Visibility Modeling Using Upgraded CALPUFF Model

Submitted for Consideration in Developing Best Available Retrofit

Technology Proposal for Navajo Generating Station

Submitted to:

United States Environmental Protection Agency, Region 9

75 Hawthorne Street  

San Francisco, California 94105  

Prepared by:

Salt River Project

P.O. Box 52025 PAB352

Phoenix, Arizona 85072‐2025

February 2011
Table of Contents

1 Executive Summary...............................................................................................................................1

2 Background ...........................................................................................................................................2

3 Modeling Approach .............................................................................................................................. 3

4 Modeling Results ..................................................................................................................................5

5 Conclusion.............................................................................................................................................7

Attachment 1.  Model Upgrade Documentation

Attachment 2.  CMAQ‐Derived Background Ammonia Concentrations

Attachment 3.  Modeling Files
1

1 Executive Summary

The Salt River Project Agricultural Improvement and Power District (SRP) is submitting this report to the 

United States Environmental Protection Agency (EPA) in order to provide the results of additional 

visibility modeling performed in support of the pending Best Available Retrofit Technology (BART) 

determination for the Navajo Generating Station (NGS). In previous correspondence with EPA, SRP 

expressed concern that the current regulatory version of the CALPUFF model (CALPUFF v5.8) has been 

shown to overpredict nitrate concentrations, particularly during winter months. The implications of this 

known issue with the CALPUFF model are significant, particularly with respect to the pending BART 

determination for NGS. Because nitrate concentrations are overestimated, the model also overestimates 

the degree of visibility improvement that can be achieved by more stringent BART control technologies 

for nitrogen oxide (NOx) emissions. This issue was acknowledged in the EPA’s 2005 Regional Haze 

Rule (70 FR 39121). EPA also committed to evaluating ways to improve the model (70 FR 39123). 

TRC Corporation, the developer of the CALPUFF model, recently released an upgraded version of the 

model (CALPUFF v6.4). CALPUFF v6.4 includes more sophisticated nitrate chemistry algorithms that 

improve the performance of the model. SRP retained AECOM to re-run the visibility modeling 

previously performed for NGS. All of the assumptions and inputs used in the upgraded model are the 

same as those that were used in the modeling presented in SRP’s comments on the Advanced Notice of 

Proposed Rulemaking (ANPR) issued by EPA in August 2009, with the exception of the background 

ammonia values. In this analysis, an additional set of background ammonia values was derived using the 

EPA’s Community Multi-Scale Air Quality (CMAQ) Model. Given that the use of CMAQ is an 

objective approach that is based on the best available science, SRP believes that it is an appropriate 

methodology for establishing ammonia values for use as input to CALPUFF v6.4. 

The incremental visibility improvement predicted by CALPUFF v6.4 from adding Selective Catalytic 

Reduction (SCR) to Low-NOx Burners and Separated Overfire Air (LNB/SOFA) on all three units at 

NGS is 0.12 deciviews (dv) on average over all Class I areas. To put this result in perspective, EPA has 

indicated that “1 dv is a small but noticeable change in haziness in most circumstances when viewing 

scenes in a Class I area” in the August 2009 ANPR. The degree of visibility improvement predicted by 

the upgraded model is well below 1 dv at all Class I areas. In certain Class I areas, visibility conditions 

are even predicted to degrade due to the increase in sulfates that are produced by SCR. 

Given the significant cost of the BART control options, and the potential economic consequences 

discussed in previous submittals, it is important that EPA analyze the BART options for NGS using the 

best available model. Accordingly, SRP urges EPA to consider the results of this additional modeling in 

developing a proposed BART determination for NGS. When considered in combination with the other 

BART statutory factors, the results obtained by running the upgraded CALPUFF model provide further 

support to conclude that a NOx emission rate of 0.24 lb/MMBtu, achieved using LNB/SOFA, is BART 

for all three units at NGS. When calculated using a modeling approach that is based on the best available 

science, the cost per deciview of SCR exceeds that of LNB/SOFA by a factor of about 100. Given the 

significant additional cost and the small incremental visibility benefit that is predicted to occur relative to 

LNB/SOFA, SCR cannot be justified as BART for NGS. 
2

2 Background

The EPA is currently in the process of developing a proposed BART determination for NGS. One of the 

statutory factors that must be considered in performing the BART analysis is the degree of visibility 

improvement that can be achieved by each BART control option, which is estimated using the CALPUFF 

model. As noted in previous correspondence with EPA, multiple studies have documented the tendency 

of CALPUFF v5.8, the model that is currently approved for regulatory use, to overpredict nitrates. This 

issue was acknowledged in the EPA’s 2005 Regional Haze Rule (70 FR 39121). EPA also committed to 

evaluating ways to improve the model (70 FR 39123). Additional information on the studies that 

demonstrate this overprediction was provided in the report submitted by SRP to EPA in September 2010. 

A recent study conducted by Atmospheric and Environmental Research, Inc. and sponsored by the 

American Petroleum Institute (“the AER study”) recommended improvements to the nitrate formation 

modules of the CALPUFF model in order to correct the general tendency of the model to overpredict 

nitrate concentrations in the winter.1

 The results of this study were presented to EPA in October 2009. 

To facilitate the CALPUFF model improvement effort, WEST Associates, a group of electric utilities in 

the Western United States, sponsored the incorporation of the nitrate chemistry improvements 

recommended and validated in the AER study into the CALPUFF model. The CALPUFF model 

upgrades were completed by TRC and the new version of the model was released as Version 6.4. The 

new version was submitted to EPA with a recommendation to adopt it as the version that is approved for 

regulatory use. The model upgrades were also presented to a group of Federal Land Managers in 

December 2010 and to EPA’s Office of Air Quality Planning and Standards (OAQPS) in February 2010. 

Attachment 1 of this report contains a memorandum from TRC that describes each of the model upgrades 

incorporated into CALPUFF v6.4. 

SRP retained AECOM to re-run the visibility modeling previously performed for NGS in support of the 

pending BART determination using the upgraded model. The purpose of this report is to provide the 

results of this supplemental modeling analysis to EPA for the agency’s consideration in developing a 

BART proposal for NGS. 

                                                           

1

  Karamchandani, P., S. Chen, R. Bronson, and D. Blewitt, 2009. Development of an Improved Chemistry Version of CALPUFF 

and Evaluation Using the 1995 SWWYTAF Data Base. Presented at the Air & Waste Management Association Specialty 

Conference on Guideline on Air Quality Models: Next Generation of Models, October 28-30, 2009, Raleigh, NC.  
3

3 Modeling Approach

All of the assumptions and inputs used in the upgraded model are the same as those that were used in the 

modeling presented in SRP’s comments on the Advanced Notice of Proposed Rulemaking (ANPR) issued 

by EPA in August 2009, with the exception of the background ammonia values. As SRP has noted in 

previous correspondence with EPA, the background ammonia values used as input to the CALPUFF 

model have a significant impact on the degree of visibility improvement predicted for each BART control 

option. SRP’s ANPR comments demonstrated that the background ammonia values used in the BART 

analysis submitted to EPA in December 2008 (hereafter referred to as “December 2008 ammonia values”) 

yield the best agreement between modeled ammonium nitrate concentrations and measured values at sites 

that are part of the Interagency Monitoring of Protected Visual Environments (IMPROVE) program. SRP 

concluded that it was necessary to utilize the December 2008 ammonia values as a basis for any visibility 

modeling conducted for NGS using CALPUFF v5.8. 

As EPA is aware, SRP conducted a monitoring study over the winter months of 2009 and 2010 with the 

goal of measuring ambient background ammonia concentrations in the area around NGS. SRP submitted 

a report to EPA in September 2010 that summarized the monitoring results and discussed the potential 

implications on future visibility modeling. As described in the report, SRP observed spatial variability 

between the gaseous ammonia concentrations measured in the two different locations where monitoring 

took place in this study. SRP acknowledged that without additional monitoring data, there was some 

uncertainty over which values would be more representative of the ammonia concentrations in other areas 

of the modeling domain. SRP also identified several factors related to human influence near the locations 

where the higher concentrations were measured. Given that those same factors are not present in the 

Class I areas addressed in the BART analysis, the presence of such factors suggests that the lower 

measured values may be more representative of the remainder of the modeling domain. SRP suggested 

that additional monitoring could be performed in other areas, but also noted that such additional 

monitoring would only be useful if EPA upgraded the CALPUFF model to address the tendency of the 

model to overpredict nitrates. Otherwise, if EPA was going to proceed with a BART determination based 

on CALPUFF v5.8, the December 2008 ammonia values should be used, since these values were 

previously shown to yield the best agreement between modeled and measured ammonium nitrate 

concentrations. 

Since the upgraded version of CALPUFF may help reduce the tendency of the model to overpredict 

nitrates, in order to provide an objective and conservative analysis that is based on the best available 

science, SRP pursued an alternate approach to derive background ammonia concentrations for use in the 

modeling presented in this report. Specifically, SRP retained TRC to utilize the EPA’s Community 

Multi-Scale Air Quality (CMAQ) Model to derive background ammonia concentrations for use in the 

visibility modeling using CALPUFF v6.4. The approach used by TRC to derive the background 

ammonia concentrations is summarized in a memorandum that is included in Attachment 2. 

The wintertime ammonia values derived using this approach are shown in Table 1, in comparison with 

those that were modeled by SRP and EPA in previous analyses, as well as those measured by SRP in the 

winter 2009-2010 study. 
4

Table 1. Winter Background Ammonia Concentrations 2

December 2008 1 CMAQ Average 2 Measured 

Average 3

EPA BackCalculation 4

January 0.2 0.3 0.4 0.8

February 0.2 0.3 0.5 0.7

March 0.5 0.5 0.4 0.7

November 0.5 0.8 - 1.0

December 0.2 0.5 0.6 0.9

1

 Values modeled by AECOM in BART analysis submitted to EPA in December 2008.

2

 Average of CMAQ-derived ammonia values over all Class I areas, which are shown in

 Attachment 2 of this report.

3

 Average of values measured at each site in SRP's ammonia monitoring program, 

 which are shown in the report submitted to EPA in September 2010.

4

 Values presented in ANPR issued by EPA in August 2009.

Background Ammonia Concentration (ppb)

Month

As can be seen in the table, the CMAQ-derived values are generally higher than the December 2008 

values, but lower than the EPA’s back-calculated values, which were shown to produce large 

overpredictions of nitrate concentrations relative to measured values. In addition, the CMAQ-derived 

values are generally similar to the average of the values measured in SRP’s monitoring program. Given 

this, and given that the use of the CMAQ model is an objective approach that is based on the best 

available science, SRP believes that it is an appropriate methodology for establishing ammonia values for 

use in visibility modeling conducted using CALPUFF v6.4. 

                                                           

2

  The “CMAQ Average” ammonia values listed represent total ammonia, including free ammonia gas, as well as ammonia 

bound in ammonium sulfate and nitrate. Although the total ammonia may overestimate the free ammonia available, the total 

ammonia predictions from CMAQ are a conservative estimate of the gaseous ammonia to mitigate any possibility of 

underpredicting the concentration of gaseous ammonia actually present.
5

4 Modeling Results

Table 2 contains the incremental visibility improvement predicted by CALPUFF v6.4 with the CMAQderived ammonia values when SCR is added to LNB/SOFA on all three units at NGS. For comparison 

purposes, the results obtained using CALPUFF v5.8 and the December 2008 ammonia values, submitted 

as part of SRP’s ANPR comments, are presented in Table 2 as well. Consistent with EPA Region 9’s 

approach, the modeled values are derived from an average over the 2001-2003 period modeled of each 

year’s 98th percentile day visibility impairment relative to natural conditions, expressed in delta-deciviews 

(delta dv). The tabulated values represent the difference in modeled visibility impacts between this 

average for the LNB/SOFA case and the scenario in which SCR controls are added to all three units. 

Table 2. Incremental Visibility Improvements 

CALPUFF v6.4, 

CMAQ NH3

CALPUFF v5.8, 

December 2008 NH3

Grand Canyon 0.01 0.24

Capitol Reef 0.57 0.57

Bryce Canyon 0.15 0.47

Zion -0.02 0.47

Canyonlands 0.30 0.70

Arches 0.23 0.59

Mesa Verde 0.08 0.52

Petrified Forest 0.07 0.42

Sycamore Canyon 0.04 0.33

Mazatzal -0.04 0.13

Pine Mountain -0.04 0.16

Average 0.12 0.42

Maximum 0.57 0.70

Minimum -0.04 0.13

Class I Area

Incremental Visibility Improvement from Adding SCR to LNB/SOFA (delta dv)

As can be seen in the table, the upgraded model (CALPUFF v6.4) generally predicts less of a benefit from 

SCR than the current regulatory model (CALPUFF v5.8). To put these results in perspective, EPA 

indicated in the August 2009 ANPR that 1 dv change in visibility is a “small but noticeable change in 

haziness in most circumstances when viewing scenes in a Class I area.” As can be seen in the table, the 

visibility improvement predicted using both versions of CALPUFF is well below 1 dv at each Class I 

area. It should also be noted that in certain Class I areas, visibility conditions are predicted to degrade 

following the installation of SCR. This occurs because visibility impairment caused by additional 

ammonium sulfate particles produced by the SCR outweighs the improvement resulting from the reduced 

NOx emissions. This occurs despite the assumption that an ultra low-oxidation SCR catalyst will be used, 
6

with a sulfur dioxide (SO2) to sulfur trioxide (SO3) conversion rate of 0.5%, which was determined to be 

Best Available Control Technology for sulfuric acid (H2SO4) for Coronado Generating Station. 

As directed by EPA’s BART rules, it is necessary to consider the cost of each control option in relation to 

the degree of visibility improvement expected. Accordingly, Table 3 shows the incremental cost 

effectiveness of adding SCR to LNB/SOFA on all three units at NGS, expressed in dollars per deciview 

($/dv). The results obtained using CALPUFF v6.4 and the CMAQ-derived ammonia values are shown in 

comparison with the results obtained using CALPUFF v5.8 and the December 2008 ammonia values, as 

presented in SRP’s ANPR comments.3

Table 3. Cost Effectiveness of Incremental Visibility Improvements 

CALPUFF v6.4, 

CMAQ NH3

CALPUFF v5.8, 

December 2008 NH3

Grand Canyon $7,015,000,000 $292,000,000

Capitol Reef $123,000,000 $123,000,000

Bryce Canyon $468,000,000 $149,000,000

Zion - $149,000,000

Canyonlands $234,000,000 $100,000,000

Arches $305,000,000 $119,000,000

Mesa Verde $877,000,000 $135,000,000

Petrified Forest $1,002,000,000 $166,000,000

Sycamore Canyon $1,754,000,000 $213,000,000

Mazatzal - $548,000,000

Pine Mountain - $434,000,000

Average $1,472,000,000 $221,000,000

Maximum $7,015,000,000 $548,000,000

Minimum $123,000,000 $100,000,000

Class I Area

Incremental Cost of Adding SCR to LNB/SOFA ($/dv)

To put these results in perspective, the degree of visibility improvement expected from the installation of 

LNB/SOFA was previously modeled to be 0.37 dv on average, and the average cost per deciview for 

LNB/SOFA over all Class I areas modeled was $14,000,000/dv. SRP previously concluded that the 

installation of SCR is expected to achieve similar incremental visibility improvements as LNB/SOFA, but 

at more than 10 times the cost per deciview. When the modeling results using the upgraded CALPUFF 

model are considered, the cost effectiveness of SCR is even further reduced. The results obtained using 

the upgraded model indicate that the installation of SCR is expected to achieve similar incremental 

visibility improvements as LNB/SOFA, but at more than 100 times the cost per deciview.

                                                           

3

  The figures in this table have been updated based on the Cost Study performed by Sargent & Lundy, submitted to EPA in 

August 2010 (Report No. SL-010214). Therefore, they are slightly different from those presented in SRP’s ANPR comments.
7

5 Conclusion

Given the significant cost of the BART control options, and the potential economic consequences 

discussed in previous submittals, it is important that EPA analyze the BART options for NGS using the 

best available model. Accordingly, SRP urges EPA to consider the results of this additional modeling in 

developing a proposed BART determination for NGS. When considered in combination with the other 

BART statutory factors, the results obtained by running the upgraded CALPUFF model provide further 

support to conclude that a NOx emission rate of 0.24 lb/MMBtu, achieved using LNB/SOFA, is BART 

for all three units at NGS. When calculated using a modeling approach that is based on the best available 

science, the cost per deciview of SCR exceeds that of LNB/SOFA by a factor of about 100. Given the 

significant additional cost and the small incremental visibility benefit that is predicted to occur relative to 

LNB/SOFA, SCR cannot be justified as BART for NGS. 
Attachment 1: Model Upgrade Documentation 
1 

CALPUFF Chemistry Updates 

Overview & Recommendations 

February 2011 

Prepared by 

TRC Environmental Corporation 

650 Suffolk Street 

Lowell, MA 01854 
2 

1.0 Introduction and Overview 

API sponsored a research program with AER to improve the performance of the CALPUFF 

model (Scire et al., 2000a,b). The findings of API-sponsored study were presented at various 

technical conferences and in project reports (Karamchandani et al., 2008, 2009). API provided 

the revised CALPUFF modules to the CALPUFF model developers at TRC and requested that 

they be evaluated and tested for incorporation into the latest CALPUFF model. This document 

describes the work TRC undertook as part of their evaluation. 

Two chemical transformation module options were introduced into Version 5.8 (v5.8) of the 

CALPUFF modeling system by Karamchandani et al. (2008) and evaluated in a second study 

(Karamchandani et al., 2009). These modules include: 

Modules 1 and 2

• Modification of the existing RIVAD chemical mechanism for the transformation of 

SO2 to SO4 and NO/NO2 to HNO3 and NO3

• Replacement of the MESOPUFF-II CHEMEQ model with the ISORROPIA (Version 

1.7) model for inorganic gas-particle equilibrium 

• Addition of a new option for aqueous-phase transformation adapted from the RADM 

cloud implementation in CMAQ/SCICHEM 

Module 2

• Addition of a new option for anthropogenic secondary organic aerosol (SOA) 

formation based on the CalTech SOA routines implemented in CMAQ-MADRID 

TRC has implemented these modules as options into a new version of CALPUFF (Version 6.4). 

This version of the model has been used to host the updated chemistry modules because it 

contains the latest Model Change Bulletin (MCB) updates, MCB-E and MCB-F, which correct 

code bugs in v5.8 of the model. MCB-E is dated June 2008 and MCB-F is dated November 

2010. In addition, v6.4 contains numerous technical enhancement options not part of v5.8. The 

v6.4 code has been found to give equivalent results as v5.8 when the same options and data are 

used once the v5.8 bug fixes are made. Therefore, the new features and technical improvements 

can be tested individually within the v6.4 framework. Alternatively, v6.4 can also be configured 

to be equivalent to v5.8. 

Module Option 1 is implemented as the 6th CALPUFF chemical transformation option (MCHEM 

= 6), and Module Option 2 is implemented as the 7th CALPUFF chemical transformation option 

(MCHEM = 7). However, one difference is that TRC has updated the gas-particle equilibrium 

model for nitrate from ISORROPIA v1.7 to ISORROPIA v2.1 (Nenes et al., 1998; Fountoukis 

and Nenes, 2007). ISORROPIA v2.1 is the only version of the model currently supported by the 

ISORROPIA model developers. 

Both new module options use the ISORROPIA v2.1 model instead of the CHEMEQ gas-particle 

equilibrium model for nitrates used with the MCHEM=1, 3 options. Since total nitrate (TNO3) is 

partitioned into the gas (HNO3) and particulate (NO3) phases based in part on the ammonia 
3 

available after preferential scavenging by sulfate, the equilibrium should be determined using the 

total amount of sulfate and nitrate (due to all sources, background, etc.) present at a particular 

location and time. This is accomplished using the ammonia-limiting method (ALM) of the 

POSTUTIL postprocessor in the CALPUFF modeling system. Because POSTUTIL also 

employs the CHEMEQ model in the ALM, Karamchandani et al. (2009) replaced CHEMEQ 

with ISORROPIA v1.7 in POSTUTIL Version 1.56 in a hardwired way which prevented 

backward compatibility. TRC has implemented the current ISORROPIA v2.1 module into a new 

POSTUTIL Version 1.64 as an option which retains backward compatibility of the POSTUTIL 

code. 

The ISORROPIA v2.1 model is documented and maintained by its developers, as described at 

the ISORROPIA home page (http://nenes.eas.gatech.edu/ISORROPIA). It is incorporated in this 

version of CALPUFF and POSTUTIL without modification, with permission from the model 

developers following the terms-of-use requirements. 

TRC found three coding bugs in the API code and several items requiring improvement. In the 

category of model bugs: 

 The code documentation associated with the RADM aqueous phase chemistry requires 

the cloud liquid water content value in kg/m3

. However, the units of cloud water actually 

passed to the routine in the API code were g/m3

. This factor of 1000 error is potentially a 

significant issue with the code when using the API version of the aqueous phase 

chemistry option. 

 The wet scavenging coefficients in the API code for species other than the set of six 

species defined by the RIVAD scheme (SO2, SO4, NO, NO2, HNO3 and NO3) were left 

undefined when the aqueous phase chemistry option is used, meaning the scavenging 

coefficients would be zero or an arbitrary erroneous value (depending on the compiler 

used) for all species other than the six RIVAD species. 

 In the API code, the aqueous phase chemistry wet scavenging coefficients for the set of 

six species in the RIVAD scheme were sometimes set to zero even though there was 

precipitation present in the grid cell. 

Several important improvements were made in the implementation of the API chemistry module. 

These include the following: 

 The API code set the cloud liquid water content in the aqueous-phase option to a constant 

value of 0.5 g/m3

 (for precipitating clouds) and 0.1 g/m3 (for non-precipitation clouds). 

In reality, there is considerable variability in the liquid water content of clouds, both 

spatially and temporally. In the revised version of the model, time- and space-varying 

liquid water content values from MM5, WRF or another numerical weather prediction 

model has been added. 

 The latest version of ISORROPIA (v2.1) has been implemented into both CALPUFF 

(v6.4) and POSTUTIL (v1.64), replacing the v1.7 ISORROPIA code used by 

Karamchandani et al. (2008) in the API version. 
4 

 The call of the ISORROPIA mechanism for small TNO3 concentrations has been revised 

so that an immediate drop to zero NO3 at TNO3=10-10 mol/m3

 has been avoided.

 Karamchandani et al. (2008) mistakenly interpreted the nighttime heterogeneous reaction 

rate for SO2 conversion in CALPUFF as being due to aqueous phase reactions alone. 

This resulted in nighttime conversion of SO2 being set equal to zero in the API code 

when there was not aqueous phase conversion. An option to use the small heterogeneous 

reaction (0.2% per hour) as before has been added. This heterogeneous term is a userspecified value, so it is changeable. 

 The format of the new file for monthly ammonia data provided for each CALPUFF layer 

has been revised to include documentation, and its use is optional, controlled by a new 

control-file variable. 

 The CALPUFF Graphical User Interfaces (GUIs) have been updated to include the new 

API chemistry options. 

The revised modeling system codes and test case datasets have been available to the public on 

the TRC CALPUFF web site (http://www.src.com) since November 3, 2010. 
5 

2.0 Model Implementation 

The FORTRAN codes introduced in the API-sponsored update to CALPUFF are made up of a 

number of subroutines and supporting include-files for declarations of parameters and variables 

stored in common. To facilitate future maintenance, all codes other than ISORROPIA are 

bundled in a single FORTRAN file (API_CHEM.FOR) that is separate from the CALPUFF file 

(CALPUFF.FOR), the code for ISORROPIA remains a separate file (ISORROPIA.FOR), and 

the included commons-files are retained without modification (aqueous.puf, newsoa.puf, 

soadat.puf, and isrpia.inc). 

The file api_chem.for includes: 

API routines based on existing CALPUFF routines 

 Subroutine CHEMRIV6 

 Subroutine CHEMRIV7 

 Subroutine CHMRIV6 

 Subroutine CHMRIV7 

 Subroutine SETBCKOC 

ISORROPIA interface routine 

 Subroutine ISODRIVER 

CALTECH SOA routines 

 Subroutine SOADRIVER 

 Subroutine CALTECH_SOA 

 Subroutine SOASUB 

AQUEOUS-CHEMISTRY ROUTINES BASED ON RADM/CMAQ 

 Subroutine AQRADM 

 Function HLCONST 

 Function INDEX1 

The API-sponsored update had been coded as CALPUFF options MCHEM=5 and MCHEM=6 

because CALPUFF v5.8 had four MCHEM options. Since then, an option for radioactive decay 

species processing was added for the United Nations International Atomic Energy Agency 

(IAEA) as MCHEM=5 in CALPUFF "v6" so that the API-sponsored updates now appear as 

MCHEM=6 and MCHEM=7. The names of the associated CHEMRIV and CHMRIV 

subroutines are changed accordingly. After repackaging the subroutines and updating calling 

routines within CALPUFF "v6", output from the v5.8-based code and the new code were 

compared to verify the implementation. Note that the Lahey LF95 compiler requires that lines 

coded as "value**-n" be recoded as "value**(-n)". 
6 

Then, based on the review of the API-sponsored updates to the chemical transformation options 

in CALPUFF, seven changes were made: 

1. A small SO4 formation rate based on heterogeneous reactions as in the current 

mechanism is restored.

2. The way the ISOPRROPIA mechanism is called for small TNO3 concentrations is revised 

so that an immediate drop to zero NO3 at TNO3=10-10 mol/m3

 is avoided.

3. The format of the new file for monthly ammonia data provided for each CALPUFF layer 

is revised to add documentation, and its use is controlled by a new control-file variable. 

4. An option is provided to read time-and-space-varying gridded cloud liquid water content 

for the aqueous-phase option. Also, CALMET has been modified to output these 

additional parameters from the 3D.DAT file (e.g., from MM5, WRF or another numerical 

weather prediction model). 

5. The cloud liquid water content value passed to subroutine AQRADM (to compute the 

aqueous-phase transformations) is changed from g/m3 to kg/m3, as defined in the 

AQRADM documentation. 

6. The scavenging coefficients for species other than the six species defined in the RIVAD 

chemical mechanism were left undefined in the API code when aqueous phase chemistry 

is used, resulting in these species having either zero or arbitrary values for the scavenging 

coefficients. The revised code was modified to assign appropriate values for the 

scavenging coefficients of all species modeled. 

7. The aqueous phase chemistry wet scavenging coefficients for the set of six species 

defined by the RIVAD scheme are sometimes erroneously set to zero during precipitation 

events. This occurs when the precipitation is recorded even though no clouds are present. 

This can and does occur because precipitation is an accumulated measure (e.g., an 

average over an hour) whereas the cloud cover, which triggers the calculation of the 

scavenging coefficients in the API code, is a snapshot of conditions at the time of the 

observation (or in the case of MM5, output of the model parameters). Precipitation can 

occur during the hour, but at the time of the cloud observation, the sky may be clear. 

This results in locations with precipitation but no wet scavenging. The revised code 

imposes a minimum cloud cover for the calculation whenever there is precipitation so 

that liquid water content can be computed. 

SO4 Heterogeneous Reactions

Both the MESOPUFF-II (MCHEM=1) and the RIVAD (MCHEM=3) transformation options in 

CALPUFF include an SO2-to-SO4 transformation rate due to heterogeneous reactions. These are 

not important during the day when gas reactions dominate, but remain active at night. The 

CALPUFF default transformation rate is 0.2%/hour for this pathway. 

Karamchandani et al. (2008) removed this SO2-to-SO4 transformation rate, and do not explicitly 

include heterogeneous reactions. Therefore, in the absence of aqueous-phase reactions the SO2-

to-SO4 transformation rate at night is identically zero in their implementation. 
7 

Inclusion of the heterogeneous reaction rate in both MCHEM=6 and MCHEM=7 has been added 

as a user-controlled option (MHETERG) in the control file. When the option is selected the 

"Nighttime SO2 loss rate" entered in the control file is added to the calculated rate. If the 

heterogeneous reaction rate is not selected, the calculated conversion rate remains as calculated 

by Karamchandani et al. (2008). 

ISORROPIA for small TNO3

CHEMRIV6 and CHEMRIV7 include a threshold concentration for total nitrate that must be 

exceeded before the ISORROPIA subroutines are called. Below this threshold, the particulate 

NO3 concentration is set identically to zero, placing all of the nitrate in the gas phase. Although 

the TNO3 threshold is "small" at 10-10 mol/m3, this may introduce an artificial step in particulate 

NO3 concentrations. 

A logic-block is added to subroutines that uses the threshold as a floor, so that the ISORROPIA 

subroutines are called with this floor concentration (when the TNO3 is less), and the resulting 

NO3/TNO3 ratio is then applied to the actual TNO3 concentration to obtain the particulate NO3

concentration rather than setting it to zero. This change is not presented to the user as an option 

in the control file, but is easily reverted to the original by resetting one logical variable to 

FALSE. 

Further testing of the ISORROPIA module at low TNO3 concentration has revealed what appears 

to be some instability in the response. Figure 1 shows the fraction of TNO3 that is particulate 

nitrate as the TNO3 concentration decreases. Step-changes in the response can be seen at the 

smallest scales, with the particulate fraction changing by a factor of two for changes in the TNO3 

in the 5th significant digit. 

Monthly Ammonia Concentration File

The use of monthly ammonia concentrations for each CALPUFF model layer was originally 

formulated to read an undocumented text-file of an array of numbers, which has no header. 

Furthermore, the model provided no option to choose between the monthly values in the control 

file (which does not vary by layer) and the set of month-and-layer values read from the new file. 

A new control variable for this option is constructed. It allows the user to choose between using 

the 12 monthly control file values or reading the month-and-layer values from the new file. 

Future selections such as reading ammonia from a full time-varying 3D gridded file can readily 

be added to the control variable. 

The file format is extended to include sufficient information to document the contents of the file. 

A header is added that provides the concentration units, the number of layers, and the row-order 

of the data. The row-order currently accepted places data for one month on a single line in 

which the data progress from layer 1 to the top-most CALPUFF layer. Therefore there are 12 
8 

data records, one for each month. The first entry of each line is the name of the month so there is 

no ambiguity. 

ISODRIVER

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.E+00 2.E-10 4.E-10 6.E-10 8.E-10 1.E-09 1.E-09

TNO3 (mol/m3)

P_NO3 Fraction

Figure 1. Example output from the ISORROPIA module for the fraction of the total nitrate that 

is particulate nitrate as a function of the total nitrate concentration. 

Gridded Cloud Water from MM5

The cloud water originally used in the aqueous chemistry was set to 0.5 g/m3

 for precipitating 

clouds and 0.1 g/m3

 for non-precipitating clouds in lieu of providing a pathway for obtaining 

more specific information. New procedures are implemented in CALMET to pass the gridded 

cloud liquid water content from MM5/WRF to a new auxiliary output file which is read by 

CALPUFF. These data currently allow CALPUFF to obtain a mean cloud water mixing ratio 

(g/kg) that is vertically-averaged over the depth of the cloud layer, when present, at the current 

puff location. 

The option is selected by means of a new control-file variable. If the user chooses not to use this 

option or if it is not available because the MM5/WRF data are not used in the CALMET 

application, then the originally coded values are used. 
9 

Cloud Water Units in AQRADM

The cloud liquid water content value passed to subroutine AQRADM to compute the aqueousphase transformations is changed from g/m3

 to kg/m3. The AQRADM subroutine states that it 

expects the kg/m3

 units. 

This change is implemented under the control of a single logical. It is not presented to the user 

as an option in the control file, but resetting the logical variable to FALSE in the code restores 

the use of the original units (g/m3

). This is provided as an option to allow testing of the 

consequences of the code error in the API version of the model. 

Wet Scavenging Coefficients from AQRADM

Scavenging coefficients from the aqueous phase chemistry are applied in CALPUFF subroutine 

WET in a loop over all species in the simulation. However, AQRADM assigns scavenging 

coefficients only for the six RIVAD species, and the coefficients for all other species in the 

simulation remain unassigned. The API version of subroutine WET is missing a screen on the 

species that allows the normal CALPUFF user-input scavenging coefficients to be used for these 

other species. That screen has been implemented in the revised code. 

Wet scavenging when there is liquid precipitation but no cloud cover is not treated properly in 

the API code. The scavenging coefficients are set to zero, turning off the scavenging in spite of 

the rain data. Precipitation is an accumulated measure (e.g., 1-hr average rate) whereas the cloud 

cover is more of a "snap-shot", whether taken from an observation or from an MM5/WRF time 

step. This can and does result in locations with non-zero precipitation rate but zero clouds. 

To address this problem, a minimum cloud cover is assigned for use in the aqueous chemistry 

module whenever the liquid precipitation rate is non-zero. 10% is currently used for this 

minimum cloud cover. 

These changes are implemented under the control of a logical variable within the affected 

subroutines (WET, CHEMRIV6, and CHEMRIV7). It is not presented to the user as an option 

in the control file, but resetting the logical variable to FALSE in the code restores the original 

API logic. This is provided as an option to allow testing of the consequences of the code error in 

the API version of the model. 

Secondary Organic Aerosol (SOA) Module

The API chemistry module includes a new option for anthropogenic secondary organic aerosol 

(SOA) formation based on the CalTech SOA routines implemented in CMAQ-MADRID. 

Anthropogenic SOA might be important for certain types of sources when the organic emissions 

of species such as toluene and xylene or other anthropogenic organic compounds are significant. 

The SOA module is activated using the MCHEM=7 switch in CALPUFF. The new SOA 

chemistry may require a substantially greater amount of computation time, with runtimes taking 
10 

approximately 7.5 times longer than when the simple chemistry without SOA is used 

(MCHEM=3).

3.0 Equivalence Testing 

A demonstration of the equivalence of the updated EPA-version of the code (v5.8) with all 

known bugs fixed and the new v6 code with API chemistry has been done using the EPA 

CALPUFF Evaluation Tool (EPA, 2008) of 11 modeling scenarios and also using a full-year 

BART visibility assessment. All CALPUFF v6 code changes are made with backward 

compatibility in mind and any API-sponsored changes that were not compatible have been made 

so. All bug-fixes identified in Model Change Bulletins E and F have been implemented in the 

EPA v5.8 code to create a CALPUFF v5.835 code for the equivalence testing. These updates to 

v5.8 do not involve the API code or any model enhancements, but rather just the bug fixes that 

are pending EPA review. This must be done to show equivalence because these bug fixes are 

already in v6 of the code.

The EPA Evaluation Tool results show that there is no difference in CALPUFF output when 

Version 6 is run with hourly time steps and the same options as Version 5. The EPA Tool fully 

matches all concentrations in time and in space, so this is the most demanding test of equivalence 

and shows that the systems produce identical results for these 11 scenarios. 

EPA CALPUFF Evaluation Tool Results for 

CALPUFF (v5.835) with CALMET (v5.812) 

compared to 

CALPUFF (v6.402) with CALMET (v6.325) 

Scenario Result 

01 No Difference 

02 No Difference 

03 No Difference 

04 No Difference 

05 No Difference 

06 No Difference 

07 No Difference 

08 No Difference 

09 No Difference 

10 No Difference 

11 No Difference 

A typical regulatory visibility assessment study has been also used to test the equivalence of 

CALPUFF v6 with v5.835. The visibility assessment reproduces BART modeling for the 

Hayden power plant in Colorado, and compares results for a full year of modeling with three 

stack configurations. CALMET v5.8 was used for all of the simulations, and CALPUFF options 
11 

are identical in both CALPUFF v5.835 and v6.402 applications. The visibility impact results are 

found to be identical. 

In terms of the controlling visibility measures, there is no difference in modeled results: 

SCENARIO

CALPUFF 

Version

Delta DV 

Rank 1

Delta DV 

Rank 8

Days 

w/Delta DV 

> 0.5 

Days 

w/Delta DV 

> 1.0 

∆ Delta DV 

Rank 1

∆ Delta DV 

Rank 8

∆ Days 

w/Delta DV 

> 0.5 

∆ Days 

w/Delta DV 

> 1.0 

Max-v2 v5.835 5.804 3.403 236 155 -- -- -- --

1-SCR-v2 v5.835 4.057 2.356 188 91 -1.747 -1.047 -48 -64

2-SCR-v2 v5.835 5.197 2.624 213 114 -0.607 -0.779 -23 -41

Max-v2 v6.402 5.804 3.403 236 155 -- -- -- --

1-SCR-v2 v6.402 4.057 2.356 188 91 -1.747 -1.047 -48 -64

2-SCR-v2 v6.402 5.197 2.624 213 114 -0.607 -0.779 -23 -41

Receptor-specific visibility measures are also compared. CALPOST output files containing the 

daily visibility extinction at each receptor are compared using file-compare utilities, and show 

that all results are identical. Furthermore, the DELVIS.DAT output files that contain the daily 

delta-deciview at each receptor for each day of the year are compared using the utility, and again 

all results from CALPUFF v5.835 are identical to all results from CALPUFF v6.402. 
12 

4.0 Conclusions and Recommendations 

A new version of CALPUFF (v6.4) has been developed that includes potentially significant 

improvements in the treatment of chemical reactions, including a revised gas phase chemical 

transformation module for SO2 conversion to sulfate and NOx conversion to nitric acid and 

nitrate, the latest ISORROPIA (Version 1.7) model for inorganic gas-particle equilibrium (Nenes 

et al., 1998; Fountoukis and Nenes, 2007), and aqueous phase oxidation and wet scavenging of 

pollutants adapted from adapted from the RADM cloud implementation in CMAQ/SCICHEM, 

and the addition of a new option for anthropogenic secondary organic aerosol (SOA) formation 

based on the CalTech SOA routines implemented in CMAQ-MADRID. 

Version 6.4 of CALPUFF has been tested with the EPA Evaluation Tool and other datasets and 

has been found to be equivalent in all tests conducted to-date to the current regulatory version of 

the model (v5.8), once known bugs in the regulatory code are corrected when the same set of 

model options are used in both runs. The significance of this finding is the v6.4 code can be 

used to test the new v6.4 features and technical improvements individually while in other 

respects being consistent with current regulatory code. Additional simulations to expand the 

range of tested equivalence continue to be performed. 

 

1. A framework should be developed for a timely regulatory review and consideration of the 

new API-sponsored model chemistry modules, including the ALM method in 

POSTUTIL, and other model improvements as well as pending Model Change Bulletin 

(MCB-E and MCB-F) updates associated with bug fixes to v5.8. 

2. The new version of the model configured with MCHEM=6 (revised API-sponsored 

chemical transformation update in v6.4 of the code) should be re-evaluated with the same 

SWWYTAF dataset used in the past to evaluate CALPUFF with the RIVAD chemical 

mechanism, and to compare with the API-sponsored CALPUFF v5.8 evaluation with 

ISORROPIA v1.7 (Karamchandani et al., 2009). In particular, the latest ISORROPIA 

v2.1 model suggests confirmation of the previous evaluation work is warranted. In 

addition, because the Karamchandani et al. (2009) study did not make use of the new 

aqueous phase chemistry or SOA modules, testing of these new components of the 

CALPUFF is also warranted. 

3. Further evaluation of the output of the ISORROPIA routines should be conducted to 

determine the cause of the variability seen at low TNO3 concentrations, and to determine 

if the minimum TNO3 threshold should be revised. In addition, the ISORROPIA model 

sometimes encounters numerical problems which should be studied further. 

ISORROPIA is now included in both CALPUFF and in POSTUTIL with the ALM 

option. 

4. Additional testing of the aqueous phase module with the SWWYTAF dataset is 

recommended in light of the code changes to the aqueous phase chemistry and the new 
13 

improvements introduced in this study related to using MM5/WRF liquid cloud water 

content values rather than constant default values. 

5. As noted previously, using the aqueous phase transformation option MAQCHEM=1 not 

only alters transformation rates, it also computes wet scavenging coefficients for the six 

RIVAD species. Without explicit gridded liquid cloud water fields, these are computed 

using cloud cover and the presence of liquid precipitation (liquid water content of 0.5 

g/kg if there is liquid precipitation or 0.1 g/kg if there are clouds but no precipitation). 

Consequently, two options should be considered if no explicit gridded liquid water 

content data are available: 

a. Use the original scavenging coefficients that are entered into the control file (both 

liquid and frozen precipitation). These are scaled by the precipitation rate, and so 

they vary both spatially and hourly. Select this by turning the aqueous phase 

transformation option off (MAQCHEM=0). 

b. Use the aqueous phase transformation option with the default liquid water content 

assumptions. Select this by turning the aqueous phase transformation option on 

(MAQCHEM=1) and setting the liquid water content flag MLWC=0. 

The first option is currently recommended because the second option has not yet been 

evaluated. 
14 

5.0 References 

EPA, 2008. Protocol for Updating the CALPUFF Modeling System. U.S. EPA, OAQPS, 

Research Triangle Park, NC. 

Fountoukis, C. and A. Nenes, 2007: ISORROPIA II: A Computationally Efficient Aerosol 

Thermodynamic Equilibrium Model for K+, Ca2+, Mg2+, NH4+, Na+, SO42-, NO3-, 

Cl-, H2O Aerosols, Atmos.Chem.Phys., 7, 4639–4659 

Karamchandani, P., S.-Y. Chen and C. Seigneur, 2008:. CALPUFF Chemistry Upgrade, AER 

Final Report CP277-07-01 prepared for API, Washington, DC, February 2008. 

Karamchandani, P., S.-Y. Chen and R. Balmori, 2009: Evaluation of Original and Improved 

Versions of CALPUFF using the 1995 SWWYTAF Data Base, AER Report CP281-09-01 

prepared for API, Washington, DC, October 2009. 

Nenes A, Pandis SN, Pilinis C, 1998: ISORROPIA: A new thermodynamic equilibrium model 

for multiphase multicomponent inorganic aerosols, Aquat.Geoch., 4, 123-152 

Scire, J.S., F.R. Robe, M.E. Fernau and R.J. Yamartino, 2000a: A User's Guide for the CALMET 

Meteorological Model. (Version 5.0). Earth Tech., Inc. Concord, MA. (Available from 

http://www.src.com). 

Scire, J.S., D.G. Strimaitis and R.J. Yamartino, 2000b: A User's Guide for the CALPUFF 

Dispersion Model. (Version 5.0). Earth Tech, Inc., Concord, MA. (Available from 

http://www.src.com). 
15 

Pending Model Change Bulletins (MCBs) 

MCB-E (June 2008) 

MCB-F (October 2010) 
Attachment 2. CMAQ-Derived Background Ammonia Concentrations 
Monthly Averages of 

Background 

Concentrations from 

CMAQ 2002 

February 2011 

Prepared For: 

APS and 

Salt River Project 

Submitted By: 

TRC 

650 Suffolk Street 

Lowell, Massachusetts 01854 

(978) 656-3627
1 

Summary Tables of the Monthly Averages Background Concentrations 

from CMAQ 2002 

The CMAQ 2002 dataset used for computing the monthly averages of background concentrations 

was run using 2002 emissions inventories (with refinements and including fire emissions) and 

2002 3-D field meteorological dataset from an MM5 run. The grid resolution of the domain is 36 

km and it covers the entire continental United States, part of Canada and Mexico. The CMAQ 

was run in a Lambert Conformal Conical (LCC) projection with the following mapping values: 

Matching Latitudes 330N, 450N and origin of Projection: 400N, 970W. It consists of 112 cells in 

the north-south direction and 148 cells in the east-west direction, and the southwest corner is 

located at X= -2,736 km and Y= -2,088 km. For reference, this CMAQ run is the CMAQ 2002 

Base Case simulation. It was prepared as part of Phase II of the Visibility Improvement State and 

Tribal Association of the Southeast (VISTAS) emissions and air quality modeling study in 2004. 

The emissions include 2002 inventory for the USA, Canada and Mexico. 

The procedure used to create the background concentration files is described below: 

Step1: 

The 36 km CMAQ gridded data are interpolated to the discrete receptors provided. 

The discrete receptor locations for each Class I area were provided by AECOM in two 

CALPUFF.INP files: 

- cf_fcpp_c0_u45_02.inp covering 16 of the 21 Class I areas 

- case4_02.inp covering 11 of the 21 Class I areas 

Step2: 

At each receptor (containing an hourly time series of interpolated CMAQ concentrations), 

monthly averages of the hourly time series are computed on a 720-hour period for each month: 

January: Hour 1 to Hour 720; February Hour 721 to Hour 1440, etc. 

Step3: 

The monthly averaged time series obtained for each receptor are used to replace the hourly time 

series of each receptor. For example, at Receptor 1, all the hours of each day of January are 
2 

replaced by the January monthly averages computed at Receptor 1, all the hours of each day of 

February are replaced by the February averages computed at Receptor 1, etc. 

Each receptor of each Class I area, may have a different monthly averaged time series. 

Spatial Averaging by Class I Area 

In the Tables 1-21 (each table is the summary for one Class I area), the monthly average CMAQ 

concentration of each species is listed. For summary in the tables, the concentrations are 

provided as spatially averaged values over all receptors in the Class I area. In the binary 

concentration files provided in CALPUFF format, the CMAQ concentrations are provided as 

monthly averages but separately for each receptor (i.e., not spatially averaged). The spatial 

averages in the tables are listed for the five species of interest: NO3, HNO3, SO4, NH3(gas) and 

TNH3 (in μg/m3

). TNH3 is the sum in ppb of concentrations: [NO3] + 2 [SO4] + [NH3(gas)]. In 

addition, in the final two columns (Max NH3 gas), the maximum monthly average ammonia 

concentration over all receptors of each Class I area is provided in both μg/m3 and in ppb. The 

conversion factor used for NH3 between μg/m3

 and ppb in the table is based on 15 deg C and 1 

atm conditions (1.39 ppb per μg/m3

 of NH3). The units of all CMAQ concentrations in the 

CALPUFF concentrations files provided are g/m3

, as required by POSTUTIL. The units of 

ammonia concentrations entered in the POSTUTIL control file (POSTUTIL.INP) are ppb. 

The recommended monthly NH3 (gas) concentrations to use in the CALPUFF runs when applying 

POSTUTIL to repartition the nitric acid-nitrate should be equal to or higher than the values used 

in POSTUTIL. Although CALPUFF will overestimate the ammonia availability because it 

applies the available ammonia on a puff-by-puff basis, POSTUTIL repartitions the nitrate account 

for all puffs together from sources modelled by CALPUFF and background sources of sulfate and 

total nitrate ([TNO3] = [HNO3] + [NO3]) as predicted by CMAQ. 
3 

Table 1. Monthly Averaged Background Concentrations at Arches NP. 

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0648 0.5563 0.9966 0.2265 0.0117 0.0150 0.021 

February 0.0829 0.5512 0.969 0.2323 0.0143 0.0205 0.028 

March 0.1997 0.4895 0.6983 0.2917 0.0635 0.0807 0.112 

April 0.2237 0.3955 0.5667 0.4717 0.2702 0.3186 0.442 

May 0.1328 0.4115 0.6934 0.506 0.3238 0.3881 0.539 

June 0.0073 0.4989 0.873 0.5609 0.3822 0.4586 0.637 

July 0.0002 0.5792 0.7327 0.4299 0.2247 0.2819 0.391 

August 0.0004 0.398 0.7138 0.4489 0.3079 0.3599 0.500 

September 0.0068 0.582 0.8426 0.4322 0.2242 0.2788 0.387 

October 0.3926 0.6389 0.8281 0.6808 0.3468 0.4213 0.585 

November 0.4512 0.7466 0.8281 0.5054 0.1173 0.1516 0.210 

December 0.1669 0.8591 1.3984 0.3819 0.0318 0.0442 0.061 

Table 2. Monthly Averaged Background Concentrations at Bandelier WA. 

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0598 0.5359 0.9291 0.2244 0.0182 0.0213 0.030 

February 0.0998 0.5227 0.9167 0.2316 0.0191 0.0216 0.030 

March 0.4847 0.5219 0.634 0.3885 0.0708 0.0760 0.106 

April 0.2712 0.473 0.6653 0.4527 0.2109 0.2334 0.324 

May 0.2637 0.4367 0.7607 0.4671 0.2401 0.2571 0.357 

June 0.0129 0.7121 1.243 0.6916 0.4359 0.4651 0.646 

July 0.0015 0.554 0.7089 0.3044 0.1078 0.1142 0.159 

August 0.0033 0.763 1.0445 0.4384 0.1673 0.1820 0.253 

September 0.0138 0.7744 1.0789 0.4544 0.1763 0.1950 0.271 

October 0.2253 0.7354 1.0236 0.5162 0.1939 0.2178 0.302 

November 0.3262 0.8022 0.8983 0.4255 0.0519 0.0581 0.081 

December 0.1069 0.9124 1.0397 0.3632 0.0107 0.0134 0.019 
4 

Table 3. Monthly Averaged Background Concentrations at Black Canyon of the 

Gunnison WA. 

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.1248 0.4797 0.6568 0.252 0.0579 0.0601 0.083 

February 0.2193 0.4473 0.544 0.2816 0.063 0.0809 0.112 

March 0.5202 0.5023 0.4015 0.6294 0.3089 0.3679 0.511 

April 0.5408 0.3808 0.2665 1.1879 0.9047 1.0121 1.405 

May 0.3474 0.3494 0.422 0.7169 0.4979 0.5661 0.786 

June 0.0904 0.5106 0.7209 0.9024 0.6968 0.7534 1.046 

July 0.0028 0.4499 0.4957 0.5478 0.3877 0.4225 0.587 

August 0.0489 0.4371 0.5845 0.7191 0.5508 0.6290 0.873 

September 0.0693 0.5439 0.5382 0.6331 0.4215 0.4838 0.672 

October 0.5314 0.5844 0.3484 0.865 0.5123 0.6011 0.835 

November 0.5568 0.6644 0.3913 0.6404 0.2525 0.3081 0.428 

December 0.2793 0.7942 0.7206 0.4724 0.1145 0.1370 0.190 

Table 4. Monthly Averaged Background Concentrations at Canyonlands NP. 

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0374 0.5398 0.9843 0.2079 0.0064 0.0115 0.016 

February 0.0527 0.5151 0.9849 0.2037 0.0068 0.0147 0.020 

March 0.1206 0.461 0.752 0.2315 0.0352 0.0583 0.081 

April 0.1691 0.3695 0.542 0.3608 0.1835 0.2768 0.384 

May 0.1228 0.4011 0.7033 0.4342 0.2585 0.3320 0.461 

June 0.0076 0.4841 0.8868 0.4859 0.3124 0.4343 0.603 

July 0.0002 0.5799 0.7308 0.3378 0.1324 0.2213 0.307 

August 0.0003 0.3902 0.7096 0.3593 0.221 0.3098 0.430 

September 0.0026 0.5825 0.7442 0.3423 0.1353 0.2335 0.324 

October 0.2787 0.6302 0.9557 0.5218 0.2222 0.3840 0.533 

November 0.3355 0.711 0.9359 0.4253 0.0815 0.1298 0.180 

December 0.0853 0.8189 1.4854 0.3285 0.0151 0.0339 0.047 
5 

Table 5. Monthly Averaged Background Concentrations at Capitol Reef NP.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.1035 0.368 0.591 0.1958 0.037 0.0675 0.094 

February 0.1646 0.3864 0.5725 0.225 0.043 0.0840 0.117 

March 0.3277 0.3453 0.3579 0.3448 0.1327 0.2221 0.308 

April 0.2818 0.3434 0.3977 0.6166 0.4177 0.6130 0.851 

May 0.2546 0.3877 0.5712 0.6862 0.4791 0.6736 0.935 

June 0.0453 0.4496 0.8012 0.7077 0.536 0.7255 1.007 

July 0.0012 0.4978 0.6822 0.5638 0.3871 0.5982 0.830 

August 0.0238 0.3387 0.7254 0.5984 0.4719 0.6551 0.909 

September 0.0618 0.5186 0.6318 0.5312 0.3306 0.5166 0.717 

October 0.5506 0.5779 0.6284 0.8934 0.5377 0.8372 1.162 

November 0.5676 0.5888 0.5921 0.5653 0.2012 0.3405 0.473 

December 0.2862 0.6294 0.9542 0.3615 0.0602 0.1186 0.165 

Table 6. Monthly Averaged Background Concentrations at Grand Canyon NP. 

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0671 0.3517 0.8242 0.1529 0.0099 0.0159 0.022 

February 0.061 0.3674 0.7886 0.1583 0.0115 0.0297 0.041 

March 0.2185 0.3445 0.5491 0.2267 0.0448 0.0866 0.120 

April 0.2448 0.3961 0.6438 0.4979 0.2905 0.4483 0.622 

May 0.1923 0.3929 0.7346 0.5632 0.3713 0.5017 0.697 

June 0.0078 0.6422 1.3161 1.0541 0.8245 1.0294 1.429 

July 0.0003 0.6536 0.7669 0.3919 0.1604 0.2447 0.340 

August 0.0006 0.4817 0.9131 0.5246 0.3538 0.4966 0.689 

September 0.0059 0.514 0.7672 0.3663 0.1827 0.2286 0.317 

October 0.1155 0.5405 0.915 0.447 0.2239 0.3848 0.534 

November 0.3792 0.571 0.877 0.4255 0.1193 0.2164 0.300 

December 0.0565 0.6473 1.0947 0.2553 0.0106 0.0292 0.041 
6 

Table 7. Monthly Averaged Background Concentrations at Great Sand Dunes WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0553 0.477 0.5206 0.2003 0.0162 0.0197 0.027 

February 0.1548 0.4066 0.3893 0.2362 0.0498 0.0625 0.087 

March 0.3912 0.4297 0.3319 0.4382 0.1787 0.2182 0.303 

April 0.5246 0.3589 0.1805 0.9251 0.6542 0.7945 1.103 

May 0.384 0.3668 0.3916 0.5229 0.2877 0.3223 0.447 

June 0.1657 0.5805 0.6978 0.8585 0.6074 0.6587 0.914 

July 0.0281 0.692 0.7211 0.4749 0.2221 0.2344 0.325 

August 0.0653 0.5113 0.5027 0.6686 0.4696 0.5493 0.763 

September 0.0834 0.5013 0.4562 0.4368 0.2363 0.2708 0.376 

October 0.4393 0.5171 0.3051 0.4917 0.1881 0.2128 0.295 

November 0.3151 0.5721 0.336 0.3929 0.1039 0.1244 0.173 

December 0.1896 0.7372 0.6564 0.3675 0.0545 0.0663 0.092 

Table 8. Monthly Averaged Background Concentrations at La Garita WA. 

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0256 0.4199 0.5908 0.1583 0.0025 0.0038 0.005 

February 0.0238 0.3739 0.5243 0.1421 0.0032 0.0049 0.007 

March 0.1269 0.4173 0.5976 0.211 0.0284 0.0352 0.049 

April 0.2211 0.3753 0.5245 0.3213 0.1278 0.1556 0.216 

May 0.3476 0.3554 0.4588 0.3522 0.131 0.1450 0.201 

June 0.2039 0.5342 0.6679 0.5836 0.3385 0.3812 0.529 

July 0.0338 0.4737 0.5006 0.3332 0.1562 0.1739 0.241 

August 0.0759 0.424 0.5324 0.313 0.142 0.1695 0.235 

September 0.0702 0.5024 0.5331 0.2713 0.0741 0.0841 0.117 

October 0.2822 0.5177 0.4809 0.346 0.0852 0.1049 0.146 

November 0.1683 0.5602 0.56 0.2666 0.0221 0.0281 0.039 

December 0.0294 0.62236 0.6797 0.2334 0.0045 0.0066 0.009 
7 

Table 9. Monthly Averaged Background Concentrations at Maroon Bells-Snowmass WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0327 0.4301 0.5861 0.1637 0.0024 0.0047 0.007 

February 0.0269 0.4057 0.6048 0.154 0.0029 0.0055 0.008 

March 0.1692 0.4325 0.6164 0.2318 0.0322 0.0475 0.066 

April 0.3646 0.3775 0.427 0.4369 0.2032 0.3038 0.422 

May 0.3027 0.32 0.3252 0.3552 0.1589 0.2291 0.318 

June 0.207 0.4541 0.5012 0.5754 0.3579 0.4150 0.576 

July 0.0255 0.4419 0.5102 0.3275 0.164 0.1928 0.268 

August 0.0862 0.3707 0.4344 0.3447 0.1898 0.2312 0.321 

September 0.0859 0.4535 0.4129 0.2876 0.1034 0.1429 0.198 

October 0.3697 0.494 0.392 0.3713 0.0949 0.1666 0.231 

November 0.1997 0.5734 0.5682 0.2794 0.0216 0.0363 0.050 

December 0.05 0.6897 0.7922 0.2636 0.0056 0.0140 0.019 

Table 10. Monthly Averaged Background Concentrations at Mesa Verde NP.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0541 0.5826 1.4306 0.2348 0.0136 0.0146 0.020 

February 0.0589 0.4971 1.0781 0.2 0.0078 0.0090 0.012 

March 0.1938 0.5533 0.9614 0.2862 0.0371 0.0434 0.060 

April 0.2931 0.4192 0.7334 0.4269 0.1981 0.2198 0.305 

May 0.2502 0.4587 1.0102 0.4956 0.2645 0.2821 0.392 

June 0.0173 0.6806 1.4468 0.6797 0.4339 0.4616 0.641 

July 0.001 0.5776 0.8608 0.3753 0.1705 0.1851 0.257 

August 0.0022 0.6723 1.3482 0.4528 0.2141 0.2322 0.322 

September 0.0251 0.7851 1.2736 0.4244 0.1395 0.1519 0.211 

October 0.3046 0.8072 1.3785 0.5619 0.1925 0.2103 0.292 

November 0.342 0.7884 1.1816 0.4385 0.0655 0.0719 0.100 

December 0.1064 1.0245 1.8357 0.4072 0.0151 0.0181 0.025 
8 

Table 11. Monthly Averaged Background Concentrations at Pecos WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0379 0.4434 0.7136 0.1808 0.0134 0.0202 0.028 

February 0.0895 0.4616 0.7213 0.2062 0.0182 0.0272 0.038 

March 0.4911 0.4971 0.493 0.4016 0.0909 0.1521 0.211 

April 0.3463 0.4652 0.4867 0.4557 0.196 0.2919 0.405 

May 0.3251 0.4242 0.5784 0.4531 0.2137 0.2895 0.402 

June 0.0928 0.7451 1.0326 0.8278 0.5385 0.6039 0.838 

July 0.0065 0.5125 0.5519 0.2914 0.1081 0.1601 0.222 

August 0.0315 0.7501 0.8425 0.4367 0.1624 0.2323 0.323 

September 0.0423 0.7497 0.7989 0.4156 0.1385 0.1913 0.266 

October 0.294 0.6654 0.7416 0.4848 0.1686 0.2511 0.349 

November 0.3114 0.692 0.6438 0.3755 0.045 0.0767 0.106 

December 0.1236 0.7795 0.8017 0.3202 0.0103 0.0220 0.031 

Table 12. Monthly Averaged Background Concentrations at Petrified Forest NP.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.1084 0.591 1.0712 0.2576 0.0186 0.0297 0.041 

February 0.2029 0.4603 1.0651 0.2383 0.0196 0.0316 0.044 

March 0.4325 0.4195 0.6469 0.3355 0.0683 0.1024 0.142 

April 0.3583 0.4359 0.8467 0.6078 0.3552 0.4628 0.643 

May 0.4647 0.4412 0.9936 0.7558 0.4721 0.5893 0.818 

June 0.0102 0.7603 1.7636 0.9546 0.6826 0.8594 1.193 

July 0.0014 0.705 0.8287 0.4058 0.1558 0.2261 0.314 

August 0.0035 0.6781 1.0827 0.5561 0.315 0.4167 0.579 

September 0.0065 0.7003 0.96 0.4853 0.2355 0.3486 0.484 

October 0.1562 0.7431 1.0262 0.5988 0.2928 0.4393 0.610 

November 0.5609 0.6801 1.0954 0.5272 0.1325 0.1822 0.253 

December 0.1151 1.0858 1.3927 0.4297 0.0135 0.0272 0.038 
9 

Table 13. Monthly Averaged Background Concentrations at San Pedro Parks WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0465 0.5584 1.0404 0.2221 0.0116 0.0119 0.017 

February 0.0782 0.527 1.1997 0.2173 0.0092 0.0095 0.013 

March 0.4299 0.5061 0.6733 0.3504 0.0532 0.0537 0.075 

April 0.3625 0.4304 0.5974 0.4116 0.1598 0.1658 0.230 

May 0.4028 0.4191 0.6522 0.4691 0.2102 0.2143 0.298 

June 0.0457 0.6964 1.2645 0.7371 0.4779 0.4915 0.682 

July 0.0026 0.5083 0.6781 0.3104 0.1297 0.1433 0.199 

August 0.0107 0.6172 0.9382 0.3889 0.1673 0.1808 0.251 

September 0.0314 0.7069 1.0381 0.405 0.1461 0.1541 0.214 

October 0.3079 0.7199 1.1654 0.4889 0.1495 0.1568 0.218 

November 0.3107 0.8419 1.0907 0.4282 0.0448 0.0479 0.066 

December 0.1133 0.9338 1.3157 0.3685 0.0067 0.0076 0.011 

Table 14. Monthly Averaged Background Concentrations at Weminuche WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0313 0.4073 0.7337 0.1555 0.0027 0.0070 0.010 

February 0.0203 0.3689 0.6401 0.1389 0.0026 0.0053 0.007 

March 0.1196 0.4191 0.8003 0.2023 0.021 0.0302 0.042 

April 0.2642 0.3674 0.6091 0.3044 0.1019 0.1353 0.188 

May 0.3448 0.3667 0.5471 0.3504 0.126 0.1463 0.203 

June 0.2142 0.5506 0.7758 0.5535 0.2998 0.3640 0.505 

July 0.0259 0.4224 0.4465 0.2842 0.1275 0.1538 0.214 

August 0.0698 0.4471 0.6461 0.2975 0.12 0.1474 0.205 

September 0.0714 0.5395 0.6685 0.2719 0.0613 0.0774 0.107 

October 0.2656 0.5535 0.6607 0.3392 0.0703 0.0982 0.136 

November 0.1813 0.5573 0.7007 0.2643 0.0172 0.0277 0.038 

December 0.029 0.6227 0.7742 0.2312 0.0027 0.0077 0.011 
10 

Table 15. Monthly Averaged Background Concentrations at West Elk WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0495 0.4488 0.6478 0.1812 0.0087 0.0199 0.028 

February 0.0657 0.4224 0.6154 0.1791 0.0115 0.0277 0.038 

March 0.2777 0.4673 0.5945 0.3327 0.091 0.1812 0.252 

April 0.4503 0.3807 0.3611 0.6777 0.4194 0.6536 0.907 

May 0.3749 0.3292 0.3262 0.4593 0.2399 0.3463 0.481 

June 0.1657 0.4933 0.6282 0.7149 0.4948 0.5946 0.825 

July 0.0123 0.4645 0.5243 0.4131 0.2452 0.3025 0.420 

August 0.0751 0.4072 0.5222 0.4618 0.2969 0.4100 0.569 

September 0.0828 0.4944 0.4871 0.3889 0.191 0.2845 0.395 

October 0.4667 0.5282 0.3421 0.5055 0.1905 0.3119 0.433 

November 0.3199 0.61 0.5162 0.3648 0.061 0.1285 0.178 

December 0.1188 0.7303 0.8215 0.3161 0.0248 0.0527 0.073 

Table 16. Monthly Averaged Background Concentrations at Wheeler WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0394 0.4342 0.6593 0.1746 0.01 0.0116 0.016 

February 0.0663 0.4285 0.6263 0.1854 0.0154 0.0169 0.023 

March 0.346 0.474 0.5471 0.3253 0.0625 0.0681 0.095 

April 0.3116 0.4107 0.4945 0.3864 0.1555 0.1591 0.221 

May 0.3287 0.3975 0.5343 0.4061 0.1752 0.1778 0.247 

June 0.1129 0.6098 0.8519 0.6175 0.3706 0.3829 0.532 

July 0.0065 0.4876 0.5245 0.2804 0.106 0.1094 0.152 

August 0.043 0.6177 0.6529 0.3694 0.1389 0.1430 0.199 

September 0.0421 0.6181 0.6695 0.3412 0.1107 0.1156 0.160 

October 0.3308 0.6145 0.6282 0.434 0.1257 0.1310 0.182 

November 0.2476 0.6424 0.5878 0.333 0.0376 0.0406 0.056 

December 0.1463 0.8231 0.8672 0.3401 0.0084 0.0096 0.013 
11 

Table 17. Monthly Averaged Background Concentrations at Bryce Canyon NP.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0823 0.3251 0.6641 0.157 0.0193 0.0324 0.045 

February 0.0845 0.3274 0.5982 0.157 0.0178 0.0278 0.039 

March 0.2995 0.3199 0.4091 0.2622 0.0668 0.0910 0.126 

April 0.3186 0.3261 0.3278 0.5133 0.3104 0.3817 0.530 

May 0.3422 0.3864 0.5241 0.6452 0.4145 0.4973 0.690 

June 0.054 0.481 0.9367 0.7779 0.5928 0.6721 0.933 

July 0.0037 0.5512 0.8112 0.5351 0.3388 0.3980 0.553 

August 0.0266 0.3558 0.8171 0.5651 0.4318 0.4997 0.694 

September 0.0579 0.4584 0.6261 0.4317 0.2535 0.3261 0.453 

October 0.5049 0.5439 0.5531 0.6776 0.3466 0.4536 0.630 

November 0.4407 0.5337 0.6545 0.433 0.1231 0.1700 0.236 

December 0.1484 0.5837 0.9756 0.2735 0.0261 0.0438 0.061 

Table 18. Monthly Averaged Background Concentrations at Mazatzal WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.2245 0.4754 1.2659 0.2713 0.0413 0.0835 0.116 

February 0.265 0.4584 1.341 0.31 0.075 0.1496 0.208 

March 0.631 0.4089 1.4642 0.5089 0.1911 0.3328 0.462 

April 0.2974 0.4914 2.3346 1.0041 0.7485 1.0555 1.465 

May 0.3767 0.4812 2.5302 1.3868 1.1131 1.5385 2.136 

June 0.013 0.8369 3.0856 1.5477 1.2477 1.5409 2.139 

July 0.0032 0.8164 1.9295 0.7103 0.4203 0.6467 0.898 

August 0.0074 0.7142 3.5579 1.0857 0.8307 1.1155 1.549 

September 0.0084 0.6744 1.6156 0.5866 0.3454 0.4445 0.617 

October 0.179 0.7183 2.5517 0.8421 0.5386 0.7141 0.991 

November 0.8057 0.6636 1.5178 0.7408 0.2849 0.4442 0.617 

December 0.1184 0.9436 1.2495 0.3956 0.0289 0.0743 0.103 
12 

Table 19. Monthly Averaged Background Concentrations at Pine Mountain WA.

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.2175 0.4504 1.2056 0.2518 0.0327 0.0388 0.054 

February 0.2319 0.4355 1.1269 0.2712 0.0534 0.0651 0.090 

March 0.5925 0.3952 1.2199 0.447 0.1446 0.1651 0.229 

April 0.1991 0.4676 1.8334 0.8148 0.5946 0.6348 0.881 

May 0.2369 0.4522 1.9603 1.1214 0.8963 0.9521 1.322 

June 0.0047 0.7023 2.2156 1.1298 0.8798 0.9152 1.271 

July 0.0021 0.7834 1.5088 0.5851 0.3071 0.3328 0.462 

August 0.0047 0.6745 2.7013 0.8996 0.6594 0.6992 0.971 

September 0.0033 0.6513 1.2414 0.5265 0.2949 0.3128 0.434 

October 0.1644 0.7004 2.2962 0.7743 0.4812 0.5137 0.713 

November 0.7614 0.6317 1.3453 0.686 0.2535 0.2795 0.388 

December 0.1024 0.9078 1.134 0.3727 0.0231 0.0271 0.038 

Table 20. Monthly Averaged Background Concentrations at Sycamore Canyon WA

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.1889 0.4143 0.967 0.2219 0.0234 0.0284 0.039 

February 0.263 0.3912 0.8662 0.2328 0.0222 0.0302 0.042 

March 0.5327 0.3705 0.6772 0.3451 0.0678 0.0891 0.124 

April 0.4197 0.4323 0.8072 0.7165 0.4483 0.5170 0.718 

May 0.3364 0.4116 1.0467 0.8495 0.6115 0.6856 0.952 

June 0.0338 0.7454 1.8269 1.3768 1.1035 1.1273 1.565 

July 0.0039 0.6552 1.0324 0.5021 0.269 0.3170 0.440 

August 0.0034 0.5795 1.3114 0.7726 0.5665 0.6254 0.868 

September 0.0088 0.5928 1.0411 0.4981 0.2857 0.3303 0.459 

October 0.3345 0.6014 1.303 0.6827 0.378 0.4591 0.637 

November 0.6801 0.5904 0.8717 0.5549 0.1593 0.1958 0.272 

December 0.1476 0.7965 1.1498 0.3396 0.0171 0.0235 0.033 
13 

Table 21. Monthly Averaged Background Concentrations at Zion NP

 Max NH3 gas 

Species 

NO3 

(μg/m3

) 

SO4 

(μg/m3

) 

HNO3

(μg/m3

) 

TNH3

(μg/m3

) 

NH3 gas 

(μg/m3

) (μg/m3) (ppb) 

January 0.0921 0.3283 0.6664 0.1737 0.0322 0.0417 0.058 

February 0.1105 0.3432 0.5948 0.1794 0.0275 0.0377 0.052 

March 0.3163 0.3325 0.3982 0.29 0.0855 0.1196 0.166 

April 0.2274 0.3551 0.5199 0.5354 0.3472 0.4199 0.583 

May 0.2103 0.4011 0.774 0.6726 0.4728 0.5289 0.734 

June 0.0156 0.5101 1.2333 0.8069 0.6219 0.6691 0.929 

July 0.0011 0.6772 1.1441 0.5956 0.3554 0.4463 0.620 

August 0.0011 0.4124 1.0754 0.6232 0.4769 0.5559 0.772 

September 0.0131 0.458 0.7115 0.4469 0.2811 0.3339 0.464 

October 0.3333 0.5523 0.8064 0.7339 0.4469 0.5186 0.720 

November 0.5135 0.5468 0.6123 0.5024 0.168 0.2055 0.285 

December 0.1804 0.6138 0.9725 0.3146 0.0477 0.0618 0.086 
Attachment 3. Modeling Files