibility Modeling Implications4. Proposed Path Forward5. Questions and Discussion

September 2010 
1. Background

September 2010 
Background

• Visibility improvement is one of five factors in 

Available Retrofit Technology (BART) determi

• SRP submitted visibility modeling results with 

December 2008 BART Proposal

• Modeling was based on a protocol that was a

by EPA in July 2008

• In an Advanced Notice of Proposed Rulemaki

(ANPR) issued in August 2009, EPA presented modeling with different assumptions/inputs

September 2010 
Importance of Ammonia

• To evaluate relative importance of EPA chang

model inputs, SRP conducted sensitivity mod

• Results showed that ambient background am

(NH

3) is most important parameter affecting 

predicted visibility impacts in winter

• Example: Incremental visibility improvement 

Canyonlands from adding SCR to LNB/SOFA

– SRP NH

3: 0.61 dv

– EPA NH

3: 1.25 dv

– All EPA Changes: 1.73 dv

+0.64 dv

+0.48 dv

Effect of 

September 2010
Basis for SRP NH

3

• Approved by EPA and Federal Land Managers 

for use in modeling performed for Desert Roc

• Consistent with previously measured ammoni

(e.g. Green River, Rocky Mountains, Mesa Ver

• More recently accepted by New Mexico Enviro

Department in BART determination for San JuGenerating Station

September 2010
EPA’s Back-Calculated NH

• In ANPR, EPA back-calculated NH

3 based on 

measured ammonium sulfate and nitrate val

• Measured values were obtained from Interag

Monitoring of Protected Visual Environments (IMPROVE) sites

• Much of measured ammonium sulfate and n

comes from regional background and emissiofrom other sources

• Therefore, back-calculation method overesti

NH

3 available to react with NGS emissions

September 2010
Comparison of NH

3 Input

Previously Modeled Values

SRP NH3

(ppb)

EPA NH3

(Mesa Verde)

(ppb)

EPA NH3

(all other are

(ppb)

January 0.2 1 0.8

February 0.2 1 0.7

March 0.5 1.3 0.7

April 0.5 1.3 1

Month

September 2010

April 0.5 1.3 1

May 1 1.3 1June 1 1.3 1.3

July 1 1.5 1

August 1 1.5 1

September 1 1.5 1

October 0.5 1.5 1

November 0.5 1.3 1

December 0.2 1 0.9
Evaluation of Back-Calculati

Method

• SRP submitted a white paper evaluating back

calculation method with ANPR Comments

• NGS emissions from 2001

-2003 were modele

CALPUFF for two scenarios:

– SRP NH

3

– EPA back-calculated NH

3

– “Low” NH

3

• Ammonium nitrate predicted by CALPUFF wa

compared with measured values at IMPROVE 

September 2010
Evaluation of Back-Calculati

Method

• Conclusions:

1. Use of back-calculated ammonia inputs y

order

-of

-magnitude overestimates comp

measured values (i.e., by a factor of 10 or more in some ca

2. Visibility modeling for NGS BART determi

should utilize SRP’s ammonia inputs

September 2010 
Results for Grand Canyon 

(Hance Camp Monitoring Site

September 2010 
2. SRP’s Ammonia Monitor

Program

September 2010 
SRP Ammonia Monitoring Pro

• In ANPR, lack of available ammonia 

measurements was cited as justification use of back-calculation method

• To provide additional data to address th

issue, SRP initiated a monitoring programeasure background ammonia

September 2010 
SRP Ammonia Monitoring Pro

• Objective: To support improved estimates of month

wintertime background NH

3 concentrations

• Guiding Considerations:

– Relevant NH

3 concentrations are those in air mas

NGS emissions would be transported

– Background NH

3 is best measured outside the inf

current transported NGS emissions and/or upwin

– NH

3 measurements should be consistent in spatia

temporal scale with resolution of CALPUFF

– Since region is free of major ammonia emission s

ambient NH

3 was expected to be quite uniform sp

September 2010 
Monitoring Program Timeli

• Draft conceptual monitoring plan provided to 

June 2009

• Final monitoring plan provided to EPA and Na

Park Service (NPS) in October 2009

• Discussed in a conference call with NPS in No

2009

• Program was initiated in early December 200

ran until end of April 2010

September 2010 
Monitoring Site Selection

• CALPUFF modeling of current NGS emissions 

indicated greatest visibility impacts at Grand 

Capitol Reef, and Canyonlands National Parks

• Therefore, 2 sites were selected:

– One representing transport conditions toward Gr

Canyon National Park

– Another representing transport conditions towar

Reef and Canyonlands National Parks

September 2010 
Selected Monitoring Sites

1. N

C

Hall’s Crossing Site

2. A

MNC

Gray Mountain Site

September 2010
Expansion of Monitoring Stud

• Initial data raised questions about representa

of Gray Mountain site

• A third site was established at Moenkopi Yard 

from Gray Mountain) in February 2010

• Passive ammonia samplers were also added a

multiple locations

– At primary monitoring sites for quality control and

calibration

– At three other locations near Gray Mountain site t

geographic coverage

• 3-week samples taken using passive samplers 

obtain lower detection limit of 0.1 ppb

September 2010 
Monitoring 

Sites in 

Cameron Area

September 2010 
Monitoring Results

• NH

3 appears to vary in both time and space

• NH

3 at Gray Mountain and Moenkopi > Hall’s Crossing

September 2010
Monitoring Results

Measured Values in SRP's 2009/2010 Monitoring Program Previously Modele

Hall's Crossing

NH3

(ppb)

Moenkopi *

NH3

(ppb)

Gray Mountain

NH3

(ppb)

SRP NH3

(ppb)

EPA NH3

(Mesa Verd

(ppb)

January 0.1 - 0.7 0.2 1 

February 0.2 0.6 0.7 0.2 1 

March 0.3 0.4 0.5 0.5 1.3 

April 0.3 0.4 0.5 0.5 1.3 

Month

* Sampling began at Moenkopi site on 2/15/2010

September 2010

May - - - 1 1.3 June - - - 1 1.3 

July - - - 1 1.5 

August - - - 1 1.5 

September - - - 1 1.5 

October - - - 0.5 1.5 

November - - - 0.5 1.3 

December 0.2 - 0.9 0.2 1 
Monitoring Results

• Passive samplers correlated well with a

monitors 

September 2010 
Spatial Variability

• While not initially expected, spatial vari

is comparable to other monitoring stud(e.g. Four Corners)

• Possible contributors to the higher amm

concentrations observed in the Camero

– Animal grazing– Highway vehicles– Sewer lagoons– Septic tanks

September 2010 
Spatial Variability

Fertilizer Application

Animal Husbandry

EPA’s national NH

3 data confirms that animal a

highway vehicles, and waste disposal are signif

0% 10% 20% 30% 40% 50% 60% 7

Other

Waste Disposal

Industrial Processes

Highway Vehicles

Fertilizer Application

September 2010
• Particulate sulfate concentrations corre

well between monitoring sites

Spatial Variability

September 2010 
Spatial Variability

• Additional observations from measurem

1. Cameron area sites are < 5 miles apart, but con

difference in measured ammonia was observed

2. Sulfates measured at SRP sites demonstrated a 

systematic relationship with IMPROVE site valu

• Above observations provide further evi

of possible local sources in Cameron are

• Ammonia measured in Cameron area m

be representative of Colorado Plateau

September 2010 
Temporal Variability

• Two distinct periods were observed:

– Period 1: December 5 – February 15

• Cold temperatures, more week-to-week variati

– Period 2: February 15 

– April 28

• Warmer temperatures, more mixing, less week

variation 

• Each period exhibited distinct meteorolo

and air quality characteristics/trends

September 2010 
Temporal Variability

• Increased mixing in Period 2

September 2010 
Temporal Variability

• Balance of ammonium and particulate 

changes from Period 1 to Period 2

September 2010 
Temporal Variability

• During Period 1, more ammonia was availabl

needed to account for measured particulate 

• During Period 2, nitrate formation was limite

availability of ammonia, but crustal cations pcrustal nitrates (small effect on visibility)

• Balance of ammonium and nitrate concentra

observed cannot be simulated by the simplifinitrate-formation modules in CALPUFF

September 2010 
Conclusions

• Higher ammonia measured at Cameron area 

may be affected by local sources, and may norepresentative of Colorado Plateau

• Balance of ammonium and nitrate concentra

observed cannot be simulated by current CA

• To determine regionally representative backg

ammonia for use with improved CALPUFF mocollaborative monitoring using passive samplshould be initiated

September 2010 
3. Visibility Modeling Implica

September 2010 
Modeling Questions

• Monitoring study results do not explain 

SRP’s ammonia inputs produce best agreement with measurements

• To further investigate, additional mode

was conducted using same approach as analysis submitted with SRP’s ANPR com

• Additional modeling was performed for 

Garden site, located part way between bottom and rim of Grand Canyon

September 2010 
Same Over-Prediction Occurs 

Indian Gardens Monitoring Si

September 2010 
CALPUFF Overprediction

• Modeling suggests a general tendency o

CALPUFF to overpredict nitrates in wint

• CALPUFF would also overpredict visibili

improvement for NO

x BART control opt

• To further investigate, a search of 

independent evaluations was conducte

• Focus of search was on studies that add

nitrate chemistry used in CALPUFF

September 2010 
Independent Evaluations

1. Morris et al. (2005) testing with MESO

chemistry

– Ran CALPUFF chemistry with entire CMAQ emissi

inventory to compare predictions to numerous IM

CASTnet, SEARCH monitors

– Jan and July 2002 simulated– CALPUFF overpredicts nitrates by a factor ranging 

in winter, depending upon specific site and region

September 2010 
Independent Evaluations

2. Colorado Department of Public Health 

Environment (CDPHE) – BART protoco

– Conducted CALPUFF sensitivity to ammonia conc

input for hypothetical sources

– Most significant difference in modeled visibility im

occurs between NH

3 inputs of 0.1 and 1 ppb

– Peak visibility impacts increase by a factor of mor

when NH

3 input is increased from 0.1 to 1 ppb

September 2010 
September 2010 38
Independent Evaluations

3. Scire et al. 2001 testing w/MESOPUFF 

chemistry 

– Conducted evaluation of CALPUFF vs. nitrate obse

Bridger Wilderness Area 

– Tests involved only variations of ammonia inputs (

and 1.0 ppb concentrations)

– Predicted/Observed nitrate concentration ratios w

• About 3 for ammonia concentration of 1.0 ppb• About 2 for ammonia concentration of 0.5 ppb• Unbiased for ammonia concentration of 0.23 ppb

September 2010 

Independent Evaluations

4. Karamchandani et al. (“KCBB” Study)

– Involved predictions at Bridger Wilderness Area IM

site and Pinedale CASTnet site

– Currently approved version of CALPUFF overpredic

by a factor of 2-3 in winter

– Improved CALPUFF chemistry implemented

• NO

x gas phase chemistry 

• ISORROPIA treatment for inorganic PM, MADRID for org• Aqueous phase chemistry upgrade

– Improved model was nearly unbiased for nitrate p

September 2010 
Conclusions

• Incremental visibility improvement asso

with BART controls is critically dependeammonia input

• EPA has suggested a sensitivity modelin

analysis of ammonia inputs could be do

• If back-calculated NH3 values are used, 

sensitivity modeling would overestimatvisibility improvement for BART control

September 2010 
Conclusions

• To compensate for the general tendenc

CALPUFF to overpredict nitrates, SRP’s ammonia inputs should be used

• Nitrate formation modules in CALPUFF 

be updated to address winter overpred

• BART determination should not be base

model with a known technical flaw

September 2010 
4. Proposed Path Forward

September 2010 
Proposed Path Forward

• SRP is partly sponsoring an effort to 

implement KCBB upgrades to nitrate min CALPUFF

• SRP intends to run updated version of m

and provide results to EPA

• If EPA intends to use currently approved 

version of CALPUFF for BART determinaSRP’s ammonia inputs should be used

September 2010 
5. Questions and Discussio

September 2010