# 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 NH 3 MONITORING PROGRAM

........................................................................... 14
Management of the SRP NH 3 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

............................................................. 891 

## EXECUTIVE SUMMARY

The United States Environmental Protection Agency (EPA) is curr ently in the process of performing a Best Available Retr ofit Technology (BART) determin ation for the Navajo Generating Station (NGS). One of the statutory factors that mus t be considered in performing the BART analysis is the degree of visibility improv ement that can be achieved by each BART control option, whic h is estimated using the CALPU FF model. Previous modeling performed by Salt River Project (SRP) indicated that w intertime background ammonia is the most important pa rameter affecting the degree of visibility improvement predicted by the model for a giv en BART control option for nitr ogen oxides (NO
x ).

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

concentrations that were the sam e as those used in previous Pre vention of Significant Deterioration (PSD) permitting p roceedings in the Southwest (“S RP’s ammonia inputs”), in accordance with a modeling proto col that was approved by EPA. E PA subsequently issued an Advanced Notice of Proposed Ru lemaking (ANPR) in which the a gency used a back‐

calculation method to establish h igher background ammonia conce ntrations for use in BART modeling for NGS.

SRP submitted comments on the ANP R that included a modeling ana lysis demonstrating

that EPA’s back‐calculation meth od overestimates the background ammonia concentration values that would be appropriat e for use in CALPUFF modeling of NGS emissions. In this analysis, actual emissions from N GS were modeled using CALPUFF with both SRP’s ammonia inputs and EPA’s higher back‐calculated values. The amm onium nitrate concentrations predicted by the model using both sets of ammoni a inputs were compared

to ammonium nitrate concentration s measured at monitoring sites (Hance Camp and Bryce Canyon) that are part of the Int eragency Monitoring of Protecte d Visual Environments (IMPROVE) network. The results s how that the use of SRP’s ammon ia inputs produces the most reasonable agreement betwee n modeled and measured ammonium nitrate

concentrations. The use of EPA’s back‐calculated ammonia concen trations produces order‐ of‐magnitude overpredictions (i.e ., by a factor of 10 or more i n many cases) of ammonium

nitrate concentrations relative to measured values.

In the ANPR, EPA cited a lack of available background ammonia m easurements in the Colorado Plateau as justificatio n for the use of its back‐calcu lation method. To provide

additional data to address this i ssue, SRP conducted a monitori ng program to measure background ammonia in the vicin ity of NGS between December 2009 and April 2010. Monitoring sites were selected b ased on a number of criteria, i ncluding accessibility, basic infrastructure, and their locati ons relative to possible transp ort paths from NGS to nearby

Class I areas. Based on these cri teria, a monitoring site was e stablished at Hall’s Crossing, Utah, and several others were es tablished near Cameron, Arizona

.2 The results obtained in SRP’s mo nitoring study showed consisten tly lower background

ammonia concentrations at the Ha ll’s Crossing site compared to those measured at the Cameron area sites. The highest measured ammonia values were cl ose to EPA’s back‐ calculated values, and the lowes t measured values were close to SRP’s ammonia inputs. While it was not initially expec ted, this spatial variability i s comparable to the variability observed in other monitoring stu dies, such as the Four Corners area study performed in 2007. There are several possible contributors to the higher amm onia concentrations observed in the Cameron area, in cluding animal grazing, highway vehicles, septic tanks, and nearby sewer lagoons.

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

whether the ammonia concentratio ns at other areas within the mo deling domain would be more accurately represented by va lues measured at the Cameron a rea sites or the Hall’s Crossing site. It is also unclear whether the higher measured c oncentrations at the

Cameron area sites are indicativ e of a local ammonia source and as such, may only be representative over a small area . Even within the two Cameron a rea sites, despite the relatively small distance that se parates them, a consistent dif ference in measured ammonia values was observed, which sugge sts that a local source could b e affecting the measured

values.

The range of ammonia concentrations measured, and the uncertain ty regarding the spatial

extent to which the measured valu es are representative, does no t provide a clear picture of which ammonia values are appropri ate for input to CALPUFF, part icularly when modeling areas where monitoring data is n ot available. In addition, the results obtained in SRP’s monitoring study do not explain w hy SRP’s ammonia inputs produc e the best agreement between model‐predicted and meas ured ammonium nitrate concentra tions.

To further investigate this issu e, SRP conducted an additional ammonia background sensitivity modeling analysis us ing the same approach that was used in the analysis that

SRP included in its comments on t he ANPR. As in the previous an alysis, ammonium nitrate concentrations were predicted us ing CALPUFF with both SRP’s and EPA’s background ammonia inputs and compared to m easured values. This new analys is was performed for

the Indian Garden IMPROVE site. The Indian Garden site is locat ed part way between the Grand Canyon floor and the rim, whereas the Hance Camp site, wh ich was included in the previous modeling analysis prese nted in SRP’s ANPR comments, is located on the rim of the canyon. The same conclusions wer e obtained in this new analysis as the previous one – the use of SRP’s ammonia inputs produ ced the most reasonable agreem ent between modeled

and measured ammonium nitrate con centrations. When considered w ith the previous modeling analysis, these results suggest a general tendency of the CALPUFF model to overpredict nitrate concentrations in the winter. This is signi ficant because it suggests that CALPUFF also overpredicts the deg ree of visibility improvement that would be expected from implementing a given NO
x BART control option.

Several independent evaluations confirm this finding. The most relevant investigation is a recent study performed by Atmosp heric and Environmental Researc h, Inc. (AER), which suggested several potential impro vements to the nitrate chemist ry algorithms in CALPUFF.3 The improvements were implemente d and an analysis was conducted in which the concentrations predicted by the improved model were compared wi th measured values. The results obtained using the i mproved version of the model sh owed significantly less overprediction of nitra te concentrations.

The modeling performed by SRP, a nd the other studies and evalua tion work presented in this report, compel the following conclusions:

• The EPA‐approved version of CAL PUFF has a general tendency to o verpredict

ammonium nitrate conce ntrations during the winter. To compensat e for this tendency, SRP’s ammonia inputs s hould be used in the visibility benefits modeling for the pending NGS BART determination.

• The incremental visibility impro vement predicted for each BART control option is critically dependent on the backg round ammonia input to the mod el. The use of the SRP’s ammonia inputs yields suff iciently conservative visibilit y 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 predict ed for more stringent BART control options. I t has been shown that the use of EPA’s back‐ calculated ammonia inputs produce s order‐of‐magnitude (i.e., by a factor of 10 or more in some cases) overestimate s compared to measured values.

• The degree of overprediction in CALPUFF has significant implica tions on many pending BART determinations. Therefore, the nitrate formation m odules in CALPUFF should be updated to ens ure that BART determinations ar e not based on a

model with a known technical flaw.

• In order to determine regionally representative background ammo nia input values for use with an improved CALPUFF model, SRP recommends the init iation of a collaborative monitoring program using a network of passive sam plers. The passive samplers can be deployed at remo te locations at a relatively lo w cost.

To facilitate the model improvem ent 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 i ntends to run the updated versi on 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 current ly approved version of the CAL PUFF model to overpredict nitrate concentrations and the degree of visibility improvement associated with more stringent BART control options, S RP urges EPA to proceed with a BART determination that is based on SRP’s ammonia inputs.4 

## INTRODUCTION

Gaseous ammonia (NH 3 ) in the atmosphere (“backgr ound ammonia”) combines with nitrogen oxides (NO
x ) and sulfur dioxide (SO 2 ) gases emitted from p ower plants and other emission sources (such as motor vehicles and oil and gas explor ation facilities) to form

ammonium nitrate and a mmoniated sulfate particles, both of whic h impair visibility.

Ammonium nitrate conce ntrations from stationary sources predict ed using the CALPUFF 1 model are highly sensitive to th e concentration of background a mmonia that is input to the model, particularly during the winter months.

The Salt River Project (SRP) sub mitted a Best Available Retrofi t Technology (BART) analysis for the Navajo Generati ng Station (NGS) in December 20 08.  In this analysis, SRP utilized the CALPUFF model to assess the degree of visibility i mprovement associated with each BART control option under c onsideration, in accordance wit h a modeling protocol approved by the U.S. Environme ntal protection Agency (EPA). 2 For these simulations, SRP used the same background ammonia concentrations as those that h ad been used in the previous PSD permit application for the Desert Rock Energy Faci lity (DREF) in northwestern New Mexico (“SRP’s ammonia inputs”). 3 More recently, the New Mexico Environmental Department used th e same background ammonia conce ntrations in its draft

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

In August 2009, EPA issued an Adv anced Notice of Proposed Rulem aking (ANPR) in which the agency used an indirect “bac k‐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 a mmonia that was involved in the formation of ammoniated sulfate‐ and nitrate‐containing particles measured a t sites that are part of the Interagency Monitoring of Protect ed Visual Environments (IMPROV E) program.  The EPA

1 The U.S. Environmental Protecti on Agency (EPA) has specified t hat 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 200 7.  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.  I n separate e‐mail correspondenc e, 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 ammon ia concentrations values had been approved by the Federal Land Managers (FLMs) for use in the DRE F modeling.

4 New Mexico Environment Departme nt Air Quality Bureau BART Dete rmination, Public Service Company of New Mexico San Juan Ge nerating Station, Units 1‐4, New Mexico E nvironment 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 i n approach by the EPA, since mo deling protocol approval letters sent by the EPA on 1 July 2008 stated that the background ammonia values us ed in the NGS BART modeling were “appropriate.”5 asserts that this back‐calculate d 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 A NPR in October 2009.  In the sub mitted comments, SRP included a technical paper 7 that demonstrated that EPA’s back‐calculation method overestimates the background ammonia concentration values that would be appropriate for use in CALPUFF modeling of N GS emissions.  Those overestima tes occur because some

of sulfates and nitrates measure d at the IMPROVE sites are due to sources other than NGS, many of which may be outside of t he region.  The ammonia that w as involved in their

formation would never have been available to interact with emis sions from NGS. Therefore, for these situations, the back‐calculation method ov erestimates the amount of

ammonia that is available to com bine with emissions from NGS to produce ammonium

sulfates and nitrates.

To evaluate the extent to which t he back‐calculation method ove restimates the amount of

ammonia available to interact wi th NGS emissions, the technical paper included an analysis performed using the CALPUFF mode l.  In this analysis, the model was used to estimate

ammonium nitrate conce ntrations at several I MPROVE sites using both SRP’s ammonia inputs and EPA’s back‐calculated ammonia inputs.  The results o f the analysis indicate that

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

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

further indicates that the use of SRP’s ammonia inputs produces the most reasonable agreement between modeled and measured ammonium nitrate concent rations.

In the ANPR, EPA cited a lack of available monitoring data for background ammonia in the Colorado Plateau as justificatio n for the use of its back‐calcu lation method.  Indeed, previous ammonia measurements pro vide only limited insight into the background

ammonia concentrations on the Col orado Plateau in southern Utah and northern Arizona – the area shown in Figure 1.  How ever, the technical paper submi tted with SRP’s comments

on the ANPR demonstrates that th ere is justifiable concern abou t the correctness of

ammonia concentrations derived u sing EPA’s back‐calculation met hod.

In order to provide a firmer basi s for estimating ambient ammon ia concentrations in this area, SRP offered to perform mon itoring of ammonia in the vicin ity of NGS.  SRP submitted a conceptual monitoring plan t o EPA in June 2009.  Despite limi ted feedback, SRP decided to pursue the monitoring and pro vided a final plan to EPA and t he National Park Service (NPS) in October 2009.  NPS provi ded feedback on the plan in a conference call in November 2009.  The monitoring pr ogram was initiated in early D ecember 2009 and ran until the end of April 2010.

7 Analysis of the U.S. EPA’s Back

­ Calculation Approach for Estimating Ammonia Concentrations , by Ivar Tombach and Robert Paine, 26 Oc tober 2009.  Report submitted to the US EPA Region 9 as Attachment 2 to comments provided by the Salt Riv er 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 NH 3 monitoring program and its findi ngs, Part B addresses the role of NH 3 in CALPUFF visibility modeling, and a final part summa rizes the conclusions of the en tire report.

In Part A, Section A‐2 describes the monitoring program in deta il; Section A‐3 presents the results of the measurements; Sec tion A‐4 interprets those measu red data; and Section A‐5

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

Section B‐1 reviews the role of NH 3 background in CALPUFF modeli ng of visibility; Section B‐2 addresses various assumptions about NH 3 concentrations; and Section B‐3 presents modeling performed by SRP using different ammonia inputs; Secti on B‐4 discusses results

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

conclusions and a brief discussi on of areas of potential future work.8 

## PART A

MEASUREMENTS OF AMBIENT BACKGROUND AMMONIA9 

## A

‐ 1. BACKGROUND

The Role of Ammonia in Nitrate Particle Formation

A brief introduction into the r ole of background ammonia (NH 3 ) in the formation of nitrate particles will illumina te some of the technical issues that wer e considered in the design of SRP’s monitoring program.  Background NH 3 reacts preferentially with SO 2 to form ammoniated sulfate particles.  If any NH 3 is left over, it may be inv olved in reactions with NO
x that form nitrate particles and gases.  Specifically , the chemical species that a re produced in these latter reactions are a mixture of gaseous nitric acid (HNO 3 ) and particulate ammonium nitrate (NH 4 NO 3 ), the

sum of which we label as total nitrate .  It is the particulate for m of ammonium nitrate that impairs visibility.

The results of these reactions d epend on the relative availabil ities of SO 2 , NO
x , and NH 3

. 8
Specifically, three scenarios de scribe the possible outcomes fo r nitrate production.  The scenarios are presented here in order of decreasing concentrati ons of background NH 3 :

• NO x ­ Limited. If the concentration of background NH 3 is sufficiently large, the NH 3 first

reacts with the SO 2 to form fully neutralized (i.e., non‐acidic) ammonium sulfate particles.

The NH 3 left over after the sulfate‐pro ducing reactions are complete i s then available to

react with NO
x to form ammonium nitrate.  If some NH 3 still remains after the nitrate‐ producing reactions, then the am ount of nitrates formed is inde pendent of the NH 3 concentration.  Rather, i t depends only on the NO
x concentration, i.e ., the formation of

nitrates is said to be “NO

x ‐Limited”.

• NH 3 ­ Limited. If the background NH 3 concentration is larg e enough to produce

neutralized ammonium sulfa te particles with some NH 3 left over, but not enough NH 3 remains to combine with all of the available NO
x , then the amount of nitrates produced depends on the NH 3 concentration, i.e., the format ion of nitrates is said to be “ NH 3 limited”.  In this case, nitrate s will be formed only to the ex tent that sufficient NH 3 is

available.

• No Nitrate Production. If the background NH 3 concentration is not even sufficient to produce fully neutralized sulfat e particles, then the resulting sulfate particles will be acidic.  In this scenario, all of the background NH 3 will be consumed in the process of forming the sulfate particles.  No NH 3 will remain to react with NO
x and there is no production of nitrate particles. (This situation occurs at tim es in the Southeast in the summer.)

The equilibrium between the gase ous and particle forms of nitra tes produced in these reactions depends on the ambient temperatu re and relative humidity.  The reactions are reversible and occur very rapidly (typically wit hin a few minutes), so the fra ction of total nitrate that is in

8 For the sake of brevity and clar ity, this presentation simplif ies the chemical processes that are involved.10 particulate form depends on the c onditions at or near the area of concern (e.g., the Class I area). Cooler temperatures and higher r elative humidity favor the form ation of particulate ammonium

nitrate.

The relationship is illustrated conceptually in Figure 2 (and i s also discussed further in the Part B report), which shows an example of how the ratio of (particulat e) ammonium nitrate to total nitrate (nitric acid gas + particulate ammonium nitrate) concen tration varies as a function of temperature and relative humidit y (RH), based on the parameteri zation of the nitrate equilibrium process that is cont ained in the CALPUFF model.  Th e example in Figure 2 is for a total nitrate concentration (exp ressed in terms of nitrate ion, NO 3 ‐ ) of 1.625 parts per billion by volume (ppbv) and a NH 3 concentration of 1 ppbv.  The c urves show the partitioning of total nitrate, so the numbers on the cur ves indicate the percentage o f the total nitrate (again in terms

of NO 3 ‐ ) 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 NO 3 ‐ ) of 1.625 ppbv and NH 3 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 concentratio n would be zero at RH less than about 85% ‐‐ i.e., all of the nitrate would be in gaseous form – for t he specific total nitrate and N H 3 conditions represented in the figure. At a colder temperatur e 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 Arring ton at the VISTAS BART Protocol Meeting, Research Triangle Park, NC. 21 September

2005. http://www.vistas-sesarm.org/documents/index.asp11 between particle and gaseous nit rate whenever the RH is less th an about 60%. At all temperatures, the fraction of par ticulate nitrate increases as the RH approaches 100%.

This example illustrates that nit rate particles could volatiliz e and become HNO 3 gas if the temperature increases or the relative humidity decreases.  Conv ersely, the gaseous HNO 3 could convert into particulate nitrate s if the temperature decreases. The behavior of nitrates is

different from sulfates: the sul fate‐forming process is not rev ersible and therefore the amount of sulfate particles at a receptor is determined by the history of the amount of SO 2 occurring in an air mass throughout its travel to the receptor.

In summary, unless the amount of ammonia is so abundant that th e formation of nitrates is limited by the amount of NO
x (Scenario #1, above), we ha ve the following situation:

• The ammonia concentration is the most important determinant of how much of the NO
x in the atmosphere is conver ted into nitrate, and

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

temperature.

A strong correlation has been found between background NH 3 concentration and ambient temperature during the course of a day. 10 This temperature dependence is consistent with observations that background ammo nia concentrations tend to be greater in summer than in winter, especially because the or ganic sources of ammonia are m ore active in warmer temperatures. Since the nitrates produced in summertime are mos tly in the gaseous form that does not affect visibility , the higher summertime NH 3 concentrations have a negligible effect on visibility.

Colder temperatures are needed t o form nitrate particles, and I MPROVE measurements show that particulate nitrate concent rations in rural areas are, ind eed, generally greatest in the winter, despite the lower ammonia concen trations at that time. This mea ns that nitrates are of greatest importance to visibility during t he cooler months of the year, hence the background ammonia concentrations during those mont hs are a critical input to the visibility modeling that is performed as part of the BART an alysis. For this reason, the SR P NH 3 measurements described in this report took place during th e relatively cooler months of D ecember through April.



### Previous Ammonia Measurements

Very few measurements have been m ade of the actual concentratio ns of background ammonia in the area shown in Figure 1, so b ackground ammonia concentration s inputs to the model must typically be estimated from othe r information.  Several studies have been performed that could provide some insight into ammonia concentrations that might int eract with emissions from NGS.

Two sets of NH 3 measurements on the Colorad o Plateau are known to us:

10 Gaseous Ammonia Fluxes and Back ground Concentrations in Terres trial Ecosystems of the United States, by A.O. Langford, F.C. Fehsenfeld, J. Za chariassen, 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 U niversity during the month of May 2003. 11 The

ammonia concentration measured d uring this late spring month av eraged 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 Co rners area, using passive sampl ers 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 mo st representative of the regional background ammonia concentrations.

The annual mean ammonia concentr ation at Mesa Verde was 0.2 ppb v, with a standard deviation of 0.2 ppbv.  The 3‐wee k average concentrations at Me sa Verde were 0.2 ppbv

or less in all but one of the sam pling periods ending in Januar y through April, and in all periods ending in September thro ugh December.  In fact, all sam pling periods ending in January and all those ending in November and December had conce ntrations of 0.1 ppbv or less.  The sampled concentrati ons at Mesa Verde were greater than 0.2 ppbv only during May through August, with a peak of 0.6 ppbv for one samp le in July.

Interestingly, the ammonia concen trations at another site (Nava jo Lake), which the investigators considered to be im pacted by emissions from oil a nd gas development, closely tracked the concentratio ns at Mesa Verde even though th e two sites were roughly 100 km (60 miles) apart.

Ammonia concentrations at the ot her three sites, all of which w ere considered by the investigators to be impacted by emissions from power plants, oi l and gas development, agriculture, mobile sources, and /or urban activities, were alwa ys greater than the concentrations at Mesa Verde and Navajo Lake.

Background ammonia concentrations have also been measured at fo ur 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 NH 3 concentration was 0.2 ppbv and it exhibited almost no diurnal v ariation.  In contrast, in summer the average daytime peak 2‐hr NH 3 concentration of about 0.6 ppbv was more than double the average nighttim e 2‐hr concentration of less th an 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 E NSR by Dr. Jeff Collett of Colorado State University.

12 Baseline ambient gaseous ammoni a concentrations in the Four Co rners area and eastern Oklahoma, USA

, by Mark E. Sather, Johnson Mathe w, 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 atmos pheric chemistry and deposition in Rocky Mountain National Park during two 5‐week p eriods in 2006 (25 March throu gh 29 April in spring and 6 July through 11 August in summer). 14 24‐hr NH 3 measurements were made daily at one location. During the 35‐day spring study, about half of the 24‐hr NH 3 concentrations there were less than 0.20 ppbv. 15 In contrast, during the 37‐ day summer study, about half

of the 24‐hr NH 3 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 Uppe r Green River Basin of central Wyoming. 16 Two samples (one each of 3 days and 4 days d uration) were taken each week f rom December 2006 through January 2008.  Monthly‐average NH 3 concentrations were well under 0.1 ppbv during the three months from December 2006 through February 200 7, and again during

the five months from November 2007 through March 2008.  Concent rations of all 3‐ or 4‐ day samples during those months were also less than 0.1 ppbv, e xcept 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 NH 3 at Big Bend National Park (in Texas on the U S‐Mexico border) dur ing July through Oct ober 1999. 17 The park has an arid, desert‐like envir onment.  The average daily NH 3 concentration during this summer and early‐fall study was 0.156 μg/m 3 (about 0.22 ppbv) and values ranged from a low of 0.003 μg/m 3 to a high of 0.624 μg/m 3 (about 0.004 to 0.89 ppbv). 18

Of the six studies described abo ve, only three (Four Corners, N iwot Ridge, and Upper Green River Basin) included NH 3 measurements in the cooler month s of late fall, winter, and ea rly spring. The average wintertime concentra tions measured in those three s tudies ranged from less than 0.1 ppbv to less than 0.2 ppbv. T hese concentrations are consis tent 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 tha t included the summer were about 0.2 ppbv in the Four Corn ers and Big Bend studies.

14 RoMANS: Rocky Mountain Atmospheric Nitrogen and Sulfur Study Report , by William C. Malm, Je ffrey L. Collett, Jr., et al., October 2009. ISSN 0737‐5352‐84.

15 Ammonia concentrations are reported in the RoMANS report in te rms of micrograms of nit rogen per cubic meter.

These values were converte d here to micrograms of NH 3 by multiplying by 17/14, the r atio of formula weights for

NH 3 and N, respectively. Conversion to units of ppbv depends on th e atmospheric pressure a nd temperature, both of which vary from hour to hour. At standard atmospheric temperatu re and pressure, 1 ppbv NH 3 = 0.7 μg/m 3 , so division by 0.7 provides the app roximate concent ration in ppbv.

16 NH 3 Monitoring in the Upper Green River Basin, Wyoming , by John V. Molenar, H. James Newell, Jeffrey Collett, et al. Extended Abstract #70, A&WMA Spe cialty Conference “Aerosol & At mospheric Optics: Visual Air Quality and Radiation”, Moab, Utah , 28 April ‐ 2 May 2008.

17 Aerosol ion characteristics during the Big Bend Regional Aeros ol and Visibility Observat ional 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 NH 3 concentration information publis hed by the investigators is in units of micrograms per cubic meter. As
noted in Footnote 14, conversion to units of ppbv depends on th e atmospheric pressure a nd temperature, both of which vary from hour to hour. At standard atmospheric temperatu re and pressure, 1 ppbv NH 3 = 0.7 μg/m 3 , which is

the conversion used here.14 

## A

‐ 2. THE SRP NH 3 

## MONITORING PROGRAM

As discussed in Section A‐1, prev ious ammonia measurements prov ide only limited insight into the background ammonia concentr ations on the Colorado Plateau i n the vicinity of NGS.  In particular, those measurements d o not support the ammonia conce ntrations derive d by the EPA using its back‐calculation metho d.  In order to provide a firme r basis for estimating ambient

ammonia concentrations in this a rea, SRP initiated a program to measure wintertime background ammonia concentratio ns in December 2009.  This secti on describes the conceptual design of the program, as well as the equipment and procedures used.



### Management of the SRP NH 3 

### Monitoring Program

The monitoring program that is t he subject of this report was t he 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., Envi ronmental 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, Quali ty Assurance Manager

• Laboratory Analyses

– Atmospheric Science Laboratory, Colorado State University (CSU)

o Jeffrey Collett, Ph.D., Da ta 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 NH 3 monitoring program:

• The ammonia concentrations of in terest are the ones that are mo st appropriate to use in the CALPOST processor of the C ALPUFF model.  CALPUFF and the as sociated POSTUTIL post‐processor provide for input of monthly average ammonia con centrations, which is

the convention the EPA used in i ts CALPUFF modeling of NGS emis sions.  Since SRP and EPA Region 9 with its BART mod eling have identified the 8 th highest day as the appropriate metric for identifying the visibility impacts at Cl ass I areas, it is not15 productive to target measurement of conditions that are associa ted 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 N GS emissions are emitted and within which they are transp orted.  Those concentrations ha ve to be measured outside of the portion of the at mosphere where the actual NGS e missions are present today, because the current emissi ons could react with the backg round ammonia and

reduce its concentration.  The a mmonia concentration upwind of NGS or immediately outside the region of influence of current NGS emissions may be st represent the

concentration that is available to react with the NO
x emissions from NGS in the model.

• Measured ammonium nitrate concent rations in the a rea are greate st during the winter months and CALPUFF modeling indic ates that particulate nitrate concentrations are most sensitive to the background ammonia concentrations during the c ooler months. Therefore, the period where background ammonia concentrations a re most important to visibility spans from the late f all through winter into early s pring.

• Much of the topography around NGS is very complex, including ne ar‐vertical cliffs and deep canyons with steep walls, s o CALPUFF is not able to simula te the details of the local flows in and around such topographic features. 19 In order to be consistent with the spatial resolution used in pract ice with CALPUFF, the backgroun d ammonia

concentrations should be measure d at locations that are represe ntative of the region in general, and are not characteris tic of conditions at geographic features, such as the bottoms of canyons or the tops of buttes, whose effects on tran sport and dispersion may not be represented in CALPUFF.

• The region between NGS and nearb y Class I areas is free of majo r ammonia emission sources, such as agricultural f eedlots.  Consequently, it was r easonable to assume that the

ambient ammonia concentrations i n this region would be relative ly uniform spatially, and therefore measurements at a poin t would likely reflect ammonia concentrations over a large area.

• While the key variable of interest is the background NH 3 concentration, other

measurements would to assist with interpreting the NH 3 results.  Specifically, the SO 2 gas concentration would provide an i ndication of the presence of em issions from coal

combustion that could react with the NH 3 .  Knowledge of local wind speed and direction could provide insight into the g eneral direction of airflow to the site.



### Site Selection Rationale

In light of the above con siderations, the SRP NH 3 measurement program w as designed to address the background NH 3 ammonia concentrations occurring along the transport routes of NGS

19 In fact, to the knowledge of the authors, no one has demonstra ted any model that is able to produce a reasonable simulation of the finer‐scale details of transport and dispersi on in much of the topogr aphically complex region

around NGS.16 emissions to the three class I ar eas for which CALPUFF modeling has predicted higher visibility impairment, i.e., Grand Canyon, Capitol Reef, and Canyonlands N ational Parks.

Since the first part of the trans port route to Canyonlands is a lmost the same as that to the southern portion of Capitol Reef, and hence the ammonia concent rations 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 conc entrations representative of the transport routes to Grand Canyon and Capitol Reef, as sy mbolized by the two arrows in Figure 3. Desirable ammonia moni toring site locations are expec ted 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 imp lied considerable freedom in pl acing the monitoring sites, since the background NH 3 concentrations were expected to be quite uniform spatially.

Background NH 3 measurements will represent the regional concentrations of ava ilable ammonia

whenever the NGS emissions are no t directly affecting the air m ass that is being measured. Because of opposite directions of the two arrows in Figure 3, a t any moment in time at least one of the two monitoring sites impl ied by the red circles is likel y to measure ammonia concentrations that are regionally representative, either becau se they are upwind of NGS or are near (but not on) the actual tra nsport route of NGS emissions t o a Class I area.



### Monitoring Program Description

Taking into account the design f actors mentioned earlier, SRP c arried out measurements of atmospheric concentrations of ammo nia gas and other gaseous and particulate species at several locations in the region around N GS from the beginning of Decemb er 2009 to the end of April

2010.  These are the coldest month s of the year, when measured particulate ammonium nitrate concentrations tend to be highest.

Monitoring Site Locations. Four criteria were used to selec t the locations of the monitor ing sites:

• The Monitoring Program Design Considerations described above

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

• Site exposure to background NH 3 and avoidance of poten tial local sources

• Logistical considerations – elec tric power, access for servicin g, and security.

Typically, all four of the criter ia could not be met simultaneo usly at any particular location, so the site selection process involv ed balancing of sometimes‐cont radictory criteria.  Ultimately,

two locations were selected to sta rt the program – a northern s ite adjacent to the little‐used Cal Black airport at Hall’s Crossing (Utah), along the Colorado Riv er some 90 km (60 mi) northeast of NGS, and southern site near a fo rmer coal‐slurry pipeline pumpi ng station at Gray Mountain, a location south of Cameron (Arizona), about 120 km (80 mi) south of NGS.  Monitoring with diffusion denuders (described bel ow) began at these two sites o n 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 NH 3 in the vicinity of these paths.18 Initial results from the first f ew weeks of measurements showed ammonia concentrations at the Gray Mountain site to be substant ially greater than those at th e Hall’s Crossing site, a difference that was larger than expected.  T o determine if t he Gray Mounta in ammonia concentrations were affected by localized sources of NH 3 , SRP added a third monitoring site, comparably equipped to the original two sites, at the A rizona Public Service Moenkopi Switchyard, approximately 8 km (5 miles) northeast of the Gray Moun tain 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 n eed electric power, at six loca tions – at all three of the instrumented monitoring sites pl us at three remote locations in the region around the two southern monitoring sites.  (The three sites with both diffusio n denuders and passive samplers are hereafter referred to as “pri mary” sites, to distinguish th em from the three sites with only passive samplers.)

The locations of all of the monit oring sites are shown on the m ap in Figure 4 and a finer scale

view of the locations of the fiv e southern sites appears in Fig ure 5. Geographic coordinates, aerial views, and photographs of the e quipment installations at all si tes 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 fi ne particulate matter, 21 and measured meteorological variab les, 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. Coll ins, 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 th e gas.19 Figure 4. Locations of NH 3 monitoring sites (circles) relative to NGS and the Grand Canyon.20 Figure 5. Detail of locations of the NH 3 monitoring sites south of NGS.21 Table 1. Summary of Variables Measured and Instrumentation Used.

Component Parameter Instrumentation Sample Period

PRIMARY SITES

Gases Ammonia (NH 3 ) URG Denuders 6

‐ day integrated
samples

Nitric Acid (HNO 3 ) URG Denuders 6

‐ day integrated
samples

Sulfur Dioxide (SO 2 ) URG Denuders 6

‐ day integrated
samples

Fine Particulate Matter Ammonium ion (NH 4 + ) URG Stacked Filter 6

‐ day integrated
samples

Nitrate ion (NO 3 ‐ ) URG Stacked Filter 6

‐ day integrated
samples

Sulfate ion (SO 4 2

‐ ) URG Stacked Filter 6

‐ day integrated
samples

Soil Constituents (K +

, Mg 2+

, Ca 2+ ) 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 (NH 3 ) Radiello Passive Samplers 18

‐ day integrated
samples

Primary Sites. The principal measurements for t he project were made on samples collected by continuously operated University Research Glassworks (URG) samp lers 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 PM 2.5 size‐selective cyclone inlet at 1.5 m (5 ft) above the ground, then through a series of two coated an nular denuders, a Nylon filter , and another denuder. 22 The first

denuder was coated with sodium c arbonate to collect gaseous nit ric acid (HNO 3 ), while the second denuder was coated with p hosphorous acid to collect ammo nia gas (NH 3 ).  After these denuders, a 37 mm nylon filter co llected particles, including t hose containing sulfate (SO 4 2‐ or
SO = 4 ), ammonium (NH 4 + ), and nitrate (NO 3 ‐ ) ions.  Lastly, a “backup” denuder collected any NH 3 volatilized from ammoni um nitrate particles on the Nylon filter . System operations and analysis

22 The typical sampling configurat ion sketched in Figure 6 does n ot 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 Figu re 7 shows the configuration used in this study, with the backup denuder on top.22 of collected samples were carrie d out in accordance with EPA pr otocols 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 di ctated by SRP’s desire to quant ify background ammonia concentrations down to 0.1 ppbv. Consequently, the denuders wer e 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 tra in was changed in the morning of

the 7 th day. In order to facil itate comparisons wi th concentrations me asured at IMPROVE sites on every third day, the 6‐day perio ds used included an IMPROVE sam pling day on the 2 nd and 5 th 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.  Relat ive humidity was measured just below the anemometer.  The URG sampling system included mea surements of ambient temperatur e, barometric pressure, and sample airflow rate.  All of thi s continuous measurement data w as recorded digitally on‐site and was telemetered daily via th e cellular network to ARS.

Passive Sampling. To provide further insight int o the representativeness of the Gray Mountain and Moenkopi site measurements, Radiello passive NH 3 samplers were installed at the three primary sites and at the three r emote locations in the vicinity of the two southern primary sites. The Radiello passive samplers co nsist of adsorbent cartridges h oused within “diffusive bodies”, which were placed 1.5 m (5 ft) a bove the ground.  The adsorbent cartridges were impregnated with phosphoric acid to collect ambient atmospheric ammonia.  F or quality assurance purposes, each site had duplicate passive s amplers, as shown in Figure 8.Figure 8. D u

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### Quality Assurance

Quality Assurance Program. Key elements of the quality a ssurance program for the study included the following:

• Written procedures for field han dling of samples, sample analys is in the laboratory, and data processing, in the QAPP

• Collection and analysis of fi eld and laboratory blanks

• Audit and calibration of the URG samplers and meteorological eq uipment, jointly performed by ARS and CSU personn el, on 27‐29 January and 5 Marc h

• Visits to all primary and passive sampler sites by the SRP proj ect team and Dr. Ivar Tombach on 12 February

• Collocated measurements of NH 3 at (1) the primary sites, by URG samplers and Radiello passive samplers, and (2) all pa ssive sites, by collocated Radi ello samplers

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

• Analysis of standards in the labo ratory (to determine analytica l accuracy)

Details of these aspects of quali ty assurance are provided in t he project QAPP and in laboratory

SOPs.

NH 3 Sampler Comparison. One means of data quality evalua tion was a consequence of the collocation of multiple samplers a t every site.  Duplicate Radi ello passive samplers were installed at all sites and URG denuder samp lers were collocated with the passive samplers at the primary

sites.  This collocation allowed evaluation of the internal con sistency of the two forms of NH 3 measurements as well as determina tion of the precision of the p assive NH 3 measurements.

Figure 9 is a plot of the blank‐corrected NH 3 concentrations determ ined from one passive sampler (Sampler A) vs. the othe r passive sampler (Sampler B) a t the same location.  The diagonal line is the 1:1 line. All 18‐day samples at all six s ites collected from the commencement of passive monitoring on 15 February throu gh the termination of sampling on 28 April (four samples per site) are represente d, with one exception.  Data fo r both Hall’s Crossing passive samples for the period beginning 23 March were discarded becaus e the blank‐corrected concentrations were negative and differed greatly from the reas onable concentration determined by the URG sampler there.26Table2.Monitoringprogramsamplingschedule,withcoloredcellsindicatingwhenvalidsampleswereobtained.Samplingperiodsareidentifiedbythedateonwhichthesamplingbegan.Doubleasterisksindicatewhentheconcentrationsofoneortwominorconstituentswerebelowtheminimumdetectionlimit(seedetailsinTable4).SamplerSiteDecemberJanuaryFebruaryMarchApril5111723294101622284*9152127511172329410162228URGHall’sCrossing****************Moenkopi**GrayMountain**PassiveHall’sCrossingMoenkopiGrayMountainWaunetaBabbittRanchBlackPointNote:*ThesamplerexchangethatwassupposedtotakeplaceonFebruary3rdactuallytookplaceonFebruary4th,resultingina7‐daysamplefollowedbya5‐daysample.27 Figure 9. Comparison of NH 3 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 bet ween the two passive samplers i n each pair was generally excellent; the measurem ents were very well correlated (R 2 = 0.98) and the Root Mean Square (RMS) discrep ancy was 0.023 ppbv.  The largest magnitude of discrepancy was ‐0.044 ppbv, whi ch represented a fractional err or of ‐11%.  The largest fractional error was ‐13% .  Based on the same data set, the relative

measurement precision of the passi ve measurements of NH 3 was determined to be

3.2%. 25 26

25 The measurement precision indicates how well the comple te 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 bein g measured – the NH 3 concentration in this instance.  It is usually straightforward to determine laboratory precision by repeated analysis of aliquots of the sa me sample, but it is d ifficult to quantify the precision of the field portion of the monitoring program – the sampling and the sample handling – because such repeated measurem ents of the same atmosphere are typically not possible.  The operation of the passive samplers in this SRP NH 3 monitoring program was a refreshing exception to this dilemma, because it was economically feasible to operate m ultiple samplers in the same atmosphere and to then analyze t he samples in the same laborato ry.  Therefore it was possible to calculate the precision of the e ntire measurement process (fiel d sampling and laboratory components) when using t he passive samplers.

26 Accuracy, Precision, and Minimum Detection Limit (MDL). Memorandum prepared by the CSU Atmospheric Science Laboratory for the SRP NH 3 monitoring program, 4 June 2010.28 Figure 10 is similar to Figure 9, but now the NH 3 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 conc urrent three con secutive 6‐day NH 3 concentrations from the URG sampler at the same site.  The data sources repres ented in this plot include only the three primary sites and the duration of the da ta is the same as in Figure 9 (taking into considerati on the discarding of the Hall’ s Crossing passive data for the 18‐day period starting on 23 March).

Figure 10. Comparison of NH 3 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 technolog ies of the URG and passive samplers (R 2 = 0.89) is not as strong as that between two collocated identical passive samplers.  The R MS difference is 0.078 ppbv, about three times as large as the corresponding value for the collocated passive sam plers.  The largest difference was 0.225 ppbv, which corresponded to the largest fr actional error of 28%.  Nevertheless, this degree of agreement is good enough to allow mingling of

NH 3 concentration data de rived by the URG and p assive samplers, an d this has been done in this report.29 The very few points plotted in F igure 10 could lead one to infe r that the passive

measurements tend to understate the URG measurements at the low er concentrations on the plot and overstate the URG values at the highest concentration.  However, this sam e stratification is also by si te, with the lower values occurring at Hall’s Crossi ng and the highest values at G ray Mountain, so the discrepancy with the passive mea surements may just reflect slig htly different calibrations of the URG sampling systems (or other systematic o r operational differences) at different sites.  Therefore, there is no eviden ce that there is a

consistent bias between the two t ypes 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 l imit (MDL), analytical precision, and analyt ical accuracy for the ion chrom atographic analyses in the CSU laboratory. 27 The MDL and analytical precisio n were calculated specifically for this monitoring program.  The accuracy was calculated by an alyzing standards. Since the accuracy can vary with concentration, a range of accu racies is given in the table.  Empty spaces in the accura cy column indicate that the a ccuracy was not determined.  Dashes appear in th e MDL (ppbv) column when the sp ecies is solid and therefore ppbv is meaningless.

In general, the MDLs are well be low the concentrations encounte red during the monitoring program.  The minor e xceptions are listed in Table 4

.

Note particularly that the MDL of NH 3 is 0.016 ppbv for the URG samplers, so the goal of quantifying the NH 3 concentration down to 0.1 p pbv was met.  In fact, even 3‐day samples would have been su fficient 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 th e CSU laboratory memo reference d in Footnote 26.30 Table 3. Minimum detection limits, precisions, and accuracies of laboratory analyses

Species MDL (ng/m 3 ) MDL (ppbv) Relative

Precision (%) Accuracy
Range (%)

GASES

NH 3 10.36 0.016 4.5

NH 3 (Passive) 26.08 0.042 3.2*

HNO 3 3.25 0.002 1.1

SO 2 3.49 0.002 1.7

SOLIDS AND LIQUIDS

SO 4 2

‐ 1.08 ‐‐ 1.5 0.1 to 1.8

NO 3 ‐ 1.76 ‐‐ 3.5 ‐ 7.0 to +2.6

NH 4 + 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

Mg 2+ 0.51 ‐‐ 3.8 ‐ 8.2 to 5.6

Ca 2+ 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 Mg 2+

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

Gray Mountain 12/23 Mg 2+

Moenkopi 2/27 Cl ‐31 

## A

‐ 3. MEASUREMENT RESULTS

The measurement systems of this program provided raw data in ma ny forms and from many sources, including the following:

• Results of chemical analyses of d enuders, filters, adsorbents a nd corresponding field and laborat ory blanks from all six sites

• Digital records of sampler flow rate, ambient temperature and r elative humidity, and wind speed and dir ection from the three primary s ites

• Station operator and laboratory logs

• Results of QA activities such as calibrations and audits

This information was processed, c hecked, and compiled into data records usable for providing credible insights int o the atmospheric chemistry in t he 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 dur ing this program.

Details of the calculations and the resulting values are contai ned 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 sampl ers were used at the three primary sites to c ollect samples of gases (NH 3 , HNO 3 , and SO 2 ), and fine particulate matter components (SO 4 2‐ , NO 3 ‐ , and NH 4 + and several elements) over 6‐ day periods.  To enable sample con centration calculations, temp erature, barometric pressure, and sample flow rate w ere recorded continuously.  Win d speed and direction were also recorded continuously.

As an overview of the results of t hese measurements, Table 5 sh ows the study‐ period averages and standard dev iations of the quantities measu red using the URG samplers. The concentrations in that table, as well as all conc entrations 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 patte rn 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 20.32Table5.Averagesandstandarddeviationsofsix‐daygas(g)andparticulate(p)componentconcentrationsmeasuredoverthe5‐monthmonitoringperiodatthethreeprimarysites.HNO3(g)SO2(g)NH3(g)NO3‐(p)SO4=(p)NH4+(p)Cl‐(p)Na+(p)K+(p)Mg2+(p)Ca2+(p)ppbVppbVppbVμg/m3μg/m3μg/m3μg/m3μg/m3μg/m3μg/m3μg/m3HALL'SCROSSINGAverage0.210.080.220.250.550.270.010.010.010.010.02Std.Dev.0.150.060.080.230.230.120.000.010.010.010.03MOENKOPI(15Februaryonward)*Average0.110.060.450.150.530.210.010.030.010.010.04Std.Dev.0.050.020.120.070.140.060.010.020.000.010.03GRAYMOUNTAINAverage0.170.060.630.340.510.290.010.020.010.010.03Std.Dev.0.050.030.250.260.210.120.010.020.010.010.03*Note:TheaveragesforMoenkopiareonlyforthe13samplingperiodsstarting15Februaryandsoarenotdirectlycomparablewiththe25samplingperiodsstarting5DecemberthatarereflectedintheHall’sCrossingandGrayMountainaverages.Mor e thre e

mon t
duri n

conc e anal y
Note peri o
in T a

Tabl e

sam p

Dec e

Jan u

Feb r

Mar

Apri

Ave r

Figu r
thre e e details ab o

e primary s i

t hly averag e

n g a given m

e ntration d u

y sts. The d a that there a

o ds startin g

a ble 2.

e 6. Monthl y

p lers at the t

e mber 2009

u ary 2010

r uary 2010

ch 2010

l 2010

r age

Notes: *

* *

r e 11. Conc e

e primary si t o ut the NH 3 i tes are pro v

e NH 3 conc e

m onth. Figu r

u ring the 5

‐

a tes on Figu r

a re gaps in

g on 12/29 a

y averages o

t hree prima

H a

= Based on 4

* = Only the l a

ntrations o f

t es. The dat e concentra t

v ided in Ta b

e ntrations fo

r e 11 displ a

‐ month stu d

r e 11 indic a the Gray M o

a nd 1/16 w a

o f NH 3 gas c o

ry sites.

a ll’s Crossing

0.19

0.14

0.23

0.27

0.28

0.22

4 samples; o n

a st 3 samplin g

f NH 3 gas (i n

e s represen

33 t ions meas u b le 6 and F i

fo r all samp l

a ys the sam p d y period, a

a te the first

o untain da t

a s not succ e

o ncentratio n

M

n e sample pe r

g periods of t

n ppbv) in in

t the first d a u red by the U

i gure 11. T a l es whose s

p le‐to‐sam p s provided day of eac h

t a because t h

e ssful, as w a

n s (in ppbv)

M oenkopi

–

–

0.58**

0.41

0.41

0.45

r iod missing d

t he month, s t

dividual 6

‐ d

a y of each s a U RG sampl e

a ble 6 show s

ampling pe r

p le values o f by the CSU

h 6‐day sam p

h e samplin g

a s indicate d

measured b

Gray

d uring each m

t arting 2/15/ 2

d ay URG sa m

a mpling pe r e rs at the

s the

r iods bega n f NH 3 and ARS

p ling cycle.

g for the

d previousl y

b y the URG

Mountain

0.89*

0.65*

0.71

0.48

0.48

0.63

m onth

2 010.

m ples at the

r iod. n

y34 Prominent features of the measured NH 3 concentrations that are displayed in Table 6 and Figure 11 include the following:

• At the beginning of the study, the NH 3 concentrations at Gray Mountain were considerably greater than those at Hall’s Crossing. This differ ence became smaller during the course of the study, as the Gray Mountain co ncentrations

decreased and those at Hall’s Cr ossing increased; the two conce ntrations were almost identical in the last (28 April) sample

• The concentrations measured at M oenkopi, which were only taken during the second half of the study, we re comparable to, but just a bi t smaller, than those at Gray Mountain.

Figure 12 and Figure 13 similarl y display the concentrations of other key species at all three primary sites during t he 5‐month study period, as mea sured by the URG samplers.  Concentrati ons of the gases HNO 3 (nitric acid) and SO 2 are displayed in Figure 12; the inorganic ions NH + 4 , (ammonium), NO 3 ‐ , and SO 4 2‐ are displayed in Figure 13; and the sum of two crustal cations (Mg 2+ + Ca 2+ ) is plotted similarly in Figure 14. Note that the concent ration scales are not the same on all plots.

Several features stand out in the se plots. Many species demonst rated expected seasonal behavior, with concentr ations measured toward the end of the 5 months of monitoring differing from those measured in the first months. T here are several strong seasonal effects, including the difference between NH 3 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 be tween concentrations

in the first months and the last months is especially prominent

.

The plots in Figure 12, Figure 13, and Figure 14 also illuminat e several short‐term events that are reflected in one or two 6‐day samples. For inst ance, in the sample taken starting 10 January and, t o a lesser degree, in the 4 Jan uary sample, Hall’s Crossing concentrations of all species except ammonia were greater than those at Gray Mountain, opposite to the s ituation at most other times. F or the 10 January sample, the concentrations of nitric acid, SO 2 , ammonium and nitrate were unusually high at Hall’s Crossing and concentrations of the sam e constituents were also elevated at Gray Mountain ( the Moenkopi site was not yet o perational then). Also, in the 28 January sample, unusually high sulfate concentr ations were observed at both the Hall’s Crossing and G ray Mountain sites and ammoniu m concentrations

were elevated at both si tes in that period.Figu r prim a e 12. Six

‐ da

a ry sites. y sample c o ncentratio n

35 s of the ga s es HNO 3 a n d SO 2 at th e threeFigu r
sulfa t e 13. Six

‐ da

t e ions at t h y sample c o
h e three pri m o ncentratio n

ary sites.

36 n s of partic u late nitrate

, ammoniu m , andFigu r
calci u

To p r plot t
part i

the e cubi c

mas s

29 An weig h

avail a

react i

ion i s g) – a weig h

ions (

of a m weig h

amm o

react

nor m have

48 n a r e 14. Six

‐ da

u m ions, M g

r ovide a di ff

t ed in Figur e
i culate spec

e arlier plots

c mete r , a q u

s balances i n

ultra‐simpli

h t in grams o

a ble or will a
i on that for m

s the sum of t

a total of 96 g h t of 48 g. O n Now, wh e ( NH + 4 ) becau s

m monium is t

h t of hydrog e

o nium ion a c The usef u with one eq

m ally react w i

already) re a And, as fo

a nograms pe y average c o

g 2+ + Ca 2+

, at

ff

erent vie w

e 13 and Fi g ies are incl u is that the c

u antity that

n chemical r

fied explana t

o f a chemical

a ccept in a r e

m ed the com p

t he atomic w

g – divided b y

n e equivalen t

e n ammoniu m

s e each am m

t he sum of t h

e n (4x1 g), a n

c cepts one e l

lness of this

uivalent of a

i th each oth e

a cted comple

r the magni t
r cubic mete o ncentratio

the three p

w point, Figu r

g ure 14, ex c

u ded in tha t

c oncentrati may be un f

r eactions. 29

t ion of neq/ m species, div i

e action, or (f o

p ound. For e

w eights of on e

y 2 because i

t weight of s u

m sulfate is f o

m onium ion i s h e atomic we

n d the sum (

l ectron. So, o concept is t h positively c h

e r have the s a tely with ea c

t ude of one n

r of sulfate i o

37 ns of the su

p rimary site s

r e 15 displ a

c ept that th e

t plot. An i m
on units in

f amiliar bu t

m 3 : An “equi v
i ded by (for i

o r compoun d xample, the f

e sulfur ato m
i t has 2 elect

u lfate thus e

o rmed, the s

s able to acc e ight of nitro g ( 18 g) is the n ne equivale n h at one equi v h arged ion. S

a me concen t

c h other.

n eq/m 3 , that a

o n, or rough l m of crusta l

s

.

a ys the sam e

e re is one p l

m portant di f

Figure 15 a

t is very us e

v alent weig h

i ons) the nu m d s) how ma n f ormula wei g

m (32 g) and f

rons to shar e quals 48 g.

ulfate ion re a

e pt only one

g en (14 g) pl

n divided by 1

n t for ammo n

v alent of a n e
S o, in Figure

t ration in ne q

a mount cor r l y 0.05 μg/ m l magnesiu m

e informati o l ot per site a f ference co m

a re nano eq u

e ful when e x

h t” is just the

m ber of elec t

n y were enga

g ht of the su l

f our oxygen

e , giving an e

a cts with tw o electron. On e
us 4 times t h

1 because e a

n ium is 18 g.

e gatively ch a 3‐4, if two i o

q /m 3 , then t h

r esponds to (

m 3 , a small a m m and

o n as that

a nd severa l

m pared to

u ivalents pe r

x ploring

formula

t rons it has ged in the

l fate ( SO = 4 ) atoms (4x1 6

e quivalent

o ammoniu m

e equivalent

h e atomic

a ch

a rged ion wil

o ns that

h ey can (or

( for example )

m ount. l

r

6

m

l

)

,Figu r prim a e 15. Conc e

a ry site, in u ntrations o f

nits of neq / f potentiall y

m 3

.

38 y

‐ reacting in organic ion s in fine par t icles at ea c h39 Figure 15 shows, for example, th at the atmospheric chemistry as sociated with the ammonium peaks on the 1/10 sample at Hall’s Crossing is very di fferent from that associated with the1/24 sample t here. In the sample taken start ing 1/10, there are nearly equal numbers of equivale nts of sulfate and nitrate, and their sum equals the number of equivalents of ammonium , so the ammonium is about equ ally 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 sulfat e particles and there is virtually no ammonium nitrate . The relationships between the chemical components plotted in Figure 15 ar e analyzed at greater length in Section A‐4.



### Radiello Passive Sampler Measurements

As described in Section A‐2, tandem Radiello samplers measured 18‐day NH 3 concentrations at six sites duri ng the last half of the monitor ing program. The adsorbent material in the sample rs was extracted and analyzed u sing the same ion‐ chromatographic analysis method that was used to determine NH 3 from the URG denuders.  The sampling protocol included dynamic field blanks.

Figure 16 is a plot of all of the passive sampler data – four 1 8‐day pairs of samples per site.  The values shown are averages of the two passive sam pler readings.  As Figure 9 showed, the differences between the readings of the tw o samplers were very small.  The plot also i ncludes 18‐day average NH 3 measurements by the URG denuder, for comparison with the passive data.

The bars in Figure 16 are arrang ed in four clusters, one for ea ch of the 18‐day sampling periods that began on t he dates shown at the bottom of the graph.  In each cluster, the bars are in order f rom the northernmost sampler at Hall’s Crossing to the southernmost samplers.  The o rder is as follows:  HC (Hall’ s Crossing), MS (Moenkopi Switchyard), GM (Gray Mountain), WA (Wauneta), BR (Ba bbitt Ranch), and BP (Black Point).  Although the order is based on location, it happens that the

first three sites on this list a re the primary ones. For compar ison, the maroon bars indicate the 18‐day average conc entrations determined by the UR G samplers during the same period as the passive sampling.

There are two prominent features in Figure 16: (1) NH 3 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) The re is a consistent geographic pattern over the four measurement periods shown, wit h the Gray Mountain site always having the highest NH 3 concentration, as derived by both the passive sampler and the URG sampl er, and the Hall’s Crossing si te showing the lowest concentration in every se t.  The passive sampling period of 2 1⁄2 months in late winter and early spring w as too short to permit drawing an y general conclusions for the winter or the year, however.Figu r

mea s (gre e

conc e

site n



### Met

Mea s mad e fro m chro m

Ane m at th e surf a fro m and s r e 16. The s p

s ured by pa s

e n bars). Th e

e ntrations b

n ames are d



### eorologic a

s urements o

e during th e

m the passiv e

m atograph i

m ometers m

e three pri m

a ce winds a n

m the anemo

s ynoptic‐sc a p atial and t e

s sive sampl e

e maroon b a
b y the URG s

efined in th



### a l Measur e

o f temperat

e study, pri n

e samplers a
i c analyses i

m ounted at a

m ary sites. T

n d their po s

meters are

a le weathe r e mporal dis t

e rs during t h

a rs represe n

s amplers at

e text.



### e ments

ure, relativ e

n cipally for

a nd for con v
i nto conce n

a height of a
T heir purp o

s sible effec t
also useful

r systems.

40 t ribution of

h e second h

n t concurre n
the primar y

e humidity, the purpos e

v erting the

n trations ex p

a bout 3 m a b

o se was to p

t s on the ch e

to evaluate NH 3 concen
alf of the m

n t measure m

y sites. The

and barom

e of calcula t
mass deter

p ressed in v

b ove groun rovide info r

e mical mea s

response t o trations (in
onitoring p r

m ents of N H

two

‐ letter c

etric press u

t ing concen

minations f

v arious uni t

d level wer

r mation ab o

s urements.

o passing m ppbv)

r ogram

H 3

c odes for th e

u re were
trations

f rom the io n

t s.

e operated

o ut localize d

The data

m esoscale e

n

d41 Table 7 tabulates some of the sur face meteorological informatio n 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 consis tent with its lower elevation, and the Hall’s Crossi ng winds were lighter than thos e at Gray Mountain, both in terms of frequency of cal m conditions and highest wind speed. The average

relative humidities at bot h 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 t o that of Figure 11 through Fig ure 15, Figure 17 shows the average wind speed at each site over each 6‐day sampl ing period. 31 Although the wind speed and dire ction can be expected to vary w idely over six days, the average wind speed is nevert heless an indicator of mesoscal e atmospheric activity. Figure 18 displays ano ther measure of atmospheric mot ion, the 6‐day average of the hourly standard d eviations of wind direction (of ten denoted sigma

theta ), which is an indicator of horiz ontal turbulent mixing near th e surface.

30 Since Table 7 addresses results over the 5‐month monitoring pe riod, data for the 21⁄2 months of Moenkopi operation a re not included.

31 Because of a data lo gger malfunction, meteo rological measureme nts 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 a verage wind at G ray Mountain wa s appreciably greater than that at Hall’s Cro ssing during all but one samplin g period. The Gray Mountain wind speed was often tw ice as great as that at Hall’s Crossing, beginning with the very first sampling per iod of 5 December. The one exce ption 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, t he average wind speed tended to increase during the cou rse of the 5‐month study. Alth ough the Moenkopi site was much closer to Gray Moun tain than to Hall’s Crossing, the wind speed at Moenkopi (which was only measured starting on 5 March 2010), te nded to be similar to or a bit greater than that at Hall’s Crossing, and i t 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 differ ences in the standard deviation of the wind direction in Figure 18 were much smaller, w ith the values of this turbule nce indicator being greater at Hall’s Crossing than at Gray Mountain most of the ti me. A notable exception was the period that be gan on 16 January, when the Gra y Mountain turbulence was considerably grea ter than that at Hall’s Crossin g. During the time

that wind measurements were made at Moenkopi (the last 10 sampl ing periods of the monitoring program), the sta ndard deviation of the wind dir ection there was greater than at the other two sit es. There was no obvious tempo ral trend in this

turbulence indicator over the co urse of the monitoring program at any of the sites.

Insight into the wind directions during the course of the monit oring program is provided by the wind roses in Fi gure 19 and Figure 20. These wi nd roses show that, in general, the measured surface winds at Halls Crossing were w eaker than those at Gray Mountain, and calm conditio ns (WS < 0.5 m/s) were much mor e 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 v ery light winds (with wind spe eds < 1 m/s or 2.24 mph), occurred 31% of the time du ring the 5‐month period, while at Gray Mountain

such low wind conditions occurred only 8% of the time. Consider ing all wind speeds, the wind at Hall’s Crossi ng was most often from a gener ally southeasterly direction, which was also the gen eral 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 d irection, although the preferred direction for the light wind speeds between 0.5 a nd 1 m/s tended to be more from a general ly 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.Figu r
the m e 19. Surfa c

m onitoring p e wind ros e

p rogram. for the Hal

44 l’s Crossing site over th e 5

‐ month d uration ofFigu r
the m r e 20. Surfa c

m onitoring p e wind ros e

p rogram. for the Gr a

45 y Mountai n site over t h e 5

‐ month duration o f46 

## A

‐ 4. ANALYSIS OF THE MEASUREMENT RESULTS

The data acquired by the monitor ing network described in Sectio n A‐3 was analyzed in various ways in order to arriv e at a description of the atmo spheric conditions and, in particular, background ammonia concentrations, in the r egion around NGS.

For these analyses, the concentr ation and meteorology data from the SRP monitoring network was supplemen ted by data from other sources during the December 2009 through April 2010 period. This additional data i ncluded the following.

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

data includes 24‐hr concent ration data for ions (SO 4 = and NO 3 ‐ ) and a variety of elements contained in fine pa rticulate matter at the IMPROVE monitoring stations at Bryce Canyon Nationa l Park (IMPROVE site identifier BRCA1), Canyonlands NP (CANY1), Capitol Reef NP (CAPI1), Grand Canyon N P (at Hance Camp on the rim, GRCA2, and at Indian Garden within the c anyon, INGA1), and Petrified Forest NP (PEFO1). The data were of norma l IMPROVE quality, except that they had not undergone a final review.

• Calculations of atmospheric stabi lity indices using meteorologi cal data inputs

from the EDAS data archive 33 of the National Oceanic and Atmospheric Administration (NOAA). Calculatio ns were carried out using the web‐based Real‐time Environmental Applicat ions and Display sYstem (READY) of the NOAA Air Resources Laboratory (ARL).

In the analyses in this section the following questions are add ressed:

1. How do the measured concentratio ns reflect changing atmospheric conditions during the course of the study?

2. What geographic areas do the res ults of the SRP monitoring prog ram represent?

3. What do the measurements imply ab out the abundance of backgroun d

NH 3 and its involvement in atmosphe ric chemical reactions at the monitoring sites, both on a verage and episodically?

32 Courtesy of the IMPROVE particu late monitoring team at the Un iversity of California at Davis.

33 EDAS (Eta Data Assimilation Sys tem) creates representations of meteorological conditions throughout the country by “Optim um Interpolation” analyses of s uccessive 3‐hr forecasts produced by the NOAA Eta meteorol ogical forecast model.47 

### Seasonal Variation

– Two Measurement Periods

The timelines of measured concen trations in Figure 11 through F igure 14 reveal

that most of the concentrations show two pattern s over the 5‐mo nth monitoring period:

• A long term pattern in which averages over many 6‐day sampling periods tend to be stable or vary slow ly from month to month; and

• Short episodes of one or two sampl ing periods duration that are associated with simultaneous high concentra tions of two or more species. T wo notable examples are the sampling period s beginning on 10 January and 2 8 January. 34

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

The long‐term pattern shows some important changes during the c ourse of the 5‐ month monitoring. The concentrat ions 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 t he early months of the study than in the final months. The change in long‐term beh avior appears to be most rapid about the middle of F ebruary, which suggests a trans ition in atmospheric conditions occurring around that time.

Specifically, beginning around m id‐February, fine particulate a mmonium (NH 4 + ) and nitrate (NO 3 ‐ ) ion concentrations decrease ap preciably and vary less from sa mpling period to sampling period and fro m site to site than they did e arlier. Other species (gaseous nitric acid, HNO 3 ; sulfur dioxide gas, SO 2 ; and fine particle sulfate, SO = 4 ) also show more consistency from site to site after this time. T he only species that does not display as clear a dichotomy is ammonia gas (NH 3 ), for which the Gray Mountain and Moenkopi concentrations are always considerably gr eater than those at Hall’s Crossing, although the difference decreases toward th e end of the study.

Coincidentally, sampling began a t Moenkopi in mid February (on the 15 th ), at the midpoint of the monitoring and at about the middle of these beh avioral changes, so

it is useful to divide the durati on of the monitoring program i nto two periods and investigate them separately:

• Period 1 – sampling at two sites (Hall’s Crossing and Gray Mountain) fr om the beginning of monitoring on 5 Dec ember through the sample beginn ing on 9 February

34 Sampling dates refer to the fir st day of a six‐day (or, for th e passive samplers , 18‐day) sampling period.48 • Period 2 – sampling at three sites (Hall’ s Crossing, Gray Mountain, and Moenkopi) from 15 February throu gh the end of monitoring on 28 April, plus passive sampling of NH 3 at these sites and three others.

The meteorology differed between these two periods. As Table 8 shows, in addition to the expected warmer temperatu res during Period 2 than Period 1, the air became drier (the RH decreased substant ially) and the average wind spe ed 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*

Tem p erature °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 differe d between Periods 1 and 2. At Hall’s Crossing, the Pe riod 1 wintertime propensity f or winds from the

southeast shifted to a less focu sed, more southwesterly tendenc y in Period 2. At Gray Mountain, similarly, a stro ng tendency for winds from the southwest during Period 1 became more diffuse, al though the chang e in wind direc tion was slight.Figu r Hall’

Gra y

e 21. Wind s Crossin g, P

y Mountain,

roses for P e P eriod 1

Period 1

e riods 1 and

49 2 at Hall’s C Gra y Hall ’

rossing an d y Mountain

, ’ s Crossing,

d Gray Mou n , Period 2 Period 2

n tain.50 The depth of the atmospheric bou ndary layer is an indicator of the coupling

between local conditions at the surface and the large‐scale atm ospheric conditions aloft.  Figure 22 shows values of the daily maximum depth of th e boundary layer (Z i ), in meters, at the location of NGS, over the duration of the monitoring program,

as estimated from EDAS simulatio ns by the web‐based NOAA‐ARL RE ADY system. Vertical lines indicate the begi nning of each 6‐day sampling pe riod; a starting date is given for every other sampling period. The red line marks the d ividing point

between the two halves of the mon itoring program, Period 1 and Period 2.

Figure 22. Daily maximum boundary layer depths (Z i , in meters) at NGS during the
monitoring program. The red line separates Period 1 and Period 2

Generally, a shallow boundary lay er depth corresponds to stabil ity at the surface and little mixing between the su rface layer and the atmosphere aloft, while a deep layer reflects extensive vertica l mixing with coupling to the m esoscale atmosphere aloft. Figure 22 shows a clear di fference in boundary layer dep ths during the monitoring program. Period 1, th e colder period, is characteriz ed by daily‐maximum

Z i values that are mostly relative ly uniform with most values in the range of 300 ‐ 800 m. In contrast, during Period 2, the increasing solar heati ng of the ground results in growing values of Z i, , from around 500 m in mid‐Feb ruary to the vicinity of 2500‐3000 m at the end of the monitoring program.

In light of the atmospheric mix ing implications of Figure 22, i t is expected that the concentrations measured during P eriod 1 might represent relativ ely local

conditions for an area with rela tively flat topography, while t hose during Period 251 would be indicative of more regi onal conditions. As discussed l ater in this section, although this is often true in the area of t he SRP sites, the c omplex topography of the region makes the actual picture more complex than this simple r epresentation, especially when the region of int erest includes the surrounding Class I areas.

Turning now to the concentration measurements, Table 9 summariz es some of the

concentration measurements over t he entire 5‐month measurement period and during Periods 1 and 2 separatel y. Both the mean measured conce ntrations and

standard deviations of the six s ampled species are shown.  Thos e data support the qualitative observatio ns noted above about d ifferent conditions before and after mid‐February.

Specifically, note the f ollowing in Table 9:

• At both Halls Crossing and Gray Mountain, the average concentra tions of SO 2 , NO 3 ‐ and NH 4 + were all less in Period 2 than i n Period 1 and their standard deviations (which indicate sampl e to sample variability) were s ubstantially

less in Period 2 than in Period 1 .  These are likely consequenc es of the increased atmospheric mixing, an d consequent increasing atmosph eric homogeneity, as the solar radiat ion increased from winter to sp ring.

• At both locations, the average sulfate (SO = 4 ) concentration increased slightly from Period 1 to Period 2, accomp anied with a substantial reduc tion in its

standard deviation.

• The average ammonia (NH 3 ) concentration increased from Period 1 to Period 2 at Hall’s Crossing, but decrea sed at Gray Mountain. In both c ases, the standard deviations during Perio d 2 were substantially less tha n those in

Period 1.

• The average nitric acid (HNO 3 ) concentration was the same during both periods at Gray Mountain (and th e standard deviation remained e ssentially the same), while both the concentration and the standard deviat ion at Hall’s Crossing were less during Per iod 2 than during Period 1.

Table 9 also reveals some of the differences in average concent rations between the three sampling sites. In general, NH 3 and NH 4 + concentrations at Gray Mountain and Moenkopi were greater than those at Hall’s Crossing, while the HNO 3 concentration was greater at Hall’s Crossing t han at the other two sites.  Fo r all species, average

inter‐site differences were sma ller (usually much smaller) duri ng Period 2 than during Period 1, which is consis tent with the inference from Fi gure 22 that, for the geographic area monitored by the SRP sites, the concentration m easurements in Period 2 would be more represent ative of regional conditions th an those of Period

1.52Table9.Averagesof6‐daymeasurementsofconcentrationsandtheirstandarddeviations(inparentheses),forthefull5‐monthdurationofthemonitoringprogramandforPeriods1and2individually.SpeciesUnitsHall’sCrossingGrayMountainMoenkopi5MonthsPeriod1Period25MonthsPeriod1Period2Period2HNO3(g)ppbV0.210.250.180.170.170.170.11(0.15)(0.19)(0.09)(0.05)(0.05)(0.06)(0.05)SO2(g)ppbV0.080.110.060.060.080.050.06(0.06)(0.08)(0.02)(0.03)(0.04)(0.02)(0.02)NH3(g)ppbV0.220.170.270.630.760.530.45(0.08)(0.08)(0.04)(0.25)(0.31)(0.13)(0.11)NO3‐(p)μg/m30.250.390.120.340.580.160.15(0.23)(0.27)(0.04)(0.26)(0.24)(0.06)(0.07)SO=4(p)μg/m30.550.530.570.510.460.540.53(0.23)(0.30)(0.15)(0.21)(0.28)(0.15)(0.14)NH4+(p)μg/m30.270.330.210.290.380.220.21(0.12)(0.14)(0.06)(0.12)(0.13)(0.06)(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 g eneral 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 NH 3 monitoring program. Yellow cells indicate the predominant relationships.

a) Period 1 ‐‐ 5 December through 9 February

Notes: 1 Except Hall’s Crossing NO 3 ‐ as much greater for 1/4 and 1/10 periods

2 Hall’s Crossing HNO 3 was much more than twice Gray Mtn. HNO 3 for 1/4 and 1/10

3 Gray Mountain NH 4 + was more than twice Hall’s Crossing NH 4 + for 1/22

4 Except Gray Mountain SO 2 was more than twice Hall’s Crossing SO 2 for 1/28

b) Period 2 ‐‐ 15 February through 28 April

Notes: 1 Gray Mountain HNO 3 vs. Hall’s Crossing HNO 3

2 Moenkopi HNO 3 vs. Hall’s Crossing HNO 3

3 Hall’s Crossing SO 2 was greater than Gray Mountain SO 2 more often than it was greater than Moenkopi SO 2

In Table 10, the yellow cells mar ked “XX” indicate the predomin ant relationships between concentrations listed vertically along the left and sit es listed across the top. Footnotes point out some notable exceptions to the general patt ern. 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

NH 3 (g) XX

NO 3 ‐ (p) XX 1

HNO 3 (g) Similar 2

NH 4 + (p) Variable 3

SO = 4 (p) XX

SO 2 (g) XX 4

Species Gray Mtn. &
Moenkopi

are Greater Gray Mountain

& Moenkopi
Usually Greater Variable or
Similar Hall’s Crossing
Usually Greater Hall’s Crossing

is Greater

NH 3 (g) XX

NO 3 ‐ (p) XX

HNO 3 (g) Variable 1 XX 2

NH 4 + (p) Similar

SO = 4 (p) Similar

SO 2 (g) XX 354 • “Greater” means that all samples during the period, with no mor e than one exception, had the indicated relationship;

• “Usually Greater” means than more than 50% of the samples behav ed as indicated;

• “Variable or Similar” means that the sample‐by‐sample relative concentrations were not consistently greater at any one site, o r that the

concentrations at all the sites were similar most of the time.

Broadly, the matrices in Table 10 show that, despite the differ ent concentration

levels in Periods 1 and 2 shown in Table 9, some general spatia l relationships held throughout the monitoring program:

• Nitrogen compounds tended to hav e higher concentrations at Gray Mountain and Moenkopi than at Hall’s Cro ssing, while sulfur compounds te nded to have higher concentrations at Ha ll’s Crossing. This pattern is stronger during Period 1 than during Period 2; i n fact, Table 9 showed that ave rage concentrations for some species were very nearly the same at al l three sites during Period 2.

• None of the measured species cons istently had highest concentra tions at Hall’s Crossing during either per iod. Figure 11 through Figure 13 showed that Hall’s Crossing had the hig hest concentrations of all meas ured non‐ crustal species except NH 3 in the 4 January and 10 January samples, though.

• NH 3 concentrations were consistentl y greatest at Gray Mountain.

Additional general observations, by species, of temporal and sp atial patterns over the two periods include the following

• NH 3 : During both periods, ammonia gas at Hall’s Crossing was almost always less than at Gray Mou ntain and Moenkopi

• NO 3 ­ : More than half the time during each period, the nitrate ion concentration in particulate ma tter at Hall’s Crossing was less than that at Gray Mountain, and also at Moenko pi during its operation in Per iod 2. Notable exceptions occurred on 4 and 10 January, when Hall’s cr ossing NO 3 ‐ was much greater tha n at Gray Mountain.55 • HNO 3 : Except for the episodes that wer e reflected in the 4 January an d 10 January samples, the sample‐by‐s ample nitric acid gas concentra tions at Hall’s crossing and Gray Mountain were relatively similar throu ghout the study. (Because of the effect of the January episode(s), the average HNO 3 concentrations for Period 1 in T able 8 are quite different for the two sites, however.) Moenkopi tended to have lower HNO 3 concentrations than Gray Mountain, so their comparisons w ith Hall’s Crossing concentrati ons differed,

in that more than half of the Pe riod 2 samples showed Moenkopi HNO 3 concentrations to be less than those at Hall’s Crossing.

• NH 4 + : Ammonium ion concentrations duri ng Period 1 were quite variable

and there is no obvious indication that one site had more ammon ium than the other. The variability dimin ished greatly in Period 2, so t hat the comparability in ammonium concent rations (now among three sites ) was now more clearly apparent.

• SO = 4 : Sulfate ion concentrations varie d widely during Period 1 and th e Halls Crossing concentrations were mor e than those at Gray Mountain i n more than half of the samples. During Period 2 the sulfate concentra tions varied less from sample to sample and te nded to be quite similar at al l three sites.

• SO 2 : During Period 1, sulfu r dioxide gas concentrations tended to be greater at Hall’s Crossing than at Gray Mountain, with the difference b eing particularly large in the 4 Janu ary and 10 January samples. The se episodic events amplify the differences in the averages in Table 8. SO 2 concentrations at all three sites were more similar during Period 2. In more t han half of the samples the Hall’s Crossi ng concentrations of SO 2 were slightly greater than those at Gray Mountain. Moenkopi SO 2 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 th an during Period 1. Although a n increase in temperature increases biologic al emission of ammonia, it also r educes the fraction

of total nitrates that is in the form of particulate ammonium n itrate. The expected particulate NO 3 ‐ behavior is shown in Table 8, where the NO 3 ‐ 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 th e Gray Mountain

ammonia concentrations decrease relative to Period 1, per haps because increased ventilation and mixing offsets the natural tendency for greater emissions.

In summary, the following observat ions can be made concerning t he seasonality of

the measurements:56 • The duration of the monitoring pr ogram encompassed two distinct meteorological and air quality r egimes, with a transition aroun d mid‐ February: Period 1, a typically c older period with little syste matic change in conditions, and Period 2, a period of increasing warming and at mospheric activity.

• During Period 1, the concentrati ons of several chemical compone nts were substantially different at Hall’ s Crossing than at Cameron and Moenkopi. The

differences were much sm aller during Period 2.

• The relative mix of the measured chemical components of the aer osol differed considerably between th e first and second halves of th e duration of the monitoring.

Period 1, with its lower tempera tures and much higher nitrate c oncentrations, is the more typical of the winter atmosp heric conditions that are most relevant to the monitoring program objectives expressed in Section A‐2.



### Spatial Relationships

One of the considerations that i nfluenced the design for the SR P monitoring program was the assumption that, because of the assumed absence of major NH 3 sources, the ambient concentration of NH 3 would be relatively uniform spatially.

Since the measurements showed that there were some differences in concentrations between the Gray Mountain and Ha ll’s Crossing sites, this subse ction is devoted to evaluating the extent of the geo graphic area represented by the measurements at each sampling site location.

In the idealized case of an abse nce of sources a nd sinks, and w ith meteorology that is uniform spatially, one would expect the concentration of an inert atmospheric constituent would be relatively uniform and inde pendent of loca tion in the study area, although there could be so me differences resulting from a ltitude. Deviations from these assumptions would resu lt 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 SO 2 + SO = 4 ) as an inert marker of atmospheric concentrations. Figu re 23 shows that the total S co ncentrations were indeed the same, or nearly so, at all of the monitoring locatio ns for more than half of the sampling periods. This sugge sts that it was likely that oth er air pollutant

concentrations would also be rela tively uniform over the area a t those times, a postulate that can be readily ch ecked against the concentration s that were plotted in Figure 11 and Figure 13.  This ch eck reveals that the sample pe riods that had similar Total S concentrations at all sites in Figure 23 also generally showed similar inter‐ site agreement for HNO 3 , SO 2 , NH 4 + , and SO = 4 . The NH 3 and NO 3 ‐ concentrations did57 not display the same level of agr eement, though, which suggests that local emissions may be playing a role.

Figure 23. Comparison of “total” sulfur (SO 2 + SO = 4 ) concentrations, in units of μg/m 3

of S, at the 3 sampling sites.

A larger geographic region can b e considered by including the I MPROVE measurements in this analysis. As mentioned at the beginning of this section, PM 2.5 chemical composition data was ob tained for six IMPROVE sites in the area surrounding NGS. IMPROVE does not measure SO 2 , 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 so mewhat less stable because it c an reflect varying degrees of conversion of SO 2 . As the analysis below show s, for the geographic area of the SRP monitoring study, the information that can be deduce d 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 like ly to be adequate for the purposes of this analysis.

Some adjustments are needed in or der to include both the SRP mo nitoring and IMPROVE sites in the same analys is. Since IMPROVE makes 24‐hr m easurements every 3 rd day while the SRP study collect ed 6‐day samples, comparison of SO = 4 measurements by the two network w ill be somewhat approximate, w ith the average of the two IMPROVE measurements (on the 2 nd and 5 th days of the SRP sampling cycle) compared against the 6‐da y average of the SRP measuremen ts. After these adjustments, Figure 24 and Fig ure 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 su lfur in Figure 23, shows

that the SO = 4 concentrations at the three SRP sites were well correlated. Fi gure 25

leads to the same conclusion for three IMPROVE sites in the vic inity of the SRP sites. Furthermore, the similarity of F igure 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 relationsh ips between the IMPROVE sites w as considered.

During Period 1, there was a pat tern of high inter‐site correla tions of SO = 4 concentration (r > 0.81 over the duration of the period) at the IMPROVE sites extending from Bryce Canyon (BRC A1) in the northwest to Indian Garden (INGA1) and Hance Camp (GRCA2) at the Grand Canyon 36 and on to Petrified Forest (PEFO1) in the southeast (see Figure 2 f or IMPROVE site locations). Eve n the correlation

between the two ends of this stri ng of sites, BRCA1 and PEFO1, was 0.82 (although

the SO = 4 concentrations at BRC A1 averaged 64% of the PEFO1 values). The se high correlations suggest a consistency in the general pattern of va riation of SO = 4 concentrations in the region fro m 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 Re ef (CAPI1) and Canyonlands (CAN Y1) ‐‐ well removed

from the locations of SRP’s measurements.

The pattern differed in Peri od 2, when the highest SO = 4 correlations were in two separated clusters of sites: BRCA1 and Capitol Reef (CAPI1) wit h r = 0.88, and

INGA1 and GRCA2 at the Grand Can yon correlated with PEFO1 with r = 0.78 and 0.83, respectively. There was n o longer any corre lation between BRCA1 and the two Grand Canyon sites, with correla tion coefficients of 0.22 and u nder – even though the average concentrations at all three locations were quite si milar, in the relatively narrow range from 0.45 to 0.54 μg/m 3 .  The three‐site con centration patterns during Period 2 in Figure 25 aren ’t as congruent as those of th e more compact SRP network were in Figure 24, most likely because of the long inte r‐site distance between PEFO1 (Petrified Forest) and the Grand Canyon sites, an d the very large

elevation difference between GRCA2 (Hance Camp) and INGA1 (Indi an Garden).

35 Note that the vertical scales are the same in Figure 23 and Fi gure 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 a t very different e levations. INGA1 is located within the Grand Cany on at an elevation of 1166 m an d GRCA2 is located on the rim at an

elevation of 2267 m – an elevati on difference of 1101 m (3611 f t). During Period 1, the correlations

between the SO = 4 concentrations at the two sites were 0.87, while during the mo re 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/m 3 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/m 3 of S, at 3 IMPROVE sampling sites near the SRP

network.60 Adding the SRP sites to this disc ussion, during Period 1, the H all’s Crossing and Gray

Mountain SO = 4 concentrations were highly corre lated, r = 0.94. The high cor relation does not necessarily indicate the concentrations at all locatio ns were identical, but rather that they tended to vary proportionally. For example, th e average Period 1

SO = 4 concentration of 0.59 μg/m 3 at Hall’s Crossing diffe red from the 0.46 μg/m 3 at Gray Mountain. During Period 1 b oth the Hall’s Crossing and Gra y Mountain SO = 4 measurements also correlated very well with the average concent rations 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 Cro ssing and r = 0.80 for Gray Mo untain).

During Period 2, the correlation between Hall’s Crossing and Gr ay Mountain SO = 4 concentrations was 0.84 vs. a co rrelation of 0.94 in Period 1. The average Period 2

SO = 4 concentrations at Hall’s Crossi ng, Moenkopi, and Gray Mountai n were within about 5% of each other, at 0.59, 0.55, and 0.56 μg/m 3 , respectively. Noting that concentrations at all three sites are nearly identical througho ut Period 2, it appears

that the smaller correlations in P eriod 2 than in Period 1 are a consequence of the smaller dynamic range of concent rations during Period 2. In par ticular, those smaller correlations do not imply larger differences in concent rations during Period

2.

As for the relationship between SRP and IMPROVE SO = 4 measurements during Period 2, the correlations betwee n Gray Mountain and PEFO1, GRC A2, 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 mani fested itself at the SRP sites. The Moenkopi correla tions were similar to those of G ray 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 d ata 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 surroga te for a regional air mass
marker, Period 1, which was char acterized by large differences in concentrations of

SO = 4 from SRP sample period to sampl e period, nevertheless showed a high degree of regional consistency in behav ior, exemplified by high correl ation of concentrations over a large geogr aphic band from Bryce Canyon t o Grand Canyon to Petrified Forest, including the SRP Hall’s Crossing and Gray Mo untain sites. Period 2 showed a high degree of regional uniformity, with nearly identi cal SO = 4 concentrations at the three SRP sites and nearly the same conce ntrations at the Grand Canyon and PEFO1 IMPROVE sites.

As a final indication of the clo se relationships between the IM PROVE and SRP

measurements of SO = 4 , Table 11 displays multivariat e regression equations for the

37 The Moenkopi vs. Gray Mountain correlation during Period 2 was r = 0.996!61 SRP site concentrations, as expr essed in terms of IMPROVE conce ntrations. 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 incrementa l improvement in

fit above a selected threshold or when an IMPROVE concentration appeared with a negative coefficient. Interestin gly, the stepwise regression pr ocess in each of the

four cases in Table 11 ended with concentrations from only a si ngle site. (A fit for the entire study period at eithe r site involved multiple sites, however.) In Table 11,

R 2 is the coefficient of determina tion, which represents the frac tion of the variability

in the SRP site concentration th at is explained by the formula, 38 and RMSE stands for the RMS error. As has been the p ractice for this discussion, al l SO = 4 concentrations are expressed in terms of μg/m 3 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 R 2 RMSE, μg/m 3 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 ar e 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, whi ch explain, respectively,

77% and 85% of the concentration v ariation at these SRP sites. The coefficients are 1.11 and 1.25, so the concentrati ons at the SRP sites are a lit tle greater than those at INGA1. Note, though, that the RM SE is comparable to the concent ration values

themselves.

38 The interpretation of R 2 and r can be confusing. The quantity R 2 represents the coefficient of determination, which indicates what fraction of the variation i n the dependent variable (the SRP site concentration) is accounted for b y the regression model. In the case of a linear regression with only one independent variable, the R 2 for the linear regression line is numerically equal to the squ are of the linear correlation coefficient (which we have written in th is section with a lower case r) between the variable being compared. In Table 11, since all of the equa tions are linear, the R 2 there equals r 2

, where the values of r have been d iscussed previously in this se ction.62 The relationships are much weake r 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 CANY 1, which is geographically in t he opposite direction from the INGA1 site of Period 1, a change that is consistent wi th the differing winds in the two periods along the Col orado River channel. The averag e Period 2 concentrations are identical at Hall’s Crossing and CANY1, howe ver, at 0.20 μg/m 3 of S. At the Gray Mountain site, which is distant from the Colo rado River channel, INGA1 remains the primary IMPROV E 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 loc ations were nearly the same; 0.19 μg/m 3 of S at Gray Mountain vs. 0.18 at INGA1.

In summary, this multifaceted ex amination of relationships betw een 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 M oenkopi) are closely linked to the atmosphere at nearby Class I areas, especially PEFO1 and INGA1, respectively. The linkage is stronger during Period 1, when c onditions often changed dramati cally 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 Pe riod 2, as solar

insolation increases vertical mi xing. As a result, the concentr ations tend to change less from one sample to the next and correlations between chang es at the sites are weaker, while the concentrations are more similar over all thes e sites.



### Chemistry of the Aerosol

The measured concentrations of t he various ions can provide ins ight into the nature of the atmospheric aerosol at th e time of sampling. For this pu rpose, concentrations will be expressed in terms of equ ivalents, i.e., the formula we ight 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 m easure all the

ions of relevance.

Charge Balance. First, consider the charge bala nce of the ions that were sampl ed during the SRP program. For an e lectrically neutral aerosol, th e sum of the cations (measured in equivalents) should equal the sum of the anions. T he measured

cations were Na + , NH 4 + , K + , Mg 2+ , and Ca 2+ . The measured anions were Cl

‐ , NO 3 ‐ , and

SO 4 2‐ . Figure 26 shows that the sum of cations equaled or exceeded t he sum of anions for every sample taken du ring the study. In other words, the combination of the sampled ions is neutral at t imes and positively charged mos t of the time.

39 The slightly different Multiple Least Squares regression proce ss produces an equation that

includes small contributions fro m other sites, which results in slightly greater R 2 for both sites.

40 See Footnote 26 for an explanation of equivalents.63 Although Figure 26 is for the ent ire study period, separate plo ts for Period 1 and

Period 2 show similar behavior.

The charge imbalance shown in Fi gure 26 is not surprising, sinc e several crustal cations were measured, but Cl

‐ was the only measured crus tal anion. If additional negative ions were to be measure d, it is likely that Figure 26 would show charge neutrality.

Figure 26. Charge balance of the measured ions.

If the focus of the charge balan ce evaluation is narrowed to on ly the non‐crustal components NH 4 + , NO 3 ‐ , and SO = 4 that make up the ammoniated sulfate and

ammonium nitrate of int erest here, the charge balance can be re presented by the

ratio of the sum of sulfate and n itrate anion equivalents to am monium cation equivalents, i.e.,

Ion Ratio = {[SO = 4 ] + [NO 3 ­ ]}/[NH 4 +

] ,  (1)

where the square brackets denote concentration in equivalents.

If the Ion Ratio is exactly unity, this suggests that the combination of sulfat es and nitrates is fully neutralized by NH 4 + , implying that their chemical forms are ammonium sulfate, (NH 4 ) 2 SO 4 , and ammonium nitrate, NH 4 NO 3

.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 u ntil about the end of February and then increases rapidly in Ma rch to a value of about 1.3, wh ere it remains for the last month of the study. The beha vior at all sites followed thi s pattern, although the

Ion Ratio at Hall’s Crossing was often a b it higher than the Gray Mounta in and Moenkopi values.

Figure 27. Temporal variation in the Ion Ratio = {[SO = 4 ] + [NO 3 ‐ ]}/[NH 4 +

] over the study
period.

To interpret this be havior, a value of [Ion Ratio] < 1, suggests that NH 4 + is paired with the sulfates and nitrates, but there is still some NH 4 + remaining, presumably in some other compound, perhaps a sa lt of an organic acid. On the other hand, a value

of [Ion Ratio] > 1 means that there is insufficient NH 4 + to pair with the total of the

sulfates and nitrates that are a ctually present, so some other cation besides NH 4 +
has reacted with some of the rem aining nitrates. Since these op posing situations of “remaining NH 4 + ” and “excess NO 3 ‐ ” cause the NH 4 + , NO 3 ‐ , and SO 4 2‐ system to respond differently to changes in NO
x or NH 3 concentrations in win ter and in spring, they warrant further discussion.

Excess NO 3 ‐

. When both sulfate and nitrate i ons are present in the atmosphe re, the ammonium ions react preferentia lly with sulfate; and then any a mmonium left over

is available to react with the ni trate. Mathematically, that le ft over ammonium is represented by the f ollowing formula”65 [Leftover NH 4 +

] =[NH 4 +

]

– [SO 4 = ] ,  (2)

Ambient ammonium and sulfate con centrations were measured durin g the monitoring program, so the lefto ver ammonium can be calculated for each sampling period by using Equation 2.

This leftover ammonium is availa ble to react with nitrate to fo rm ammonium nitrate. By reasoning analogous to that used above, the amount of any of the

measured nitrate that is left ov er after reactions between the nitrate ions and ammonium ions have gone to compl etion can be calculated. This l eft over or

“excess” nitrate is then

[Excess NO 3 ­ ] = [NO 3 ­ ]

– [Leftover NH 4 +

] .  (3)

The Excess NO 3 ­ concentration is the difference (in equivalents) between the

measured NO 3 ‐ concentration and the amount of NO 3 ‐ that could have been produced with the ammonium left over after the formation of sul fates was completed. The above two formula s can be combined to give a for mula for calculating Excess NO 3 ­ from the measured concentrations,

[Excess NO 3 ­ ] = [NO 3 ­ ] ­ {[NH 4 +

] ­ [SO = 4 ]} (4)

= [NO 3 ­ ] + [SO = 4 ] ­ [NH 4 +

].

Note that the right hand side of Equation 4 contains all the te rms of both the

numerator and denominator of the Ion Ratio . Consequently, if Ion Ratio > 1 then [Excess NO 3 ­ ] > 0, and if Ion Ratio < 1 then [Excess NO 3 ­ ] < 0

. Hence Ion Ratio and [Excess NO 3 ­ ] can be considered two sides of the same coin.

Both the average Ion Ratio and the average amount of Excess NO 3 ­ are tabulated in

Table 12 for the full 5‐month stud y period and for Periods 1 an d 2 separately. The

standard deviation values noted in brackets are a measure of th e sample‐to‐sample variability of these variables d uring each period, and not a me asure of the uncertainty of a given measureme nt 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 NO 3 ­ ] was negative at the two sites t hat were operational then. In contrast, during Period 2 the average Ion Ratio was greater than unity and [Excess

NO 3 ­ ] was positive (actually greater than +1) at all three sites.66 Table 12. Values of Ion Ratio and [Excess NO 3 ‐ ] 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 NO 3 ‐ ], 0.24 ‐ 0.95 1.33 ‐ 0.15 ‐ 2.01 1.28 1.62 neq/m 3 (1.70) (1.29) (1.25) (2.30) (1.16) (1.89) (2.24)

Figure 28 shows the temporal variation of [Excess NO 3 ­ ] 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 ext ensively and somewhat independently during the first p art of the study and then becom e smoother and

more consistent toward the end o f the study. In Figure 28, all but two 6‐day values

of excess NO 3 ‐ are negative from the initiatio n of monitoring up to the March 5 th sampling period. After that time, the 6‐day excess NO 3 ­ values are all consistently positive.

Figure 28. Values of [Excess NO 3 ‐ ] for each sample during the monitoring program.

For most of the samples in Period 1, [Excess NO 3 ­ ] was negative (and equivalently,

Ion Ratio < 1 ). During those times there was sufficient NH 4 + in the atmosphere to67 fully react with all the available NO 3 ‐ and SO = 4 , thereby forming ammonium nitrate and ammonium sulfate, and ye t still have a surplus of NH 4 + left over. The chemical form of the compound that contains the surplus NH 4 + is unknown. Organic acid (e.g., oxalate) ammonium salts are a po ssibility, since organic acids are widely emitted by vegetation and can be in condens ed form in cold weather, but th eir presence was not measured in this monitoring program.

A positive value of [Excess NO 3 ­ ] , which is the case for a ll of Period 2 except the beginning (and for the Hall’s Cr ossing sample of 1/28 in Period 1) means that there

was insufficient NH 4 + to react with all of the measure d sulfates and nitrates and so

some of the measured NO 3 ‐ must have been in some other form than ammonium nitrate. An analysis b elow will suggest that crustal nitrates a re the likely form of the

additional nitrates.

In summary, the results in Tab le 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 accoun t for the measured particulate nitrate. 42 In contrast, on average, du ring Period 2 the nitrate

formation would have been limited by the availability of ammoni um, except that (as discussed below) crustal catio ns appear to have stepped in to e nable 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 HNO 3 reacts is sitting on th e ground (in fact, it is the ground) and has no effect on vis ibility at all. Furthermore, th e small amount of crustal material in the air, pick ed up by the wind, is composed of coarse particles that are nearly all larger than the 2.5 μm upper cut point of t he fine particle samplers used in this study. The “tail” of their size distribut ion 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 dete rmine light extinction from particle compone nt measurements, calculates the total light extinction by adding together th e contributions of ammonium sul fate, ammonium nitrate, organic matter, light‐ab sorbing carbon, and soil, all of which are in the fine

41 Note that the standard deviations of the [Excess NO 3 ­ ] averages in Table 12 ar e quite large during both of the two periods, so not every sample will conform to th e 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 co ntribution of coarse matter. 43 Each component contribution to extinction is calculated by m ultiplying its concentration by a factor. For ammonium nitrate the factor depe nds on relative humidity, but i n practice ranges from 3.0 to over 10, while the f actor for fine soil (i.e., fine crustal matter) is 1.0. This means that fine crustal matter produces light extinction that i s mostly in the range
of 10% to 33% of the extinction caused by an equal mass of ammo nium nitrate.

That contribution is further t empered when considering an increase in light

extinction due to the f ormation of crustal n itrates because the mineral portion of the crustal mass (silicon, aluminum, iron, potassium, etc.) is natu rally present and the additional light extinction cont ribution due to the formation o f crustal nitrates is no more than the portion attributab le to the nitrate that was adde d to the crust.

Although coarse matter was not m easured in this study, the effe ct on extinction by

the coarse crustal nitrates is e ven smaller, because the coarse matter factor in the IMPROVE algorithm is 0.6.

Therefore, any crustal nitrates will have a minute effect on vi sibility compared to ammonium nitrate because (1) the crustal nitrates are mostly on the ground; (2)

those crustal nitrates that do b ecome airborne contribute much less to light extinction than an equal mass of ammonium nitrate; and (3) the contribution of the crustal nitrates to increasing t he light extinction is only due to the portion of the

crustal mass that is nitrate.

Effect of Different NH 3 Concentrations. Now, how would the situation differ if the

ambient ammonia concentration di ffered? The result will differ depending on

whether the new NH 3 concentration is less or more than the original

concentration 44 and whether the new [Excess NO 3 ­ ] is positive or negative. 45

In the case of negative [Excess NO 3 ­ ] , where there is a surplus of NH 4 + , if the new

condition involves an increase in NH 3 , this means that there will be even more NH 4 + left over after completing the fo rmation of ammonium sulfate an d ammonium nitrate, so there will be no furth er change in the concentratio ns of ammonium

sulfate and ammonium nitrate. If the new condition has less NH 3 , the result will depend on the size of the NH 3 concentration difference relative to the amount of

43 For simplicity, the discussion here is based on the original I MPROVE algorithm. A new algorithm developed a few years ago is bas ed on the same principles, but the multipliers for the concentrations are more complex. It includes te rms for light scattering by sea salt and light absorption by NO 2 gas, neither of which is an important contributor to light extinctio n in the geographic area studied here. For purposes of this discussion, there are no material differen ces between the original and new algorithms.

44 The discussion here will be limit ed to ammonia co ncentration d ifferences that are small enough that they don’t change the sign of [Excess NO 3 ­ ]. Also, large differences in NH 3 concentration may bring other factors into play that could limit the resulting di fference in the ammonium nitrate particle concentration.

45 The discussion here assumes a steady state situation.69 surplus NH 4 + . If the ammonia concentration reduction is small enough that s ome surplus NH 4 + still remains, 46 then the same situation of unchanged ammonium

sulfate and nitrate wi ll still exist. An NH 3 concentration that is s o low that there is no surplus NH 4 + changes the state of the system to one where [Excess NO 3 ­ ] has changed sign and is now positive.

In the case of positive [Excess NO 3 ­ ] , where there are some crustal nitrates, an NH 3 concentration that is less will result in a reduced NH 4 + concentration, which in turn

will result in a smaller amount of ammonium nitrate particles. As a consequence, the [Excess NO 3 ­ ] will be greater and there may be more crustal nitrate particles . On the other hand, a larger concentration of NH 3 will mean that more ammonium nitrate particles are formed, which mean s there will be less nitrate av ailable to pair up with

crustal matter and so the amount of crustal nitrates will be le ss. Note that this case of positive [Excess NO 3 ­ ] is the only situation where different NH 3 concentrations could result in parallel changed ammonium nitrate particle conc entrations.

Let us look at the implications of the above discussion on a si te‐by‐site basis. At Hall’s Crossing during Period 1, Table 9 showed slightly greate r average measured

sulfate concentrations and notic eably lower nitrate concentrati ons there than at Gray Mountain, along with much l ower ammonia gas concentrations and somewhat greater nitric acid gas concentrations. The Ion Ratio < 1 and negative [Excess NO

3 ] values in Table 12 suggest that, o n average, there was sufficie nt 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 p resent there, it is unlikely that more ammonium nitra te than the amount currently m easured would be formed. Reducing the ambient ammonia concentration will also have no effect on

current nitrate concentrations until [NH 4 +

] is reduced sufficiently to eliminate the negative [Excess NO 3 ­ ]

.

The average [Excess NO 3 ­ ] value at Gray Mountain is even more negative than that at Hall’s Crossing, which implies th at the availability of ammonia there is even greater

relative to the sulfate and nitra te concentrations, and therefo re the current nitrate

concentration there is even more immune to any di fferences in a mmonia concentration. (This is another indication that the Gray Mounta in ammonia concentrations may include a con tribution from local sources.)

During Period 2, on average, [Excess NO 3 ­ ] is greater than unity at all three sites. In

this situation the ammonia that w as present in the atmosphere w ould have been insufficient to pair with all the nitrate and so some other cat ions were stepping in to partially fill the void. For this case, the excess NO 3 in Table 12 is the amount of particulate nitrate (in neq/m 3 ) that was formed by other means than reaction with ammonia. In this situation, when the formation of ammonium nitr ate is limited by the availability of ammonia, a c hange in the ammonia concentrat ion is likely to result in a similar change in th e ammonium nitrate concentratio n.

46 NH 3 gas and the NH 4 + ion are in equilibrium in a reversible relationship.70 Contribution of Crustal Ions. The inference that the cations that contribute to [Excess

NO 3 ­ ] in the latter portion of the mon itoring period are crustal in origin is based on a strong correlation between [Excess NO 3 ­ ] and the sum of the two crustal cations, Mg 2+ and Ca 2+ , as shown in Figure 29. Negative values of [Excess NO 3 ­ ] are associated

with low concentrations of the c rustal cations while positive v alues of [Excess NO 3 ­ ] 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 ag ain to the conclusion [Excess NO

3 ] was negative early in the study and was positive late in the s tudy.

Figure 29. Demonstration of correlation between [Excess NO 3 ‐ ] and crustal ion

concentrations (in neq/m 3 ). The solid lines are linear regression lines; the
corresponding R 2 values are noted next to the site names.71 

## A

‐ 5. CONCLUSIONS AND POTENTIAL FUTURE WORK

The following points summarize t he key findings from SRP’s ammo nia monitoring

program:

• The average ammonia concentrations measured at the SRP sites du ring the monitoring program from December through April were 0.22 ppb at Hall’s Crossing and 0.63 ppb at Gray Mou ntain. The average at Moenkopi was 0.45 ppb during its shorter period of oper ation from mid‐February to the end of April.

• The SRP monitoring program spanne d two distinct meteorological and air quality regimes:

o Period 1. A winter period (from the beg inning of Decembe r to February

15 th ) with cold temperatures, shallow boundary layer depth, and lar ge changes in conditions from one sa mpling period to the next driv en by changing weather systems; and

o Period 2. A transitional spring period (from February 15 th to the end of April) with increasing temperatures and boundary layer depths, more vigorous atmospheric mixing, and less variation from one sampli ng period to the next.

• Consistently lower background a mmonia concentrations were measu red at the Hall’s Crossing site than at the Cameron area sites (Gray Mount ain and Moenkopi). While it was not init ially expected, this spatial va riability is comparable to that observed in ot her monitoring studies, such a s the Four Corners Area study.

• Although the two Cameron area si tes are separated by less than 5 miles, a

consistent difference in measure d ammonia values was observed b etween the two sites. This suggests that loc al sources could be affecting the measured

values. Possible local sources c ould include animal grazing, hi ghway vehicles, sewer lagoons, and septic tanks.

• The sulfate concentrations measured at the SRP sites demonstrat ed a systematic relationship with those measured at the IMPROVE sites. This deg ree of consistent regional behavior was not observed in the ammonia me asurements, suggesting the possible influence of local sources affecting th e measured

ammonia values.

• On average, during Period 1, there appears to be more ammonia a vailable than what would be needed to fully acco unt for the measured particul ate nitrate. On72 average, during Period 2, the ni trate formation would have been limited by the availability of ammonia, but cru stal cations appear to have ste pped in to enable production of additional nitrate c ompounds. Crustal nitrates ha ve a minute effect on visibility compar ed to ammonium nitrate.

• The sensitivity of the ammonium n itrate concentra tions to diffe rences in the ambient ammonia concentrations differs during the two periods.

o During Period 1, an increased am monia concentration is unlikely to result in any difference in the concent ration of ammonium nitrate part icles.

o During Period 2, a higher ammonia concentration could result in a parallel increase in th e ammonium nitrate particle concentratio n during the colder part of the period.  T he magnitude of this increase will diminish as the ambient temperat ure increases and the formation of particulate ammonium nitra te 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 si mplified nitrate‐

formation modules of the CALPUFF system.  Therefore, the model cannot
simulate similar conditions. Mor e advanced representation of am monia‐nitrate chemistry, such as that describe d in Section B‐4 in Part B of t his report, has the potential of producing more re alistic 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 t herefore may not be representat ive of background

ammonia concentrations on the Colorado Plateau.

• The balance of ammonium and nitra te concentrations observed in the monitoring program cannot be sim ulated by the simplified nitrat e‐formation modules in CALPUFF. Improvements to these modules could result in more

realistic nitrate estimates.

• In order to determine regionally representative background ammo nia input values for use with an improved CALPUFF model, SRP recommends t he initiation of a collaborative monitoring pr ogram using a network of passiv e samplers. The passive samplers were shown to co rrelate well with the URG samp lers, and can be deployed at remote location s at a relatively low cost.73 

## PART B

VISIBILITY MODELING IMPLICATIONS74 

## B

‐ 1. BACKGROUND

Role of Background Ammonia in CALPUFF

Secondary pollutants such as nitrates and sulfates contribute t o light extinction in Class I areas. The CALPUFF model was approved b y EPA for use in BART determina tions to evaluate the effect of these pollutants on visibilit y in Class I areas. CALPUFF use s the EPA‐approved MESOPUFF II

chemical reaction mechanism to convert SO 2 and NO
x emissions to secondary sulfates and

nitrates. This section describes how secondary pollutants, spec ifically nitrates, are formed and the factors affecting their formation, especially as formulated in CALPUFF.

In the CALPUFF model, the oxidation of NO
x to nitric acid (HNO 3 ) depends on the NO
x concentration, ambient ozone conc entration, and atmospheric sta bility. Some of the nitric acid is

then combined with available ammo nia in the atmosphere to form ammonium nitrate aerosol in an equilibrium state that is a f unction of temperature, relativ e humidity, and ambient ammonia concentration, as shown in Figur e 30 (taken from the CALPUFF us er’s guide).

Figure 30. MESOPUFF II NOx Oxidation.

In CALPUFF, total nitrate (TNO 3 =HNO 3 + NO 3 ) is partitioned into gaseous HNO 3 and NO 3 particles according to the equil ibrium relationship between the two species. This equilibrium is a function of ambient temperature a nd relative humidity. Moreover , the formation of nitrate particles strongly depends on availability of NH 3 to form ammonium nitrate , as shown in Figure 31 (taken from CALPUFF courses gi ven by TRC). The figure on the left 47 shows that with 1 ppb of available ammonia and fixed temp erature and humidity (for examp le, 275 K and 80% humidity), only 50% of the total nitrate forms particulate matter. When th e available ammonia is increased to 2 ppb, as shown in the figure on the right, as much as 80% o f the total nitrate is in the

47 A larger image of the left panel appears in Figure 2.75 particulate form. Figure 31 als o 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 re lative humidity create favorabl e conditions to form nitrate particulate matter, and therefore more ammonium nitrate is formed;

• Warm temperatures and lower rela tive humidity create less favor able conditions to form nitrate particulate matter, and therefore less ammonium nitrate is formed;

• Sulfate preferentially scavenges ammonia over nitrates.  In are as where sulfate concentrations are high and ambient ammonia concentrations are low, there is less

ammonia available to react with nitrate, and therefore less amm onium nitrate is formed.

For the NGS BART analysis, the e ffects of temperature and backg round ammonia concentrations

on the nitrate formati on are the key to und erstanding the effec ts of various NO
x control options. For the reasons discussed above, the periods of low temperature s are the most likely to be

sensitive to ammonium n itrate formation.

Figure 31. NO 3 /HNO 3 Equilibrium Dependency on Temperature and Humidity.76 

### Sensitivity of CALPUFF Predictions to Ammonia Concentration Input

Previous modeling performed by S RP indicated that wintertime ba ckground ammonia is the most important parameter affecti ng the degree of visibility imp rovement predicted by CALPUFF for a given BART control option. A more detailed discussion of this modeling analysis was

included in SRP’s comments s ubmitted on the EPA’s ANPR. 48

In an independent analysis, the Colorado Department of Public H ealth and Environment (CDPHE) performed a sensitivity modeling analysis to explore th e effect of the ammonia concentration input to CALPUFF on the predicted visibility impa cts for a source with high NO
x emissions relative to SO 2 emissions such as NGS. 49 The results of the sensitivity modeling are shown in Figure 32. It is notewor thy that the largest sensitivi ty occurs for ammonia input values between 1 and 0.1 ppb. In that range, the difference in the pea k visibility impacts predicted by CALPUFF is slightly more than a factor of 3 between ammonia con centration input values of 1 and 0.1 ppb. This sensitivity ana lysis shows that the choice of background ammonia is very important in terms of the magnitu de of visibility impacts predi cted by CALPUFF.

48 Comments submitted by SRP to EP A on ANPR, Section 6.3, Pages 46‐49.

49 Supplemental BART Analysis: CALPU FF Protocol for Class I Federa l Area Visibility Improvement Modeling Analysis (DRAFT), revised June 25, 2010, available at http://ww w.colorado.gov/airquality/documents/Draft‐
ColoradoSupplementalBARTAnalysi sCALPUFFProtocol‐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 fo r NGS in December 2008. In this analysis, SRP used the

CALPUFF model to assess the visi bility improvement associated w ith each BART control option under consideration, in accordan ce with a modeling protocol app roved by the EPA. 50 For these simulations, SRP used the same b ackground ammonia concentration s as those used in the previous PSD permit application for the Desert Rock Energy Faci lity (DREF) in northwestern New Mexico (“SRP’s ammonia inputs”). 51 Recently, the New Mexico E nvironmental Department

used the same ammonia concentrations in its BART determination for the San Juan Generating

Station. 52 SRP’s ammonia inputs are shown f or reference in Table 13.  The average ammonia

values measured in SRP’s monitor ing 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 ANP R in which the agency used an indirect “back‐calculation” method to infer higher background ammonia concentration estimat es than those used by SRP. 53

EPA’s method involves a determin ation of the amount of ammonia that was involved in the formation of ammoniated sulfate‐ and nitrate‐containing particl es measured at sites that are part of the Interagency Monitoring of Protected Visual Environments (IMPROVE) program. The EPA’s back‐calculated ammonia inputs a re shown for reference in Table 13, along with the values SRP used in its modeling and the val ues measured during the SRP NH 3 monitoring program.

50 SRP submitted a BART modeling protocol to EPA in September 200 7. 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 separa te 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‐OA R‐2007‐1110‐0012. These ammoni a concentration valu es had been approved by the Federal Land Managers (FLMs) for use in the DREF modelin g.

52 New Mexico Environment Departme nt Air Quality Bureau BART Det ermination, Public Service Company of New Mexico San Juan Generating Stati on, Units 1‐4, New Mexico Envir onment 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 Moenk opi is for only the last half o f the month.



### Importance of Ammonia Inputs

As discussed in Section B‐1, both previous modeling performed b y SRP and the independent evaluation performed by CDPHE in dicated that the background amm onia concentrations input to CALPUFF has a significant impact on the degree of visibility im provement predicted by the model

for different NO
x BART control options. The ammon ia concentrations assumed by EP A for the winter months range from 0.8 to 1 ppb, which are much higher th an the value of 0.2 ppb that was used in the modeling performed b y SRP. This is precisely the ra nge of ammonia input values for which the CDPHE sensitivity anal ysis showed the largest differe nces in visibility impacts. Therefore, it is clear that the differences in the ammonia conc entration input to CALPUFF between SRP and EPA have very im portant implications for the mo deled effectiveness of BART

NO
x emission controls.

Since the observed ammonia conce ntrations vary in time and spac e over this critical input range, the best way to determine the app ropriate input values to CALPU FF is to compare model predictions to measured values f or the different background amm onia inputs and select the inputs that yield the best agreem ent between the modeled result s and the measured values. The next two sections address the qu estion of which ammonia input v alues provide the best agreement between modeled and measured data.

54 From Table 22 of EPA’s Advance No tice of Proposed Rulemaking, 74 Federal Register , pages 44326‐44327. Previously Modeled Values Measured Values in SRP's 2009/2010 Monitoring Program

SRP NH 3 (ppb) EPA NH 3 (Mesa Verde)
(ppb) EPA NH 3 (all other areas)
(ppb) Hall's Crossing

NH 3 (ppb) Moenkopi *

NH 3 (ppb) Gray Mountain

NH 3 (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

Month80 

## B

‐ 3. MODELING OF SRP AND EPA AMMONIA INPUTS

Review of Previous Modeling

In the SRP comments on the EPA’s ANPR, CALPUFF modeling analyse s were performed to compare the ammonium nitrate con centrations predicted by the mo del with measured values for different background ammonia inpu ts. In these analyses, each da y within the BART meteorological modeling period of 2001‐2003 was modeled using d aily meteorological data and actual daily emissions of SO 2 and NO
x for each NGS unit. Three runs were made, one for each of the background ammonia concentrations sets given in Table 14. T he “SRP” values are those that

were used in the December 2008 BA RT analysis submitted to EPA. The “EPA” values are the

back‐calculated ammonia concentr ations that were used by EPA in the modeling presented in the

ANPR. An alternative set of “Low ” values presents a case with l ower ammonia concentrations than the SRP values to provide a dditional information on the ch ange in CALPUFF prediction performance with changes in the ammonia input values.  The resu lting sulfate and nitrate concentrations were predicted at receptors placed at the locati ons of nearby IMPROVE monitors. The modeling was performed at Gr and Canyon and Bryce Canyon, si nce 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 m onitor of interest and there was an elevated m easurement of sulfate concentrations. Such days were ones where modeled ammonium sulfate concentrations, which

are not sensitive to the background ammo nia concentration, were simila r to the measured ones

and the likelihood of NGS emissi ons reaching the monitor of int erest was corroborated by back trajectories. On such days, it i s expected for a well‐performin g model that predicted ammonium

nitrate concentrations should be compar able to, but somewhat less than , the measured values. The possibility of lesser concentrations would allow for the co ntributions of other anthropogenic

and natural sources that were not included in the CALPUFF model ing.

Eleven days in 2001 through 2003 met the criteria identified ab ove 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 NG S emissions reaching the Hance Camp (and Indian Garden, discussed later) IMPROVE sites tended to be among the c leanest days monitored dur ing the 3‐year period. Seven of the 11 days at Hance C amp were among the 20% of the da ys with best visibility during their respective years, and 5 of the 7 at Indian Garden were in the 20% best cat egory.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 se lected 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 concen trations, for which there could be several causes. In such a case, the bias in the CALPUFF predicti on of sulfate concentrations wa s 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 r emove this potential bias on th e case study days, whenever the

modeled 24‐hr ammonium sulfate concentration exceeded the me asured sulfate concentration,

the modeled ammonium nitrate concentrations were scaled down ward 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 result ed in reducing the possible ov erprediction tendency of CALPUFF for the predicted vs. ob served nitrate comparison. In a ddition, an upward adjustment was not made in predicted nitrat es for days when CALPUFF undere stimated the measured sulfate concentration because, even if the modeled prediction w as perfect, some of the measured sulfates are likely to come from other sources and regional bac kground. Consequently, some

underestimation of total ambient sulfates should be expected fr om CALPUFF if it were providing unbiased model predictions.82 

### Additional Modeling for the Indian Garden Monitoring Site

In this report, to supplement th e previous modeling performed f or the Grand Canyon Hance Camp and Bryce Canyon IMPROVE si tes, the same model comparison analysis was performed for

the Indian Garden IMPROVE site. The Indian Garden site is locat ed part way between the Grand Canyon floor and the rim, wherea s the Hance Camp site, which wa s included in the previous modeling analysis, is located on the rim of the canyon.  The fo llowing section summarizes the results of the analysis for the Indian Garden site and compares them with the results obtained previously for the Hance Camp si te. While eleven days in 2001 t hrough 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 Cany on IMPROVE sites provides a mor e extensive test of the appropriate selection of the ammo nia concentration input to CAL PUFF.



### Comparison of Model Performance at Hance Camp and Indian Garden Sites

Figure 33 compares the measu red and modeled ammonium nitrate concentrations on the 11 case study days at Hance Camp an d the 7 case study days at Indi an Garden. (The re sults for Bryce Canyon, involving fewer days, are not repeated here, and can be found in Attachment 2 of SRP’s ANPR comments.) Asterisks mark t he modeled values that were adj usted 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 modele d ammonium nitrate concentrations on some days, the nitrate concentrations calcula ted 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 p redicted ammonium sulfate concentration never exceeded the measured value, so no scaling was required.)

The ammonium nitrate concentrati ons predicted by CALPUFF using the SRP concentrations of ammonia (green bars) also exceed t he measured concentrations on most days, but by a considerably smaller margin. The ammonium nitrate concentration s 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 Gard en 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 mo deling for Hance Camp and the n ew analysis presented here

for Indian Garden confirm that t he use of EPA’s background ammo nia inputs produce significant overpredictions of nitrate conce ntrations relative to actual me asured values. SRP’s ammonia inputs produce some overpredicti on, but to a significantly less er 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 perfor mance evaluation described in Section B‐3 of this report indicate that SRP’s ammoni a inputs produce the best agre ement between

modeled and measured ammonium nit rate concentrations.  However, the monitoring study described in Part A of thi s report, and the study conduct ed 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 q uestion of why CALPUFF appears to yield the most reasonable results with lower ammonia values, even though actua l ammonia concentrations may not be consis tently that low throughout the entire modeling domain.  To further investigate this question, a search of inde pendent evaluations of the CALPUFF model was conducted, with a focus on identifying st udies that address the nitrate chemistry used in the model.

Morris et al. 56 reported that the CALPUFF MESO PUFF II transformation rates wer e developed using temperatures of 86, 68 and 50°F. Therefore, the 50°F minimum temperature used in development of the model could result in ov erestimating sulfate

and nitrate formation in colder conditions. These investigators found that CALPUFF tended to overpredict nitrate con centrations during winter by a factor of about 3.

A recent independent study that is relevant to the CALPUFF perf ormance for nitrate prediction was performed by Atmospheric and Environmental Resea rch, Inc. (AER) and presented at the October 2009 Air & Waste Management Associ ation Specialty Conference in Raleigh, North Car olina, by Karamchandani et al. 57 (“the KCBB study”). This study presented several imp rovements to the RIVAD chemistr y option in CALPUFF, an alternative treatmen t that was more amenable to an upgrade than the MESOPUFF II chemistry option. Amon g other items, the improvemen ts included the replacement of the orig inal CALPUFF secondary particulate matte r (PM) modules by newer algorithms that are used i n current state‐of‐the‐art regi onal air quality models such as CMAQ, CMAQ‐MADRID, CAMx and REMSAD, and in advanced puf f models such as SCICHEM. In addition, th e improvements included the inc orporation of an aqueous‐phase chemistry module b ased on the treatment in CMAQ. Excerpts from the study papers describing each of t he improvements made to CALPUF F in the KCBB study are repeated below.

56 Morris, R., Steven Lau and Bon young Koo, 2005. Evaluation of t he CALPUFF Chemistry Algorithms.

Presented at A&WMA 98th Annual C onference 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 an d Evaluation Using the 1995 SWWY TAF Data Base.   Presented at the Air & Waste Management Associatio n Specialty Conf erence on Guid eline on Air Quality Models: Next Generation of Models, Octobe r 28‐30, 2009, Raleigh, NC.85 Gas‐Phase Chemistry Improvements

The KCBB study applied a correct ion to CALPUFF in that the upgr aded model was modified to keep track of the puff ozone concentrations between time steps. The authors also updated the oxidation rates of SO 2 and nitrogen dioxide (NO 2 ) by the hydroxide ion (OH

‐ ) to the rates employed in con temporary photochemical and regional PM models.

Treatment of Inorganic Particulate Matter

The KCBB study scientists noted that the EPA‐approved version o f CALPUFF currently uses a simple approach to simulate the partitioning o f nitrate and sulfate between the gas and particulate phases. In this approach, sulfa te is appropriately assumed to be entirely present in the particulate phase, while nitrate is assumed to be formed by the reaction betwee n nitric acid and ammonia, as n oted in Section B‐1.

The KCBB study implemented an addi tional treatment for inorgani c gas‐particle equilibrium, based upon an advan ced 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. Wit h this new module, the improved CALPUFF model developed in the KCBB study incl udes a treatment of inorganic P M formation that is

consistent with the state of the science in air quality modelin g, and is critical for the prediction of regional haze due to secondary nit rate formation from NO
x 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 tr eatment is based on the Model of Aerosol Dynamics, Reaction, Ionization and Diss olution (MADRID) 59,60 , which treats SOA formation fro m both anthropogenic and biogen ic volatile organic compound emissions.

58 Nenes A., Pilinis C., and Pandis S.N. (1998) Continued Developm ent 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. Vijayarag havan, S.‐Y. Wu, C. Seigneur, S . Pandis, M. Jacobson, A. Nenes and J.H. Seinfeld, 2004. Development and Ap plication of the Model of Aer osol 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 partit ioning module for thr ee‐dimensional air q uality applications, 24th Annual

American Association for Aerosol Research Conference, Austin, T X, October 17‐21, 2005.86 Aqueous‐Phase Chemistry

The current aqueous‐phase forma tion 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. T here is no explicit treatment of aqueous‐phase SO 2 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 CALPU FF and the version with the im proved chemistry options were evaluated using the 1995 Southwest Wyoming Technic al Air Forum (SWWYTAF) database 61 , available from the Wyoming D epartment 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 CAL PUFF simulations. Several sens itivity studies were also conducted to investigate the effect of background NH 3 concentrations on model predictions of PM nitrate.

Twice‐weekly background NH 3 concentrations were provided from monitoring

station observations for the Pin edale, Wyoming area. These data were processed to calculate seasonally a veraged background NH 3 concentrations for CALPUFF.

Two versions of CALPUFF with diffe rent chemistry modules were e valuated with this

database:

1. MESOPUFF II chemistry using the Federal Land Managers' Air Qual ity Related Values Work Group (FLAG) recommended background NH 3 concentration of 1 ppb for arid land. As discussed previously, the MESOPUFF II alg orithm is the basis for the currently approved v ersion of CALPUFF that is bei ng used in the

BART determination for NGS.

2. Improved CALPUFF RIVAD/ARM3 chemi stry using background values o f NH 3 concentrations based on measurements in the Pinedale, Wyoming a rea, as described above.

PM sulfate and nitrate were pred icted by the two models and com pared with actual measured values obtained at the Bridger Wilderness Area site fr om the IMPROVE

61 Background and database des cription are available at http://deq.state.wy.us/aqd/prop/2003AppF.pdf.87 network and the Pinedale site fr om the Clean Air Status and Tre nds Network (CASTNET). For the two model con figurations evaluated in this s tudy, the results for PM sulfate were very similar, wh ich was expected since the impr ovements to the CALPUFF chemistry were anticipat ed to have the most impact on P M nitrate predictions. Therefore, the rema ining discussion focuses on the performance of each model with respect to PM nitrate.

The EPA‐approved CALPUFF model was found to significantly overp redict PM nitrate

concentrations at the two monitor ing locations, by a factor of 2 to 3. The performance

of the version of CALPU FF 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 anal ysis conducted within the KCBB study, both the EPA‐ approved version of CALPUFF and the improved version were run w ith a constant ammonia background of 1 ppb. 62 The results were similar t o those noted above:  the improved CALPUFF predictions were about 2‐3 times lower than th ose from the EPA‐ approved version of CALPUFF. Thi s result is similar to the resu lts using the seasonal observed values of ammonia, and i ndicates that the sensitivity of the improved CALPUFF model to the ammonia inpu t value is potentially much le ss than that of the current EPA‐approved model.

Similar sensitivity was noted by Scire et al. in their original work in the SWWYATF study 63 , in which they tested seasonally va rying 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 form ation shows very similar results to those reported in the KCBB study.

62 This is a recommendation from t he Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendat ions for Long‐Range Transport M odeling, 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 – Vol ume 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 CALPU FF is highly sensitive to the background ammonia value used as input, par ticularly as the input ranges f rom 0.1 ppb to 1 ppb. Case studies conducted by modeli ng NGS emissions using CALPUFF showed that the

EPA’s back‐calculated ammonia in put values resulted in order‐of ‐magnitude (i.e., by a factor of 10 in some cases) over predictions of ammonium nitrate concentrations. The ammonia inputs used by SRP i n the December 2008 BART analys is produce the best agreement between modeled a nd measured ammonium nitrate co ncentrations.

In monitoring studies of ammonia concentrations such as the one conducted by SRP

as described in Part A of this r eport, spatial gradients in amm onia concentrations have been observed during winter. However, as discussed in deta il in Part A of this report, further study would be r equired to determine whether th e measured concentrations are representativ e over a larger area, or just a t certain locations.

The sensitivity modeling perform ed by SRP, and the other studie s and evaluation work presented in this report, compel the following conclusions :

• To compensate for the general te ndency of the current EPA‐appro ved version of CALPUFF to overpredict nitrat es, the background ammonia valu es that should be used as input in the m odeling of NGS emissions should be much

lower than the EPA back‐calculat ed values.  If EPA intends to u se the currently approved version of CALPUFF in t he BART determination for NGS, the visibility benefits modeling sho uld be conducted using the back ground 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 deg ree of visibility improvement predicted for more stringent BART control opti ons. It has been shown that the use of EPA’s back‐calculated ammonia inputs p roduces order‐of‐magnitude (i.e ., by a factor of 10 or more in some cases) over estimates compared to measured values.

• The nitrate formation modules in CALPUFF should be updated in o rder to correct the general tendency of the model to overpredict nitrat e concentrations in the winter. The degree of overprediction in C ALPUFF has significant implications on many pending BART determinations. A requirement to install costly emi ssion controls should not be b ased on results obtained using a model with a technical flaw, unless the measur es described above (use of lower ammonia backg round concentration input valu es) are taken to compensate for th e known overprediction.90 To facilitate the model improvem ent effort, SRP has joined with several other western utilities to sponsor t he implementation of t he nitrate chemistr y improvements that were developed in the KCBB study into the CALPUFF model. This w ork is being undertaken by TRC, the CALPUFF mo del developer. Upon completing the improvements, TRC will submit th e revised model algorithms to E PA for review and approval. Although the review pr ocess may requir e considerable time and effort, SRP intends to run the updated version of the model to provide addi tional information to

EPA for use in the BART determin ation for NGS. In the meantime, given the tendency of the EPA‐approved version of the C ALPUFF model to overpredict ni trate concentrations and the degree of visibility improvement associated with more s tringent BART control options, SRP urges EPA to proceed with a BART determination tha t is based on the ammonia input values used by SRP in the December 2008 BART anal ysis.Measurements of Ambient BackgroundAmmonia on the Colorado Plateau andVisibility Modeling ImplicationsSeptember 28, 2010Outline1. Background2. SRP’s Ammonia Monitoring Program3.Visibility Modeling Implications4. Proposed Path Forward5. Questions and Discussion2September 20101. Background3September 2010Background•Visibility improvement is one of five factors in BestAvailable Retrofit Technology (BART) determination•SRP submitted visibility modeling results withDecember 2008 BART Proposal•Modeling was based on a protocol that was acceptedby EPA in July 2008•In an Advanced Notice of Proposed Rulemaking(ANPR) issued in August 2009, EPA presentedmodeling with different assumptions/inputs4September 2010Importance of Ammonia•To evaluate relative importance of EPA changes tomodel inputs, SRP conducted sensitivity modeling•Results showed that ambient background ammonia(NH3) is most important parameter affecting modelpredicted visibility impacts in winter5•Example: Incremental visibility improvement atCanyonlands from adding SCR to LNB/SOFA–SRP NH3: 0.61 dv–EPA NH3: 1.25 dv–All EPA Changes: 1.73 dv+0.64 dv+0.48 dvEffect of NH3September 2010Basis for SRP NH3•Approved by EPA and Federal Land Managers (FLMs)for use in modeling performed for Desert Rock permit•Consistent with previously measured ammonia values(e.g. Green River, Rocky Mountains, Mesa Verde)•More recently accepted by New Mexico EnvironmentalDepartment in BART determination for San JuanGenerating Station6September 2010EPA’s Back-Calculated NH3•In ANPR, EPA back-calculated NH3based onmeasured ammonium sulfate and nitrate values•Measured values were obtained from InteragencyMonitoring of Protected Visual Environments(IMPROVE) sites•Much of measured ammonium sulfate and nitratecomes from regional background and emissionsfrom other sources•Therefore, back-calculation method overestimatesNH3available to react with NGS emissions7September 2010Comparison of NH3InputsPreviously Modeled ValuesSRP NH3(ppb)EPA NH3(Mesa Verde)(ppb)EPA NH3(all other areas)(ppb)January 0.2 1 0.8February 0.2 1 0.7March 0.5 1.3 0.7April0.51.31Month8September 2010April0.51.31May 1 1.3 1June 1 1.3 1.3July 1 1.5 1August 1 1.5 1September 1 1.5 1October 0.5 1.5 1November 0.5 1.3 1December 0.2 1 0.9Evaluation of Back-CalculationMethod•SRP submitted a white paper evaluating backcalculation method with ANPR Comments•NGS emissions from 2001-2003 were modeled usingCALPUFF for two scenarios:–SRP NH3–EPA back-calculated NH3–“Low” NH3•Ammonium nitrate predicted by CALPUFF wascompared with measured values at IMPROVE sites9September 2010Evaluation of Back-CalculationMethod•Conclusions:1. Use of back-calculated ammonia inputs yieldsorder-of-magnitude overestimates compared tomeasured values(i.e., by a factor of 10 or more in some cases)2. Visibility modeling for NGS BART determinationshould utilize SRP’s ammonia inputs10September 2010Results for Grand Canyon(Hance Camp Monitoring Site)11September 20102. SRP’s Ammonia MonitoringProgram12September 2010SRP Ammonia Monitoring Program•In ANPR, lack of available ammoniameasurements was cited as justification foruse of back-calculation method•To provide additional data to address thisissue, SRP initiated a monitoring program tomeasure background ammonia13September 2010SRP Ammonia Monitoring Program•Objective: To support improved estimates of monthlywintertime background NH3concentrations•Guiding Considerations:–Relevant NH3concentrations are those in air mass in whichNGS emissions would be transported–Background NH3is best measured outside the influence ofcurrent transported NGS emissions and/or upwind ofNGS–NH3measurements should be consistent in spatial andtemporal scale with resolution of CALPUFF–Since region is free of major ammonia emission sources,ambient NH3was expected to be quite uniform spatially14September 2010Monitoring Program Timeline•Draft conceptual monitoring plan provided to EPA inJune 2009•Final monitoring plan provided to EPA and NationalPark Service (NPS) in October 2009•Discussed in a conference call with NPS in November2009•Program was initiated in early December 2009 andran until end of April 201015September 2010Monitoring Site Selection•CALPUFF modeling of current NGS emissionsindicated greatest visibility impacts at Grand Canyon,Capitol Reef, and Canyonlands National Parks•Therefore, 2 sites were selected:–One representing transport conditions toward GrandCanyon National Park–Another representing transport conditions toward CapitolReef and Canyonlands National Parks16September 2010Selected Monitoring Sites1. Near Hall’sCrossing, UTHall’s Crossing Site2. At GrayMountainNearCameron, AZ17Gray Mountain SiteSeptember 2010Expansion of Monitoring Study•Initial data raised questions about representativenessof Gray Mountain site•A third site was established at Moenkopi Yard (5 milesfrom Gray Mountain) in February 2010•Passive ammonia samplers were also added atmultiple locations–At primary monitoring sites for quality control andcalibration–At three other locations near Gray Mountain site toexpandgeographic coverage•3-week samples taken using passive samplers toobtain lower detection limit of 0.1 ppb18September 2010MonitoringSites inCameron Area19September 2010Monitoring Results20•NH3appears to vary in both time andspace•NH3at Gray Mountain and Moenkopi > Hall’s CrossingSeptember 2010Monitoring ResultsMeasured Values in SRP's 2009/2010 Monitoring Program Previously Modeled ValuesHall's CrossingNH3(ppb)Moenkopi *NH3(ppb)Gray MountainNH3(ppb)SRP NH3(ppb)EPA NH3(Mesa Verde)(ppb)EPA NH3(all other areas)(ppb)January 0.1 - 0.7 0.2 1 0.8February 0.2 0.6 0.7 0.2 1 0.7March 0.3 0.4 0.5 0.5 1.3 0.7April 0.3 0.4 0.5 0.5 1.3 1Month21* Sampling began at Moenkopi site on 2/15/2010September 2010May - - - 1 1.3 1June - - - 1 1.3 1.3July - - - 1 1.5 1August - - - 1 1.5 1September - - - 1 1.5 1October - - - 0.5 1.5 1November - - - 0.5 1.3 1December 0.2 - 0.9 0.2 1 0.9Monitoring Results•Passive samplers correlated well with activemonitors22September 2010Spatial Variability•While not initially expected, spatial variabilityis comparable to other monitoring studies(e.g. Four Corners)•Possible contributors to the higher ammoniaconcentrations observed in the Cameron area:–Animal grazing–Highway vehicles–Sewer lagoons–Septic tanks23September 2010Spatial VariabilityFertilizer ApplicationAnimal HusbandryEPA’s national NH3data confirms that animal activity,highway vehicles, and waste disposal are significant:240% 10% 20% 30% 40% 50% 60% 70% 80%OtherWaste DisposalIndustrial ProcessesHighway VehiclesFertilizer ApplicationSeptember 2010•Particulate sulfate concentrations correlatedwell between monitoring sitesSpatial Variability25September 2010Spatial Variability•Additional observations from measurements:1. Cameron area sites are < 5 miles apart, but consistentdifference in measured ammonia was observed2. Sulfates measured at SRP sites demonstrated asystematic relationship with IMPROVE site values•Above observations provide further evidenceof possible local sources in Cameron area•Ammonia measured in Cameron area may notbe representative of Colorado Plateau26September 2010Temporal Variability•Two distinct periods were observed:–Period 1: December 5 – February 15•Cold temperatures, more week-to-week variation–Period 2: February 15–April 28•Warmer temperatures, more mixing, less week-to-weekvariation•Each period exhibited distinct meteorologicaland air quality characteristics/trends27September 2010Temporal Variability•Increased mixing in Period 228September 2010Temporal Variability•Balance of ammonium and particulate nitratechanges from Period 1 to Period 229September 2010Temporal Variability•During Period 1, more ammonia was available thanneeded to account for measured particulate nitrate•During Period 2, nitrate formation was limited byavailability of ammonia, but crustal cations producedcrustal nitrates (small effect on visibility)•Balance of ammonium and nitrate concentrationsobserved cannot be simulated by the simplifiednitrate-formation modules in CALPUFF30September 2010Conclusions•Higher ammonia measured at Cameron area sitesmay be affected by local sources, and may not berepresentative of Colorado Plateau•Balance of ammonium and nitrate concentrationsobserved cannot be simulated by current CALPUFF•To determine regionally representative backgroundammonia for use with improved CALPUFF model,collaborative monitoring using passive samplersshould be initiated31September 20103. Visibility Modeling Implications32September 2010Modeling Questions•Monitoring study results do not explain whySRP’s ammonia inputs produce bestagreement with measurements•To further investigate, additional modelingwas conducted using same approach asanalysis submitted with SRP’s ANPR comments•Additional modeling was performed for IndianGarden site, located part way betweenbottom and rim of Grand Canyon33September 2010Same Over-Prediction Occurs atIndian Gardens Monitoring Site34September 2010CALPUFF Overprediction•Modeling suggests a general tendency ofCALPUFF to overpredict nitrates in winter•CALPUFF would also overpredict visibilityimprovement for NOxBART control options•To further investigate, a search ofindependent evaluations was conducted•Focus of search was on studies that addressnitrate chemistry used in CALPUFF35September 2010Independent Evaluations1. Morris et al. (2005) testing with MESOPUFFchemistry–Ran CALPUFF chemistry with entire CMAQ emissionsinventory to compare predictions to numerous IMPROVE,CASTnet, SEARCH monitors–Jan and July 2002 simulated–CALPUFF overpredicts nitrates by a factor ranging from 2-4in winter, depending upon specific site and region36September 2010Independent Evaluations2. Colorado Department of Public Health andEnvironment (CDPHE) – BART protocol (2005)–Conducted CALPUFF sensitivity to ammonia concentrationinput for hypothetical sources–Most significant difference in modeled visibility impactsoccurs between NH3inputs of 0.1 and 1 ppb–Peak visibility impacts increase by a factor of more than 3when NH3input is increased from 0.1 to 1 ppb37September 201038September 2010Independent Evaluations3. Scire et al. 2001 testing w/MESOPUFFchemistry–Conducted evaluation of CALPUFF vs. nitrate observations atBridger Wilderness Area–Tests involved only variations of ammonia inputs (0.23, 0.5,and 1.0 ppb concentrations)–Predicted/Observed nitrate concentration ratios were about:•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 ppb39September 2010Independent Evaluations4. Karamchandani et al. (“KCBB” Study)–Involved predictions at Bridger Wilderness Area IMPROVEsite and Pinedale CASTnet site–Currently approved version of CALPUFF overpredictsnitratesby a factor of 2-3 in winter–Improved CALPUFF chemistry implemented•NOxgas phase chemistry•ISORROPIA treatment for inorganic PM, MADRID for organic PM•Aqueous phase chemistry upgrade–Improved model was nearly unbiased for nitrate prediction41September 2010Conclusions•Incremental visibility improvement associatedwith BART controls is critically dependent onammonia input•EPA has suggested a sensitivity modelinganalysis of ammonia inputs could be done•If back-calculated NH3values are used,sensitivity modeling would overestimatevisibility improvement for BART controls42September 2010Conclusions•To compensate for the general tendency ofCALPUFF to overpredict nitrates, SRP’sammonia inputs should be used•Nitrate formation modules in CALPUFF shouldbe updated to address winter overprediction•BART determination should not be based on amodel with a known technical flaw43September 20104. Proposed Path Forward44September 2010Proposed Path Forward•SRP is partly sponsoring an effort toimplement KCBB upgrades to nitrate modulesin CALPUFF•SRP intends to run updated version of modeland provide results to EPA•If EPA intends to use currently approvedversion of CALPUFF for BART determination,SRP’s ammonia inputs should be used45September 20105. Questions and DiscussionSeptember 2010