BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

July 28, 2006 Page D-7 

### Cape Romain Class I Area Lambert Conformal X (km) Lambert Conformal Y (km) Height (m)
1609.918 -633.619 0 1610.682 -633.471 1 1610.506 -632.561 1 1611.27 -632.414 1 1611.094 -631.504 1 1611.858 -631.356 1 1610.154 -630.742 1 1610.918 -630.594 1 1611.682 -630.446 1 1612.446 -630.298 1 1610.742 -629.684 1 1611.506 -629.537 1 1612.27 -629.389 1 1610.566 -628.775 0 1611.33 -628.627 1 1612.093 -628.479 1 1612.857 -628.331 1 1610.39 -627.865 1 1611.154 -627.717 1 1610.214 -626.955 1 1610.978 -626.807 1 1611.741 -626.66 1 1610.802 -625.898 1 1611.565 -625.75 1 1610.626 -624.988 1 1611.389 -624.84 2 1612.152 -624.693 1 1615.968 -623.952 0 1611.213 -623.931 1 1611.976 -623.783 1 1616.554 -622.895 0 1611.037 -623.021 1

1611.8 -622.873 1 1612.387 -621.816 1 1613.149 -621.668 1 1617.727 -620.779 0 1612.973 -620.758 1 1613.736 -620.61 1 1621.949 -618.069 0 1622.712 -617.92 1 1623.474 -617.772 1BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

July 28, 2006 Page D-8 

### Cape Romain Class I Area Lambert Conformal X (km) Lambert Conformal Y (km) Height (m)
1614.146 -618.643 1 1623.297 -616.862 1 1624.059 -616.713 1 1624.822 -616.564 1 1625.584 -616.415 1 1633.208 -614.921 1 1633.97 -614.771 0 1614.732 -617.586 1 1621.594 -616.25 0 1622.357 -616.102 1 1623.119 -615.953 1 1623.882 -615.804 1 1624.644 -615.655 1 1625.406 -615.506 1 1626.169 -615.357 1 1626.931 -615.208 1 1627.693 -615.059 1 1628.456 -614.909 2 1629.218 -614.76 2 1629.98 -614.611 1 1630.742 -614.461 2 1631.505 -614.311 1 1633.791 -613.862 0 1615.318 -616.528 1 1616.08 -616.38 1 1616.843 -616.232 1 1620.655 -615.49 0 1621.417 -615.341 1 1622.179 -615.192 1 1622.942 -615.044 1 1623.704 -614.895 1 1624.466 -614.746 1 1625.229 -614.597 1 1628.278 -614 1 1629.04 -613.851 1 1632.088 -613.253 1 1634.375 -612.803 0 1619.715 -614.729 0 1620.477 -614.581 1 1621.24 -614.432 1 1622.002 -614.283 1BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

July 28, 2006 Page D-9 

### Cape Romain Class I Area Lambert Conformal X (km) Lambert Conformal Y (km) Height (m)
1622.764 -614.134 1 1623.526 -613.986 1 1624.289 -613.837 1 1625.051 -613.688 1 1625.813 -613.539 1 1626.575 -613.389 1 1627.337 -613.24 1 1629.624 -612.792 1 1630.386 -612.643 1 1631.148 -612.493 1 1631.91 -612.344 1 1634.196 -611.894 0 1618.776 -613.968 1 1619.538 -613.82 1

1620.3 -613.671 1 1621.062 -613.523 1 1621.824 -613.374 1 1622.587 -613.225 1 1623.349 -613.076 1 1624.111 -612.927 1 1624.873 -612.778 1 1625.635 -612.629 1 1626.397 -612.48 1 1627.159 -612.331 1 1627.921 -612.182 1 1628.683 -612.033 1 1629.445 -611.883 1 1630.207 -611.734 1 1630.969 -611.584 1 1634.017 -610.985 0 1620.123 -612.762 1 1620.885 -612.613 1 1621.647 -612.465 1 1622.409 -612.316 1 1623.171 -612.167 1 1623.933 -612.018 1 1624.695 -611.869 1 1625.457 -611.72 1 1626.219 -611.571 1 1626.981 -611.422 1 1627.743 -611.273 1BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

July 28, 2006 Page D-10 

### Cape Romain Class I Area
Lambert Conformal X (km) Lambert Conformal Y (km) Height (m)
1628.505 -611.123 1 1633.077 -610.226 0 1633.839 -610.076 1

1634.6 -609.927 1 1621.47 -611.555 1 1622.232 -611.407 1 1622.994 -611.258 1 1623.756 -611.109 1 1624.517 -610.96 1 1625.279 -610.811 1 1626.041 -610.662 1 1626.803 -610.513 1 1627.565 -610.364 1 1628.327 -610.214 1 1629.089 -610.065 1 1629.851 -609.916 1 1630.613 -609.766 1 1634.422 -609.018 0 1624.34 -610.051 1 1625.102 -609.902 1 1625.863 -609.753 1 1626.625 -609.604 1 1627.387 -609.455 1 1628.149 -609.305 1 1628.911 -609.156 1 1629.672 -609.007 1 1630.434 -608.857 1 1634.243 -608.109 0 1628.732 -608.247 1 1629.494 -608.098 1 1630.256 -607.948 1 1631.017 -607.799 1 1629.316 -607.189 1 1630.077 -607.039 1 1630.839 -606.89 1 1631.601 -606.74 1 1629.899 -606.13 1 1630.66 -605.981 1 1631.422 -605.831 1 1629.721 -605.221 1 1630.482 -605.072 1BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

July 28, 2006 Page D-11 

### Joyce Kilmer Slickrock Lambert

Confor mal X (km) Lambert Conforma

l Y (km) Height (m) Lambert Conforma

l X (km) Lambert Conforma

l Y (km) Height (m) Lambert Conformal

X (km) Lambert Conforma

l Y (km) Height (m)

1174.116 -429.607 1154 1168.724 -423.854 1065 1174.861 -429.499 1056 1169.468 -423.747 836 1175.607 -429.391 903 1170.213 -423.64 700 1176.352 -429.283 820 1170.958 -423.532 782 1177.097 -429.175 818 1171.703 -423.425 961 1173.239 -428.8 1278 1173.937 -423.102 809 1173.984 -428.693 1069 1174.682 -422.995 941 1174.729 -428.585 913 1167.847 -423.047 991 1175.475 -428.477 776 1168.592 -422.94 817 1176.22 -428.369 788 1169.337 -422.833 751 1176.965 -428.262 750 1170.081 -422.726 635 1172.362 -427.994 1327 1170.826 -422.619 772 1173.107 -427.887 1085 1171.571 -422.511 882 1173.852 -427.779 985 1168.461 -422.026 954 1174.597 -427.671 934 1169.205 -421.919 739 1175.342 -427.563 978 1169.95 -421.812 620 1176.088 -427.456 982 1170.694 -421.705 635 1176.833 -427.348 824 1171.439 -421.597 700 1169.995 -427.402 1317 1172.184 -421.49 865 1170.74 -427.295 1394 1168.329 -421.113 943 1171.485 -427.188 1487 1169.074 -421.005 742 1172.23 -427.08 1308 1169.818 -420.898 505 1172.975 -426.973 1237 1170.563 -420.791 558 1173.72 -426.865 1336 1171.307 -420.684 538 1174.465 -426.757 1290 1172.052 -420.576 686 1175.21 -426.65 1304 1168.198 -420.199 946 1175.955 -426.542 1111 1168.942 -420.092 760

1176.7 -426.434 1066 1169.686 -419.984 644 1169.863 -426.489 1189 1170.431 -419.877 491 1170.608 -426.381 1127 1171.175 -419.77 442 1171.353 -426.274 1299 1171.92 -419.662 491 1172.098 -426.166 1380 1168.066 -419.285 959 1172.843 -426.059 1553 1168.81 -419.178 752 1173.588 -425.951 1336 1169.555 -419.071 528 1174.333 -425.844 1219 1170.299 -418.963 598 1175.078 -425.736 1109 1171.043 -418.856 515 1175.823 -425.628 1133 1171.788 -418.749 400 1176.568 -425.52 1226 1172.532 -418.641 523 1168.987 -425.682 1280 1167.935 -418.371 712 1169.731 -425.575 1136 1170.167 -418.05 743 1170.476 -425.467 906 1170.912 -417.942 684 1171.221 -425.36 999 1171.656 -417.835 538 1171.966 -425.253 1170 1172.4 -417.727 361 1172.711 -425.145 1376 1173.456 -425.037 1318 1174.201 -424.93 1369 1174.946 -424.822 1152 1175.691 -424.714 924 1176.436 -424.607 882 1168.11 -424.875 1151 1168.855 -424.768 1116

1169.6 -424.661 1044 1170.345 -424.554 772 1171.09 -424.446 866 1171.834 -424.339 1090 1173.324 -424.124 1020 1174.069 -424.016 1067 1167.979 -423.961 1224BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

July 28, 2006 Page D-12 

### Linville Gorge Lambert Conforma

l X (km) Lambert Conforma

l Y (km) Height (m) Lambert Conforma

l X (km) Lambert Conformal

Y (km) Height (m) Lambert Conforma

l X (km) Lambert Conforma

l Y (km) Height (m)
1349.498 -350.859 468 1346.472 -337.331 924 1350.236 -350.735 457 1347.209 -337.207 901 1349.345 -349.949 478 1347.946 -337.084 1114 1350.083 -349.825 576 1348.683 -336.96 1219 1347.717 -349.286 550 1345.583 -336.544 946 1348.455 -349.162 472 1346.32 -336.421 983 1349.193 -349.039 721 1347.056 -336.297 1142 1349.931 -348.915 759 1346.167 -335.511 1063 1350.669 -348.791 600 1351.406 -348.667 517 1347.564 -348.376 641 1348.302 -348.252 439 1349.04 -348.129 607 1349.778 -348.005 881 1350.516 -347.881 758 1347.412 -347.466 920 1348.15 -347.342 665 1348.887 -347.219 458 1349.625 -347.095 585 1350.363 -346.971 870 1347.997 -346.432 826 1348.735 -346.308 518 1349.473 -346.185 525 1350.21 -346.061 695 1350.948 -345.937 892 1347.845 -345.522 874 1348.582 -345.398 581 1349.32 -345.275 573 1350.057 -345.151 664 1348.43 -344.488 728 1349.167 -344.364 694 1349.905 -344.241 914 1348.277 -343.578 756 1349.015 -343.454 631 1349.752 -343.331 944 1348.125 -342.668 826 1348.862 -342.544 624 1349.599 -342.42 965 1347.972 -341.758 732 1348.709 -341.634 685 1349.446 -341.51 889 1347.819 -340.848 890 1348.557 -340.724 697 1349.294 -340.6 827 1346.93 -340.061 1096 1347.667 -339.938 749 1348.404 -339.814 816 1349.141 -339.69 1106 1346.777 -339.151 1055 1347.514 -339.027 762 1348.251 -338.904 909 1348.988 -338.78 1092 1345.888 -338.364 941 1346.625 -338.241 813 1347.362 -338.117 953 1348.099 -337.994 1097 1348.835 -337.87 1083 1345.735 -337.454 923

### September 22, 2006

Mr. Dale Herendeen Bowater Incorporated 5300 Cureton Ferry Road P.O. Box 7 Catawba, SC 29704

Re: BART Exemption Modeling

Dear Mr. Herendeen:

SC Department of Health and Environmental Contro l (SC DHEC) Bureau of Air Quality (BAQ) is in the process of reviewing the Best Available Retrof it Technology (BART) modeling report and associated CALPUFF modeling submitted by Bowater for the Cata wba facility. We have not yet completed our review and the consultation process with the EPA a nd Federal Land Managers, but anticipate completing that process shortly.

During our review, we confirmed as you reported, th at all of the particulate for the Bowater BART sources was assigned to the .48 micron size category during the CALPUFF run, as opposed to the data that is presented in your particle speciation Table 2-2. In order to help us complete our review, please provide a rationale as to how this treatment of the par ticulate speciation is at least as conservative as if the particulate species were modeled as listed in Table 2-2. We will also need a letter from the responsible official for the Bowater Catawba facility confirmi ng that the BART modeling report dated July 28, 2006 you submitted is accurate.
Please comply with this request at your earliest convenience. If you will be unable to respond by Friday, October 6, please let me know. Once we have all the information we need, we will be able to complete our consultation process and make our fina l BART determination for the Bowater facility.

We appreciate your assistance in helping us fulfill the BART requirements. If you have any questions concerning this request or the BART process, please contact me.

Sincerely,

John P. Glass, Jr. Modeling Section Manager Bureau of Air Quality glassjp@dhec.sc.gov 803-898-4074

## B EST 

## A VAILABLE 

## R ETROFIT 

## T ECHNOLOGY 

## A PPLICABILITY 

## A NALYSIS 

## A IR 

## Q UALITY 

## M ODELING 

## P ROTOCOL 

## DAK A MERICAS 

## , LLC  C OOPER 

## R IVER 

## F ACILITY

M ONCKS 

### CORNER, S OUTH 

### C AROLINA



### T RINITY 

### C ONSULTANTS 

### 5821 Fairview Road Suite 400 Charlotte, NC 28209 (704) 553-7747

May 2006

Project 053401.0002

### DAK Americas, LLC- Cooper River Facility i Trinity Consultants BART Applicability Modeling Protocol 

## T ABLE OF 

## C ONTENTS



### 1. I NTRODUCTION 

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

### R EGIONAL 

### H AZE 

### R ULE AND 

### BART R EQUIREMENTS 

### ..................... 1-2 1.1.1 D ETERMINATION OF 

### BART- ELIGIBILITY 

### ............................................................1-2 1.1.2 A SSESSMENT OF 

### C ONTRIBUTION TO 

### V ISIBILITY 

### I MPAIRMENT AND 

### BART A PPLICABILITY 

### .................................................................................................................1-3 

### 1.2 S CHEDULE FOR 

### BART I MPLEMENTATION IN 

### S OUTH 

### C AROLINA 

### ........................... 1-7 1.3 O RGANIZATION OF 

### M ODELING 

### P ROTOCOL 

### ............................................................ 1-8

2. BART-E LIGIBLE 

### S OURCE 

### D ESCRIPTION 

### ........................................................... 2-9 2.1 BART-E LIGIBLE 

### E MISSION 

### U NITS 

### ........................................................................ 2-9 2.2 BART- ELIGIBLE 

### S OURCE 

### M ODEL 

### E MISSIONS 

### I NVENTORY 

### ................................. 2-10 2.2.1 P ARTICULATE 

### S PECIATION 

### F OR 

### B OILERS AND 

### P ROCESS 

### H EATERS 

### ..................2-13 

### 2.3 M ODELED 

### S TACK 

### P ARAMETERS AND 

### E MISSIONS 

### ................................................ 2-17

3. G EOPHYSICAL AND 

### M ETEOROLOGICAL 

### D ATA 

### ................................................ 3-20 3.1 T ERRAIN 

### E LEVATIONS WITHIN THE 

### M ODELING 

### D OMAIN 

### .................................... 3-20 3.2 L AND 

### U SE AND 

### C OVER WITHIN THE 

### M ODELING 

### D OMAIN 

### ................................... 3-22 3.3 M ETEOROLOGICAL 

### D ATABASE 

### ........................................................................... 3-22 3.3.1 MM5 S IMULATIONS 

### ..........................................................................................3-23 3.3.2 M EASUREMENTS AND 

### O BSERVATIONS 

### .............................................................3-23 

### 3.4 A IR 

### Q UALITY 

### D ATABASE 

### .................................................................................... 3-24 3.4.1 O ZONE 

### B ACKGROUND 

### C ONCENTRATIONS 

### ........................................................3-24 3.4.2 A MMONIA 

### B ACKGROUND 

### C ONCENTRATIONS 

### ..................................................3-25 3.4.3 O THER 

### P OLLUTANT 

### B ACKGROUND AND 

### B OUNDARY 

### C ONDITIONS 

### .................3-25 

### 3.5 N ATURAL 

### C ONDITIONS AT 

### C LASS 

### I A REAS 

### ......................................................... 3-25

4. A IR 

### Q UALITY 

### M ODELING 

### M ETHODOLOGY 

### ...................................................... 4-28 4.1 P LUME 

### M ODEL 

### S ELECTION 

### ................................................................................. 4-28 4.1.1 M AJOR 

### R ELEVANT 

### F EATURES OF 

### CALMET ....................................................4-29 4.1.2 M AJOR 

### R ELEVANT 

### F EATURES OF 

### CALPUFF ...................................................4-31 

### 4.2 M ODELING 

### D OMAIN 

### C ONFIGURATION 

### ................................................................ 4-33 4.3 CALMET M ETEOROLOGICAL 

### M ODELING 

### .......................................................... 4-33 4.4 CALPUFF C OMPUTATIONAL 

### D OMAIN AND 

### R ECEPTORS 

### .................................... 4-34 4.5 CALPUFF M ODELING 

### O PTION 

### S ELECTIONS 

### ...................................................... 4-34 4.6 CALPOST P ROCESSING 

### O PTION 

### S ELECTIONS FOR 

### L IGHT 

### E XTINCTION AND 

### H AZE 

### I MPACT 

### C ALCULATIONS 

### .................................................................................................. 4-36 4.7 M ODELING 

### P RODUCTS 

### ........................................................................................ 4-36

5. Q UALITY 

### A SSURANCE 

### M ETHODS 

### ...................................................................... 5-38 5.1 CALMET F IELDS 

### ............................................................................................... 5-38 5.2 CALPUFF, CALPOST, AND 

### POSTUTIL R ESULTS 

### .......................................... 5-38

### DAK Americas, LLC- Cooper River Facility ii Trinity Consultants BART Applicability Modeling Protocol 

### 6. R EFERENCES 

### ...................................................................................................... 6-41

A PPENDIX 

### A- VISTAS BART M ODELING 

### P ROTOCOL



### A PPENDIX 

### B- A MBIENT 

### O ZONE 

### S TATIONS

### DAK Americas, LLC- Cooper River Facility 1-1 Trinity Consultants BART Applicability Modeling Protocol 

## 1. I NTRODUCTION

DAK Americas, LLC (DAK) operates a facility (known as the Cooper River Plant) in Moncks Corner, South Carolina, just north of Charleston. The Cooper River plant, formerly operated by DuPont, has three main process area s: (1) a continuous polymerization (CP) area for polyethylene terephthalate (PET) production; (2) a PET finishing area to produce fiber products; and (3) a PET finishing area to produce bottle resin products. Support operations at the facility include an ethylene glycol re fining area, combustion sources for steam and process heat, raw material and product storage vessels, and a wastewater treatment plant. The Cooper River plant is a major source of ai r emissions and operates under a Part 70 (Title V) major source operating permit No. 0420-0089 i ssued by the South Carolina Department of Health and Environmental Control.

The facility is considered eligible to be regulated under the Best Available Retrofit Technology (BART) provisions of the Regiona l Haze Rule. Air quality modeling will be used to determine whether the emissions from DAK’s BART-eligible sources causes or contribute to visibility impairment at any fede rally protected Class I area, and hence whether a BART determination is necessary.

This modeling protocol is presented to describ e the procedures, analytical techniques, and data resources DAK proposes to use to make the applicability determination. DAK’s evaluation of BART-eligibility and the proposed modeling methods to determine applicability of BART as described in this protocol are consistent with the following guidance documents:

▲ 

### U.S. EPA, “Regional Haze Regulations and Guideline for Best Available Retrofit Technology (BART) Determinations,” Federal Register Volume 70, Number 128, July 6, 2005.

▲ 

### U.S. EPA, Guidance for Tracking Progress under the Regional Haze Rule (EPA-54/B-03-004), September 2003.

▲ 

### U.S. EPA, Guidance for Estimating Natural Visib ility Conditions under the Regional Haze Rule (EPA-454/B-03-005), October 2003.

▲ 

### U.S. EPA, Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report (EPA-454/R-98-019), December 1998.

▲ 

### U.S. EPA, Guideline on Air Quality Models , 40 CFR Part 51, Appendix W (Revised, November 9, 2005).

▲ 

### VISTAS, Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART) , December 22, 2005, revised March 9, 2006.

▲ 

### VISTAS and U.S. EPA, “Q&A for Source by Sour ce BART Rule” (Draft), October 28, 2005.

The VISTAS BART Modeling Protocolis incorporated by reference for DAK’s sourcespecific modeling protocol, and is presented in Appendix A of this modeling protocol document. This BART applicability modeling protocol is submitted to SCDHEC to provide

### DAK Americas, LLC- Cooper River Facility 1-2 Trinity Consultants BART Applicability Modeling Protocol the opportunity for review and comment in c onjunction with review by the U.S. EPA and Federal Land Managers (FLMs) responsible for oversight of the federally protected Class I areas potentially affected by DAK’s BART-eligible operations. As of the date of this protocol, certain modeling data resources have not yet been made available by VISTAS, as identified in this document. In addition to SCDHEC’s review and comment on this protocol, DAK anticipates additional communication with SCDHEC throughout the BART implementation process as details become availabl e between the time this modeling protocol is submitted and SCDHEC’s BART Applicability An alysis is submitted to ensure that refined analyses are conducted using mutually agr eeable data resources and processing options.



## 1.1 O VERVIEW OF 

## R EGIONAL 

## H AZE 

## R ULE AND 

## BART R EQUIREMENTS
The Regional Haze Rule requires that major sources of visibility-affecting pollutants belonging to one or more of 26 specific indus trial source categories evaluate BART if the source was “in existence” (i.e., built or recons tructed) before Augu st 7, 1977 and began operation after August 7, 1962. Such sources are termed “BART-eligible sources.” Major sources of visibility-affecting pollutants have th e potential to emit 250 tons per year (tpy) of one or more of oxides of nitrogen (NO X 

### ), sulfur dioxide (SO 2 

### ), and particulate matter less than 10 micrometers in aerodynamic diameter (PM 10 

### ). Chemical process plants are one of the listed source categories, and include process units and support facilities that are considered to be part of major Standard Industrial Classi fication 28 – Chemicals and allied products. Hereafter, the “BART-eligible source” is taken to mean the collection of sources at a facility in existence during the relevant time period with in one or more BART source categories that has potential emissions of one or more visibility-affecting pollutants in excess of 250 tpy. The BART-eligible source may include multiple emission units, but need not include the entire facility.



### 1.1.1 D ETERMINATION OF 

### BART- ELIGIBILITY


### The U.S. EPA BART guidelines define the fo llowing three steps for determining which sources 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. 3. Compare potential emissions to the 250 tpy threshold.

DAK’s Cooper River facility has determined that six (6) emission units comprise the BART-eligible source because the units operate at a chemical processing facility, were in existence on August 7, 1977, a nd began operation after August 7, 1962. This collection of emission units has potential emissions of 488 tpy NO X 

### , 38 tpy PM 10 

### , and 1,478 tpy SO 2 

### . Accordingly, the BART-eligible emission units at DAK’s Cooper River facility will be analyzed to evaluate whether a BART determin ation is required or whether the facility can be considered exempt from BART. Specific information about these emission units is provided in Section 2 of this modeling protocol.

### DAK Americas, LLC- Cooper River Facility 1-3 Trinity Consultants BART Applicability Modeling Protocol 

### 1.1.2 A SSESSMENT OF 

### C ONTRIBUTION TO 

### V ISIBILITY 

### I MPAIRMENT AND 

### BART A PPLICABILITY


### In its role as technical analysis coordi nator, VISTAS developed a common modeling protocol and data resources for use by state regulatory agencies and BART-eligible sources. The final VISTAS BART Modeling Protocol was issued on December 22, 2005 (and has been revised twice since, most recently on March 9, 2006), and prescribes modeling techniques and data resources to c onduct screening and refined analyses to assess whether a BART-eligible source is subject to BART.

A BART-eligible source is determined to be subject to BART if the source causes or contributes to visibility impairment at a federally protected Class I area. Causation is defined as a single-source impact of 1.0 deciviews (dv) or more; contribution is defined as a single-source impact of 0.5 dv or more (each evaluated on a 24-hour average basis). The deciview is a metric used to represent normali zed light extinction attributable to visibilityaffecting pollutants. To determine whethe r a BART-eligible facility causes or contributes to visibility impairment, 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 s ource. Because contribution to visibility impairment is sufficient cause to require a BART determination, 0.5 dv is the critical threshold for assessment of BART applicability.

Regional haze is measured using th e light extinction coefficient ( b ext 

### ), which is expressed in terms of the haze index expressed in dv. The haze index ( 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 HI for estimated natural background conditions with the impact of the source and without the impact of the source. The background extinction coefficient b ext, background 

### is affected by various chemical species and the Rayleigh scattering phenomenon and can be calculated as shown in the following equation.

rayap coarse soil OC NO SO background ext bbbbbbbkm b ++++++= −

3 4 )( 1

,

### DAK Americas, LLC- Cooper River Facility 1-4 Trinity Consultants BART Applicability Modeling Protocol where, 

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

3 Ray 34 424

μg/minion Concentrat ][ factor adjustment humidity relative f(RH) Scattering Rayleigh b Carbon] Elemental[01.0 Mass Coarse 0006.0 Soil001.0 OC004.0 )(NONH003.0 )(SO)(NH003.0

3 4

= =

= = = = = = =

ap Coarse Soil OC NO SO

b b b b RHf b RHf b



### Values for the parameters listed above specifi c to the natural background conditions at the Class I area considered in this analysis are provided on an annual average basis in the U.S. EPA’s Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule

. 1 

### More detailed information about the natural background c onditions particular to Class I areas potentially affected by DAK’ s operations at the Cooper River plant are provided in Section 3.5 of this protocol.

Particulate species that affect visibility are emitted from anthropogenic sources in various phases and include coarse particulate matter (PMC), fine particulate matter (PMF), secondary organic aerosols (SOA), and elemental carbon (EC), as well as precursors to fine particulate matter such as SO 2 

### and NO X 

### . DAK’s calculation of speciated visibilityaffecting pollutant emissions is presented in Section 2 of this modeling protocol. The extinction coefficient due to emissions of visibility-affecting pollutants from a single BART-eligible source b ext,source 

### is calculated using an air quality model. The extinction due to the BART-eligible source will be calcula ted as shown in the following equation. 

# ( )
EC SOA PMF PMC NO SO sourceext bbbbbbkmb +++++= −

3 4 1

,


### where, 

### [ ] [ ]

3 34 424

μg/minion Concentrat ][ factor adjustment humidity relative)( ][01.0 ][004.0

][001.0 ][0006.0 )(NONH003.0 )(SO)(NH003.0

3 4

= = = =

= = = =

RHf EC b SOA b PMF b PMC b RHf b RHf b

EC

soa PMF PMC NO SO

1 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.

### DAK Americas, LLC- Cooper River Facility 1-5 Trinity Consultants BART Applicability Modeling Protocol DAK proposes to utilize screening modeling techniques and, if necessary, refined modeling techniques as described in the VISTAS BART Modeling Protocol to determine whether BART-eligible operations at the Cooper River plant contribute to visibility impairment at any Class I area. The CALPUFF V5.754 modeling system will be used to determine the 24-hour average visibility impair ment attributable to DAK to assess whether the 0.5 dv contribution threshold is exceeded , and if so, the freque ncy, duration, and magnitude of any exceedance events. The U. S. EPA BART guidelines prescribe that the 98 th 

### percentile 24-hour average visibility impact computed in a modeling analysis that evaluates three years of meteorological data should be compared to the contribution threshold. However, VISTAS prescribes that the maximum computed visibility impact be used as the basis for comparison in the screening analysis and the 98 th 

### percentile impact in the refined analysis. To assess whether BART -eligible operations contribute to visibility impairment, DAK’s applicability modeling analysis will quantify the top eight 24-hour average visibility impacts of each year m odeled to illustrate the distribution (i.e., frequency, duration, and magnitude) of peak vi sibility impairment episodes attributable to the Cooper River plant.

CALPUFF is a refined air quality modeling syst em that is capable of simulating the dispersion, chemical transformation, and long-range transport of multiple visibilityaffecting pollutant emissions from a single source and is therefore preferred for BART applicability and determination analyses. The CALPUFF modeling system is described in technical detail in the VISTAS BART Modeling Protocol and its use in screening and refined analyses for BART applicability asse ssment of DAK’s Cooper River plant is described in Sections 3, 4, a nd 5 of this modeling protocol.

The VISTAS BART Modeling Protocol specifies that all Class I areas within 300 km of a BART-eligible source must be initially ev aluated to determine whether the source contributes to visibility impairment. Tabl e 1-1 summarizes the distances separating the Cooper River plant from all Class I areas with in the VISTAS region and adjacent states. Consistent with the VISTAS BART Modeling Protocol , only those Class I areas within 300 km are considered further in the BART applicability modeling analysis.

### DAK Americas, LLC- Cooper River Facility 1-6 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 1-1. D ISTANCES 

### ( KILOMETERS 

### ) S EPARATING 

### C LASS 

### I A REAS AND 

### C OOPER 

### R IVER FACILITY

Class I Area Distance (km)

Breton (LA/MS) 911 Cape Romain (SC) 31 Chassahowitzka (FL) 545 Cohutta (GA) 468 Dolly Sods (WV) 656 Everglades (FL) 818 Great Smoky Mountains (NC/TN) 402 Hercules Glade (AR) 1242 James River Face (VA) 501 Joyce Kilmer/Slickrock (NC) 446 Linville Gorge (NC) 354 Mammoth Cave (KY) 715 Mingo (MO) 1024 Okefenokee (GA) 311 Otter Creek (WV) 651 Shenandoah (VA) 561 Shining Rock (NC) 366 Sipsey (AL) 699 St. Marks (FL) 503 Swanquarter (NC) 411 Wolf Island (GA) 228



### Figure 1-1 illustrates the location of the Cooper River facility and its location relative to the following federally protected Class I ar ea that is located within 300 km of DAK’s Cooper River operations:

▲ 

### Cape Romain National Wildlife Refuge locat ed approximately 31 km east of the Cooper River facility

▲ 

### Wolf Island National Wildlife Refuge lo cated approximately 228 km southwest of the Cooper River facility

The location of the Class I areas and receptor lo cations evaluated in the modeling analysis were determined by, and obtained from, the U. S. Fish and Wildlife Service, which is the FLM for these Class I areas. 2

2 National Park Service compilation of Class I area re ceptors, http://www2.nature. nps.gov/air/maps/receptors/.

### DAK Americas, LLC- Cooper River Facility 1-7 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 1-1. L OCATION OF 

### DAK’ S 

### C OOPER 

### R IVER 

### F ACILITY 

### R ELATIVE TO 

### C LASS 

### I A REAS 

### W ITHIN 

### 300 KM



## 1.2 S CHEDULE FOR 

## BART I MPLEMENTATION IN 

## S OUTH 

## C AROLINA
Each state within the VISTAS region has established its own schedule for BART implementation, which includes separate deadlines for BART app licability assessment and determination protocols and final reports. SCDHEC has established the fo llowing milestones for BART implementation, with which DAK will conform throughout the implementation process provided that SCDHEC and VISTAS continue to make common resources available in a timely manner.

▲ 

### BART Applicability Exemption Protocol due May 8, 2006

▲ 

### BART Exemption Modeling Dem onstration due May 19, 2006

▲ 

### BART Determination Modeling Pr otocol due August 15, 2006

▲ 

### BART Determination Permit Application Includ ing Modeling Analysis due October 17, 2006

### DAK Americas, LLC- Cooper River Facility 1-8 Trinity Consultants BART Applicability Modeling Protocol 

## 1.3 O RGANIZATION OF 

## M ODELING 

## P ROTOCOL
The remainder of this modeling protocol is organized as follows.

▲ 

### Section 2 describes the BART-eligible emission units at DAK and the emission rates to be modeled in the BART applicability analysis.

▲ 

### Section 3 describes the procedural and technical guidance for conducting Class I area analyses.

▲ 

### Section 4 describes the proposed approach for CALPUFF modeling, including the data resources and technical modeling options to be used in the CALMET, CALPUFF, and CALPOST analyses.

▲ 

### Section 5 describes the presentation of results fro m, and quality assurance techniques for, the BART applicability modeling analysis.

The air quality modeling analysis methodology will conform to the VISTAS BART Modeling Protocol , which is provided in Appendix A of this re port for reference. A listing of the receptor locations and ozone stations used in the an alysis are also provided in the Appendix.

### DAK Americas, LLC – Cooper River facility 2-9 Trinity Consultants BART Applicability Modeling Protocol 

## 2. BART-E LIGIBLE 

## S OURCE 

## D ESCRIPTION

This section of the modeling protocol describes the emission units that comprise the BART-eligible source at DAK’s Cooper River facility. Emissions and exhaust characteristics of each source are quantified to demonstrate how each source w ill be represented in the modeling analysis.



## 2.1 BART-E LIGIBLE 

## E MISSION 

## U NITS
DAK reviewed the criteria for BART-eligibility and de termined that six emission units described in Table 2-1 comprise the BART-eligible source at the Cooper River facility.

T ABLE 

### 2-1. S UMMARY OF 

### BART- ELIGIBLE 

### E MISSION 

### U NITS

Emission Unit Source Code/ Stack ID BART Source Category Date † Built or Modified Potential SO 2 Emissions

(tpy)* Potential NO X Emissions

(tpy)* Potential PM 10 Emissions

(tpy)* Potential VOC Emissions

(tpy)*

Boiler #1 BOIL 1 21 1976 512.74 169.31 13.15 3.00 Boiler #2 BOIL 2 21 1976 621.32 205.16 15.93 3.00 Vaporizer #1 VAPOR 1 21 1977 114.61 37.84 2.94

- Vaporizer #2 VAPOR 2 21 1977 114.61 37.84 2.94

- Vaporizer #3 VAPOR 3 21 1977 114.61 37.84 2.94

- Continuous Polymerization POLY 21 1975 - - - 18.7

Total Potential Emissions (tpy) 1477.9 488.0 37.9 25.7
† Dates indicate range of years during which source was constructed or reconstructed. * Potentials based on permit limits on emissions



### DAK’s Cooper River facility is BART-eligible sin ce potential emissions exceed 250 tpy for at least one of NO X 

### , SO 2 

### , and PM 10 

### , which are considered visibility-affecting pollutants. Note that for the purposes of this BART analysis, VISTAS and SC DHEC have determined that volatile organic compounds (VOC) are not visibility-affecting pollutants. Section 4.1.3 of the VISTAS BART Modeling Protocol describes the regional modeling analyses showing that cumulative VOC emissions do not contribute to visibility impairment within the VISTAS region:

VOC emissions from all anthropogenic point sources in each VISTAS State are being reduced. Given that the impact of elimin ating all VOC emissions from all point sources in a State is less than 0.5 dv, th en the impact of any one BART-eligible source would be less than 0.5 dv. Based on these analyses, the VISTAS States have concluded that VOC emissions should not be subject to BART.

As a result of this determination, emissions of VOC from DAK’s Cooper River facility continuous polymerization are not further evaluated. The five (5) remaining emission sources indicated in Table 2-1 are considered the BART-eligible source at DAK’s Cooper River facility.

### DAK Americas, LLC – Cooper River facility 2-10 Trinity Consultants BART Applicability Modeling Protocol 

## 2.2 BART- ELIGIBLE 

## S OURCE 

## M ODEL 

## E MISSIONS 

## I NVENTORY
Whereas the BART eligibility determination relies on potential emissions of visibility-affecting pollutants, the BART applicability modeling analysis utilizes maximum actual 24-hour average emission rates of NO X 

### , SO 2 

### , and PM 10 

### . The VISTAS BART Modeling Protocol specifies the following hierarchy of information resources to establish the maximum actual 24-hour average emission rate for BART applicability modeling over the prior three-to-five year period:

▲ 

### 24-hour maximum emissions observed using a Continuous Emission Monitor (CEM) for the period 2001 through 2003

▲ 

### 24-hour maximum emissions observed using a CEM for any representative period

▲ 

### Facility stack test emissions

▲ 

### Potential to emit

▲ 

### Permit allowable emissions

▲ 

### Emissions factors from U.S. EPA AP-42 source profiles

DAK is using on-site stack testing conducted June 7-9 of 2005 to determine the 24-hour average maximum actual emission rates of visibility-affecting pollutants. Three runs were used and averaged to calculate the 24-hour average maximum rates. Table 2-2 summarizes these emission rates from each BART-eligible emissions unit considered in the applicability modeling analysis.

T ABLE 

### 2-2. S UMMARY OF 

### 24- HOUR 

### A VERAGE 

### M AXIMUM 

### A CTUAL 

### E MISSION 

### R ATES

Emission Unit NO X Emissions (g/s) SO 2 Emissions (g/s) PM 10 Emissions (g/s)

Boiler #1 9.6010 20.5087 3.4259 Boiler #2 11.2819 22.7225 4.1516 Vaporizer #1 1.3079 3.5846 0.8946 Vaporizer #2 1.7640 3.9614 0.8946 Vaporizer #3 1.9227 3.7421 0.8946



### Modeling of visibility impairment requires that th e components of the exhaus t stream be speciated because different types of particulate matter affect vi sibility to varying extents. The amount by which a mass of a certain species scatters or absorbs light is termed the extinction efficiency or extinction coefficient , and ranges from values of 0.6 m 2 

### /g for coarse particulate matter to 10 m 2 

### /g for elemental carbon. Fine particulate matter (1 m 2 

### /g) and organic aerosols (4 m 2 

### /g) scatter light with intermediate efficiencies, and ammonium sulfate and ammonium nitrate (that forms from precursor SO 2 

### and NO X 

### emissions in the presence of ambient ammonia) are hygroscopic species that are particularly efficient light scatters in the presence of ambient water vapor (3 f

(RH) m 2 

### /g, where f (RH) is a function of the relative humidity). The size distribution of particle species is also important, since smaller particles may be transported longer distances than larger particles and dispersed differently under prevailing ambient conditions. Figure 2-1 depicts the speciation of visibility-affecting pollutant emissions as represented in the VISTAS BART Modeling Protocol

.

### DAK Americas, LLC – Cooper River facility 2-11 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 2-1. P ARTICULATE 

### M ATTER 

### S PECIATION 

### ( AFTER 

### F IGURE 

### 4-3 OF THE 

### VISTAS BART M ODELING 

### P ROTOCOL 

### )

The FLM has published speciation guidance for cer tain types of sources for Particulate Matter speciation. VISTAS has also provided example sp eciation calculations via the Earthtech website available for download. Speciation was performed for the five (5) eligible sources using the Earthtech example template and the appropriate Cumulative Mass Size Distribution percentages listed for the categorized SCC code for each source from A.P.-42. The resulting speciation values account for the filterable and condensable particulate matter and for the sulfate portion of the emissions (H 2 

### SO 4 

### mist).

While few data are available to estimate speciated emissions, DAK has reviewed what data are available to arrive at a conservative, yet reasonabl e estimate of speciated emissions. However, it should be noted that the data quality on PM speciation is inadequate for setting regulatory emission limits and are provided here solely as the best estimated data for a scientific analysis of potential impacts on visibility impairment at Class I areas using CALPUFF modeling. The following analysis does not represent source test r esults for specific sources at the Cooper River facility.

NO X 

### emissions from the Cooper River facility’s BART-eligible sources result from combustion operations. Similarly, SO 2 

### emissions emanate from combustion sources, and result from the incomplete oxidation of fuel-bound sulfur. Primary em issions of sulfuric acid mists or vapors, if any, are assumed to occur as only a small percentage of the primary SO 2 

### emissions unless an emission factor has otherwise been published. Because of th e condensable nature of such emissions and the distinct effect on visibility caused by sulfates, pr imary sulfate is evaluated as a distinct, speciated

### DAK Americas, LLC – Cooper River facility 2-12 Trinity Consultants BART Applicability Modeling Protocol particulate fraction. Condensable particulate matt er (CPM) emissions, unlike filterable emissions, have not historically been measured or regulated for the affected sources.

PM emissions can be differentiated with respect to size, point of formation, and composition. Table 2-3 gives definitions for the nomenclature used herein.

T ABLE 

### 2-3. N OMENCLATURE FOR 

### E MISSIONS 

### S PECIATION 

### A NALYSIS

Nomenclature Description

PMC800 Filterable coarse particulate matter with an aerodynamic diameter > 6 and < 10 μ m PMC425 Filterable coarse particulate matter with an aerodynamic diameter > 2.5 and < 6 μ m PMF187 Filterable fine particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μ m PMF112 Filterable fine particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μ m PMF081 Filterable fine particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μ m PMF056 Filterable fine particulate matter with an aerodynamic diameter > 0.50 and < 0.625 μ m EC187 * Elemental Carbon particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μ m EC112 * Elemental Carbon particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μ m EC081 * Elemental Carbon particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μ m EC056 * Elemental Carbon particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μ m POC081 Condensable organic particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μ m POC056 Condensable organic particulate matter with an aerodynamic diameter > 0.50 and < 0.625 μ m PIC081 Condensable inorganic particulate matter with an aerodynamic diamet er > 0.625 and < 1.0 μ m PIC056 Condensable inorganic particulate matter with an aerodynamic diameter > 0.50 and < 0.625 μ m * The Elemental Carbon portion is equal to 3.5% of the filtera ble fine particulate matter base d on recommendations from NPS



### These PM classifications are necessar y in the Class I visibility analysis because each type of PM has a different effect on visibility as defined by the extin ction efficiency. The emission rates of each of these particulate phases and size categories are modeled in CALPUFF and grouped according to visibility-affecting characteristics as was illustrated in Figure 2-1. Elemental carbon (EC), if emitted, typically results from unburned carbonaceous fuel a nd is distinguished from other PM types because of its light extinction characteristics. Coarse PM (PMC) comprises PMC425 and PMC800 and is typically filterable in nature. Fine PM (PMF) comprises PMF056, PMF081, PMF112, and PMF187, and is typically considered to be filterable. Condensable particulate matter (CPM) comprises both organic and inorganic species. The organic fraction of CPM is represented in CALPUFF primary organic condensable (POC) emissions, which are direct emissions but are sometimes referred to as secondary organic aerosols (SOA) by convention an d due to the representation of their visibilityaffecting characteristics in the light extinction equa tion. Primary emissions of inorganic CPM (PIC) may contain hygroscopic sulfates (SO 4 

### ) and nitrates (NO 3 

### ), as well as other salts (e.g., carbonates) that may be hygroscopic to a lesser degree, and hence are considered in a manner similar to PMF. 3 

### Therefore, it is important to distinguish inorganic CPM since certain hygroscopic species (i.e., sulfate and nitrate species) will have a greater extinction coefficient than non-hygroscopic (i.e., non-sulfate and non-nitrate) species. Even the distinction be tween primary sulfate and nitrate emissions is important since primary nitrate emissions will be affected by the partitioning of nitrate and nitric acid

3 The U.S. EPA’s Guidance for Tracking Progress under the Regional Haze Rule identifies carbonates, magnesium oxides, and sodium oxides as components of the soil mass concentrat ion when analyzed to assess natural background visibility (Malm 1994).

### DAK Americas, LLC – Cooper River facility 2-13 Trinity Consultants BART Applicability Modeling Protocol in the presence of ambient ammonia, which is m odeled explicitly in CALPUFF and can be corrected when the ammonia limiting method (as described in Section 4 of this protocol) is applied. DAK distinguishes primary emissions of sulfates and nitr ates, which would be assigned to the appropriate modeled PM type (i.e., SO 4 

### and NO 3 

### , respectively), from non-hygroscopic species (e.g., carbonates), which would be modeled as non-h ygroscopic PIC species. Inorganic condensable emissions are assumed to be PIC unless a specific emission factor for primary sulfate emissions is available.

Table 2-4 summarizes the grouping of PM species and extinction coefficient of each component. A discussion of the PM speciation methodologies for each of the BART-eligible sources at the Cooper River plant is presented in the fo llowing sections of this protocol.

T ABLE 

### 2-4. A SSIGNMENT OF 

### E MITTED 

### PM S PECIES TO 

### M ODELED 

### PM C ATEGORIES

Modeled PM Category † Components Output Category ‡ Extinction Coefficient (m 2 /g)
PMC Filterable coarse particles (PM 6-10 , PM 2.5-6 ) PMC 0.6 PMF Filterable fine particles (PM 1.25-2.5 , PM 1-1.25 , PM 0.625-1 , PM 0.5-0.625 ) SOIL 1 PIC Non-hygroscopic, primary inorganic condensable (PIC) emissions* SOIL 1 SO4 Primary inorganic condensable emissions of sulfates SO4 3 f

(RH) NO3 Primary inorganic condensable emissions of nitrates* NO3 3 f

(RH) POC Primary organic condensable emissions SOA 4 EC Uncombusted carbonaceous fuel EC 10
* In the screening analyses, all condensable, non-sulfate inorganic emissions will be c onservatively represented as PIC emissio ns. The refined analysis, if necessary, would distingu ish between primary nitrate and other emissions. † Modeled PM Category denotes the input of emissions data into CALPUFF. ‡ Output Category denotes the assignment of modeled emissions in POSTUTIL for the visibility calculations in CALPOST.



### 2.2.1 P ARTICULATE 

### S PECIATION 

### F OR 

### B OILERS AND 

### P ROCESS 

### H EATERS


### DAK operates five (5) BART-eligible boilers and process heaters at the Cooper River plant. PM emissions from these sources are due to the combustion of No. 6 fuel oil for Boiler #1 and the Vaporizers, and the combusti on of coal for Boiler #2. To speciate the PM emissions from these combustion sources, DAK utilized PM size fractions and CPM emissions information from Section 1.3 of U.S. EPA’s AP-42 emission factor database. 4 

### Table 2-5 summarizes the relevant data for this category of sources for No. 6 fuel oil combustion and Table 2-6 for coal combustion.

The PM10 emissions from these sources are from on-site stack test data using Method 5. Method 5 only produces the results of filterabl e PM and does not include the condensable portion. Therefore, to calculate the conde nsable portion, it is necessary to ratio the filterable values by the 90.0% to calculate the condensable value, with the knowledge that 10.0% of emissions are from condensable PM. The same is done for the coal boiler using the appropriate apportionment.

4 AP-42, Section 1.3, Fuel Oil Combustion, Table 1.3-5 , “Cumulative Partic le Size Distribution and Size Specific Emission Factors for Industria l Boilers Firing Residual Oil,” Sept 1998

.

### DAK Americas, LLC – Cooper River facility 2-14 Trinity Consultants BART Applicability Modeling Protocol hour CPM lb 11.0 FPM%0.90

TPM
TPM CPM %0.10 hr FPM lb 0.1 10

10 10

10 10 10 =                  



### T ABLE 

### 2-5. N O 

### . 6 F UEL 

### O IL 

### B OILERS AND 

### H EATERS 

### S PECIATION 

### D ATA

Speciation Data Value Reference
Filterable portion of PM 10 90.00% Condensable portion of PM 10 10.00% VISTAS Speciation data source a Sulfate 5% of Condensable Portion b Non-sulfate Condensable portion 95% of Condensable Portion Organic Condensable PM 10 15% of non-sulfate condensable c Inorganic Condensable PM 10 85% of non-sulfate condensable c PM 6-10 as a % of PM 10 PM 2.5-6 as a % of PM 10 PM 1.25-2.5 as a % of PM 10 PM 1-1.25 as a % of PM 10 PM 0.625-1 as a % of PM 10 PM 0.5-0.625 as a % of PM 10 AP-42, Section 1.3; Table 1.3-5 (see breakdown in table 2-7)a

Elemental Carbon (PM 0.5-2.5 ) 3.50% Non-EC Fine Filterable (PM 0.5-2.5 ) 96.50% Per discussion with the National Parks Service

a PM Speciation for an Uncontrolled Industrial Boiler b Conservative estimate based on Earthtech example speciation for a point source c AP-42 Section 1.3; Table 1.3-2



### T ABLE 

### 2-6: C OAL 

### B OILER 

### S PECIATION 

### D ATA

Speciation Data Value Reference
Filterable portion of PM 10 8.3% Condensable portion of PM 10 91.7% VISTAS Speciation data source a Sulfate 5% of Condensable Portion b Non-sulfate Condensable portion 95% of Condensable Portion Organic Condensable PM 10 20% of non-sulfate condensable c Inorganic Condensable PM 10 80% of non-sulfate condensable c PM 6-10 as a % of PM 10 PM 2.5-6 as a % of PM 10 PM 1.25-2.5 as a % of PM 10 PM 1-1.25 as a % of PM 10 PM 0.625-1 as a % of PM 10 PM 0.5-0.625 as a % of PM 10 AP-42, Section 1.1; Table 1.1-9 (see breakdown in table 2-7)a

Elemental Carbon (PM 0.5-2.5 ) 3.50% Non-EC Fine Filterable (PM 0.5-2.5 ) 96.50% Per discussion with the National Parks Service

a PM Speciation for Spreader Stoker with Fabric Filter Control Device b Conservative estimate based on Earthtech example speciation for a point source c AP-42 Section 1.3; Table 1.3-2

### DAK Americas, LLC – Cooper River facility 2-15 Trinity Consultants BART Applicability Modeling Protocol Condensable emissions are allocated between primary organic (POC) and inorganic species (PIC), which includes sulfates and n itrates. DAK has represented maximum actual emissions as potential sulfur dioxide emissi ons for the purposes of BART applicability modeling, and further conservatively estimated that primary sulfuric acid emissions are equivalent to 5% of the total conden sable portion of particulate emissions.

Organic CPM emissions were determin ed by multiplying the calculated TPM 10 

### emissions by the condensable percentage of TPM 10 

### and the organic percentage of CPM. Organic CPM was assumed to be evenly split be tween the two size categories, 0.625-1.0 μ m and 0.5-0.625 μ m. An organic CPM calculation for an example TPM 10 

### emission rate of 1 lb/hr is shown below.

hour category sizeeach for CPM organic lb 0310.72/ CPM-SO4non

CPM organic %15 4 %95 TPM CPM %00.10 hr TPM lb 0.1

10 10 −=            

−             E CPM CPMSOnon



### PIC emissions were calculated using a similar methodology, yielding 0.0404 lb/hr of nonsulfate for a TPM 10 

### emission rate of 1 lb/hr.

Filterable PM 10 

### emissions were divided amongst six different size categories by multiplying TPM 10 

### emissions by the filterable % and then by the % of PM 10 

### for each size category, yielding hourly emissions of PM 6-10 

### , PM 2.5-6 

### , PM 1.25-2.5 

### , PM 1-1.25 

### , PM 0.625-1 

### , and PM 0.5-0.625 

### . A sample calculation for PM 0.5-0.625 

### for TPM 10 

### emissions of 1 lb/hr is shown below.

hour PM lb 1047.0 FPM PM %47.10 PM lb FPM %00.90 hr PM lb 1.0 10-6

10 10-6

10 10 10

=                    



### Table 2-7 presents a summary of the speciated PM emissions for the boilers and vaporizers, using a sample emission rate of 1 lb/hr of TPM 10 

### . Figure 2-3 presents a graphical representation of the TPM 10 

### speciation used for each of these units, based on the sample 1 lb/hr TPM 10 

### emission rate.

### DAK Americas, LLC – Cooper River facility 2-16 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 2-7. B OILER 

### /V APORIZER 

### PM 10 

### S PECIATION 

### (B ASED ON 

### 1 LB 

### / HR 

### )

Coal (Boiler #2) Lb/hr
PMC800 1.94E-02 PMC425 2.77E-02 PMF187 1.07E-02 PMF112 4.00E-03 PMF081 1.07E-02 PMF056 9.30E-03 EC187 4.00E-04 EC112 1.00E-04 EC081 4.00E-04 EC056 3.00E-04 POC081 8.71E-02 POC056 8.71E-02 PIC081 3.50E-01 PIC056 3.50E-01 Sulfate 4.59E-02



### F IGURE 

### 2-3. B OILER 

### /V APORIZER 

### PM 10 

### S PECIATION 

### (B ASED ON 

### 1 LB 

### / HR 

### ) No. 6 Fuel Oil (Boiler #1, Vapor 1-3) Lb/hr
PMC800 9.42E-02 PMC425 2.20E-01 PMF187 1.72E-01 PMF112 3.03E-02 PMF081 6.06E-02 PMF056 3.03E-01 EC187 6.20E-03 EC112 1.10E-03 EC081 2.20E-03 EC056 1.10E-02 POC081 7.10E-03 POC056 7.10E-03 PIC081 4.04E-02 PIC056 4.04E-02 Sulfate 5.00E-03

No. 6 Fuel Oil

PMC800 PMC425

PMF187 PMF112

PMF081 PMF056

EC187 EC112

EC081 EC056

POC081 POC056

PIC081 PIC056

Sulfate

Coal

PMC800 PMC425

PMF187 PMF112

PMF081 PMF056

EC187 EC112

EC081 EC056

POC081 POC056

PIC081 PIC056

Sulfate

### DAK Americas, LLC – Cooper River facility 2-17 Trinity Consultants BART Applicability Modeling Protocol 

## 2.3 M ODELED 

## S TACK 

## P ARAMETERS AND 

## E MISSIONS
Actual stack parameters will be input to the CA LPUFF model to represent the point of visibilityaffecting pollutant emissions. The location of each point source will be represented consistently in the Lambert Conformal Coordinate system used for the screening and refined meteorological data analyses prepared by VISTAS. Each exhaust discharg es vertically without obstruction. The closest Class I area is 31 km from the facility. By the decision of the state of South Carolina, downwash will not be utilized for facilities within 50 km of class I areas. The VISTAS protocol gives the state discretion as to whether or not downwash is included in BART analyses. Tables 2-8 and 2-9 summarize the stack parameters and modeled emission rates for BART-eligible emission units at DAK’s Cooper River plant.DAK Americas, LLC – Cooper River facility2-18Trinity ConsultantsBART Applicability Modeling ProtocolTABLE2-8. STACKPARAMETERS FORBART-ELIGIBLEEMISSIONUNITSEmission UnitStack IDLambertConformalCoordinateEast(km)LambertConformalCoordinateNorth(km)StackHeight(m)BaseElevation(m)StackDiameter(m)ExhaustTemperature(°K)ExhaustVelocity(m/sec)Boiler #1BOIL 1 1580.083 -620.12545.72 9.10131.521448 10.7351Boiler #2BOIL 2 1580.074 -620.11045.72 9.10131.521433 13.0089Vaporizer #1VAPOR 1 1580.090 -620.13745.72 9.10131.3015605 4.5811Vaporizer #2VAPOR 2 1580.098 -620.13445.72 9.10131.3015605 4.5811Vaporizer #3VAPOR 3 1580.106 -620.12945.72 9.10131.3015605 4.5811DAK Americas, LLC – Cooper River facility2-19Trinity ConsultantsBART Applicability Modeling ProtocolTABLE2-9. MODELEDEMISSIONRATES FORBART-ELIGIBLEEMISSIONUNITS(G/S)PollutantBoiler #1 Boiler #2 Vaporizer #1 Vaporizer #2 Vaporizer #3SO220.5087 22.7225 3.5846 3.9614 3.7421NOx9.6010 11.2819 1.3079 1.7640 1.9227Sulfate3.60E-03 5.533E-01 2.50E-03 3.00E-03 1.90E-03PMC8006.87E-02 2.337E-01 4.79E-02 5.58E-02 3.61E-02COARSEPMC4251.603E-01 3.339E-01 1.117E-01 1.301E-01 8.43E-02PMF1871.252E-01 1.289E-01 8.72E-02 1.017E-01 6.59E-02PMF1122.21E-02 4.83E-02 1.54E-02 1.79E-02 1.16E-02PMF0814.42E-02 1.289E-01 3.08E-02 3.59E-02 2.32E-02FINEPMF0562.21E-01 1.128E-01 1.54E-01 1.794E-01 1.162E-01EC1874.50E-03 4.70E-03 3.20E-03 3.70E-03 2.40E03EC1128.00E-04 1.80E-03 6.00E-04 7.00E-04 4.00E-04EC0811.60E-03 4.70E-03 1.10E-03 1.30E-03 8.00E-04ELEMENTALCARBONEC0568.00E-03 4.10E-03 5.60E-03 6.50E-03 4.20E-03POC0815.20E-03 1.0513 3.60E-03 4.20E-03 2.70E-03ORGANICPOC0565.20E-03 1.0513 3.60E-03 4.20E-03 2.70E-03PIC0812.94E-02 4.2054 2.05E-02 2.39E-02 1.55E-02INORGANICPIC0562.94E-02 4.2054 2.05E-02 2.39E-02 1.55E-02

### DAK Americas, LLC – Cooper River facility 3-20 Trinity Consultants BART Applicability Modeling Protocol 

## 3. G EOPHYSICAL AND 

## M ETEOROLOGICAL 

## D ATA

Section 3 of this BART applicability modeling protocol for DAK’s Cooper River plant describes the geophysical and meteorological data that will be used in the screening and refined analyses. The information in this Section 3 is largely adapted from the VISTAS BART Modeling Protocol , which is presented in Appendix A of this source-specific protoc ol for reference, and sample model files made available on the VISTAS technical contractor website. 5



### CALMET requires geophysical data about the domain to characterize the terrain and land use parameters that potentially affect dispersion. Te rrain features affect flows and create turbulence in the atmosphere and are potentially subjected to highe r concentrations of elevated puffs, and different land uses exhibit variable characteristics such as surface roughness, albedo, Bowen ratio, and leafarea index that also affect turbulence and dispersion.



## 3.1 T ERRAIN 

## E LEVATIONS WITHIN THE 

## M ODELING 

## D OMAIN
Terrain elevations within the modeling domain were processed from SRTM-GTOPO30 digital terrain data format with 30-arcsec resolution. SRTM30 is a digital elevation data set that spans the globe from 60° north latitude to 56° south latitude, appr oximately from the southern tip of Greenland to below the southern tip of South America. It has a horizontal grid spacing of 30 arc-seconds (approximately 1 kilometer). GTOPO30 is a global digital elevation model with a horizontal grid spacing of 30 arc-seconds (approximately 1 kilometer) that was derived from several raster and vector sources of topographic information that include U.S. Geological Survey digital elevation models. The VISTAS technical contractor used data preproces sors to format and assimilate these data into a single geophysical data file for processing by CALM ET. The representation of terrain in the regional screening analysis resolves the terrain onto the 12-km regional grid depicted in Figure 3-1.

Refined analyses may be necessary as part of th e DAK BART applicability analysis, in which case CALMET grid sizes of 4 km or less (e.g., 1 km) would be used to represent the modeling domain. As described in the VISTAS BART Modeling Protocol , VISTAS provides 2001 through 2003 CALMET files for five 4-km sub-regional domains, one of which includes DAK’s Cooper River facility and the Cape Romain and Wolf Island National Wildlife Refuges. The VISTAS BART Modeling Protocol envisions the use of higher resolution terrain DEM data (e.g., 3 arc-second U.S. Geological Survey data) for this analysis.

5 http://src.com/verio/download/ download.htm#VISTAS_VERSION.

### DAK Americas, LLC – Cooper River facility 3-21 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 3-1. T ERRAIN 

### R EPRESENTATION IN THE 

### 12- KM 

### R EGIONAL 

### S CREENING 

### G RID 

### (A FTER 

### F IGURE 

### 4-2 OF THE 

### VISTAS BART M ODELING 

### P ROTOCOL 

### )

### DAK Americas, LLC – Cooper River facility 3-22 Trinity Consultants BART Applicability Modeling Protocol 

## 3.2 L AND 

## U SE AND 

## C OVER WITHIN THE 

## M ODELING 

## D OMAIN
Land use and land cover (LULC) within the modeling domain was assimilated by the VISTAS technical contractor into a single geophysical data file for processing by CALMET using Composite Theme Grid (CTG) data archived by the U.S. Ge ological Survey at a resolution of 200 meters. CALMET was used to calculate the fractional land use types within each cell of the 12-km size regional grid. LULC in each grid cell was used by CALMET to compute the micrometeorological parameters (i.e., surface roughness, Bowen ratio, al bedo, soil heat flux) that affect turbulent dispersion in the boundary layer.

Refined analyses may be necessary as part of th e DAK BART applicability analysis, in which case CALMET grid resolutions of 4 km or less (e.g., 1 km) would be used to represent the modeling domain. As described in the VISTAS BART Modeling Protocol , VISTAS provides 2001 through 2003 CALMET files for five 4-km sub-regional domains , one of which appears to include DAK’s Cooper River plant and the Cape Romain and Wolf Island National Wildlife Refuges. The VISTAS BART Modeling Protocol does not explicitly indicate that higher r esolution LULC data would be used for a refined analysis; therefore the 200-meter CTG format will provide adequate resolution for CALMET grid spacing down to 1 km. Therefore, refined an alyses performed by DAK, if necessary, will utilize the same CTG LULC data.



## 3.3 M ETEOROLOGICAL 

## D ATABASE
CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields from mesoscale model (MM) output, raw obser vations of surface and upper air conditions, precipitation measurements, and geop hysical parameters into a single hourly, gridded data set for input to CALPUFF. The federal Guideline for CALPUFF processing provides the following recommendations for the meteorological data period at Section 9.3.1.2:

Less than five, but at least three, years of meteorological data (need not be consecutive) may be used if mesoscale me teorological fields are available, as discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used in conjunction with available standar d [National Weather Service] NWS or comparable meteorological observations w ithin and near the modeling domain.

The VISTAS BART Modeling Protocol describes a regional domain and a set of pre-computed regional CALMET meteorological files with 12 km grid size for the years 2001, 2002, and 2003, prepared by the VISTAS technical contractor to allow any Class I areas within the VISTAS area to be evaluated with a consistent meteorological data base and consistent CALPU FF modeling options. The CALMET modeling output files in the form of CA LPUFF-ready three-dimensional meteorological files were made available on external hard drives to the States and other parties. These data were obtained by DAK’s technical contractor and will be utilized in the regional screening analyses. Should refined analyses be necessary, DAK will initially utilize one of the 4-km refined CALMET grids provided by VISTAS, also for the 2001 through 2003 data period. Higher resolution analyses, for example with 1 km grid spacing, would also be prepared for this data period using the resources described in the following sections.

### DAK Americas, LLC – Cooper River facility 3-23 Trinity Consultants BART Applicability Modeling Protocol 

### 3.3.1 MM5 S IMULATIONS



### MM data are used as “observed” or “fir st-guess” fields in CALMET due to its highresolution representation of meteorological conditions on a uniform three-dimensional grid. The following three years of MM5 meteorological data have been assembled by VISTAS for use in the regional CALPUFF V5.754 modeling effort:

▲ 

### 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA)

▲ 

### 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS)

▲ 

### 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning Organization).

These data sets were provided to the VISTAS technical contractor, which produced annual CALMET meteorological files at for the 12-km grid resolution regional domain. The development of the regional CALMET mete orological fields from MM5 data were conducted in No-Observations (“No-Obs”) m ode since the MM5 data already reflect assimilation of observational data and are likel y to adequately characterize regional wind patterns that are consistent with the 12-km grid scale.

When the 12-km MM5 (2001 and 2002) data are used, the diagnostic CALMET terrain adjustments were turned off since the grid r esolution of the MM5 data is the same as the CALMET grid and the terrain adjustments on the 12-km grid scale will already be reflected in the MM5 dataset. In this case, the MM5 winds will be interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules will compute mixing heights, turbulence parameters and other meteorological parameters that are required by CALPUFF. For 2003, the 36-km MM5 data were used as CALMET’s initial guess field and then the CALMET diagnostic te rrain adjustments (see Section 3.1.1 of the VISTAS BART Modeling Protocol ) were applied to reflect terrain on the scale of the CALMET grid (i.e., 12 km).



### 3.3.2 M EASUREMENTS AND 

### O BSERVATIONS


### The finer grid (4 km) CALMET simulations, which DAK anticipates utilizing as part of a refined analysis, will be run by VISTAS techni cal contractor in hybrid mode, using both MM5 data to define the initial guess fields and NWS meteorological observational data in the Step 2 calculations. In this manner, actual observations of three-dimensional meteorological conditions can be used in the model to smooth the coarse MM5 resolution to better represent areas in which terrain f eatures and coastlines may have an important effect on meteorological conditions, but not be well resolved in the mesoscale model. Surface, upper air, precipitation, and offshore buoy observation points are readily available for use in CALMET. As of the date of this modeling protocol, the VISTAS technical contractor has not completed the refined CALMET analyses; hence the specific observations used have not been identified. The following generally describes the use of NWS observations in Step 2 of the CALMET analyses.

Parameters affecting turbulent dispersion that are observed hourly at surface stations include wind speed and direction, temperatur e, cloud cover and ceiling, relative humidity,

### DAK Americas, LLC – Cooper River facility 3-24 Trinity Consultants BART Applicability Modeling Protocol and precipitation type. Surface da ta would be selected from the available data inventory to optimize spatial coverage and representation of the domain. Raw observations would be obtained from the National Climatic Data Ce nter (NCDC), quality assured, and merged using the SMERGE pre-processor to create a single assimilated data file of surface observations for each year analyzed.

Observations of meteorological conditions in the upper atmosphere provide a profile of turbulence from the surface through the depth of the boundary layer in which dispersion occurs. Upper air data are collected by balloons launched simultaneously across the observation network at 0000 Greenwich Mean Time (GMT) (6 o’clock PM in South Carolina) and 1200 GMT (6 o’clock AM in South Carolina). Sensors observe pressure, wind speed and direction, and temperature (among other parameters) as the balloon rises through the atmosphere. The upper air observation network is less dense than surface observation points since upper air conditions va ry less and are generally not as affected by local effects (e.g., terrain or coastlines). Upper air data would be extracted from the NCDC’s available data inventory to optimi ze spatial coverage and representation of the domain, and utilization from year to year may vary due to availability and data quality.

The effects of aqueous phase chemical tr ansformations and wet deposition processes on ambient pollutant concentrations are an important part of the BART applicability analysis. Therefore, it is necessary to include observati ons of precipitation in the CALMET analysis. Precipitation data would be collected from se lected surface meteorological data stations included in the analysis, plus Cooperative Ob servation Network (COOP) stations nearer to or within the domain. Precipitation data we re extracted from among the NCDC’s available data inventory to optimize spatial covera ge and representation of the domain. Raw observations from these stations would be quality assured and merged using the PMERGE pre-processor to create a single assimilated data file of precipitation observations.



## 3.4 A IR 

## Q UALITY 

## D ATABASE
The CALPUFF V5.754 model is capable of simulating linear chemical transformation effects by using pseudo-first-order chemical reacti on mechanisms for the conversions of SO 2 

### to SO 4 

### , and NO X 

### , which consists of nitrogen oxide (NO) and nitrogen dioxide (NO 2 

### ), to nitrate (NO 3 

### ) and nitric acid (HNO 3 

### ). In this study, chemical transformations involving five species (SO 2 

### , SO 4 

### , NO X 

### , HNO 3 

### , and NO 3 

### ) will be modeled using the MESOPUFF II chemical transformation scheme. Ambient concentrations of ammonia and ozone concentr ations as represented in the model affect the MESOPUFF II chemical transformation simulation.



### 3.4.1 O ZONE 

### B ACKGROUND 

### C ONCENTRATIONS


### Both screening and refined analyses will u tilize observed ozone data for 2001 through 2003 from non-urban CASTNet and AIRS sta tions compiled by the VISTAS technical contractor for the regional domain. Monthly average ozone background values will be computed based on daytime average ozone concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations co mputed by month) for substitution should all observations be missing for a particular hour of the dataset.

### DAK Americas, LLC – Cooper River facility 3-25 Trinity Consultants BART Applicability Modeling Protocol 

### 3.4.2 A MMONIA 

### B ACKGROUND 

### C ONCENTRATIONS


### In the screening, a constant background value (0.5 ppb) for ammonia will be utilized. The VISTAS BART Modeling Protocol envisions that CMAQ-modeled NH 3 

### data for the Cape Romain and Wolf Island Class I areas would be used during postprocessing of refined analyses to repartition HNO 3 

### and NO 3 

### using the ammonia limiting method.



### 3.4.3 O THER 

### P OLLUTANT 

### B ACKGROUND AND 

### B OUNDARY 

### C ONDITIONS



### The VISTAS BART Modeling Protocol envisions the use of modeled boundary conditions of ammonia and sulfates in the refined analysis of chemical transformations involving these species and nitrates. The VISTAS BART Modeling Protocol did not provide specific details about these boundary conditions, and as of the data of this source-specific modeling protocol for DAK’s Cooper River plant, VISTAS has not yet provided this information. DAK does not anticipate the necessity to complete refined analyses for the BART-eligible source at the Cooper River plant. Therefore, appropriate boundary c onditions will be used in the refined analyses and appropriate do cumentation will be provided in the BART applicability modeling final report if refined modeling is necessary.



## 3.5 N ATURAL 

## C ONDITIONS AT 

## C LASS 

## I A REAS
The visibility goal of the Clean Air Act is both the remedying of existing visibility impairment, and prevention of future visibility impairment. In its BART Implementation Guidance , U.S. EPA affirms that it interprets the goal to mean return atmospheri c conditions to “natural visibility conditions.” For the purposes of the Regional Haze Rule program, the U.S. EPA’s Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule and Guidance for Tracking Progress under the Regional Haze Rule documents specify the 20% best days at each Class I area as the natural visibility goal.

For the Class I area within 300 km of the DAK fac ility and potentially affected by DAK’s operations, Table 3-1 summarizes the default natural backgroun d conditions as tabulated in Appendix B of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule

.

### DAK Americas, LLC – Cooper River facility 3-26 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 3-1. N ATURAL 

### B ACKGROUND 

### C ONCENTRATIONS ON 

### 20% B EST 

### D AYS FOR 

### C LASS 

### I A REAS 

### P OTENTIALLY 

### A FFECTED BY THE 

### DAK C OOPER 

### R IVER 

### P LANT

Class I Area b ext (Mm -1 ) Annual Average Haze

Index (dv) Best Days Haze Index (dv) Worst Days Haze Index (dv) Soil b back Annual Avg ( μ g/m 3 ) Soil b back Best Days

( μ g/m 3 )
Cape Romain 21.22 7.52 3.68 11.36 11.21 4.45 Wolf Island 21.33 7.58 3.74 11.42 11.34 4.54
* As tabulated in Appendix B of U.S. EPA’s Guidance for Estimating Natural Visib ility Conditions under the Regional Haze Rule (2003).



### To represent natural conditions in the absence of anthropogenic sources of sulfates and nitrates, the VISTAS BART Modeling Protocol expresses monthly background extinc tion coefficient in terms of Rayleigh scattering and scattering due to soils (i.e., fi ne particles) based on the 20% best visibility days, and is calculated from the tabulated “b est days” values and the following equations.

     

= 10

exp10 HI b back 

### ,

where HI is Haze Index expressed in units of deciviews (dv). Therefore, total b back 

### for the best days at Cape Romain and Wolf Island, including the Raylei gh scattering coefficient is calculated as shown in the following equations using the Cape Ro main and Wolf Island Best Days values.



### I Classfor Mm 45.4 Mm 10 Mm 14.48 10 68.3 exp10 1 1 1

− − − 

### =⇒+ =+= =      

= soil soil soil ray back 

### bb bb CRb

I Classfor Mm 54.4 Mm 10 Mm 14.54 10 74.3 exp10 1 1 1

− − − 

### =⇒+ =+= =      

= soil soil soil ray back 

### bb bb WIb

As is described in Section 4 of this protocol, the effects of relative humidity to amplify the visibility impairment of hygroscopic sulfates and nitrat es will be characterized using “Method 6,” which computes ∆ b ext 

### using a monthly average relative humidity adjustment particular to each Class I area applied to background and modeled sulfate and nitr ate. Table 3-2 summarizes the monthly average humidity values that will be applied for the Class I ar ea considered in this analysis, as tabulated in Table A-3 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule

.

### DAK Americas, LLC – Cooper River facility 3-27 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 3-2. M ONTHLY 

### A VERAGE 

### f (RH) FOR 

### S ELECTED 

### C LASS 

### I A REA 

### *

Class I Area January February Ma rch April May June July August September October November December
Cape Romain 3.3 3.0 2.9 2.8 3. 2 3.7 3.6 4.1 4.0 3.7 3.4 3.2

Wolf Island 3.4 3.1 3.1 3.0 3.3 3.7 3.7 4.1 4.0 3.7 3.5 3.5

* As tabulated in Table A-3 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule (2003).



### As noted in the VISTAS BART Modeling Protocol , the U.S. EPA and the Regional Planning Organizations (including VISTAS) are evaluating wh ether refinements are warranted to the methods recommended in U.S. EPA’s guidance to calculate de fault estimates of natural background visibility. In addition, the Interagency Monitoring of Prot ected Visual Environments (IMPROVE) work group has recently approved an alternative to the default formula used to estimate extinction from particle concentration measurements. 6 

### Refinements in the revised IMPR OVE formula include the following:

▲ 

### Adding a sea salt term, including a growth factor due to relative humidity

▲ 

### Increasing the factor used to calculate the mass of particulate organic matter from organic carbon measurements

▲ 

### Modifying the relative humidity growth fo rmula, f(RH), for sulfates and nitrates

▲ 

### Revising the extinction efficiencies (the numeri cal constants in Equation 3-1) for sulfates, nitrates, and organic carbon so that they vary with concentration

▲ 

### Adding a site-specific Rayleigh scattering term to the formula. Values will be calculated by IMPROVE for all Class I areas.

For the purposes of calculating current, future, and natural background visibility at Class I areas as part of its reasonable progress analyses, VISTAS intends to present regional air quality modeling results using both the current U.S. EPA recomme nded assumptions and the newly revised IMPROVE light extinction formula. Accordingly, in any refined BART applicability analyses that may be required, DAK may evaluate the results of the anal ysis using both formulas to assess the frequency, duration, and magnitude of visibility impairment events.

6 Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005. Revised IMPROVE Algorithm for Estimating Light Extinction from Particle Speciation Data . Report to IMPROVE Steering Committee, November 2005.

### DAK Americas, LLC – Cooper River facility 4-28 Trinity Consultants BART Applicability Modeling Protocol 

## 4. A IR 

## Q UALITY 

## M ODELING 

## M ETHODOLOGY

Section 4 of this BART applicability modeling protocol for DAK’s Cooper River plant describes the air quality modeling methodology that will be used in the screening and refined analyses. The information in this Section 4 is largely adapted from the VISTAS BART Modeling Protocol , which is presented in Appendix A of this source-specific protoc ol for reference, and sample model files made available on the VISTAS technical contractor website. 7



### Section 2.2 of the VISTAS BART Modeling Protocol summarizes recommendations for the air quality modeling analyses required to assess applicability of BART by determining whether DAK’s Cooper River plant contributes to visibility impairment at the Cape Romain Class I area (31 km) and/or the Wolf Island Class I area (228 km). The CALPUFF V5.754 modeling system is recommended as the preferred modeling approach for use in the BART analyses.



## 4.1 P LUME 

## M ODEL 

## S ELECTION
CALPUFF and its meteorological model, CALMET, are designed to handle the complexities posed by the complex terrain, the large source-receptor distances, chemical transformation and deposition, and other issues related to Class I visibility im pacts. The CALPUFF modeling system has been adopted by the U.S. 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 situa tions for shorter distances, as is the case with Cape Romain. CALPUFF is recommended for Class I impact assessments by FLAG and IWAQM. The final BART guidance recommends CALPUFF as “t he best modeling application available for predicting a single source’s contribution to vi sibility impairment.” As a result of these recommendations, the VISTAS BART Modeling Protocol is based on the use of CALPUFF for its BART determinations. Specifically, VISTAS CALMET Version 5.7 and CALPUFF Version 5.754 will be used in the CALPUFF analyses for BART applicability assessment, or other appropriate versions as specified by VISTA S and it technical contractor. 8



### This source-specific modeling protocol for DAK’s C ooper River plant incorporates by reference the VISTAS BART Modeling Protocol , which is provided in Appendix A of this document. The following sections present a brief summary of major features of the CALMET and CALPUFF models, and further detailed information should be obtained from the VISTAS BART Modeling Protocol and documentation referenced therein.

7 http://src.com/verio/download/ download.htm#VISTAS_VERSION.

8 Although the VISTAS BART Modeling Protocol specifies CALMET Version 5.7 and CALPUFF Version 5.8, as of the date of this modeling protocol , the technical contractor website ma intains CALPUFF Version 5.754 and CALMET Version 5.711.

### DAK Americas, LLC – Cooper River facility 4-29 Trinity Consultants BART Applicability Modeling Protocol 

### 4.1.1 M AJOR 

### R ELEVANT 

### F EATURES OF 

### CALMET
The CALMET meteorological model consists of a diagnostic wind field module and boundary layer micrometeorological modules for overwater and overland boundary layers. Over land surfaces, the energy balance method of Holtslag and van Ulden (1983) is used tocompute hourly gridded fields of the sensible heat flux, surface friction velocity, MoninObukhov length, and convective velocity scale. Mixing heights are determined from the computed hourly surface heat fluxes and observed temperature soundings using a modified Carson (1973) method based on Maul (1980). The model also determines gridded fields of Pasquill-Gifford-Turner (PGT) stability class and hourly precipitation rates.

The aerodynamic and thermal properties of water surfaces suggest that a different method is best suited for calculating the boundary laye r parameters in the marine environment. A profile technique, using air-sea temperature differences, is used in CALMET to compute the micro-meteorological parameters in th e marine boundary layer. The version of CALMET being used by VISTAS contains improvements in the overwater boundary layer parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response Experiment (COARE) and enha ncements in the calculation of overwater mixed layer heights (Batchvarova and Gryning, 1991, 1994) . Further details and the results of an evaluation of the model containing these enhancements are described in Scire et al. (2005). An upwind-looking spatial averaging scheme is optionally applied to the mixing heights and three-dimensional temperature fields in order to account for important advective effects.

The diagnostic wind field module uses a two-step approach to the computation of the wind fields (Douglas and Kessler, 1988). In the firs t step, an initial-guess wind field is adjusted for kinematic effects of terrain, slope flows, and terrain blocking effects to produce a Step 1 wind field. Gridded MM5 can be used to define the initial guess field. The second step consists of an objective analysis pro cedure to introduce observational data into the Step 1 wind field to produce a final wind field.

Development of the Step 1 wind field begins with the initial guess field defined by the MM5 prognostic meteorological model. No rmally, the CALMET computational domain is specified to be at finer grid resolution than the MM5 dataset used to initialize the initial guess field. For example, 36-km MM5 data available for VISTAS modeling may be used to develop the initial guess field on a 12-km or even a 1-km CALMET grid. The Step 1 algorithms in CALMET described below apply te rrain adjustments to the initial guess field on the fine-scale CALMET grid. Thus, the CALMET winds are adjusted to respond to fine-scale terrain features not necessarily seen by the coarser scale MM5 model.

The approach of Liu and Yocke (1980) is used to evaluate the effects of the kinematic terrain on the wind field. The initial guess field winds are used to compute a terrain-forced vertical velocity, subject to an exponential, stability-dependent decay function. The effectsof terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind fi eld. The divergence minimization scheme is applied iteratively until the three-dimensiona l divergence is less than a threshold value.

### DAK Americas, LLC – Cooper River facility 4-30 Trinity Consultants BART Applicability Modeling Protocol The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes both advective-gravity and equilibrium 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 added to the wind field adjusted for kinematic effects.

The thermodynamic blocking effects 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 grid point is less than a critical value and the wind has an uphill component, the wind direction is adjusted to be tangent to the terrain.

The wind field resulting from the preceding ad justments of the initial-guess wind is the Step 1 wind field. The second step of the procedure may involve introduction of observational data into the Step 1 wind field through an objective analysis procedure. An inverse-distance squared interpolation scheme is used which weights observational data heavily in the vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind field in regions with no observational data. The resulting wind field is subject to smoothing, an optional adjustment of vertical velocities based on the O’Brien (1970) method, and divergence minimization to produce a final Step 2 wind field.

The introduction of observational data in the Step 2 calculation is an option. It is also possible to run the model in “no observations” (No-Obs) mode, which involves the use only of MM5 gridded data for the initial guess field followed by fine-scale terrain adjustments by CALMET. In No-Obs mode, obser vational data are not used in the Step 2 calculations. The No-Obs mode is appropria te when the MM5 simulations adequately characterize the regional wind patterns and when local observations, especially surface observations, reflect local conditions on a scale smaller than that of the CALMET domain and hence their spatial representativeness may be limited. Such situations are most likely to occur when the CALMET grid scale is relativ ely large i.e., coarser than the scale of variation of the true wind field, which is partic ularly likely to occur in complex terrain or along the seashore.

As was described in Section 3.1.1 of this protocol, when the 12-km MM5 (2001 and 2002) data are used, the diagnostic CALMET terrain ad justments were turned off since the grid resolution of the MM5 data is the same as the CALMET grid and the terrain adjustments on the 12-km grid scale will already be reflect ed in the MM5 dataset. In this case, the MM5 winds will be interpolated by CA LMET to the CALMET layers and CALMET’s boundary layer modules will compute mixing he ights, turbulence parameters and other meteorological parameters that are require d by CALPUFF. For 2003, the 36-km MM5 data were used as CALMET’s initial guess fi eld and then the CALMET diagnostic terrain adjustments (see Section 3.1.1 of the VISTAS BART Modeling Protocol ) were applied to reflect terrain on the scale of the CALMET grid (i.e., 12 km). Refined analyses, if

### DAK Americas, LLC – Cooper River facility 4-31 Trinity Consultants BART Applicability Modeling Protocol required, will utilize the MM5 data as the fi rst-guess wind field, apply the diagnostic algorithms to create the Step 1 winds, and use NWS data for smoothing in Step 2.



### 4.1.2 M AJOR 

### R ELEVANT 

### F EATURES OF 

### CALPUFF
By its puff-based formulation and through th e use of three-dimensional meteorological data developed by the CALMET meteorologi cal model, CALPUFF can simulate the effects of time- and space-varying meteorol ogical conditions on pollutant transport from sources in complex terrain. The major features and options of the CALPUFF model are summarized in Table 3-2 of the VISTAS BART Modeling Protocol . Some of the technical algorithms are briefly described as follows.

▲ 

### Complex Terrain: The effects of complex terrain on puff transport are derived from the CALMET winds. In addition, puff-terrain interactions at gridded and discrete receptor locations are simulated using one of two algorithms that modify the puff-height (either that of ISCST3 or a gene ral “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical dispersion derived from the weakly-stratified flow and dispersion m odule of the Complex Terrain Dispersion Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms rely on the receptor elevation (relative to the elevation at the source) and 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 in CALPUFF, CTSG treats terrain features that are not resolved by the gridded terrain field, and is based on the CTDMPLUS (Perry et al., 1989). Plume impingement on subgrid-scale hills is evaluated at the CTSG subgroup of recep tors 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 fields. As in CTDMPLUS, each feature is modeled in is olation with its own set of receptors.

▲ 

### Puff Sampling Functions: A set of accurate and computationally efficient puff sampling routines is included in CALPUFF, which solve many of the computational difficulties encountered when applying a puff model to near-field releases. For near-field applications during rapidly- varying meteorological conditions, an elongated puff (slug) sampling function may be used. An integrated puff approach may be used during less demanding conditions. Both techniques reproduce continuous plume results under the appropriate steady state conditions.

▲ 

### Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are both incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the choice on a stack-by-stack and wind sector-by-wind sector basis. Both algorithms have been implemented in such a way as to allow the use of wind direction specific building dimensions. The PRIME building downwash model (Schulman et al., 2000) is also included in CALPUFF as an option.

▲ 

### Dispersion Coefficients: 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

### DAK Americas, LLC – Cooper River facility 4-32 Trinity Consultants BART Applicability Modeling Protocol heat and momentum fluxes, or the use of Pasquill-Gifford (PG) or McElroy-Pooler (MP) dispersion coefficients, or dispersion equations based on the CTDM. Options are provided to apply an averaging time correction or surface roughness length adjustments to the PG coefficients. In Version 5.754 of CALPUFF being used by VISTAS, an option is provided to u se the AERMOD turbulence profiles for determining dispersion rates, which is the mo st recent approach to dispersion in EPAapproved regulatory modeling. In addition, turbulence advection is included. For additional details on these features, see Scire et al. (2005).

▲ 

### Overwater and Coastal Interaction Effects: Because the CALMET meteorological model contains both overwater and overland boundary layer algorithms, the effects of water bodies on plume transport, dispersion, and deposition can be simulated with CALPUFF. The puff formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion conditions, including the abrupt changes that occur at the coastline of a major body of water.

▲ 

### Dry Deposition: A resistance model is provided in CALPUFF for the computation of dry deposition rates of gases and particulate matter as a function of geophysical parameters, meteorological c onditions, and pollutant species. For particles, sourcespecific mass distributions may be provided for use in the resistance model. Of particular interest for BART analyses is the ability to separately model the deposition of fine particulate matter (< 2.5 μ m diameter) from coarse particulate matter (2.5-10
μ m diameter).

▲ 

### Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that allows vertical wind shear effects across individual puffs to be simulated. Differential rates of dispersion and trans port among the “new” puffs generated from the original, well-mixed puff can substa ntially increase the effective rate of horizontal spread of the material. Puffs ma y also be split in the horizontal when the puff size becomes large relative to the grid size, to account for wind shear across the puffs.

▲ 

### Wet Deposition: An empirical scavenging co efficient approach is used in CALPUFF to compute the depletion and wet depositi on 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).

▲ 

### Chemical Transformation: CALPUFF includ es options for parameterizing chemical transformation effects using the five species scheme (SO 2 

### , SO 4 = 

### , NO X 

### , HNO 3 

### , and NO 3 - 

### ) employed in the MESOPUFF II model or a set of user-specified, diurnallyvarying transformation rates. The MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine particulate matter (sulfate and nitrate) from emissions of SO 2 

### and NO X 

### and thus allows analyses of visibility impacts. Ambient ozone concentrations are used in the parameterized chemical transformation module as a surrogate for OH radicals during daylight hours. Ambient ammonia concentrations are used together with a temperature and relative humidity-dependent equilibrium relationship to partition nitric acid and nitrate on an hour-by-hour and receptor-by-receptor basis.

### DAK Americas, LLC – Cooper River facility 4-33 Trinity Consultants BART Applicability Modeling Protocol 

## 4.2 M ODELING 

## D OMAIN 

## C ONFIGURATION
The VISTAS regional modeling domain was illustrated in Figure 3-1 in the preceding section of this protocol, and was designed to allow any Class I areas within the VISTAS area to be evaluated with a single meteorological database and consistent CA LPUFF modeling options. The horizontal domain is comprised of grid cells, each containing a central grid point at which meteorological and computational parameters are calculated at each time step. For the initial regional analysis, a grid spacing interval of 12 km was selected. Given this interval, the domain consists of 160 by 172 grid cells. A Lambert Conformal Coordinate projection system is used to describe the horizontal grid, with origin at 40 degrees North latitude and 97 de grees West longitude. Standard parallels for the projection were set at 33 degrees North and 45 degrees North.

Table 4-1 summarizes the vertical grid structure selected for this analysis, which comprises ten vertical layers. The cell face height of each layer indicates its vertical extent. The vertical domain is composed of terrain-following grid cells, the number and size of which are chosen so as to constrain the boundary layer in which dispersion and chemical transformations take place. The highest cell face was selected to be 4,000 meters to constrain th e default maximum mixing height of 3,000 meters.

T ABLE 

### 4-1. V ERTICAL 

### G RID 

### S TRUCTURE

Vertical Grid Cell Cell Face Height (meters)

1 20 2 40 3 80 4 160 5 320 6 640 7 1,200 8 2,000 9 3,000 10 4,000



### Refined analyses, if necessary, will be conducted using the Domain #1 4-km grids that the VISTAS technical contractor will provide. If necessary, the a ppropriate subregional grid would be utilized. Additional runs at higher resolution (e.g., 1 km ) may be performed and the modeling subdomain would be selected appropriately from the VISTAS regional grid.



## 4.3 CALMET M ETEOROLOGICAL 

## M ODELING
CALMET meteorological modeling for the initial regional screening analysis was conducted over the entire VISTAS regional domain described in section 4.2. The major features of CALMET were described in Section 4.1.1 of th is protocol, and the geophysical and meteorological databases were described in Section 3. CALMET processing w as conducted generally in accordance with the recommendations of IWAQM Phase 2 Summary Report and Recommendations for Modeling Long Range Transport Impacts , with the following exceptions and/or specifications of non-default values.

### DAK Americas, LLC – Cooper River facility 4-34 Trinity Consultants BART Applicability Modeling Protocol ▲ 

### Modeling period: 2001 through 2003

▲ 

### Meteorological inputs: MM5 data provide initial guess fields in CALMET

▲ 

### CALMET mode: No-Observations mode including option to read overwater data directly from MM5

▲ 

### Diagnostic options: IWAQM default values, ex cept as follows: diagnostic terrain blocking and slope flow algorithms used for 2003 simulations (using 36-km MM5 data), but no diagnostic terrain adjustments in 2001 a nd 2002 simulation (using 12-km MM5 data)

▲ 

### CALMET options dealing with radius of in fluence parameters (R1, R2, RMAX1, RMAX2, RMAX3), BIAS, ICALM parameters are not used in No-Observations mode.

▲ 

### TERRAD (terrain scale) is required for runs w ith diagnostic terrain adjustments (i.e., the 2003 simulations). Values of ~10-20 km will be tested, and a value of 15 km was selected by the VISTAS technical contractor.

▲ 

### Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature allows the temperature field over large bodies of water such as the Atlantic Ocean and the Great lakes to be properly characterized by buoy observations.

▲ 

### Mixing height averaging parameter (MNMDAV) was determined by the VISTAS technical contractor to be 1 grid cell for regional simula tions based on sensitivity tests. The purpose of the testing was to optimize the variable to allo w spatial variability in the mixing height field, but without excessive noise.

Refined analyses, if necessary, will be prepared using appropriate CALMET model settings as described by the VISTAS technical contractor for the 4 km subregional grids utilized. Should even higher resolution analyses be conducted, appropria te CALMET processing options will be evaluated and documented in the final applicability modeling report.



## 4.4 CALPUFF C OMPUTATIONAL 

## D OMAIN AND 

## R ECEPTORS
CALPUFF analyses to assess the visibility impacts attr ibutable to DAK’s Cooper River facility will be performed on a computational domain that is a su bset of the VISTAS regional domain. The size of the domain will be selected to encompass the Cooper River plant and the Cape Romain and Wolf Island Class I Areas, and to extend at least 50 km be yond in all directions. The size of the domain allows for the possible recirculation of puffs beyond the facility and areas being evaluated.

Class I receptors: Use FLM Class I receptor list with receptor elevations provided (available from the NPS). Ambient impacts will be predicted at recep tors specified by the FLM to represent the Cape Romain and Wolf Island Class I Areas 9



### .



## 4.5 CALPUFF M ODELING 

## O PTION 

## S ELECTIONS
The CALPUFF analysis will be conducted generally in accordance with the recommendations of IWAQM Phase 2 Summary Report and Recommendations for Modeling Long Range Transport Impacts , with the following exceptions and/or specifications of non-default values.

9 http://www2.nature.nps.gov/ai r/maps/Receptors/index.htm

### DAK Americas, LLC – Cooper River facility 4-35 Trinity Consultants BART Applicability Modeling Protocol ▲ 

### Chemical mechanism: MESOPUFF II module

▲ 

### Background concentrations of SO 4 

### and TNO 3 

### (HNO 3 

### + NO 3 

### ) from CMAQ 2001-2003 annual runs. SCDHEC has provided the appropriate va lues in response to this modeling protocol should refined modeling be necessary.

▲ 

### Species modeled: SO 2 

### , SO 4 

### , NO X 

### , HNO 3 

### , NO 3 

### and particulate matter in size categories of <0.625 μ m, 0.625-1.0 μ m, 1.0-1.25 μ m, 1.25-2.5 μ m, 2.5-6.0 μ m and 6-10 μ m aerodynamic diameters. Because DAK’s BART-eligible source comprises more than one emission point, emissions of each particle type (i.e., PIC, POC, PMC, PMF, and EC) will be explicitly modeled.

▲ 

### Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual emission rate with normal operations from th e highest emitting day of the meteorological period modeled (excluding days where star t-up, shutdown or malfunctions occurred sometime during the day). Note that potential emissions are used to determine if a source is BART-eligible, but 24-hour average maximu m emissions are used for modeling purposes (70 FR 39162). Pollutants considered include SO 2 

### , NO X 

### and PM 10 

### . The basis for modeled emission rates was described in Section 2 of this protocol.

▲ 

### Condensable emissions are considered as prim ary fine particulate matter and allocated equally to the two sub-micrometer particle si ze classes. Industry-specific emission factors were utilized to analyze the phase, size, and character of PM emissions as described in Section 2 of this protocol.

▲ 

### Excluded from the modeling are pollutants with plant-wide emissions less than de minimis levels (40 tons per year for SO 2 

### and NO X 

### and 15 tons per year for PM 10 

### ). De minimis levels are plant wide for each visibility-impairing pollutant, so individual units may be modeled even if they have emissions below de minimis if the plant total is greater than de minimis

.

▲ 

### Particulate emissions speciation: Break down, as appropriate, filterable and condensable particulate matter into the following species ca tegories: elemental carbon (soot), “soil” (fine PM < 2.5 μ m diameter), coarse particulate matter (2.5-10 μ m diameter) and organics. Industry-specific emission factors were utilized to analyze the phase, size, and character of PM emissions as described in Section 2 of this protocol.

▲ 

### CALPUFF model options: Use IWAQM guidance, including Pasquill-Gifford (ISC-like) dispersion coefficients. The use of turbulen ce-based dispersion coefficients and probability density function (pdf) dispersion, as used in the AERMOD model and originally proposed by VISTAS, will be evaluated should refined modeling be necessary.

▲ 

### Ozone dataset: use observed ozone data fo r 2001 through 2003 from CASTNet and AIRS stations. Only non-urban ozone stations shoul d be used in the OZONE.DAT file. Monthly average ozone (backup) background values are to be computed based on daytime average ozone concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations computed by month).

▲ 

### Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) values for ammonia. In refined analyses (if required), an application of the ammonia limiting method with CMAQ NH 3 

### data will be used for each Class I area in POSTUTIL to repartition HNO 3 

### and NO 3

▲ 

### Puff representation: integrated puff sampling methodology.

### DAK Americas, LLC – Cooper River facility 4-36 Trinity Consultants BART Applicability Modeling Protocol ▲ 

### Building downwash: The Cape Romain Class I area is 31 km from the source. Therefore, downwash must be included in the calpuff run for receptors at the Cape Romain area.



## 4.6 CALPOST P ROCESSING 

## O PTION 

## S ELECTIONS FOR 

## L IGHT 

## E XTINCTION AND 

## H AZE 

## I MPACT 

## C ALCULATIONS
The following postprocessing techniques will be used to compute the 24-hour average visibility impacts at the Class I area located within 300 km of DAK’s Cooper River plant.

▲ 

### Species considered in visibility analysis: SO 4 

### , NO 3 

### , EC, SOA (i.e., condensable organic emissions), soil, coarse PM

▲ 

### Visibility Method 6 will be used with Class I area-specific, monthly average, relative humidity values as described in Section 3.5 of this protocol using both the 20% best days and annual average natural concentrations of visibility-affecting pollutants.

▲ 

### Natural background light extinction will be re presented at the Class I Areas as described in Section 3.5 of this protocol.

▲ 

### Light extinction efficiencies: Use EPA (2003a) values. DAK will evaluate the use of the 2005 IMPROVE algorithm for calculating light extinction, which the VISTAS BART Modeling Protocol indicates may be used in addition to the default IMPROVE algorithm. Calculations would be pe rformed outside CALPOST unless CALPOST is modified to accommodate the new algorithm.

▲ 

### Ammonia Limiting Method: Refined analyses (if necessary) would use ammonia from CMAQ to define NH 3 

### for the Class I area. Choose ammonia from either the CMAQ grid cell where the IMPROVE monitor is located or the grid cell of the centroid of the Class I area (the latter in the case that the IMPROVE monitor is located outside the Class I area or there is no IMPROVE monitor.)

The initial run results will be based on the highest change in light extinction (deciviews) from natural conditions over the three-year modeling period fo r the Class I area considered. Predicted changes exceeding the “contribution” threshold (0.5 decivi ews) will trigger a finer grid CALPUFF modeling analysis. To assess whether BART-eligible opera tions contribute to visibility impairment, DAK’s applicability modeling analysis will demonstrate the top eight 24-hour average visibility impacts of each year modeled to illustrate the distribution (i .e., frequency, dur ation, and magnitude) of peak visibility impairment episodes attributable to the Cooper River plant. Only the 98 th 

### percentile 24-hour average visibility impact would be evalua ted in the refined analysis, if necessary.



## 4.7 M ODELING 

## P RODUCTS
DAK will prepare and submit a BART applicabilit y analysis result describing the modeling procedures, data resources, and results of screen ing and refined modeling (if necessary) used to assess whether the Cooper River plant is subject to BART. The presentation of modeling results will generally conform to the expectations described in the VISTAS BART Modeling Protocol and described as follows. The Results section of the CALPUFF modeling report should contain the following information:

### DAK Americas, LLC – Cooper River facility 4-37 Trinity Consultants BART Applicability Modeling Protocol 1. Map of source location and Class I areas within 300 km of the source

2. For the VISTAS 12-km CALPUFF initial exem ption modeling domain, a table listing all Class I areas in the VISTAS domain and those in neighboring states and impacts at those Class I areas within 300 km of the source 3. A discussion of the number of Class I areas with visibility impairment from the source on 98 th 

### percentile days in each year greater than 0.5 dv (total visibility impairment minus impairment on 20% best days for natural background visib ility equals delta-dv, the visibility impact attributed to the source). 4. For the Class I area with the maximum impact, discussion of the number of days below the 98 th 

### percentile that the impact of the source ex ceeds 0.5 dv, the number of receptors in the Class I area where the impact exceeds 0.5 dv, and the maximum impact. 5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which impacts of the source exceeded 0.5 dv in the 12-km initial exemption modeling. Report same results as provided for 12-km initial exemption modeling.

### DAK Americas, LLC – Cooper River facility 5-38 Trinity Consultants BART Applicability Modeling Protocol 

## 5. Q UALITY 

## A SSURANCE 

## M ETHODS

DAK will conduct quality assurance of CALMET, CALPUFF, and CALPOST analyses in a manner the generally conforms to the VISTAS BART Modeling Protocol . A description of the quality assurance methods and products (e.g., test case simu lations, graphic representations of model fields and performance) will be provided in DAK’ s BART Applicability Modeling Report. The following sections describe techniques that can be used to visualize and quality assure performance of each model component as described in the VISTAS BART Modeling Protocol

.



## 5.1 CALMET F IELDS

Section 4 of the VISTAS BART Modeling Protocol describes the methods and procedures for use in conducting regional scale screening modeling to dete rmine the whether a particular source or group of sources is subject to BART controls. In the initial application, the regional CALPUFF-ready meteorological data files will be provided by VISTAS. The amount of effort for end-users performing QA of these pre-defined meteorological fi elds will be reduced from what is required in developing source-specific meteorological fields, as described below. Also, VISTAS is planning to provide five subregional CALMET meteorological datasets in a CALPUFF-ready format. The development of these CALMET datasets will be subject to a QA program as part of their development, so the necessary quality assurance ac tivity of end-users is again reduced from what would be required in the development of the dat aset. It is not expected that the quality assurance steps in the development will be re peated in each application.

The critical CALMET input parameters depend on the mode in which the model is run (i.e., observations mode, hybrid mode, or no-observa tions mode), and the location and spatial representativeness of any observational data. In a site -specific protocol involving the development of a meteorological dataset, the elements of the QA process include preparation of wind rose (using observed, MM5 and CALMET-derived data), includi ng examination of the data as a function of season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses, and presentation of 2-D vector plots illustrating terrain effects/sea breeze circulation or other features of the flow expected to occur within the domain. For example, 2-D vector plots produced during light wind speed stable conditions (e.g., early morning su ch as 4 am) are good for assessing the performance of the CALMET model configuration and switches in reproducing terrain effects because these conditions are likely to maximize the terrain impacts in the model. Season wind roses at 4 am, 10 am and 4 pm would be expected to show the development of sea breeze circulations that may be important for certain applications. Customization of the QA process for the individual site-specific domain based on the availability of da ta and the physical processes expected to be important at that location will be conducted as part of th e site-specific QA plan development.



## 5.2 CALPUFF, CALPOST, AND 

## POSTUTIL R ESULTS
Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report contains a list of recommended CALPUFF switch setti ngs. Except as modified in Chapter 4 of the VISTAS BART Modeling Protocol or in a source-specific protocol, the IWAQM guidance should be

### DAK Americas, LLC – Cooper River facility 5-39 Trinity Consultants BART Applicability Modeling Protocol used in setting up the CALPUFF simulations. Th e CALPUFF model obtains the switch settings from an ASCII “control file” with a default name called the CALPUFF.INP file. As is the case with the comparable CALMET file, it is essential that the cont rol file be reviewed manually as part of the CALPUFF QA analysis. To facilitate this pr ocess, as was the case w ith the CALMET GUI, the CALPUFF GUI retains all of the input descriptive information that is part of the standard CALPUFF.INP file structure.

CALPOST is run separately for each Class I area in or der to obtain the necessary visibility statistics for evaluating compliance with the BART screening and finer grid modeling thresholds. The inputs to CALPOST involve selection of the visibility method (Method 6 in the standard EPA BART guidance), entry of Class I area-specific data fo r computing background extinction and monthly relative humidity factors for hygroscopic aerosol s. CALPOST contains a receptor screening that allow subsets of a receptor network modeling in CALPUFF to be selected for processing in a given CALPOST run. This is how receptors within a single Class I area are selected for processing from a CALPUFF output file that may contain receptors from several Class I areas. CALPOST contains options for creating plot files that will help in the confirmation that the proper receptor subset is extracted.

The CALPOST output file contains a listing of the highest visibility impact each day of the model simulation over all receptors included in CALPOST an alysis. Receptors will normally be selected in each CALPOST run so that each CALPOST run represents the impacts at a single Class I area. For a screening assessment, the peak value of the change in extinction is shown at the bottom of the visibility table. For a finer grid simulation, the 98 th 

### percentile value (8 th 

### highest day) is used for comparison against the BART threshold of 0.5 deciviews. It is necessary to import the results of the CALPOST table into a sorting program such as a spre adsheet to rank the dail y change in extinction values such as is presented in Table 4-2.

The CALPOST inputs that will be carefully ch ecked as part of the CALPOST quality assurance include the following:

▲ 

### Visibility technique (Method 6)

▲ 

### Monthly Class I-specific relative humidity factors for Method 6

▲ 

### Background light extinction values

▲ 

### Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, organics), coarse and fine particulate ma tter, and elemental carbon.

▲ 

### Appropriate species names for coarse PM used

▲ 

### Extinction efficiencies for each species

▲ 

### Appropriate Rayleigh scattering term (10 Mm -1 

### for screening modeling but Class I area specific value for finer grid modeling)

▲ 

### Screen to select appropriate Class I receptors for each CALPOST simulation.

POSTUTIL allows the user to sum the contributi ons of sources from different CALPUFF simulations into a total concentration file. In addition, it contains options to scale the concentrations from different modeled species (e.g., diffe rent particle sizes) into speci es-dependent size distributions for the particulate matter. For example, PM is often simulated with unit emission rates for each particle

### DAK Americas, LLC – Cooper River facility 5-40 Trinity Consultants BART Applicability Modeling Protocol size category and, in the POSTUTIL stage, the contributions of each size category based on the species being considered (e.g., elemental carbon, coarse particulate matter, etc.) are combined to form the species concentrations for inpu t into CALPOST. This process, although simple, requires a careful review of the weighting factors for each source. PO STUTIL also allows a repartitioning of nitric acid and nitrate to account for the eff ects of ammonia limiting conditions.

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, performance of the model in reproducing observe d CASTNet or IMPROVE sulfate and nitrate concentrations at measurement sites within the s ite-specific modeling domain should be evaluated. The use of alternative ammonia concentration data (e.g., CMAQ output rather than derived ammonia based on aerosol measurements) will require an ev aluation of the model performance relative to the techniques in the VISTAS common protocol.

### DAK Americas, LLC – Cooper River facility 6-41 Trinity Consultants BART Applicability Modeling Protocol 

## 6. R EFERENCES

The following guidance documents, regulations, and technical publications were referenced in preparation of this BART Applicability Modeling Protocol for DAK and the VISTAS BART Modeling Protocol

.
Allwine, K.J. and C.D. Whiteman, 1985: MELSA R: A Mesoscale Air Quality Model for Complex Terrain: Volume 1--Overview, Technical Description and User's Guide. Pacific Northwest Laboratory, Richland, Washington.

Batchvarova, E. and S.-E. Gryning, 1991: Applied model for the growth of the daytime mixed layer. Boundary-Layer Meteorol
., 56 , 261-274.

Batchvarova, E. and S.-E. Gryning, 1994: An applied model for the height of the daytime mixed layer and the entrainment zone. Boundary-Layer Meteorol
., 71 , 311-323.

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Ma nikin, 2004: An Hourly Assimilation Forecast Cycle: the RUC, M on. Wea. Rev ., 132 , 495-518.

Carson, D.J., 1973: The development of a dry, in version-capped, convectively unstable boundary layer. Quart. J. Roy. Meteor. Soc
., 99 , 450-467.

Chang, J.C. P. Franzese, K. Chayantrakom and S. R. Hanna, 2001: Evaluations of CALPUFF, HPAC and VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology
, 42 (4): 453-466.

Department of the Interior, 2003: Letter from the Assist ant Secretary of the Interior to the Director of the Montana Department of Environmental Quality.

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air Resources Board, Sacramento, California.

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Network. Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes, Consequences and Controvers ies, Asheville, NC, October 2004.

England, Glenn. Development of Fine Particulate Emission Factors and Speciation Profiles for Oiland Gas-Fired Combustion Systems. October 2004.

Environmental Protection Agency, 2003a: Guidan ce for Tracking Progress under the Regional Haze Rule; EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC.

### DAK Americas, LLC – Cooper River facility 6-42 Trinity Consultants BART Applicability Modeling Protocol Environmental Protection Agency, 2003b: Guidan ce for Estimating Natural Visibility Conditions under the Regional Haze Rule. EPA- 454/B-03-005. U.S. Environmental Protection Agency, Research Triangle Park, NC. Environmental Protection Agency, 1998: Ph ase 2 Summary Report and Recommendations for Modeling Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA-454/R-98-019, U.S. Environmental Protection Agency, RTP, NC.

Escoffier-Czaja, C., and J. Scire. 2002: The E ffects of Ammonia Limitation on Nitrate Aerosol Formation and Visibility Impacts in Class I Areas. Paper J5.13, 12 th 

### AMS/A&WMA Conference on the Applications of Air Pollution Meteorology, Norfolk, VA. May 2002.

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grach ev, and J.B. Edson, 2003: Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm. Journal of Climate
, 16 , 571-591.

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi-Vol Sampling for Carbon-14 Analysis of PM 2.5 Aerosols. Draft Report, Lawr ence Livermore National Laboratory.

FLAG, 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report. U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn State/NCAR MM5, NCAR TN-398-STR, NCAR Technical Note.

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. Atmos. Environ.
, 23 , 1733-1747.

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., 22 , 517-529.

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications , XII. Edited by S.E.

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy , 4
, 10:16.

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci
., 39 , 2701-2711.

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity Generating Bureau MID/SSD/80/0026/R. Nottingham, England.

Morris, R., C. Tang and G. Yarwood, 2003: Eval uation of the Sulfate and Nitrate Formation Mechanism in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference – Guideline on Air Quality Models: The Pa th Forward. Mystic, CT, 22-24 October 2003.

### DAK Americas, LLC – Cooper River facility 6-43 Trinity Consultants BART Applicability Modeling Protocol Morrison, K., Z-X Wu, J.S. Scire, J. Chenie r and T. Jeffs-Schonewille, 2003: CALPUFF-Based Predictive and Reactive Emissions Control System. 96 th 

### A&WMA Annual Conference & Exhibition, 22-26 June 2003, San Diego, CA.

National Council for Air and Stream Improvement. Performance of EPA stack sampling methods for PM 10 

### , PM 2.5 

### and condensible particulate matter on sources equipped with electrostatic precipitators (Technical Bulletin No. 852), September 2002.

National Council for Air and Stream Improvement. Condensible particulate matter emissions from sources equipped with wet scrubbers (Tec hnical Bulletin No. 898), March 2005.

National Council for Air and Stream Improvement. Compilation of criteria air pollutant emissions data for sources at pulp and paper mills including boilers (Technical Bulletin No. 884), April 2005 (Revised).

O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coe fficient in the planetary boundary layer. J. Atmos. Sci
., 27 , 1213-1215.

Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised IMPROVE Algorithm for Estimating Light Extinctio n from Particle Speciation Data. Report to IMPROVE Steering Committee, November 2005

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLU S) Volume 1: Model Description and User Instructions. EPA/600/8-89/041, U.S. EPA, Research Triangle Park, South Carolina.

Schulman, L.L., D.G. Strimatitis, and J.S. Scire, 2000: Development and Evaluation of the PRIME Plume Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50 , 378-390.

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II Dispersion Model. EPA-600/3-84-057, U.S. Envi ronmental Protection Agency, Research Triangle Park, NC.

Scire, J.S. and F.R. Robe, 1997: Fine-Scale App lication of the CALMET Meteorological Model to a Complex Terrain Site. Paper 97-A1313. Air & Wast e Management Association 90th Annual Meeting & Exhibition, Toronto, Ontario, Canada. 8-13 June 1997.

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

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

### DAK Americas, LLC – Cooper River facility 6-44 Trinity Consultants BART Applicability Modeling Protocol Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of Environmental Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA.

Scire, J.S., Z.-X. Wu, 2003: Evaluation of th e CALPUFF Model in Predicting Concentration, Visibility and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty Conference, Guideline on Air Quality Models: Th e Path Forward. Mystic, CT, 22-24 October 2003.

Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005: Evaluation of enhancements to the CALPUFF model for offshore and coastal applications. Proceedings of the Tenth International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), Crete, Greece. 17-20 October 2005.

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1 998: Evaluation of the CALPUFF Dispersion Model with Two Power Plant Data Sets. Tenth Joint Conference on the Applications of Air Pollution Meteorology, Phoenix, Arizona. American Meteorological Society. 11-16 January 1998.

Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at the AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 7-11 February 2005.

### DAK Americas, LLC – Cooper River facility A - 1 Trinity Consultants BART Applicability Modeling Protocol 

## A PPENDIX 

## A

VISTAS BART M ODELING 

### P ROTOCOL

# Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART)

December 22, 2005

(Revision 2

– 3/9/06)



## Visibility Improvement State and Tribal Association of the Southeast (VISTAS)Table of Contents iv 

## TABLE OF CONTENTS

Page

SUMMARY S

- 1

1. INTRODUCTION AND PRO TOCOL OBJECTIVES 1 1.1 Background 1 1.2 Objective of this Protocol 2

2. REVIEW OF EPA ’S GUIDANCE FOR BART MODELING 4 2.1 Overview of the Regional Haze BART Process 4 2.2 Model Recommendations for the BART Analysis 6 2.3 Performance of a Cap and Trade Program 7

3. OVERVIEW OF THE CALP UFF MODELING SYSTEM 8 3.1 Capabilities and features of CALPUFF 8 3.1.1 Major Features of CALMET 10 3.1.2 Major Features of CALPUFF 12 3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL) 16 3.2 Discussion of CALPUFF Applicability and Limitations 17 3 .2.1 Transport and Diffusion 17 3.2.2 Aerosol Constituents 20 3.2.3 Regional Haze 28

4. VISTAS’ COMMON MODEL ING PROTOCOL 32 4.1 Overv iew of Common Modeling Approach 32 4.1.1 BART Exemption Analysis 32 4.1.2 BART Control Evaluation 34 4.1.3 VISTAS’ Treatment of VOC, NH 3 , and PM 34 4.2 Optional Source

- Specific Modeling 35 4.3 Initial Procedure for BART Exemption 36 4.3.1 Overview of Initial Approach 36 4.3.2 Discussion of Regi onal Initial Modeling Approach 36 4.3.3 Model Configuration and Settings for Initial Analysis 39 4.4 Finer Grid Modeling Procedures 43 4.4.1 Rationale for and Over view of Finer Grid Modeling Approach 43 4.4.2 Model Configuration and Settings for Finer Grid Modeling 44 4.5 Presentation of Modeling Results 46 4.6 VISTAS Contri bution to CALPUFF Modeling of BART Eligible Sources 51

5

. SOURCE

- SPECIFIC MODELING PR OTOCOL 52Table of Contents iv 6. QUALITY ASSURANCE 55 6.1 Scope and Purpose of the QA program 55 6.2 QA Procedures for Common Protocol Modeling 56 6.2.1 Quality Control of Input Data 56 6.2.2 Quality Control of Application of CALMET 57 6.2.3 Quality Control of Application of CALPUFF 58 6.2.4 Quality Control of Application of CALPOST and POSTUTIL 59 6.3 Additional QA Issues for Alternative Source

- Specific Modeling 60 6.4 Assessment of Uncertainty in Modeling Results 61

7. REFERENCES 62Summary S

- 1 

## SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available
R etrofit Technology (BART) for the VISTAS Regional Planning Organization (RPO) describes common procedures for carrying out air quality modeling to support BART determinations that are consistent with guidelines of the U.S. Environmental Protection Agency i n 40 CFR Part 51 Appendix W and Appendix Y. The Protocol is intended to serve as the basis for a common understanding among the organizations that will be performing BART analyses or reviewing the BART modeling results in the VISTAS region.


### Background

Be st Available Retrofit Technology is required for any BART

- eligible source that ‘‘emits any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of visibility” in any mandatory Class I federal area. According to 40 CFR Part 51 Appendix Y, “ You can use dispersion modeling to determine that an individual source cannot reasonably be anticipated to cause or contribute to visibility impairment in a Class I area and thus is not subject to BART .” In the “individual source attri bution approach,” a BART

- eligible source that is responsible for a 1.0 deciview (dv) change or more is considered to “cause” visibility impairment. A BART

- eligible source that is responsible for a 0.5 dv change or more is considered to “contribute” to visi bility impairment in a Class I area. Any source determined to cause or contribute to visibility impairment in any Class I area is subject to BART.

The member states of the VISTAS RPO agreed to develop a common BART Modeling Protocol to guide them, their s ources, and reviewers in the BART determination and review effort. The Protocol has been in preparation within VISTAS since January 2005. The original authors are Pat Brewer, VISTAS Technical Coordinator, and Ivar Tombach, VISTAS Technical Advisor. The V ISTAS state BART contacts, particularly Tom Rogers, FL, Chris Arrington, WV, Leigh Bacon, AL, and Michael Kiss, VA, have directed and extensively reviewed the Protocol. The Protocol was enhanced and completed with the assistance of Joseph Scire, Christell e Escoffier

- Czaja and Jelena Popovic of Earth Tech, Inc. and it has received extensive contributions and review from the VISTAS federal partners: Federal Land Managers and USEPA. The VISTAS RPO held a meeting on September 21, 2005 in Research Triangle Par k, NC to discuss the Protocol with participants before starting a public comment period. The Protocol underwent formal external review during the period between September 26, 2005 and October 31, 2005. Numerous comments were received. All comments were carefully considered and discussed with VISTAS participants and federal partners. VISTAS gratefully acknowledges the very useful contributions of those that provided comments. On November 1 st , 2005 VISTAS held another meeting with its participants in Nas hville, TN to present and discuss the comments being considered for inclusion in the Protocol. No formal document will be prepared to address all the comments received on the Protocol.Summary S

- 2 

### Objectives

The objectives of the Protocol (discussed in Chapter 1) are to provide:

 A consistent approach to determine if a source is subject to BART

 A consistent model (CALPUFF) and modeling guidelines for BART determinations

 Clearly delineated modeling steps

 A common CALPUFF configuration

 Guidance for site

- specific mode ling

 Common expectations for reporting model results

The Protocol is not intended to define the engineering analyses required by the USEPA BART Guidance, nor address model alternatives to the CALPUFF model, nor address emissions trading.

Chapter 2 is in tended to provide summary background on EPA’s guidance for BART modeling. The CALPUFF model system is reviewed in Chapter 3, while specific recommendations for applying the CALPUFF model for BART purposes appear in Chapter 4. Chapter 5 describes the
spec ific information that should be included in site

- specific protocols. Chapter 6 describes the quality assurance requirements for BART analyses in the VISTAS RPO.



### Recommendations

The major recommendations for VISTAS BART modeling included in this Protocol a re:

I. Process

Follow the BART process steps discussed in Chapter 2 :

1. Identify BART eligible sources

2. Identify which pollutants have greater than de minimis emission levels

3. Identify sources that are subject to BART

4. Identify baseline visibility impact of each BART source

5. Identify feasible controls and emission changes

6. Identify the change in visibility impact for each candidate BART control option

7. Compare the visibility improvement of BART control options to other statutory factors in the engineering analys isSummary S

- 3 II. CALPUFF Model Configuration

Use the CALPUFF dispersion modeling system, as described in Chapter 4, to determine if a single source is subject to BART. VISTAS will use CALPUFF Version 5.754 and CALMET Version 5.7. These versions contain enhancemen ts funded by the Minerals Management Service (MMS) and VISTAS. They were developed by Earth Tech, Inc. and they are maintained on Earth Tech’s Atmospheric Studies Group CALPUFF website ( www.src.com ) for public access.

VISTAS is making publicly available 12

- km CALMET output files for the entire VISTAS modeling domain (eastern United States) and intends to also provide CALMET output files for five 4

- km grid subdomains covering the VISTAS states and VISTAS Class I areas. To create the CALMET input files, Earth Tech used the MM5 databases developed by EPA for 2001, VISTAS for 2002, and Midwest RPO for 2003. For the 12 km grid large domain covering the entire VISTAS region, Earth Tech used the No

- Obs setting (i.e., did not i nclude additional surface and upper air observations beyond those incorporated in the MM5 calculations). For finer resolution subdomains (4 km grid or less), available surface and upper air observations will be used in addition to MM5 meteorological model outputs. The specific model settings will be provided with the CALMET files and via the CALPUFF website so that users can review or replicate the work.

For CALPUFF modeling, source emissions should be defined using the maximum 24

- hour actual emission ra te during normal operation for the most recent 3 or 5 years. If maximum 24

- hr actual emissions are not available, continuous emissions data, permit allowable emissions, potential emissions, and emissions factors from AP

- 42 source profiles may be used as a vailable.

Key points from comments received on the specific CALPUFF, CALPOST, and POSTUTIL configurations are highlighted below.

• Use CMAQ modeling data from 2001

- 2003 to determine background concentrations of SO 4 and total NO 3 (HNO 3 + NO 3 ). CMAQ data in CALPUFF

- ready format will be provided for each Class I area by VISTAS. After running CALPUFF for an individual facility, repartition NO 3 in POSTUTIL using the CMAQ background data, including that for NH 3 .

• Use ozone data from non

- urban monitors as the b ackground ozone input.

• Use the Pasquill

- Gifford dispersion method. 1

1 The Fina l VISTAS BART Modeling Protocol (Dec. 22, 2005) recommended using turbulence

- based AERMOD dispersion methods, citing EPA’s Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218

- 68261. 9 November 2005. Subsequently, EPA Region IV notified the VISTAS states that using turbulence

- based dispersion methods would be considered a non

- guideline application of CALPUFF. Thus this Protocol has been revised to indicate Pasquill

- Gifford dispersion coefficients should be used.Summary S

- 4 • In CALPOST, use Method 6 with monthly average RH for calculating extinction, as recommended by the EPA.

• Use EPA default calculations of light extinction under current and natural backgro und conditions. In addition to the default assumptions, a source may choose to also calculate visibility using the recently revised IMPROVE algorithm described by Pitchford, et al., (2005).

Provide results in tables as illustrated in Chapter 4 that descr ibe, for each source:

• Number of receptors within a single Class I area with impact > 0.5 dv

• Number of days at all receptors in the Class I area with impact > 0.5 dv

• Number of Class I areas with impacts > 0.5 dv

III. CALPUFF Application for BART

For determ ining if a BART

- eligible source is subject to BART CALPUFF modeling, use a two

tier approach. For the initial exemption modeling use CALPUFF with 12

- km grid CALMET. For finer resolution of meteorological fields, use CALPUFF with CALMET of 4

- km or smaller g rid size.

VISTAS States are accepting EPA guidance that the threshold value to establish that a source contributes to visibility impairment is 0.5 deciview.

VISTAS States are using emissions (tons per year) divided by distance (km) from a Class I are a boundary (Q/d) as a presumptive indicator that a BART

- eligible source is subject to BART. If Q/d for SO 2 is greater than 10 for 2002 actual annual emissions, then the State presumes that the source is subject to BART and no exemption modeling will be pe rformed using VISTAS funds. If the source agrees with this presumption, then the source can proceed to the BART determination using CALPUFF to evaluate impacts of control options and perform the engineering analyses. If a source disagrees, the source may p erform fine grid modeling to determine if its impact is <0.5 dv.

For sources with Q/d less than or equal to 10, VISTAS intends to fund Earth Tech to assist States with the initial CALPUFF exemption modeling. Each State will prioritize which sources will be offered modeling by VISTAS. Modeling of these sources will be conducted in priority order to first accommodate States with nearer term timing constraints in their SIP development process. To conserve VISTAS resources, modeling will begin with sources at lower Q/d values and continue with sources with higher Q/d values until a Q/d value that consistently results in a greater than 0.5 dv impact is identified. Chapter 4 addresses the number of VISTAS sources eligible for BART based on Q/d analysis.Summary S

- 5 Not e that VISTAS does not propose to use Q/d to exempt BART

- eligible sources, but only to prioritize sources for modeling purposes. Thus this application is consistent with EPA guidance not to use Q/d for exemption purposes.

For the 12

- km initial modeling exemption test, compare the highest single 24

- hour average value across all receptors in the Class I area to the threshold value of 0.5 dv. If the highest 24

- hr average value is below 0.5 dv at all Class I areas, then the source is not subject to BART. I f the highest 24

- hr average value is greater than 0.5 dv, then the source may choose to perform finer grid modeling for exemption purposes or may accept determination that the source is subject to BART and proceed to establish visibility impacts prior to a nd after BART controls. If using the single highest 24

- hr average value proves, after initial 12

- km grid CALPUFF modeling, to be too conservative a screening level, VISTAS may allow some exceedances of the threshold value for exemption purposes, up to no more than the 98 th percentile value.

The 12

- km modeling results can be used to focus finer grid modeling for exemption purposes on only those Class I areas where impacts greater than 0.5 dv were projected in the 12

- km modeling.

For finer grid (4 km or l ess) analyses, use the 98 th percentile impact value for the 24

- hr average. Use either the 8 th highest day in each year or the 22 nd highest day in the 3

- year period, whichever is more conservative, for comparison to the exemption threshold.

Use the same mod el assumptions for pre

- BART visibility impact and for BART control options modeling: establish baseline visibility from the pre

- BART run; change one control at a time; and evaluate the change in visibility impact, i.e. the delta

- deciview. Note that “no co ntrol” may constitute BART.

Visibility impact is one of the five factors considered in the engineering analysis required under the USEPA BART guideline. If a source accepts to institute the most stringent control, the engineering analyses are not required

. This common VISTAS Protocol consistently recommends conservative assumptions. Individual States ultimately have responsibility to determine which, if any, BART controls are recommended in their State Implementation Plans (SIPs).Introduction and Protocol Objectives 1 

## 1. INTRODUCTION AND PRO TOC OL OBJECTIVES

1.1 Background

Under regional haze regulations, the Environmental Protection Agency (EPA) has issued final guidelines dated July 6, 2005 for Best Available Retrofit Technology (BART) determinations (70 FR 39104

- 39172). The regional haze rul e includes a requirement for BART for certain large stationary sources. Sources are BART

- eligible if they meet three criteria including potential emissions of at least 250 tons per year of a visibility

- impairing pollutant, were put in place between August 7, 1962 and August 7, 1977, and fall within one of the 26 listed source categories in the guidance. A BART engineering evaluation using five statutory factors

-- 1) existing controls; 2) cost; 3) energy and non

- air environmental impacts; 4) remaining usef ul life of the source; 5) degree of visibility improvement expected from the application of controls

-- is required for any BART

- eligible source that can be reasonably expected to cause or contribute to impairment of visibility in any of the 156 federal pa rks and wilderness (Class I) areas protected under the regional haze rule. (Note that, depending on the five factors, the evaluation may result in no control.) Air quality modeling is an important tool available to the States to determine whether a source can be reasonably expected to contribute to visibility impairment in a Class I

area.
Throughout this document the term “BART

- eligible emission unit” is defined as any single emission unit that meets the criteria described above. A “BART

- eligible source” i s defined as the total of all BART

- eligible emission units at a single facility. If a source has several emission units, only those that meet the BART

- eligible criteria are included in the definition “BART

eligible source”.

One of the listed categories i s steam electric plants of more than 250 million BTU/hr heat input. To determine if such a plant has greater than 250 million BTU/hr heat input and is potentially subject to BART, the boiler capacities of all electric generating units (EGUs) should be adde d together regardless of construction date. In this category, electric generating sources greater than 750 MW have presumptive SO 2 and NO x emission limits. States may presume the same limits for EGU sources between 250

- 750 MW. However, units at those sou rces constructed after the BART

- eligibility dates are not subject to a BART engineering evaluation. EPA, in the Clean Air Interstate Rule (CAIR), determined that an EGU participating in the CAIR trading program satisfies the BART requirements for SO 2 and NO x . VISTAS states are tentatively accepting this guidance. CAIR does not cover PM so EGUs would still need to evaluate impacts of PM if PM emissions are above de minimis values.

As illustrated in Table 1

- 1, as of December 5, 2005, VISTAS States had iden tified a total of 274 BART

- eligible sources that fall into 20 of the 26 BART source categories. Of the 274 sources with BART

- eligible units, 84 sources are utility EGUs and 190 are non

- EGU industrial sources. (Note that these numbers are not final and ar e subject to slight adjustments and refinements.) No BART sources are located on Tribal lands.Introduction and Protocol Objectives 2 Table 1

- 1. VISTAS BART Eligible Sources



### State Total Number of Sources EGU Sources Non

- EGU Sources

AL 48 8 40

FL 50 23 27

GA 24 10 14

KY 29 12 17

MS 18 8 10

NC 16 5 11

SC 31 6 25

TN 13 2 11

VA 18 3 15

WV 26 7 19


### Total 273 84 189



### 1.2 Objective of this Protocol

The objective of this VISTAS’ BART Modeling Protocol is to describe common procedures for air quality modeling to support BART determinations that are consistent with the EPA guidelines. The protocol will serve as the basis for establishing a common understanding among the organizations who will be performing the BART analyses or reviewing the BART modeling results, including VISTAS State and Local air regulatory agencies, EPA, Federal Land Managers (FLMs), source operators, and contractors for the sources. This final protocol incorporates EPA final guidance and comments that were received on VISTAS’ draft protocol 2 and provides additional des cription of modeling procedures.

The VISTAS States have accepted EPA’s guidance to use the CALPUFF modeling system to comply with the BART modeling requirements of the regional haze rule. A BART

- eligible source will be required to submit a site

- specific modeling protocol to the State for review and approval prior to performing CALPUFF modeling. States will consult with FLMs and the EPA when evaluating the site

- specific BART protocols. The site

- specific protocol will include the

source

- specific data on s ource location, stack parameters, and emissions. The methods of the

2 Draft Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART). VISTAS , March 22, 2005 and September 20, 2005.Introduction and Protocol Objectives 3 VISTAS common modeling protocol will be followed in the site

- specific protocol unless the source proposes to the State, and the State approves, alternative methods or assumptions.

Each VISTAS State or Local agency retains responsibility for the specific procedures and processes it will follow in working with the BART sources under its jurisdiction, the FLMs, EPA, and public to determine BART controls for sources in the State. Nothing i n the VISTAS process replaces States’ responsibility to determine BART controls.

The remainder of this document describes the CALPUFF modeling system and the application of CALPUFF to two situations:

• Air quality modeling to determine whether a BART

- eligi ble source is “subject to BART” and therefore the BART analysis process must be applied to its operations.

• Air quality modeling of emissions from sources that have been found to be subject to BART, to evaluate regional haze benefits of alternative control options and to document the benefits of the preferred option.

Chapters 2 and 3 of this document are intended to provide background information on EPA’s guidance for BART analysis modeling and on the CALPUFF modeling system. Subsequent chapters include more specific recommendations. Chapter 2 of this document reviews EPA’s guidance for regional haze BART analysis modeling, as outlined in the 6 July 2005 Federal Register notice. The CALPUFF model is the preferred model recommended by the EPA for BART modeling analyses and its characteristics and limitations are discussed in Chapter 3. The specific steps to determine whether a BART

- eligible source is subject to BART and to evaluate BART controls are described in Chapter 4. The procedures include initial modeli ng of BART

eligible sources using CALPUFF run in a conservative mode with regional meteorological datasets. For sources determined to be subject to BART based on these first modeling analyses, further finer grid CALPUFF analyses would be performed. The m odel configuration for the common modeling protocol is described in Chapter 4. Details of the source

- specific protocol are described in Chapter 5. A quality assurance plan is outlined in Chapter 6.

EPA’s guidance allows for the use of appropriate alter native models, however VISTAS will not develop a protocol for alternative models. This protocol focuses on guidance for the application of the preferred CALPUFF modeling approach. If a source wants to use an alternative model in its BART demonstration, th e source will need to submit a detailed written justification to the State for review and approval. The State will provide the documentation to the EPA and Federal Land Managers for their review.

Also, this protocol does not address a preferred modeling approach to demonstrate the effectiveness of an optional emissions cap and trade program. Such a cap and trade program is not required, but can be implemented in lieu of BART if desired by the VISTAS States. VISTAS States are not pursuing a regional trad ing alternative under the proposed EPA trading guidance (70 FR 44154

- 44175) that is to be promulgated early in 2006.Review of EPA’s Guidance 4 

## 2. REVIEW OF EPA’S GUID ANCE FOR BART MODELI NG

The final guidance for regional haze BART determinations was published in the Federal Register on 6 July 2005 (70 FR 39104 to 39172). It prescribes the modeling approaches that are to be used for various stages of the BART analysis process.

This chapter provides a summary of EPA’s guidance for BART modeling. It is not inte nded to provide a comprehensive review of the guidance. Nor does this chapter address specific recommendations for VISTAS’ approach to CALPUFF BART modeling. Those recommendations appear in Chapter 4.


### 2.1 Overview of the Regional Haze BART Process

Th e process of establishing BART emission limitations consists of four steps:

1) Identify whether a source is “BART

- eligible” based on its source category, when it was put in service, and the magnitude of its emissions of one or more “visibility

- impairing” a ir pollutants. The BART guidelines list 26 source categories of stationary sources that are BART

- eligible. Sources must have been put in service between August 7, 1962 and August 7, 1977 in order to be BART

eligible. Finally, a source is eligible for BAR T if potential e missions of visibility

- impairing air pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate matter (PM 10 ) and gaseous precursors to secondary fine particulate matter, such as SO 2 and NO x . Whether a mmonia or volatile organic compounds (VOCs) should be included as visibility

impairing pollutants for BART eligibility is left for the States to determine on a case

- by

- case basis. The guidance states that high molecular weight VOCs with 25 or more carbon atoms and low vapor pressure should be considered as primary PM 2.5 emissions and not VOCs for BART purposes.

(Note: If the source is subject to BART because one visibility impairing pollutant has potential emissions > 250 TPY, the State may determine that other visibility impairing pollutants are not subject to BART if their potential emissions are less than the de minimis levels (40 TPY for SO 2 and NO x and 15 TPY of PM 10 or PM 2.5 . This assumes that the other BART

- eligibility criteria are met.)

2) Determin e whether a BART

- eligible source can be excluded from BART controls by demonstrating that the source cannot be reasonably expected to cause or contribute to visibility impairment in a Class I area. The preferred approach is an assessment with an air quali ty model such as CALPUFF or other appropriate model followed by comparison of the estimated 24

- hr visibility impacts against a threshold above estimated natural conditions to be determined by the States. 3 The threshold to determine whether a single source “causes” visibility impairment is set at

3 The le vel of the natural conditions baseline that is to be used is described differently in the BART guideline and in the preamble to the BART rule. The BART guideline text says “natural conditions” at 70 FR 39162, col. 3, while the preamble says “natural visibi lity baseline for the 20% best visibility days” at 70 FR 39125, col. 1. Clarification received from Todd Hawes, US EPA, is that the intent was the 20% best days.Review of EPA’s Guidance 5 1.0 deciview change from natural conditions over a 24

- hour averaging period in the final BART rule (70 FR 39118). The guidance also states that the proposed threshold at which a source may “contribute” to visibility impairment should not be higher than 0.5 deciviews although, depending on factors affecting a specific Class I area, it may be set lower than 0.5 deciviews. The test against the threshold is “driven” by the contribution level, since if a source “causes”, by definition it “contributes”.

EPA recommends that the 98 th percentile value from the modeling be compared to the contribution threshold of 0.5 deciviews (or a lower level set by a State) to determine if a source does not contribute to visibility impai rment and therefore is not subject to BART. Whether or not the 98 th percentile value exceeds the threshold must be determined at each Class I area. Over an annual period, this implies the 8 th highest 24

- hr value at a particular Class I area is compared to the contribution threshold. Over a 3

- year modeling period, the 98 th percentile value may be interpreted as the highest of the three annual 98 th percentile values at a particular Class I area or the 22 nd highest value in the combined three year record, w hichever is more conservative.

Alternatively, States have the option of considering that all BART

- eligible sources within the State are subject to BART and skipping the initial impact analysis. In rare cases, a State might be able to do exactly the opposi te, and use regional modeling to conclude that all BART

- eligible sources in the State do not cumulatively contribute to “measurable” visibility impairment in any Class I areas. Also, the States have an option to exempt individual sources based on model pl ant analysis conducted by EPA in finalizing the BART rule. Under this option, sources with potential emissions of SO 2 plus NO x of less than 500 tons and a distance from any Class I area greater than 50 kilometers or sources with SO 2 plus NO x potential emi ssions of less than 1000 tons and a distance from any Class I area greater than 100 kilometers can be exempted. PM emissions are not specifically addressed in the model plant analysis, but subsequent discussions with EPA staff indicate that PM may be cons idered along with SO 2 and NO x , so that a plant could be exempted if the combined potential emissions of SO 2 , NO x , plus PM meet the criteria above.

3) Determine BART controls for the source by considering various control options and selecting the “best” al ternative, taking into consideration:

a) Any pollution control equipment in use at the source (which affects the availability of options and their impacts),

b) The costs of compliance with control options,

c) The remaining useful life of the facility,

d) The energy and non air

- quality environmental impacts of compliance, and

e) The degree of improvement in visibility that may reasonably be anticipated to result from the use of such technology.Review of EPA’s Guidance 6 Note that if a source agrees to apply the most stringent c ontrols available to BART

- eligible units, the BART analysis is essentially complete and no further analysis is necessary (70 FR 39165).

4) Incorporate the BART determination into the State Implementation Plan for Regional Haze, which is due by December 20 07.

Instead of applying BART on a source

- by

- source basis, a State (or a group of States) has the option of implementing an emissions trading program that is designed to achieve regional haze improvements that are greater than the visibility improvements th at could be expected from BART. If the geographic distributions of emissions under the two approaches are similar, determining whether trading is “better than BART” may be possible by simply comparing emissions expected under the trading program against th e emissions that could be expected if BART was applied to eligible sources. If the geographic distributions of emissions are likely to be different, however, air quality modeling comparing the expected improvements in visibility from the trading program an d from BART would be required. (See the proposed BART Alternative rule, at 70 FR 44160.) EPA suggests that regional modeling using a photochemical grid model may be more appropriate than CALPUFF for this purpose.

Note that EPA has indicated in the BART rul e (70 FR 39138

- 39139) that emissions reductions under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO 2 and NO x control for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR and therefore a BART anal ysis may still be required for PM.


### 2.2 Model Recommendations for the BART Analysis

To evaluate the visibility impacts of a BART

- eligible source at Class I areas beyond 50 km from the source, the EPA guidance recommends the use of the CALPUFF model as “ the best regulatory modeling application currently available for predicting a single source’s contribution to visibility impairment” (70 FR 39162). The use of another “appropriate model” is allowed although the EPA prefers the use of CALPUFF. If a source wants to use an alternative model, the source needs to submit a written justification and source

- specific modeling protocol to its State for review and approval. As part of the consultation process, the State will provide documentation to EPA and FLM.

For modeling the impact of a source closer than 50 km to a Class I area, EPA’s BART guidance recommends that expert modeling judgment be used, “giving consideration to both CALPUFF and other methods.” The PLUVUE

- II plume visibility model is mentioned as a possible model to consider instead of CALPUFF for a source within less than 50 km of a Class I area

.
The EPA guidance notes that “regional scale photochemical grid models may have merit, but such models have been designed to assess cumulative impacts, not impacts from individual sources” and they are “very resource intensive and time consuming relative to CALPUFF”, but States may consider their use for SIP development in the future as they may be adapted and “demonstrated to be appropriate for single so urce applications” (70 FR 39123). Photochemical grid models may beReview of EPA’s Guidance 7 more appropriate for cumulative modeling options such as in the determination of the aggregate contribution of all

- BART

- eligible sources to visibility impairment, but such use should invol ve consultation with the appropriate EPA Regional Office (70 FR 39163).

According to the BART guidance, a modeling protocol should be submitted for all modeling demonstrations regardless of the distance from the BART

- eligible source to the Class I area. E PA’s role in the development of the protocol is only advisory as the “States better understand the BART

eligible source configurations” and factors affecting their particular Class I areas (70 FR 39126).

In the BART modeling analyses the EPA recommends that the State use the highest 24

- hour average actual emission rate for the most recent three to five

- year period of record. Emissions on days influenced by periods of start

- up, shutdown and malfunction are not to be considered in determining the appropri ate emission rates. (70 FR 39129).

If a source is found to be subject to BART, CALPUFF or another appropriate model should be used to evaluate the improvement in visibility resulting from the application of BART controls. Visibility improvements may be e valuated on a pollutant

- specific basis in the BART determination (70 FR 39129).

For evaluating the improvement in visibility resulting from the application of BART, the EPA guidelines state that States are “encouraged to account for the magnitude, frequenc y, and duration of the contributions to visibility impairment caused by the source based on the natural variability of meteorology” (70 FR 39129).


### 2.3 Performance of a Cap and Trade Program

If a State or States elect to pursue an optional cap and trade program, they are required to demonstrate greater “reasonable progress” in reducing haze than would result if BART were applied to the same sources. In some cases, a State may simply be able to demonstrate that a trading program that achieves greater progr ess at reducing emissions will also achieve greater progress at reducing haze. Such would be the case if the likely geographic distribution of emissions under the trading program would not be greatly different from the distribution if BART was in place.

I f the expected distribution of emissions is different under the two approaches, then “dispersion modeling” of all sources must be used to determine the difference in visibility at each impacted Class I area, in order to establish that the optional trading program will result in visibility improvements aggregated over all Class I areas that are “better than BART” (70 FR 39137

- 39138). The BART guidance does not specify the method to be used for this modeling. From a technical perspective, either applying CALP UFF to every source or using a regional photochemical model would satisfy the need.

A rulemaking procedure is currently underway to establish final guidance for such alternatives to BART (70 FR 44154

- 44175). The rule is expected to be finalized early in 2 006.Ove rview of the CALPUFF Modeling System 8 

## 3. OVERVIEW OF THE CALP UFF MODELING SYSTEM

This chapter contains a general description of the CALPUFF modeling system and its capabilities and limitations. It does not include specific recommendations regarding the use of the mo del for BART analysis in the VISTAS region. These specific recommendations can be found in Chapter 4.


### 3.1 Capabilities and features of CALPUFF

The CALPUFF modeling system (Scire et al., 2000a, b) is recommended as the preferred modeling approach for u se in the BART analyses. CALPUFF and its meteorological model, CALMET, are designed to handle the complexities posed by the complex terrain, the large

source

- receptor distances, chemical transformation and deposition, and other issues related to Class I vi sibility impacts. 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 situations for shorter distances (68 FR 18440

- 18482). CALPU FF is recommended for Class I impact assessments by the Federal Land Managers Workgroup (FLAG 2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) (EPA 1998). The final BART guidance recommends CALPUFF as “the best modeling application avai lable for predicting a singe source’s contribution to visibility impairment” (70 FR 39122). As a result of these recommendations, the VISTAS modeling protocol is based on the use of CALPUFF for its BART determinations.

The main components of the CALPUFF m odeling system are shown in Figure 3

- 1. CALMET is a diagnostic meteorological model that is used to drive the CALPUFF dispersion model. It produces three

- dimensional wind and temperature fields and two

- dimensional fields of mixing heights and other meteor ological fields. It contains slope flow effects, terrain channeling, and kinematic effects of terrain. CALMET includes special algorithms for treating the overwater boundary layer and coastal interaction effects. CALMET can use meteorological observationa l data and/or three

- dimensional output from prognostic numerical meteorological models such as MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) in the developments of its fine

- scale meteorological fields.

CALPUFF is a non

- steady

- state Lagrangian puf f transport and dispersion model that advects Gaussian puffs of multiple pollutants from modeled sources. CALPUFF’s algorithms have been designed to be applicable on spatial scales from a few tens of meters to hundreds of kilometers from a source. It incl udes algorithms for near

- field effects such as building downwash, stack tip downwash and transitional plume rise as well as processes important in the far

- field such as chemical transformation, wet deposition, and dry deposition. CALPUFF contains an option to allow puff splitting in the horizontal and vertical directions, which extends the distance range of the model. The primary outputs from CALPUFF are hourly concentrations and hourly deposition fluxes evaluated at user

- specified receptor locations.Ove rview of the CALPUFF Modeling System 9 Fig ure 3

- 1. CALPUFF modeling system components.

A set of postprocessing programs associated with CALPUFF computes visibility effects and allows cumulative source impacts to be assessed, including potential non

- linear effects of ammonia limitation on nitrat e formation. The CALPOST postprocessor contains several options for computing change in extinction and deciviews for visibility assessments. The POSTUTIL postprocessor includes options for summing contributions of individual sources or groups of

sources to assess cumulative impacts. POSTUTIL also contains CALPUFF’s nitric acid

- nitrate chemical equilibrium module, which allows the cumulative effects of ammonia consumption by background sources to be assessed in the postprocessor. In addition, the combina tion of CALPUFF and POSTUTIL allows the effects of source emissions of ammonia to be incrementally added to background ammonia levels when determining nitrate formation.

The rest of this chapter summarizes the capabilities and features of the CALPUFF model ing components in more detail.Ove rview of the CALPUFF Modeling System 10 3.1.1 Major Features of CALMET

The CALMET meteorological model consists of a diagnostic wind field module and micrometeorological modules for overwater and overland boundary layers. When modeling a large geographical area, as would be necessary for the regional VISTAS 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. T he techniques used in the CALMET model are briefly described below.

Table 3

- 1. Major Features of the CALMET Meteorological Model

• Boundary Layer Modules of CALMET

- Overland B oundary Layer

- Energy Balance Method

- Overwater Boundary Layer

- Profile Method

-- COARE algorithm

-- OCD

- based 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 Field Module of CALMET

- Slope Flows

- Kinematic Terrain Effects

- Terrain Blocking Effects

- Divergence Minimizati on

- 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

CALMET Boundary Layer Models

The CALMET model contains two boundary layer models for application to overland and overwater grid cells.

Overland Boundary Layer Model : Over land surfaces, the energy balance method of Holtslag and van Ulden (1983) is used to compute hourly gridded fields of the sensible heat flux, surfaceOve rview of the CALPUFF Modeling System 11 friction velocity, Monin

- Obukhov length, and convective velocity scale. Mixing heights are determined from the computed hourly surface heat fluxes and observed temperature soundings using a modified Carson (1973) method base d on Maul (1980). The model also determines gridded fields of Pasquill

- Gifford

- Turner (PGT) stability class and hourly precipitation rates.

Overwater Boundary Layer Model : The aerodynamic and thermal properties of water surfaces suggest that a different me thod is best suited for calculating the 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. The

ver sion of CALMET being used by VISTAS contains improvements in the overwater boundary layer parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response Experiment (COARE) and enhancements in the calculation of overwater mixed laye r heights (Batchvarova and Gryning, 1991, 1994). Further details and the results of an evaluation of the model containing these enhancements are described in Scire et al. (2005). An upwind

- looking spatial averaging scheme is optionally applied to the mixi ng heights and three

- dimensional temperature fields in order to account for important advective effects.

Diagnostic Wind Field Module

The diagnostic wind field module uses a two

- step approach to the computation of the wind fields (Douglas and Kessler, 1988 ). In the first step, an initial

- guess wind field is adjusted for kinematic effects of terrain, slope flows, and terrain blocking effects to produce a Step 1 wind field. Gridded MM5 can be used to define the initial guess field. The second step consists of an objective analysis procedure to introduce observational data into the Step 1 wind field to produce a final wind field.

Step 1 Wind Field. Development of the Step 1 wind field begins with the initial guess field defined by the MM5 prognostic meteorologi cal model. Normally, the CALMET computational domain is specified to be at finer grid resolution than the MM5 dataset used to initialize the initial guess field. For example, 36

- km MM5 data available for VISTAS modeling may be used to develop the initial guess field on a 12

- km or even a 1

- km CALMET grid. The Step 1 algorithms in CALMET described below apply terrain adjustments to the initial guess field on the fine

- scale CALMET grid. Thus, the CALMET winds are adjusted to respond to fine

- scale terrain fe atures not necessarily seen by the coarser scale MM5 model.

Kinematic Effects of Terrain : The approach of Liu and Yocke (1980) is used to evaluate the effects of the terrain on the wind field. The initial guess field winds are used to compute a terrain

for ced vertical velocity, subject to an exponential, stability

- dependent decay function. 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 minimi zation scheme is applied iteratively until the three

- dimensional divergence is less than a threshold value.

Slope Flows : The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 1997) based on the shooting flow algorithm of Mahrt (19 82). This scheme includes bothOve rview of the CALPUFF Modeling System 12 advective

- gravity and equilibrium 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 m agnitude of the slope flow depends on the local surface sensible heat flux and local terrain gradients. The slope flow wind components are added to the wind field adjusted for kinematic effects.

Blocking Effects : The thermodynamic blocking effects of terra in on the wind flow are parameterized in terms of the local Froude number (Allwine and Whiteman, 1985). If the Froude number at a particular grid point is less than a critical value and the wind has an uphill component, the wind direction is adjusted to be tangent to the terrain.

Step 2 Wind Field. The wind field resulting from the above adjustments of the initial

- guess wind is the Step 1 wind field. The second step of the procedure may involve introduction of observational data into the Step 1 wind field through an objective analysis procedure. An inverse

distance squared interpolation scheme is used which weights observational data heavily in the vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind field in re gions with no observational data. The resulting wind field is subject to smoothing, an optional adjustment of vertical velocities based on the O’Brien (1970) method, and divergence minimization to produce a final Step 2 wind field.

The introduction of ob servational data in the Step 2 calculation is an option. It is also possible to run the model in “no observations” (No

- Obs) mode, which involves the use only of MM5 gridded data for the initial guess field followed by fine

- scale terrain adjustments by CAL MET. In No

Obs mode, observational data are not used in the Step 2 calculations. The No

- Obs mode is appropriate when the MM5 simulations adequately characterize the regional wind patterns and when local observations, especially surface observations, refle ct local conditions on a scale smaller than that of the CALMET domain and hence their spatial representativeness may be limited. Such situations are most likely to occur when the CALMET grid scale is relatively large i.e., coarser than the scale of variati on of the true wind field, which is particularly likely to occur in complex terrain or along the seashore,

3.1.2 Major Features of CALPUFF

By its puff

- based formulation and through the use of three

- dimensional meteorological data developed by the CALMET me teorological model, CALPUFF can simulate the effects of time

and space

- varying meteorological conditions on pollutant transport from sources in complex terrain. The major features and options of the CALPUFF model are summarized in Table 3

- 2. Some of the technical algorithms are briefly described below.

Complex Terrain: The effects of complex terrain on puff transport are derived from the CALMET winds. In addition, puff

- terrain interactions at gridded and discrete receptor locations are simulated using o ne of two algorithms that modify the puff

- height (either that of ISCST3 or a general “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical dispersion derived from the weakly

- stratified flow and dispersion module of the Comp lex TerrainOve rview of the CALPUFF Modeling System 13 Dispersion Model (CTDMPLUS) (Perry et al., 1989). The puff

- height adjustment algorithms rely on the receptor elevation (relative to the elevation at the source) and 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 in CALPUFF, CTSG treats terrain features that are not resolved by the gridded terrain field, and is bas ed on the CTDMPLUS (Perry et al., 1989). Plume impingement 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 fields. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Puff Sampling Func tions: A set of accurate and computationally efficient puff sampling routines is included in CALPUFF, which solve many of the computational difficulties encountered when applying a puff model to near

- field releases. For near

- field applications during rap idly

- varying meteorological conditions, an elongated puff (slug) sampling function may be used. An integrated puff approach may be used during less demanding conditions. Both techniques reproduce continuous plume results under the appropriate steady stat e conditions.

Building Downwash: The Huber

- Snyder and Schulman

- Scire downwash models are both incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the choice on a stack

- by

- stack and wind sector

- by

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

Dispersion Coefficients: 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 momentum fluxes, or the use of Pasqui ll

- Gifford (PG) or McElroy

- Pooler (MP) dispersion coefficients, or dispersion equations based on the CTDM. Options are provided to apply an averaging time correction or surface roughness length adjustments to the PG coefficients. In version 5.754 of CALPUFF being used by VISTAS, an option is provided to use the AERMOD turbulence profiles for determining dispersion rates, which is the most recent approach to dispersion in EPA

- approved regulatory modeling. In addition, turbulence advection is included. For a dditional details on these features, see Scire et al. (2005).

Overwater and Coastal Interaction Effects: Because the CALMET meteorological model contains both overwater and overland boundary layer algorithms, the effects of water bodies on plume transport, dispersion, and deposition can be simulated with CALPUFF. The puff formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion conditions, including the abrupt changes that occur at the coastline of a major body of water.Ove rview of the CALPUFF Modeling System 14 Dry Deposition: A resistance model is provided in CALPUFF for the computation of dry deposition rates of gases and particulate matter as a function of geophysical parameters, meteorological conditions, and pollutant species. For particles, source

- specif ic mass distributions may be provided for use in the resistance model. Of particular interest for BART analyses is the ability to separately model the deposition of fine particulate matter (< 2.5 μ m diameter) from coarse particulate matter (2.5

- 10 μ m diame ter).

Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that allows vertical wind shear effects across individual puffs to be simulated. Differential rates of dispersion and transport among the “new” puffs generated from the origi nal, well

- mixed puff can substantially increase the effective rate of horizontal spread of the material. Puffs may also be split in the horizontal when the puff size becomes large relative to the grid size, to account for wind shear across the puffs.

We t Deposition: An empirical scavenging coefficient approach is used in CALPUFF 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 ty pe (i.e., frozen vs. liquid precipitation).

Chemical Transformation: CALPUFF includes options for parameterizing chemical transformation effects using the five species scheme (SO 2 , SO 4 = , NO x , HNO 3 , and NO 3 - ) employed in the MESOPUFF II model or a set of u ser

- specified, diurnally

- varying transformation rates. The MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine particulate matter (sulfate and nitrate) from emissions of SO 2 and NO x and thus allows analyses of visibility impacts. Ambie nt ozone concentrations are used in the parameterized chemical transformation module as a surrogate for OH radicals during daylight hours. Ambient ammonia concentrations are used together with a temperature and relative humidity

- dependent equilibrium rela tionship to partition nitric acid and nitrate on an hour

- by

- hour and receptor

- by

- receptor basis.Ove rview of the CALPUFF Modeling System 15 Table 3

- 2. Major Features of the CALPUFF Dispersion Model

• Source types

- Point sources (constant or variable emissions)

- Line sources (constant o r 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, temperatu re)

- Spatially

- variable fields of mixing height, friction velocity, convective velocity scale, Monin

- Obukhov length, precipitation rate

- Vertically and horizontally

- varying turbulence and dispersion rates

- Time

- dependent source and emissio ns data for point, area, and volume sources

- Temporal or wind

- dependent scaling factors for emission rates, for all source types

• Interface to the Emissions Production Model (EPM)

- Time

- varying heat flux and emissions from controlled burns and w ildfires

• 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 theor y

-- AERMOD turbulence profiles

-- Original turbulence profiles

- Pasquill

- Gifford (PG) dispersion coefficients (rural areas)

- McElroy

- Pooler (MP) dispersion coefficients (urban areas)

- CTDM dispersion coefficients (neutral/stable)

• Ve rtical 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

- Huber

- Snyder method

- Schulman

- Scire method

- PRIME method

• Complex terrain

- Steering effects in CALMET wind field

- Optional puff height adjustment: ISC3 or "plume path coefficient"

- Optional enhanced vertical dispersion (neutral/we akly stable flow in CTDMPLUS)Ove rview of the CALPUFF Modeling System 16 Table 3

- 2. Major Features of the CALPUFF Dispersion Model (Cont’d)

• Subgrid scale complex terrain (CTSG option)

- Dividing streamline, H d , as in CTDMPLUS:

- Above H d , material flows over the hill and experiences alt ered 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 resis tance model

- User

- specified diurnal cycles for each pollutant

- No dry deposition

• Overwater and coastal interaction effects

- Overwater boundary layer parameters (COARE algorithm or OCD

- based method)

- Abrupt change in meteorological con ditions, 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 mech anism 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

• Wet Removal

- Scavenging coefficient approach

- Removal rate a function o f precipitation intensity and precipitation type

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL)

The two main postprocessors of interest for BART applications are the CALPOST and POSTUTIL programs. CALPOST is used to process the CALPUFF outputs, producing tabulations that summarize the results of the simulations, identifying, for example, the highest and second

- highest hourly

- average concentrations at each receptor. When performing visibility

related modeling, CALPOST uses concentrations from CALPUFF to compute light extinction and related measures of visibility (haze index in deciviews), reporting these for a 24

- hour averaging time.

The CALPOST processor contains several options for evaluating visibility impacts, including the method de scribed in the BART guidance, which uses monthly average relative humidity values. CALPOST contains implementations of the IWAQM

- recommended and FLAG

- recommended visibility techniques and additional options to evaluate the impact of natural weather events (fog, rain and snow) on background visibility and visibility impacts from modeled sources.Ove rview of the CALPUFF Modeling System 17 The POSTUTIL processor is a program that allows the cumulative impacts of multiple sources from different simulations to be summed, can compute the difference betwe en two sets of predicted impacts (useful for evaluating the benefits of BART controls), and contains a chemistry module to evaluate the equilibrium relationship between nitric acid and nitrate aerosols. This capability allows the potential non

- linear effe cts of ammonia scavenging by sulfate and nitrate sources to be evaluated in the formation of nitrate from an individual source. CALPUFF makes the full ambient ammonia concentration available to each puff without regard for any scavenging by other puffs. PO STUTIL corrects for such scavenging when the puffs generated by the CALPUFF model overlap, as could be the case for a single source when the wind speed is low, or when nitrate formation is to be attributed to each of several sources that are in a cluster a nd whose plumes overlap,

POSTUTIL will also compute the impacts of individual sources or groups of sources on sulfur and nitrogen deposition into aquatic, forest and coastal ecosystems. The postprocessor allows the changes in deposition fluxes resulting from changes in emissions to be quantified. For example the output of POSTUTIL and CALPOST can be used as input into an Acid Neutralizing Capacity (ANC) analysis, or for comparison to Deposition Analysis Thresholds (DATs).


### 3.2 Discussion of CALPUFF Appl icability and Limitations

3.2.1 Transport and Diffusion

According to the IWAQM Phase 2 report (page 18), “CALPUFF is recommended for transport distances of 200 km or less. Use of CALPUFF for characterizing transport beyond 200 to 300 km should be done caut iously with an awareness of the likely problems involved.” 4

IWAQM’s 200

- km limitation derives from the observation that, when compared to the data of the Cross Appalachian Tracer Experiment (CAPTEX), the basic configuration of CALPUFF overestimated inert tracer concentrations by factors of 3 to 4 at receptors that were 300 to 1000 km from the source. The apparent reason was insufficient horizontal dispersion of the simulated plume, presumably because an actual large plume does not remain coherent in the pr esence of vertical wind shears that typically occur, especially during the night, and of horizontal wind shears over the large puffs that arise over long transport distances.

To better represent such situations, an optional puff splitting algorithm has si nce been added to CALPUFF to simulate wind shear effects across a well

- mixed individual puff by dividing the puff horizontally and vertically into two or more pieces. Differential rates of transport among the

4 The IWAQM presentation at EPA’s 6 th Modeling Conference provides the background for this rec ommendation: “The IWAQM concludes that CALPUFF be recommended as providing unbiased estimates of concentration impacts for transport distances of order 200 km and less, and for transport times of order 12 hours or less. For larger transport times and dista nces, our experience thus far is that CALPUFF tends to underestimate the horizontal extent of the dispersion and hence tends to overestimate the surface

- level concentration maxima. This does not preclude the use of CALPUFF for transport beyond 300 km, but it does suggest that results in such instances be used cautiously and with some understanding.” (From page D

- 12 of the IWAQM Phase 2 report.)Ove rview of the CALPUFF Modeling System 18 new puffs thus generated can increase the horiz ontal spread of the material in the plume due to vertical wind speed shear and wind direction shear. The horizontal puff splitting algorithm is designed to allow large puffs that may grow to be several grid cells or more in size to split into smaller puff s that can then more accurately respond to variations in the local wind field across the original large puff. This will also tend to increase horizontal dispersion of the plume. Since the creation of additional puffs via puff splitting will increase the computational requirements of the model, possibly substantially, puff splitting is not enabled by default, but can be turned on at the option of the user. Puff splitting may be appropriate for transport distances over 200 to 300 km, or possibly over shorte r distances in complex terrain.

Turning to the shorter distance end of the transport range, the CALPUFF section of Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states, “CALPUFF is intended for use on scales from tens of meters from a source to hundreds of kilometers.” This is supported by the IWAQM Phase 2 report, which indicates that the diffusion algorithms in CALPUFF were designed to be suitable for both short and long distances. In this regard, CALPUFF does contain algorithm s for such near

- field effects as plume rise, building downwash, and terrain impingement and includes routines that deal with the computational difficulties encountered when applying a puff model in the field near to a source.

The recommendations for regula tory use in Appendix A of the Guideline on Air Quality Models state, “CALPUFF is appropriate for long range transport (source

- receptor distance of 50 to several hundred kilometers)”, but provisions for using CALPUFF in the near

- field in “complex flow” situ ations are also included in the regulatory guidance. Complex flow situations may include complex terrain, coastal areas, situations where plume fumigation is likely, and areas where stagnation, flow reversals, recirculation or spatial variability in wind fields (e.g., as due to changes in valley orientation) are important.

The tracer studies with which CALPUFF transport and diffusion capabilities were evaluated in the IWAQM Phase 2 report were generally over distances greater than 50 km. More recently, ad ditional studies of model performance have been performed at shorter distances, including at a power plant in New York state in complex terrain (at source

- receptor distances of 2 to 8.5 km) and a second 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). Other CALPUFF evaluation studies over short

- distances include ones by Chang et al. (2001) and Morrison et al. (2003). These studies demonstrate good model performance over source

- receptor distances from a few hundred meters to 50 km.

An important factor in the performance of CALPUFF is the choice of dispersion coefficients. The EPA has defined the "regulatory default" option in CALPUFF to allow either Pasqui ll

- Gifford (PG) or turbulence

- based dispersion coefficients. CALPUFF has been evaluated and shown to perform better using turbulence

- based dispersion for tall stacks (Strimaitis et al, 1998). CALPUFF with turbulence

- based dispersion has also been evaluated for overwater transport and coastal situations (Scire et al., 2005). In many other studies, including AERMOD evaluation studies conducted by EPA, the use of PG

- dispersion, or more specifically the lack of a convectiveOve rview of the CALPUFF Modeling System 19 probability density function (pdf) mo dule, has been demonstrated to result in underprediction of peak concentrations.

In November 2005, EPA approved the AERMOD model, which relies on turbulence

- based dispersion, as a regulatory Guideline Model 5 . The ISCST3 model and its PG dispersion coeffic ients are being phased out as an acceptable regulatory approach. However, EPA Region IV has indicated that the application of turbulence

- based dispersion coefficients in CALPUFF needs to be further demonstrated before they are approved for BART applicati on. They will consider accepting the use of turbulence dispersion coefficients on a case

- by

- case basis for sources that are close to Class I areas.

For regional haze light extinction calculations, use of a plume

- simulating model such as CALPUFF is appro priate 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 start ing 30 to 50 km from a source. In this light, the BART guidance strongly recommends using CALPUFF for

source

- receptor distances greater than 50 km but also presents CALPUFF as an option that can be considered for shorter transport distances.

As discussed a bove, there do not appear to be any scientific reasons why CALPUFF cannot be used for even shorter transport distances than 30 km, though, 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 structure 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. For extremely short transport distances, depiction of the concent ration distribution will require a grid that is finer than 1 km. (For reference, the width of a Gaussian plume, 2 σ y , is roughly 1 km after 10 km of travel distance, assuming Pasquill

- Gifford dispersion rates under neutral conditions.)

As an additional con sideration, if the plume width is small compared to the visual range, the atmospheric extinction along a typical sight path of tens of kilometers through the plume will be inhomogeneous and the simple CALPOST point estimate of regional light extinction at a receptor point will not be correct. However, the effect of averaging light extinction estimates for 24 hours, during which the plume location shifts over various receptor points, is likely to mitigate this problem to some degree and suggests that using C ALPUFF at distances under 30 km will often be appropriate. For the narrow plumes that result from short transport distances, though, the modeled peak 24

- hr average extinction at a receptor will tend to overstate the effect of the source on regional haze.

5 Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terra in) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218

- 68261. 9 November 2005.Ove rview of the CALPUFF Modeling System 20 The U.S. EPA has suggested that the plume visibility model, PLUVUE

- II, could be used in lieu of CALPUFF for simulating visibility effects at such short distances. 6 PLUVUE

- II is a Gaussian model that simulates the dispersion, chemical conversion, and optica l effects of emissions of particles, SO 2 , and NO x from a single source. Its outputs include the discoloration of the sky by the plume (so called “plume blight”) and the effect of the plume on visibility along user

- selected sight paths that pass through the plume. The impacts of the plume on visibility depend not only on the plume composition, but also on the sight path chosen and its direction relative to the axis of the plume and the location of the sun. It isn’t clear how such sight

- path dependent results could be compared to the 0.5 and 1.0 deciview thresholds in the BART guidance. Since CALPUFF is designed to be useful for short transport distances (with features such as the simulation of plume downwash caused by structures at the source), CALPUFF seems more appropriate than PLUVUE

- II for evaluating source impact at short distances for BART assessment purposes.

3.2.2 Aerosol Constituents

Primary PM 2.5
Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that CALPUFF can treat p rimary pollutants such as PM 10 . In actuality, CALPUFF can simulate PM 10 or PM 2.5 or some other size range, because the assumed size distribution of the particles is a user input. The smaller the particles, the more they disperse like an inert gas. In most cases, the dispersion of inert PM 2.5 particles will be only minutely different from that of an inert gas, but the behavior of larger particles will differ.

A particularly important contributor to PM concentrations is the rate of deposition to the surface.
PM 2.5 particles, which have a mass median diameter around 0.5 μ m, have an average net deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine particles is usually not significant except for ground

- level emissions. On the o ther hand, coarse particles (those PM 10 particles larger than PM 2.5 ) have an average deposition velocity of more than 1 m/min (or 1440 m/day), which is significant, even for emissions from elevated stacks.

CALPUFF includes parametric representations of pa rticle and gas deposition in terms of atmospheric, deposition layer, and vegetation layer “resistances” and, for particles, the gravitational settling speed. Gravitational settling, which is of particular importance for the coarse fraction of PM 10 , is acco unted for in the calculation of the deposition velocity. Effects of inertial impaction (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 μ m diameter), which has higher light extinction efficiency than coarse particulate matter (2.5

- 10 μ m diameters), should be treated separately in the model. CALPUFF allows for user

- specified s ize categories to be treated as

6 However, for the reasons given in this paragraph, VISTAS does not recommend PLUVUE

- II for BART applicationOve rview of the CALPUFF Modeling System 21 separate species, which includes calculating size

- specific dry deposition velocities for each size category.

A primary PM 2.5 emission from coal

- fired electric generating units (EGUs) that is of relevance to visibility calcul ations is that of primary sulfate. Although primary sulfate emissions account for only a small fraction of the total sulfur emissions from such sources, it may be important to simulate their effect with CALPUFF, especially at shorter distances before signi ficant formation of secondary sulfate conversion from SO 2 has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate

The MESOPUFF

- II chemistry algorithm used in CALPUFF 7 simulates the gas phase oxidation of sulfur dioxide to sulfate by a linear tr ansformation rate that was developed using regression relationships derived from the analysis of chemical conversion rates produced by a complex photochemical box model (see Scire et al., 1984, for a description of the development of the chemical module). As in all empirically

- derived models, the relationships are based on easily

computed or observed parameters that are used as surrogates for the factors that control SO 2 oxidation.

The surrogate factors included in the parameterized chemistry during the daytime hours include solar radiation intensity, ambient ozone concentration, and atmospheric stability class. For example, gas phase SO 2 oxidation is a function of OH radical concentrations. Ozone concentrations are correlated with OH radical concentrat ions during daytime hours, and their use in the daytime SO 2 conversion rate in CALPUFF is based on this correlation relationship. The philosophy is that OH radical measurements are not available and cannot easily be computed within a model like CALPUFF, b ut ozone is commonly measured throughout the country, so the use of the well

- known surrogate variable (ozone) is more useful in the empirical relationship than factors that are unknown or have a high degree of uncertainty. The same logic applies to the
ot her variables in the relationship. They are surrogates for factors that the regression analysis has shown to be important in SO 2 oxidation rates. At night, the SO 2 conversion is set to a constant low value (default is 0.2%/hr). Aqueous phase oxidation of SO 2 is represented by an additive term that varies with relative humidity and peaks at 3%/hr at 100% relative humidity. CALPUFF represents the chemical conversion as a linear process because it requires linear independence between puffs, although as expl ained below, non

- linear behavior in nitrate formation can be modeled.

7 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO 2 , SO 4 = , NO x , HNO 3 , and NO 3 - ) MESOPUFF

- II system and the 6 species RIVAD system (which treats NO and NO 2 separately). IWAQM recommends using the MESOPUFF

- II system with CALPUFF. The RIVAD system is believed to be more appr opriate for clean environments, however, and therefore was used in the Southwest Wyoming Regional CALPUFF Air Quality Modeling Study in 2001. For the VISTAS region, the IWAQM

- and FLM

- recommended MESOPUFF

- II chemistry is most appropriate.Ove rview of the CALPUFF Modeling System 22 The IWAQM Phase 2 report concludes that this chemistry algorithm is adequate for representing the gas phase sulfate formation but that it does not adequately account for the aqueous phas e oxidation of SO 2 . Actual aqueous phase oxidation in clouds or fog can proceed at rates much greater than 3% per hour, leading IWAQM to suggest that sulfate might be underestimated in such situations. However, aqueous phase oxidation depends on liquid wat er content, not relative humidity. In reality, liquid water does not exist in the atmosphere at relative humidity much below 100%, while the CALPUFF aqueous reaction term produces sulfate at lower relative humidity. This can lead CALPUFF to overestimate s ulfate concentrations when the humidity is high but the cloud water that enables aqueous conversion is not present. Therefore, the direction of the bias in the aqueous chemistry simulation of sulfate formation can vary.

Other potential sources of error in the sulfate formation mechanism of CALPUFF include (1) overestimation of sulfate formation when NO x concentrations in the plume are high and in actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO 2 ; and (2) lac k of direct consideration of the effect of temperature on the conversion rates, which may cause the model to overstate sulfate formation on cold days (below 10C or 50 ° F) (Morris et al., 2003). However, in CALPUFF, the effects of temperature are, to some de gree, compensated for indirectly by the use of the solar radiation surrogate variable in the empirical conversion equations.

Whether these potential errors are important will depend on the setting. For example, Figure 3

- 2 shows a comparison of predicted and observed 24

- hour sulfate concentrations, due to a large number of SO 2 sources, at the Pinedale IMPROVE site in Wyoming for the 1995 period (Scire et al., 2001). Overall, in this case there was very little bias in the sulfate predictions. Whether CALP UFF predictions would compare as well with measurements in the Southeast remains to be

seen.
CALPUFF does not identify the chemical form of the sulfate compound that results from its reactions, which will generally be some form of ammoniated sulfate whose degree of neutralization will depend on the availability of ammonia in the atmosphere. This consideration, which has been found to be relevant for calculating light extinction in the VISTAS region, is not addressed by CALPUFF or CALPOST.

In most applicati ons, the ozone concentrations required for the sulfate formation calculations are derived from ambient measurements, although concentrations simulated by regional models can be used.Ove rview of the CALPUFF Modeling System 23 Figure 3

- 2. Observed vs. CALPUFF

- predicted 24

- hour sulfate concentr ations at the IMPROVE monitoring site in Pinedale, Wyoming for 1995.

NO x and Secondary Ammonium Nitrate

The MESOPUFF

- II chemistry algorithm used in CALPUFF simulates the oxidation of NO x to nitric acid and organic nitrates (both gases) by transformation r ates that depend on NO x concentration, ambient ozone concentration, and atmospheric stability class during the day. The conversion rate at night is set at to a constant value (default is 2.0 %/hr). The temperature

- and humidity

- dependent equilibrium betwee n nitric acid gas and ammonium nitrate particles is taken into account when estimating the ammonium nitrate particle concentration, an equilibrium that depends on the ambient concentration of ammonia. The user supplies the value of the ambient concentratio n of ammonia. CALPUFF assumes that the sulfate reacts preferentially with that ammonia to form ammonium sulfate and the left over ammonia is available to form ammonium nitrate.

The IWAQM Phase 2 report considers that this mechanism is adequate for represe nting nitrate chemistry. Potential situations where this assumption may not be correct, however, include (1) plumes with high concentrations of NO x that deplete the ambient ozone and thus limit theOve rview of the CALPUFF Modeling System 24 transformation of NO x to nitric acid in the plume; and (2) when ambient temperature is below 10 C, and thus the transformation rate is much slower and the nitrate concentration may be lower than that simulated by CALPUFF (Morris et al., 2003). In both cases, CALPUFF may overestimate the amount of nitrate that is produced. In particular, the impact of ammonium nitrate concentrations on visibility at Class I areas in the VISTAS region is greatest in the winter, when temperatures are lowest, the nitrate concentrations are the greatest, and the sulfate concentrations tend to be the least. CALPUFF may overstate the impacts of NO x emissions at those times, especially in the colder northern states

. This potential overestimate of nitrate was not evident, however, in an evaluation of CALPUFF

- modeled nitrate against actual o bservational data in the Wyoming study, as shown in Figure 3

- 3a (Scire et al., 2001),

Another factor in the calculation of nitrate is that CALPUFF makes the full amount of the background concentration of ammonia available to each puff, and that amount is scavenged by the sulfate in the puff. If puffs overlap, then that approach could overstate the amount of ammonium nitrate that is formed in total if, in reality, the combined scavenging by the overlapping puffs at a location would deplete the available amm onia enough that the combined nitrate formation was limited by the availability of ammonia. This effect of such ammonia limiting can be large in summer; for a source 75 km west of Mammoth Cave National Park, one modeling analysis found the maximum light ex tinction impact of the source to be 7.4% (roughly 0.74 deciviews) at the park when CALPUFF was used without consideration of ammonia limiting and about 30% less, between 5.5 and 5.8% (roughly 0.55 to 0.58 dv), when the effect of ammonia limiting was consid ered (Escoffier

- Czaja and Scire, 2002).

To address the issue, since 1999 (i.e., after the IWAQM Phase 2 report) the CALPUFF system has included the optional POSTUTIL postprocessing program, which repartitions the ammonia and nitric acid concentrations esti mated by CALPUFF to reflect potential ammonia

- limiting effects on the development of nitrate

. This allows non

- linearity associated with ammonia limiting effects to be included in the CALPUFF model estimates. POSTUTIL computes the total sulfate concentrati ons from all sources (modeled sources plus inflow boundary conditions) and estimates the amount of ammonia available for total nitrate formation after the preferential scavenging of ammonia by sulfate. That is, as new sulfate, nitrate or ammonia from the source of interest is added to an existing mix of pollutants, POSTUTIL will estimate both the nitrate formed from the new source and the change in background nitrate as a result of the incremental depletion of ammonia (due to the new sulfate and nitrate) o r addition of ammonia (from a new source of ammonia).

Reliable estimates of the ambient concentrations of ammonia, especially with the temporal and spatial resolution that would be optimal for use with CALPUFF, are needed to take full advantage of the incr eased accuracy provided by POSTUTIL. The processor requires estimated concentrations of ammonia throughout the modeling domain and period. Such estimates can be inferred from CASTNet measurements, which are integrated over a week, from 24

- hr SEARCH

measure ments, or from the output of a regional photochemical model such as CMAQ or CAMx. The CASTNet network is fairly sparse and the uncertainty in the ammonia measurements is large,Ove rview of the CALPUFF Modeling System 25 so defining the ammonia concentration throughout the Southeast would require ex tensive interpolation or extrapolation from the measured values. The quality of the SEARCH measurements is much better, but there are only 8 sites and they do not cover the entire VISTAS domain. Modeled concentrations have the advantage of being resolved i n space and time, but their accuracy should be evaluated by comparison with measurements wherever possible.

Benefit is obtained by considering seasonal trends of ammonia and using POSTUTIL to determine the diurnal variability in available ammonia due to the daily cycle of nitrate formation associated with temperature and relative humidity effects. For example, results of the Wyoming study (see Figure 3

- 3a) show that POSTUTIL adjustments produced daily average nitrate concentrations well within the facto r of two lines and with very little mean bias. On the other hand, analysis of the same results with use of constant ammonia of 0.5 ppb or 1.0 ppb produced consistent overpredictions of nitrate by factors of 2

- 3 and 3

- 4, respectively, as shown in Figure 3

- 3b (Scire et al., 2003).

Figure 3

- 3a. Observed vs. CALPUFF

- predicted 24

- hour nitrate concentrations at the IMPROVE monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method. (Scire et al., 2001)Ove rview of the CALPUFF Modeling System 26 Figure 3

- 3b. Observed vs. CALP UFF

- predicted 24

- hour nitrate concentrations at the IMPROVE monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method (blue), constant ammonia at 0.5 ppb (pink) and constant ammonia at 1.0 ppb (green). (Scire et al., 2003)

Secondary Organic Aerosol

Ongoing research studies at several Class I areas throughout the country (Fallon and Bench, 2004) and at SEARCH sites in the Southeast (Edgerton et al., 2004) are finding that, typically, 90 to 95% of the rural organic carbon fine particle concentration consists of modern carbon (e.g., that from the burning of vegetation and deriving from VOC emissions from vegetation) and only 5 to 10% is attributable to man’s burning of fossil fuels. In addition, a field study at Great Smoky Mountains Nati onal Park in August 2002 (Tanner, et al., 2005) found that an average of 83% of the fine carbon was modern carbon

According to IMPROVE measurements, organics account for roughly 10% of the particle

- caused light extinction in Class I areas in the Southeast. We can thus conclude that, in general, secondary organic carbon particles derived from anthropogenic fossil fuel burning emissions are unlikely to have a large impact (around 1%) on current visibility. (Man

- caused burning of vegetation can have significan t localized, short

- term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of the 1.4 μ g/m 3 concentration assumed for natural conditions by the EPA, which means that current fossil fuel burni ng would contribute less than 2% to visibility in an atmosphere that represents natural conditions. Thus, it is unlikely that VOC and organic particle contributions from BART [NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IM PROVE Observations

0 0.2 0.4 0.6 0.8

1 1.2

0 0.2 0.4 0.6 0.8 1 1.2
Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3 CALPUFF Summed Repartition (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb)
CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4 [NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IMPROVE Observations

0 0.2 0.4 0.6 0.8

1 1.2

0 0.2 0.4 0.6 0.8 1 1.2 Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3 CALPUFF Summed Repartition (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb) CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4Ove rview of the CALPUFF Modeling System 27 sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 d v) localized impact from a particularly large VOC source cannot be dismissed out of hand.

CALPUFF has only rudimentary capabilities for addressing formation of visibility

- impairing organic particles from some forms of volatile organic carbon (VOC). The ca pabilities that do exist include the following.

First, PM 10 emissions (such as from power plants) are often divided into filterable and condensable components, with the condensable mass being 100

- 200% of the filterable mass. For purposes of visibility a nalyses with CALPUFF, a fraction of the condensable part is typically treated as organic particles, i.e., it is assumed that a fraction of the condensable components in the
PM 10 emissions condense into organic PM 2.5 particles. The size of this organic frac tion varies with process and process equipment, and can range from 20 to 100% of the condensable mass. These fine organic particles can be readily modeled by CALPUFF. (The remaining condensable material may be sulfuric, hydrochloric, or hydrofluoric acid.)

Second, a module that treats the formation of secondary organic particles from organic emissions was recently developed and is now part of the CALPUFF system. (Scire et al., 2001). This simplified secondary organic aerosol (SOA) module is a linear, parame terized representation that is currently considered best suited for biogenic organics. It relies on the conventional wisdom that only hydrocarbons with more than six carbon atoms can form significant SOA (Grosjean and Seinfeld, 1989). For example, accordin g to this rule, isoprene (C 5 H 8 ) does not make SOA but terpenes do, making pine trees more important biogenic contributors to SOA than oak trees. 8

Limited evaluation of the performance of CALPUFF at simulating SOA with its biogenic SOA module at one IMPROVE site in a regional modeling study in Wyoming found that 95% of 101 estimated 24

- hr SOA concentrations were within 2% of the measured values (Scire et al., 2001). This performance seems promising, although the developers view the SOA module as needing

more testing and evaluation.

Thus, CALPUFF includes approaches for dealing with condensable VOC emissions that are characterized as condensable PM 10 and with biogenic VOCs, although the soundness of concentration estimates by these approaches when modeling a p lume from a single source is largely untested. 9 The CALPUFF simulation of VOC emissions from sources whose VOC emissions are predominantly anthropogenic is problematic, however. Perhaps the approach used for the simplified biogenic SOA module may be extend ed to anthropogenic VOCs when speciated VOC emissions information is available. If only those VOCs with more than six carbon atoms are presumed to be of importance, this eliminates many anthropogenic sources of VOC emissions. For example, the fugitive emis sions of butane and ethane during petroleum processing

8 Recent researc h suggests that isoprene may be a SOA precursor, however.

9 Note that neither of these VOC

- related simulation approaches is described in the current (Version 5) CALPUFF User’s Guide dated January 2001. See the Wyoming report referenced above for a descrip tion of this module.Ove rview of the CALPUFF Modeling System 28 are not important, while aromatic emissions (such as of toluene and xylene) are considered by the SOA module’s mechanism. Development, testing, and evaluation would be needed before one could rely on s uch a module for estimating SOA from anthropogenic SOA emissions, though.

Therefore, to demonstrate the visibility impacts of VOC emissions from BART

- eligible sources, means other than CALPUFF will be needed. A technical approach using a regional photoche mical model to evaluate visibility impacts of VOC emissions is presented in Section 4.1.3. CALPUFF can be used to estimate the contribution from the primary condensable fraction of PM 10 emissions, though.

3.2.3 Regional Haze

Calculation of the impact of t he simulated plume particulate matter component concentrations on light extinction is carried out in the CALPOST postprocessor. The formula used is the usual IMPROVE/EPA formula, which is applied to determine a change in light extinction due to changes in component concentrations. Using the notation of CALPOST, the formula is the following:

b 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] + b Ray (3

- 1)

The concentrations, in square brackets, are in μ g/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).

There are a few important differences in detail and in notation between t he CALPOST formula for estimating light extinction (i.e., Equation 3

- 1) and that of IMPROVE and EPA. First, the OC in the formula above represents organic carbonaceous matter (OMC in IMPROVE’s notation), which is 1.4 times the OC (i.e., organic carbon alon e) in the IMPROVE formula. The EC above is synonymous with LAC in the IMPROVE formula. CALPOST still uses the old IMPROVE f(RH) curve, whose values are documented in the December 2000 FLAG report. That curve differs slightly from the f(RH) now used by IMPR OVE and EPA (as documented in EPA’s regional haze guidance documents), 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%.

The haze index (H I) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (b ext /10) (3

- 2)

where HI is in units of deciviews (dv) and b ext is in Mm - 1 . The impact of a source is determined by comparing HI for estimated natural background conditions with the impact of the source and without the impact of the source.Ove rview of the CALPUFF Modeling System 29 CALPOST Methods

CALPOST uses Equation 3

- 1 to calculate the extinction increment due to the source of interest and provides various methods for estimating the background extin ction 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 (called Methods 1

- 7) . These are 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 avera ge of 24

- hour ratios). A brief summary of the techniques is provided below. Method 2 is the current default, recommended by both IWAQM (EPA, 1998) and FLAG (2000) for refined analyses. Method 6 is recommended by EPA’s BART guidance (70 FR 39162).

Methods 4 and 5 use optically measured hourly background extinctions, which represent current actual levels of extinction and thus are not consistent with the “natural conditions” the BART proposal says should be used as a baseline. Methods 1 through 3 and 6 and 7 allow for user inputs of estimated (e.g., natural conditions) background extinction or component concentrations, and thus are consistent with the BART proposal.

Method 1 allows the user to specify a single value of a “dry” background extinction coefficie nt for each receptor, specify that a certain fraction of that coefficient is due to hygroscopic species, and use relative humidity measurements to vary the extinction hourly via a 1993 IWAQM f(RH) curve or, optionally, the EPA regional haze f(RH) curve (EP A, 2003b). The RH is capped at 98%

or a user

- selected value (95% for the EPA curve). The same f(RH) is applied to both the modeled sulfate and nitrate.

For an example of the use of Method 1, one could use the dry particle extinction coefficient of 9.09 Mm - 1 that results from EPA’s default natural conditions concentrations, together with an assumption that for natural conditions, say, 0.9 Mm

– 1 (or 10%) of this amount results from hygroscopic ammonium sulfate and ammonium nitrate, and then apply f(RH) to thi s 10%.

In Method 2, user

- specified, speciated monthly 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 concentra tions 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 concentrations and hourly, site

- specifi c f(RH) from a 1993 IWAQM curve (a different oneOve rview of the CALPUFF Modeling System 30 than that in Method 1) or, optionally, the EPA regional haze f(RH) curve. 10 Again the RH is capped at either 98% (default) or a user

- selected value (most commonly at 95%).

Method 3 is the same as Method 2, e xcept that any hour in which the RH exceeds 98% (or the selected maximum) is dropped from the analysis. When 24

- hr extinction is computed, no fewer than 6 valid hours are accepted at each receptor; otherwise the value for the day is tabulated as “missing”.

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly climatologically representative values in EPA (2003a, b)) are used in place of hourly values for calculating both the extinction impact of the source emissions and the backg round conditions extinction. Hourly source impacts, with the effect on extinction due to sulfates and nitrates calculated using the monthly

- average relative humidity in f(RH), are compared against the monthly default natural background concentrations. Thus the monthly

- averaged relative humidity is applied to the hygroscopic components (i.e., sulfate and nitrate) of both the source impact and the background extinction with Method 6

. Method 7 is a new variant of Method 2 that was developed as a result of a r uling by the Assistant Secretary of the Interior for Fish and Wildlife and Parks, in response to a New Source Review case in Montana, that “natural conditions” should reflect the visibility impairment caused by significant meteorological events such as fog , precipitation, or naturally occurring haze (DOI, 2003). 11 Under Method 7, during hours when visibility is obscured by meteorological conditions, the actual measured visibility is used to represent natural conditions instead of the value that is calculated from EPA’s default natural conditions concentrations under Method 2. A recent modification developed in response to FLM comments on Method 7, in which the daily average natural extinction is calculated somewhat differently, is called Method 7’, i.e., “7 p rime”.

Refined Estimates of Extinction and Natural Background Visibilit y
Separate from the BART discussions, IMPROVE, EPA, and the Regional Planning Organizations are evaluating whether refinements are warranted to the methods recommended in EPA’s guidance to calculate default estimates of natural background visibility. In addition, IMPROVE has recently approved an alternative to the formula (Eq. 3

- 1) it uses to estimate extinction from particle concentration measurements (Pitchford et al., 2005).

Refinemen ts in the revised IMPROVE formula include the following:

- Adding a sea salt term, including a growth factor due to relative humidity

10 Note that the hourly

- varying natural background extinction in this method is not consistent with that prescribed by the EPA’s natural conditions guidance (EPA, 2003b), for which a “climatologically

- representative” f(RH) that only var ies monthly is to be used. Method 6 uses these monthly average humidity values.

11 The Secretary’s guidance applies only to Federal Land Managers. EPA’s position on this interpretation of natural conditions is unknown.Ove rview of the CALPUFF Modeling System 31 - Increasing the factor used to calculate the mass of particulate organic matter (OC in Eq. 3

- 1) from organic carbon measurem ents

- Modifying the relative humidity growth formula, f(RH), for sulfates and nitrates

- Revising the extinction efficiencies (the numerical constants in Equation 3

- 1) for sulfates, nitrates, and organic carbon so that they vary with concentration

- Adding a si te

- specific Rayleigh scattering term to the formula. Values will be calculated by IMPROVE for all Class I areas.

For the purposes of calculating current, future, and natural background visibility at VISTAS Class I areas as part of the reasonable progress analyses, VISTAS intends to present regional air quality modeling results using both the current EPA recommended assumptions and the newly revised aerosol extinction formula. If a BART

- eligible source chooses to consider its projected impacts using the new ly revised formula as well as the current formula, then modifications would need to be made to CALPOST to carry out calculations with the new algorithm.VISTAS’ Common Modeling Protocol 32 

### 4. VISTAS’ COMMON MODEL ING PROTOCOL
4.1 Overview of Common Modeling Appr oach

In this section, guidance is provided on the use of the CALPUFF modeling system for two
purposes:
1) Evaluating whether a BART

- eligible source is exempt from BART controls because it is not reasonably expected to cause or contribute to impairment of v isibility in Class I areas, and

2) Quantifying the visibility benefits of BART control options.

For purpose 1), States must determine whether a source emits any air pollutant (SO 2 , NO x , PM, and in certain cases VOC and NH 3 ) that “may reasonably be antici pated to cause or contribute to any impairment of visibility” in a Class I area. The States have 3 options to accomplish this:

A) Conclude that all BART

- eligible sources in State are subject to BART.

B) Demonstrate that all BART

- eligible sources in the State together do not cause or contribute to any visibility impairment

C) Determine if the impact from each individual BART

- eligible source is greater than a threshold value.

VISTAS States intend to follow Option C (determine if the visibility impact fr om individual sources exceeds a contribution threshold) for SO 2 , NO x , and PM emissions. The methods for Option C are described in Section 4.1.1. Option B (demonstrate that all BART eligible sources in a State do not impact visibility) is being pursued for VOC and NH 3 emissions, and potentially for PM emissions. Methods for Option B are described in Section 4.1.3.

4.1.1 BART Exemption Analysis

As illustrated in Figure 4

- 1, three steps will evaluate whether a BART

- eligible source of SO 2 , NO x , or PM is sub ject to BART:

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d for SO 2 > 10 for 2002 actual emissions, then the State presumes that the source is subject to BART. If the source agrees with this presumption , then no exemption modeling is required and the source can proceed to the BART determination using CALPUFF to evaluate impacts of control options and can perform the engineering analyses. If a source disagrees, the source may perform fine grid modeling as described in Section 4.4 to determine if its impact is < 0.5 dv.VISTAS’ Common Modeling Protocol 33 Figure 4

- 1. Flow chart showing the components of the VISTAS common modeling protocol. Assessment should be made for each Class I Area. (If a source agrees to install the most stringent controls then the modeling steps indicated above and engineering analyses and visibility impact modeling would not be required.)

2) An optional initial modeling assessment using the CALPUFF model with the coarse scale 12

- km regional VISTAS dom ain can be used to answer questions whether (a) a particular source may be exempted from further BART analyses and (b) if finer grid CALPUFF analysis were to be undertaken, which Class I areas should be included. Assumptions for the initial modeling asses sment are conservative so that a source that contributes to visibility impairment is not exempted in error. If a source is shown not to contribute to visibility impairment using the initial modeling assessment, the source would not be subject to BART and would be exempted from further BART analyses. If a source is shown to contribute to visibility impairment using the initial modeling assessment, the source has the option to undertake finer grid CALPUFF modeling to evaluate further whether it is subject t o BART.

3) A finer grid CALPUFF modeling analysis using a subregional CALMET domain will be the definitive test as to whether a source is subject to BART.

For large sources that will clearly exceed the initial screening thresholds, this step can be ski pped and the analysis may proceed directly to the finer grid modeling analysis, which is described in Section 4.4.VISTAS’ Common Modeling Protocol 34 4.1.2 BART Control Evaluation

For sources that are determined to be subject to BART controls, part of the BART review process involves eval uating the visibility benefits of different BART control measures. These benefits will be determined by making additional CALPUFF simulations using the same CALMET and CALPUFF configuration as those used in the finer grid analysis of Step 2. The only exce ption is that the source and emissions data used in the CALPUFF control evaluation simulations will reflect the BART control measures being evaluated. Using the same model configuration will produce an “apples

- to

- apples” comparison, where differences in i mpacts are due to the effectiveness of the controls rather than model configuration differences. For example, a control scenario evaluation that uses more conservative assumptions than the base case simulation may produce results showing no or little impr ovement in visibility impacts. That control scenario run with the same model configuration as the base case may show significant visibility improvement. Therefore, in order to not obscure the response to predicted visibility improvements by differences i n the modeling approach, the same model configuration should be used in the BART control evaluation simulation as in the base case simulation.

The base case to which the effectiveness of BART controls is to be compared is the “current emissions” scenario f or which the finer grid Step 2 modeling was performed. The postprocessing steps and procedures are the same as in the BART eligibility simulation. Side

- by

- side comparison of the visibility impacts will be tabulated to quantify the effectiveness of each c ontrol scenario relative to the base case.

The modeling evaluation is a unit

- by

- unit evaluation and can be conducted on a pollutant specific basis. Modeling results are used with the other four statutory factors mentioned in Section 2.1 to decide which co ntrol technology, if any, is appropriate. Finally, if a source decides to use the most stringent control technology available, the BART control analysis, including modeling, is not necessary.
4.1.3 VISTAS’ Treatment of VOC, NH 3 , and PM

Volatile Organic Co mpounds

CALPUFF is currently not recommended for addressing visibility impacts from VOC because its capability to simulate secondary organic aerosol formation from VOC emissions is not adequately tested, especially for anthropogenic emissions. (Separate ly, condensable organic carbon can be calculated from PM 10 .)

VISTAS is currently performing a weight of evidence analysis to demonstrate, using the CMAQ regional air quality model, that all VOC emissions from all point sources (BART

- eligible and

non

- BAR T) in each State do not contribute to visibility impairment. Emissions sensitivity simulations run for VISTAS by Georgia Institute of Technology using VISTAS’ 12 x 12 km grid and CMAQ v 4.3 for episodes in July 2001 and January 2002 demonstrated very low to no response of organic carbon levels and light extinction at Class I areas to changing VOC emissionsVISTAS’ Common Modeling Protocol 35 from all anthropogenic sources in the VISTAS 12

- km modeling domain (eastern US). Georgia Tech is currently repeating the sensitivity analyses for VISTA S using the VISTAS 12

- km domain and CMAQ v 4.4 with a refined SOA module for a summer (Jun 1

- Jul 10) and winter (Nov 19

- Dec 19) period in 2002. VOC emissions from all anthropogenic point sources in each VISTAS State are being reduced. Given that the imp act of eliminating all VOC emissions from all point sources in a State is less than 0.5 dv, then the impact of any one BART

- eligible source would be less than 0.5 dv. Based on these analyses, the VISTAS States have concluded that VOC emissions should not be subject to BART. When similar analyses for NH 3 and PM have been completed, a technical justification for treatment of all three of these pollutants will be presented to EPA and FLMs for their review.

Ammonia

EPA has given states the option to addr ess ammonia (NH 3 ) emissions from BART

- eligible sources. VISTAS has also contracted with Georgia Tech to perform emissions sensitivities using CMAQ v 4.4 with a refined SOA module and the Jun

- Jul and Nov

- Dec periods in 2002. All NH 3 emissions from point s ources (BART

- eligible and not

- BART) in each State are reduced for these analyses. If the sensitivity evaluation shows that the collective impact of all point NH 3 emissions is less than 0.5 dv, then the impact of a single BART eligible source would be less than 0.5 dv. In that case, the VISTAS States would recommend that NH 3 emissions not be subject to BART.

Primary Particulate Matter

Primary particulate matter is considered a visibility impairing pollutant. However, the extent to which primary PM from BA RT

- eligible sources contributes to impairment at Class I areas in the southeastern US is not clear. For EGUs, the EPA has determined that emissions reductions of SO 2 and NO x under the CAIR rule meet the BART requirements, but these EGUs may still be subje ct to BART for primary PM. To determine the potential impacts of PM from EGU and non

EGU sources in the VISTAS states, two CMAQ sensitivity runs are underway for the first and third quarters of 2002. In one run, all primary PM from EGUs is removed while in the other run all primary PM from non

- EGU sources is removed. All other CMAQ modeling components are held constant. The results will help determine at which, if any, Class I areas in the VISTAS region primary PM emissions contribute to regional haze. If the sensitivity evaluation shows that the collective impact of all EGU or non

- EGU point primary PM emissions is less than 0.5 dv, then the impact of primary PM from any single BART eligible source would necessarily be less than 0.5 dv. These results wi ll be reported at the same time as results for VOC and NH 3 . 

### 4.2 Optional Source

- Specific Modeling

In some circumstances, a source may want to apply techniques designed to evaluate the impacts in a more detailed way than the standard VISTAS common protoc ol. A source may propose

source

- specific modeling procedures to address special issues to the State for State review. For example, sources very close to Class I areas may be better treated by a finer grid resolution that the generic Step 2 “fine” grid re solution meteorological fields provided by VISTAS. In someVISTAS’ Common Modeling Protocol 36 situations, higher resolution MM5 or other prognostic meteorological datasets may be available than the standard 12

- km or 36

- km MM5 datasets provided by VISTAS. Because it is not possible to anti cipate all of the situations where there would be a benefit to conducting more detailed

source

- specific analyses, the option to pursue this option is left as an open issue, to be resolved and justified based on specific factors relevant for the source in q uestion.

A source

- specific modeling protocol is required for each source. This document should describe the data sources and model configuration, and provide rationale for any changes in the model approach from the common protocol. This source

- specific pr otocol must be provided for review and approval by the State. The State will share the protocol with EPA and the Federal Land Managers for their review. Discussion of approaches to source

- specific modeling and an outline of the typical contents of the so urce

- specific protocol are presented in Chapter 5. Discussions with the regulatory authorities should be conducted prior to development of a source

- specific protocol to ensure all of the relevant issues are included in the protocol.



### 4.3 Initial Procedure for BART Exemption

4.3.1 Overview of Initial Approach

The first step in the common protocol, the initial assessment in Figure 4

- 1, is a simple procedure to evaluate whether a source can be exempted from BART controls using a consistent set of meteorolog ical and dispersion options. A pre

- computed set of meteorological files and a pre

defined CALPUFF input option configuration, based on guidance in the final BART rule (70 FR 39104

- 39172) and other EPA and FLAG model guidance, will allow relatively simple initial simulations. The regional initial domain is designed to allow any Class I areas within the VISTAS area to be evaluated with a single meteorological database and consistent CALPUFF modeling options. The second important question that this first sc reening step will answer is, if initial modeling indicates a source may impact visibility significantly, what Class I areas should be included in a finer grid analysis? Due to the multitude of factors affecting the contribution of a source to visibility in a Class I area, simple screens or rules of thumb alone (such as that the closest Class I area will produce the controlling visibility impacts) are not likely to be universally reliable.

4.3.2 Discussion of 12

- km Initial Exemption Modeling

Meteorological Fields

A regional initial domain and a set of pre

- computed regional CALMET meteorological files will be prepared for VISTAS, to allow any Class I areas within the VISTAS area to be evaluated with a consistent meteorological database and consistent CALPUFF modeling options.

The following three years of MM5 meteorological data have been assembled by VISTAS for use in the regional CALPUFF modeling effort:

- 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA)VISTAS’ Common Modeling Protocol 37 - 2002 MM5 dataset at 12 km and 36 km g rid (developed by VISTAS)

- 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning Organization).

These data sets have been provided to Earth Tech by VISTAS, and from them Earth Tech has produced annual CALMET meteorological files at 12

- km grid resolution for the domain shown in Figure 4

- 2. The CALMET modeling output files in the form of CALPUFF

- ready three

dimensional meteorological files will be available on external hard drives to the States and other parties.

The initial procedure t o determine if a BART

- eligible source is subject to BART uses the pre

computed CALMET meteorological fields for the years 2001

- 2003 on the 12

- km CALMET domain in Figure 4

- 2 and simulates with CALPUFF any BART

- eligible source to be screened. The CALMET sim ulations will be developed using the highest resolution MM5 data available for each year (i.e., 36

- km MM5 data for 2003, 12

- km MM5 data for 2001 and 2002).

The development of the regional CALMET meteorological fields from MM5 data will be conducted in No

- Observations (“No

- Obs”) mode. The MM5 data already reflect assimilation of observational data and are likely to adequately characterize regional wind patterns that are consistent with the 12

- km grid scale. Blending of MM5 data with local observations (whic h are mainly at the surface) could lead to wind structures that may not be realistic under some conditions and may result in poorer characterization of the regional winds. Thus, the effort required to prepare observational data sets for CALMET for the larg e regional domain involves considerable effort that may not provide corresponding improvement of the wind field.

For 2003, the 36

- km MM5 data will be used as CALMET’s initial guess field and then the CALMET diagnostic terrain adjustments (see Section 3.1. 1) will be applied to reflect terrain on the scale of the CALMET grid (i.e., 12

- km). When the 12

- km MM5 (2001 and 2002) data are used, the diagnostic CALMET terrain adjustments will be turned off since the grid resolution of the MM5 data is the same as th e CALMET grid and the terrain adjustments on the 12

- km grid scale will already be reflected in the MM5 dataset. In this case, the MM5 winds will be interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules will compute mixing heights , turbulence parameters and other meteorological parameters that are required by CALPUFF.