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 1 

Appendix H.6 - 250
BART 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 1 

Appendix H.6 - 251
BART 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 1 

Appendix H.6 - 252
BART 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 1 

Appendix H.6 - 253
BART 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 1224 

Appendix H.6 - 254
BART 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 

Appendix H.6 - 255
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 Control (SC DHEC) Bureau of Air Quality (BAQ) is in 

the process of reviewing the Best Available Retrofit Technology (BART) modeling report and associated 

CALPUFF modeling submitted by Bowater for the Catawba facility. We have not yet completed our 

review and the consultation process with the EPA and Federal Land Managers, but anticipate completing 

that process shortly. 

During our review, we confirmed as you reported, that 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 particulate 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 confirming 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 final 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 

Appendix H.6 - 256
 

BEST AVAILABLE RETROFIT TECHNOLOGY APPLICABILITY ANALYSIS

AIR QUALITY MODELING PROTOCOL

DAK AMERICAS, LLC  COOPER RIVER FACILITY

MONCKS CORNER, SOUTH CAROLINA

TRINITY CONSULTANTS

5821 Fairview Road 

Suite 400 

Charlotte, NC 28209 

(704) 553-7747 

May 2006 

Project 053401.0002 

Appendix H.6 - 257
DAK Americas, LLC- Cooper River Facility i Trinity Consultants 

BART Applicability Modeling Protocol 

TABLE OF CONTENTS

1. INTRODUCTION.................................................................................................... 1-1

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS ..................... 1-2 

1.1.1 DETERMINATION OF BART-ELIGIBILITY............................................................1-2

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND BART 

APPLICABILITY.................................................................................................................1-3

1.2 SCHEDULE FOR BART IMPLEMENTATION IN SOUTH CAROLINA........................... 1-7 

1.3 ORGANIZATION OF MODELING PROTOCOL............................................................ 1-8 

2. BART-ELIGIBLE SOURCE DESCRIPTION ........................................................... 2-9

2.1 BART-ELIGIBLE EMISSION UNITS........................................................................ 2-9 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY ................................. 2-10 

2.2.1 PARTICULATE SPECIATION FOR BOILERS AND PROCESS HEATERS..................2-13

2.3 MODELED STACK PARAMETERS AND EMISSIONS................................................ 2-17 

3. GEOPHYSICAL AND METEOROLOGICAL DATA ................................................ 3-20

3.1 TERRAIN ELEVATIONS WITHIN THE MODELING DOMAIN .................................... 3-20 

3.2 LAND USE AND COVER WITHIN THE MODELING DOMAIN................................... 3-22 

3.3 METEOROLOGICAL DATABASE ........................................................................... 3-22 

3.3.1 MM5 SIMULATIONS..........................................................................................3-23

3.3.2 MEASUREMENTS AND OBSERVATIONS .............................................................3-23

3.4 AIR QUALITY DATABASE.................................................................................... 3-24 

3.4.1 OZONE BACKGROUND CONCENTRATIONS........................................................3-24

3.4.2 AMMONIA BACKGROUND CONCENTRATIONS ..................................................3-25

3.4.3 OTHER POLLUTANT BACKGROUND AND BOUNDARY CONDITIONS .................3-25

3.5 NATURAL CONDITIONS AT CLASS I AREAS......................................................... 3-25 

4. AIR QUALITY MODELING METHODOLOGY...................................................... 4-28

4.1 PLUME MODEL SELECTION ................................................................................. 4-28 

4.1.1 MAJOR RELEVANT FEATURES OF CALMET ....................................................4-29

4.1.2 MAJOR RELEVANT FEATURES OF CALPUFF ...................................................4-31

4.2 MODELING DOMAIN CONFIGURATION ................................................................ 4-33 

4.3 CALMET METEOROLOGICAL MODELING.......................................................... 4-33 

4.4 CALPUFF COMPUTATIONAL DOMAIN AND RECEPTORS.................................... 4-34 

4.5 CALPUFF MODELING OPTION SELECTIONS ...................................................... 4-34 

4.6 CALPOST PROCESSING OPTION SELECTIONS FOR LIGHT EXTINCTION AND HAZE 

IMPACT CALCULATIONS.................................................................................................. 4-36 

4.7 MODELING PRODUCTS ........................................................................................ 4-36 

5. QUALITY ASSURANCE METHODS...................................................................... 5-38

5.1 CALMET FIELDS............................................................................................... 5-38 

5.2 CALPUFF, CALPOST, AND POSTUTIL RESULTS .......................................... 5-38 

Appendix H.6 - 258
DAK Americas, LLC- Cooper River Facility ii Trinity Consultants 

BART Applicability Modeling Protocol 

6. REFERENCES...................................................................................................... 6-41

APPENDIX A- VISTAS BART MODELING PROTOCOL

APPENDIX B- AMBIENT OZONE STATIONS

Appendix H.6 - 259
DAK Americas, LLC- Cooper River Facility 1-1 Trinity Consultants 

BART Applicability Modeling Protocol 

1. INTRODUCTION

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 areas: (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 refining 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 air emissions and operates under a Part 70 (Title 

V) major source operating permit No. 0420-0089 issued 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 Regional 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 federally protected Class I area, and hence whether 

a BART determination is necessary. 

This modeling protocol is presented to describe 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 Visibility 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 Source BART Rule” (Draft), October 28, 2005. 

The VISTAS BART Modeling Protocol is 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 

Appendix H.6 - 260
DAK Americas, LLC- Cooper River Facility 1-2 Trinity Consultants 

BART Applicability Modeling Protocol 

the opportunity for review and comment in conjunction 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 available between the time this modeling protocol 

is submitted and SCDHEC’s BART Applicability Analysis is submitted to ensure that refined 

analyses are conducted using mutually agreeable data resources and processing options. 

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS

The Regional Haze Rule requires that major sources of visibility-affecting pollutants 

belonging to one or more of 26 specific industrial source categories evaluate BART if the 

source was “in existence” (i.e., built or reconstructed) before August 7, 1977 and began 

operation after August 7, 1962. Such sources are termed “BART-eligible sources.” Major 

sources of visibility-affecting pollutants have the potential to emit 250 tons per year (tpy) of 

one or more of oxides of nitrogen (NOX), sulfur dioxide (SO2), and particulate matter less 

than 10 micrometers in aerodynamic diameter (PM10). 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 Classification 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 within 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 DETERMINATION OF BART-ELIGIBILITY

The U.S. EPA BART guidelines define the following 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, and began operation after August 7, 1962. This collection of 

emission units has potential emissions of 488 tpy NOX, 38 tpy PM10, and 1,478 tpy SO2. 

Accordingly, the BART-eligible emission units at DAK’s Cooper River facility will be 

analyzed to evaluate whether a BART determination 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. 

Appendix H.6 - 261
DAK Americas, LLC- Cooper River Facility 1-3 Trinity Consultants 

BART Applicability Modeling Protocol 

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND

BART APPLICABILITY

In its role as technical analysis coordinator, 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 conduct 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 normalized light extinction attributable to visibilityaffecting pollutants. To determine whether 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 source. 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 the light extinction coefficient (bext), 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 bext 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 bext, background is affected by various chemical 

species and the Rayleigh scattering phenomenon and can be calculated as shown in the 

following equation. 

bext background SO NO OC soil coarse ++++++= bbbbbbbkm rayap

−

4 3

)(

1

,

Appendix H.6 - 262
DAK Americas, LLC- Cooper River Facility 1-4 Trinity Consultants 

BART Applicability Modeling Protocol 

where, 

[ ]

[ ]

[ ]

[ ]

[ ]

3

Ray

34

424

][ Concentrat μg/minion

f(RH) relative humidity adjustment factor

b Rayleigh Scattering

Elemental[01.0 Carbon]

0006.0 Coarse Mass

Soil001.0

OC004.0

)(NONH003.0

)(SO)(NH003.0

3

4

=

=

=

=

=

=

=

=

=

ap

Coarse

Soil

OC

NO

SO

b

b

b

b

b RHf

b RHf

Values for the parameters listed above specific 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 conditions 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 SO2 and NOX. 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 bext,source is calculated using an air quality model. The extinction due 

to the BART-eligible source will be calculated as shown in the following equation. 

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

4 3

1

,

where, 

[ ]

[ ]

3

34

424

][ Concentrat μg/minion

relative)( humidity adjustment factor

][01.0

][004.0

][001.0

][0006.0

)(NONH003.0

)(SO)(NH003.0

3

4

=

=

=

=

=

=

=

=

RHf

b EC

b SOA

b PMF

b PMC

b RHf

b RHf

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. 

Appendix H.6 - 263
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 impairment attributable to DAK to assess whether 

the 0.5 dv contribution threshold is exceeded, and if so, the frequency, duration, and 

magnitude of any exceedance events. The U.S. EPA BART guidelines prescribe that the 

98th 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 98th 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 modeled to illustrate the distribution (i.e., 

frequency, duration, and magnitude) of peak visibility impairment episodes attributable to 

the Cooper River plant. 

CALPUFF is a refined air quality modeling system 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 assessment of DAK’s Cooper River plant is 

described in Sections 3, 4, and 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 evaluated to determine whether the source 

contributes to visibility impairment. Table 1-1 summarizes the distances separating the 

Cooper River plant from all Class I areas within 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. 

Appendix H.6 - 264
DAK Americas, LLC- Cooper River Facility 1-6 Trinity Consultants 

BART Applicability Modeling Protocol 

TABLE 1-1. DISTANCES (KILOMETERS) SEPARATING CLASS I AREAS AND

COOPER RIVER 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 area that is located within 300 km of DAK’s 

Cooper River operations: 

▲ Cape Romain National Wildlife Refuge located approximately 31 km east of the 

Cooper River facility 

▲ Wolf Island National Wildlife Refuge located approximately 228 km southwest 

of the Cooper River facility 

The location of the Class I areas and receptor locations 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 receptors, http://www2.nature.nps.gov/air/maps/receptors/. 

Appendix H.6 - 265
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BART Applicability Modeling Protocol 

FIGURE 1-1. LOCATION OF DAK’S COOPER RIVER FACILITY RELATIVE TO CLASS I 

AREAS WITHIN 300 KM

1.2 SCHEDULE FOR BART IMPLEMENTATION IN SOUTH CAROLINA

Each state within the VISTAS region has established its own schedule for BART implementation, 

which includes separate deadlines for BART applicability assessment and determination protocols 

and final reports. SCDHEC has established the following 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 Demonstration due May 19, 2006 

▲ BART Determination Modeling Protocol due August 15, 2006 

▲ BART Determination Permit Application Including Modeling Analysis due October 17, 2006 

Appendix H.6 - 266
DAK Americas, LLC- Cooper River Facility 1-8 Trinity Consultants 

BART Applicability Modeling Protocol 

1.3 ORGANIZATION OF MODELING PROTOCOL 

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 from, 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 report for reference. A listing of the receptor 

locations and ozone stations used in the analysis are also provided in the Appendix. 

Appendix H.6 - 267
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BART Applicability Modeling Protocol 

2. BART-ELIGIBLE SOURCE DESCRIPTION

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 will be represented in the modeling analysis. 

2.1 BART-ELIGIBLE EMISSION UNITS

DAK reviewed the criteria for BART-eligibility and determined that six emission units described in 

Table 2-1 comprise the BART-eligible source at the Cooper River facility. 

TABLE 2-1. SUMMARY OF BART-ELIGIBLE EMISSION UNITS

 

Emission Unit 

Source 

Code/ 

Stack ID 

BART 

Source 

Category 

Date†

Built or 

Modified 

Potential

SO2

Emissions

(tpy)* 

Potential 

NOX

Emissions 

(tpy)* 

Potential

PM10

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 since potential emissions exceed 250 tpy for at least 

one of NOX, SO2, and PM10, which are considered visibility-affecting pollutants. Note that for the 

purposes of this BART analysis, VISTAS and SCDHEC 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 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. 

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. 

Appendix H.6 - 268
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BART Applicability Modeling Protocol 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY

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 NOX, SO2, and PM10. 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. 

TABLE 2-2. SUMMARY OF 24-HOUR AVERAGE MAXIMUM ACTUAL EMISSION RATES

 

Emission Unit 

NOX

Emissions

(g/s) 

SO2

Emissions

(g/s) 

PM10

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 the components of the exhaust stream be speciated 

because different types of particulate matter affect visibility 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 m2

/g for coarse particulate matter to 10 m2/g for elemental 

carbon. Fine particulate matter (1 m2

/g) and organic aerosols (4 m2/g) scatter light with intermediate 

efficiencies, and ammonium sulfate and ammonium nitrate (that forms from precursor SO2 and NOX

emissions in the presence of ambient ammonia) are hygroscopic species that are particularly efficient 

light scatters in the presence of ambient water vapor (3f(RH) m2

/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. 

Appendix H.6 - 269
DAK Americas, LLC – Cooper River facility 2-11 Trinity Consultants 

BART Applicability Modeling Protocol 

FIGURE 2-1. PARTICULATE MATTER SPECIATION

(AFTER FIGURE 4-3 OF THE VISTAS BART MODELING PROTOCOL) 

The FLM has published speciation guidance for certain types of sources for Particulate Matter 

speciation. VISTAS has also provided example speciation 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 

(H2SO4 mist). 

While few data are available to estimate speciated emissions, DAK has reviewed what data are 

available to arrive at a conservative, yet reasonable 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 results for specific sources at the Cooper River 

facility. 

NOX emissions from the Cooper River facility’s BART-eligible sources result from combustion 

operations. Similarly, SO2 emissions emanate from combustion sources, and result from the 

incomplete oxidation of fuel-bound sulfur. Primary emissions of sulfuric acid mists or vapors, if any, 

are assumed to occur as only a small percentage of the primary SO2 emissions unless an emission 

factor has otherwise been published. Because of the condensable nature of such emissions and the 

distinct effect on visibility caused by sulfates, primary sulfate is evaluated as a distinct, speciated 

Appendix H.6 - 270
DAK Americas, LLC – Cooper River facility 2-12 Trinity Consultants 

BART Applicability Modeling Protocol 

particulate fraction. Condensable particulate matter (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. 

TABLE 2-3. NOMENCLATURE FOR EMISSIONS SPECIATION ANALYSIS

 

Nomenclature Description 

 

PMC800 Filterable coarse particulate matter with an aerodynamic diameter > 6 and < 10 μm 

PMC425 Filterable coarse particulate matter with an aerodynamic diameter > 2.5 and < 6 μm 

PMF187 Filterable fine particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μm 

PMF112 Filterable fine particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μm 

PMF081 Filterable fine particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μm 

PMF056 Filterable fine particulate matter with an aerodynamic diameter > 0.50 and < 0.625 μm 

EC187* Elemental Carbon particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μm 

EC112* Elemental Carbon particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μm 

EC081* Elemental Carbon particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μm 

EC056* Elemental Carbon particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μm 

POC081 Condensable organic particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μm 

POC056 Condensable organic particulate matter with an aerodynamic diameter > 0.50 and < 0.625 μm 

PIC081 Condensable inorganic particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μm 

PIC056 Condensable inorganic particulate matter with an aerodynamic diameter > 0.50 and < 0.625 μm 

* The Elemental Carbon portion is equal to 3.5% of the filterable fine particulate matter based on recommendations from NPS 

These PM classifications are necessary in the Class I visibility analysis because each type of PM has a 

different effect on visibility as defined by the extinction 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 and 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 and due to the representation of their visibilityaffecting characteristics in the light extinction equation. Primary emissions of inorganic CPM (PIC) 

may contain hygroscopic sulfates (SO4) and nitrates (NO3), 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 between 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 concentration when analyzed to assess natural 

background visibility (Malm 1994). 

Appendix H.6 - 271
DAK Americas, LLC – Cooper River facility 2-13 Trinity Consultants 

BART Applicability Modeling Protocol 

in the presence of ambient ammonia, which is modeled 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 nitrates, which would be assigned to the appropriate 

modeled PM type (i.e., SO4 and NO3, respectively), from non-hygroscopic species (e.g., carbonates), 

which would be modeled as non-hygroscopic 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 following sections of this protocol. 

TABLE 2-4. ASSIGNMENT OF EMITTED PM SPECIES TO MODELED PM CATEGORIES

 

Modeled PM 

Category†

 Components 

Output 

Category‡

Extinction 

Coefficient 

(m2

/g) 

PMC Filterable coarse particles (PM6-10, PM2.5-6) PMC 0.6 

PMF Filterable fine particles (PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625) SOIL 1 

PIC Non-hygroscopic, primary inorganic condensable (PIC) emissions* SOIL 1 

SO4 Primary inorganic condensable emissions of sulfates SO4 3f(RH) 

NO3 Primary inorganic condensable emissions of nitrates* NO3 3f(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 conservatively represented as PIC emissions. 

The refined analysis, if necessary, would distinguish 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 PARTICULATE SPECIATION FOR BOILERS AND PROCESS HEATERS

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 combustion 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 filterable PM and does not include the condensable 

portion. Therefore, to calculate the condensable 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 Particle Size Distribution and Size Specific 

Emission Factors for Industrial Boilers Firing Residual Oil,” Sept 1998. 

Appendix H.6 - 272
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  =



 









 









 





TABLE 2-5. NO. 6 FUEL OIL BOILERS AND HEATERS SPECIATION DATA

 

Speciation Data Value Reference 

Filterable portion of PM10 90.00%

Condensable portion of PM10 10.00% VISTAS Speciation data sourcea

Sulfate 5% of Condensable Portion b 

Non-sulfate Condensable portion 95% of Condensable Portion 

Organic Condensable PM10 15% of non-sulfate condensable c

Inorganic Condensable PM10 85% of non-sulfate condensable c

PM6-10 as a % of PM10 

PM2.5-6 as a % of PM10 

PM1.25-2.5 as a % of PM10 

PM1-1.25 as a % of PM10 

PM0.625-1 as a % of PM10 

PM0.5-0.625 as a % of PM10 

AP-42, Section 1.3; Table 1.3-5 (see breakdown in 

table 2-7)a 

Elemental Carbon (PM0.5-2.5 ) 3.50% 

Non-EC Fine Filterable (PM0.5-2.5 ) 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 

TABLE 2-6: COAL BOILER SPECIATION DATA

 

Speciation Data Value Reference 

Filterable portion of PM10 8.3% 

Condensable portion of PM10 91.7% VISTAS Speciation data sourcea

Sulfate 5% of Condensable Portion b

Non-sulfate Condensable portion 95% of Condensable Portion 

Organic Condensable PM10 20% of non-sulfate condensable c

Inorganic Condensable PM10 80% of non-sulfate condensable c

PM6-10 as a % of PM10 

PM2.5-6 as a % of PM10 

PM1.25-2.5 as a % of PM10 

PM1-1.25 as a % of PM10 

PM0.625-1 as a % of PM10 

PM0.5-0.625 as a % of PM10 

AP-42, Section 1.1; Table 1.1-9 (see breakdown in 

table 2-7)a 

Elemental Carbon (PM0.5-2.5 ) 3.50% 

Non-EC Fine Filterable (PM0.5-2.5 ) 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 

Appendix H.6 - 273
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BART Applicability Modeling Protocol 

Condensable emissions are allocated between primary organic (POC) and inorganic 

species (PIC), which includes sulfates and nitrates. DAK has represented maximum actual 

emissions as potential sulfur dioxide emissions for the purposes of BART applicability 

modeling, and further conservatively estimated that primary sulfuric acid emissions are 

equivalent to 5% of the total condensable portion of particulate emissions. 

Organic CPM emissions were determined by multiplying the calculated TPM10 emissions 

by the condensable percentage of TPM10 and the organic percentage of CPM. Organic 

CPM was assumed to be evenly split between the two size categories, 0.625-1.0 μm and 

0.5-0.625 μm. An organic CPM calculation for an example TPM10 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 TPM10 emission rate of 1 lb/hr. 

Filterable PM10 emissions were divided amongst six different size categories by 

multiplying TPM10 emissions by the filterable % and then by the % of PM10 for each size 

category, yielding hourly emissions of PM6-10, PM2.5-6, PM1.25-2.5, PM1-1.25, PM0.625-1, and 

PM0.5-0.625. A sample calculation for PM0.5-0.625 for TPM10 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 TPM10. Figure 2-3 presents a 

graphical representation of the TPM10 speciation used for each of these units, based on the 

sample 1 lb/hr TPM10 emission rate. 

Appendix H.6 - 274
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BART Applicability Modeling Protocol 

TABLE 2-7. BOILER/VAPORIZER PM10 SPECIATION (BASED 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 

FIGURE 2-3. BOILER/VAPORIZER PM10 SPECIATION (BASED 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

Appendix H.6 - 275
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2.3 MODELED STACK PARAMETERS AND EMISSIONS

Actual stack parameters will be input to the CALPUFF 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 discharges 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. 

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TABLE 2-8. STACK PARAMETERS FOR BART-ELIGIBLE EMISSION UNITS

Emission Unit Stack ID 

Lambert 

Conformal 

Coordinate 

East 

(km) 

Lambert 

Conformal 

Coordinate 

North 

(km) 

Stack 

Height 

(m) 

Base 

Elevation 

(m) 

Stack 

Diameter 

(m) 

Te

Boiler #1 BOIL 1 1580.083 -620.125 45.72 9.1013 1.521 Boiler #2 BOIL 2 1580.074 -620.110 45.72 9.1013 1.521 Vaporizer #1 VAPOR 1 1580.090 -620.137 45.72 9.1013 1.3015 Vaporizer #2 VAPOR 2 1580.098 -620.134 45.72 9.1013 1.3015 Vaporizer #3 VAPOR 3 1580.106 -620.129 45.72 9.1013 1.3015 
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TABLE 2-9. MODELED EMISSION RATES FOR BART-ELIGIBLE EMISSION UNITS (G/S) 

Pollutant Boiler #1 Boiler #2 Vaporizer #1 Vaporizer #2 Vapori

SO2 20.5087 22.7225 3.5846 3.9614 3.74

NOx 9.6010 11.2819 1.3079 1.7640 1.92

Sulfate 3.60E-03 5.533E-01 2.50E-03 3.00E-03 1.90E

PMC800 6.87E-02 2.337E-01 4.79E-02 5.58E-02 3.61E

COARSE 

PMC425 1.603E-01 3.339E-01 1.117E-01 1.301E-01 8.43E

PMF187 1.252E-01 1.289E-01 8.72E-02 1.017E-01 6.59EPMF112 2.21E-02 4.83E-02 1.54E-02 1.79E-02 1.16EPMF081 4.42E-02 1.289E-01 3.08E-02 3.59E-02 2.32E

FINE 

PMF056 2.21E-01 1.128E-01 1.54E-01 1.794E-01 1.162E

EC187 4.50E-03 4.70E-03 3.20E-03 3.70E-03 2.40EEC112 8.00E-04 1.80E-03 6.00E-04 7.00E-04 4.00EEC081 1.60E-03 4.70E-03 1.10E-03 1.30E-03 8.00E

ELEMENTAL 

CARBON 

EC056 8.00E-03 4.10E-03 5.60E-03 6.50E-03 4.20E

POC081 5.20E-03 1.0513 3.60E-03 4.20E-03 2.70E

ORGANIC 

POC056 5.20E-03 1.0513 3.60E-03 4.20E-03 2.70E

PIC081 2.94E-02 4.2054 2.05E-02 2.39E-02 1.55E

INORGANIC 

PIC056 2.94E-02 4.2054 2.05E-02 2.39E-02 1.55E
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3. GEOPHYSICAL AND METEOROLOGICAL DATA

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 protocol 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. Terrain features affect flows and create turbulence in 

the atmosphere and are potentially subjected to higher 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 TERRAIN ELEVATIONS WITHIN THE MODELING DOMAIN

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, approximately 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 preprocessors to format and assimilate these data into a 

single geophysical data file for processing by CALMET. 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 the 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. 

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FIGURE 3-1. TERRAIN REPRESENTATION IN THE 12-KM REGIONAL SCREENING GRID

(AFTER FIGURE 4-2 OF THE VISTAS BART MODELING PROTOCOL)

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3.2 LAND USE AND COVER WITHIN THE MODELING DOMAIN

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. Geological 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, albedo, soil heat flux) that affect turbulent 

dispersion in the boundary layer. 

Refined analyses may be necessary as part of the 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 resolution 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 analyses performed by DAK, if necessary, will utilize 

the same CTG LULC data. 

3.3 METEOROLOGICAL DATABASE

CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields 

from mesoscale model (MM) output, raw observations of surface and upper air conditions, 

precipitation measurements, and geophysical 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 meteorological fields are available, as 

discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used 

in conjunction with available standard [National Weather Service] NWS or 

comparable meteorological observations within 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 database and consistent CALPUFF modeling options. The 

CALMET modeling output files in the form of CALPUFF-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. 

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3.3.1 MM5 SIMULATIONS

MM data are used as “observed” or “first-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 meteorological fields from MM5 data were 

conducted in No-Observations (“No-Obs”) mode since 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. 

When the 12-km MM5 (2001 and 2002) data are used, the diagnostic CALMET terrain 

adjustments 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 

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 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). 

3.3.2 MEASUREMENTS AND OBSERVATIONS

The finer grid (4 km) CALMET simulations, which DAK anticipates utilizing as part of a 

refined analysis, will be run by VISTAS technical 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 features 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, temperature, cloud cover and ceiling, relative humidity, 

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and precipitation type. Surface data 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 Center (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 vary 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 optimize 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 transformations and wet deposition processes on 

ambient pollutant concentrations are an important part of the BART applicability analysis. 

Therefore, it is necessary to include observations of precipitation in the CALMET analysis. 

Precipitation data would be collected from selected surface meteorological data stations 

included in the analysis, plus Cooperative Observation Network (COOP) stations nearer to 

or within the domain. Precipitation data were extracted from among the NCDC’s available 

data inventory to optimize spatial coverage 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 AIR QUALITY DATABASE

The CALPUFF V5.754 model is capable of simulating linear chemical transformation effects by 

using pseudo-first-order chemical reaction mechanisms for the conversions of SO2 to SO4, and NOX, 

which consists of nitrogen oxide (NO) and nitrogen dioxide (NO2), to nitrate (NO3) and nitric acid 

(HNO3). In this study, chemical transformations involving five species (SO2, SO4, NOX, HNO3, and 

NO3) will be modeled using the MESOPUFF II chemical transformation scheme. Ambient 

concentrations of ammonia and ozone concentrations as represented in the model affect the 

MESOPUFF II chemical transformation simulation. 

3.4.1 OZONE BACKGROUND CONCENTRATIONS

Both screening and refined analyses will utilize observed ozone data for 2001 through 

2003 from non-urban CASTNet and AIRS stations 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 computed by month) for substitution should all 

observations be missing for a particular hour of the dataset. 

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3.4.2 AMMONIA BACKGROUND CONCENTRATIONS

In the screening, a constant background value (0.5 ppb) for ammonia will be utilized. The 

VISTAS BART Modeling Protocol envisions that CMAQ-modeled NH3 data for the Cape 

Romain and Wolf Island Class I areas would be used during postprocessing of refined 

analyses to repartition HNO3 and NO3 using the ammonia limiting method. 

3.4.3 OTHER POLLUTANT BACKGROUND AND BOUNDARY CONDITIONS

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 conditions will be used 

in the refined analyses and appropriate documentation will be provided in the BART 

applicability modeling final report if refined modeling is necessary. 

3.5 NATURAL CONDITIONS AT CLASS I AREAS

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 atmospheric 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 facility and potentially affected by DAK’s operations, 

Table 3-1 summarizes the default natural background conditions as tabulated in Appendix B of U.S. 

EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule. 

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TABLE 3-1. NATURAL BACKGROUND CONCENTRATIONS ON 20% BEST DAYS

FOR CLASS I AREAS POTENTIALLY AFFECTED BY THE DAK COOPER RIVER PLANT

 

Class I Area 

bext

(Mm-1) 

Annual 

Average Haze 

Index (dv) 

Best Days 

Haze Index 

(dv) 

Worst Days 

Haze Index 

(dv) 

Soil bback 

Annual Avg 

(μg/m3

) 

Soil bback 

Best Days 

(μg/m3

) 

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 Visibility 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 extinction coefficient in terms of 

Rayleigh scattering and scattering due to soils (i.e., fine particles) based on the 20% best visibility 

days, and is calculated from the tabulated “best days” values and the following equations. 





 



 = 10 exp10 HI bback , 

where HI is Haze Index expressed in units of deciviews (dv). Therefore, total bback for the best days at 

Cape Romain and Wolf Island, including the Rayleigh scattering coefficient is calculated as shown in 

the following equations using the Cape Romain and Wolf Island Best Days values. 

Mm 14.48 Mm 10 I Classfor Mm 45.4

10

68.3 exp10 −1 −1 −1  = =+= =⇒+ 

 



 CRbback = ray bb soil soil bb soil

Mm 14.54 Mm 10 I Classfor Mm 54.4

10

74.3 exp10 −1 −1 −1  = =+= =⇒+ 

 



 WIbback = ray bb soil soil bb soil

As is described in Section 4 of this protocol, the effects of relative humidity to amplify the visibility 

impairment of hygroscopic sulfates and nitrates will be characterized using “Method 6,” which 

computes ∆bext using a monthly average relative humidity adjustment particular to each Class I area 

applied to background and modeled sulfate and nitrate. Table 3-2 summarizes the monthly average 

humidity values that will be applied for the Class I area 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. 

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TABLE 3-2. MONTHLY AVERAGE f(RH) FOR SELECTED CLASS I AREA* 

 

Class I Area January February March 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 whether refinements are warranted to the methods 

recommended in U.S. EPA’s guidance to calculate default estimates of natural background visibility. 

In addition, the Interagency Monitoring of Protected 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 IMPROVE 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 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 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 recommended 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 analysis 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. 

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4. AIR QUALITY MODELING METHODOLOGY

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 protocol 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 PLUME MODEL SELECTION

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 impacts. 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 situations 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 “the best modeling application available for 

predicting a single source’s contribution to visibility 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 VISTAS and it technical contractor.8

This source-specific modeling protocol for DAK’s Cooper 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 maintains CALPUFF Version 5.754 and CALMET 

Version 5.711. 

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4.1.1 MAJOR RELEVANT FEATURES 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 to 

compute 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 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 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 enhancements 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 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. 

Development of the Step 1 wind field begins with the initial guess field defined by the 

MM5 prognostic meteorological 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 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 effects 

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme 

is applied iteratively until the three-dimensional divergence is less than a threshold value. 

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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 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 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, 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, 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 relatively large i.e., coarser than the scale of 

variation of the true wind field, which is particularly 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 adjustments 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 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 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 

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required, will utilize the MM5 data as the first-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 MAJOR RELEVANT FEATURES OF CALPUFF 

By its puff-based formulation and through the use of three-dimensional meteorological 

data developed by the CALMET meteorological 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 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 general “plume path coefficient” adjustment), 

or an algorithm that simulates enhanced vertical dispersion derived from the 

weakly-stratified flow and dispersion module 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 receptors using a dividing streamline height 

(Hd) to determine which pollutant material is deflected around the sides of a hill 

(below Hd) and which material is advected over the hill (above Hd). The local flow 

(near the feature) used to define Hd is taken from the gridded CALMET fields. As in 

CTDMPLUS, each feature is modeled in isolation 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 

(σv and σw), the use of similarity theory to estimate σv and σw from modeled surface 

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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 use the AERMOD turbulence profiles for 

determining dispersion rates, which is the most 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 conditions, 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 μm diameter) from coarse particulate matter (2.5-10 

μm 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 transport among the “new” puffs generated from 

the original, 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. 

▲ Wet 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 type (i.e., frozen vs. liquid precipitation). 

▲ Chemical Transformation: CALPUFF includes options for parameterizing chemical 

transformation effects using the five species scheme (SO2, SO4

=

, NOX, HNO3, and 

NO3

-

) 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 SO2 and NOX 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. 

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4.2 MODELING DOMAIN CONFIGURATION

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 CALPUFF 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 degrees 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 the default maximum mixing height of 3,000 meters. 

TABLE 4-1. VERTICAL GRID STRUCTURE

 

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 appropriate 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 METEOROLOGICAL MODELING

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 this protocol, and the geophysical and meteorological databases were 

described in Section 3. CALMET processing was 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. 

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▲ 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, except 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 and 2002 simulation (using 12-km MM5 data)

▲ CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, 

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode. 

▲ TERRAD (terrain scale) is required for runs with 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 simulations based on sensitivity tests. The purpose of 

the testing was to optimize the variable to allow 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, appropriate CALMET processing options will be evaluated 

and documented in the final applicability modeling report. 

4.4 CALPUFF COMPUTATIONAL DOMAIN AND RECEPTORS

CALPUFF analyses to assess the visibility impacts attributable to DAK’s Cooper River facility will 

be performed on a computational domain that is a subset 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 beyond 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 receptors specified by the FLM to represent the Cape 

Romain and Wolf Island Class I Areas9

. 

4.5 CALPUFF MODELING OPTION SELECTIONS

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/air/maps/Receptors/index.htm 

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▲ Chemical mechanism: MESOPUFF II module 

▲ Background concentrations of SO4 and TNO3 (HNO3 + NO3) from CMAQ 2001-2003 annual 

runs. SCDHEC has provided the appropriate values in response to this modeling protocol 

should refined modeling be necessary. 

▲ Species modeled: SO2, SO4, NOX, HNO3, NO3 and particulate matter in size categories of 

<0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm 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 the highest emitting day of the meteorological 

period modeled (excluding days where start-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 maximum emissions are used for modeling purposes 

(70 FR 39162). Pollutants considered include SO2, NOX and PM10. The basis for modeled 

emission rates was described in Section 2 of this protocol. 

▲ Condensable emissions are considered as primary fine particulate matter and allocated 

equally to the two sub-micrometer particle size 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 SO2 and NOX and 15 tons per year for PM10). 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 categories: elemental carbon (soot), “soil” (fine 

PM < 2.5 μm diameter), coarse particulate matter (2.5-10 μm 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 turbulence-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 for 2001 through 2003 from CASTNet and AIRS 

stations. Only non-urban ozone stations should 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 NH3 data will be used for each Class I area in POSTUTIL to repartition HNO3

and NO3

▲ Puff representation: integrated puff sampling methodology. 

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▲ 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 PROCESSING OPTION SELECTIONS FOR

LIGHT EXTINCTION AND HAZE IMPACT CALCULATIONS

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: SO4, NO3, 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 represented 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 performed 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 NH3 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 for the Class I area considered. Predicted changes 

exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid CALPUFF modeling 

analysis. To assess whether BART-eligible operations 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, duration, and magnitude) of peak 

visibility impairment episodes attributable to the Cooper River plant. Only the 98th percentile 24-hour 

average visibility impact would be evaluated in the refined analysis, if necessary. 

4.7 MODELING PRODUCTS

DAK will prepare and submit a BART applicability analysis result describing the modeling 

procedures, data resources, and results of screening 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: 

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1. Map of source location and Class I areas within 300 km of the source 

2. For the VISTAS 12-km CALPUFF initial exemption 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 98th

percentile days in each year greater than 0.5 dv (total visibility impairment minus impairment 

on 20% best days for natural background visibility 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 

98th percentile that the impact of the source exceeds 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. 

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5. QUALITY ASSURANCE METHODS

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 simulations, 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 FIELDS 

Section 4 of the VISTAS BART Modeling Protocol describes the methods and procedures for use in 

conducting regional scale screening modeling to determine 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 fields 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 activity of end-users is again reduced from what 

would be required in the development of the dataset. It is not expected that the quality assurance 

steps in the development will be repeated 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-observations 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), including 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 such 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 data and the physical processes expected to be important at that 

location will be conducted as part of the site-specific QA plan development. 

5.2 CALPUFF, CALPOST, AND POSTUTIL RESULTS

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report 

contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 of the 

VISTAS BART Modeling Protocol or in a source-specific protocol, the IWAQM guidance should be 

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used in setting up the CALPUFF simulations. The 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 control file be reviewed manually as part of the 

CALPUFF QA analysis. To facilitate this process, as was the case with 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 order 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 for computing background extinction and monthly 

relative humidity factors for hygroscopic aerosols. 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 analysis. 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 98th percentile value (8th 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 spreadsheet to rank the daily change in extinction 

values such as is presented in Table 4-2. 

The CALPOST inputs that will be carefully checked 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 matter, 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 contributions 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., different particle sizes) into species-dependent size distributions for 

the particulate matter. For example, PM is often simulated with unit emission rates for each particle 

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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 input into CALPOST. This process, although simple, requires a careful 

review of the weighting factors for each source. POSTUTIL also allows a repartitioning of nitric acid 

and nitrate to account for the effects of ammonia limiting conditions. 

 

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, 

performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate 

concentrations at measurement sites within the site-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 evaluation of the model performance relative to the 

techniques in the VISTAS common protocol. 

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6. REFERENCES

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: MELSAR: 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. Manikin, 2004: An Hourly Assimilation Forecast 

Cycle: the RUC, Mon. Wea. Rev., 132, 495-518. 

Carson, D.J., 1973: The development of a dry, inversion-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 Assistant 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 Controversies, 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: Guidance for Tracking Progress under the Regional Haze 

Rule; EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

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Environmental Protection Agency, 2003b: Guidance 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: Phase 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 Effects of Ammonia Limitation on Nitrate Aerosol 

Formation and Visibility Impacts in Class I Areas. Paper J5.13, 12th 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. Grachev, 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, Lawrence 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. 

Grosjean, D., and J. Seinfeld, 1989: Parameterization of the Formation Potential of Secondary 

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

Gryning and F.A. Schiermeier. Plenum Press, New York, NY. 

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface 

fluxes from routine weather data. J. Clim. and Appl. Meteor., 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: Evaluation 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 Path Forward. Mystic, CT, 22-24 October 2003. 

Appendix H.6 - 301
DAK Americas, LLC – Cooper River facility 6-43 Trinity Consultants 

BART Applicability Modeling Protocol 

Morrison, K., Z-X Wu, J.S. Scire, J. Chenier and T. Jeffs-Schonewille, 2003: CALPUFF-Based 

Predictive and Reactive Emissions Control System. 96th 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 

PM10, PM2.5 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 (Technical 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 coefficient 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 Extinction 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 (CTDMPLUS) 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. Environmental Protection Agency, Research Triangle 

Park, NC. 

Scire, J.S. and F.R. Robe, 1997: Fine-Scale Application of the CALMET Meteorological Model to a 

Complex Terrain Site. Paper 97-A1313. Air & Waste 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. 

Appendix H.6 - 302
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 the 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: The 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, 1998: 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. 

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BART Applicability Modeling Protocol 

APPENDIX A 

VISTAS BART MODELING PROTOCOL

Appendix H.6 - 304
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)

Appendix H.6 - 305
Table of Contents iv

TABLE OF CONTENTS

Page

SUMMARY S-1

1. INTRODUCTION AND PROTOCOL 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 CALPUFF 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 MODELING PROTOCOL 32

4.1 Overview 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, NH3, 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 Regional 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 Overview 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 Contribution to CALPUFF Modeling of BART Eligible Sources 51

5. SOURCE-SPECIFIC MODELING PROTOCOL 52

Appendix H.6 - 306
Table 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 62

Appendix H.6 - 307
Summary S-1

SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available 

Retrofit 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 in 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

Best 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 attribution 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 visibility 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 sources, 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 

VISTAS 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, Christelle 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 Park, 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 1st, 2005 VISTAS held another meeting with its 

participants in Nashville, 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. 

Appendix H.6 - 308
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 modeling

 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 intended 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 

specific 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 are:

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 analysis

Appendix H.6 - 309
Summary 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 enhancements 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 include 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 rate 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 available. 

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 

SO4 and total NO3 (HNO3 + NO3). CMAQ data in CALPUFF-ready format will be 

provided for each Class I area by VISTAS. After running CALPUFF for an individual 

facility, repartition NO3 in POSTUTIL using the CMAQ background data, including that 

for NH3.

• Use ozone data from non-urban monitors as the background ozone input. 

• Use the Pasquill-Gifford dispersion method.1

 

1 The Final 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. 

Appendix H.6 - 310
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 background 

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 describe, 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 determining if a BART-eligible source is subject to BART CALPUFF modeling, use a twotier 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 grid 

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 area 

boundary (Q/d) as a presumptive indicator that a BART-eligible source is subject to BART. If 

Q/d for SO2 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 performed 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 perform 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. 

Appendix H.6 - 311
Summary S-5

Note 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. If 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 and 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 98th 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 less) analyses, use the 98th percentile impact value for the 24-hr average. 

Use either the 8th highest day in each year or the 22nd highest day in the 3-year period, whichever 

is more conservative, for comparison to the exemption threshold.

Use the same model 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 control” 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).

Appendix H.6 - 312
Introduction and Protocol Objectives 1

1. INTRODUCTION AND PROTOCOL 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 rule 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 useful 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 parks 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” is 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 “BARTeligible source”. 

One of the listed categories is 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 added 

together regardless of construction date. In this category, electric generating sources greater than 

750 MW have presumptive SO2 and NOx emission limits. States may presume the same limits for 

EGU sources between 250-750 MW. However, units at those sources 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 SO2 and NOx. 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 identified 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 are subject to slight adjustments and refinements.) No 

BART sources are located on Tribal lands.

Appendix H.6 - 313
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 protocol2 and provides 

additional description 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 source 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.

Appendix H.6 - 314
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 in 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-eligible 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 modeling of BARTeligible 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 model 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 alternative 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, the 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 trading alternative under the proposed EPA trading guidance 

(70 FR 44154-44175) that is to be promulgated early in 2006.

Appendix H.6 - 315
Review of EPA’s Guidance 4

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING

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 intended 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

The 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” air 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 BARTeligible. Finally, a source is eligible for BART if potential emissions of visibility-impairing air 

pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate 

matter (PM10) and gaseous precursors to secondary fine particulate matter, such as SO2 and NOx. 

Whether ammonia or volatile organic compounds (VOCs) should be included as visibilityimpairing 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 PM2.5 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 SO2

and NOx and 15 TPY of PM10 or PM2.5. This assumes that the other BART-eligibility criteria are 

met.)

2) Determine 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 quality 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 level 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 visibility 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.

Appendix H.6 - 316
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 98th 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 impairment and therefore is not subject to BART. Whether or not the 98th

percentile value exceeds the threshold must be determined at each Class I area. Over an annual 

period, this implies the 8th highest 24-hr value at a particular Class I area is compared to the 

contribution threshold. Over a 3-year modeling period, the 98th percentile value may be interpreted 

as the highest of the three annual 98th percentile values at a particular Class I area or the 22nd highest 

value in the combined three year record, whichever 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 opposite, 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 plant 

analysis conducted by EPA in finalizing the BART rule. Under this option, sources with 

potential emissions of SO2 plus NOx of less than 500 tons and a distance from any Class I area 

greater than 50 kilometers or sources with SO2 plus NOx potential emissions 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 considered along with SO2 and NOx, so that a plant could 

be exempted if the combined potential emissions of SO2, NOx, plus PM meet the criteria above.

3) Determine BART controls for the source by considering various control options and selecting 

the “best” alternative, 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.

Appendix H.6 - 317
Review of EPA’s Guidance 6

Note that if a source agrees to apply the most stringent controls 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 2007.

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 that 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 the 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 and 

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 rule (70 FR 39138-39139) that emissions reductions 

under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO2 and NOx control 

for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR 

and therefore a BART analysis 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 source applications” (70 FR 39123). Photochemical grid models may be 

Appendix H.6 - 318
Review 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 involve 

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. EPA’s 

role in the development of the protocol is only advisory as the “States better understand the BARTeligible 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 appropriate 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 evaluated 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, frequency, 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 progress 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. 

If 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 CALPUFF 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 2006.

Appendix H.6 - 319
Overview of the CALPUFF Modeling System 8

3. OVERVIEW OF THE CALPUFF 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 model 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 use 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 visibility 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). CALPUFF 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 available 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 modeling 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 meteorological 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 observational 

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 puff 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 includes 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.

Appendix H.6 - 320
Overview of the CALPUFF Modeling System 9

Figure 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 nitrate 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 combination 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 modeling 

components in more detail.

Appendix H.6 - 321
Overview 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. The 

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 Boundary 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 Minimization

- Produces Gridded Fields of U, V, W Wind Components

- Inputs Include Domain-Scale Winds, Observations, and

 (optionally) Coarse-Grid Prognostic Model Winds

- Lambert Conformal Projection Capability

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, surface 

Appendix H.6 - 322
Overview 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 based 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 method 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 

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 enhancements 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.

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 meteorological 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 features 

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 terrainforced vertical velocity, subject to an exponential, stability-dependent decay function. The effects 

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme is 

applied iteratively until the three-dimensional divergence is less than a threshold value. 

Slope Flows: The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 

1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes both 

Appendix H.6 - 323
Overview 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 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.

Blocking 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. 

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 inversedistance 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 NoObs 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, 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 relatively large 

i.e., coarser than the scale of variation 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 meteorological model, CALPUFF can simulate the effects of timeand 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 one 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 Complex Terrain 

Appendix H.6 - 324
Overview 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 based 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 (Hd) to determine which pollutant material is 

deflected around the sides of a hill (below Hd) and which material is advected over the hill (above 

Hd). The local flow (near the feature) used to define Hd is taken from the gridded CALMET 

fields. As in CTDMPLUS, each feature is modeled in isolation 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 (σv and σw), the use of 

similarity theory to estimate σv and σw from modeled surface 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 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 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.

Appendix H.6 - 325
Overview 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-specific 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 μm diameter) from 

coarse particulate matter (2.5-10 μm 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 

transport among the “new” puffs generated from the original, 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. 

Wet 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 type (i.e., frozen vs. 

liquid precipitation).

Chemical Transformation: CALPUFF includes options for parameterizing chemical 

transformation effects using the five species scheme (SO2, SO4

=

, NOx, HNO3, and NO3

-

) 

employed in the MESOPUFF II model or a set of user-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 SO2 and NOx 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.

Appendix H.6 - 326
Overview 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 or variable emissions)

- Volume sources (constant or variable emissions)

- Area sources (constant or variable emissions)

• Non-steady-state emissions and meteorological conditions

- Gridded 3-D fields of meteorological variables (winds, temperature)

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

 Monin-Obukhov length, precipitation rate

- Vertically and horizontally-varying turbulence and dispersion rates

- Time-dependent source and emissions 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 wildfires

• Efficient sampling functions

- Integrated puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient (σy, σz) options

- Direct measurements of σv and σw

- Estimated values of σv and σw based on similarity theory 

-- 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)

• Vertical wind shear

- Puff splitting

- Differential advection and dispersion

• Plume rise

- Buoyant and momentum rise

- Stack tip effects

- Building downwash effects

- Partial penetration

- Vertical wind shear

• Building downwash

- 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/weakly stable flow in CTDMPLUS)

Appendix H.6 - 327
Overview 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, Hd, as in CTDMPLUS:

- Above Hd, material flows over the hill and experiences altered diffusion rates

- Below Hd, material deflects around the hill, splits, and wraps around the hill

• Dry Deposition

- Gases and particulate matter

- Three options:

- Full treatment of space and time variations of deposition with a resistance model

- User-specified 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 conditions, plume dispersion at coastal boundary

- Plume fumigation

• Chemical transformation options

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NOx, HNO3, and NO3

(MESOPUFF II method)

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NO, NO2, HNO3, and NO3 

(RIVAD/ARM3 method)

- User-specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal

- Scavenging coefficient approach

- Removal rate a function of 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 visibilityrelated 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 described 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.

Appendix H.6 - 328
Overview 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 between 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 effects 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. POSTUTIL 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 and 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 Applicability 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 cautiously 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 presence 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 since 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 6th Modeling Conference provides the background for this recommendation: 

“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 distances, 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.)

Appendix H.6 - 329
Overview of the CALPUFF Modeling System 18

new puffs thus generated can increase the horizontal 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 puffs 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 shorter 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 

algorithms 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 regulatory 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” situations 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, 

additional 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 Pasquill-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 convective 

Appendix H.6 - 330
Overview of the CALPUFF Modeling System 19

probability density function (pdf) module, 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 Model5. The ISCST3 model and its PG dispersion 

coefficients 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 application. 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 appropriate only when the plume is sufficiently diffuse that it is not visually 

discernible as a plume per se, but nevertheless its presence could alter the visibility through the 

background haze. The IWAQM Phase 2 report states that such conditions occur starting 30 to 50 

km from a source. 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 above, 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 concentration distribution will require a grid that is finer than 1 km. (For 

reference, the width of a Gaussian plume, 2σy, is roughly 1 km after 10 km of travel distance, 

assuming Pasquill-Gifford dispersion rates under neutral conditions.) 

As an additional consideration, 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 CALPUFF 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 

Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-68261. 9 November 2005.

Appendix H.6 - 331
Overview 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 optical effects of emissions of 

particles, SO2, and NOx 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 PM2.5

Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that 

CALPUFF can treat primary pollutants such as PM10. In actuality, CALPUFF can simulate PM10

or PM2.5 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 PM2.5 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. 

PM2.5 particles, which have a mass median diameter around 0.5 μm, have an average net 

deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine 

particles is usually not significant except for ground-level emissions. On the other hand, coarse 

particles (those PM10 particles larger than PM2.5) 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 particle 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 PM10, is accounted for in the calculation of the deposition velocity. Effects of inertial 

impaction (important for the upper part of the PM10 distribution) and Brownian motion (important 

for small, sub-micron particles) and wet scavenging are also addressed. The BART guidance 

recommends that fine particulate matter (less than 2.5 μm diameter), which has higher light 

extinction efficiency than coarse particulate matter (2.5-10 μm diameters), should be treated 

separately in the model. CALPUFF allows for user-specified size categories to be treated as 

 

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

application

Appendix H.6 - 332
Overview of the CALPUFF Modeling System 21

separate species, which includes calculating size-specific dry deposition velocities for each size 

category.

A primary PM2.5 emission from coal-fired electric generating units (EGUs) that is of relevance to 

visibility calculations 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 significant formation 

of secondary sulfate conversion from SO2 has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate 

The MESOPUFF-II chemistry algorithm used in CALPUFF7 simulates the gas phase oxidation 

of sulfur dioxide to sulfate by a linear transformation 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 easilycomputed or observed parameters that are used as surrogates for the factors that control SO2

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 SO2 oxidation is a function of OH radical concentrations. Ozone 

concentrations are correlated with OH radical concentrations during daytime hours, and their use 

in the daytime SO2 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, but 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 

other variables in the relationship. They are surrogates for factors that the regression analysis has 

shown to be important in SO2 oxidation rates. At night, the SO2 conversion is set to a constant 

low value (default is 0.2%/hr). Aqueous phase oxidation of SO2 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 explained below, non-linear behavior in nitrate formation can be 

modeled.

 

7 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO2, SO4

=

, NOx, HNO3, 

and NO3

-

) MESOPUFF-II system and the 6 species RIVAD system (which treats NO and NO2 separately). 

IWAQM recommends using the MESOPUFF-II system with CALPUFF. The RIVAD system is believed to be 

more appropriate 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. 

Appendix H.6 - 333
Overview 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 phase 

oxidation of SO2. 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 water 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 sulfate 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 NOx concentrations in the plume are high and in 

actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO2; 

and (2) lack 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 degree, 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 SO2 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 

CALPUFF 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 applications, the ozone concentrations required for the sulfate formation calculations are 

derived from ambient measurements, although concentrations simulated by regional models can 

be used. 

Appendix H.6 - 334
Overview of the CALPUFF Modeling System 23

Figure 3-2. Observed vs. CALPUFF-predicted 24-hour sulfate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995.

NOx and Secondary Ammonium Nitrate

The MESOPUFF-II chemistry algorithm used in CALPUFF simulates the oxidation of NOx to 

nitric acid and organic nitrates (both gases) by transformation rates that depend on NOx

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 between 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 

concentration 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 representing nitrate 

chemistry. Potential situations where this assumption may not be correct, however, include (1) 

plumes with high concentrations of NOx that deplete the ambient ozone and thus limit the 

[SO4] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Bridger IM PROVE[SO4].Units:ug/m**3 of SO4

CALPUFF Predicted [SO4]. Units: ug/m**3 of SO4

Appendix H.6 - 335
Overview of the CALPUFF Modeling System 24

transformation of NOx 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 NOx 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 observational 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 ammonia 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 extinction 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 considered (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 estimated 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 

concentrations 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) or 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 increased 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 

measurements, 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, 

Appendix H.6 - 336
Overview of the CALPUFF Modeling System 25

so defining the ammonia concentration throughout the Southeast would require extensive 

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 in 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 factor 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)

[NO3] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

Bridger IM PROVE[NO3].Units:ug/m**3 of NO3

CALPUFF Predicted [NO3]. Units: ug/m**3 of NO3

Appendix H.6 - 337
Overview of the CALPUFF Modeling System 26

Figure 3-3b. Observed vs. CALPUFF-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 National 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 significant localized, short-term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of 

the 1.4 μg/m3 concentration assumed for natural conditions by the EPA, which means that current 

fossil fuel burning 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 (

CALPUFF Separated Repartition 

CALPUFF Separated Repartition 

Factor 1

Factor 2 Factor 3

Factor 4

Factor 2

Factor 3

Factor 4

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 

or 3

Factor 2

Factor 3

Factor 4

Appendix H.6 - 338
Overview of the CALPUFF Modeling System 27

sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 dv) 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 capabilities that do 

exist include the following. 

First, PM10 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 analyses 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 

PM10 emissions condense into organic PM2.5 particles. The size of this organic fraction 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, parameterized 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, according to this rule, isoprene (C5H8) 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 PM10 and with biogenic VOCs, although the soundness of 

concentration estimates by these approaches when modeling a plume 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 extended 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 emissions of butane and ethane during petroleum processing 

 

8 Recent research 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 description of this module.

Appendix H.6 - 339
Overview 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 such 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 

photochemical 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 PM10 emissions, though.

3.2.3 Regional Haze

Calculation of the impact of the 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:

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] +

 + 0.6[Coarse Mass] + 10[EC] + bRay (3-1)

The concentrations, in square brackets, are in μg/m3 and bext is in units of Mm-1. The Rayleigh 

scattering term (bRay) 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 the 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 alone) 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 IMPROVE 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 (HI) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (bext/10) (3-2)

where HI is in units of deciviews (dv) and bext 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. 

Appendix H.6 - 340
Overview 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 extinction against which the 

increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processor contains seven techniques for computing the 

change in light extinction due to a source or group of sources (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 average 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 coefficient 

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 (EPA, 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 this 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 concentrations would have to be used 

throughout the year (unless potential refinements to those default values resulted in 

concentrations that vary during the year). Hourly background extinction is then calculated using 

these concentrations and hourly, site-specific f(RH) from a 1993 IWAQM curve (a different one 

Appendix H.6 - 341
Overview 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, except 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 background 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 ruling 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 prime”.

Refined Estimates of Extinction and Natural Background Visibility

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).

Refinements 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 varies 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.

Appendix H.6 - 342
Overview 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 measurements

- 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 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 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 newly 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. 

 

Appendix H.6 - 343
VISTAS’ Common Modeling Protocol 32

4. VISTAS’ COMMON MODELING PROTOCOL

4.1 Overview of Common Modeling Approach

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 visibility 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 (SO2, NOx, PM, 

and in certain cases VOC and NH3) that “may reasonably be anticipated 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 from individual 

sources exceeds a contribution threshold) for SO2, NOx, 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 NH3 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 SO2, 

NOx, or PM is subject to BART: 

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d 

for SO2 > 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.

Appendix H.6 - 344
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 domain 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 assessment 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 to 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 skipped 

and the analysis may proceed directly to the finer grid modeling analysis, which is described in 

Section 4.4. 

Appendix H.6 - 345
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 evaluating 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 exception 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 impacts 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 improvement 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 in 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 for 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 control 

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 control 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, NH3, and PM

Volatile Organic Compounds 

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. (Separately, condensable organic carbon can be 

calculated from PM10.) 

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-BART) 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 emissions 

Appendix H.6 - 346
VISTAS’ 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 VISTAS 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 impact 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 NH3 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 address ammonia (NH3) 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 NH3

emissions from point sources (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 NH3 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 NH3 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 BART-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 

SO2 and NOx under the CAIR rule meet the BART requirements, but these EGUs may still be 

subject to BART for primary PM. To determine the potential impacts of PM from EGU and nonEGU 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 will be reported at the same time as results for VOC and NH3. 

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 protocol. 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 resolution meteorological fields provided by VISTAS. In some 

Appendix H.6 - 347
VISTAS’ 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 anticipate 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 question.

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 protocol 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 source-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 

meteorological and dispersion options. A pre-computed set of meteorological files and a predefined 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 screening 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)

Appendix H.6 - 348
VISTAS’ Common Modeling Protocol 37

- 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 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 threedimensional meteorological files will be available on external hard drives to the States and other 

parties.

The initial procedure to determine if a BART-eligible source is subject to BART uses the precomputed 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 simulations 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 (which 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 large 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 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. 

Appendix H.6 - 349