# 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 Files1 

## 1 Executive Summary

The Salt River Project Agricultural Improvement and Po wer 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 pe nding Best Available Retrofit Technology (BART) determination for the Navajo Gene rating Station (NGS). In previous correspondence with EPA, SRP expressed concern that the current regulatory vers ion of the CALPUFF model (CALPUFF v5.8) has been shown to overpredict nitrate concentrations, particular ly 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 concentrati ons are overestimated, the model also overestimates the degree of visibility improvement that can be ach ieved by more stringent BART control technologies for nitrogen oxide (NO x ) emissions. This issue was acknowle dged in the EPA’s 2005 Regional Haze Rule (70 FR 39121). EPA also committed to evalua ting 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 s ophisticated nitrate chemistry algorithms that improve the performance of the model. SRP re tained 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 pr esented 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 be lieves that it is an appropriate methodology for establishing ammonia values for use as input to CALPUFF v6.4.
The incremental visibility improvement predicte d by CALPUFF v6.4 from adding Selective Catalytic Reduction (SCR) to Low-NO x 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 cha nge 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 incr ease in sulfates that are produced by SCR.
Given the significant cost of the BART contro l 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 NO x emission rate of 0.24 lb/MMBtu, achieved using LNB/SOFA, is BART for all three units at NGS. When calculated using a m odeling 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 visi bility 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 perfo rming 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 391 23). Additional information on the studies that demonstrate this overprediction was provided in the re port submitted by SRP to EPA in September 2010.
A recent study conducted by Atmospheric and Envir onmental 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 th e 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 recommendati on to adopt it as the version that is approved for regulatory use. The model upgrades were also pr esented to a group of Federal Land Managers in December 2010 and to EPA’s Office of Air Quality Pl anning 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 modeli ng 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, Octobe r 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 visi bility 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 (hereafte r referred to as “December 2008 ammonia values”) yield the best agreement between modeled ammonium n itrate concentrations and measured values at sites that are part of the Interagency Monitoring of Prot ected 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 con centrations 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 descr ibed 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 wi thout additional monitoring data, there was some uncertainty over which values would be more represen tative of the ammonia concentrations in other areas of the modeling domain. SRP also identified several f actors related to human influence near the locations where the higher concentrations were measured. Give n that those same factors are not present in the Class I areas addressed in the BART analysis, the pr esence 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 valu es 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 re duce the tendency of the model to overpredict nitrates, in order to provide an objective and conser vative 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 appr oach 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 appr oach 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-calcula ted values, which were shown to produce large overpredictions of nitrate concentr ations 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 appr opriate 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 amm onia 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 impr ovement 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 Ta ble 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 98 th percentile day visibility impairment relative to natural conditions, expressed in delta-deciviews (delta dv). The tabulated values represent the diffe rence in modeled visibility impacts between this average for the LNB/SOFA case and the scenario in wh ich SCR controls are added to all three units.
Table 2. Incremental Visibility Improvements

CALPUFF v6.4, CMAQ NH 3 CALPUFF v5.8, December 2008 NH 3 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 (C ALPUFF 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 b ecause visibility impairment caused by additional ammonium sulfate particles produced by the SCR ou tweighs the improvement resulting from the reduced NO x emissions. This occurs despite the assumption that an ultra low-oxidation SCR catalyst will be used,6 with a sulfur dioxide (SO 2 ) to sulfur trioxide (SO 3 ) conversion rate of 0.5%, which was determined to be Best Available Control Technology for sulfuric acid (H 2 SO 4 ) for Coronado Generating Station.
As directed by EPA’s BART rules, it is necessary to c onsider the cost of each control option in relation to the degree of visibility improvement expected. A ccordingly, Table 3 shows the incremental cost effectiveness of adding SCR to LNB/SOFA on all th ree 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 CALPU FF 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 NH 3 CALPUFF v5.8, December 2008 NH 3 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 vi sibility 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 simila r 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 slig htly different from those presented in SRP’s ANPR comments.7 

## 5 Conclusion

Given the significant cost of the BART contro l 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 NO x emission rate of 0.24 lb/MMBtu, achieved using LNB/SOFA, is BART for all three units at NGS. When calculated using a m odeling 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 visi bility 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 fo r incorporation into the latest CALPUFF model. This document describes the work TRC undertook as part of their evaluation.
Two chemical transformation module options we re introduced into Version 5.8 (v5.8) of the CALPUFF modeling system by Kara mchandani 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 SO 2 

### to SO 4 

### and NO/NO 2 

### to HNO 3 

### and NO 3



### • Replacement of the MESOPUFF-II CHEMEQ model with the ISORROPIA (Version 1.7) model for inorganic ga s-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 anthropoge nic secondary organic aerosol (SOA) formation based on the CalTech SOA r outines implemented in CMAQ-MADRID
TRC has implemented these modules as options in to 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 (M CB) 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 resu lts 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 with in the v6.4 framework. Alternatively, v6.4 can also be configured to be equivalent to v5.8.
Module Option 1 is implemented as the 6 th 

### CALPUFF chemical tran sformation option (MCHEM = 6), and Module Option 2 is implemented as the 7 th 

### 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 IS ORROPIA v2.1 (Nenes et al., 1998; Fountoukis and Nenes, 2007). ISORROPIA v2.1 is the only ve rsion of the model curre ntly 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 (TNO 3 

### ) is partitioned into the gas (HNO 3 

### ) and particulate (NO 3 

### ) phases based in part on the ammonia

### 3 available after preferential scav enging by sulfate, the equilibrium should be determined using the total amount of sulfate and nitrat e (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, Ka ramchandani 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 reta ins backward compatibility of the POSTUTIL code.

The ISORROPIA v2.1 model is docum ented 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 m odification, with permission from the model developers following the te rms-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 co ntent value in kg/m 3



### . However, the units of cloud water actually passed to the routine in the API code were g/m 3 

### . This factor of 1000 error is potentially a significant issue with the c ode when using the API version of the aqueous phase chemistry option.

 The wet scavenging coefficients in the API c ode for species other than the set of six species defined by the RIVAD scheme (SO 2 

### , SO 4 

### , NO, NO 2 

### , HNO 3 

### and NO 3 

### ) were left undefined when the aqueous phase chemistr y option is used, meaning the scavenging coefficients would be zero or an arbitrar y erroneous value (dep ending 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 some times set to zero even though there was precipitation present in the grid cell.
Several important improvements were made in th e implementation of the API chemistry module. These include the following:

 The API code set the cloud liquid water conten t in the aqueous-phase option to a constant value of 0.5 g/m 3 

### (for precipitating clouds) and 0.1 g/m 3 

### (for non-precipitation clouds). In reality, there is considerable variability in the liquid water content of clouds, both spatially and temporally. In the revised ve rsion 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) ha s been implemented into both CALPUFF (v6.4) and POSTUTIL (v1.64), replacing the v1.7 ISORROPIA code used by Karamchandani et al. (20 08) in the API version.

### 4  

### The call of the ISORROPIA mechanism for small TNO 3 

### concentrations has been revised so that an immediate drop to zero NO 3 

### at TNO 3 

### =10 -10 

### mol/m 3 

### has been avoided.

 Karamchandani et al. (2008) mistakenly inte rpreted the nighttime heterogeneous reaction rate for SO 2 

### conversion in CALPUFF as being due to aqueous phase reactions alone. This resulted in nighttime conversion of SO 2 

### 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 befo re has been added. This heterogeneous term is a userspecified value, so it is changeable.

 The format of the new file for monthly amm onia data provided for each CALPUFF layer has been revised to include documentation, a nd 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 b een 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-spon sored update to CALPUFF are made up of a number of subroutines and supportin g include-files for declarations of parameters and variables stored in common. To facilita te future maintenance, all c odes 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 rema ins a separate file (ISORROPIA.FOR), and the included commons-files are retained w ithout modification (a queous.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 Na tions 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 re packaging 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 th at the Lahey LF95 compile r 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 SO 4 

### formation rate based on heterogeneous reactions as in the current mechanism is restored. 2. 

### The way the ISOPRROPIA mechanism is called for small TNO 3 

### concentrations is revised so that an immediate drop to zero NO 3 

### at TNO 3 

### =10 -10 

### mol/m 3 

### is avoided. 3. The format of the new file for monthly amm onia 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-sp ace-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 passe d to subroutine AQRADM (to compute the aqueous-phase transformati ons) is changed from g/m 3 

### to kg/m 3 

### , as defined in the AQRADM documentation. 6. The scavenging coefficients for species othe r than the six specie s defined in the RIVAD chemical mechanism were left undefined in the API code when aqueous phase chemistry is used, resulting in these speci es having either zero or arbi trary 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 err oneously 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 th e cloud observation, the sky may be clear. This results in locations w ith precipitation but no wet scavenging. The revised code imposes a minimum cloud cover for the calcula tion whenever there is precipitation so that liquid water content can be computed.

SO 4 

### Heterogeneous Reactions
Both the MESOPUFF-II (MCHEM=1) and the RI VAD (MCHEM=3) transformation options in CALPUFF include an SO 2 

### -to-SO 4 

### transformation rate due to hete rogeneous 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 SO 2 

### -to-SO 4 

### transformation rate, and do not explicitly include heterogeneous reactions. Therefore, in the absence of aqueous-phase reactions the SO 2 

### - to-SO 4 

### transformation rate at night is id entically 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 c ontrol file. When the option is selected the "Nighttime SO 2 

### loss rate" entered in the control file is added to the calculated rate. If the heterogeneous reaction rate is not selected, the calculated conver sion rate remains as calculated by Karamchandani et al. (2008).

ISORROPIA for small TNO 3


### CHEMRIV6 and CHEMRIV7 include a threshold c oncentration for total nitrate that must be exceeded before the ISORROPIA subroutines are called. Below this threshold, the particulate NO 3 

### concentration is set identically to zero, placi ng all of the nitrate in the gas phase. Although the TNO 3 

### threshold is "small" at 10 -10 

### mol/m 3 

### , this may introduce an artificial step in particulate NO 3 

### concentrations.
A logic-block is added to subrou tines that uses the threshold as a floor, so that the ISORROPIA subroutines are called with this floor concentratio n (when the TNO 3 

### is less), and the resulting NO 3 

### /TNO 3 

### ratio is then applied to the actual TNO 3 

### concentration to obtain the particulate NO 3 

### 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 IS ORROPIA module at low TNO 3 

### concentration has revealed what appears to be some instability in the respon se. Figure 1 shows the fraction of TNO 3 

### that is particulate nitrate as the TNO 3 

### concentration decreases. Step-changes in the response can be seen at the smallest scales, with the particulate fraction chan ging by a factor of two for changes in the TNO3 in the 5 th 

### 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 choos e between the monthly values in the control file (which does not vary by laye r) and the set of month-and-layer values read from the new file.
A new control variable for this option is construc ted. It allows the user to choose between using the 12 monthly control file values or reading the month-and-laye r values from the new file. Future selections such as read ing ammonia from a full time-varyi ng 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 concentratio n units, the number of layers, and the row-order of the data. The row-order currently accepted pl aces data for one month on a single line in which the data progress from layer 1 to the t op-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 modul e for the fraction of th e 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 th e aqueous chemistry was set to 0.5 g/m 3 

### for precipitating clouds and 0.1 g/m 3 

### for non-precipitating clouds in lieu of providing a pathway for obtaining more specific information. New procedures ar e 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 CALP UFF to obtain a mean cloud water mixing ratio (g/kg) that is vertically-average d 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 ava ilable 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/m 3 

### to kg/m 3 

### . The AQRADM subroutine states that it expects the kg/m 3 

### units.
This change is implemented under th e 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/m 3 

### ). 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 simu lation. However, AQRADM assigns scavenging coefficients only for the six RI VAD 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 b een implemented in the revised code.
Wet scavenging when there is liq uid precipitation but no cloud cove r 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 precip itation rate but zero clouds.
To address this problem, a minimum cloud cover is assigned for use in the aqueous chemistry module whenever the liquid precip itation 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 presen ted to the user as an option in the control file, but resetting the logical variab le to FALSE in the code restores the original API logic. This is provided as an option to allow testi ng 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 othe r anthropogenic organic co mpounds are significant. The SOA module is activated using the MC HEM=7 switch in CALPUFF. The new SOA chemistry may require a substant ially greater amount of computat ion time, with runtimes taking

### 10 approximately 7.5 times longer than when th e simple chemistry without SOA is used (MCHEM=3).



## 3.0 Equivalence Testing

A demonstration of the equivalence of the upda ted EPA-version of the code (v5.8) with all known bugs fixed and the new v6 code with AP I chemistry has been done using the EPA CALPUFF Evaluation Tool (EPA, 2008) of 11 mode ling 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 change s that were not compatible have been made so. All bug-fixes identified in Model Change Bu lletins E and F have been implemented in the EPA v5.8 code to create a CALPUFF v5.835 code for the equivalence testi ng. These updates to v5.8 do not involve the API code or any model enha ncements, 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 th ere 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 identi cal 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 as sessment study has been also used to test the equivalence of CALPUFF v6 with v5.835. The visibility asse ssment reproduces BART modeling for the Hayden power plant in Colorado, and compares re sults for a full year of modeling with three stack configurations. CALMET v5.8 was used fo r all of the simulations, and CALPUFF options

### 11 are identical in both CALPUFF v5.835 and v6.402 applic ations. The visibility impact results are found to be identical.
In terms of the controlling vi sibility 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 de veloped that includes potentially significant improvements in the treatment of chemical reac tions, including a revise d gas phase chemical transformation module for SO 2 

### conversion to sulfate and NO x 

### conversion to nitric acid and nitrate, the latest ISORROPIA (V ersion 1.7) model for inorganic gas-particle equilibrium (Nenes et al., 1998; Fountoukis and Nene s, 2007), and aqueous phase oxi dation and wet scavenging of pollutants adapted from adapted from the RADM cloud implementation in CMAQ/SCICHEM, and the addition of a new option for anthropogeni c secondary organic ae rosol (SOA) formation based on the CalTech SOA routines implemented in CMAQ-MADRID.

Version 6.4 of CALPUFF has been tested with th e EPA Evaluation Tool and other datasets and has been found to be equivalent in all tests conduct ed 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 signi ficance of this finding is the v6.4 code can be used to test the new v6.4 featur es and technical improvements individually while in other respects being consistent with current regulatory c ode. 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 m odules, 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 ev aluate 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 previ ous 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 IS ORROPIA routines should be conducted to determine the cause of the va riability seen at low TNO 3 

### concentrations, and to determine if the minimum TNO 3 

### 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 phas e 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 re lated to using MM5/W RF 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 com putes wet scavenging coefficients for the six RIVAD species. Without explicit gridded li quid cloud water fields, these are computed using cloud cover and the pr esence 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 consid ered 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 ar e scaled by the precipitation rate, and so they vary both spatially and hourly. Se lect this by turning the aqueous phase transformation option off (MAQCHEM=0). b. Use the aqueous phase transformation opti on with the default liquid water content assumptions. Select this by turning th e aqueous phase transformation option on (MAQCHEM=1) and setting the liquid water content flag MLWC=0. The first option is currently recommended b ecause the second option has not yet been evaluated.

### 14 

## 5.0 References

EPA, 2008. Protocol for Updating the CALP UFF Modeling System. U.S. EPA, OAQPS, Research Triangle Park, NC.
Fountoukis, C. and A. Nenes, 2007: ISORROPIA II: A Comput ationally 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: ISORRO PIA: 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. Yama rtino, 2000b: A User's Guide for the CALPUFF Dispersion Model. (Version 5.0). Earth T ech, 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 Av erages Background Concentrations from CMAQ 2002

The CMAQ 2002 dataset used for computing the mont hly 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 St ates, part of Canada and Mexico. The CMAQ was run in a Lambert Conformal Conical (LCC) projection with the following mapping values: Matching Latitudes 33 0 N, 45 0 N and origin of Projection: 40 0 N, 97 0 W. 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) em issions 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 Recep tor 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 su mmary in the tables, the concentrations are provided as spatially averaged values over all r eceptors 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 fo r the five species of interest: NO 3 , HNO 3 , SO 4 , NH 3 (gas) and
TNH 3 (in μ g/m 3 ). TNH 3 is the sum in ppb of concentrations: [NO 3 ] + 2 [SO 4 ] + [NH 3 (gas)]. In addition, in the final two columns ( Max NH 3 gas ), the maximum monthly average ammonia concentration over all receptors of each Class I area is provided in both μ g/m 3 and in ppb. The
conversion factor used for NH 3 between μ g/m 3 and ppb in the table is based on 15 deg C and 1 atm conditions (1.39 ppb per μ g/m 3 of NH 3 ). The units of all CMAQ concentrations in the CALPUFF concentrations files provided are g/m 3 , as required by POSTUTIL. The units of
ammonia concentrations entered in the POSTU TIL control file (POSTUTIL.INP) are ppb.

The recommended monthly NH 3 (gas) concentrations to use in the CALPUFF runs when applying POSTUTIL to repartition the nitric acid-nitrate shoul d be equal to or higher than the values used in POSTUTIL. Although CALPUFF will over estimate the ammonia availability because it applies the available ammonia on a puff-by-puff b asis, POSTUTIL repartitions the nitrate account for all puffs together from sources modelled by CALPUFF and background sources of sulfate and total nitrate ([TNO 3 ] = [HNO 3 ] + [NO 3 ]) as predicted by CMAQ.

### 3 Table 1. Monthly Averaged Background Concentrations at Arches NP.

Max NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 C oncentrations at Canyonlands NP.

Max NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 Con centrations at La Garita WA.

Max NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 Concentr ations at Maroon Bells-Snowmass WA.

Max NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 Concen trations at San Pedro Parks WA.

Max NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 C oncentrations at Weminuche WA.

Max NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 NH 3 gas

Species NO 3 ( μ g/m 3 ) SO 4 ( μ g/m 3 ) HNO 3 ( μ g/m 3 ) TNH 3 ( μ g/m 3 ) NH 3 gas ( μ g/m 3 ) ( μ g/m 3 ) (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 0.086Attachment 3. Modeling Files