## B EST 

## A VAILABLE 

## R ETROFIT 

## T ECHNOLOGY 

## D ETERMINATION



## D OMTAR 

## I NDUSTRIES 

## I NC



## .  A SHDOWN 

## M ILL 

## (AFIN 41-00002)

Prepared by:

T RINITY C ONSULTANTS 10809 Executive Center Drive Suite 120, The Searcy Building

Little Rock, Arkansas 72211 (501) 225-6400

Jeremy W. Jewell  Senior Consultant Amanda S. Barber  Consultant

T RINITY C ONSULTANTS 25055 West Valley Parkway Suite 101 Olathe, Kansas 66061 (913) 390-9700

Kasi Dubbs  Senior Consultant

T RINITY C ONSULTANTS 12770 Merit Drive Suite 900 Dallas, Texas 75251 (972) 661-8100

Weiping Dai, PhD  Managing Consultant

October 31, 2006 Revised on March 26, 2007

Project 060401.0081Domtar Industries i Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## T ABLE OF 

## C ONTENTS



### 1. I NTRODUCTION 

### ................................................................................................................ 1-1 1.1 O VERVIEW OF 

### R EGIONAL 

### H AZE 

### R ULE AND 

### BART G UIDELINES 

### ........................... 1-1
1.1.1 BART A PPLICABILITY ........................................................................................1-1 1.1.2 BART D ETERMINATION .....................................................................................1-3



### 2. BART-E LIGIBLE 

### E MISSION 

### U NITS 

### ................................................................................. 2-1 2.1 N O



### . 1 P OWER 

### B OILER 

### ........................................................................................... 2-1 2.2 N O



### . 2 P OWER 

### B OILER 

### ........................................................................................... 2-2

3. BART A PPLICABILITY 

### A NALYSIS 

### .................................................................................. 3-1 3.1 M ODELED 

### A SHDOWN 

### M ILL 

### E MISSIONS 

### ................................................................ 3-1 3.2 M ODELED 

### A SHDOWN 

### M ILL 

### S TACK 

### P ARAMETERS 

### ................................................. 3-1 3.3 P OTENTIALLY 

### A FFECTED 

### C LASS 

### I A REAS 

### ............................................................. 3-2 3.4 BART A PPLICABILITY 

### A NALYSIS 

### R ESULTS 

### .......................................................... 3-3

4. BART D ETERMINATION 

### A NALYSIS 

### ................................................................................ 4-1 4.1 BART D ETERMINATION FOR 

### PM.......................................................................... 4-1 4.2 BART D ETERMINATION FOR 

### SO 2 

### – N O



### . 1 P OWER 

### B OILER 

### ................................... 4-3 4.3 BART D ETERMINATION FOR 

### SO 2 

### – N O



### . 2 P OWER 

### B OILER 

### ................................... 4-3 4.4 BART D ETERMINATION 

### A NALYSIS FOR 

### NO X 

### ....................................................... 4-4
4.4.1 S TEP 1

- I DENTIFY A LL A VAILABLE R ETROFIT C ONTROL T ECHNOLOGIES ........4-4 4.4.2 S TEP 2

– E LIMINATE T ECHNICALLY I NFEASIBLE O PTIONS ...............................4-13 4.4.3 S TEP 3

– E VALUATE C ONTROL E FFECTIVENESS OF R EMAINING C ONTROL T ECHNOLOGIES ..............................................................................................................4-18 4.4.4 S TEP 4

– E VALUATE I MPACTS AND D OCUMENT R ESULTS ................................4-19 

### 4.5 S TEP 

### 5

– E VALUATE 

### V ISIBILITY 

### I MPACTS 

### ........................................................... 4-22

A PPENDIX 

### A

– ADEQ’ S 

### D RAFT 

### BART M ODELING 

### P ROTOCOL 

### ........................................ A-1

A PPENDIX 

### B

– C ONTROLS 

### C OST 

### A NALYSES 

### .........................................................................B-1

A PPENDIX 

### C

– C OMPLIANCE 

### P LAN AND 

### S CHEDULE 

### ........................................................... C-1

A PPENDIX 

### D

– R EQUESTED 

### D ESIGN 

### D ETAILS OF 

### N O



### . 1 P OWER 

### B OILER 

### ........................... D-1Domtar Industries ii Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## L IST OF 

## T ABLES

T ABLE 2-1. S UMMARY OF BART- ELIGIBLE E MISSION U NITS ............................................................2-1

T ABLE 3-1. S UMMARY OF 24- HOUR A VERAGE M AXIMUM A CTUAL E MISSION R ATES ......................3-1

T ABLE 3-2. S TACK P ARAMETERS .......................................................................................................3-2

T ABLE 3-3. M ODELED C LASS I A REAS ...............................................................................................3-3

T ABLE 3-4. S UMMARY OF BART A PPLICABILITY A NALYSIS R ESULTS .............................................3-3

T ABLE 4-1. BART / B OILER MACT-B ASED PM E MISSION R ATES ....................................................4-2

T ABLE 4-2. R ANKING OF C ONTROL S TRATEGIES .............................................................................4-19

T ABLE 4-3. C ONTROLS C OSTS S UMMARY ........................................................................................4-20

T ABLE 4-4. S UMMARY OF P ROPOSED BART D ETERMINATIONS ......................................................4-21

T ABLE 4-5. S UMMARY OF 24- HOUR A VERAGE M AXIMUM P OST -C ONTROL E MISSION R ATES ........4-22

T ABLE 4-6. P OST -C ONTROL S TACK P ARAMETERS ...........................................................................4-22

T ABLE 4-7. S UMMARY OF V ISIBILITY I MPROVEMENT A NALYSIS R ESULTS .....................................4-23

T ABLE 4-8. E MISSION U NIT & P OLLUTANT S PECIFIC M ODELING R ESULTS ....................................4-23Domtar Industries iii Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## L IST OF 

## F IGURES

F IGURE 3-1. L OCATION OF A SHDOWN M ILL R ELATIVE TO M ODELED C LASS I A REAS .....................3-2

F IGURE 4-1. P RIMARY SNCR R EACTION S EQUENCES ......................................................................4-10Domtar Industries 1-1 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## 1. I NTRODUCTION

Domtar Industries Inc. (Domtar) owns and operates a kraft paper mill located at 285 Highway 71 South in Ashdown, Arkansas (the Ashdown Mill). The Ashdown Mill is a major source as defined in Arkansas Pollution Control and Ecology Commission (ADP&E) Regulation 26, Regulations of the Arkansas Operating Air Permit Program , and currently operates under the authority of Arkansas Department of Environmental Quality (ADEQ) Operating Air Permit 0287-AOP-R6, which was issued on July 12, 2006.

The ADEQ has determined that the Ashdown Mill operates two emission units – No. 1 and No. 2 Power Boilers – that are eligible to be regulat ed under the Best Available Retrofit Technology (BART) provisions of the U.S. Environmental Protection Agency’s (EPA) Regional Haze Rule in Title 40 of the Code of Federal Regulations (40 CFR) Part 51. BART is the primary mechanism identified for regulating haze-forming pollutants fro m stationary sources for the first implementation period under the Regional Haze Rule. The ADEQ ha s also determined, based on air dispersion modeling, that emissions from the Ashdown Mill BA RT-eligible source contributes to visibility impairment at a federally protected Class I area. Therefore, Domtar has prepared this report to document its BART determination in accordance with Appendix Y to Part 51 – Guidelines for BART Determinations Under the Regional Haze Rule (the BART Guidelines).

An overview of the Regional Haze Rule and BA RT Guidelines is provided in Section 1.1. Descriptions of the Ashdown Mill’ s BART-eligible emission units are included in Section 2. Section 3 describes the BART applicability analysis co mpleted by the ADEQ for the Ashdown Mill BARTeligible source. Domtar’s BART determin ation analysis is included in Section 4.



## 1.1 O VERVIEW OF 

## R EGIONAL 

## H AZE 

## R ULE AND 

## BART G UIDELINES

The Regional Haze Rule requires that major sources of visibility-affecting pollutants belonging to one or more of 26 specific industrial source categories evaluate BART if the source was in existencebefore August 7, 1977 and began operation after August 7, 1962. “Major sources of visibilityaffecting pollutants” are sources that have the potential to emit 250 tons per year (tpy) or more of any of the following: oxides of nitrogen (NO X ), sulfur dioxide (SO 2 ), or particulate matter (PM). 1 The “BART-eligible source” is the collection of sources at a facility meeting the applicability criteria.



### 1.1.1 BART A PPLICABILITY
In the BART applicability analysis, a BART-eligib le source is determined to be subject to BART if it causes or contributes to visibility impairment at one or more of the 156 federally protected Class I areas. Per the U.S. EPA’s BART Modeling Guidance, “an individual source will be considered to ‘cause visibility impairment’ if the emissions

1 As allowed in the BART Guidelines, the ADEQ has determined that volatile organic compounds (VOC) and ammonia are not visibility-affecting pollutants for the purposes of BART analyses.Domtar Industries 1-2 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc results in a change (delta Δ ) in deciviews (dv) 2 that is greater than or equal to 1.0 deciview on the visibility in a Class I area...if the emis sions from a source results in a change in visibility that is greater than or equal to 0.5 dv in a Class I area the source will be considered to ‘contribute to visibility impa irment.’” To determine whether a BARTeligible facility causes or contributes to visibility impairment, the U.S. EPA guidance requires the use of an air quality model, specifically recommending the CALPUFF modeling system, to quantify the impacts attributable to a single BART-eligible source. Because contribution to visibility impairme nt is sufficient cause to require a BART determination, 0.5 dv is the critical thre shold for assessment of BART applicability.

Regional haze is quantified using the light extinction coefficient ( b ext ), which is expressed in terms of the haze index ( HI ) expressed in dv. The HI is calculated as shown in the following equation.
⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= 10

ln10 ext b HI

The impact of a BART-eligible source is determined by comparing the HI attributable to a source to estimated natural background cond itions. That is, a single-source visibility impact is measured as the change in light ex tinction versus backgrou nd, and is referred to

as Δ dv. The background extinction coefficient is affected by various chemical species and the Rayleigh scattering phenomenon and can be calculated as shown in the following equation. 

# ( ) RayEC Coarse Soil OC NO SO background ext bbbbbbb b ++++++= −
3 4 1

, Mm

where:



### ( ) 

## [ ] ( ) 

### [ ] ( ) 

### [ ] [ ] [ ] [ ] 

# ( ) ( ) 

### [ ] 3 1 34 4

2 4

m μ ginion Concentrat Function Humidity Relative defaultby Mm 10 Scattering Rayleigh EC10

Mass Coarse6.0 Soil1 OC4 NONH3 SONH3

3 4

= =

= = = = = = =

− RHf b b b b b RHf b RHf b

Ray

EC Coarse Soil OC NO SO 

### ( ) 

## [ ] [ ] [ ] [ ] [ ] [ ]

molecules air todue scattering is Scattering Rayleigh carbon elemental ofion concentrat thedenotes EC dusts coarse ofion concentrat thedenotes Mass Coarse soils fine ofion concentrat thedenotes Soil carbon organic ofion concentrat thedenotes OC ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4 2 4

Values for the parameters listed above speci fic to the natural background conditions at each Class I area are provided on an annual-average basis in the U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule

. 3

2 The deciview (dv) is a metric used to represent normalized light extinction attributable to visibility-affecting pollutants.
3 U.S. EPA, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule , Table 2-1, Attachment A, September 2003, EPA-454/B-03-005.Domtar Industries 1-3 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc Particulate species that affect visibility are emitted from anthropogenic (human-caused) sources and include coarse particulate matter (PMC), fine particulate matter (PMF), and elemental carbon (EC) as well as precursors to secondary organic aerosols (SOA) and fine particulate matter such as SO 2 and NO X . The extinction coefficient due to emissions of visibility-affecting pollutants from a single BART-eligible source is calculated according to the following equation. 

# ( )

EC PMC PMF SOA NO SO sourceext bbbbbb b +++++= −
3 4 1

, Mm

where:


### ( ) 

## [ ] ( ) 

## [ ] ( ) 

## [ ] [ ]

[ ] [ ] ( ) 

## [ ] 3 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative EC10

PMC6.0 PMF1 SOA4 NONH3 SONH3

= =

=

= = = = =

RHf b b b b RHf b RHf b

EC

PMC PMF SOA NO SO 

### ( ) 

## [ ] [ ] [ ] [ ] [ ] [ ] carbon elemental ofion concentrat thedenotes EC PM coarse ofion concentrat thedenotes PMC PM fine ofion concentrat thedenotes PMF aerosols organic secondary ofion concentrat thedenotes SOA ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4 2 4



### 1.1.1.1 CALPUFF M ODELING 

### A NALYSES
As stated above, the BART Guidelines recommend using the CALPUFF modeling system to compute the 24-hour average visibility impairment attributable to a BART-eligible source to assess whether the 0.5 Δ dv contribution threshold is exceeded, and if so, the frequency, duration, and magnitude of any exceedance events. CALPUFF is a refined air quality modeling system that is capable of simulating the dispersion, chemical transformation, and long-range transport of multiple visibility-affecting pollutant emissions and is therefore preferred for BART applicability and determination analyses.



### 1.1.2 BART D ETERMINATION
BART-eligible sources that are found to cause or contribute to visibility impairment at a Class I area are required to make a BART determination. The BART Guidelines define BART as follows:

BART means an emission limitation based on the degree of reduction achievable through the application of the best system of continuous emission reduction for each pollutant which is emitted by...[a BART-eligible source]. The emission limitation must be established, on a case-by-case basis, taking into consideration the technology available, the costs of compliance, the energy and non-air quality environmental impacts of compliance, any pollution control equipment in use or in existence at the source, the remaining useful lifeDomtar Industries 1-4 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc of the source, and the degree of improvement in visibility which may reasonably be anticipated to result from the use of such technology. The BART analysis identifies the best system of continuous emission reduction taking into

account:

(1) The available retrofit control options, (2) Any pollution control equipment in use at the source (which affects the availability of options and their impacts), (3) The costs of compliance with control options, (4) The remaining useful life of the facility, (5) The energy and non-air quality environmental impacts of control options [ , and ] (6) The visibility impacts analysis.Domtar Industries 2-1 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## 2. BART-E LIGIBLE 

## E MISSION 

## U NITS

The BART Guidelines define the following three st eps for determining which emission units at a facility are BART-eligible:

1. Identify the emission units in the BART source categories, 2. Identify the start-up dates of those units, and 3. Compare potential emissions to the 250 ton/yr cutoff.

“Fossil-fuel boilers of more than 250 million BTUs per hour heat input” are one of the listed BART source categories. The Ashdown Mill’s No. 1 and No. 2 Power Boilers are each greater than 250 million British thermal units per hour (MMBtu/hr), were in existence on August 7, 1977, began operation after August 7, 1962, an d each have potential emissions greater than 250 tpy of PM, NO X , or SO 2 ; therefore, these units make up the Ashdown Mill’ s BART-eligible source. A summary of the BART eligibility criteria for each emissi on unit is provided in Table 2-1.

T ABLE 2-1. S UMMARY OF BART- ELIGIBLE E MISSION U NITS

Emission Unit Source
Number BART
Source Category Year of Completion of
Construction or
Reconstruction Potential
SO 2 Emissions (tpy) Potential
NO X Emissions (tpy) Potential
PM/PM 10 Emissions (tpy)

No. 1 Power Boiler SN-03 Boiler a 1968 214.0 1,084.1 1,502.3
No. 2 Power Boiler SN-05 Boiler a 1976 4,305.5 2,514.1 359.2

a Fossil-fuel boilers of more than 250 million BTUs per hour heat input.

Detailed descriptions of each unit ar e provided in the sub-sections below.



## 2.1 N O



## . 1 P OWER 

## B OILER

The No. 1 Power Boiler (SN-03), also known as the Bark Boiler, was installed in 1968. It has a heat input rating of 580 MMBtu/hr and an average steam generation rate of approximately 120,000 pounds per hour (lb/hr). It combusts primarily bark (approximately 75 percent of the heat input is supplied by bark), but is also permitted to burn bark and wood chips used to absorb oil spills, wood waste, recycled sanitary products composed of cellulose a nd polypropylene, pelletized paper fuel (PPF), tirederived fuel (TDF), municipal yard waste, No. 6 fu el oil, reprocessed fuel oil, used oil generated on site, and natural gas. Natural gas is only used to supplement other fuels during high steam demand periods. Fuel oil usage is limited to 2,700,000 gall ons per year, and the sulfur content of the fuel oil used is limited to 3.0 percent by weight. TDF u sage (total for No.1, No. 2, and No. 3 Power Boilers) is limited to 220 tons per day.

The No. 1 Power Boiler is equipped with a traveling grate, a combustion air system, andDomtar Industries 2-2 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc multiclones.

The No. 1 Power Boiler is not subject to any New Source Performance Standards (NSPS) in 40 CFR Part 60. It is subject to 40 CFR Part 63, Subpart DDDDD, National Emissions Standard for Hazardous Air Pollutants (NESHAP) for Industrial, Commercial, and Institutional Boilers and
Process Heaters . NESHAP DDDDD establishes Maximum Achievable Control Technology (MACT) limits and is commonly referred to as “the Boiler MACT.”

To meet the applicable Boiler MACT PM emission st andard of 0.07 lb/Mmbtu, Domtar is preparing to install a wet electrostatic precipita tor (WESP) on the No. 1 Power Boiler.



## 2.2 N O



## . 2 P OWER 

## B OILER

The No. 2 Power Boiler (SN-05) started operations in Fe bruary 1976. It has a heat input rating of 820 MMBtu/hr and an average steam generation rate of approximately 600,000 lb/hr. It combusts primarily bituminous coal (over 80 percent of the heat input is supplied by coal), but is also permitted to burn bark, bark and wood chips used to absorb oil spills, wood waste, petroleum coke (pet coke), recycled sanitary products based on cellulose and pol ypropylene, PPF, TDF, municipal yard waste, No. 6 fuel oil, reprocessed fuel oil, used oil gene rated on site, natural gas, and non-condensable gases (NCGs). The NCGs are produced in the pulp area (from the cooking of chips) and evaporator area (where weak black liquor is concentrated) and c onsist of nitrogen, total reduced sulfur (TRS) compounds, methanol, acetone, SO 2 , and minor quantities of other compounds such as methyl ethyl ketone (MEK). Under normal operating c onditions, natural gas is not combusted.

The No. 2 Power Boiler is equipped with a traveli ng grate, combustion air system including overfire air, multiclones, and two parallel venturi scrubbers. The SO 2 loading to the boiler is significant since the boiler burns coal and NCGs. Therefore, the scrubbing fluid includes water and a source of alkali, such as sodium hydroxide (i.e., caustic) and/or pulp mill extraction stage filtrate.

The No. 2 Power Boiler is subject to 40 CFR 60, Subpart D, Standards of Performance for FossilFuel-Fired Steam Generators for Which Construction is Commenced After August 17, 1971 , 40 CFR 60, Subpart BB, Standards of Performance for Kraft Pulp and Paper Mills (since it combusts NCGs), and 40 CFR Part 63, Subpart DDDDD, National Emissions Standard for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters

.
The No. 2 Power Boiler is equipped with Continuous Emissions Monitoring Systems (CEMS) for NO X , SO 2 , and carbon monoxide (CO). In accordance w ith 40 CFR 60, Subpart BB, the No. 2 Power Boiler also has a continuous flame pyrometer to measure the temperature at the point of NCG injection (the temperature at the injection point must remain at or above 1200 oF for at least 0.5 seconds at all times that NCGs are being burned).Domtar Industries 3-1 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## 3. BART A PPLICABILITY 

## A NALYSIS

This section summarizes the source-specific inputs an d results of the BART applicability analysis conducted by the ADEQ for Domtar’s Ashdown Mill BART-eligible source. The screening modeling methodologies and data resources used by the ADEQ in executing the CALPUFF modeling system are described in technical detail in the ADEQ’s Draft BART Modeling Protocol (the Protocol), dated June 7, 2006, and in the Central Regi onal Air Planning Association (CENRAP) BART Modeling Guidelines (issued on December 22, 2005, and re-issued on February 3, 2006). A copy of the Protocol is included in Appendix A.



## 3.1 M ODELED 

## A SHDOWN 

## M ILL 

## E MISSIONS

Whereas the BART eligibility determination relies on current potential emissions of visibilityaffecting pollutants, the BART applicability modeling analysis is based on maximum 24-hour average actual emission rates of NO X , SO 2 , and PM 10 for the modeled three-year period (i.e., 2001, 2002, & 2003). 4 At the ADEQ’s request, Domtar estimated the 24-hour average maximum actual emission rates of visibility-affecting pollutants from the No. 1 and No. 2 Power Boilers using a combination of CEMS data, source-specific stack testing results, and emission factors from U.S. EPA’s AP-42. These emission rates are summarized in Table 3-1.

T ABLE 3-1. S UMMARY OF 24- HOUR A VERAGE M AXIMUM A CTUAL E MISSION R ATES

Emission Unit NO X Emissions

(lb/hr) SO 2 Emissions

(lb/hr) PM 10 /PMF
Emissions

(lb/hr)

No. 1 Power Boiler 179.6 442.5 169.5
No. 2 Power Boiler 526.8 788.2 81.6



## 3.2 M ODELED 

## A SHDOWN 

## M ILL 

## S TACK 

## P ARAMETERS

Actual stack parameters were input to the CALPUFF model to represent each emissions point. The location of each point was represented using the Lambert Conformal Coordinate (LCC) system. According to the Protocol, because the BART modeling focuses on mesoscale transport to Class I areas, effects of building downwash were not c onsidered in the ADEQ’s analysis. Table 3-2 summarizes the stack parameters modeled for th e BART-eligible emission units at Domtar’s Ashdown Mill.

4 The ADEQ assumed all PM 10 emissions were PMF for modeling purposes.Domtar Industries 3-2 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc T ABLE 3-2. S TACK P ARAMETERS

Emission Unit LCC East (km) LCC North (km) Base
Elevation (m) Stack Height

(m) Stack
Diameter (m) Exhaust Temperature (K) Exhaust Velocity

(m/s)

No. 1 Power Boiler 267.47491 -698.66686 97.5 66.1 1.890 522 26.76
No. 2 Power Boiler 267.48245 -698.74355 97.5 71.6 3.659 325 11.92



## 3.3 P OTENTIALLY 

## A FFECTED 

## C LASS 

## I A REAS

Regardless of distance from the BART-eligible s ource, the ADEQ evaluated all Class I areas within 300 km of the Arkansas state boundary in all analyses. Figure 3-1 illustrates the location of the Ashdown Mill relative to each of the modeled Class I areas.



### F IGURE 

### 3-1. L OCATION OF 

### A SHDOWN 

### M ILL 

### R ELATIVE TO 

### M ODELED 

### C LASS 

### I A REAS

Base ma p p rovided b y ADE Q

. Ashdown MillDomtar Industries 3-3 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc Table 3-3 presents the Class I areas (and respons ible Federal Land Manage r [FLM]) included in ADEQ’s analyses and the approximate dist ance from each area to the Ashdown Mill.

T ABLE 3-3. M ODELED C LASS I A REAS

Class I Area FLM a Approximate Distance from Ashdown Mill (km)

Caney Creek Wilderness FS 85 Upper Buffalo Wilderness FS 250
Hercules-Glades Wilderness FS 350 Mingo Refuge FWS 510 Sipsey Wilderness FS 620

a FS = Forest Service (Department of Agriculture), FWS = Fish and Wildlife Service (Department of Interior).



## 3.4 BART A PPLICABILITY 

## A NALYSIS 

## R ESULTS

The ADEQ’s BART applicability analysis showed that Domtar’s Ashdown Mill contributes to visibility impairment, since the maximum modeled 24-hour average impacts were greater than 0.5

Δ dv, in the Caney Creek, Upper Buffalo, Hercules-Glades, and Mingo Class I areas. The results of the ADEQ’s BART applicability anal ysis for Domtar’s Ashdown Mill are summarized in Table 3-4.

T ABLE 3-4. S UMMARY OF BART A PPLICABILITY A NALYSIS R ESULTS

Class I Area Maximum 24-hour Impact ( **Δ** dv) a Number of Days > 0.5 **Δ** dv a Number of Days > 1.0 **Δ** dv a

Caney Creek 2.262 159 50 Upper Buffalo 1.181 18 1
Hercules-Glades 0.701 3 0 Mingo 0.923 2 0 Sipsey 0.341 0 0

a For total modeled period: years 2001, 2002, and 2003.

Since the ADEQ’s BART applicability analysis shows that Domtar’s Ashdown Mill BART-eligible source contributes to visibility impairment in at least one Class I area, Domtar must conduct a BART determination analysis for the No. 1 and No. 2 Power Boilers.Domtar Industries 4-1 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## 4. BART D ETERMINATION 

## A NALYSIS

In general, BART is determined for each eligible emissions unit using the following five (5) steps from Section IV.D of the BART Guidelines:

Step 1 – Identify all available retrofit control technologies, Step 2 – Eliminate technically infeasible options, Step 3 – Evaluate control effectiven ess of remaining control technologies, Step 4 – Evaluate impacts and document the results, and Step 5 – Evaluate visibility impacts.

However, in the preamble to the BART Guidelin es, the U.S. EPA clearly encourages the use of streamlined approaches for BART determinations so that states and industry can focus their resources on the main contributors to visibility impairment. 5 Domtar asserts that streamlined BART determinations are appropriate for emissions of PM and SO 2 from the Ashdown Mill’s No. 1 and No. 2 Power Boilers. The streamlined BART determinations for PM and SO 2 are presented in Sections 4.1, 4.2, and 4.3. Steps 1 through 4 of the BART determination analysis for NO X emissions from the No. 1 and No. 2 Power Boilers are presented in S ection 4.3. Section 4.5 presents the visibility impacts evaluation for all pollutants.



## 4.1 BART D ETERMINATION FOR 

## PM

Section IV.C of the BART Guidelines describes a streamlined approach for evaluating BART for certain sources that are subject to MACT standards (i.e., NESHAP in 40 CFR 63). The Ashdown Mill’s No. 1 and No. 2 Power Boilers are affected sources (in the existing, large, solid fuel subcategory) under the Boiler MACT, and are subject to a PM emissions standard of 0.07 lb/MMBtu. Since the Boiler MACT standard was established recent ly the technology analys is is up-to-date. The No. 1 and No. 2 Power Boilers must be in complianc e with the Boiler MACT standards by September 13, 2007, in advance of the anticipated 2013 BART compliance deadline. Domtar is planning to equip the No. 1 Power Boiler with a WESP to meet the PM standard. The No. 2 Power Boiler is equipped with a wet scrubber and can meet the Boiler MACT PM emission standard. Domtar has not identified any feasible upgrades to the No. 2 Po wer Boiler’s wet scrubber. At ADEQ’s request, Domtar evaluated the costs for installing a WESP on the No. 2 Power Boiler. The estimated cost effectiveness, based on the estimates given in the proposal for the WESP on the No. 1 Power Boiler, is at a minimum $30,000/ton – clearly infeasible. Table 4-1 presents the maximum PM emission rates from each power boiler based on heat i nput capacity and the Boiler MACT standard.

5 Federal Register, Vol. 70, No. 1 28, July 6, 2005, pp 39107 and 39116.Domtar Industries 4-2 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc T ABLE 4-1. BART / B OILER MACT-B ASED PM E MISSION R ATES

Emission Unit PM Emissions (lb/hr) PM Emissions (tpy)

No. 1 (Bark) Power Boiler 40.6 177.9 No. 2 (Coal) Power Boiler 57.4 251.5

The recent Boiler MACT PM emission standard is presumptively relied upon to meet BART requirements. Accordingly, a comprehensive BA RT determination analysis is not necessary to determine BART for PM emissions from the Ashdow n Mill’s No. 1 and No. 2 Power Boilers. The ADEQ agreed to allow this streamlined MACT-equa ls-BART option in a September 8, 2006, letter, but required that Domtar “consult with the ADEQ Air Division regarding whether the wet electrostatic precipitator (MACT Control) is the best available and cost effective control technology for PM 2.5 .” 6 Domtar provides the following evidence that a WESP is the best choice for control of the No. 1 Power Bark Boiler.

Particulate emissions from wood-fired boilers are t ypically controlled by one of four technologies: baghouse (fabric filter), ESP (wet or dry), wet scrubber, or cyclone. Cyclones provide for the lowest control efficiencies of the options at up to 65 percent, and particulate collection efficiencies of 85 percent or greater have been reported for venturi [wet] scrubbers operating on wood-fired boilers. 7 To achieve control efficiencies of 90 percent or greater, a baghouse or ESP is used. The normal PM control efficiency range for a fabric filter is 95 to 99+ percent, and the normal PM control efficiency range for a WESP is 98 to 99+ percent. 8 Fabric filters are rarely u sed on wood-fired boilers due to concerns about bag flammability. 9 The principal drawback is a fire danger arising from the collection of combustible carbonaceous fly ash. 10 Both types (i.e., wet and dry) of ESPs are capable of greater than 99 percent removal of pa rticle sizes above 1 micron. 11 An additional benefit of WESPs is that the wash used in WESPs can also have some c ontrol effect on other pollutant gases via absorption and can help condense other emissions due to the cooling of the stream. 12 Based on the comparison of control efficiencies and the applicability of each control device, Domtar asserts that the WESP is the best control technology (i.e., BART) for the No. 1 Power Boiler.

6 Mike Bates (ADEQ), letter to Kelle y Crouch (Domtar), September 8, 2006.

7 U.S. EPA, Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area Source (AP42), Fifth Edition, Section 1.6 – Wood Resi due Combustion in Boilers, September 2003.
8 MACTEC, Midwest RPO Boiler BART Engineering Analysis , March 30, 2005.
9 NCASI, Information on Retrofit Cont rol Measures for Kraft Pulp Mill Sources and Boilers for NO X , SO 2 and PM Emissions , Corporate Correspondence Memo 06-014.
10 U.S. EPA, Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area Source (AP42), Fifth Edition, Section 1.6 – Wood Resi due Combustion in Boilers, September 2003.

11 Northeast States for Coordinated Air Use Manageme nt (NESCAUM) and Mid-Atla ntic/Northeast Visibility Union (MANE-VU), Assessment of Control Technology Options for B ART-Eligible Sources – St eam Electric Boilers, Industrial Boilers, Cement Plant and Paper and Pulp Facilities , March 2005.

12 MACTEC, Midwest RPO Boiler BART Engineering Analysis , March 30, 2005.Domtar Industries 4-3 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## 4.2 BART D ETERMINATION FOR 

## SO 2 

## – N O



## . 1 P OWER 

## B OILER

Generally, pre-combustion SO 2 control strategies involve fuel switching/blending or fuel cleaning so that less fuel-bound sulfur enters the process. However, because wood already contains very little sulfur, pre-combustion SO 2 controls are ineffective.

Post-combustion SO 2 control is accomplished by reacting the SO 2 in the gas with a reagent (usually calcium-based [e.g., lime or limestone] or sodium-based [e.g., caustic]) and removing the resulting product (a sulfate/sulfite) for disposal or commercial use. SO 2 reduction technologies are commonly referred to as flue gas desulfurization (FGD) and/or scrubbers and are usually described in terms of the process conditions (wet versus dry), byproduc t utilization (throwaway versus saleable) and reagent utilization (once-th rough versus regenerable). 13 Post-combustion SO 2 controls have not been installed on wood-fired boilers because of the relatively low SO 2 emissions from wood-combustion (due to the low sulfur content of wood).

Due to the low fuel sulfur input, emissions fro m wood combustion are inherently low and have a negligible impact on visibility impairment. Theref ore, Domtar proposes no additional add-on control, i.e., only the existing fuel restrictions (fuel oil su lfur content and usage limitations) and no additional SO 2 removal as BART for SO 2 emissions from the No. 1 Power Boiler.



## 4.3 BART D ETERMINATION FOR 

## SO 2 

## – N O



## . 2 P OWER 

## B OILER

Section IV.D.1.9 of the BART Guidelines provid es an option to skip th e comprehensive BART determination analysis for BART-eligible emission units that are already equipped with the most stringent controls available (i ncluding any possible improvements to the control device) “as long these most stringent controls available are ma de federally enforceable for the purpose of implementing BART for that source.” The Ashdow n Mill’s No. 2 Power Boiler is equipped with a wet scrubber for control of SO 2 (and particulate) emissions. Th e existing wet scrubber achieves an SO 2 control efficiency of approximately 90 percent, which is within the normal range for the highest efficiency SO 2 control strategies and is the BART-based control efficiency presumed by the Central Regional Air Planning Association (CENRAP) a nd the Midwest Regional Planning Organization (MRPO) for pulp and paper industry power boilers. 14,15

The No. 2 Power Boiler is equipped with a CEMS for SO 2 . Thus, Domtar is able to immediately identify needs for both ongoing operational adjust ments and periodic maintenance and/or scrubber improvements to maintain high levels of SO 2 control. Domtar has not identified any feasible upgrades to the existing wet scrubber. It should be no ted that the No. 2 Power Boiler is operated such that SO 2 emissions are well below any applicable limits/standards. Since wet scrubbing is the most effective method of controlling SO 2 emissions, no additional analysis is needed for SO 2 emissions from the No. 2 Power Boiler. Domtar proposes no additional SO 2 removal as BART for the No. 2 Power Boiler.

13 NESCAUM and MANE-VU, Assessment of Control Technology Opti ons for BART-Eligible Sources – Steam Electric Boilers, Industrial Boilers, Ce ment Plant and Paper and Pulp Facilities , March 2005.

14 CENRAP’s Control Estimates Spr eadsheet dated January 10, 2006.
15 MRPO, Interim White Paper – Midwest RPO Candidate Control Measures , March 29, 2005.Domtar Industries 4-4 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## 4.4 BART D ETERMINATION 

## A NALYSIS FOR 

## NO X
Each required step of the BART determination analysis for emissions of NO X from the No. 1 and No. 2 Power Boilers is presented below.



### 4.4.1 S TEP 

### 1

- I DENTIFY 

### A LL 

### A VAILABLE 

### R ETROFIT 

### C ONTROL 

### T ECHNOLOGIES
The BART Guidelines require the consideration of all “control technologies with a practical potential for application to the em issions unit and the regulated pollutant under evaluation.” The list of available control options should include “the most stringent option and a reasonable set of options for analysis...[, but] it is not necessary to list all permutations of available control levels that exist for a given technology – the list is complete if it includes the maximum level of control each technology is capable of achieving.”

Per the BART Guidelines, the BART determ ination analysis must “take into account technology transfer of controls that have been applied to similar source categories and gas streams [in addition to] existing controls for the source category in question.” However, “technologies which have not yet been applied to (or permitted for) full scale operations need not be considered as available; [the U. S. EPA does] not expect the source owner to purchase or construct a process or control device that has not already been demonstrated in practice.” The BART Guidelines provides th e following additional considerations for preparing the list of potential control options:

3⁄4 One of the control options should reflect the level of control equivalent to any applicable NSPS, 3⁄4 Source redesign should not be considered, 3⁄4 Fuel switching should not be considered, and 3⁄4 For emission units with existing control measures or devices, one of the control options should involve improvements to the existing controls.

Potential NO X control technologies and resulting emission control quantities for the Ashdown Mill’s No. 1 and No. 2 Power Boilers were identified from the exhaustive review of the U.S. EPA’s Clean Air Technology Center, including the RACT/BACT/LAER Clearinghouse (RBLC), control equipment vendor information, publicly-available air permits and applications, and technical literature published by the U.S. EPA, the Regional Planning Organizations (RPOs), and industry groups such as the National Council for Air and Stream Improvement, Inc. (NCASI). 16 In fact, Domtar has largely relied upon the extensive research conducted by NCASI rega rding the applicability and effectiveness of each control option for coal- and wood-fired pulp and paper mill power boilers. Each NO X

16 NCASI is an independent, non-profit research institute that focuses on environmental topics of interest to the forest products industry. NCASI was es tablished in 1943...In the years since, NC ASI has developed t echnical expertise spanning the spectrum of environmental challenges facing the forest products industry, and is today recognized as the leading source of reliable data on envir onmental issues affecting this industry. (http://www.ncasi.org/about/default.aspx)Domtar Industries 4-5 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc control option identified as potentially applicable to either power boiler is listed below and explained in detail in the following subsections.

3⁄4 Selective Non-Catalytic Reduction (SNCR) / NO X OUT 3⁄4 Selective Catalytic Reduction (SCR) 3⁄4 Low NO X Burners (LNB) and Ultra Low NO X Burners (ULNB) 3⁄4 Over-fire Air (OFA) 3⁄4 Reburning / Methane de-NO X (MdN) 3⁄4 Flue Gas Recirculation (FGR ) (Internal and External) 3⁄4 Fuel Blending / Boiler Operational Modifications / Tuning / Optimization

For this analysis, utility boiler control t echnology determinations were generally not considered since utility boilers and pulp and paper mill power boilers are considered too dissimilar.

The greatest difference in utility and po wer boiler operations is the fluctuating steam demand characteristic of pulp and paper mill operations which requires that power boilers continuously adjust fuel firing rates and excess air levels. Even with the most sophisticated combusti on controls, it is not practical or safe to maintain excess air continuously at minimum levels. Consequently, power boilers have characteristically and inherently higher NO X emissions.

...NO X reduction measures are particularly d ifficult to implement in small, low capacity facilities because a) residence ti me is limited and often inadequate for applying OFA without excessive loss of thermal efficiency or induced smoking; b) relatively small furnace dimensions limit combustion modifications that increase flame length and tend to cause the flame to impinge on tube wall;, c) peak boiler efficiency and minimized NO X

emissions occur close to minimum flue-gas O 2 content, which is at the threshold of smoke or combustibleemissions formation; d) steam is u sed far more effectively in industrial applications than in conventional elect ric utility plants and, consequently, emission limits based on boiler heat input or volume of flue gas do not recognize such efficiency. 17

Combustion-related NO X emissions are formed by two mechanisms. NO X formed from oxidation of molecular nitrogen (N 2 ) in combustion air is referred to as “thermal NO X ” and is dependent on high temper atures (approximately 2,800 ° F) and an excess of combustion air. NO X formed by oxidation of nitrogen compounds in fuel is referred to as “fuel NO X .” The NO X formed from coal combustion is primarily fuel NO X . 18 Fuel NO X is also the dominant NO X formation mechanism operative during wood combustion because wood combustion in boilers seldom reaches high enough temperatures. 19,20

17 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.
18 MACTEC, Midwest RPO Boiler BART Engineering Analysis , March 30, 2005.
19 NCASI, Information on Retrofit Cont rol Measures for Kraft Pulp Mill Sources and Boilers for NO X , SO 2 and PM Emissions , Corporate Correspondence Memo 06-014.Domtar Industries 4-6 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc The possible NO X emissions control technologies generally fit into one of two categories: combustion modifications, which are often associated with improving boiler performance, or flue gas treatment (i.e., post-combustion c ontrols). Pre-combustion techniques to reduce fuel NO X have shown little promise. 21 Combustion modifications are the most common, commercially available means of controlling NO X emissions from fossil fuel-fired boilers. 22 However, since wood-fired boilers normally burn at lower temperatures (around 1,500 °F), the units have inherently lower NO X emissions, and, as a result, NO X combustion control technologies are not applicable to wood-fired boilers

. 23 During the past decade, LNB with FGR and LNB alone were the most commonly recommended NO X control technologies for oil/gas and coal-fired boilers, respectively, while good combustion control was typically the only recommendation for wood waste-fired boilers. 24



### C OMBUSTION 

### M ODIFICATIONS



### 4.4.1.1 F LUE 

### G AS 

### R ECIRCULATION
Generally, FGR involves extracting a portion (15 to 30 percent) of the flue gas and readmitting it to the furnace through the burner window. When the flue gas is extracted from the economizer or ai r heater outlet, a separate fan/blower is needed to withdraw the flue gas. This setup is referred to as external or forced FGR. Internal or induced FGR re fers to the setup where the flue gas is extracted from upstream of the stack using the forced draft (FD) fan instead of a separate FGR fan. In either setup, the recirculated flue gas acts as a thermal diluent (i.e., heat sink) to reduce combus tion temperatures. It also dilutes the combustion reactants and reduces the excess air requirements thereby reducing the concentration of oxygen in the combustion zone. Thus, thermal NO X formation is inhibited. 25 The onset of thermal NO X occurs around 2,800 ° F, and NO X generation increases exponentially with temperatures beyond 2,800

° F. As only thermal NO X can be controlled by this technique, it is especially effective only in oil and gas-fired units. 26



### 4.4.1.2 L OW 

### NO X 

### B URNERS 

### / U LTRA 

### L OW 

### NO X 

### B URNERS
LNB technology utilizes advanced burner design to reduce NO X formation through the restriction of oxygen, flame temperature, and/or residence time. A

20 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.

21 Ibid.

22 Ibid.
23 STAPPA and ALAPCO, Controlling Fine Particulate Matter Unde r the Clean Air Act: A Menu of Options
, March 2006.

24 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.
25 U.S. EPA, Clean Air Technology Center, Nitrogen Oxides (NO X ), Why and How They Are Controlled

. Research Triangle Park, North Carolina, EPA-456/F-99-006R, November 1999.
26 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.Domtar Industries 4-7 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc LNB is a staged combustion process that is designed to split fuel combustion into two zones, primary combustion and secondary combustion. Two general types of LNB exist: staged fuel and staged air. Lower emission rates can be achieved with a staged fuel burner than with a staged air burner. Staged fuel LNB separate the combustion zone into tw o regions. The first region is a lean primary combustion region where the total quantity of combustion air is supplied with a fraction of the fuel. Combustion in the primary region (first stage) takes place in the presence of a la rge excess of oxygen at substantially lower temperatures than a standard burne r. In the second region, the remaining fuel is injected and combusted with any oxygen left over from the primary region. The remaining fuel is introduced in the second stage outside of the primary combustion zone so that the fu el/oxygen are mixed diffusively (rather than turbulently), which maximizes th e reducing conditions. This technique inhibits the formation of thermal NO X , but has little effect on fuel NO X . By increasing residence times staged air LNB provide reducing conditions, which have a greater impact on fuel NO X than staged fuel burners. The estimated NO X control efficiency for LNB in high temperature applications is 25 percent. 27

The application of LNB is often limited by the longer flames produced as a consequence of improved air distribution control. While there is generally ample room for LNB flames in utility furnaces, their use on smaller power boilers can result in flame impingement on furnace walls, leading to tube wall overheating and mechanical failure. Flame impingement can also result in premature flame quenching and increased soot and CO emissions. 28

ULNB combine LNB and FGR technologies and may incorporate other techniques such steam injection. Th e FGR design within ULNB recirculates flue gas from the flame or firebox back into the combustion zone in an effort to reduce oxygen concentrations without significantly reducing flame temperature. Reduced oxygen concentrations in the flame have a strong impact on fuel NO X . 29 ULNB also tend to have large diameters, but shorter flame lengths and may be easier to retrofit. 30

Combustion modification with LNB is used in both gas/oil-fired and coal-fired units. 31 LNB are not used for wood-fired boilers. The No. 1 Power Boiler burns only a small amount of fuel for which LNB technology exists. Therefore, LNB is not considered further for the No. 1 Power Boiler.

27 MACTEC, Midwest RPO Boiler BART Engineering Analysis , March 30, 2005.
28 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.
29 MACTEC, Midwest RPO Boiler BART Engineering Analysis , March 30, 2005.
30 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.
31 Ibid.Domtar Industries 4-8 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

### 4.4.1.3 O VERFIRE 

### A IR
In OFA, about 10 to 20 percent of the combustion air flow is directed to separate air ports located downstream of the burners. OFA works by reducing the excess air in the burner zone, ther eby enhancing the combustion staging effect and theoretically reducing NO X emissions. Residual unburned material, such as CO and unburned carbon, whic h inevitably escapes the main burner zone, is oxidized as the OFA is admixed later. 32

OFA vendors (e.g., Jansen Combustion and Boiler Technologies, Inc.) have informed Domtar that while OF A often results in decreased NO X emissions, the primary purpose is combustion optimization, and implementation of OFA can actually increase NO X emissions in certain circumstances. Domtar has experienced this potential adverse effect. A recent OFA upgrade to the Ashdown Mill’s No. 3 Power Boiler (not a BART-eligible unit) is still in startup mode, but so far Domtar has measured a noteworthy increase in NO X emissions.

Domtar does not consider OFA to be a potential NO X control technology, and OFA is not considered further in this analysis.



### 4.4.1.4 R EBURNING 

### / M ETHANE DE 

### -NO X
In reburning, also known as “off-stoichiometric combustion” or “fuel staging,” a fraction (5 to 25 percent) of the total fuel heat input is diverted to a second combustion zone downstream of the primary zone. The fuel in the fuel-rich secondary zone acts as a reducing agent, reducing NO, which is formed in the primary zone, to N 2 . Low nitrogen-containing fuels such as natural gas and distillate oil are typically used for reburning to minimize further NO X formation. Generally, it is more economical for a facility to use the same fuel for reburning as it does for primary combustion, although there are exceptions. In order to use coal as a reburning fuel, it must be finely ground, which requires additional pulverizing equipment. 33

MdN utilizes the injection of natural gas together with recirculated flue gases (for enhanced mixing) to create an oxygen-rich zone above the combustion grate. Air is then injected at a higher furnace elevation to burn out the combustibles. This process is claimed to yield between 50 and 70 percent NO X reduction and to be suitable for all solid fuel-fired stoker boilers. However, as of 2002, MdN had only been demonstrated for a short duration in one pulp mill wood-fired stoker boiler that also burned small amounts of waste treatment plant residuals, with NO X reductions of 40 to 50 percent reported. 34

32 Ibid.
33 STAPPA and ALAPCO, Controlling Fine Particulate Matter Unde r the Clean Air Act: A Menu of Options
, March 2006.
34 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.Domtar Industries 4-9 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc More recently, MdN is being applie d to kraft pulp mill stoker boilers by utilizing the VOC content of NCGs to partially replace the natural gas (by up to 25 percent). This technology has been tested for over a year at one pulp mill boiler, and is being tested at several boilers within one forest products industry (FPI) company. 35



### 4.4.1.5 F UEL 

### B LENDING
Since wood is inherently low in nitrogen content, fuel blending is not feasible for wood-fired boilers. Therefore, this c ontrol strategy is not considered for the Ashdown Mill’s No. 1 Power Boiler.

Coal-fired boilers could experience a decrease in NO X emissions from fuel blending. Preliminary results show that the co-firing of up to 7 percent biomass, on a heat-input basis, with crushed or pulverized coal can lower NO X emissions by as much as 15 percent. 36 However, fuel biasing on an industrial boiler subject to rapid and excessive load swings could result in too rich or too lean firing conditions, which can lead to flame stability problems and explosive conditions. 37 In addition, unlike utilities, which can specify the nitrogen content of their large oil purchases, most industrial mills cannot. 38

Domtar historically mixes 10 to 15 percent (heat input basis) wood with coal in the No. 2 Power Boiler. Therefore, fuel blending is considered part of the base case for the No. 2 Power Boiler.



### 4.4.1.6 B OILER 

### O PERATIONAL 

### M ODIFICATIONS 

### / T UNING 

### / O PTIMIZATION
Combustion optimization efforts can lead to improvements in NO X emissions of 5 to 15 percent. Recent developmen ts of intelligent controls – softwarebased systems that "learn" to operate a unit and then maintain its performance during normal operation – are expected help in keeping plants well tuned. 39 Domtar has employed, and will continue to employ, the latest boiler optimization and tuning techniques. This control strategy is considered part of the base case for the Ashdown Mill’ s No. 1 and No. 2 Power Boilers.

35 Ibid.
36 Ibid.
37 Ibid.
38 Ibid.
39 NESCAUM and MANE-VU, Assessment of Control Technology Opti ons for BART-Eligible Sources – Steam Electric Boilers, Industrial Boilers, Ce ment Plant and Paper and Pulp Facilities , March 2005.Domtar Industries 4-10 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

### P OST 

### -C OMBUSTION 

### C ONTROLS



### 4.4.1.7 S ELECTIVE 

### N ON 

### -C ATALYTIC 

### R EDUCTION
SNCR is a post-combustion NO X control technology based on the reaction of urea or ammonia (NH 3 ) and NO X . In the SNCR chemical reaction, urea or ammonia-based chemicals are injected in to the combustion gas path to reduce the NO X to nitrogen and water. The primary SNCR reaction sequences are shown in Figure 4-1. 40



### F IGURE 

### 4-1. P RIMARY 

### SNCR R EACTION 

### S EQUENCES

Typical NO X removal efficiency for SNCR is 30 to 65 percent. For industrial coal-fired boilers, SNCR can achieve approximately 40 percent NO X control. 41 An important consideration for implementing SNCR is the operating temperature range. The optimum temperat ure range is approximately 1,600 to 2,000 °F. 42 Operation at temperatures below this range results in ammonia slip. Operation above this range results in ox idation of ammonia, forming additional NO X . In addition, the urea must have sufficient residence time, about 3 to 5 seconds, at the optimum operating temperatures for efficient NO X reduction. Therefore, the injection point is typical ly prior to convective heat recovery. 43

40 ABB Power Plant Laboratories, Engineering development of coal-fir ed high performance power systems – Phase II topical report , Selective Non-Catalytic Reduction System De velopment Subcontract to United Technologies Research Center, Contract No. DE-AC22-95PC95144, February 24, 1997 (reprinted in NCASI’s Special Report No. 03-04).

41 MRPO, Interim White Paper – Midwest RPO Candidate Control Measures , March 29, 2005.

42 U.S. EPA, Clean Air Technology Center, Nitrogen Oxides (NO X ), Why and How They Are Controlled

. Research Triangle Park, North Carolina, EPA-456/F-99-006R, November 1999.
43 U.S. EPA. Summary of NO X Control Technologies and their Avai lability and Extent of Application . Research Triangle Park, North Carolina. EPA-450/3-92-004, February 1992.Domtar Industries 4-11 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc According to the U.S. EPA, the performan ce of an SNCR system is affected by six factors.

These are a) inlet NO X l evel, b) temperature, c) mixing, d) residence time, e) reagent-to- NO X ratio, and f) fuel sulfur content. Lower inlet NO X concentrations reduce the reaction kinetics and hence the achievable NO X emissions reductions. As mentioned above, temperatures below the d esired window result in ammonia emissions (slip), and temperatures above the desired window result
in NH 3 being oxidized to NO X . Mixing becomes an important consideration in regions distant fr om an injection nozzle where the level of turbulence is reduced and stratification of the reagent and flue gas will probably be a greater problem, especially at low boiler loads. Residence time becomes important to allow the desired reactions to go to comp letion. Small, packaged, water tube boilers and boilers with va rying steam loads are therefore difficult applications for SNCR. As higher than the theoretical NH 3 to NO X ratios are generally required to achieve desired NO X emission reductions, a trade-off exists between NO X control and the presence of NH 3 in the flue gas. The main disadvantage of SNCR is the low NO X reduction that is experienced when the allowable ammonia slip is low. Finally, in the case of high sulfur fuels, excess NH 3 can react with sulfur trioxide to form ammonium sulfate salt compounds that deposit on downstream equipment leading to plugging and reduced heat transfer efficiencies. 44

One concern about the SNCR process is its ability to perform adequately under changing load and fuel conditions. 45 Based on its research regarding this concern, NCASI concludes that SNCR is most widely used for base-loaded boilers, and is not suited for power boilers that experience wide temperature variances, i.e., high load swings. NCASI also points out that the use of SNCR systems on coal-fired boilers is still in the development stage. 46

The NO X OUT process is an SNCR hybrid based on the following chemical reaction that ideally occurs in the temperature range of 1700 to 2000 °F:

2 NO + NH 2 CONH 2 + 1/2 O 2 → 2 N 2 + CO 2 + 2 H 2 O

44 U.S. EPA, New source performance standards, subpart Db – t echnical support for proposed revisions to NO X , EPA-453-/R-95-012 (republished in NCASI’s Special Report 03-04).
45 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.
46 Ibid.Domtar Industries 4-12 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc The problems with typical SNCR systems (e.g., ammonia slippage and heat transfer surface fouling with byproduct formation) also exist with the NO X OUT
process.



### 4.4.1.8 S ELECTIVE 

### C ATALYTIC 

### R EDUCTION
SCR is a post-combustion gas treatment process in which NH 3 is injected into the exhaust gas in the presence of a catalyst bed usually located between the boiler and air preheater. The catalyst lowers the activation energy required for NO X decomposition. 47 On the catalyst surface, NH 3 and nitric oxide (NO) react to form diatomic nitrogen and water. The overall chemical reaction can be expressed as:

4NO + 4NH 3 + O 2 → 4N 2 + 6H 2 O

When operated within the optimum temp erature range of approximately 575 to 750 ° F, the reaction can result in removal efficiencies between 70 and 90 percent. For coal-fired industrial boilers, SCR can achieve approximately 80 percent NO X control. 48 The specific temperature ranges are 600 to 750 ° F for conventional (vanadium or tita nium) catalysts, 470 to 510 ° F for platinum catalysts, and 600 to 1000 ° F for high-temperature zeolite catalysts. 49 SCR units have the ability to function eff ectively under fluctuating temperature conditions (usually ± 50 ° F), although fluctuation in exhaust gas temperature reduces removal efficiency by disturbing the chemical kinetics (speed) of the NO X -removal reaction.

According to the U.S. EPA, the performan ce of an SCR system is affected by six factors.

These are a) NO X level at SCR inlet, b) flue gas temperature, c)
NH 3 -to-NO x ratio, d) fuel sulfur content, e) gas flow rate, and f) catalyst condition. For SCR, when inlet NO X concentrations fall below 150 ppm, the reduction efficiencies decrease with decreasing NO X concentrations. Each type of catalyst has an optimum operating temperature range . Temperatures below this range result in ammonia emissions (slip), and temperatures above the desired range result in NH 3 being oxidized to NO X. For up to about 80 percent NO X reduction efficiencies, a 1:1 NH 3 :NO X ratio is sufficient. For higher efficiencies, higher reagent to NO X ratios are required which may result in higher NH 3 slip. In the case of high sulfur fuels, excess NH 3 can react with sulfur trioxide to form ammonium sulfate salt compounds that deposit and foul downstream equipment. SCR application experience in the case of

47 MACTEC, Midwest RPO Boiler BART Engineering Analysis , March 30, 2005.
48 MRPO, Interim White Paper – Midwest RPO Candidate Control Measures , March 29, 2005.
49 MACTEC, Midwest RPO Boiler BART Engineering Analysis , March 30, 2005.Domtar Industries 4-13 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc medium-to-high sulfur fuels is limited. For a given flue gas flow rate, the catalyst structural design should be chosen so that the residence time needed for the re duction reactions to take place on the catalyst surface is achievable. 50



### 4.4.2 S TEP 

### 2

– E LIMINATE 

### T ECHNICALLY 

### I NFEASIBLE 

### O PTIONS

Per the BART Guidelines, documentation of infeasibility should “explain, based on physical, chemical, or engineering principles, why technical difficulties would preclude the successful use of the control option under revi ew.” The BART Guidelines use the two key concepts of “availability” and “applicability” to determine if a control option is technically feasible. These concepts are defined in Section IV.D.2:

...a technology is considered "availabl e" if the source owner may obtain it through commercial channels, or it is ot herwise available within the common sense meaning of the term. An available technology is "applicable" if it can reasonably be installed and operated on the source type under consideration.

The typical stages for bringing a contro l technology concept to reality as a commercial product are:

• concept stage;

• research and patenting;

• bench scale or laboratory testing;

• pilot scale testing;

• licensing and commercial demonstration; and

• commercial sales.

A control technique is considered ava ilable, within the context presented above, if it has reached the stage of licensing and commercial availability. Similarly, we do not expect a source own er to conduct extended trials to learn how to apply a technology on a totally new and dissimilar source type. Consequently, you would not consider t echnologies in the pilot scale testing stages of development as “available” for purposes of BART review.

In general, a commercially avail able control option will be presumed applicable if it has been used on the sa me or a similar source type. Absent a showing of this type, you evaluate technical feasibility by examining the physical and chemical characteristics of the pollutant-bearing gas stream, and comparing them to the gas stream charac teristics of the source types to which the technology had been applied previously.



### C OMBUSTION 

### M ODIFICATIONS

50 U.S. EPA, New source performance standards, subpart Db – t echnical support for proposed revisions to NO X , EPA-453-/R-95-012 (republished in NCASI’s Special Report 03-04).Domtar Industries 4-14 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

### 4.4.2.1 F LUE 

### G AS 

### R ECIRCULATION

FGR is used to reduce thermal NO X formation. Emissions due to fuel-bound NO X , which are significant for coal-fired boilers, are not meaningfully affected by FGR. Therefore, FGR is not technically feasible to control NO X emissions from coal-fired boilers. 51 Similarly, FGR would not be effective in wood combustion since most of the NO X generated during wood combustion is also from the fuel NO X pathway. 52 Recent refusals by vendors (e.g., Entropy Technology & Environmental Consultants LP 53 ) to provide budgetary estimates for installing FGR are further evidence that FGR is not applicable for the Ashdown Mill’s No. 1 and No. 2 Power Boilers.



### 4.4.2.2 R EBURNING 

### / M ETHANE DE 

### -NO X
Generally, Domtar considers MdN not feasible because (1) it is not fully demonstrated and (2) it incorporates FGR, which is clearly technically infeasible (see Section 4.4.2.1). However, Domtar was able to obtain equipment cost estimates from vendors of MdN. Therefore, MdN is considered further in this analysis.



### P OST 

### -C OMBUSTION 

### M ODIFICATIONS NCASI points out the following issues of concern for post-combustion NO X controls (i.e., SNCR and SCR) for pulp and paper mill power boilers: 54

Load Swings - Pulp mill combination and power boilers frequently exhibit wide and rapid load swings that are not consistent with the steady conditions required for effective use of either SNCR or SCR NO X control technologies. The load swings produce variable temperature conditions in the boiler, causing the temperature zone for NO X reduction to fluctuate, making it more difficult to know where to inject the reactants.

Temperature Incompatibility - Combination and power boilers are affected by temperature profile incompatibility. To obtain the required temperature window, the only location to install this technology is upstream of the par ticulate matter control device, yet this is where flue gases are dirty and can foul the catalyst rapidly.

51 U.S. EPA. Alternative Control Technologies Document: NO X Emissions from Utility Boilers. (EPA-453/R-94- 023).
52 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.
53 Steve Wood (ETEC), e-mail to Joel Martin (Dom tar), September 20, 2006: “Based on the design and operational data provided rega rding #2 Coal Boiler, ETEC would decline to bid the application Induced Flue Gas Recirculation for Boiler #2 NO X control. Flue gas recirculation technology is very effective in reducing natural gas and light oil fuel NO X emissions, but is not for No.6 fuel oil, coal, bark and other solid fuels. To the best of our knowledge, flue gas recirculation for NO X control has never been installed on a coal fired boiler.”
54 Ibid.Domtar Industries 4-15 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc Downstream of the PM control device, the temperature is too low for the catalyst to be effective.

Unproven – SCR or SNCR controls, technologies which, for the most part, are untested and infeasible for pulp and paper mill boilers. These technologies must be operated on a continuous basis within a specified temperature range in order to be effective. The type of fuel burned influences the design of the technology, and FPI facilities’ frequent fuel changes and co-firing of multiple fuels would result in design and operational problems.

Lack of Guarantee for FPI Boilers – Boiler owners are finding that vendors of SCR and SNCR technologies are unwilling to provide performance guarantees that the controls will meet the level of reduction called for in [NSPS Subpart Db (promulgated on September 16, 1998)].



### 4.4.2.3 S ELECTIVE 

### N ON 

### -C ATALYTIC 

### R EDUCTION
Most boilers in the pulp and paper industry operate in the swing load mode, a consequence of supplying steam as required to the various components of the process. The problem with control of the required flue gas temperature window is an inherent difficulty with use of SNCR for load-following boilers, whether wood or fossil fuel. 55

Controlling flue gas temperatures over th e entire range of operating loads that the boiler is expected to experience will be very difficult to achieve. Boilers in the pulp and paper industry rarely ope rate under base loaded conditions. Consequently, the location of the desired temperature window is expected to change constantly. Accurate, instanta neous temperature measurement, as well as the ability to accurately ad just the location of the injection nozzle, would be necessary. Ammonia slip would be a recurring problem associated with the application of the SNCR process to indust rial boilers with fluctuating loads. 56

Inadequate reagent dispersion in the re gion of reagent injection in wood-fired boilers is also a factor mitigating against the use of SNCR technology. 57 Good dispersion of the reagent in the flue gas is needed to get good utilization of the reagent and to avoid excessive ammonia s lip from the process. The need for a

55 NCASI, Information on Retrofit Cont rol Measures for Kraft Pulp Mill Sources and Boilers for NO X , SO 2 and PM Emissions , Corporate Correspondence Memo 06-014.
56 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.

57 NCASI, Information on Retrofit Cont rol Measures for Kraft Pulp Mill Sources and Boilers for NO X , SO 2 and PM Emissions , Corporate Correspondence Memo 06-014.Domtar Industries 4-16 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc sufficient volume in the boiler at the right temperature window precludes the application of SNCR in all types of industrial boilers. 58

Additional issues with SNCR include the potential for formation of ammonium sulfate salts (if sulfur oxides are present in the gas stream where they can react with excess ammonia from the SNCR process to form ammonium salts), which cause plugging problems. Ammonia also poses potential water quality issues - ammonia slip released to the atmosphere could contaminate surface waters by deposition.

SNCR has been applied to a few ba se-loaded wood and combination woodfired boilers, mainly in the electric gene rating industry. However, its efficacy on wood-fired boilers with changing loads has not been demonstrated, except when used as a polishing step. Early u se of ammonia injection in the case of one pulp mill wood-fired boiler met with significant problems and had to be abandoned (significant ammonia slip, caused by inefficient dispersion of the reagent within the boiler, was to blam e). The boiler was unable to meet the manufacturer guarantee unless operated at less than half load. Even then, reducing NO X to near permitted limits consum ed considerably more ammonia than anticipated, leading to the formation of a visible ammonium chloride plume. A similar problem was encounter ed at a second FPI mill where nearly half the urea (on a molar basis) injected was being emitted as ammonia. 59

The use of SNCR on stoker type wood-fired boilers that have significant load swings has not been demonstrated. Excessive ammonia slip is a primary concern when adequate dispersion of the SNCR chemical is not achieved in the boiler ductwork within the range of residence times available and temperatures needed for the NO X reduction reactions to go to completion. Additional concerns include the impact of interference from higher CO levels present in many wood-fired boilers, the possibility of appreciable SNCR chemical being absorbed onto the ash matrix in a wood-fi red boiler, and the extent and fate of ammonia in scrubber purge streams. 60

The MRPO concludes, “if combustion zone temperatures within the boiler do not fall into [the ideal temperature range], then SNCR would be infeasible.” 61



### 4.4.2.4 S ELECTIVE 

### C ATALYTIC 

### R EDUCTION
The use of SCR on boilers operating in the FPI has also never been successfully demonstrated for wood boilers, and would face the same inherent problem of requiring it to be post PM-control to protect the catalyst, and

58 NESCAUM and MANE-VU, Assessment of Control Technology Opti ons for BART-Eligible Sources – Steam Electric Boilers, Industrial Boilers, Ce ment Plant and Paper and Pulp Facilities , March 2005.
59 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.
60 Ibid.
61 MACTEC, Midwest RPO Boiler BART Engineering Analysis , March 30, 2005.Domtar Industries 4-17 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc achieving and maintaining the required temperature window for effective NO X control. 62 There are numerous other issues with using SCR including catalyst plugging and soluble alkali poisoning as well as increased energy consumption. 63

The use of SCR technology would be considered technically infeasible based upon the fact that post-particulate removal flue gas temperatures are typically significantly lower than those desired for this application. Many boilers are equipped with wet scrubbers for particulat e emission (PM) control. Reheating the scrubbed flue gases from these boilers to bring them within the desiredtemperature window would involve a significant energy penalty. For preparticulate removal flue gas application, catalyst deactivation from high particulate loading would be a serious concern, in addition to the impact of fluctuating loads on flue gas temperat ures. Deactivation and/or poisoning could result from the size and density of fly ash particulate, and from their unique chemical and physical nature. Water-soluble alkali (such as Mg or Na) in particulate-laden gas streams has been known to poison SCR catalysts. Space considerations for installing a catalyst section in an existing boiler’s ductwork are also important. Also note the use of solid fuels can result in catalyst contamination even with efficient PM control system and high moisture levels in exhaust air woul d result in inefficient SCR operation. 64

Most boilers feature a flue gas temperature at the economizer exit that is below the ammonium sulfate/bisulfate dew point. Air heater surfaces must withstand corrosion from ammonium sulfates and bisulfates, be easily cleaned with conventional soot blowing, and surviv e corrosion-inducing water washing. SO 3 produced by the catalyst may condense on cooler surfaces, depending on the temperature, during both steady-state and non-steady-state operation. Higher levels of SO 2 to SO 3 conversion could cause accelerated corrosion or higher SO 3 -induced plume opacity. Minimizing ammonia levels in the stack (typically <2 to 3 ppm) is required to avoid problems with disposal of scrubber byproduct contaminated by ammonia. The use of a particular catalyst puts restrictions on the fuel flexibility for a boiler. For example, purchasing coal with fly ash containing calcium oxide and arsenic outside the defined range absolves the catalyst supplier from responsibility for arsenic poisoning. 65

The only “wood-fired” boiler SCR application in service in the U.S. was located at a woodworking facility in Oh io. This SCR was located downstream of a mechanical collector and electrosta tic precipitator, operating in flue gas temperatures ranging from 550 to 650 ° F. The only problem reported at this

62 NCASI, Information on Retrofit Cont rol Measures for Kraft Pulp Mill Sources and Boilers for NO X , SO 2 and PM Emissions , Corporate Correspondence Memo 06-014.
63 NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.
64 Ibid.
65 Ibid.Domtar Industries 4-18 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc installation was minor catalyst blinding due to the deposition of fine particulate that escaped the PM collection devices. It was learned the operating temperature for this SCR system allo wed the use of conventional catalysts designed to accommodate high dust applications. For these catalysts, the catalyst openings through which the flue gas flows are sized to provide proper surface area contact and sufficient flue gas velocity to minimize fouling. Low temperature catalyst designs are considerably different and would not be recommended for use on any high dust a pplication. Based on this description of the air pollution control system configuration and the operating conditions for this particular wood-fired boiler, it is important to identify several specific differences between this installation and those that operate in the FPI. First, due to the requirement to provide hot air to burn all but the driest of wood fuels, wood-fired boilers are usually equi pped with air preheaters. Thus, even when dry particulate control devices like an ESP are utilized, the installation of an SCR catalyst section after a PM control device is not amenable for adaptation to such boilers without, of course, incurring a severe energy penalty. Second, a significant portion of the FPI’s wood-fired boilers is controlled for PM emissions by multiclones and wet scr ubbers. Therefore the PM emissions from these would be higher than the exampl e situation. Third, it is unclear how the Ohio facility’s SCR system would ha ve worked under the fluctuating boiler load characteristics common to many FPI boilers. Finally, sawdust, which was the fuel fired in the Ohio facility’s bo iler, is a low moisture fuel and the particulate matter present in the flue gases from its combustion is likely to be of different composition than when bark or hog fuel (typically much higher moisture) is burned. 66

Hence the use of SCR technology has clearly not been demonstrated for industrial wood, biomass or combination fuel-fired boilers in the FPI. 67



### 4.4.3 S TEP 

### 3

– E VALUATE 

### C ONTROL 

### E FFECTIVENESS OF 

### R EMAINING 

### C ONTROL 

### T ECHNOLOGIES
Table 4-2 presents a ranking of the technically feasible control strategies in order of their effectiveness (i.e., potential control efficiency). For controls with a range of performance levels, the BART Guidelines note:

It is not [the U.S. EPA’s] intent to require analysis of each possible level of efficiency for a control technique as such an analysis would result in a large number of options. It is important, how ever, that in analyzing the technology you take into account the most stringent emission control level that the technology is capable of achieving.

66 Ibid.
67 Ibid.Domtar Industries 4-19 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc T ABLE 4-2. R ANKING OF C ONTROL S TRATEGIES

Control Strategy Applicability Potential Control Efficiency (%)

MdN No. 1 & No. 2 Boilers 50 a
LNB No. 2 Boiler Only 30 b,c Original OFA + Boiler Tuning/Optimization No. 1 Boiler Base Case

Original OFA + NO X Air + Fuel Blending + Boiler Tuning/ Optimization No. 2 Boiler Base Case

a Based on estimate from Energy System Associates.
b NCASI, NO X Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry Experience , Special Report 03-04.

c Based on estimate from B&W.

Note that MdN is included in Table 4-2 d espite its questionable technical feasibility.



### 4.4.4 S TEP 

### 4

– E VALUATE 

### I MPACTS AND 

### D OCUMENT 

### R ESULTS
The technically feasible control technologies are evaluated on the basis of (1) costs of compliance, including consideration of the remaining useful life, (2) energy impacts, and (3) non-air quality environmental impacts.

For the purposes of this analysis, energy a nd non-air quality environmental impacts are considered minimal for all the technically feasibl e control options listed in Table 4-2. Per the BART Guidelines, the costs of compliance analysis for each control option consists of comparisons of the average cost effectiveness and the incremental cost effectiveness, which are defined in Section IV.D.4 as follows:

Average cost effectiveness means the tota l annualized costs of control divided by the annual emissions reduction (t he difference between baseline annual emissions and the estimate of emissions after controls), using the following formula:

Average cost effectiveness (dollars per ton removed) = Control option annualized cost ÷ (Baseline annual emissions – Annual emissions with Control option)

...the incremental cost effectiveness calculation compares the costs of performance level of a control option to those of the next most stringent option, as shown in the following formula (with respect to cost per emissions reduction):

Incremental Cost Effectiveness (dollars per incremental ton removed) = (Total annualized costs of control option) – (Total annualized costs of next controlDomtar Industries 4-20 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc option) ÷ (Control option annual emissions) – (Next control option annual emissions)

The average and incremental (where applicable) cost effectiveness for each feasible control option for the Ashdown Mill’s No. 1 and No. 2 Power Boilers are summarized in Table 4-3. Detailed control costs calculations are presented in Appendix B.

T ABLE 4-3. C ONTROLS C OSTS S UMMARY

Total NO X Cost Effectiveness

Emission Unit Control Strategy Annualized Cost (MM$) Removed (tpy) Average ($/ton) Incremental ($/ton)

No. 1 Power Boiler MdN 3.94 542 7,262 17,354 No. 2 Power Boiler MdN 5.35 1,257 4,259 9,571
LNB 1.10 754 1,465 b N/A

b This estimate is consistent with NCASI’s Special Repo rt 03-04, which states, “for pulverized coal boilers, a 30

percent NO X reduction could be achieved with LNB at a cost of <$2,000/ton.”

Based on Domtar’s analysis, MdN is considered cost prohibitive for both the No. 1 and No. 2 Power Boilers and is ruled out as a BART option. Based on steps 1 through 4 of the BART determination analysis, no retrofit cont rols are available for the No. 1 Power Boiler and LNB is the best available retrofit control technology for the No. 2 Power Boiler.



### P ROPOSED 

### BART D ETERMINATIONS FOR 

### NO X
For the No. 1 and No. 2 Power Boilers, Domtar proposes NO X BART limits of 179.6 lb/hr and 368.7 lb/hr, respectively.Domtar Industries 4-21 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc A summary of all proposed BART determinations is provided in Table 4-4. Please note that while example control technologies theoretically capable of achieving the proposed BART limits are listed, Domtar reserves the right to implement other equivalent control strategies between now and the BART effectiv e date (~2013) to meet the same emission limits.

T ABLE 4-4. S UMMARY OF P ROPOSED BART D ETERMINATIONS

Emission Unit Pollutant BART Limit Example Control Technology

No. 1 Power Boiler PM 0.07 lb/MMBtu (Boiler MACT) WESP
SO 2 442.5 lb/hr No additional add-on controls (existing fuel restrictions)
NO X 179.6 lb/hr No add-on controls No. 2 Power Boiler PM 0.07 lb/MMBtu (Boiler MACT) Wet Scrubber
SO 2 788.2 lb/hr Wet Scrubber
NO X 368.7 lb/hr (30 Percent Control) LNBDomtar Industries 4-22 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## 4.5 S TEP 

## 5

– E VALUATE 

## V ISIBILITY 

## I MPACTS

The degree of visibility improvement is assessed base d on the change in modeled impacts for the precontrol (i.e., the BART applicability analysis) and post-control (i.e., the predicted maximum 24-hour emission rate after implementation of BART) emission scenarios. Per the BART Guidelines, this assessment “may consider the frequency, magn itude, and duration components of [visibility] impairment.”

The post-control modeling for the visibility improvement analysis was conducted using the CALPUFF modeling system in the same manner as the ADEQ’s BART applicability analysis, which is described in Section 3 of this report and in the Protocol (see Appendix A). In fact, the post-control modeling was conducted using the same CALPU FF, POSTUTIL, and CALPOST input files generated by the ADEQ for the applicability analysis. The only changes made to these files for the post-control modeling was to the emissions rates and stack parameter changes associated with implementing the chosen BART controls. Table 4-5 and Table 4-6 summarize the maximum 24-hour average emission rates and the stack parameters, resp ectively, that were modeled in the post-control analysis.

T ABLE 4-5. S UMMARY OF 24- HOUR A VERAGE M AXIMUM P OST -C ONTROL E MISSION R ATES

Emission Unit NO X Emissions

(lb/hr) SO 2 Emissions

(lb/hr) Total PM
Emissions

(lb/hr)

No. 1 Power Boiler 179.6 442.5 40.6
No. 2 Power Boiler 368.7 788.2 57.4

T ABLE 4-6. P OST -C ONTROL S TACK P ARAMETERS

Emission Unit LCC East (km) LCC North (km) Elevation (m) Stack Height

(m) Stack
Diameter (m) Exhaust Temperature (K) Exhaust Velocity

(m/s)

No. 1 Power Boiler 267.47491 -698.66686 97.5 66.1 1.890 522 26.76
No. 2 Power Boiler 267.48245 -698.74355 97.5 71.6 3.659 325 11.92

Visibility improvement is quantified and judged in a cumulative matter. That is, to compare to the pre-control modeling analysis executed by the ADEQ, Domtar’s post-control modeling analysis simulated all emissions reductions from both emission units. Note that since maximum applicability analysis impacts were less than 0.5 Δ dv for the Sipsey Class I area, this area was not evaluated in the post-control scenario. Table 4-7 summarizes the r esults of the visibility improvement analysis.Domtar Industries 4-23 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc T ABLE 4-7. S UMMARY OF V ISIBILITY I MPROVEMENT A NALYSIS R ESULTS

Class I Area Maximum 24-hour Impact ( **Δ** dv) a Number of Days > 0.5 **Δ** dv a Number of Days > 1.0 **Δ** dv a

Caney Creek 2.039 118 29 Upper Buffalo 1.029 14 1 Mingo 0.836 2 0
Hercules-Glades 0.631 2 0

a For total modeled period: years 2001, 2002, and 2003.

As shown in Table 4-7, the application of BART on the Ashdown Mill’s No. 1 and No. 2 Power Boilers results in significant visibility impacts improv ement in the affected Class I areas. Visibility impairment at Upper Buffalo was reduced by 29 percent while impairment at Caney Creek was reduced by 32 percent (based on total impact and excluding any days with impacts less than 0.50

Δ dv). The number of days within the modeled th ree-year period with impacts greater than 0.50 Δ dv decreased from 159 to 118 for the Caney Creek Clas s I area and from 18 to 14 for the Upper Buffalo Class I area.

In addition to the cumulative analysis, the ADE Q requested emission unit specific and pollutant specific modeling. Since cumulative analysis impacts in the Upper Buffalo and Mingo Class I areas are minimal, the emission unit and pollutant specific modeling was only conducted for the Caney Creek Class I area. The results of these pre- a nd post-control analyses (each conducted for the entire modeling period: year 2001, 2002, a nd 2003) are presented in Table 4-8.

T ABLE 4-8. E MISSION U NIT & P OLLUTANT S PECIFIC M ODELING R ESULTS

Pre-Control Scenario Post-Control Scenario
Emission
Unit Pollutant Max. 24-hour Impact ( **Δ** dv) Number of Days > 0.5 **Δ** dv Max. 24-hour Impact ( **Δ** dv) Number of Days > 0.5 **Δ** dv

PM 0.252 0 0.065 0
SO 2 0.575 2 0.575 2 No. 1
Power Boiler NO X 0.398 0 0.398 0
PM 0.135 0 0.095 0
SO 2 1.036 5 1.036 5 No. 2
Power Boiler NO X 1.072 35 0.762 14
PM 0.391 0 0.156 0
SO 2 1.542 30 1.542 30 No. 1 & 2

Power Boilers NO X 1.427 54 1.129 36Domtar Industries 4-24 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc Additionally, as requested by the ADEQ in its Sept ember 8, 2006, letter, Do mtar’s post-control (and pre-control, where different from the ADEQ’s applicability modeling files) CALPUFF, POSTUTIL, and CALPOST input files and CALPOST output files are included with this report on electronic media. The file naming convention is explained be low. Note that all filenames contain the “doas” root (characters 4 through 7) to denote Do mtar – As hdown. Note also that path names will need to be modified to represent the user’s directory structure when replicating these analyses.

File Naming Convention:

CALPUFF & POSTUTIL x x _doas yy(v*)

. fff

xx = Model: cp = CALPUFF pu = POSTUTIL

yy = Year: 01 = 2001 02 = 2002 03 = 2003

v* = Pollutant Run Identifier A = PM Pre-controls B = PM Post-controls C = SO 2 Pre- and Post-controls D = NO X Pre-controls E = NO X Post-controls

fff = File type: inp = Input

CALPOST x x _doas yyz(v*)

. fff

xx = Model: ct = CALPOST

yy = Year: 01 = 2001 02 = 2002 03 = 2003

z = Class I area: c = Caney Creek

m = Mingo u = Upper Buffalo h = Hercules-Glades s = Sipsey

v* = Pollutant Run Identifier A = PM Pre-controls B = PM Post-controls C = SO 2 Pre- and Post-controls D = NO X Pre-controls E = NO X Post-controls

fff = File type: inp = Input lst = Output The “v” designator is used only for the unit and source specific model runs requested by ADEQ.Domtar Industries A-1 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## A PPENDIX 

## A

– ADEQ’ S 

## D RAFT 

## BART M ODELING 

## P ROTOCOL

### STATE OF ARKANSAS DEPARTMENT OF ENVIRONMENTAL QUALITY 8001 NATIONAL DRIVE P.O. BOX 8913 LITTLE ROCK, ARKANSAS 72219-8913 PHONE: (501) 682-0744

FAX: (501) 682-0753

Draft BEST AVAILABLE RETROFIT TECHNOLOGY (BART) MODELING PROTOCOL

June 7, 2006

Prepared by: Mary Pettyjohn, Senior Epidemiologist Planning and Air Quality Analysis Branch Air Division

### ii 

## Table of Contents

List of Figures..................................................................................................... iii List of Tables ...................................................................................................... iv

I. Introduction...........................................................................................................1 II. Background...........................................................................................................2 III. BART-Eligible Sources ........................................................................................4 IV. CAIR and Arkansas ..............................................................................................8 V. BART Air Quality Modeling Approach ...............................................................8 VI. BART-Eligible Units Physical Parameters.........................................................10 A. Stack parameters ............................................................................................10 B. Emission rates ................................................................................................10 VII. Air Quality Model and Inputs.............................................................................11 A. CALPUFF No-obs and refined screening modeling......................................12 1. Modeling domain......................................................................................12 2. CALPUFF system implementation...........................................................13 3. Meteorological data modeling (CALMET) ..............................................13 4. Dispersion modeling (CALPUFF)............................................................14 5. Post-processing (POSTUTIL/CALPOST)................................................16 6. Measuring visibility impacts.....................................................................17 VIII. Change in Visibility Due to BART Controls......................................................19 IX. References...........................................................................................................20

Appendix A. Proposed Time-line.............................................................................. A-1 Appendix B. Map of Receptors..................................................................................B-1 Appendix C. BART-Eligible Sources’ Stack Parameters, Base Elevation at Ground Level and Stack Coordinates ...............................................C-1 Appendix D. BART-Eligible Emission Rates used for the No-obs Modeling Run.......................................................................... D-1 Appendix E.Chapter 5 of the CENRAP BART Modeling Guidelines ......................E-1 Appendix F.CALMET Input Control Files................................................................F-1 Appendix G.CALPUFF Input Control Files............................................................. G-1 Appendix H.POSTUTIL Input Control Parameters...................................................F-1 Appendix I. CALPOST Input Control Parameters .....................................................I-1 Appendix J. Chapter 3 of the CENRAP BART Modeling Guidelines.......................J-1

### iii 

## List of Figures

Figure 1 Mandatory Class I federal areas in the U.S.....................................................3

Figure 2 Arkansas’ Class I areas ...................................................................................3

Figure 3 Map showing the 300 km radius buffer zones around five separate receptors (north, south, east, west , and center) located in the following Class I areas: Upper Buffalo, Cane y Creek, Hercules Glade, Mingo, and Sipsey. This map was developed to determine which Class I areas will Bee assessed during the BA RT determination modeling ...............................4

Figure 4 Map indicating the locations of Upper Buffalo, Caney Creek, and the eighteen BART-eligible fac ilities located in Arkansas...................................7

Figure 5 Map indicating the locations of Upper Buffalo, Caney Creek, Hercules Glade, Mingo, Sipsey, and the ei ghteen BART-eligible facilities located in Arkansas.........................................................................................8

Figure 6 Map of the 6 km CENR AP Central CALPUFF domain...............................12

### iv 

## List of Tables

Table 1 Class I areas and their locations as well as the supervising agencies ADEQ will be assessing..................................................................................4

Table 2 Fossil fuel-fired steam elect ric plants > 250 MMBtu/hr and Kraft pulp mills facilities with BART-eligible emissions units...............................5

Table 3 Petroleum refineries, sinteri ng plants and chemical processing plants facilities with BART-eligible emission units .......................................6

Table 4 CALPUFF modeling components.................................................................13

Table 5 Species modeled in BART screening analyses.............................................15

Table 6 EPA recomme nded monthly average f ( RH 

### ) for the five Class I areas ADEQ are assessing......................................................................................17

Table 7 Average annual natural levels of aerosol components ( μ g/m 3 

### ) ....................18

### 1 

## I. Introduction

On 6 July 2005, the U.S. Environmental Protection Agency (EPA) published final amendments to its 1999 Regional Haze Rule in the Federal Register , including Appendix Y, the final guidance for Best Available Re trofit Technology (BART) determinations (70 FR 39104-39172). The BART rule requires the in stallation of BART on emission sources that fit specific criteria and “may reasonably be anticipated to cause or co ntribute” to visibility impairment in any Class I area. Air quality modeling is the preferred method for establishing which emission sources cause or contribute to visi bility impairment. Arkansas’ BART modeling prot ocol is provided herein.

According to the Regional Haze Regulations a nd Guidelines for Best Available Retrofit Technology (BART) Determination; Final Ru le (40 CFR Part 51, p 39125), each state is required to develop a BART Modeling Protocol that describes the required methodology to assess the levels of controls needed on sources subject to BART. The aforementioned regulation also requires states to work in partnership with all st akeholders including Tribes, EPA, Federal Land Managers (FLMs), Regional Planning Organizations (RPOs) and the various source operators. Although stat es are required to wo rk in concert with the previously mentioned stakeholders, EPA has the ultimate authority to approve or disapprove a state’s State Im plementation Plan (SIP).

The main objective of this protocol is co mpliance with the RHR visibility improvement goals. To accomplish this goal, the Arkans as Department of Environmental Quality (ADEQ) has set forth three functions of this protocol. First, ADEQ will use the protocol to determine which BART-eligible units are subject-to-BART and must perform a BART-analysis. Second, facilities that ADEQ notifies that are subject-to-BART will use this protocol to conduct post-control modeli ng required for their BA RT-analysis. Third, the results from this protocol will be used to conduct cumulative modeling to show the change in visibility impact on Class I ar eas based on ADEQ’s BART determination and the BART emission limits for facilities base d on their BART-analysi s. The subject-toBART and final modeling will be submitted to the EPA as part of the BART section of the Arkansas State Implementation Plan (SIP) for Regional Haze.

The AR RH SIP submittal deadline to EPA as set forth in the Regional Haze Regulations and Guidelines for Best Available Retrof it Technology (BART) De termination; Final Rule (40 CFR Part 51, p 39156) is December 17, 2007. To meet this deadline, ADEQ has developed a schedule for completing BART determinations and implementing the BART strategy in order to meet the mandato ry SIP submittal deadline (Appendix A). As shown in Appendix A, the modeling results must be completed no later than March 1, 2007.

The Central States Regional Planning Asso ciation (CENRAP) cont racted with Alpine Geophysics, LLC to develop a modeling protocol for the states within CENRAP’s region of which the state of Arkansas is a memb er. On December 22, 2005, Alpine Geophysics, LLC delivered the final version of the CENR AP BART Modeling Guidelines (Tesche, et

### 2 al, 2005). However, comments from EPA Re gions VI and VII and the Federal Land Managers (FLMs) were not incorporated in to the guidelines; t hus, Alpine Geophysics, LLC rewrote the guidelines to reflect the comm ents from Regions VI and VII and FLMs. These guidelines were re-issued February 3, 2006. Hence, CENRAP’s BART Modeling Guidelines (Tesche, et al, 2005) have been approved by Regions VI and VII and the FLMs. Therefore, the Planning and Air Qual ity Analysis Branch, Air Division, Arkansas Department of Environmental Quality ha s chosen to adopt the CENRAP BART Modeling Guidelines as ADEQ’s BART Modeli ng Protocol. Additionally, in preparing this draft protocol, ADEQ also consulted th e following draft BART modeling protocols:

1. Best Available Retrofit Technology (BART) Modeling Protocol to Determine Sources Subject to BART in the State of Kansas draft version February 24, 2006 2. Best Available Retrofit Technology (BART) Modeling Protocol to Determine Sources Subject to BART in the State of Minnesota draft version February 24, 2006

This draft protocol is most similar to th e CENRAP BART Modeling Guidelines. These guidelines were developed to ensure “consiste ncy between states in the development of BART modeling protocols and to harmonize the approaches between adjacent RPOs” (Tesche, et al, 2005).

Soon after the finalization of this modeli ng protocol, ADEQ will notify sources subjectto-BART. For those facilities subject-to -BART, ADEQ will provide guidance for conducting their BART-analyses.



## II. Background

The Clean Air Act Amendments (CAAA) of 1977 established 156 Class I areas where visibility was determined to be an important value (Figure 1). Areas designated as Class I areas are those national parks exceeding 6000 acres, wi lderness areas and national memorial parks exceeding 5000 ar eas, and all international park s that were in existence on August 7, 1977. While Rainbow Lake W ilderness Area, Wisconsin has been designated as a Class I area, the FLMs have in dicated that visibility is not a valuable characteristic and therefore, is not included in BART or other RH analyses.

The state of Arkansas has w ithin her boundary two mandatory Class I federal areas (Class I area), Upper Buffalo Wilderness Area a nd Caney Creek Wilderness Area which are managed by the United States Forest Service (F igure 2). However, there are two Class I areas in southern Missouri that are located downwind of facilities operating in Arkansas. The Missouri Class I areas are Hercules-Glade Wilderness Area (US Forest Service) and Mingo National Wildlife Refuge (US Fish and Wildlife). While EPA has not listed the maximum distance from a Class I area to model, this criteria has been set by CENRAP as 300 km. As shown in Figure 3, the eastern po rtion of Arkansas is within the 300 km radius of Sipsey Wilderness Area (US Forest Service), Alabama. Therefore, there are

### 3 five Class I areas Arkansas will be perfor ming BART determination/exemption modeling (Table 1).

Figure 1 Mandatory Class I federal areas in the United States of America

Figure 2 Arkansas’s Class I areas

### 4 Figure 3 Map showing the 300 km radius buffe r zones around five separate receptors (north, south, east, west, and center) locat ed in the following Class I areas: Upper Buffalo, Caney Creek, Hercules Glade, Mingo, and Sipsey. This map was developed to determine which Class I areas will be asse ssed during the BART determination modeling

Table 1 Class I areas and the State they are locat ed in as well as the supervising agencies ADEQ will evaluate during the BART determination/exemption modeling Class I Area State Supervising Agency Upper Buffalo Wilderness Area AR U.S. Forest Service Caney Creek Wilderness AR U.S. Forest Service Hercules Glade Wilderness Area MO U.S. Forest Service Mingo NWS MO U.S. Fish and Wildlife Sipsey Wilderness Area AL U.S. Forest Service 

## III. BART-Eligible Sources

The BART requirements in the RHR are inte nded to reduce emissions specifically from large emission units that, due to age, were exempted from other control requirements of the CAAA. For an emissions unit to be cons idered eligible for BART, it must fall into one of 26 specified categories, must have the potential to emit at l east 250 tons per year of certain haze-forming pollutants, and must have been in existence on August 7, 1977, but not in operation before August 7, 1962.

ADEQ staff determined Arkansas’ BART-eligib le sources by first identifying which of Arkansas’ stationary sources fit the first crit eria of being listed in the BART 26 specific categories. After identifying the sources which fit the first criteria, a database search of

### 5 these facilities was performed to determine whether or not these emitting units’ potential to emit were at least 250 tons pe r year of sulfur dioxide (SO 2 

### ), nitrogen oxides (NO x 

### ), and particulate matter (PM). The next stage of determining BART-eligibility was to research the permit applications for the year the point source was placed into operation. The final step in the process was to contact facilities for the exact date of operation especially for sources that were placed into operation in the years 1977 and 1962. Tables 2 and 3 contain the list of BART-e ligible facilities ( 18) by BART source category and the number of BART-eligible emitting units (27) within each facility. Figure 4 is a map of Arkansas which shows the location of the 18 BART-eligible facilities located in Arkansas. Figure 5 depicts the five Class I areas Arkansas will be assessing and the BART-eligible sources in Arkansas. Appendix B contains maps showing the receptors at each Class I area ADEQ will be assessing.

Table 2 Fossil fuel-fired steam electric plants > 250 MMBtu/hr and Kraft pulp mills facilities with BART-eligible emission units BART Source Category Number and Name Facility Name/Location Facility ID AFIN Unit ID Unit Description

American Electric Power (SWEPCO)/Gentry 05-007- 00107 04- 0017 SN-01 Boiler

AR Electric Cooperative/Augusta 05-147- 00024 74- 00024 SN-01 Boiler 1350mm

AR Electric Cooperative/Camden 05-103- 00055 52- 00055 SN-01 Boiler

Entergy – Lake Catherine/Jones Mill 05-059- 00011 30- 00011 SN-03 Unit 4 Boiler

Entergy – Ritchie Plant/Helena 05-107- 00017 54- 00017 SN-02 Unit 2

Entergy – White Bluff/ Redfield 05-069- 00110 35- 00110 SN-01 Unit 1

Entergy – White Bluff/Redfield 05-069- 00110 35- 00110 SN-02 Unit 2 1. Fossil fuel-fired Electric Plants > 250 MMbtu/hour – Electric Generating Units (EGUs)

Entergy – White Bluff/Redfield 05-069- 00110 35- 00110 SN-05 Auxiliary Boiler

Domtar, Inc./Ashdown 05-081- 00002 41- 00002 SN-03 #1 Power Boiler

Domtar, Inc./Ashdown 05-081- 00002 41- 00002 SN-05 #2 Power Boiler

Delta Natural Kraft/Pine Bluff 05-069- 00017 35- 00017 SN-02 Recovery Boiler

Georgia – Pacific Paper/Crossett 05-003- 00013 02- 00013 SN-22 9A Boiler

Green Bay Packing/ Morrilton 05-029- 00001 15- 00001 SN05A Recover Boiler 3. Kraft Pulp Mills

Potlatch/McGehee 05-041- 00036 21- 00036 SN-04 Power Boiler

### 6 Table 3 Petroleum refineries, sintering plants and chemical proce ssing plant facilities with BART-eligible emissions units BART Source Category Number and Name Facility Name/Location Facility ID AFIN Unit ID Unit Description

11. Petroleum Refineries Lion Oil/El Dorado 05-139- 00016 70- 00016 SN809 #7 Catalyst Regenerator 19. Sintering Plants Big River Industries /West Memphis 05-035- 00082 198- 00082 SN-01 Kiln A

Albermarle – South Plant/Magnolia 05-027- 00028 14- 00028 SR-01 Tail Gas Incinerator Albermarle – South Plant/Magnolia 05-027- 00028 14- 00028 BH-01 Boiler #1

Albermarle – South Plant/Magnolia 05-027- 00028 14- 00028 BH-02 Boiler #2

Eastman Chemical/Batesville 05-063- 00036 32- 00036 6M01- 01 3 Coal Boilers

El Dorado Chemical/El Dorado 05-139- 00040 70- 00040 SN-08 West Nitric Acid Plant El Dorado Chemical/El Dorado 05-139- 00040 70- 00040 SN-09 East Nitric Acid Plant 21. Chemical Processing Plants

El Dorado Chemical/El Dorado 05-139- 00040 70- 00040 SN-10 Nitric Acid Concentrator

### 7 Figure 4 Map indicating the locations of U pper Buffalo, Caney Creek and the eighteen BART-eligible facilities located in Arkansas

### 8 Figure 5 Map indicating the locations of U pper Buffalo, Caney Creek, Hercules Glade, Mingo and the eighteen BART-eligible facilities located in Arkansas



## IV. CAIR and Arkansas

The Clean Air Interstate Rule was finalized in May 2005 by EPA and applies to states in the eastern U.S. Reconsiderations were fi nalized March 2006. This rule address air pollution transport across state borders. EPA determined which states must reduce which pollutants based on modeling which showed how the travel of pollution affects nonattainment in other states. CAIR requires states to reduce NO x 

### and/or SO 2 

### emissions. Of the three programs in CAIR, Arkansas is re quired to participate in only the Ozone-Season NO x 

### reductions program. Although EPA’s BA RT Modeling Guidance allows CAIR states to participate in the CAIR cap and trade program, the state of Arkansas is not eligible for the aforementioned trading progr am because Arkansas is in CAIR only for NO x 

### during the ozone season. Therefore, in Arkansas CAIR is not better than BART. Thus BART-eligible EGUs will be modele d for BART determination/exemption by ADEQ.



## V. BART Air Quality Modeling Approach

According to EPA’s BART Modeling Guid ance, “CALPUFF is the best regulatory modeling application currentl y available ... and is curre ntly the only EPA-approved model...” (p 45); therefore, ADEQ and CENRAP have chosen to use CALPUFF in the

### 9 BART determination process as well as in the post-control analys is. One of the air quality modeling approaches suggested by EP A in the BART guidan ce is an individual source attribution approach. This is the appr oach ADEQ proposes to take. Specifically, this entails modeling source-sp ecific units and comparing mode led impacts to a particular deciview threshold (described below). ADEQ has decided to c onduct the subject-toBART modeling, rather than have each BART -eligible facility either conduct the modeling or hire a contractor. This plan will eliminate the need for ADEQ to quickly review many air quality mode ling analyses conducted usi ng varying approaches. This plan will also satisfy the need to use a c onsistent approach among the modeling analyses. Once the subject-to-BART modeling is co mplete, all the modeling inputs will be available to facilities subject to BART for them or their consultants to conduct modeling for making BART analyses.

ADEQ will follow EPA’s BART Modeling Guidance (p 42) in sitting a threshold limit in determining whether a BART-eligible source is either subject-to-BART or exempt. According to the aforementioned modeling guidance, an individual source will be considered to “cause visibility impairment” if the emissions results in a change (delta ∆ ) in deciviews (dv) that is greater than or equa l to 1.0 deciview on the visibility in a Class I area. Additionally, if the emissions from a sour ce results in a change in visibility that is greater than or equal to 0 .5 dv in a Class I area the sour ce will be considered to “contribute to visibility impairment” (B ART Final Rule, 40 CFR 51 p 39113). Thus, ADEQ has set the threshold limit at 0.5 dv

. The modeling approach discussed here is specifically designed for conducting the subject-to-BART screening analyses. There may be differences between modeling for conducting BART analyses and that for conducti ng a visibility analysis for a New Source Review permit, which may involve similar em ission sources and the same air dispersion model used here.

To ensure that no sources pass the screening test when they should fail, the simple approach, by its nature, must be the most c onservative of all the c onditions likely to be examined for the source in question. Fo r example, many factors influence the contribution of a source to the Class I area other than distance. The frequency of winds transporting the pollutants toward the Class I ar ea may often be important to include for a reliable screening analysis . Also, a more distant Class I area downwind in the predominant wind direction from a source may receive a higher visibility impact than a closer Class I area that is infrequently downwind of the source. Another example of conservatism in the screening process is th e use of the latest beta version of the CALMET/CALPUFF modeling system using the no-observation (no-obs) mode (the prognostic meteorological model MM5). Thus, the maximum impact instead of the 98 th 

### percentile will be used to determine if a sour ce has an impact on visibility in a Class I.

Additionally, the BART analysis process includes several othe r steps in addition to the modeling described in this protocol (EPA, 2005). These steps, none of which are addressed in this document, include detailed analysis of:

### 10  Costs of compliance among the various retrofit control options

 Energy and non-air quality impacts

 Existing pollution control technologies in use at the BART-eligible unit particularly with respect to their a ffecting the choice of retrofit options

 Remaining useful life of the units and/or facility

 Improvements in visibility expected from the use of BART controls.



## VI. BART-Eligible Units Physical Parameters

The physical characteristics of the BART-elig ible point sources to be used for the screening stage one analysis will be pr ovided by ADEQ staff. For the stage two screening analysis, ADEQ staff will work w ith the BART-eligible facilities in the development of actual emissions.



## A. Stack Parameters

Stack parameters required for modeling BART -eligible units were extracted from the permit applications. Stack parameters include height of the stack opening from ground in meters, inside diameter in meters, exit veloci ty in meters per sec ond, exit gas temperature in Kelvin, ground elevation of the stack base in meters, and location coordinates of the stack in Lambert Conformal Conical (LCC). The stack coordinates were taken (in Universal Transverse Mertcator, UTM, and then converted to LCC) by ADEQ staff and then verified using ArcMap. Because the BART modeling focuses on mesoscale transport to Class I areas, other source term parameters (needed to calculate localized impacts) such as building heights and widths for calculating downwash will not be used. Appendix C contains tables indicating the stack parameters and coordinates for each BART-eligible emitting unit.



## B. Emission rates

ADEQ notified by email the BART-eligible facilities to provide the 24-hour average actual emission rate with normal operations from the highest emitting day of the year. Excluded from consideration are days where st art-up, shutdown or ma lfunctions occurred unless these activities are re gular, frequently occurring components of the source’s operation cycle.

ADEQ does not intend to use emissions of VOCs and ammonia from facilities for subject-to-BART analysis. Only specific VOC compounds form secondary organic aerosols that affect visibility. These compounds are a fraction of th e total VOCs reported in the emissions inventory, and ADEQ does not have the breakdown of VOC emissions necessary to model those that only impair visibility. Furthe r, the prescribed screening model (CALPUFF) cannot simulate formation of particles from anthropogenic VOCs, nor their visibility impacts. Ammonia from sp ecific sources will not be evaluated in this process, although ammonia is included in the modeling as a background concentration— this will be discussed later in this modeling protocol. The appropriate VOCs and

### 11 ammonia emission data can, and will be, incl uded in regional scale modeling used for the Regional Haze SIP. 

## VII. Air Quality Model and Inputs

As stated in the previous section, CALPUFF is the prefer red regulatory air dispersion model for long distance and th erefore is the model ADEQ will be using in the BART determination process. ADEQ recognizes that CALPUFF has limited ability to simulate the complex atmospheric chemistry involved in the estimation of secondary particulate formation. However, for purposes of the subj ect-to-BART analysis, ADEQ intends to use CALPUFF for the following reasons:

1. The increased level of effort required fo r conducting particulate apportionment in the regional scale, full-chemistry Eulera in model (CAMx) to acquire individual source contributions to Cla ss I areas, relative to the simplicity of the CALPUFF model 2. The lack of a plume-in-grid feature with the particulate apportionment technique currently available in CAMx 3. The desire to be consistent with othe r CENRAP states, which all (except Texas and Iowa) appear to be using CALPUFF 4. The limited scope of what this modeling is to determine 5. The additional modeling of BART controls that will be conducted as part of the Regional Haze SIP with the CAMx or CMAQ model(s). EPA’s BART guidance states that States should follow the EPA’s Interagency Workgroup on Air Quality Modeling (IWAQM) guidance, Phase 2 recommendations for long-range transport. The IWAQM guidance was devel oped to address air quality impacts as assessed through the Prevention of Signifi cant Deterioration (PSD) program at Class I areas, where the source generally is located beyond 50 km of the Class I area. The IWAQM guidance doe s not specifically addre ss the type of assessment that will occur with the BART analysis.

EPA recommends in their BART modeling gu idelines (2005) that States follow theInteragency Workgroup on Air Quality Modeling (IWAQM) Phase II (1998) for longrange transport. The IWAQM guidance was developed to address air quality impact – as assessed through the Prevention of Significant De terioration (PSD) program – at Class I areas, where the source generally is locat ed beyond 50 km of the Class I area. The IWAQM guidance does not specifically address the type of assessment that will occur with the BART modeling.

### 12 

## A. CALPUFF Screening Modeling:

CALPUFF modeling will be pe rformed on all Arkansas BART-eligible sources. ADEQ intends to closely follow the CENRAP BART mo deling protocol for most of the settings and inputs. Kansas attempted puff splitting and found this method to be computationally prohibitive on the current domain (State of Ka nsas, 2006). Also, according to Tesche, et al (2005),

“There is no quantitative evidence that th e horizontal and vertical puff-splitting algorithms in CALPUFF yield improved accuracy and precision in model estimates of inert or linearly react ive pollutants although conceptually the methods have appeal in that they attemp t to mimic lateral and vertical wind speed and direction shears.” (p 6-6)

Therefore, ADEQ will not invoke puff splitting in the no-obs screening analysis nor in the refined screening analysis. However, if a potentially subject-to-BART facility wishes to invoke the puff splitting mode, they will be required to notify ADEQ in writing of their intent and provide a protocol for appr oval prior to performing the analysis.



### 1. Modeling domain The CALPUFF modeling will be conducted on the CENRAP central 6 km grid. The extent of the proposed CALPUFF domain is shown in Figure 2.

Figure 6 6 km CENRAP Central CALPU FF domain (Tesche, et al, 2005)

### 13 CALPUFF will be applied to each source for three annual simulations spanning the years 2001 through 2003. The IWAQM Phase II guidance allows the use of fewer than 5 years of meteorological data if a meteorological model using four-dimensional data assimilation is used to supply data. This is the case in this modeling analysis. See the section on meteorology for more information.

2. CALPUFF system implementation There are three main compone nts to the CALPUFF model:

1. Meteorological Data Modeling (CALMET); 2. Dispersion Modeling (CALPUFF); and 3. Post-processing (CALPOST)

Versions of the modeling components to use in this BART analysis are shown in Table 4. Table 4 CALPUFF Modeling Components

Processor Version Level
TERREL 3.311 030709 CTGCOMP 2.42 030709

CTGPROC 2.42 030709 MAKEGEO 2.22 030709 CALMM5 2.4 050413 CALMET 5.53a 040716 CALPUFF 5.753 051130 POSTUTIL 1.4 040818 CALPOST 5.6392 051130 

### The specific use of each of these components in the BART analysis is described in more detail below.

For screening applications, ADEQ will use the VI STAS version which is the latest ‘beta’ versions of the CALMET/CALPUFF mode ling system. Note that these are not the EPA guideline codes but rather an updated version containing recent (as of this writing) science improvements and bug fixes. The curre nt guideline CALPUF F code is version 5.7, level 030402. This substitution results fr om EPA phasing out the use of the legacy Pasquill-Gifford (P-G) dispersion paramete rs with the introduction of AERMOD as a new guideline model. CALPUFF employs the AERMOD turbulence-based dispersion coefficients and probability density function (pdf) dispersion methods scheme instead of P-G.

The appropriate model codes may be downloaded from www.src.com or purchased with the latest graphical user in terface (GUI) from the model de veloper. The sequence of model processors listed in Table 4 corres ponds to the order in which the programs are typically run.

3. Meteorological data modeling (CALMET) ADEQ will use the 2001-2003 CENRAP deve loped no-obs CALMET dataset for the screening analysis.* This decision was ba sed on EPA Regions VI and VII written comments on the CENRAP BART Modeling Gu idelines (Tesche, et al, 2005) which state,

### 14 “Normally, in accordance with Section 8.3.1.2 (d) of the Guideline on Air Quality Models, the EPA would require that observations be in corporated in conjunction with prognostic meteorological data. Wh ile the idea of use of prognostic data alone holds promise, it is our opinion th at this option require s further evaluation to insure that this approach does not bias CALPUFF towards underestimation (Guideline on Air Quality Models, S ection 3.2.2 (d)(iv)). While we have significant concern regarding the use of the CALMET fiel ds as they have been developed under the procedures documented in this protocol, we would consider the use of the CALMET meteorological fields provided the screening methodology described in Section 6.1 of the protocol is strictly adhered to. In this case, we feel that the use of the maximu m visibility impact rather than the 98 th 

### percentile value is conservative in its application, a nd would overcome concerns of a potential bias towards underprediction [sic] of the “no-observation” mode. Under these circumstances, we would c onsider the use of the CALMET fields acceptable for the CALPUFF screening procedure.” (EPA, 2005)

As stated in Section V. BART Air Qu ality Modeling Approach, ADEQ will use the maximum impact instead of the 98 th 

### percentile to determine if a source has an impact on visibility in a Class I.

However, subject-to-BART facilities have the option of using the CENRAP CALMET processed data or incorporating obser vational meteorological data into the aforementioned CALMET data. If a subject- to-BART facility opts to use the CENRAP CALMET processed data, then the facility will be required to use the maximum impact instead of the 98 th 

### percentile (8 th 

### day). If a subject-to-BART facility decides they would rather use the 98 th 

### percentile, then the facility will be required to incorporate observational data and provide a protocol as well as a performance evaluation which will need to be approved by ADEQ, EPA, and the FLMs.

Appendix F contains the detailed informati on on all CALMET setting that was used to develop the post-processe d no-obs data fields.

4. Dispersion modeling (CALPUFF) The CALMET output is used as input to the CALPUFF model, which simulates the effects of the meteorological conditions on the transport and dispersion of pollutants from an individual source. In gene ral, ADEQ proposes to use the recommended default options in the CALPUFF model. There are some devia tions, which are discussed below. Table 5 indicates the species that will be modeled an d/or emitted in the no-obs and refined BART analyses.

### 15 Table 5 Species modeled in BART screening analyses Species Modeled Emitted Dry Deposited
SO 2 Yes Yes Computed-gas SO 4 -2 Yes No Computed-particle NO x Yes Yes Computed-gas HNO 3 Yes No Computed-gas NO 3 - Yes No Computed-particle PM-fine* Yes Yes Computed-particle PM 10 * Yes Yes Computed-particle 

### *Please refer to Section VI subsection B for a detailed discussion on PM-fine and PM 10


### . Emissions Speciation: ADEQ does not intend to model sulfate (SO 4 -2 

### ), nitrate (NO 3 - 

### ), elemental carbon (EC), and secondary orga nic aerosols (SOA) during the screening analyses. However, ADEQ recognizes the impa ct EC and SOA have on visibility. For instance, the light extinction ( β ext 

### ) coefficient for EC is 10 and for SOA it is 4. Currently, data are quite limited on appropriate speciation of organic/inorganic and filterable/condensable emissions by source category. Although there are speciation profiles available for gas- and oil-fired combustion turbines and coal combustion processes, currently there are no detailed profiles for the full range of BART-eligible sources. Thus, in the case of a subject-to-BART source wh ere the PM profile for SO42-, EC, and SOA are known, ADEQ recommends the aforementioned species be modeled as separate species in CALPUFF in the post-control modeling analysis.

Condensable Emissions : According to Tesche, et al (p 6-5 2005), “condensable emissions are considered primary fine partic ulate.” ADEQ is aware of the inability to measure PM 2.5 

### emissions. Thus, BART-eligible fac ilities will be most likely use AP-42 emission factors to develop the “actual” highest average 24-hr emission rate for this pollutant. In the development of this emissi on rate, ADEQ will require these facilities to use the AP-42 emission factors for condensable PM 2.5 

### . For sources where AP-42 factors are not available, assumptions for partitio ning need to be resolved with ADEQ.

Size Classification of Primary PM Emissions : Particle size parameters are entered in the CALPUFF input file for dry deposition of partic les. There are default values for “aerosol” species (i.e., SO 4 -2 

### , NO 3 - 

### , and PM 2.5 

### ). The default value for ea ch of these species is 0.48 μm geometric mass mean diameter and 2.0 μm geometric standard deviation. The main sources of these particles are fuel com bustion. A way to account for this, without including EC and SOA in the modeling, is to use particle speciation in the postprocessing step. This is discussed below in the CALPOST section.

As stated in a prev ious section, all PM 10 

### emissions will be modeled as PM 2.5 

### for the noobs model simulations (Tesche, et al, 2005).

Background Ozone concentrations: Ozone (O 3 

### ) can be input to CA LPUFF as hourly or monthly background values. Hourly values of ozone concentrations were obtained from two rural monitoring sites in Arkansas: Deer , Newton County monitoring site and Eagle Mountain, Montgomery County monitoring site. The hourly ozone concentrations were adjusted for the time differences between the post-processed pr ognostic meteorological

### 16 file (0 GMT) and the collection time of the ozone (LST). Also, the concentrations were adjusted from parts per million (ppm) to parts per billion (ppb). These hourly ozone values will be used in this modeling.

Background Ammonia concentrations: Background ammonia concentration is assumed to be temporally and spatially invariant a nd will be fixed at 3 ppb across the entire domain for all months. It may be possible to derive NH 3 

### concentrations from regional modeling outputs that CENRAP is curre ntly developing. At this time these NH 3 

### values are not available in a model ready form.

Receptors: Receptors are locations where model results are calculated and provided in the CALPUFF output files. Receptor locations were derived from the National Park Service’s Class I area receptor database at http://www2.nature.nps.gov/ai r/maps/receptors/index.cfm . Only these discrete NPS receptors will be modeled in CALPUFF. Th e discrete receptors are necessary for calculating visibility impacts in the nine sel ected Class I areas that will be evaluated by ADEQ. All the discrete receptors will be pl aced with enough dens ity that the highest visibility impacts should be evident. The NPS provides recepto rs in all the Class I areas on a 1 km basis. These receptors will be kept at the 1 km spacing for the BART modeling, and all receptors will be retained. NPS also provides a conversion program to convert the coordinates of the receptors fr om latitude/longitude (lat/long) to Lambert Conformal Conical (LCC). ADEQ used this conversion program to convert the receptors located in the five Class I areas it is assessing from lat/long to LCC.

Outputs: The CALPUFF modeling resu lts will be displayed in units of micrograms per cubic meter (μg/m 3 

### ). In order to determine visibility impacts, the CALPUFF outputs must be post-processed.

Detailed information on all CALPUFF setting to be used in this sc reening analysis is located in Appendix G.

5. Post-processing (POSTUTIL/CALPOST) Hourly concentration out puts from CALPUFF are pro cessed through POSTUTIL and CALPOST to determine visibility cond itions. Specifically, POSTUTIL takes the concentration file output from CALPUFF and recalculates the nitric acid and nitrate partition based on total available sulfate and ammonia. The ammonia-limiting method (ALM) in CALPUFF repartitions nitric acid and nitrate on a receptor-by-receptor and hour-by-hour basis to account for the mode ls systematic over-prediction due to overlapping puffs. For both screening applic ations, the parameter MNIRATE=1 is set in POSTUTIL to implement this approximat e correction in its simplest form. The background ammonia concentration that was obtained from CENRAP’s regional modeling effort will be used to maintain re gional consistency in the CENRAP region. CALPOST uses the concentration file proc essed through POSTUTIL, along with relative humidity (RH) data, to perform visibility ca lculations. For the BART analysis, the only modeling results out of the CA LPUFF modeling system of in terest are the visibility impacts.

### 17 Please see Appendix H and I for detailed settings for POSTUTIL and CALPOST.

Light extinction: Light extinction must be computed in order to calculate visibility. CALPOST has seven methods fo r computing light extinction. This BART screening analysis will use Method 6, which computes extinction from speciated particulate matter with monthly Class I area-specific relative hum idity adjustment factor s, and is implied by the BART guidance. Relative humidity (RH) is an important factor in determining light extinction (and therefore visibility) because SO 4 -2 

### and NO 3 - 

### aerosols, which absorb moisture from the air, have greater extinc tion efficiencies with greater RH. All BART analyses will apply relative humidity correction factors ( f

(RH) 

### s) to SO 4 -2 

### and NO 3 - 

### concentrations outputs from CALPUFF, which were obtained from EPA’s “Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule (EPA, 2003). The f

(RH) 

### values for the Class I areas that will be assessed are provided in Table 5.

Table 6 EPA recommended monthly averaged f ( RH 

### ) for the five Class I areas ADEQ is assessing (EPA, 2003) Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Caney Creek 3.4 3.1 2.9 3.0 3.6 3.6 3.4 3.4 3.6 3.5 3.4 3.5HerculesGlades 3.2 2.9 2.7 2.7 3.3 3.3 3.3 3.3 3.4 3.1 3.1 3.3 Mingo 3.3 3.0 2.8 2.6 3.0 3.2 3.3 3.5 3.5 3.1 3.1 3.3 Sipsey 3.3 3.0 2.8 2.7 3.1 3.4 3.5 3.5 3.5 3.3 3.1 3.3 Upper Buffalo 3.3 3.0 2.7 2.8 3.4 3.4 3.4 3.4 3.6 3.3 3.2 3.3



### The PM 2.5 

### concentrations are considered part of the dry light extinction equation and do not have a humidity adjustment factor. The lig ht extinction equation is the sum of the wet SO 4 -2 

### and NO 3 - 

### and dry components PM 2.5 

### plus Rayleigh scattering ( β Ray 

### ), which is 10 inverse megameters (Mm -1 

### ).

To account for sources modeled with a known PM speciation profile for EC, SOA, and SO 4 

### , an adjustment to the extinction coefficient for the PM components will be made in CALPOST. ADEQ intends to follow the method outlined in the FLM CALPUFF Reviewer’s Guide (Gebhart, 2005) wh ich is located in Appendix K.

6. Measuring visibility impacts The recommended procedure for quantifying visib ility impacts can be found in Chapter 3 of the CENRAP BART Modeling Guidelines (Tesche, et al, 2005) which is located in Appendix J. The key point is that the light extinction coefficient ( β ext 

### ) can be calculated from the IMPROVE equation as:

β ext = 3 f ( RH ) [(NH 4 ) 2 SO 4 ] + 3 f ( RH ) [NH 4 NO 3 ] + 4[OC] + 1[Soil] +

+ 0.6[Coarse Mass] + 10[EC] + β Ray

### 18 The monthly site-specific f ( RH 

### ) values were obtained for th e five Class I Area ADEQ is assessing from Table A-3 in the EPA (2003) guidance document. Then, the haze index (HI), in dv, is calculate d in terms of the extin ction coefficient via:

HI = 10 ln ( β ext /10)



### The change in visibility (measured in terms of ∆ dv) is then compared against background conditions. The ∆ dv value is calculated from the source’s contribution to extinction, β source 

### , and background extinction, β background 

### , as follows:

∆ dv = 10 ln 

### ({ β background 

### + β

source 

### }/ β background 

### )

If the ∆ dv value is greater than or equal to 0.5 dv , the source is said to contribute to visibility impairment and is thus subject-t o-BART controls. If not, it is BART-exempt.

The annual average natural levels of aeros ol components at each Class I area being evaluated by ADEQ are shown in Table 7. Natural conditions by component in Table 6 are based on whether the Class I area is in the eastern or the western part of the United States. In this BART analysis, all Class I areas are located in the East. The source of this data is from EPA’s Guidance for Estimati ng Natural Visibility Conditions Under the Regional Haze Rule (EPA, 2003).

Table 7 Average annual natural le vels of aerosol components ( μ g/m 3 

### ) (EPA, 2003) Class I Area Region SO 4 

### NO 3 

### OC EC Soil Coarse Mass Caney Creek East 0.23 0.10 1.40 0.02 0.50 3.00 Hercules-Glades East 0.23 0.10 1.40 0.02 0.50 3.00 Mingo East 0.23 0.10 1.40 0.02 0.50 3.00 Sipsey East 0.12 0.10 0.47 0.02 0.50 3.00 Upper Buffalo East 0.23 0.10 1.40 0.02 0.50 3.00



### As stated in section V, in a cooperative agreement with EPA Regions VI and VII and FLMs, CENRAP guidance deviates from use of the 98 th 

### percentile impact. The CALMET datasets as described in this protoc ol were processed w ith the no-obs options (i.e., surface observations were not used in the CALMET wind field interpolation). Aware that exercising CALMET with no-obs may lead in some applications to potentially less conservatism in the CALPUFF vi sibility results compared with the use of CALMET with observations, CENRAP has agre ed to EPA’s recommendation that the maximum visibility impact, rather than the 98 th 

### percentile value, should be used for the no-obs screening analysis using the CENRAP-developed CALMET datasets.

If the no-obs screening analys is results indicate a BART-e ligible facility’s maximum ∆ dv on a Class I area is less that 0.5 dv, then they will be considered exempt from BART and will be notified by ADEQ of their status. However, if the maximum ∆ dv is equal to or greater than 0.5 dv, the source will be c onsidered to be subject-to-BART. ADEQ will notify these subject-to-BART facilities.

### 19 

## VIII. Change in Visibility Due to BART Controls

Once a facility is determined to be subj ect-to-BART, this facility must perform an engineering analysis and a post-control m odeling analysis using CALPUFF. This modeling analysis must be compared to the pr e-control modeling result s. Please note that this will be a source specific (i.e. emitting unit specific) and pollutant specific modeling analysis using CALPUFF. If a subjec t-to-BART facility opts to use the 98 th 

### percentile rather than the maximum impact, the subjec t-to-BART facility will be required to be incorporate observational data with the post processed CALMET prognostic meteorological data. Also these facilities wi ll be required to submit their meteorological modeling protocol, model performance eval uation, and CALPUFF modeling protocol to ADEQ, EPA Region VI, and FLMs for approva l. However, if the subject-to-BART facility opts to use the maximum impact rather than the 98 th 

### percentile, these facilities may use the post-processed CALMET MM5 data.

Additionally, one control measure that a source may opt to use is to revise their Title V permit to provide for synthetic minor limits so that it falls under the BART emission cap. That permit modification must be done prior to the State going to public hearing on its RH SIP. The limits must be in place for as long as the RH SIP is applicable or for as long as the source is operational. However, the source will still need to do a post-control CALPUFF modeling analysis to determine the amount of emissi ons it needs to reduce for visibility improvement. (Note: ADEQ strongly recommends that all subject-to-BART facilities work closely with ADEQ in their engi neering analyses.) Also, after all of the post-control results are submitted to and approved by ADEQ, these results will then be inputted into either CAMx or CMAQ for a cumulative model run. If the control measures proposed by the BART facilities still impact a Class I area, the BART facilities will need to implement additio nal control. Please note that all post-modeling results are due to ADEQ no later than October 23, 2006.

### 20 

## IX. References

Gebhart, Howard (2005). Draft CALPUFF REVIEWER’S GUIDE

. Air Resource Specialists, Inc., Fort Collins, Colorado.

Kansas Department of Health and Environm ent, Bureau of Air and Radiation (March, 2006). Best Available Retrofit Technology (B ART) Modeling Protocol to Determine Sources Subject to BART in the State of Kansas

Minnesota Pollution Control Agency (2005). Best Available Retrofit Technology (BART) Modeling Protocol to Determine S ources Subject to BART in the State of Minnesota.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino. (2000). A User’s Guide for the CALPUFF Dispersion Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino. (2002). A User’s Guide for the CALMET Dispersion Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

Tesche, T.W, et al (2005) CENRAP BART Modeling Guidelines. Alpine Geophysics, LLC. Ft. Wright, KY

U.S. EPA. (1998). Interagency Workgroup on Air Quality Modeling (IWAQM) Phase2—Summary Report and Recommendations for Modeling Long Range Transport Impacts

. EPA-454/R98-019.

U.S. EPA (2003). Regional Haze: Estimating Natural Visibility Conditions Under the Regional Haze Rule. EPA-454/B-03-005.

U.S. EPA. (2005). Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) Determinations. Federal Register Vol. 70, No. 128.

U.S. EPA, Regions VI & VII ( 2005). BART Protocol Comments.

U.S. EPA, Region VI (2006). Personal communication.

### A-1 

## Appendix A. Proposed Time-line

Figure A-1 ADEQ’s proposed time-line to m eet the RH SIP deadline of December 17, 2007 as set forth by EPA in its Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) De termination (40 CFR Part 51, p 39156)

### B-1 

## Appendix B. Map of receptors

Figure B-1 Receptors located in Cane y Creek Wilderness Area, Arkansas

Figure B-2 Receptors located in Herc ules-Glade Wilderness Area, Missouri

### B-2 Figure B-3 Receptors located in Mingo Wilderness Area, Missouri

Figure B-4 Receptors located in Sipsey Wilderness, Alabama

### B-3 Figure B-4 Receptors located in Uppe r Buffalo Wilderness Area, Arkansas

### C-1 

## Appendix C. BART-Eligible Sour ces’ Stack Parameters, Base Elevation at Ground Level, and Stack Coordinates

Table C-1 BART-eligible sources’ stack parameters

STATIONARY SOURCE NAME/LOCATION Emission Unit ID Stack Height Meters Stack Diameter Meters Exit Velocity m/sec Temperature

o K Albermarle-South Plant / Magno lia SR-01 57.9 0.814 15.24 922 Albermarle-South Plant / Magno lia BH-01 6.4 2.591 9.14 505 Albermarle-South Plant / Magno lia BH-02 6.4 2.591 9.14 505 American Elect. Power (SWEPCO) / Gentry SN-01 164.6 6.096 34.14 408 AR Elect. Coop - Bailey Plant / Augusta SN-01 50.9 3.000 28.04 444 AR Elect. Coop - McClellan Plant / Camden SN-01 48.8 3.301 28.04 444 Big River Industries / W. Memphis SN-01 30.5 1.524 21.88 330 Delta Natural Kraft / Pine Bluff SN-02 50.3 2.134 13.29 348 Domtar, Inc. / Ashdown SN-03 66.1 1.890 26.76 522 Domtar, Inc. / Ashdown SN-05 71.6 3.659 11.92 325 Eastman Chemical / Batesville 6M01-01 61.0 2.743 9.45 422 El Dorado Chemical / El Dorado SN-08 22.9 1.219 33.53 505 El Dorado Chemical / El Dorado SN-09 22.9 1.219 32.00 500 El Dorado Chemical / El Dorado SN-10 23.8 0.152 23.77 313 Entergy - Lake Catherine / Jones Mill SN-03 59.4 5.182 3.08 396 Entergy - Ritchie Plant / Helena SN-02 71.9 3.658 28.62 390 Entergy - White Bluff / Redfield SN-01 304.8 7.833 27.43 434 Entergy - White Bluff / Redfield SN-02 304.8 7.833 27.43 434 Entergy - White Bluff / Redfield SN-05 4.6 0.914 19.81 519 Georgia-Pacific Paper / Crossett SN-22 53.3 3.658 10.45 341 Great Lakes Chemical / El Dorado SN-302A 9.1 0.762 40.54 555 Green Bay Packaging / Morrilton SN-05A 30.8 1.798 25.60 456 Lion Oil / El Dorado SN-809 61.0 1.753 9.75 533 Potlatch Corp. / McGehee SN-04 89.6 2.743 14.78 444

### C-2 Table C-2 BART-Eligible Emission Units’ Base Elevation and Lambert Conformal Conical (LCC) Coordinates

STATIONARY SOURCE NAME/LOCATION (BART File Name) Emission UNIT ID Base Elevation, meters (m) X Easting LCC x Y Northing LCC y Albermarle-South Plant / Magnolia SR-01 86.9 352.81836 -747.03381 Albermarle-South Plant / Magnolia BH-01 88.4 352.67618 -746.98114 Albermarle-South Plant / Magnolia BH-02 88.4 352.65801 -746.98190 American Elect. Power (SWEPCO) / Gentry SN-01 349.9 221.58128 -410.39077 Ark. Elect. Coop - Bailey Plant / Augusta SN-01 61.3 510.86643 -507.71488 Ark. Elect. Coop - McClellan Plant / Camden SN-01 33.5 390.21870 -702.15534 Big River Industries (General Shale)/ W. Memphis SN-01 60.0 609.12652 -517.70639 Delta Natural Kraft / Pine Bl uff SN-02 66.4 457.00824 -621.20692 Domtar, Inc. / Ashdown SN-03 97.5 267.47491 -698.66686 Domtar, Inc. / Ashdown SN-05 97.5 267.48245 -698.74355 Eastman Chemical / Batesville 6M01-01 82.3 493.14724 -458.02938 El Dorado Chemical / El Dorado SN-08 63.1 401.11728 -734.65321 El Dorado Chemical / El Dorado SN-09 63.1 401.13533 -734.65236 El Dorado Chemical / El Dorado SN-10 62.2 401.19594 -734.67412 Entergy - Lake Catherine / Jon es Mill SN-03 100.0 375.45658 -606.40861 Entergy - Ritchie Plant / Helena SN-02 54.9 586.25363 -591.07129 Entergy - White Bluff / Redfield SN-01 94.2 446.73457 -625.11197 Entergy - White Bluff / Redfield SN-02 94.2 445.61252 -604.15523 Entergy - White Bluff / Redfield SN-05 94.2 445.61539 -604.25671 Georgia-Pacific Paper / Crossett SN-22 46.0 469.03486 -745.02133 Green Bay Packaging / Morrilton SN-05A 98.5 387.29077 -532.44265 Lion Oil / El Dorado SN-809 75.6 403.01817 -741.82948 Potlatch Corp. / McGehee SN-04 43.9 533.13136 -678.59798

### D-1 

## Appendix D. BART-Eligible Emis sion Rates used for the NoObs Modeling Run

Table D-1 BART-eligible units’ highes t 24-hour actual emi ssion rates for SO 2, 

### NO x 

### , PM 10 * 

### and PM 2.5 

### in grams per second (g/sec)

Highest 24-Hour Actual Emission Rates (g/sec)

BART-Eligible Facilities/ Locations Emission Unit ID Number SO 2 NO x PM 10 PM 2.5 Albermarle-South Plant / Magnolia SR-01 48.126 0.076 0.000 0.009 Albermarle-South Plant / Magnolia BH-01 0.353 2.075 0.000 0.136 Albermarle-South Plant / Magnolia BH-02 0.535 2.578 0.000 0.128

American Elect. Po wer (SWEPCO) / Gentry SN-01 595.781 245.066 21.725 5.531 AR Elect. Coop - Bailey Plant / Augusta SN-01 299.344 36.933 21.729 21.729 AR Elect. Coop - McClellan Plant / Camden SN-01 346.189 47.124 28.764 28.764 Big River Industries/ W. Memphis SN-01 0.000 8.589 0.000 7.076
Delta Natural Kraft / Pine Bluff SN-02 0.239 1.701 1.058 0.529 Domtar, Inc. / Ashdown SN-03 0.774 22.632 0.000 21.354 Domtar, Inc. / Ashdown SN-05 70.175 52.008 0.000 7.881
Eastman Chemical / Batesville 6M01-01 54.046 11.045 0.290 0.217
El Dorado Chemical / El Dorado SN-08 0.000 20.060 0.000 0.000
El Dorado Chemical / El Dorado SN-09 0.000 15.645 0.000 0.000
El Dorado Chemical / El Dorado SN-10 0.000 0.415 0.000 0.000 Entergy - Lake Catherine / Jones Mill SN-03 0.420 309.535 0.365 0.246 Entergy - Ritchie Plant / Helena SN-02 0.105 17.640 0.997 0.997 Entergy - White Bluff / Redfield SN-01 978.164 550.821 15.592 11.802 Entergy - White Bluff / Redfield SN-02 985.933 596.075 16.653 12.915 Entergy - White Bluff / Redfield SN-05 4.095 3.811 0.365 0.246 Georgia-Pacific Paper / Crossett SN-22 77.275 182.677 0.000 9.310 Green Bay Packaging / Morrilton SN-05A 4.934 8.771 0.000 1.165
Lion Oil / El Dorado SN-809 23.142 5.980 0.000 7.696 Potlatch Corp. / McGehee SN-04 6.942 10.533` 2.752 2.752

### E-1 

## Appendix E. Chapter 5 of the CENRAP BART Modeling Guidelines (Tesche, et al, 2005)

5.0 DATA BASES FOR CA LPUFF MODELING

To support BART modeling by the states and source operators, both meteorological and aerometric data sets are re quired. Regional meteorological data sets generated by the CALMET model suitable fo r direct input to the CALPUFF modeling system have been developed and archived. These data sets cover calendar years 2001, 2002, and 2003 for three sub-regional grid dom ains shown in Figures 5-1 through 5-4. The procedures used in developing the CA LMET data sets generally follow the IWAQM recommendations (EPA, 1998), except for a few notable refinements. The processed CALMET files, in CALPUFF-ready input fo rmat, are available from CENRAP on hard disk drives to interested states and stakeholders.

This chapter describes how these meteor ological modeling sets were developed and evaluated. The basic CALMET model confi guration used to generate the three years of CALPUFF-ready meteorology is described in detail so that users of this information have a clear understanding of the data sets and their applicability. In addition, for those states or source operators who elect to conduct more source-specific CALMET/CALPUFF modeling, the information in this chapter may be helpful in guiding specification of revised CALM ET model inputs and generati on of revised CALMET data
sets.

Also included in Section 5 .2 is a disc ussion of routinely available air quality monitoring data sets available to the states and source operators in support of screening and source-specific BART modeling exercises.

5.1 Development of CALMET Meteorological Files

5.1.1 MM5 Data Sets

Alpine Geophysics developed a c onsistent set of CALMET regional meteorological modeling data sets for use by the CENRAP States, BART eligible sources within the region and others. These meteorol ogical modeling data sets were constructed through the joint use of the CALMET processo r and results from existing annual threedimensional MM5 meteorological simulati ons. The specific annual prognostic model simulations available for CENR AP BART modeling included:

> 2001 MM5 data set at 36/12 km re solution developed for EPA by Alpine Geophysics (McNally and Te sche, 2002; McNally 2003);

> 2002 MM5 data set at 36 km resolution developed for CENRAP by Iowa DNR (Johnson, 2003a,b),

### E-2 > 2003 MM5 data set at 36 km resolution developed for the Midwest RPO (Baker, 2005; Baker et al., 2004; Kembell-Cook et al., 2005)

Each of these studies included a performance evaluation of the MM5 generated data sets against surface meteorological observations and the results of these evaluations are contained in the reports or pr esentations cited above. While there exists a set of annual 12 km MM5 meteorology for 2002, this data set was developed by four independent CENRAP modeling centers and th ese data sets have not been concatenated into one master data base. More im portantly, there has been no systematic, rigorous model performance evaluation performed on the CENRAP 2002 12 km MM5 data yet. Accordingly, until such time as the 2002 12 km data set has been evaluated and shown to be of comparable reliability as the aforementioned MM5 data sets, it’s use is contraindicated.

5.1.2 CALMET Model Configuration

The CALMET modeling procedures used to construct meteorological inputs to CALPUFF for visibility scr eening of BART eligible sources generally follows the IWAQM recommendations (EPA, 1998), except as noted below.

CALMET Model Options

. The CALMET model has a numbe r of user-selected options, parameter settings, and ‘switches’ that must be defined prior to exercising the processing system. These options and settings are we ll-described in the CALMET User’s Guide (Scire et al., 2000a) and in the CALMET input file to the executable code. Appendix A of this protocol summarize s the CALMET configurations used in developing the processed 6 km meteorological fields over the three CENRAP BART modeling domains. Also included in the tables in Appe ndix A are the default CALMET options and parameter settings recommended in the IWAQM Phase 2 Report (EPA, 1998).

CALMET Domain. Three slightly overlapping m odeling domains were defined by CENRAP to support BART modeling. These do mains are shown in Figures 5-1 through 5-4 and Table 5-1. The processors used to generate the domain, land use, and elevation data for the CALMET/CALPUFF system include TERREL, CTGPROC, and MAKEGEO, as described below.

### E-3 > TERREL is the terrain pre-processor that averages terrain features to the modeling grid resolution; TERREL constr ucts the basic properties of the gridded domain and defines the c oordinates upon which meteorological data are stored. Key parameters in clude specification of grid type, location, resolution and terrain elevation.

> CTGPROC computes the fractional land use for the modeling grid resolution. Land use characteristics for each grid cell are assigned using CTGPROC. The primary variable adju stment associated with CTGRPOC is selection of an appr opriate land use database. Version 2.0 of the North American Land Cover Characteristics database is used.

> MAKEGEO is the final pre-processor th at combines the terrain and land use data for input to CALMET. Generating the appropriate MAKEGEO.INP control file require s only minimal alteration of the default assignments. Key modificat ions include specifying domain attributes and ensuring input f iles are correctly referenced.

Terrain . CALMET requires both te rrain height and land us e/land cover for the application region. These are generated using the CALMET CTGPROC, TERREL and MAKEGEO processors. The terrain data were created using the TERREL (version 3.311, level 030709) processor and the Shuttle Radar Topography Mission (SRTM)-GTOPO 30 second (~1 km) reso lution dataset.

Land Use . The landuse data set was created using the Composite Theme Grid CTGROC processor (version 2.42, level 030709) and the United States Geological Survey (USGS) Global Land Cover Characterization (GLCC) version 2.0 database. The GLCC database is available at 30 second (~1km) resolution. References for these and other modeling datasets can be found at www.src.com

. Vertical Layer Structure. The vertical layer stru cture for the CALMET/CALPUFF screening applications is more refined than the ge neral suggestions of IWAQM. The CENRAP vertical structure wa s designed to reduce the need for vertical interpolation while simultaneously improving vertical re solution within the pl anetary boundary layer (PBL). Table 5-2 identifies the 11 layer interface s required to define the 10 layer vertical CALMET grid structure. The top interface in the CALMET simulation is 4000 meters.

Use of Observations

. Based on considerable disc ussions with State and Federal managers and agency personnel, CENRAP has elected to use the No-Obs mode in CALMET for constructing the 6 km meteor ological fields for CALPUFF screening exercises. The three annual MM5 simula tions (2001, 2002, and 2003) will be used as the sole source for meteorological data within CALMET. Blending observational data with the MM5 data within CALMET (i.e., use of th e “OBS” option is esse ntially a redundant use of the same data. Substantial improvement in the MM5 initialization data and in the use of four dimensional data assimilation (FDDA) has been achieved in recent years using observational data. Th e ETA analysis data used in initial and boundary conditions estimates as well as within the FDDA fiel ds derive from 3-hourly, 40 km objective

### E-4 analysis fields computed using an extens ive supply of observational data (National Weather Service surface and upper air data, GOES satellite precip itable water; VAD wind profiles from NEXRAD; ACARS aircraft temperature data; SSM/I oceanic surface winds; daily NESDIS snow cover and sea-ic e analysis data; RAOB balloon drift; GOES and TOVS-1B radiance data; 2D-VAR SST fr om NCEP Ocean Modeling Branch; radar estimated rainfall; and surface rainfall). The complexity, resolution, and accuracy of the ETA data that is used to in itialize and ‘nudge’ the MM5 for ecasts is extensive indeed. Particularly at the 12-36 km horizontal grid scales over the flat to modestly rolling topography of the CENRAP domain, there is no need to introduce local meteorological observations in order to retrieve local terrain effects, for example. Thus, mesoscale wind patterns are likely to be adequately characterized by the MM5 simulations.

Many observations, especially surface obser vations, reflect local conditions on a scale smaller than the 6 km CENRAP CALM ET fields. The introduction of the local observations into the regional modeling domain may extend the influence of the observational data beyond its true representati veness and result in internally inconsistent flow features. In particular the time interpolation of the 12-hourly upper air sounding data may wash out structure in the MM5 fields that are appropr iate to retain. Given that the CENRAP domain as a whole includes area s of moderately rolling terrain, coastal regions and relatively flat terrain, a single set of representative weights 1 

### that allows significant influence of the observations where appropriate, will involve a considerable effort and substantial testing. The internally consistent MM5 fields are considered likely to be appropriate for the regi onal simulations, and the increm ental benefit of adding the observational data into the regi onal CALMET simulations is not considered worthwhile.

However, on the smaller domains likely to be considered in source-specific modeling (e.g., 1-4 km in scale) with the hi gher CALMET grid resolu tion and the smaller domain size, more control over the region of influence of the mete orological observations can be achieved. It is easier for the diagnostic model to a llow the local flow observations to have appropriate influence in the vicinity of the observation, but allow terrain-adjusted flow to dominate away from the observations. Given that the fine scale source-specific domains will be used especially in irregular and/or meteorologically complex settings, the relatively coarser-scale MM5 simulations are less likely to be fully adequate, and the introduction observational data into CALMET is more likely to achieve improvements in the resulting meteorological fields.

Diagnostic Model Settings

A number of diagnostic m odel settings must be selected for CALMET to properly process representative diagnostic meteorologi cal data sets. Thes e are summarized in Appendix A, compared to the default CALMET settings, and discussed in the following:

1 Weights are assigned in CALMET to control the ‘blending’ of observations and MM5 predictions

.

### E-5 > CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, RMAX3), BIAS, ICALM parameters are not used in No-Observations mode;

> Gridded cloud data were inferred from the MM5 relative humidity fields (ICLOUD=3);

> Given that all state variables are MM5-derived (IPROG=14; ITPROG=2), surface layer winds were not extrapolat ed to the upper layers (IEXTRP = - 1);

> The IWAQM recommendation for disa bling the computation of kinematic effects in the wind field options and parameters was selected. This was selected in light of the very mode st elevated terrain in the CENRAP domain, relative to the mountainous re gions in the U.S. and Alps where the kinematic parameterizations were originally developed. Thus, the option for computing kinematic effects was disabled (IKINE = 0).

> The BIAS array was set to 0. in the CALMET control file because surface and upper air data were not used (NOOBS = 2);

> Because the MM5 wind fields supply CALMET with the initial guess fields to the diagnostic wind mo del (IWFCOD =1, IPROG = 14) and observational data are not reintroduced, the following variables were set to nominal values:

 The minimum distance for which extrapolation of surface winds should occur was set to -1 (RMIN2 = -1.).

 RMIN was left at the IWAQM recommendation of 0.1 km.

 RMAX1 and RMAX2 were each assigned a value of 30 km. RMAX3 was assigned a value of 50 km.

 R1 and R2 were each assigned the value of 1.0.

 ISURFT and IUPT were assigned placeholder values of 4 and 2, respectively.

> The radius of influence regarding terrain features is comparable to the resolution of the processed terrain data: 12 km.

> The radius of influence for temp erature interpolation is set to 36 km (TRADKM), a value considered appr opriate given the 6 km CALMET domain and 36/12 km MM5 domain.

> The beginning/ending land use cate gories for temperature interpolation over water are assigned category 55: (JWAT1 = JWAT2 = 55).

### E-6 > SIGMAP was set to 50 km, while the IWAQM recommendation is 100 km, but with no supporting documentat ion. Because precipitation rates are explicitly incorporated from the MM5 data, a lower radius of influence was deemed appropriate.

> Diagnostic options: IWAQM default values were used (see Appendix A);

> TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 2003 simulations ). Values of ~10-20 km were tested, and an appropriate value determined.

> Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature allows the te mperature field over large bodies of water such as the Gulf of Mexico and the Great lakes to be properly characterized by buoy observations.

> Mixing height averaging parame ter (MNMDAV) were determined sensitivity tests. The purpose of the testing is to optimize the variable to allow spatial variability in the mixing height field, but without excessive noise.

Obviously, there are some instances where mo re advanced and/or recently developed procedures for constructing the CALMET fiel ds have been used compared with the IWAQM (1998) guidance. For example, one ag ency expressed concer n about the choice to employ prognostic model-derived gridded cloud cover data in CALMET (ICLOUD = 3). While this is admittedly a ‘non-guideli ne’ option, in our view it represents the best science option currently available. In par ticular, the EPA CAIR and CAMR rulemaking modeling and the CAMx/CMAQ modeling being performed by the RPOs for regional haze all utilize the gridded moisture fields in the MM5 model as a basis for estimating cloud. Presumably, if the method is suitable fo r such advanced visib ility modeling, it is adequate for CALPUFF modeling. Of course , in the protocol negotiation, the States, source operators, and regulat ory agencies have an oppor tunity to re-examine the CALMET diagnostic model settings used in creating the CENRAP gridded fields and modify them if warranted.

In summary, the development of the regional CALMET meteorological fields from MM5 data was conducted in No-Observations (“No-Obs”) mode. CALMET’s boundary layer modules were used to comput e mixing heights, turbulence coefficients and other meteorological parameters required as input to CALPUFF.

5.1.3 MM5/CALMET Processing

Construction of the CALPUFF-ready mete orological fields en tails a two-step process. First, the MM5 prognostic model out put fields are extrac ted and processed for input to CALMET. This step entails runn ing various extraction software routines

### E-7 followed by the CALMM5 code. Then, CALM ET is exercised for the full three year period over each sub-regional CENRAP domain.

CALMM5. Previous applications of the prognostic Mesos cale Meteorological model version 5 (MM5) served as th e source of the gridded meteor ological fields for calendar years 2001, 2002, and 2003. The actual CAL MM5 configuration entailed modification of a few user-specified variables. Howeve r, two setting are of primary importance:

> All vertical layers from MM5 were extracted, providing CALMET configuration flexibility, and

> Vertical velocity, relative humidity, cloud/rain fields, an d ice/snow fields were extracted. (Graupel was extrac ted for 2001, the only year where the data were available in the MM5 datasets.)

CALMET. CALMET (v5.53a, lev 040716) was appl ied consistent with CENRAP’s recommendation that the 6 km be generate d using the ‘No-Obs’ option. The specific options used have been discussed abov e and are summarized in Appendix A.

5.1.4 Evaluation of the CALPUFF-Re ady Meteorological Data Sets

In typical applications th e adequacy of the CALMET fields is seldom evaluated using independent measurements. Often, only cursory visual examin ation of wind vector plots or time series is considered. This evaluation is important because the CALMET performance analysis gives dir ect insight into the adequacy of the model-processed fields on a subregional basis. It also serves as an independent quality assurance tool. Alpine’s MAPS evaluation software to perform an independent evaluation of the processed CALMET data bases. MAPS was used in conjunction with the NCAR DS472 TDL data sets to evaluate the surface winds and temperatures for 2001-2003 across all three domains. Since only a sma ll portion of the meteorological content of these data were ingested in the MM5 data as similation routines (see Johnson, 2003a), these data sets are essentially an independent, quantitative means for evaluating the adequacy of the meteorological fields input to CALPUFF.

CALMET Evaluation Methodology

Several statistical measures were calculated as part of the CALMET meteorological evaluation usi ng established procedures (e .g., Tesche et al., 1990; Emery et al., 2001). Additional plots and graphs are used to presen t these statistics on both hourly and daily time frames over the fu ll annual cycle. For this study, evaluation measures were calculated for wind, temper ature, and relative humidity because these parameters are the principal meteorological inputs to CA LPUFF. The full set of CALMET evaluation statistics and graphical displays generated with the AG-MAPS

### E-8 software (McNally and Tesche,1994) are contained on a DVD available from CENRAP.

The statistics used to evaluate the me teorological fields for 2001-2003 are generated in both absolute terms (e.g., wi nd speed error in m/s), and relative terms (percent error) as is commonly done for ai r quality assessments. Obviously, a very different significance is a ssociated with a given re lative error for different meteorological parameters. For example, a 10% error for wind speed measured at 10 m/s is an absolute error of 1 m/s, a minor error. Yet a 10% error for temperature at 300 K is an absolute error of 30 K, a ridi culously large error. On the other hand, pollutant concentration errors of 10% at 1 ppb or 10 ppm carry practically the same significance.

Three key meteorological metrics incl ude the bias, erro r, and index of agreement (IOA) for wind speed, temperatur e and relative humidity . These measures are defined as follows:

Bias (B) : Calculated as the mean difference in prediction-observation pairings with valid data within a given an alysis region and fo r a given time period (hourly or daily):

Error (E) : Calculated as the mean absolute difference in prediction-observation pairings with valid data within a given analysis region and for a given time period

(hourly or daily).

Note that the bias and gross error for wind s are calculated from the predicted-observed residuals in speed and directi on (not from vector components u and v ). The direction error for a given prediction-observation pa iring is limited to range from 0 to ± 180 °

. Index of Agreement (IOA) : calculated following the a pproach of Willmont (1981). This metric condenses all the differences between model estimates and observations within a given analysis region and for a gi ven time period (hourly and daily) into one statistical quantity. It is th e ratio of the total RMSE to the sum of two differences – between each prediction and the observed m ean, and each observation and the observed

mean: 

# ( ) ∑∑

= = 

### − = J

j I

i i j i j 

### OP IJ B

1 1 

### 1



# ∑∑

= = 

### − = J

j I

i i j i j 

### OP IJ E

1 1 

### 1



###    

 

   

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−+− ⋅

−= 

# ∑∑

= = J

j I

i o i jo i j 

### MOMP

RMSEIJ IOA

1 1 2 

### 1

### E-9 Viewed from another perspective, the inde x of agreement is a measure of the match between the departure of each prediction fr om the observed mean and the departure of each observation from the observed m ean. Thus, the correspondence between predicted and observed values across the domain at a gi ven time may be quantified in a single metric and displayed as a time series. The index of agreem ent has a theoretical range of 0 to 1, the latter sc ore suggesting perfect agreement.

CALMET Evaluation Results

Table 5-5 summarizes the statistical measures, averaged over the month, for temperature, wind speed, and relative humidity for all three years. The CALMET evaluation DVD contains a full compilation of the statistic al and graphical results. Figures 5-7 through 5-31 present a variety of graphical displays of processed and observed surface temperature, relative humidity, and wind across the three CENRAP subdomains for the three-year period 2001- 2003. Figures 5-28 through 5-31 provide convenient summaries of the bi as and error in the relative humidity, temperature, and wind speed fields across the conti nuous 36 month period by subdomain.

Thorough discussion of the performance fi ndings is beyond the scope of these guidelines. However, a few ke y findings of the evaluation ar e worth noting here. From Table 5-5, the wind speed index of agreement, a general measure of correlation between measured and observed winds, is systematically greater than a value of 0.8 for virtually every month. These values are typically bette r than those generally achieved in urbanand regional-scale model applications for oz one SIPs. For example, the statistical benchmark for IOA suggested by Emery et al., (2001) is IOA > 0.6. Thus, the wind speed agreement for all three domains and al l three years appears quite good relative to other MM5/RAMS model applic ations. From Figure 5-11, the wind speed root mean square error for the Central domain for 2002 is generally below 2.0 m/s, the performance goal for this parameter. From Figure 5-29 (as well as in Table 5-5), the temperature bias results for the 36 month are ge nerally quite close to the + 0.5 deg C performance goal. As shown in Figure 5-30 the temperature error re sults are slightly poor er than the 2 deg C performance goal for 2001 and 2003, but are below the 2.0 deg C threshold for 2002. Note that the benchmarks were developed not to provide a pass/fail standard to which all modeling results should be held, but rather to put the results in to an historical context.

In summary, we find that:

 Relative Humidity
 Bias over three-year peri od near zero all domains
 For some months over- and under-predi ction (up to 10% or more) is evident – no discernable trend
 Errors typically diminish from 2001 through 2003, and are generally < 12% after 1st quarter of 2001.

 Surface Temperatures

### E-10  Monthly averaged temperatures are sy stematically biased low (cooler) by 0.25 to 1.25 deg C.
 The errors in monthly averaged temperatures typically range between 1.8 and 2.6 deg C
 Average error over all mont hs is about 2.2 deg C.

 Surface Wind Speeds
 IOA typically between 0.8 0-0.9
 Seasonally variable
 Central subdomain gives best correlation

 Results from MM5/CALMET evaluation prov ide potentially useful information for diagnosing BART visibility modeling analyses

 MM5/CALMET fields exhibit good statisti cal agreement with observations, in part because observations figure prominently in the construction of the interpolated CALMET fields.

 MM5/CALMET fields for the three CENRAP subdomains are quite sufficient for use in CALPUFF modeling.

5.1.5 Meteorological Data Archive and Distribution

All models, scripts and CALMET data (e xcepting MM5 outputs) are available from CENRAP on appropriate external combination Firewire/USB drives.

5.2 Aerometric Monitoring Networks

Data from ambient monitoring networks for both gas-phase and aerosol species are available for use in CENRAP BART modeling analyses. Table 5-4 summarizes ambient monitoring networks. Data for 2002 have been compiled for all networks covering the CENRAP domain with the exce ption of the PAMS and PM Supersites. These data sets may be obtained from CE NRAP. Figures 5-5 and 5-6 display the locations of monitori ng sites in and near the CENRAP States.

### E-11 Table 5-1. CENRAP Lambert Conic Conf ormal Modeling Domain Specifications (40.97 degree projection origin; 33 and 45 degree matching parallels).

Domain Southwest Coordinate (km) Number of X grid cells Number of Y grid cells Horizontal Resolution

CALMET South -1008, -1620 306 246 6 km Central -1008, -864 388 234 6 km North -1008, 0 300 193 6 km

Table 5-2. Vertical Layer Structure in CALMET Fields. (Heights are in meters.)

L AYER
NUMBER 

### L AYER 

### H EIGHT 

### L AYER
NUMBER 

### L AYER 

### H EIGHT 

### 0 0. 6 640. 1 20. 7 1200. 2 40. 8 2000. 3 80. 9 3000. 4 160. 10 4000. 5 320.

Table 5-3. Meteorological Model File Sizes for CENRAP BART Modeling.

Domain Monthly A nnual 3 Years Domain Grid 3 years North 4.6 55.2 165.6 2001 12 km 1370 Central 6.6 79.2 237.6 2002 36 km 430 South 6.0 72.0 216.0 2003 36 km 430 total 17.2 206.4 619.2 total 2230 CALMET 6 km File Sizes, (Gbytes) MM5 File Sizes, (Gbytes)

### E-12 Table 5-4. Statistical Evaluation of th e CALMET Meteorological Fields for 2001- 2003.

Jan Feb Ma r Ap r May Jun Jul A ug Sep Oct Nov Dec Mean RH Bias (%) North 4.54 3.19 0.17 -14.55 -12.09 -4.35 -0.62 1.17 -2.07 -7.98 -6.62 -4.22 -3.62 Central -2.60 -7.28 -11.38 -10.69 -8.62 -2.90 0.66 1.07 -1.44 -5.46 -6.16 -7.78 -5.21 South -10.23 -11.53 -13.78 -4.24 -2.08 0.99 4.12 3.16 -0.12 -2.12 -3.44 -9.76 -4.09 RH Error (%) North 10.06 10.31 14.03 18.77 16.28 12.39 11.82 11.76 13.26 15.54 13.53 12.89 13.39 Central 13.32 15.86 17.45 17.05 14.50 11.67 11.52 11.32 12.26 15.52 14.79 14.95 14.18 South 16.22 18.37 18.17 13.26 12.15 11.51 12.09 12.40 11.82 14.85 14.73 16.19 14.31 Temp Bias ( 0 C) North -1.63 -1.23 -1.23 -0.24 0.08 -0.29 -0.23 -0.54 -0.55 -0.09 -0.40 -1.27 -0.64 Central -0.99 -0.65 -0.54 -0.16 0.13 -0.23 -0.43 -0.54 -0.36 -0.34 -0.30 -0.74 -0.43 South -0.47 -0.42 0.03 -0.31 -0.33 -0.63 -0.99 -0.85 -0.52 -0.36 -0.19 -0.21 -0.44 Temp Error ( 0 C) North 3.10 2.88 2.54 2.49 2.44 2.43 2.42 2.49 2.58 2.48 2.89 2.55 2.61 Central 2.38 2.25 1.99 2.18 1.99 2.01 2.07 2.11 2.21 2.52 2.61 2.42 2.23 South 2.31 2.28 1.92 2.13 2.01 2.17 2.19 2.21 2.19 2.70 2.49 2.50 2.26 Wind Speed IO A North 0.79 0.83 0.83 0.87 0.86 0.85 0.81 0.84 0.84 0.82 0.81 0.79 0.83 Central 0.85 0.87 0.88 0.88 0.89 0.86 0.84 0.86 0.87 0.86 0.85 0.84 0.86 South 0.81 0.80 0.85 0.79 0.83 0.83 0.78 0.80 0.82 0.82 0.80 0.82 0.81

Jan Feb Ma r Ap r May Jun Jul A ug Sep Oct Nov Dec Mean RH Bias (%) North 8.33 9.52 6.63 0.95 -2.42 1.25 2.43 1.60 0.57 0.47 4.47 7.73 3.46 Central 7.43 5.13 4.60 1.65 -1.02 1.52 2.50 1.88 -0.27 -1.40 -0.01 4.35 2.20 South 3.08 -1.19 2.53 2.32 1.26 1.98 2.51 2.62 -0.80 -2.42 -4.45 -1.03 0.53 RH Error (%) North 11.85 13.18 11.61 11.13 11.90 10.04 9.54 9.08 10.26 10.26 11.55 11.61 11.00 Central 12.21 12.43 11.26 10.58 10.72 9.89 9.55 9.54 10.22 10.25 11.42 11.26 10.78 South 11.24 11.76 10.34 8.95 9.30 9.49 9.46 9.61 9.68 9.33 11.63 10.95 10.14 Temp Bias ( 0 C) North -0.70 -0.82 -0.96 -0.52 -0.25 -0.36 -0.53 -0.49 -0.44 -0.67 -0.76 -0.69 -0.60 Central -0.57 -0.65 -0.79 -0.62 -0.41 -0.68 -0.81 -0.74 -0.49 -0.54 -0.55 -0.52 -0.61 South -0.23 -0.13 -0.52 -0.61 -0.61 -0.94 -0.94 -1.07 -0.65 -0.47 0.04 -0.13 -0.52 Temp Error ( 0 C) North 2.15 2.07 2.04 1.89 1.86 1.83 1.86 1.80 1.95 1.78 1.99 2.15 1.95 Central 2.12 2.05 2.14 1.95 1.91 1.93 1.93 1.92 2.02 1.77 2.00 2.00 1.98 South 2.18 2.05 2.17 1.83 1.89 1.91 1.88 2.00 1.92 1.68 2.06 1.93 1.96 Wind Speed IO A North 0.82 0.84 0.86 0.88 0.86 0.85 0.85 0.83 0.85 0.85 0.81 0.78 0.84 Central 0.87 0.88 0.90 0.90 0.88 0.87 0.84 0.84 0.87 0.88 0.85 0.85 0.87 South 0.86 0.86 0.85 0.85 0.84 0.82 0.79 0.80 0.83 0.83 0.83 0.82 0.83

Jan Feb Ma r Ap r May Jun Jul A ug Sep Oct Nov Dec Mean RH Bias (%) North 10.15 7.40 6.01 0.93 -3.76 -0.38 1.38 2.04 -1.66 -1.99 2.96 7.68 2.56 Central 6.94 4.76 4.15 0.42 -2.18 0.17 2.08 2.13 -2.05 -4.13 0.00 5.47 1.48 South 0.00 0.00 0.47 -1.10 -0.37 0.54 1.77 2.89 -3.31 -6.01 -3.66 -0.33 -0.76 RH Error (%) North 13.30 11.21 12.32 11.70 11.65 10.03 9.70 9.57 11.13 12.68 11.53 11.85 11.39 Central 12.77 10.95 11.61 11.18 10.33 9.91 9.49 9.50 10.70 12.69 12.10 12.43 11.14 South 11.18 10.00 9.85 10.17 9.20 9.54 8.90 9.91 10.21 12.12 12.15 12.39 10.47 Temp Bias ( 0 C) North -1.24 -0.99 -0.63 -0.29 -0.11 -0.10 -0.22 -0.49 -0.34 0.29 -0.85 -1.34 -0.53 Central -0.84 -0.80 -0.64 -0.47 -0.27 -0.36 -0.60 -0.66 -0.32 0.30 -0.54 -0.89 -0.51 South -0.17 -0.27 -0.36 -0.43 -0.46 -0.62 -0.91 -0.98 -0.28 0.53 0.00 -0.03 -0.33 Temp Error ( 0 C) North 2.31 2.15 2.14 2.02 1.81 1.77 1.91 1.98 2.25 2.57 2.30 2.67 2.16 Central 2.14 2.03 2.15 2.13 1.80 1.81 1.96 1.99 2.16 2.54 2.31 2.45 2.12 South 2.10 1.90 2.00 2.08 1.84 1.81 1.88 2.06 1.94 2.40 2.28 2.48 2.06 Wind Speed IO A North 0.79 0.81 0.83 0.86 0.87 0.85 0.86 0.87 0.84 0.82 0.80 0.82 0.83 Central 0.85 0.88 0.87 0.89 0.90 0.87 0.87 0.86 0.87 0.87 0.86 0.86 0.87 South 0.83 0.83 0.85 0.83 0.85 0.81 0.83 0.82 0.85 0.84 0.82 0.82 0.83 CALMET Model Evaluation Statistics for 2001.

CALMET Model Evaluation Statistics for 2002.

CALMET Model Evaluation Statistics for 2003.

### E-13 Table 5-5. Overview of Ambient Data Moni toring Networks Covering the CENRAP Domain.

Monitoring Network Chemical Species Measured Sampling Period Data Availability/Source

The Interagency Monitoring of Protected Visual Environments (IMPROVE) Speciated PM25 and PM10 (see species mappings) 1 in 3 days; 24 hr
average http://vista.cira.colostate.e du/improve/Data/IMPRO VE/improve_data.htm

Clean Air Status and Trends Network (CASTNET) Speciated PM25, Ozone (see species mappings) Approximately 1- week average http://www.epa.gov/castne t/data.html

National Atmospheric Deposition Program (NADP) Wet deposition (hydrogen (acidity as pH), sulfate, nitrate, ammonium, chloride, and base cations (such as calcium, magnesium, potassium and sodium)), Mercury 1-week average http://nadp.sws.uiuc.edu/

Air Quality System (AQS) Aka Aerometric Information Retrieval System (AIRS) CO, NO2, O3, SO2, PM25, PM10, Pb Typically hourly
average http://www.epa.gov/air/dat a/

Speciation Trends Network (STN) Speciated PM 24-hour average http://www.epa.gov/ttn/am tic/amticpm.html

Southeastern Aerosol Research and Characterization (SEARCH) (Southeastern US only) 24-hr PM25 (FRM Mass, OC, BC, SO4, NO3, NH4, Elem.); 24-hr PM coarse (SO4, NO3, NH4, elements); Hourly PM2.5 (Mass, SO4, NO3, NH4, EC, TC); Hourly gases (O3, NO, NO2, NOy, HNO3, SO2, CO) Hourly or 24-hour average, depending on parameter. Electric Power Research Institute (EPRI), Southern Company, and other companies.http://www.atmosphericresearch.com

EPA Particulate Matter Supersites (Includes St. Louis in the CENRAP region) Speciated PM25 http://www.epa.gov/ttn/am tic/supersites.html

Photochemical Assessment Monitoring Stations (PAMS) Varies for each of 4 station types. http://www.epa.gov/ttn/am tic/pamsmain.html

National Park Service Gaseous Pollutant Monitoring Network Acid deposition (Dry; SO4, NO3, HNO3, NH4, SO2), O3, meteorological data Hourly http://www2.nature.nps.go v/ard/gas/netdata1.htm

### E-14 Figure 5-1. CENRAP North, Central, a nd South 6 km Meteorological Domains.

### E-15 Figure 5-2. CENRAP South Domain.

### E-16 Figure 5-3. CENRAP Central Domain.

### E-17 Figure 5-4. CENRAP North Domain.

### E-18 Figure 5-5. Locations of IMPROVE, CASTNe t, SEARCH, STN and NADP Monitoring Sites in and Near the CENRAP States.

### E-19 Figure 5-6. Locations of AQS Monitoring Si tes in and Near the CENRAP States.

### E-20 Figure 5-7. Spatial Mean Relative Humidity (%) over the Central Domain: July 2002.

Figure 5-8. Spatial Mean Surface Temperature (deg C) over the Central Domain: July 2002.

### E-21 Figure 5-9. Wind Speed Index of Agreement over the Central Domain: July 2002.

Figure 5-10. Standard Deviation in Wind Sp eed (m/s) over the Central Domain: July 2002.

### E-22 Figure 5-11. Root Mean Square Error in Wi nd Speed (m/s) over the Central Domain: July 2002

.

Figure 5-12. Scalar Mean Wind Speed (m /s) over the Central Domain: July 2002.

### E-23 Figure 5-13. Vector Mean Wind Speed (m/s) over the Central Domain: July 2002.

Figure 5-14. Normalized Bias in Relative Hu midity (%) over the Central Domain: July 2002.

### E-24 Figure 5-15. Normalized Error in Relative Hu midity (%) over the Central Domain: July 2002.

Figure 5-16. Relative Humidity (%) at Kenosha, WI: July 2002.

### E-25 Figure 5-17. Relative Humidity (%) at Topeka, KS: July 2002.

Figure 5-18. Normalized Bias in Surface Temperature (%) over the Central Domain: July 2002.

### E-26 Figure 5-19. Normalized Error in Surface Temperature over the Central Domain: July 2002.

Figure 5-20. Surface Temperature (deg C) at Kenosha, WI: July 2002.

### E-27 Figure 5-21. Surface Temperature at Topeka, KS: July 2002.

### E-28 Figure 5-22. MM5/CALMET Relative Humidity Bias (%) by Month for Three BART Modeling Years (2001, 2003, and 2003).

### E-29 Figure 5-23. MM5/CALMET Relative Humidity Error (%) by Month for Three BART Modeling Years (2001, 2003, and 2003).

### E-30 Figure 5-24. MM5/CALMET Temperature Bias (deg C) by Month for Three BART Modeling Years (2001, 2003, and 2003).

### E-31 Figure 5-25. MM5/CALMET Temperature Erro r (deg C) by Month for Three BART Modeling Years (2001, 2003, and 2003).

### E-32 Figure 5-26. MM5/CALMET Wind Speed Inde x of Agreement by Month for Three BART Modeling Years (2001, 2003, and 2003).

### E-33 Figure 5-27. MM5/CALMET Relative Humidity Bias (%) over Three Years in All CENRAP Domains.

### E-34 Figure 5-28. MM5/CALMET Relative Humidity Error (%) over Three Years in All CENRAP Domains.

### E-35 Figure 5-29. MM5/CALMET Surface Temperature Bias (deg C) over Three Years in All CENRAP Domains.

### E-36 Figure 5-30. MM5/CALMET Surface Temperature Error (deg C) over Three Years in All CENRAP Domains.

### E-37 Figure 5-31. MM5/CALMET Wind Speed Index of Agreement over Three Years in All CENRAP Domains.

### F-1 

## Appendix F. CALMET Input Control Parameters

Table F-1 Input Groups in the CALMET Control File. Input Group Description Applicable 0 Input and output file names Yes 

### 1 General run control parameters Yes 

### 2 Map Projection and Grid Control Parameters Yes 

### 3 Output Options Yes 

### 4 Meteorological Data Options Yes 

### 5 Wind field Options and Parameters Yes 

### 6 Mixing Height, Temperature and Precipitation Parameters Yes 

### 7 Surface Meteorological Station Parameters Yes 

### 8 Upper Air Meteorological Station Parameters Yes 

### 9 Precipitation Station Parameters Yes



### Table F-2 CALMET Model Input Group 0: Input and Output File Names

Parameter Default Value Comments Input GEO.DAT GEO.DAT Input SURF.DAT SURF.DAT Input CLOUD.DAT CLOUD.DAT Input PRECIP.DAT PRECIP.DAT Input MM4.DAT MM4.DAT Input WT.DAT WT.DAT Output CALMET.LST CALMET.LST Output CALMET.DAT CALMET.DAT Output PACOUT.DAT PACOUT.DAT NUSTA -- 0 Number of upper air stations NOWSTA -- 0 Number of over water stations Input UP1.DAT UP1.DAT Input UP2.DAT UP2.DAT Input UP3.DAT UP3.DAT Input SEA1.DAT SEA1.DAT Input DIAG.DAT DIAG.DAT Input PROG.DAT PROG.DAT Output TEST.PRT TEST.PRT Output TEST.OUT TEST.OUT Output TEST.KIN TEST.KIN Output TEST.FRD TEST.FRD Output TEST.SLP TEST.SLP

### F-2 Table F-3 CALMET Model Input Group 1: General Run Control Parameters

Parameter Default Value Comments IBYR - 2001 Starting year IBMO - 1 Starting month IBDY - 1 Starting day IBHR - 1 Starting hour IBTZ - 6 Base time zone IRLG - 8760 Length of run IRTYPE 1 1 Run type (must = 1 to run CALPUFF) LCALGRD T F Compute CALGRID data fields ITEST 2 2 Stop run after SETUP to do input QA



### Table F-4 CALMET Model Input Group 2: Map Projection and Grid Control Parameters

Parameter Default Value Comments PMAP UTM LCC Map Projection RLATO -- 40N Latitude (dec. degrees) of projection origin RLONO -- 97W Longitude (dec. degrees) of projection origin XLAT1 -- 33N Matching parallel(s) of latitude for projection XLAT2 -- 45N Matching parallel(s) of latitude for projection DATUM WGS-G WGS-G NX -- 300 Number of X grid cells in meteorological grid NY -- 192 Number of Y grid cells in meteorological grid DGRIDKM -- 6.0 Grid spacing, km XORIGKM -- -1008. Ref. Coordinate of SW corner of grid cell (1,1) YORIGKM -- 0.0 Ref. Coordinate of SW corner of grid cell (1,1) NZ -- 10 No. of vertical layers ZFACE -- 0, 20 40, 80, 160, 320, 640, 1200, 2000, 3000, 4000 Cell face heights in arbitrary vertical grid, m

### F-3 Table F-5 CALMET Model Input Group 3: Output Options

Parameter Default Value Comments LSAVE T T Disk output option IFORMO 1 1 Type of unformatted output file LPRINT F F Print met fields IPRINF 1 1 Print intervals IUVOUT(NZ) NZ*0 NZ*0 Specify layers of u,v wind components to print IWOUT(NZ) NZ*0 NZ*0 Specify layers of w wind component to print ITOUT(NZ) NZ*0 NZ*0 Specify levels of 3-D temperature field to print LDB F F Print input met data and variables NN1 1 1 First time step for debug data to be printed NN2 1 1 Last time step for debug data to be printed IOUTD 0 0 Control variable for writing test/debug wind fields NZPRN2 1 0 Number of levels starting at surface to print IPR0 0 0 Print interpolated wind components IPR1 0 0 Print terrain adjusted surface wind components IPR2 0 0 Print initial divergence fields IPR3 0 0 Print final wind speed and direction IPR4 0 0 Print final divergence fields IPR5 0 0 Print winds after kinematic effects IPR6 0 0 Print winds after Froude number adjustment IPR7 0 0 Print winds after slope flows are added IPR8 0 0 Print final wind field components



### Table F-6 CALMET Model Input Group 4: Meteorological Data Options

Parameter Default Value Comments NOOBS 0 2 2 = No surface, over water, or upper air observations; use MM5 for surface, over water, and upper air data NSSTA -- 0 Number of meteorological surface stations NPSTA -- 0 Number of precipitation stations ICLOUD -- 3 Gridded cloud fields IFORMS 2 2 Formatted surface meteorological data file IFORMP 2 2 Formatted surface precipitation data file IFORMC 2 2 Formatted cloud data file

### F-4 Table F-7 CALMET Model Input Group 5: Wind field Options and Parameters

Parameter Default CENRAP Comments
IWFCOD 1 1 Model selection variable IFRADJ 1 1 Compute Froude number adjustment effects? IKINE 0 0 Compute kinematic effects? IOBR 0 0 Use O’Brien (1970) vertical velocity adjustment? ISLSOPE 1 1 Compute slope flow effects? IEXTRP -4 -1 Extrapolate surface wind obs to upper levels?

ICALM 0 0 Extrapolate surface winds even if calm?

BIAS NZ*0 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 Layer-dependent biases weighting aloft
measurements RMIN2 4. -1.0 Minimum vertical extrapolation distance IPROG 0 14 14 = Yes, use winds from MM5.DAT file as initial guess field [IWFCOD = 1 ISTEPPG 1 1 MM5 output time step LVARY F T Use varying radius of influence RMAX1

-- 30. Maximum radius of influence over land in sfc layer RMAX2 -- 30. Maximum radius of influence over land aloft RMAX3 -- 50. Maximum radius of influence over water RMIN 0.1 0.1 Minimum radius of influence used anywhere TERRAD -- 12. Terrain features radius of influence R1 -- 1. Weighting of first guess surface field R2 -- 1. Weighting of first guess aloft field RPROG -- 0. MM5 windfield weighting parameter DIVLIM 5.E-6 5.E-6 Minimum divergence criterion NITER 50 50 Number of divergence minimization iterations NSMMTH 2, 4, 4, 4, 4, 4, 4 2, 4, 4, 4, 4, 4, 4 Number of passes through smoothing filter in each layer of CALMET NITR2 99. 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 Maximum number of stations used in each layer for the interpolation of data to a grid point CRITFN 1.0 1.0 Critical Froude number ALPHA 0.1 0.1 Kinematic effects parameter FEXTR2 NZ*0.0 NZ*0.0 Scaling factor for extrapolating sfc winds aloft NBAR 0 0 Number of terrain barriers IDIOTP1 0 0 Surface temperature computation switch ISURFT -- 4 Number of sfc met stations to use for temp calcs IDIOPT2 0 0 Domain-avera ged lapse rate switch IUPT 0 2 Upper air stations to use for lapse rate calculation ZUPT 200. 200. Depth through which lapse rate is calculated IDIOPT3 0 0 Domain-averaged wind component switch

### F-5 IUPWND -1 -1 Number of aloft stations to use for wind calc ZUPWND 1., 1000. 1., 1000. Bottom and top of layer through which the domain-scale winds are computed IDIOPT4 0 0 Observed surface wind component switch IDIOPT5 0 0 Observed aloft wind component switch LLBREZE F F Use Lake Breeze Module NBOX 0 0 Number of lake breeze regions NLB -- 0 Number of stations in the region METBXID(NLB) -- 0 Station ID’s in the region



### Table F-8 CALMET Model Input Group 6: Mixing Height, Temperature and Precipitation

Parameter Default Value Comments CONSTB 1.41 1.41 Neutral stability mixing height coefficient CONSTE 0.15 0.15 Convective stability mixing height coefficient CONSTN 2400. 2400. Stable stability maxing height coefficient CONSTW 0.16 0.16 Over water mixing height coefficient FCORIOL 1.E-4 1.E-4 Absolute value of Coriolis parameter IAVEZI 1 1 Conduct spatial averaging? Yes = 1 MNMDAV 1 10 Maximum search radius in averaging process HAFANG 30. 30. Half-angle of upwind looking cone for averaging ILEVZI 1 1 Layers of wind use in upwind averaging DPTMIN 0.001 0.001 Minimum potential temperature lapse rate in the stable layer above the current convective mixing ht DZZI 200. 200. Depth of layer a bove current conv. mixing height through which lapse rate is computed ZIMIN 50. 50. Minimum overland mixing height ZIMAX 3000. 3000. Maximum overland mixing height ZIMINW 50. 50. Minimum over water mixing height ZIMAXW 3000. 3000. Maximum over water mixing height ITPROG 0 2 3D temperature from observations or from MM5? IRAD 1 1 Type of interpolation; 1 = 1/R TRADKM 500. 36. Temperature interpolation radius of influence NUMTS 5 5 Max number of stations for temp interpolation IAVET 1 1 Spatially average temperatures? 1 = yes TGDEFB -.0098 -.0098 Temp gradient below mixing height over water TGDEFA -.0045 -.0045 Temp gradient above mixing height over water JWAT1 -- 55 Beginning land use categories over water JWAT2 -- 55 Ending land use categories for water NFLAGP 2 2 Precipitation interpolation flag; 2 = 1/R-squared
SIGMAP 100. 50. Radius of influence for precipitation interpolation CUTP 0.01 0.01 Minimum precipitation rate cutoff (mm/hr)

### G-1 

## Appendix G. CALPUFF Input Control Parameters

Table G-1 Input Groups in the CALPUFF Control File Input Group Description Applicable 0 Input and output file names Yes 1 General run control parameters Yes 2 Technical options Yes 3 Species list Yes 4 Grid control parameters Yes 5 Output options Yes 6 Sub grid scale complex terrain inputs Yes 7 Dry deposition parameters for gases Yes 8 Dry deposition parameters for particles Yes 9 Miscellaneous dry deposition for parameters Yes 10 Wet deposition parameters Yes 11 Chemistry parameters Yes 12 Diffusion and computational parameters Yes 13 Point source parameters Yes 14 Area source parameters No 15 Line source parameters No 16 Volume source parameters No 17 Discrete receptor information Yes



### Table G-2 CALPUFF Model Input Group 0: Input and Output File Names Parameter Default Value Comments METDAT CALMET.DAT Not used Input file name PUFLST CALPUFF.LST Varies with facility CALPUFF output file name CONDAT CONC.DAT Varies with facility Concentration output file

name DFDAT DFLX.DAT Varies with facility Dry flux output file name WFDAT WFLX.DAT Varies with facility Wet flux output file name VISDAT VISB.DAT Varies with facility Visibility output file name OZDAT OZONE.DAT Varies with year Ozone input file name LCFILES - T File names converted to lower

case NMETDAT 1 12 Number of CALMET.DAT files for run CALMET.DAT - METDAT=/location of CALMET.DAT files 12 entries one for each month

### G-2 Table G-3 CALPUFF Model Input Group 1: General Run Control Parameters Parameter Default Value Comments METRUN 0 0 All model periods in met file(s) will be run IBYR

- See note 1 below Starting year

IBMO - 1 Starting month IBDY - 1 Starting day IBHR - 1 Starting hour XBTZ - 0 Time zone for met files (0 = GMT) IRLG

- See note 2 below Length of run

NSPEC 5 10 Number of MESOPUFF II chemical species NSE 3 See note 3 below Number of chemical species to be emitted

ITEST 2 2 Program is executed after SETUP phase MRESTART 0 0 Do not read or write a restart file during run NRESPD 0 0 File written only at last period METFM 1 1 CALMET binary file (CALMET.MET) AVET 60 60 Averaging time in minutes PGTIME 60 60 PG Averaging time in minutes 

### Note 1: Enter the year being modeled (i.e. 2001, 2002, or 2003) Note 2: Enter 8760 for the years 2001 and 2002 but enter 8748 for the year 2003 Note 3: Enter 6 for the no-obs run and 7 for the refined run

Table G-4 CALPUFF Model Inpu t Group 2: Technical Options Parameter Default Value Comments MGAUSS 1 1 Gaussian distribution used in near field MCTADJ 3 3 Partial plume path terrain adjustment MCTSG 0 0 Sub-grid-scale complex terrain not modeled MSLUG 0 0 Near-field puffs not modeled as elongated MTRANS 1 1 Transitional plume rise modeled MTIP 1 1 Stack tip downwash used MSHEAR 0 0 (0, 1) Vertical wind shear (not modeled, modeled) MSPLIT 0 0 Puffs are not split MCHEM 1 1 MESOPUFF II chemical parameterization scheme MAQCHEM 0 0 Aqueous phase transformation not modeled MWET 1 1 Wet removal modeled MDRY 1 1 Dry deposition modeled MDISP 3 2 AERMOD dispersion coefficients MTURBVW 3 3 Use both σ v and σ w from PROFILE.DAT to compute σ y and σ z (n/a) MDISP2 3 2 AERMOD dispersion coefficients
MROUGH 0 0 PG σ y and σ z not adjusted for roughness MPARTL 1 1 No partial plume penetration of elevated inversion

### G-3 MTINV 0 0 Strength of temperature inversion computed from default gradients MPDF 0 0 PDF not used for dispersion under convective conditions MSGTIBL 0 0 Sub-grid TIBL module not used for shoreline MBCON 0 0 Boundary concentration conditions not modeled MFOG 0 0 Do not configure for FOG model output MREG 1 1 Technical options must conform to USEPA Long Range Transport (LRT) guidance



### Table G-5 CALPUFF Model Input Group 3: Species List-Chemistry Options

CSPEC Modeled 1 

### Emitted 2 

### Dry Deposition 3 

### Output Group Number

SO 2 1 1 1 0 SO 4 -2 1 0 2 0 NOx 1 1 1 0 HNO 3 1 0 1 0 NO 3 - 1 0 2 0 NH 3 0 0 1 0 PM 10 4 1 1 2 0 PMF 4 1 1 2 0 EC 5 1 1 2 0 SOA 5 1 1 2 0 

### Note 1: 0 = No, 1 = Yes Note 2: 0 = No, 1 = Yes (Depends on if species is being modeled or not) Note 3: 0 = none, 1 = computed gas, 2 = computed particle, 3 = user specified Note 4: Only PMF will be modeled and emitted in the no-obs run; however, both PM 10 

### and PMF will be modeled and emitted in the refined analysis Note 5: EC and SOA will not be modeled nor will it be emitted during the no-obs and the refined runs

### G-4 Table G-6 CALPUFF Model Input Group 4: Map Projection and Grid Control Parameters Parameter Default Value Comments PMAP UTM LCC Map Projection FEAST 0.0 0.000 False Easting FNORTH 0.0 0.000 False Northing RLATO None 40N Latitude and L ongitude of projection origin RLONO None 97W Latitude and Long itude of projection origin XLAT1 None 33N Matching parallel of latitude for map projection XLAT2 None 45N Matching parallel of latitude for map projection DATUM WGS-84 WGS-G Datum region for output coordinates NX None 366 Number of X grid cells in meteorological grid NY None 234 Number of Y grid cells in meteorological grid NZ None 10 Number of vertical layers in meteorological grid DGRIDKM None 6 Grid spacing (km) ZFACE None 0, 20 40, 80, 160, 320, 640, 1200, 2000, 3000, 4000 Cell face heights in meteorological grid (m)

XORIGKM None -1008 Reference X coordinate for SW corner of grid cell (1,1) of meteorological grid (km) YORIGKM None -864 Reference Y coordinate for SW corner of grid cell (1,1) of meteorological grid (km) IBCOMP None 1 X index of lower left corner of the computational grid JBCOMP None 1 Y index of lower left corner of the computational grids IECOMP None 366 X index of the upper right corner of the computational grid JECOMP None 234 Y index of the upper right corner of the computational grid LSAMP T F Sampling grid is not used IBSAMP None 1 X index of lower left corner of the sampling grid JBSAMP None 1 Y index of lower left corner of the sampling grid IESAMP None 366 X index of upper right corner of the sampling grid JESAMP None 234 Y index of upper right corner of the sampling grid MESHDN 1 1 Nesting factor of the sampling grid

### G-5 Table G-7 CALPUFF Model Input Group 5: Output Options Parameter Default Value Comments
ICON 1 1 Output file CONC.DAT containing concentrations is created IDRY 1 1 Output file DFLX.DAT containing dry fluxes is created IWET 1 1 Output file WFLX.DAT containing wet fluxes is created IVIS 1 1 Output file containing relati ve humidity data is created LCOMPRS T T Perform data compression in output file IMFLX 0 0 Do not calculate mass fluxes across specific boundaries IMBAL 0 0 Mass balances for each species not reported hourly ICPRT 0 1 Print concentration fields to the output list file IDPRT 0 0 Do not print dry flux fields to the output list file IWPRT 0 0 Do not print wet flux fields to the output list file ICFRQ 1 1 Concentration fields are printed to output list file every hour (hr) IDFRQ 1 1 Dry flux fields are printed to output list file every 1 hour IWFRQ 1 1 Wet flux fields are printed to output list file every 1 hour IPRTU 1 3 Units for line printer output are in g/m3 for concentration and g/m 2 /s for deposition IMESG 2 2 Messages tracking the progress of run written to

screen LDEBUG F F Logical value for debug output IPFDEB 1 1 First puff to track NPFDEB 1 1 Number of puffs to track NN1 1 1 Meteorological period to start output NN2 10 10 Meteorological period to end output

### G-6 Table G-8 CALPUFF Model Input Group 6: Sub-Grid Scale Complex Terrain Input Parameter Default Value Comments NHILL 0 0 Number of terrain features NCTREC 0 0 Number of special complex terrain receptors MHILL - 2 Input terrain and receptor data for CTSG hills input in CTDM format XHILL2M 1 1 Conversion factor for changing horizontal dimensions to meters ZHILL2M 1 1 Conversion factor for changing vertical dimensions
to meters XCTDMKM None 0.0 E+00 X origin of CTDM system relative to CALPUFF coordinate system (km) YCTDMKM None 0.0 E+00 Y origin of CTDM system relative to CALPUFF coordinate system (km)



### Table G-9 CALPUFF Model Input Group 7: Dry Deposition Parameters for Gases Species Default Value Comments 0.1509 0.1509 Diffusivity 1000. 1000. Alpha star 8.0 8.0 Reactivity
0.0 0.0 Mesophyll resistance SO 2

0.04 0.04 Henry’s Law coefficient 0.1656 0.1656 Diffusivity
1.0 1.0 Alpha star 8.0 8.0 Reactivity
5.0 5.0 Mesophyll resistance NO x

3.5 3.5 Henry’s Law coefficient 0.1628 0.1628 Diffusivity
1.0 1.0 Alpha star 18.0 18.0 Reactivity
0.0 0.0 Mesophyll resistance 8.0E-8 8.0E-8 Henry’s Law coefficient HNO 3

0.000359 0.000359 Henry’s Law coefficient

### G-7 Table G-10 CALPUFF Model I nput Group 8: Dry Deposition Parameters for Particles Species Default Value Comments
SO 4 -2 0.48 0.48 Geometric mass mean diameter of SO 4 -2 (μm) NO 3 - 2.0 0.48 Geometric mass mean diameter of NO 3 - (μm) PM 10 2.0 6.0 Geometric mass mean diameter of PMC (μm) PMF 2.0 0.48 Geometric mass mean diameter of PMF (μm) EC 2.0 0.48 Geometric mass mean diameter of EC (μm) SOA 0.48 0.48 Geometric mass mean diameter of SOA (μm) 

### (Geometric Standard Deviation for all species assumed to be 2.0 μm).

Table G-11 CALPUFF Model I nput Group 9: Miscellaneous Dry Deposition Parameters Parameter Default Value Comments RCUTR 30 30 Reference cuticle resistance (s/cm) RGR 10 10 Reference ground resistance (s/cm) REACTR 8 8 Reference pollutant reactivity NINT 9 9 Number of particle size intervals for effective particle deposition velocity IVEG 1 1 Vegetation in non-irrigated areas is active and unstressed



### Table G-12 CALPUFF Mode l Input Group 10: Wet Deposition Parameters Species Default Value Comments 3.21E-05 3.21E-05 Scavenging coefficient for liquid precipitation (s -1 ) SO 2 0.0 0.0 Scavenging coefficient for frozen precipitation (s -1 ) 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s -1 ) SO 4 -2 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s -1 ) 6.0E-05 6.0E-05 Scavenging coefficient for liquid precipitation (s -1 ) HNO 3 0.0 0.0 Scavenging coefficient for frozen precipitation (s -1 ) 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s -1 ) NO 3 - 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s -1 ) 8.0E-05 8.0E-05 Scavenging coefficient for liquid precipitation (s -1 ) NH 3 0.0 0.0 Scavenging coefficient for frozen precipitation (s -1 ) 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s -1 ) PM 10 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s -1 ) 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s -1 ) PMF 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s -1 ) 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s -1 ) EC 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s -1 ) 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s -1 ) OC 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s -1 )

### G-8 Table G-13 CALPUFF Model Input Group 11: Chemistry Parameters Parameter Default Value Comments
MOZ 1 1 Read ozone background concentrations from ozone.dat file (measured values). BCKO 3 12*80 12*40 Background ozone concentration (ppb) BCKNH 3 12*10 12*3 Background ammonia concentration (ppb) RNITE1 0.2 0.2 Nighttime NO 2 loss rate in percent/hour RNITE2 2 2 Nighttime NO X loss rate in percent/hour RNITE3 2 2 Nighttime HNO 3 loss rate in percent/hour MH 2 0 2 1 1 Background H 2 O 2 concentrations (Aqueous phase transformations not modeled )

BCKH 2 0 2 1 1 Background monthly H 2 O 2 concentrations (Aqueous phase transformations not modeled )

### G-9 Table G-14 CALPUFF Model Input Group 12: Dispersion/Computational Parameters Parameter Default Value Comments SYDEP 550 550 Horizontal size of a puff in meters beyond which the time dependant dispersion equation of Heffter (1965) is used MHFTSZ 0 0 Do not use Heffter formulas for sigma z JSUP 5 5 Stability class used to determine dispersion rates for puffs above boundary layer CONK1 0.01 0.01 Vertical dispersion constant for stable conditions CONK2 0.1 0.1 Vertical disp ersion constant for neutral/stable conditions TBD 0.5 0.5 Use ISC transition point for determining the transition point between the Schulman-Scire to Huber-Snyder Building Downwash scheme IURB1 10 10 Lower range of land use categories for which urban dispersion is assumed IURB2 19 19 Upper range of land use categories for which urban dispersion is assumed ILANDUIN 20 * Land use category for modeling domain XLAIIN 3.0 * Leaf area index for modeling domain ZOIN -0.25 * Roughness length in meters for modeling domain ELEVIN 0.0 * Elevation above sea level XLATIN -999 - North latitude of station in degrees XLONIN -999 - South latitude of station in degrees ANEMHT 10 10 Anemometer height in meters ISIGMAV 1 1 Sigma-v is read for lateral turbulence data IMIXCTDM 0 0 Predicted mixing heights are used XMXLEN 1 1 Maximum length of emitted slug in meteorological grid units XSAMLEN 1 10 Maximum travel distance of slug or puff in meteorological grid units during one sampling unit MXNEW 99 60 Maximum number of puffs or slugs released from one source during one time step MXSAM 99 60 Maximum number of sampling steps during one time step for a puff or slug NCOUNT 2 2 Number of iterations used when computing the transport wind for a sampling step that includes transitional plume rise SYMIN 1 1 Minimum sigma y in meters for a new puff or slug SZMIN 1 1 Minimum sigma z in meters for a new puff or slug SVMIN .50 .50 Minimum lateral turbulence velocities (m/s) SWMIN 0.20, 0.12, 0.08, 0.06, 0.20, 0.12, 0.08, 0.06, Minimum vertical turbulence velocities (m/s)

### G-10 0.03, 0.016 0.03, 0.016 WSCALM 0.5 0.5 Minimum non-calm wind speeds (m/s) XMAXZI 3000. 3000. Maximum mixing height (m) XMINZI 50. 20. Minimum mixing height (m) SL2PF 10. 10. Maximum Sy/puff length PLXO 0.07, 0.07, 0.10, 0.15, 0.35, 0.55 0.07, 0.07, 0.10, 0.15, 0.35, 0.55 Wind speed power-law exponents

WSCAT 1.54, 3.09, 5.14, 8.23, 10.80 1.54, 3.09, 5.14, 8.23, 10.80 Upper bounds of 1 st 5 wind speed classes

PGGO 0.020, 0.035 0.020, 0. 035 Potential temp gradients PG E & F (deg/km) CDIV 0.01 0.01 Divergence criterion for dw/dz (1/s) PPC 0.5, 0.5, 0.5, 0.5, 0.35, 0.35 0.5, 0.5, 0.5, 0.5, 0.35, 0.35 Plume path coefficients (only if MCTADJ=3)

NSPLIT 3 3 Number of puffs when puffs split IRESPLIT - 1900 Hour(s) when puff is eligible to split ZISPLIT 100 100 Previous hour’s minimum mixing height, m ROLDMAX 0.25 0.25 Previous Max mixing height/current mixing height ratio, must be less than this value to allow puff to split NSPLITH 5 5 Number of puffs resulting from a split SYSPLITH 1.0 1.0 Minimum sigma-y of puff before it may split SHSPLITH 2.0 2.0 Minimum puff elongation rate from wind shear before puff may split CNSPLITH 1.0E-07 1.0E-07 Minimum species concentration before a puff may split EPSSLUG 1.0E-04 1.0E-04 Criterion for SLUG sampling EPSAREA 1.0E-06 1.0E-06 Criterion for area source integration DSRISE 1.0 1.0 Trajectory step length for numerical rise algorithm 

### Note: Values indicated by an asterisk (*) were allowed to vary spatially across the domain and were obtained from CALMET

Table G-15 CALPUFF Mode l Input Group 13: Point Source Parameters Parameter Default Value Comments NPT1 None Varies by scenario Number of point sources with stack parameters

IPTU 1 1 Units for point source emission rates are g/s NSPT1 0 0 Number of source-species combinations with variable emissions scaling factors NPT2 None 0 Number of point sources with variable emission parameters provided in external file MISC None Point source parameters and emission data Point source inputs include stack height (H), stack diameter (d), exit temperature (T), exit velocity (v) emissions by species, and coordinate of stack (LCC)

### G-11 Table G-16 CALPUFF Model Input Group 17: Discrete Receptor Information Parameter Default Value Comments NREC None 427 Number of discrete receptors



### Please note that ADEQ will not be modeli ng area, line and volume sources which are input groups 14, 15, and 16 respectively.

### H-1 

## Appendix H. POSTUTIL Input Control Parameters

Table H-1 Input Groups in the PO STUTIL Processor Control File Sub Group Description Applicable 0a Input and output file names Yes 1 NMET – Number of CALMET data files (365) Yes 2 NFILES – Number of CALPUFF data files Yes



### Table H-2 POSTUTIL Processor Input Group 1: General Run Control Parameters Parameter Default Value Comments
ISYR None See note 1 below Starting year

ISMO None 1 Starting month ISDY None 1 Starting day ISHR None 0 Starting hour NPER None See note 2 below Number of periods to process

NSPECINP None See note 3 below Number of CALPUFF species to process

NSPECOUT None See note 3 below Number of species to output

NSPECCMP None 0 Number of species to derive MDUPLCT None 1 Stop run if duplicate name NSCALED None 0 Number of CALPUFF files to ‘scale’ MNITRATE None 1 Re-compute the HNO 3 /NO 3 partition for CALPUFF modeled concentrations? 1 = yes for all sources combined BCKNH3 10. 3. Default NH 3 concentration (ppb) for HNO 3 /NO 3 partitioning 

### Note 1: Enter the modeled year for the CALPUFF run Note 2: Enter 8760 for years 2001 and 2002, but enter 8748 for the year 2003 Note 3: Enter 6 for the no-obs run and 7 for the refined run

### H-2 Table H-3 POSTUTIL Processor Input Group 2: Species Processing Information Parameter Default Value Comments
ASPECI None SO 2 , SO 4 , NO x , HNO 3 , NO 3 , PM 10 , PMF See Note 1 Below Species to post-process

ASPECO None SO 2 , SO 4 , NO x , HNO 3 , NO 3 , PM 10 , PMF See Note 4 Below Species to output

CSPECCMP None CSPECCMP = N SO 2 = 0.0 SO 4 = 0.291667 NO = 0.466667 NO 2 = 0.304348 HNO 3 = 0.222222 NO 3 = 0.451613 PM 10 = 0.0 Nitrogen species to be computed by scaling and summing one or more of the processed input species using the scaling factors for each of the NSPECINP input species

CSPECCMP None CSPECCMP = S SO 2 = 0.50 SO 4 = 0.333333 NO = 0.0 NO 2 = 0.0 HNO 3 = 0.0 NO 3 = 0.0 PM 10 = 0.0 Sulfur species to be computed by scaling and summing one or more of the processed input species using the scaling factors for each of the NSPECINP input species

MODDAT None A (Default=1.0) SO 2 = 1.1 SO 4 = 1.5 HNO3 = 0.8 NO 3 = 0.1

B (Default=0.0) SO 2 = 0.0 SO 4 = 0.0 HNO 3 = 0.0 NO 3 = 0.0 Each species in NSCALED CALPUFF data files may be scaled before processing (e.g., to change the emission rate for all sources modeled in the run that produced a data file). For each scaled species the scaling factors are A and B where x' = Ax + B.



### Note 4: In the no-obs run just enter PMF, but in the refined run enter PM 10 

### and PMF

### I-1 

## Appendix I. CALPOST Input Control Parameters

Table I-1Input Groups in the CALPOST Processor Control File Group Description Applicable 0 Input and output file names Yes 1 General Run Control Parameters Yes 2 Visibility Parameters Yes 3 Output Options Yes



### Table I-2 CALPOST Processor Input Gr oup 1: General Run Control Parameters Parameter Default Value Comments
METRUN 0 1 1 = Run all met pe riods in CALPUFF data file ISYR None 2001, 2002, 2003 Starting year

ISMO None 1 Starting month ISDY None 1 Starting day ISHR None 0 Starting hour NPER None See note 1 below Number of periods to process

NREP 1 1 Process every hour of data? Yes = 1 ASPEC None VISIB Process species for visibility ILAYER 1 1 Layer/deposition code; 1 for CALPUFF concentrations A 0.0 0.0 Scaling factor, slope B 0.0 0.0 Scaling factor, intercept LBACK F F Add hourly background concentrations or fluxes MSOURCE 0 0 Process only total reported contribution LG F F Process gridded receptors LD F T Process discrete receptors LCT F F Process complex terrain receptors LDRING F F Report receptor ring results NDRECP -1 See note 2 below To select the Class I area’s receptors enter *1 after the number of receptors otherwise enter *0 IBGRID -1 -1 X index of LL corner of receptor grid JBGRID -1 -1 Y index of LL corner of receptor grid IEGRID -1 -1 X index of UR corner of receptor grid JEGRID -1 -1 X index of UR corner of receptor grid NGONOFF 0 0 Number of gridded receptor rows NGXRECP 1 0 Exclude specific gridded receptors, Yes = 0 

### Note 1: Enter 8760 for the years 2001 and 2002, but enter 8748 for the year 2003. Note 2: CALPOST is to be run for each Class I area assessed. 3⁄4 The following are the number of receptors for each Class I area being assessed: 1. Caney Creek = 80 2. Hercules-Glade = 47 3. Mingo Wilderness = 80 4. Sipsey = 148 5. Upper Buffalo = 72

### I-2 Table I-3 CALPOST Processor Input Gr oup 2: Species Processing Information Parameter Default Value Comments RHMAX 98 95 Maximum RH (%) used in particle growth curve LVSO4 T T Compute light extinction for sulfate? LVNO3 T T Compute light extinction for nitrate? LVOC T T Compute light extinction for organic carbon? LVMPC T T Compute light extinction for coarse particles? LVMPF T T Compute light extinction for fine particles? LVEC T T Compute light extinction for elemental carbon? LVBK T T Include background in extinction calculation? SPECPMC PMC PMC Coarse particulate species SPECPMF PMF PMF Fine particulate species EEPMC 0.6 0.6 Extinction efficiency for coarse particulates EEPMF 1.0 1.0 Extinction efficiency for fine particulates EEPMCBK 0.6 0.6 Extinction efficiency for coarse part. background EESO4 3.0 3.0 Extinction efficiency for ammonium sulfate EENO3 3.0 3.0 Extinction efficiency for ammonium nitrate EEOC 4.0 4.0 Extinction efficiency for organic carbon EESOIL 1.0 1.0 Extinction efficiency for soil EEEC 10.0 10.0 Extinction efficiency for elemental carbon MVISBK 2 6 Method 6 for background light extinction: Compute extinction from speciated PM measurements. FLAG RH adjustment factor applied to observed & modeled sulfate and nitrate BEXTBTBK -- 12 Background extinction for MVISBK=1 (1/Mm) RHFRAC -- 10 Percentage of particles affected by RH RHFAC 12*value Depends on Class I Area Extinction coefficients for modeled and background hygroscopic species computed using EPA (2003) monthly RH adjustment factors BKSEC 0.02 0.02 Eastern background elemental carbon β ext BKSO4 0.23 0.23 Eastern background sulfate β ext BKNO3 0.10 0.10 Eastern background nitrate β ext BKPMC 3.00 3.00 Eastern background PMC β ext BKSOC 1.40 1.40 Easter background organic carbon β ext BKSSOIL 0.50 0.50 Eastern background soil β ext BKSEC 0.02 0.02 Eastern background elem. β ext BEXTRAY 10.0 10.0 Extinction due to Rayleigh scattering (1/Mm)

### I-3 Table I-4 CALPOST Processor Input Group 3: Output Options Parameter Default Value Comments
LDOC F F Print documentation image IPRTU 1 3 Print output units (μg/m 3 ) for concentrations and (μg/m 2 /sec) for deposition L1HR T F Report 1 hr averaging times L3HR T F Report 3 hr averaging times L24HR T T Report 24 hr averaging times LRUNL T F Report run-length (annual) averaging times LT50 T F Top 50 table LTOPN F F Top ‘N’ table NTOP 4 4 Number of ‘Top-N’ values at each receptor ITOP 1,2,3,4 1,2,3,4 Ranks of ‘Top-N’ values at each receptor LEXCD F F Threshold exceedances counts THRESH1 -1.0 -1.0 Averaging time threshold for 1 hr averages THRESH3 -1.0 -1.0 Averaging time threshold for 3 hr averages THRESH24 -1.0 -1.0 Averaging time threshold for 24 hr averages THRESHN -1.0 -1.0 Averaging ti me threshold for NAVG-hr averages NDAY 0 0 Accumulation period, days NCOUNT 1 1 Number of exceedances allowed LECHO F F Echo option LTIME F F Time series option LPLT F F Plot file option LGRD F F Use grid format instead of DATA format LDEBUG F F Output information for debugging?

### J-1 

## Appendix J. Chapter 3 of the CENRAP BART Modeling Guidelines (Tesche, et al, 2005)

3.0 CALPUFF FORMULATION AND IMPLEMENTATION

The RHR relates visibility attenuation to extinction coefficient (b ext 

### ) which is a measure of light scattering and absorption due to atmospheri c constituents. Values for b ext 

### are estimated using an empirically derived equation which relates the extinction coefficient to relative humidity and the follo wing components of particulate matter mass: (a) sulfates (SO 4 

### ); (b) nitrates (NO 3 

### ); (c) organic carbon (OC); (d) elemental carbon (EC); (f) particulate matter (IP) (“crustal mate rial”); and (g) coarse mass (CM) (i.e., PM 10 

### – PM 2.5 

### ). The BART guidance requires the us e of modeled concentrations of these components, together with a “humidity corre ction factor”, to es timate values for b ext 

### on all days within a three year period. These estimates, when compared with naturally occurring background extinction, are used to determine whether a source is causing or contributing to visibility impairment and also to measure the effectiveness of emissions controls on the source aimed at mitigating such effects. EPA notes that secondary particulate matter constitutes an important fraction of PM 2.5 

### and that the modeling requirements for secondary and primary particul ate matter differ in their need to consider atmospheric chemistry and in the degree of spatial resolution needed for the modeling (EPA, 2001, pg 22).

This chapter introduces the formulation of the CALPUFF modeling system. We summarize the model capabilities as describe d in the user’s manuals (Scire et al., 2000a,b) and discuss the capabilities and limitations of the model. Equipped with this information, states and source operators can id entify those situations for which screening and/or source-specific applications of CALPUFF are appropriate.

In most cases, we expect that application of the CALPUFF system will be sufficient to meet the BART Rule requirements. For that subset of conditions requiring advanced methods, Chapter 5 provides details on full-science alternative models and available data bases for BART modeling. Such conditions might include a situation where the default modeling shows that a sour ce just barely causes or contributes to visibility degradation or in negotiations over the final BART determination that weighs technical and economic feasibility against expected air quality benefits. In both situations, a more accurate estimate of a s ource’s impacts may be very important to source operators.

3.1 Original Model Development

The CALPUFF modeling system was origin ally developed as a component of a three-part modeling system sponsored by th e California Air Resources Board (ARB) in the mid-1980s. The ARB sought to develop a new puff-based model, a new grid-based model and an improved meteorological proce ssor that would support application of the two. CALGRID was the urban-scale photochemical grid model resulting from the

### J-2 project (Yamartino et al., 1992) comparable in science a nd capabilities to the Urban Airshed Model (UAM-IV) (Scheffe and Morris, 1993). The model formulation was aimed at overcoming the deficiencies in EPA’ s steady-state Gaussian plume models that were routinely used in California for inert and linearly reactive ma terials (principally SO 2 

### ) from elevated point sources. Thus, th e CALGRID model was designed to treat the complexities of urban-scale photochemical pr ocesses while CALPUFF was formulated to treat the non-steady state transport, diffusion, linear reaction, and deposition of primary pollutants from point sources. CALPUFF wa s not designed to address photochemical oxidants or and secondary aerosol formation production processes in a scientifically rigorous manner.

In recent years, CALPUFF and its meteorological pre-processor (CALMET) have been used in a range of regu latory modeling studies to addr ess point source issues that include complexities posed by complex terrain, large source-receptor distances, parameterized chemical transformation and deposition, and issues related to Class I visibility impacts. These applications ar e more complex than the California ARB’s nonsteady-state, linear chemistry fo rmulation of the mid-1980s.

The CALPUFF modeling system has been adopted by the EPA as a guideline model for source-receptor distances greater than 50 km, and for use on a case-by-case basis in complex flow situati ons for shorter distances. It was recommended for Class I impact assessments by the FLM Workgr oup (FLAG, 2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) (E PA, 1998). As directed in the BART guidance, CALPUFF is the primary modeling system for screening and source-specific BART applications in the CENRAP re gion. Thus, examination of the model’s formulation provides the context for assessing the extent to which it suitable for simulating the various physical processes an d gas-phase, aerosol , and aqueous-phase chemical processes that influences visibility.

3.2 CALPUFF Model Formulation

The CALPUFF user’s guide (Scire et al ., 2000a) depicts the modeling system as shown in Figure 3-1. CALMET is a diag nostic/interpolation model that provides meteorological inputs to CA LPUFF. These fields in clude hourly-averaged threedimensional wind and temperatur e fields and two-dimensiona l fields of mixing heights and other meteorological parameters. CALMET uses routine surface and aloft meteorological observations and/or three-dimensional output from prognostic numerical models such as MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) to construct the meteorological inputs. Other inputs to the air quality program include emissions information, receptor locations, ancillary geophysical information, and estimated concentrations of ambient pollutants that ar e entrained by the modeled puffs as each is carried downwind. Tabl es 3-1 and 3-2 summarize the key features of the CALMET/CALPUFF models as desc ribed in the user’s guides.

Two post-processor routines are included to facilitate cumulative source impacts (POSTUTIL) and estimates of light extinc tion and visibility attenuation at Class I

### J-3 receptors of interest (CALPOST). In par ticular, CALPOST contains several options for computing change in extinction and decivi ews for visibility assessments while the POSTUTIL postprocessor includes options fo r summing contributions of individual sources or groups of sources to assess cumula tive impacts. POSTUTIL also contains an empirical nitric acid-nitrate chemical e quilibrium module to estimate the cumulative effects of ammonia consumption by b ackground sources once the simulation is completed.

3.2.1 Model Concept and Governing Equations

The starting point for the CALPUFF development was the choice of the fundamental reference system of which ther e are two: Eulerian and Lagrangian. Consistent with the original ARB design crite ria, the Lagrangian (moving puff) reference system was chosen for CALPUFF. In the Eule rian approach, the behavior of pollutants is described relative to a fixed coordinate sy stem. The Lagrangian reference frame, in contrast, relates the behavior of pollutants relative to a coordi nate system that moves with the average wind. These two approaches yield different mathematical relationships for pollutant concentrations that are equally valid . The choice of which approach to adopt depends upon the specific design goals of the modeling system.

The advantages and drawbacks of each a pproach are thoroughly discussed in the literature (Tesche, 1983; Seinfeld and Pandis, 1998; Jacobson, 1999; Russell and Dennis, 2000). One of the criticisms of early Eulerian grid models was their ‘over-dilution’ of point source emissions into the fixed grid cells; but for the past twenty years, this limitation has been overcome through with the development of sub-grid-scale, plume-ingrid algorithms (Seigneur, et al., 1981; Godowitch, 2004; Karamcha ndani et al., 2005; Emery and Yarwood, 2005) and the use of multi- scale nested grids (Russell and Dennis, 2000). While the Lagrangian approach is conceptually simple, flexible, and computationally inexpensive, the governing e quations are not directly applicable to situations involving non- linear chemical reactions (Seinfel d and Pandis, (1998) and it is awkward to handle a large numbe r of sources realistically.

3.2.2 Transport and Dispersion

Adopting the Lagrangian concept, CALPU FF simulates the transport, dispersion, linear chemical transf ormation, and deposition of indivi dual puffs carried downwind by the three-dimensional fields generated by CALMET. The model’s implementation follows puffs from the near source region (a few tens of meters) to hundreds of kilometers downwind. Its puff-based formula tion, in conjunction with three-dimensional hourly meteorological data, allow CALPUFF to simulate the effects of time- and spacevarying meteorological conditions on pollutant s emitted from a variety of source types. The major features and options of the CALPUFF model are summarized below:

Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are both incorporated into CALPUFF. An option is provided to use eith er model for all stacks, or make the choice on a stack-by-stack and wi nd sector-by-wind sector basis. Both

### J-4 algorithms have been implemented in such a way as to allow the use of wind direction specific building dimensions. The PRIME bu ilding downwash model (Schulman et al., 2000) is also included in CALPUFF as an option.

Dispersion Coefficients: Turbulent dispersion in CALPUF F is treated with the K-theory (flux-gradient) closure scheme, defined for a Lagrangian frame of reference. Several options are provided in CALPUFF for the computation of dispersion coefficients, including the use of turbulence measurements ( σ v 

### and σ w 

### ), the use of similarity theory to estimate σ v 

### and σ w 

### from modeled surface heat and mo mentum fluxes, or the use of Pasquill-Gifford (PG) or McElroy-Pooler (M P) dispersion coefficients, or dispersion equations based on the CTDM. Options ar e provided to apply an averaging time correction or surface roughness length adjustments to the PG coefficients. Recently, the EPA AERMOD dispersion parameters have b een included in CALPUFF and are used regularly.

Puff Sampling Functions: Puff sampling routines are included in CALPUFF to address computational difficulties encountered when a pplying a puff model to near-field releases. For near-field applications during rapi dly-varying meteorolog ical conditions, an elongated puff (slug) sampling function may be used. An integrated puff approach may be used during less demanding conditions. Both techniques repr oduce continuous plume results under the appropriat e steady state conditions.

Wind Shear Effects : A key underpinning of the Lagrangi an concept is that the modeled puffs retain their identity over the time- and spatial-scale asso ciated with the effects the model is attempting to predict (i.e., visibi lity impairment at 200 km or beyond) While discrete puffs emitted from a source retain thei r physical integrity for a period of time, at some point the action of horizontal and vertic al variations in wi nd speed and direction (i.e. ‘wind shear’) shred the puff into multiple elements. These new puff parcels, composed of remnants of the old puff, continue to be diffused and dispersed by the wind. The point where significant pu ff shredding occurs is difficult to define sin ce it depends substantially upon the complexity of the me teorological conditions and the underlying terrain. But when shredding occurs, the Lagr angian concept in CA LPUFF breaks down. By ignoring puff shredding (i.e., by keeping puffs intact), the model will systematically over-predict pollutant concentrations.

To deal with this conceptual limita tion, CALPUFF contains an optional puff splitting algorithm to simulate vertical wind shear effects across individual puffs. Differential rates of dispersion and transpor t among the “new” puffs generated from the original, well-mixed puff act to increase the e ffective rate of horiz ontal spread of the material as would be expected in the real atmosphere. Puffs may also be split in the horizontal when the puff size b ecomes large relative to the gr id size to account for wind shear across the puffs. Detailed guidance on when and how the puff-splitting algorithm should be used and actual verification studies demonstrating that the technique operates as intended are not discussed in the mode l documentation or presented in the science literature.

### J-5 Complex Terrain: Effects of complex terrain on puf f transport are derived from the CALMET winds. In addition, puff-terrain intera ctions at gridded and discrete receptor locations are simulated using one of two algor ithms that modify the puff-height (either that of ISCST3 or a general “plume path coef ficient” adjustment), or an algorithm that simulates enhanced vertical dispersion deri ved from the weakly-stratified flow and dispersion module of the Complex Terrain Disp ersion Model (CTDMPLU S) (Perry et al., 1989). The puff-height adjustme nt algorithms rely on the receptor elevation (relative to the elevation at the source) a nd the height of the puff above the surface. The enhanced dispersion adjustment relies on the slope of the gridded terrain in the direction of transport during the time step.

Subgrid Scale Complex Terrain (CTSG): An optional module, CTSG treats terrain features that are not resolved by the gr idded terrain field, a nd is based on the CTDMPLUS (Perry et al., 1989). Plume impi ngement on subgrid-scale hills is evaluated at the CTSG subgroup of receptors using a dividing streamline height (H d 

### ) to determine which pollutant material is deflected around the sides of a hill (below H d 

### ) and which material is advected over the hill (above H d 

### ). The local flow (near the feature) used to define H d 

### is taken from the gridded CALMET fiel ds. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Overwater and Coastal Interaction Effects : The CALMET processor contains overwater and overland boundary layer parameterizations allowing certain of the effects of water bodies on plume transport, dispersion, and de position to be estimated. In a sense, CALPUFF operates as a hybrid model, by uti lizing gridded fields of meteorology and dispersion conditions as well as grid-based de scriptions of underlying land use. This includes the abrupt changes that occur at the coastline of a major body of water.

Dry Deposition : A resistance model is used for th e computation of dr y deposition rates of gases and particulate matter as a functi on of geophysical parameters, meteorological conditions, and pollutant species. For partic les, source-specific mass distributions may be provided for use in the resist ance model. Of particular interest for BART analyses is the ability to separately model the de position of fine particulate matter (< 2.5 μ m diameter) from coarse particulate matter (2.5-10 μ m diameter).

Wet Deposition : An empirical scavenging coefficient approach is used to compute the depletion and wet deposition fluxes due to precipitation scavenging. The scavenging coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen vs. liquid precipitation).

3.2.3 Primary Particulates

CALPUFF is designed to simulate PM 10 

### or PM 2.5 

### or other user defined size distributions of particles. The smaller the part icles, the more they disperse like an inert gas. In most cases, the dispersion of inert PM 2.5 

### particles will differ only slightly from that of an inert gas. A key primary PM 2.5 

### emission from coal-fired electric generating units (EGUs) of relevance to visibility calculations is particulate sulfate. Although

### J-6 primary sulfate emissions accoun t for only a small fraction of the total sulfur emissions from such sources, it is appropriate to incl ude their effect if r easonable estimates of primary sulfate emissions from the source are available. Treat ing primary sulfate emissions is likely to be most important at short distances from the stack before significant SO 2 

### to secondary sulfate conversion has taken place.

3.2.4 Gas-Phase Chemistry

Chemical reactions in the ga s-phase play an important role in secondary aerosol formation by generating radical concentrations (e.g., the hydroxyl radical). These radical species oxidize SO 2 

### and NOx, providing the precursors to aqueous–phase chemistry (i.e., chemistry in liquid water droplets) that convert SO 2 

### to sulfate (e.g., H 2 

### O 2 

### and O 3 

### ), and form condensable gases from some volatile organic compounds (VOCs) that can then condense into particulate secondary organic aerosols (SOA). The levels of NOx, VOC, and O 3 

### concentrations along with the reactivity of the VOCs, sunlight, temperature, and water vapor are all key variables that influe nce the radical cycle and consequent sulfate and nitrate formation rates.

CALPUFF neglects realistic gas-phase pr ocesses entirely. The chemistry in CALPUFF parameterizes chemical transfor mation effects using five species (SO 2 

### , SO 4 =



### , NO x 

### , HNO 3 

### , and NO 3 - 

### ) via a set of user-specified, diur nally-varying transf ormation rates. The model estimates secondary fine partic ulate matter (sulfate and nitrate) from emissions of gas-phase SO 2 

### and NO x 

### . Rather than simulating important non-linear gas phase oxidant chemistry, the model employs a user-supplied hourly ozone concentration as a surrogate for the hydroxyl radical and other oxidizing radical species. Ambient ammonia concentrations are also a user input along with temperature and relative humidity.

Although simplifications of photochemistry have been attempted in the past, correct representation of th e gas-phase photochemistry a nd the radical cycles are critically important in order to properly char acterize sulfate and nitrate formation in the real atmosphere. Seigneur et al., (2000) demonstrated this fact in their evaluation of fullscience representations of photochemistry against simplif ied representations (but more advanced than CALPUFF). They conclude d that simplified linearized transformation schemes are inadequate for describing sulfate and nitrate formation processes:

“These results indicate that the accu rate prediction of source-receptor relationships for PM 2.5 

### requires a comprehens ive treatment of PM 2.5 

### formation from gaseous precursors for the secondary components of PM 2.5 

### and a spatially resolved treatment of trans port processes for primary PM 2.5 

### . Simplified treatments of either atmospheric chemistry or transport are appropriate only when the secondary or primary components of PM 2.5 

### , respectively, are not significant. Therefore, the development of source-receptor relationships for PM 2.5 

### should be based on air quality models that pr ovide comprehensive descriptions of atmospheric chemistry and transport.”

### J-7 Morris et al., (1998) also compared the sulfat e and nitrate particulate estimates from a comprehensive full-science regional model w ith those from a model incorporating a simplified empirical chemical mechanism developed in a manner similar to the mechanism in CALPUFF. Evaluating the fu ll-science and empirical chemistry models against observed concentrations, Mo rris and co-workers concluded:

“Given the importance of the radical cycle for determining secondary PM formation rates, it appears that empirical gas-phase algorithms are inadequate for determining secondary PM formation.”

The uncertainty and potential bi ases introduced into the CA LPUFF visibility estimates due to neglect of gas phase oxidant chemistry remain unknown.

3.2.5 Aerosol Chemistry

Formation of secondary fine particulate matter (e. g., nitrates, sulfates, organic aerosols) in point source plumes is strongly dependent on the rate of mixing with ambient (background) air and the chemical compositi on of this background. The rates of oxidation of sulfur dioxide (SO 2 

### ) and nitrogen dioxide (NO 2 

### ) to sulfate and nitric acid can be very different within a power plant or industrial plume compared to that in the background air (Gillani and Godowitch, 1999; Karamchandani et al., 2000). Similarly, the formation of secondary organic aerosol s from emitted VOCs and those from other anthropogenic and biogenic sources, adds yet another pathway in the formation of visibility-impairing aerosols. The presence of atmospheric amm onia introduces further nonlinearities into the gas phase and aerosol reactions. Accordingly, for a model to realistically simulate the production of secondary particulate sulfate, nitrate, and organic aerosols from a potential BART source, th e mixing processes and chemical reactions within and outside of the plume must be treated realistically. If the chemical interactions between these two fundamentally different an d interactive chemical environments are overly-simplified or neglected altogether, the ability of the model to correctly calculate plume concentrations, deposition, or visibility impacts is lost.

Sulfate and Nitrate Formation. Two SO 2 

### and NOx chemical transformation schemes are available in CALPUFF: the MESOPUFF-II algorithm (Scire et al., 1983; Atkinson et al., 1982) and the RIVAD algorithm (Latimer et al ., 1986). These algorithms calculate sulfate and nitrate formation rates based on the puff concentr ations, background environmental parameters provided by CA LMET, and background ozone and ammonia concentrations provided as input by the user . SOA particulates are not treated by either mechanism. The parameters used are as fo llows (note that each method does not use all of these parameters).

Puff Average Concentrations (from CALPUFF)

• NOx concentration

• SO 2 

### concentration

Environmental Parameters (from CALMET)

### J-8 • Temperature

• Surface Relative Humidity (RH)

• Atmospheric Stability

• Solar Radiation

Background Concentrations (User Input)

• Ozone (O 3 

### )

• Ammonia (NH 3 

### )

The MESOPUFF-II chemical transformation scheme is EPA’s recommended approach for Class I area impact assessment (IWAQM, 1998). It entails pathways for five active pollutants (SO 2 

### , SO 4 

### , NOx, HNO 3 

### , and NO 3 

### ) as follows:

k 1 

### SO 2 

### Æ SO 4 

### k 2 

### NOx Æ HNO 3 

### (+RNO 3 

### ) k 3 

### NOx Æ HNO 3 

### NH 3 

### HNO 3 

### (g) ÅÆ NO 3 

### (PM)

where,

SO 2 

### is the puff average sulfur dioxide concentration; NOx is the puff average oxides of nitrogen concentrations; SO 4 

### is sulfate concentrations formed from the SO 2 

### ; HNO 3 

### is the nitric acid formed from the NOx; NO 3 

### is the particulate nitrate that is in equilibrium with the nitric acid; and NH 3 

### is the background ammonia concentration.

Daytime Rates
k 1 

### = 36 x R 0.55 

### x [O 3 

### ] 0.71 

### x S -1.29 

### + k 1(aq)



### k 1(aq) 

### = 3 x 10 -8 

### x RH 4 

### (added to k 1 

### above during the day)

k 2 

### = 1206 x [O 3 

### ] 1.5 

### x S -1.41 

### x [NOx] -0.33



### k 3 

### = 1261 x [O 3 

### ] 1.45 

### x S -1.34 

### x [NOx] -0.12



### Nighttime Rates
k 1 

### = 0.20 (%/hr) k 2 

### = 0.00 (%/hr) k 3 

### = 2.00 (%/hr)

with,

### J-9 k 1 

### is the SO 2 

### to SO 4 

### gas-phase transformation rate (%/hr) k 1(aq) 

### is the SO 2 

### to SO 4 

### aqueous-phase transformation rate (%/hr) k 2 

### is the NOx to HNO 3 

### +RNO 3 

### transformation rate (%/hr) k 3 

### is the NOx to HNO 3 

### (only) transforma tion rate (%/hr) S is the stability index ranging from 2 to 6 (PGT class A&B=2, C=3, D=4, E=5, F=6) R is the total solar radiation intensity (kw/m 2 

### ) RH is the relative humidity (%) [O 3 

### ] is the user provided backgr ound ozone concentrations (ppm) [NOx] is the plume average NOx concentration (ppm) NH 3 

### is the user provided bac kground ammonia concentrations

Daytime chemical transformations are based on statistically analyzed hourly transformation rates (Scire et al., 1983) obtained from box model simulations using the Atkinson et al., (1982) photochemical mechanis m. In this scheme, gas-phase oxidation of SO 2 

### and NOx depends on the hydroxyl (OH) radical conc entrations for which background ozone, solar intensity (R), and stab ility index are used as surrogates. At night, OH concentrations are much lower and default SO 2 

### and NOx oxidation rates of 0.2 %/hr and 2.0 %/hr are assumed. The k 1(aq) 

### sulfate formation rate is added to the k 1 

### rate during the day as a surrogate for aqueous-phase sulfate formation which begins to assume importance above approximately 50% RH (~0.2 %/ hr sulfate formation rate) and peaks at 100% RH (3%/hr sulfat e formation rate).

The sulfate and nitrate formation rate equations used in the MESOPUFF II scheme were originally generated by deve loping regression equations for a few key variables on the resu lts of 144 box model simulations th at used the 1982 photochemical mechanism of Atkinson et al. These box mode l simulations varied ambient temperature, ozone concentration, sunlight intensity, VOC concentrations , atmospheric stability, and plume NOx concentrations as shown in Tabl e 3-1. The actual environmental conditions used to generate the sulfate and nitrate transformation equations were extremely limited. For example, the transformation rates did not cover temperatures below 10 deg C (50 deg F) or cleaner rural atmospheric conditions with VOC concentrati ons less than 50 ppbC.

The CALPUFF MESOPUFF-II chemistry clea rly neglects several environmental parameters and chemical processes that are important in simulating sulfate and nitrate formation in NO X 

### /SO 2 

### emissions source plumes. In many cases these deficiencies lead to an overestimation bias of the source’s sulfate and nitrate impacts. Factors that lead such a bias include:

Lack of Temperature Effects : Photochemistry is known to be highly temperature sensitive, as evidenced by the fact that elevated ozone concentrations tend to occur on hot summer days. Lower temperatures produce lower OH and other radical concentrations and consequently lower sulfate and nitrate formation rates. The CALPUFF sulfate and nitrate forma tion rates, however, do not adequately incorporate temperature effects. Th e MESOPUFF-II chemical transformation algorithm was developed under conditions with a minimum temperature of only

### J-10 10 ° C (50 ° F). Thus, under conditions colder than 10 ° C, CALPUFF will overpredict sulfate and nitrat e formation rates and impacts. CALPUFF typically estimates maximum sulfate and visibility impacts during the late fall/early spring and winter months; these are the same months when the CALPUFF overestimation bias from not considering temperature effects will be greatest. In addition, under colder temperatures, NOx will be converted to peroxyacetyl nitrate (PAN) so that the NOx is no longer available to be conve rted to nitrate. Since the CALPUFF chemistry ignor es the PAN sink for NOx, it will systematically overpredict nitrate impacts.

Effects of NOx Emissions on Sulfate Chemistry : Downwind of a point source with significant NOx/SO 2 

### emissions, high NOx and SO 2 

### concentrations co-exist. Under high NOx concentrations, radical concentrations are greatly reduced, resulting in very low ozone, sulfate, and nitr ate formation rates. This is due to the NOx inhibition effect on photochemistry whereby: (1) the titration of NO with ozone eliminates ozone and its source as a radical generator; and (2) the high NO 2 

### concentrations eliminate the OH radical via the NO 2 

### + OH reaction thereby effectively shutting down photochemistry. Thus, in a NOx/SO 2 

### point source plume near the source, there will be very low OH radical and ozone concentrations and consequently very low sulfate and nitrate formation. Since the simple MESOPUFF-II transformation equations cannot account for the NOx effect on the sulfate formation, CALP UFF will tend to over-predict sulfate formation rate in a NOx/SO 2 

### point source plume near the source, which in turn leads to overstating the sulfate formation rate. Because NOx/SO 2 

### point sources are typically buoyant, they are frequently be emitted aloft in a stable layer where the high NOx concentrations and inhibi ted sulfate and nitrate formation rates could persist 100 km or more downwind.

Aqueous-Phase Sulfate Formation Algorithm. CALPUFF’s MESOPUFF-II chemistry treats aqueous-phase sulfate formation solely as a function of re lative humidity (RH), which actually has no direct affect on aqueous -phase sulfate formation chemistry. The CALPUFF MESOPUFF-II aqueous-phase sulfate formation rate ranges from values of approximately 0.2 %/hr at 50% RH to 3.0 %/hr at 100% RH. Relative humidity (RH) is a measure of the content of water vapor in th e atmosphere. However, in reality aqueousphase sulfate formation will depend on the amount of atmospheric liquid water content (LWC) in cloud or fog droplets, the pH of the water droplets, and the level of H 2 

### O 2 

### , ozone, and SO 2 

### concentrations. Accordingly, in the atmosphere, aqueous-phase sulfate formation chemistry is not affected by RH. Thus, the CALPUFF aqueous-phase chemistry parameterization is incorrect. Although under conditions of clouds and fog there will be high RH, the occurrence of high RH with very little or no clouds or fog can be quite frequent.

In a liquid water droplet, the reaction of SO 2 

### with H 2 

### O 2 

### to form sulfate is essentially instantaneous and is usually limited by the amount of H 2 

### O 2 

### present (i.e., oxidant limited) for a NOx/SO 2 

### point source. Once the H 2 

### O 2 

### is reacted away within the water droplet, sulfate formation via th is pathway slows to the rate of H 2 

### O 2 

### formation,

### J-11 which would be extremely slow to nonexisten t in a large point source plume due to the scavenging of radicals by th e high NOx concentrations. This introduces an inaccurate representation of sulfate formation in CALP UFF that creates uncertainties and bias in modeled visibility impacts. Whether this unc ertainty results in an under- or overestimate of sulfate formation is difficult to determine since the approach is scientifically invalid. Under conditions of high RH and little clouds or little pl ume interaction with clouds, it will clearly overstate sulfat e formation. However, under conditions of cloudy conditions with available photochem ical oxidants (i.e., H 2 

### O 2 

### and O 3 

### ) and a dilute NOx/Sox point source plume, it may understate sulfate formation. Near large NOx/SO 2 

### point source where the elevated NOx concentrations s cavenge and limit photochemical oxidants, the MESOPUFF-II algorithm will likely overstates sulfate formation.

Thus, the CALPUFF aerosol chemistry fa ils to account for many environmental parameters that are necessary to simulate sulfate and nitrate formation rates, including VOCs and their reactivity, temperature, liquid wa ter content, and NOx concentrations. In their evaluations against full-sc ience PM models and observati ons, Seigneur et al., (2000) and Morris et al., (1998) both independently fo und that the empirical chemistry modules, such as employed by CALPUFF, are inadeq uate for estimating sulfate and nitrate formation

. These findings are supported by EPA’s PM 2.5 

### and Regional Haze SIP modeling guidance (EPA, 2001) that recommends against using Lagrangian models such as CALPUFF for simulating secondary PM.

From the foregoing, it is clear that the CALPUFF chemical transformation algorithms neglect important chemical processes necessary to accurately estimate the sulfate and nitrate impacts due to SO 2 

### and NO X 

### emissions. Given that EPA recommends the model for BART determinations, a key question is “What is the influence of the simplified chemistry on modeled estimates of vi sibility impacts from BART sources? In some cases, the inadequacies in the CA LPUFF chemistry algorithms may simply introduce broader uncertainties into the cal culation of estimated sulfate and nitrate impacts. In many cases, however, the simplif ications made in the CALPUFF description of chemical processes result in a systematic bias in the estimated concentrations and visibility impacts due to SO 2 

### and NO X 

### emissions sources. For large point sources that emit SO 2 

### and NOx emissions, such as EGUs, petroc hemical process heaters, cement plant kilns, etc., many of the limitations in the CALPUFF MESOPUFF-II SO 2 

### and NOx transformation algorithms would result in an overestimation bias. While models that are systematically biased high (i.e., over-predict impacts) may be appealing to regulatory decision-makers because they are ‘conserva tive’, the overpredic tion tendency may well lead to unwarranted and excessive control of emissions from some sources. Thus, the tradeoff between simplicity and conservativism on the one hand and te chnical credibility and unbiased answers on the other is a key element in the negotiation of modeling protocols developed by the states or source operators.

3.2.6 Surface Removal

An especially important contributor to pa rticulate concentrations is the rate of deposition to the surface. PM 2.5 

### particles, which have a ma ss median diameter around 0.5

### J-12 μm, have an average net deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine particles is not usually significant except for ground-level emissions. On the other hand, coarse particles (those PM 10 

### particles larger than PM 2.5 

### ) have an average deposition ve locity of more than 1 m/ min (or 1440 m/day), which is significant, even for emissions from elevated stacks.

CALPUFF includes parametric representati ons of particle and gas deposition in terms of atmospheric, deposition layer, and vegetation layer “r esistances” and, for particles, the gravita tional settling speed. Gr avitational settling, which is of particular importance for the coarse fraction of PM 10 

### , is accounted for in th e calculation of the deposition velocity. Effects of inertial imp action (important for the upper part of the PM 10 

### distribution) and Brownian motion (important for small, sub-micron particles) and wet scavenging are also addressed. The BART guidance recommends that fine particulate matter (less than 2.5 μm diameter), which has higher light extinc tion efficiency than coarse particulate matter (2.5-10 μm diameter s), should be treated separately in the model. CALPUFF allows for user-specified size categories to be treated as separate species, which includes calculating size-specif ic dry deposition velocities for each size category.

3.3 CALMET Meteorological Preprocessor

The CALMET meteorological model consis ts of a diagnostic wind field module and micrometeorological modules for over- water and overland boundary layers. When modeling a large geographical area such as th e CENRAP domain, the user has the option to use a Lambert Conformal Projection coordinate system to account for Earth’s curvature. The major features and options of the meteorological model are summarized in Table 3-1. The techniques used in the CA LMET model are briefly described below.

3.3.1 Boundary Layer Modules

The CALMET processor contains two boundary layer modules for application to overland and overwater grid cells.

Overland Boundary Layer Module : Over land surfaces, the energy balance method of Holtslag and van Ulden (1983) is used to comp ute hourly gridded fiel ds of the sensible heat flux, surface friction velocity, Moni n-Obukhov length, and convective velocity scale. Mixing heights are determined from the computed hourly surface heat fluxes and observed temperature soundings using a modi fied Carson (1973) method based on Maul (1980). The module also determines gridded fi elds of PGT stability class and hourly precipitation rates.

Overwater Boundary Layer Module: The aerodynamic and thermal properties of water surfaces suggest that a different method is needed for estimating boundary layer parameters in the marine environment. A profile technique, using air-sea temperature differences, is used in CALMET to compute the micro-meteorological parameters in the marine boundary layer. An upwind-looking spatial averaging scheme is optionally

### J-13 applied to the mixing heights and three-dimensional temperature fields in order to account for important advective effects.

3.3.2 CALMET Diagnostic Wind Field Module

The CALMET wind model was constructe d from two other meteorological models used in California in the late 1970s. One was the Calif ornia Institute of Technology (CIT) mass consistent interp olation model described by Goodin et al., (1980). The other was the Complex Terrain Wind Model (CTWM) developed at Systems Applications, Inc. (Tesche and Yocke, 1978; Yocke and Liu, 1978). The CTWM terrain adjustments used to modify the flow fields were assembled in the 1970s as part of research into fire spread and avalanche for ecasting in mountainous regions of California. Various heuristic algorithms were developed to approximate down slope drainage flows, terrain blocking and channeling (Geiger, 1965), thermal heat islands (Stern and Malkus, 1953), surface friction retarda tion, capping by an elevated i nversion and so on. These algorithms were based on empirical stud ies in wind tunnels, numerical modeling experiments, and field studies in the Alps, some dating back to the 1930s (Defant, 1933). Later work by Tesche et al., (1986), Kessler et al., (1987) and Douglas and Kessler (1988) integrated the CIT and CTWM mode ling system into a single meteorological model that included algor ithms to blend observationa l data with prognostic meteorological model output. The combined m odel was used extensively for urban-scale ozone studies throughout the U.S. prior to the switch to MM5 as the preferred meteorological model for SIP studies in the mid-1990s.

The CALMET model development incorporat ed the main features of the CTWM and CIT wind model and significantly updat ed the physical parameterizations and improved model input/output (I/O ) schemes (Scire et al., 2000a). Today, CALMET uses the CTWM two-step approach to the computati on of the wind fields. In the first step, an ‘initial-guess’ wind field is c onstructed and then adjusted to approximate the kinematic effects of terrain, slope flows, and terrain blocking. Currently, the gridded MM5 field is used as the initial guess prio r to terrain-perturbation. The second step consists of an objective analysis procedure to blend the MM5 field with observational data to produce a final wind field. This introduction of obser vational data in the second step of the CALMET wind field development is optional. It is also possible to run the model in “no observations” (No-Obs) mode, wh ich involves the use only of MM5 gridded data for the initial guess field followed by fine-scale terr ain adjustments on the scale of the CALMET domain.

Normally, the CALMET computational domain is specified to be at smaller grid spacing than the MM5 dataset used to initia lize the initial guess field. For example, 36/12 km MM5 data sets available for 2000- 2003 over the CENRAP domain have been used to develop the 6 km CALMET grids shown in Figures 5-1 through 5-4.

The current thermal, kinematic, and dyna mic effects parameterized in CALMET, used in the first step of the windf ield development, are as follows:

### J-14 Kinematic Effects of Terrain : The CTWM algorithms for kinematic effects (Liu and Yocke, 1980) is used to evaluate the influen ce of the terrain on the wind field. The initial guess field winds are used to compute a terrai n-forced vertical velo city, subject to an exponential, stability-dependent decay functi on. The effects of terrain on the horizontal wind components are evaluated by applying a divergence-minimization scheme to the initial guess wind field. The divergence minimization scheme is applied iteratively until the three-dimensional divergence is less than a threshold value.

Slope Flows : The original slope flow algorithm (D efant, 1933) has been upgraded (Scire and Robe, 1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes both advective-gravity and equilibriu m flow regimes. At night, the slope flow model parameterizes the flow down the sides of the valley walls into the floor of the valley, and during the day, upslope flows are parameterized. The magnitude of the slope flow depends on the local surface sensible heat flux and local terrain gradients. The slope flow wind components are adde d to the wind field adjusted for kinematic effects.

Blocking Effects : The thermodynamic blocking effect s of terrain on the wind flow are parameterized in terms of the local Froude number (Allwine and Whiteman, 1985). If the Froude number at a particular gr id point is less than a crit ical value and the wind has an uphill component, the wind direction is adju sted to be tangent to the terrain.

3.4 Estimation of Region al Haze Contributions

The default procedure for quantifying visibility impacts is described in several documents (IWAQM, 1998; FL AG, 2000). Implementation of these procedures in CALPUFF is described in t he user’s documentation (Scire et al., 2000b). Generally, ‘visibilit y’ may be quantified either by visual range (the greatest distance that a la rge object can be seen) or by the light extinction coefficient, which is a measure of the li ght attenuation per unit distance due to scattering and absorption by gases and particles . Visibility is impaired when light is scattered in and out of t he line of sight an d by light absorbed along the line of sight. The light extinction coefficient (b ext 

### ) considers light extinction by scattering (b scat 

### ) and absorption (b abs 

### ):

b ext 

### = b scat 

### + b abs


### The scattering components of extinction (b scat 

### ) are represented by light scattering due to air molecules (i.e., Rayleigh scattering, b rayleigh 

### ) and light scattering due to particles, b sp 

### . The absorption components of extinction (b abs 

### ) include light absorption due to gases (b ag 

### ) and particles (b ap 

### ). Furthermore, particle scattering, b sp 

### , can be expressed by its components:

b sp 

### = b SO4 

### + b NO3 

### + b OC 

### + b SOIL 

### + b Coarse


### where the chemical species and soot scattering coefficients are given as:

### J-15 b SO4 

### = 3 [(NH 4 

### ) 2 

### SO 4 

### ] f(RH)

b NO3 

### = 3 [NH 4 

### NO 3 

### ]f(RH)

b OC 

### = 4 [OC]

b SOIL 

### = [Soil]

b Coarse 

### = 0.6 [Coarse Mass]

b ap 

### = 10 [EC]

The numeric coefficient at the beginning of each equation is the dry scattering or absorption efficiency in meters-squared per gram. The f(RH) te rm is a monthlyaverage relative humidity adjustment factor . The terms in the brackets are the estimated concentrations fro CALPUFF (or other model) in micrograms per cubic meter (μg/m 3 

### ).

Finally, the total atmospheric extinction is estimated as:

b ext 

### = b SO4 

### + b NO3 

### + b OC 

### + b SOIL 

### + b Coarse 

### + b ap 

### + b rayleigh


### or, substituting in the above terms,

b ext 

### = 3 f(RH) [(NH 4 

### ) 2 

### SO 4 

### ] + 3 f(RH) [NH 4 

### NO 3 

### ] + 4[OC] + 1[Soil] + (3-1) + 0.6[Coarse Mass] + 10[EC] + b Ray



### This is the so-called IMPROVE exti nction equation currently recommended by EPA (2003). Note that the sulfate (SO 4 

### ) and nitrate (NO 3 

### ) components are hygroscopic because their extinction coe fficients depend upon relative humidity. The concentrations, in square brackets, are in μg/m 3 

### and b ext 

### is in units of Mm -1



### . The Rayleigh scattering term (b Ray 

### ) has a default value of 10 Mm -1 

### , as recommended in EPA guidance for tracking reasonable progress (EPA, 2003a). The effect of relative humidity variabili ty on the extinction coefficients for SO 4 

### and NO 3 

### can be estimated in several ways, but following the EPA BART guidelines, the Class I area-specific m onthly f(RH) values shown in Table 6-1 should be used.

Modeled ground level concentrations of each of the above visibility impairing pollutants are used with the IMPROVE equation to deduce the extinction coefficient. The ch ange in visibility (measured in terms of ‘deciviews’) is compared against background c onditions. The delta-deciview, ∆ dv, value is

### J-16 calculated from the source’s contribution to extinction, b source 

### , and background extinction, b background 

### , as follows:

∆ dv = 10 ln ((b background 

### + b

source 

### )/ b background 

### )

The impact of a source is determined by comparing the ∆ dv, or haze index (HI), for estimated natural background conditions with th e impact of the source and without the impact of the source . If the ∆ dv value is greater than the 0. 5 dv threshold the source is said to contribute to visibility impairmen t and is thus subject to BART controls.

CALPOST uses a previous IMPROVE f(RH) curve (FLAG, 2000) which differs slightly from the f(RH) now used by IMPROVE and EPA (2003), mainly at high relative humidity. Also, CALPOST sets the maximum RH at 98% by default (although the user can change it), while the EPA’s guidance now caps it at 95% (easily modified in the CALPUFF input file).

For regional haze light extinction calcula tions, use of a plume-simulating model such as CALPUFF is appropriate only when the plume is sufficiently diffuse that it is not visually discernible as a plume per se , but nevertheless its presence could alter the visibility through the background haze. The IWAQM Phase 2 report states that such conditions occur starting 30 to 50 km from a source. This is consistent with the BART guidance recommendation for us ing CALPUFF for source-recepto r distances greater than 50 km. But, CALPUFF is also recommended by EPA as an option that can be considered for shorter transport distances when the pl ume may in fact be discernible from the background haze.

Apart from the chemistry issues discusse d previously, there do not appear to be any major reasons why CALPUFF cannot be us ed for even shorter transport distances than 30 km, as long as the scale of the plume is larger than the scale of the output grid so that the maximum concentrations and the width of the plume are adequately represented and so that the sub-grid details of plume st ructure can be ignored when estimating effects on light extinction. The standard 1-km output grid that has been established for Class I area analyses should serve down to source- receptor distances somewhat under 30 km; how much closer than 30 km will depend on the topography and meteorology of the area and should be evaluated on a case-by-case basis with individual CENRAP State modelers. (For reference, the width of a Gaussian plume, 2 σ y 

### , is roughly 1 km after 10 km of travel distance, assuming Pasqui ll-Gifford dispersi on rates under neutral conditions.)

3.4.1 CALPOST Methods

Calculation of the impact of the simu lated plume particulate matter component concentrations on light extinction is carried out in the CALPOST postprocessor. For BART applications, this processor is of considerable importance.

### J-17 CALPOST is used to process the CALP UFF outputs, producing tabulations that summarize the results of the simulations, iden tifying for example, the highest and secondhighest hourly-average concentrations at each receptor. When performing visibilityrelated modeling, CALPOST uses concentra tions from CALPUFF to compute light extinction and related measures of visibil ity (deciviews), reporting these for a 24-hour averaging time. The CALPOST processor contai ns several options for evaluating visibility impacts, including the method described in the BART guidance, which uses monthly average relative humidity values. CALPOS T contains implementations of the IWAQMrecommended and FLAG-recomme nded visibility techniques and additional options to evaluate the impact of natura l weather events (fog, rain and snow) on background visibility and visibility impacts from m odeled sources. CALPOST uses Equation 3-1 to calculate the extinction increment due to the source of interest and provides various methods for estimating the background extinction against which the increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processor contains seven techniques for computing the change in light extinction due to a source or group of sources (i.e., Methods 1 through 7). These ar e usually reported as 24-hour average values, consistent with EPA and FLM guidance. In addition, there are two techniques for computing the 24-hour average change in extinction (i.e., as the ratio of 24-hour average extinctions, or as the average of 24-hour ratios). Method 2 is the current default, recommended by both IWAQM (EPA, 1998) and FLAG (2000) for sour ce-specific. Method 6 is recommended by EPA’s BART guidance (70 FR 39162).

In Method 2, user-specified, speciated mont hly concentration values are used to describe the background. When applied to natural conditions, for which EPA’s default natural conditions concentrations are annual averages, the same component concentrations would have to be used throughout the year (unless potential refinements to those default values resulted in concentrations that vary during the year). Hourly background extinction is then calculated using these conc entrations and hourly, sitespecific f(RH) from a 1993 IWAQM curve or, optionally, the EPA regional haze f(RH)

curve. 2 

### Again the RH is capped at either 98% (default) or a user-selected value (most commonly at 95%).

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly climatologically representative values ) are used in place of hourly values for calculating both the extinction impact of the source emissions and the background conditions extinction. Hourly source impacts, w ith the effect on extinc tion due to sulfates and nitrates calculated usi ng the monthly-average relati ve humidity in f(RH), are compared against the monthly default natu ral background concen trations. Thus the monthly-averaged relative humidity is appl ied to the hygroscopi c components (i.e., sulfate and nitrate) of both the source impact and the b ackground extinction with Method 6.

2 Note that the hourly-varying natural background extinc tion here is not consistent with that prescribed by the EPA’s natural conditions guidance (EPA, 2003b) , for which a “climatologically-representative” f(RH) that only varies monthly is to be used. Method 6 uses these monthly average humidity values.

### J-18 3.4.2 POSTUTIL

The POSTUTIL processor allows the cumulative impacts of multiple sources from different simulations to be summed, including computing the difference between two sets of predicted impacts (useful for evalua ting the benefits of BART controls). It also contains a chemistry module to evaluate the equilibrium relati onship between nitric acid and nitrate aerosols. Th is capability allows the potential non-linear effects of ammonia scavenging by background sulfate and nitrate sources to be approximated in the formation of nitrate from an individual sour ce. The processor can compute the impacts of individual sources or groups of sources on sulfur and nitr ogen deposition into aquatic, forest and coastal ecosystems, thereby allo wing changes in depos ition fluxes resulting from changes in emissions to be quantified.

The POSTUTIL processor attempts to overcome the bias introduced when CALPUFF assumes that the full background a mmonia concentration is entrained into each discrete puff. For a single puff, this may be satisfactory, but the model overestimates the production of ammonium nitrate when multiple puffs co-exist and overlap. The POSTUTIL processor re-p artitions the ammoni a and nitric acid concentrations to conform to the ammoni a-limiting processes influencing nitrate formation. Though based on recognized science, this approximate post-processing method is fundamentally dependent on reliable estimates of ambient NH 3 

### at the Class I receptor of interest.

3.4.3 Refined Extinction and Back ground Visibility Estimates

EPA, the IMPROVE Steering Committee, and the RPOs are evaluating whether refinements are warranted to the methods recommended for calculating extinction and the default estimate of natural background visibi lity. Whether EPA will approve of any changes to the IMPROVE equation is uncertain at this time. Also, the responsibility for incorporating any changes to the algorithm s in CALPUFF (e.g., new f(RH) curves) is unclear. If changes to these methods are recommended by EPA, CENRAP is encouraged to adopt them. However, details of the proc ess for incorporation of any refinements to the IMPROVE equations in the CALPUFF system should be addressed in the State’s or source operators modeling protocol.

3.5 Model Availability

The EPA-approved version of the CALPUF F modeling system is available from Earth Tech, Inc., ( http://www.src.com/calpuff/calpuff1.htm ). The main models (CALMET, CALPUFF, and CALPOST), their GUIs, and many of the processors are available to download. One may also register to receive no tices of model updates. The most recent update to the system (25 May 2005) is a new version of CALMM5 (MM5 V3) that has been added to the Download BETA-Test page. This version of CALMM5 processes MM5 Version 3 output data directly.

### J-19 Earth Tech offers CALPUFF training cour ses that include a description of the technical formulation of the models, overvie ws of each of the processor programs, and hands-on application of the models to seve ral case study data sets . Attendees of the course receive a training notebook, a wor kbook of case study problems, exercises, and data sets, updates on recent and future mode l enhancements, and the latest (proprietary) versions of the models and Graphical User Interfaces (GUIs). Othe r third-party training courses and materials are also available.

3.6 CALPUFF Evaluation Studies

Tesche (2002, 2003) reviewed results of various CALPUFF evaluation studies and reached the following conclusions:

> There is a paucity of model ev aluation information for CALPUFF at scales of 50 to 200 km and beyond;

> Based on the limited information available, CALPUFF may be able to give unbiased estimates of short-term (i.e., 3-10 hr) concentrations of nonreactive contaminants to within a factor of two (e.g. 200%) out to distances of about 200 km from a source. This level or uncertainty in a 200 km radius around a source is incr eased if one examines CALPUFF’s predictions in a particular modeli ng cell (e.g., one containing a population center) at a specific hour as opposed to considering the question of bias generally over the entire 200 km regi on irrespective of location and time of occurrence;

> For time periods of a day or less, CALPUFF is unable to produce reliable predictions of non-reactive concentrations at a specific location and time;

> What limited experimental data do exist suggest that the accuracy and reliability of the model’s predictions degrade as the distance scale increases;

> While the IWAQM recommendations on the range of applicability of the CALPUFF model (50 to 200 km) rests on very sparse model evaluation information, EPA’s suggestion that th e model can be used for scales beyond 200 km, even with case-by-case approval, is not based on model evaluation data; and

> For chemically reactive pollutants such as SO 2 

### , NO x 

### , sulfate, nitrate, nitric acid, and other secondary reaction products, the testing of CALPUFF model over extended spatial scales (50 km and beyond) has not been attempted in a rigorous manner.

Scire et al., (2001) report an evaluati on of CALPUFF sulfate, nitrate, light extinction, and sulfur and nitrogen deposition at a Cla ss I areas over a range of source### J-20 receptor distances. In this study, in whic h a large number of sources were modeled simultaneously, sulfate and nitrate predicti ons at the CASTNet monitoring site in Pinedale, Wyoming were evaluated against obs ervations, and light extinction predictions were evaluated using transmissometer m easurements. Wet sulfur and nitrogen predictions were compared to observations at several acid deposition monitoring sites. This study is especially releva nt because it evaluates the performance of the model to predict variables of direct interest in Class I visibility analyses, such as sulfate and nitrate concentrations and light extinction coefficients

More recently, Chang et al., (2003) repor ted an intercomparison of CALPUFF with two other transport and disp ersion models with high resolution fi eld data. CALPUFF predictions for inert SF 6 

### were compared using two recent mesoscale fi eld datasets: the Dipole Pride 26 (DP26) and the Overla nd Along-wind Dispersion (OLAD). Both fi eld experiments involved instantane ous releases of sulfur hexa fl uoride tracer gas in a mesoscale region with desert basins and m ountains. Tracer concentrations were observed along lines of samplers at distances up to 20 km. CALPUFF predictions were evaluated using the maximum 3-h dosage (concentrati on integrated over time) along a sampling line. At the DP26 sampler array, CALPUFF had mean biases with in 35% and random scatters of about a factor of 3–4. About 50%–60% of the CALPU FF predictions were within a factor of 2 of th e observations. At the OLAD site, the model underpredicted by a factor of 2–3, on average, with random scat ters of a factor of 3–7. Only about 25%– 30% of the CALPUFF predictions of inert SF 6 

### were within a factor of 2 of observations.

The tracer studies with which CALPUFF tr ansport and diffusion capabilities were evaluated in the IWAQM Phase 2 report were generally over distances greater than 50 km. More recently, model performance has been performed at shorte r distances including a power plant in Illinois in simple terrain at source-receptor distances in arcs ranging from 0.5 km to 50 km from the stack (Strimaitis et al., 1998). Another CALPUFF evaluation study over short-distances is re ported by Morrison et al. (2003). These studies address model performance over s ource-receptor distances from a few hundred meters to 50 km.

### J-21 Figure 3-1. CALPUFF Modeling Syst em Components. (Scire et al., 2000a)

### J-22 Table 3-1. Major Features of the CALME T Meteorological Model. (Scire et al., 2000b)

• Boundary Layer Modules of CALMET

- Overland Boundary Layer - Energy Balance Method - Overwater Boundary Layer - Profile Method - Produces Gridded Fields of: -- Surface Friction Velocity -- Convective Velocity Scale -- Monin-Obukhov Length -- Mixing Height -- PGT Stability Class -- Air Temperature (3-D) -- Precipitation Rate

• Diagnostic Wind Fiel d Module of CALMET - Slope Flows - Kinematic Terrain Effects - Terrain Blocking Effects - Divergence Minimization - Produces Gridded Fields of U, V, W Wind Components - Inputs Include Domain-Scale Winds, Observations, and (optionally) Coarse-Grid Prognostic Model Winds - Lambert Conformal Projection Capability

### J-23 Table 3-2. Major Features of the CALPU FF Dispersion Model (Scire et al., 2000a)

• Source types - Point sources (constant or variable emissions) - Line sources (consta nt or variable emissions) - Volume sources (constant or variable emissions) - Area sources (constant or variable emissions)

• Non-steady-state emissions and meteorological conditions - Gridded 3-D fields of meteorological variables (winds, temperature) - Spatially-variable fields of mixi ng height, friction velocity, convective velocity scale, Monin-Obukhov le ngth, precipitation rate - Vertically and horizontally-var ying turbulence and dispersion rates - Time-dependent source and emi ssions data for point, area, and volume

sources - Temporal or wind-dependent scal ing factors for emission rates, for all source types

• Interface to the Emission s Production Model (EPM) - Time-varying heat flux and em issions from controlled burns and wildfires

• Efficient sampling functions - Integrated puff formulation - Elongated puff (slug) formulation

• Dispersion coefficient ( σ y 

### , σ z 

### ) options - Direct measurements of σ v 

### and σ w 

### - Estimated values of σ v 

### and σ w 

### based on similarity theory - Pasquill-Gifford (PG) disp ersion coefficients (rural areas) - McElroy-Pooler (MP) dispersion coefficients (urban areas) - CTDM dispersion coe fficients (neutral/stable)

• Vertical wind shear - Puff splitting - Differential advection and dispersion

• Plume rise - Buoyant and momentum rise - Stack tip effects - Building downwash effects - Partial penetration - Vertical wind shear

• Building downwash

### J-24 - Huber-Snyder method - Schulman-Scire method

-

 PRIME method

Table 3-2. Major Features of the CALPUFF Dispersion Model (Concluded).

• Complex terrain - Steering effects in CALMET wind field - Optional puff height adjustment: ISC3 or "plume path coefficient" - Optional enhanced vertical disp ersion (neutral/weakly stable flow in CTDMPLUS)

• Subgrid scale complex terrain (CTSG option) - Dividing streamline, H d 

### , as in CTDMPLUS: - Above H d 

### , material flows over the hill and experiences altered diffusion rates - Below H d 

### , material deflects around the hill, splits, and wraps around the hill

• Dry Deposition - Gases and particulate matter - Three options: - Full treatment of space and time variations of deposition with a resistance model - User-specified diur nal cycles for each pollutant - No dry deposition

• Overwater and coastal interaction effects - Overwater boundary layer parameters - Abrupt change in meteorological conditions, plume dispersion at coastal boundary - Plume fumigation

• Chemical transformation options - Pseudo-first-order chemical mechanism for SO 2 

### , SO = 4 

### , NO x 

### , HNO 3 

### , and NO 3 

### (MESOPUFF II method) - Pseudo-first-order chemical mechanism for SO 2 

### , SO = 4 

### , NO, NO 2 

### HNO 3 

### , and NO 3 

### (RIVAD/ARM3 method) - User-specified diurnal cycles of transformation rates - No chemical conversion

### J-25 • Wet Removal - Scavenging coefficient approach

- Removal rate a function of precip itation intensity and precipitation 

### type

### J-26 Table 3-3. Parameter Variations in Box Model Simulations Used to Develop the CALPUFF Sulfate and Nitrate Forma tion Algorithms. (Morris et al., 2003).

Surrogate Parameter Number of Variations Model Input Parameters And Variations

Season 3 Temperatures of 30, 20 and 10 ° C were used for the, respectively, summer, fall and winter seasons. Diurnally varying clear sk ies solar radiation was assumed for each season corresponding to a latitude of 40 °

. Background Air Reactivity 4 For the summer season the following four levels of background ozone and VOCs were used: Ozone (ppb) VOC (ppbC) 20 50 50 250 80 500 200 2,000 For fall and winter the ozone concentrations were assumed to be 75% and 50% of the summer levels. Dispersion 2 Two different rates of plume dispersion were used: (1) a stable case with a wi nd speed of 1.5 m/s and; (2) a slightly unstable case w ith a wind speed of 5.0 m/s. Release Time 2 Photochemical box model simulations were performed with release tim es of sunrise and noon. Plume NOx Concentration 3 Initial plume NOx concentrations of 7, 350 and 1400 ppb were used.Domtar Industries B-1 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## A PPENDIX 

## B

– C ONTROLS 

## C OST 

## A NALYSESCosts ($)

Purchased Equipment Cost a 1,050,000 $ Budgetary Qualifier (+/-25%) 262,500 1,312,500 Direct installation costs Foundation and supports Handling and erection Electrical Piping Insulation Painting Direct installation Costs 656,250 1,968,750 $

=1.0 x Total Direct Cost Engineering Construction and field expenses Contrator fees Start-up Performance test Contingencies Structural Modification (4%) 1,968,750 $

3,937,500 $

Operating Labor c 9,022 Operating Labor Supervision d 1,353 Maintenance Labor e 9,922 Maintenance Materials f 9,922 Utilities - Electricity g 112,560 Utilities - Natural Gas l 3,108,000 Waste Treatment & Disposal Total Direct Operating Costs 3,250,779 $

Indirect Operating Costs Overhead h 18,131 Property Tax i 39,375 Insurance i 39,375 Administration j 78,750 Capital Recovery (5% interest, 10 year life) k 509,924 685,555 $

3,936,334 $ Total Annualized Costs 3,936,334 1084

0.5 542 7,262 $

a Based on the equipment cost estimate from Energy Systems Associates

b Factored estimate based on recent capital project installations

c Operating labor = 0.75 hours/day @ $34.37/hr rate for 350 days/year

d Supervisor pay = 15% of Operator pay

e Maintenance = 240 hours @ $41.34/hr

f Maintenance Materials = 100% of Maintenance Labor

g Electrical usage (335kW) associated with running fans; from Energy Systems Associates estimate for OFA/FGR combo

h Overhead = 60% of Labor & Material

I =1% TCI (Total Capital Investment)
j =2% TCI (Total Capital Investment) k =factor of 0.129504575 for 5% interest on 10 year life
l = Natural gas usage at $370/hr at $8/MMBT U Estimated Average Cost ($/ton) of Methane DeNOx on No. 1 Power Boiler - NOx Control

CAPITAL COSTS Direct Costs

Total Uncontrolled NOx Emissions (tpy) TOTAL ANNUALIZED COSTS (TAC = DOC + IOC) OPERATING COSTS Direct Operating Costs

Total Indirect Operating

Pollutant Removed(tpy) Cost/Ton Pollutant Removed Total Direct Capital Cost = Equip Cost + 1.5*Equip Cost b

Indirect Capital Costs

Total Indirect Capital Costs b

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

Removal EffciencyCosts ($)

Purchased Equipment Cost a 2,980,000 $ Budgetary Qualifier (+/-25%) 745,000 3,725,000 Direct installation costs Foundation and supports Handling and erection Electrical Piping Insulation Painting Direct installation Costs 1,862,500 5,587,500 $

=1.0 x Total Direct Cost Engineering Construction and field expenses Contrator fees Start-up Performance test Contingencies Structural Modification (4%) 5,587,500 $

11,175,000 $

Operating Labor c 9,022 Operating Labor Supervision d 1,353 Maintenance Labor e 9,922 Maintenance Materials f 9,922 Utilities - Electricity g 112,560 Utilities - Water Waste Treatment & Disposal Total Direct Operating Costs 142,779 $

Indirect Operating Costs Overhead h 18,131 Property Tax i 111,750 Insurance i 111,750 Administration j 223,500 Capital Recovery (5% interest, 10 year life) k 1,447,214 1,912,345 $

2,055,123 $ Total Annualized Costs 2,055,123 1084

0.4 434 4,740 $

a Based on the equipment cost estimate provided by Jansen Combustion and Boiler Technologies Inc.

b Factored estimate based on recent capital project installations

c Operating labor = 0.75 hours/day @ $34.37/hr rate for 350 days/yea

d Supervisor pay = 15% of Operator pa y

e Maintenance = 240 hours @ $41.34/h r
f Maintenance Materials = 100% of Maintenance Labo

g Electrical usage (335kW) associated with running fans; from Energy Systems Associates estimate for OFA/FGR comb o
h Overhead = 60% of Labor & Materia l

I =1% TCI (Total Capital Investment ) j =2% TCI (Total Capital Investment ) k =factor of 0.129504575 for 5% interest on 10 year life Pollutant Removed(tpy) Cost/Ton Pollutant Removed Total Direct Capital Cost = Equip Cost + 1.5*Equip Cost b

Indirect Capital Costs

Total Indirect Capital Costs b

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

Removal Effciency

Estimated Average Cost ($/ton) of OFA System Upgrade on No. 1 Power Boiler - NOx Control

CAPITAL COSTS Direct Costs

Total Uncontrolled NOx Emissions (tpy) TOTAL ANNUALIZED COSTS (TAC = DOC + IOC) OPERATING COSTS Direct Operating Costs

Total Indirect OperatingCosts ($)

Purchased Equipment Cost a 1,200,000 $ Budgetary Qualifier (+/-25%) 300,000 1,500,000 Direct installation costs Foundation and supports Handling and erection Electrical Piping Insulation Painting Direct installation Costs 750,000 2,250,000 $

=1.0 x Total Direct Cost Engineering Construction and field expenses Contrator fees Start-up Performance test Contingencies Structural Modification (4%) 2,250,000 $

4,500,000 $

Operating Labor c 9,022 Operating Labor Supervision d 1,353 Maintenance Labor e 9,922 Maintenance Materials f 9,922 Utilities - Electricity g 141,120 Utilities - Natural Gas l 4,401,600 Waste Treatment & Disposal Total Direct Operating Costs 4,572,939 $

Indirect Operating Costs Overhead h 18,131 Property Tax i 45,000 Insurance i 45,000 Administration j 90,000 Capital Recovery (5% interest, 10 year life) k 582,771 780,902 $

5,353,840 $ Total Annualized Costs 5,353,840 2514

0.5 1257 4,259 $

a Based on the equipment cost estimate from Energy Systems Associates

b Factored estimate based on recent capital project installations

c Operating labor = 0.75 hours/day @ $34.37/hr rate for 350 days/year

d Supervisor pay = 15% of Operator pay

e Maintenance = 240 hours @ $41.34/hr

f Maintenance Materials = 100% of Maintenance Labor

g Electrical usage (420kW) associated with running fans; from Energy Systems Associates estimate for OFA/FGR combo

h Overhead = 60% of Labor & Material

I =1% TCI (Total Capital Investment)
j =2% TCI (Total Capital Investment) k =factor of 0.129504575 for 5% interest on 10 year life

l = Natural gas usage at $524/hr at $8/MMBTU Estimated Average Cost ($/ton) of Methane DeNOx on No. 2 Power Boiler - NOx Control

CAPITAL COSTS Direct Costs

Total Uncontrolled NOx Emissions (tpy) TOTAL ANNUALIZED COSTS (TAC = DOC + IOC) OPERATING COSTS Direct Operating Costs

Total Indirect Operating

Pollutant Removed(tpy) Cost/Ton Pollutant Removed Total Direct Capital Cost = Equip Cost + 1.5*Equip Cost b

Indirect Capital Costs

Total Indirect Capital Costs b

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

Removal EffciencyCosts ($)

Purchased Equipment Cost a 4,338,880 $ Budgetary Qualifier (+/-25%) 1,084,720 5,423,600 Direct installation costs Foundation and supports Handling and erection Electrical Piping Insulation Painting Direct installation Costs 2,711,800 8,135,400 $

=1.0 x Total Direct Cost Engineering Construction and field expenses Contrator fees Start-up Performance test Contingencies Structural Modification (4%) 8,135,400 $

16,270,800 $

Operating Labor c 9,022 Operating Labor Supervision d 1,353 Maintenance Labor e 9,922 Maintenance Materials f 9,922 Utilities - Electricity g 141,120 Utilities - Water Waste Treatment & Disposal Total Direct Operating Costs 171,339 $

Indirect Operating Costs Overhead h 18,131 Property Tax i 162,708 Insurance i 162,708 Administration j 325,416 Capital Recovery (5% interest, 10 year life) k 2,107,143 2,776,106 $

2,947,445 $ Total Annualized Costs 2,947,445 2514

0.4 1006 2,931 $

a Scaled from quote for #1 PB based on six-tenths factor rule for cost estimation from Peters, Max S. and Timmerhaus, Klaus D., Plant Design and Economics for Chemical Engineers, Fourth Edition, McGraw-Hill, Inc., 1991, p. 169.

b Factored estimate based on recent capital project installations

c Operating labor = 0.75 hours/day @ $34.37/hr rate for 350 days/year

d Supervisor pay = 15% of Operator pay

e Maintenance = 240 hours @ $41.34/hr

f Maintenance Materials = 100% of Maintenance Labor

g Electrical usage assumption (420kW) associated with running auxiliary equipment; from Energy Systems Associates
estimate for OFA/FGR combo

h Overhead = 60% of Labor & Material

I =1% TCI (Total Capital Investment)
j =2% TCI (Total Capital Investment) k =factor of 0.129504575 for 5% interest on 10 year life Estimated Average Cost ($/ton) of OFA System Upgrade on No. 2 Power Boiler - NOx Control

CAPITAL COSTS Direct Costs

Total Uncontrolled NOx Emissions (tpy) TOTAL ANNUALIZED COSTS (TAC = DOC + IOC) OPERATING COSTS Direct Operating Costs

Total Indirect Operating

Pollutant Removed(tpy) Cost/Ton Pollutant Removed Total Direct Capital Cost = Equip Cost + 1.5*Equip Cost b

Indirect Capital Costs

Total Indirect Capital Costs b

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

Removal EffciencyCosts ($)

Purchased Equipment Cost a 1,800,000 $

Direct installation costs Foundation and supports Handling and erection Electrical Piping Insulation Painting Direct installation Costs 900,000 2,700,000 $

=1.0 x Total Direct Cost Engineering Construction and field expenses Contrator fees Start-up Performance test Contingencies Structural Modification (4%) 2,700,000 $

5,400,000 $

Operating Labor c 9,022 Operating Labor Supervision d 1,353 Maintenance Labor e 9,922 Maintenance Materials f 9,922 Utilities - Electricity g 141,120 Utilities - Water Waste Treatment & Disposal Total Direct Operating Costs 171,339 $

Indirect Operating Costs Overhead h 18,131 Property Tax i 54,000 Insurance i 54,000 Administration j 108,000 Capital Recovery (5% interest, 10 year life) k 699,325 933,456 $

1,104,79 5 $ Total Annualized Costs 1,104,795 2514

0.3 754 1,465 $ Estimated Average Cost ($/ton) of Low NOx Burners on No. 2 Power Boiler - NOx Control

CAPITAL COSTS Direct Costs

Total Direct Capital Cost = Equip Cost + 1.5*Equip Cost b

Indirect Capital Costs

Total Indirect Capital Costs b

TOTAL CAPITAL INVESTMENT (TCI = DC+IC) OPERATING COSTS

Removal Effciency Pollutant Removed(tpy) Cost/Ton Pollutant Removed Direct Operating Costs

Total Indirect Operating

TOTAL ANNUALIZED COSTS (TAC = DOC + IOC)

Total Uncontrolled NOx Emissions (tpy)Domtar Industries C-1 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## A PPENDIX 

## C

– C OMPLIANCE 

## P LAN AND 

## S CHEDULE

Domtar will implement control measures or other options for reducing emissions to comply with the proposed BART limits as expeditiously as possible and before the date five years after EPA approval of ADEQ’s BART State Implementation Plan (SIP), as required by Regional Haze Rule and BART Guidelines.

Proposed BART Compliance Timeline

May 14-20, 2007 Installation of WESP on No. 1 Power Boiler complete May 21, 2007 Tentative startup of WESP on No. 1 Power Boiler September 13, 2007 Boiler MACT Compliance Deadline March 11, 2008 Last day to show complian ce with Boiler MACT = Date achieve BART Particulate Matter limits Early 2010 NO X Reduction Technology Evaluation Late 2011 NO X Reduction Technology Selected Late 2012 Installation of selected NO X Reduction Technology Early 2013 BART Compliance DeadlineDomtar Industries D-1 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc 

## A PPENDIX 

## D

– R EQUESTED 

## D ESIGN 

## D ETAILS OF 

## N O



## . 1 P OWER 

## B OILER

The No. 1 power boiler, built by Babcock & Wilcox in 1967, is a balanced draft, two drum sterling boiler designed to burn natural gas, fuel oil and bark for the production of steam.

The No. 1 power boiler has a maximum continuous steam rating of 275,000 lbs/hr at 850 psig and 850°F. The boiler discharges steam into the mill's 850# high pressure header system.

The No. 1 power boiler is typically a swing boiler (adjusts its fuel firing rate) to follow the 850 psig header pressure.

The fuel system consists of the three separate subsystems listed below that deliver combustible material into the boiler furnace.

- Bark System - supplies bark, wood waste, pelletized paper fuel, tire-derived fuel and municipal yard waste from the woodyard area and distributes it onto the grate for burning. Bark is the primary fuel source for No. 1 power boiler.

- Natural Gas System - supplies gas from th e main mill pipeline to the boiler's six burners and ignitors. Natural gas is used to warm up the boiler during start-up and to supplement bark combustion to maintain load.

- No. 6 Fuel Oil System - prepares and supplies No. 6 fuel oil, used oil generated on site or reprocessed fuel oil to the boiler's burners. Oil serves primarily as a backup to natural gas and is not normally fired.

The combustion air system consists of the three s ubsystems listed below that provide the oxygen for fuel combustion.

- Air Supply & Preheat System - provides a steady supply of combustion air at the necessary flows and temperatures to ensure efficient combustion.

- Burner Air System - provides air for the combustion of natural gas and/or fuel oil at the burners.

- Bark Air System - provides air for drying and burning of bark system fuels on the grates (undergrate air), combustion of bark system fuels above the grates (overfire air) and distribution of bark system fuels onto the grates (distribution air).

The flue gas system consists of several components listed below that handle the by-products of combustion.

- Tubular Air Heater - transfers heat fro m the flue gas to the combustion air.Domtar Industries D-2 Trinity Consultants Ashdown Mill H:\2DJ\Domtar\P06_081\BA RT17_Submitted on 2007-03-27.doc - Mechanical Dust Collector - removes e nvironmentally harmful particulate from the flue gas prior to atmospheric discharge.

- ID Fan - removes the flue gas from the fu rnace at a controlled rate to maintain a balanced draft.

- Stack - discharges the flue gas to atmosphere.

- Sootblowers - clean the tube surfaces of ash and slag deposited from the flue gas.

The No. 1 Power Boiler will undergo a modification in May 2007 that will entail the installation of a Wet Electrostatic Precipitator to bring the boiler into compliance with the Boiler MACT regulation for particulate matter emissions.