# Appendix A

Annual 2002 and 2018 Point Source Emissions in Tons in

Minnesota (MRPO) 

# Case for

Minnesota by StackCOUNTY FACID STKID FAC_NAMENOX SO2 VOC PM25 NH3 COUNTY FACID STKID FAC_NAMENOX SO2 VOC PM25 NH3Becker ORIS700127 41 GENERIC UNIT213 0 12Beltrami ORIS700227 41 GENERIC UNIT213 0 12Beltrami ORIS7947 41 Solway CT50Benton ORIS800127 41 GENERIC UNIT1101Blue_Earth 2701300015 SV001 NSP dba Xcel Energy - Key City 299 2216 Blue_Earth ORIS900127 41 GENERIC UNIT543 851 18 110 16Blue_Earth 2701300015 SV002 NSP dba Xcel Energy - Key City 307 3416Blue_Earth 2701300015 SV004 NSP dba Xcel Energy - Key City30Blue_Earth 2701300015 SV005 NSP dba Xcel Energy - Key City20Blue_Earth 2701300015 SV006 NSP dba Xcel Energy - Key City30Blue_Earth 2701300015 SV007 NSP dba Xcel Energy - Key City20Blue_Earth 2701300078 GN001 Lake Crystal Utilities Commiss2Brown 2701500008 GN001 Sleepy Eye Public Utility20Brown 2701500008 42 Sleepy Eye Public Utility12Brown 2701500010 SV001 New Ulm Public Utilities-Munic71 0Brown 2701500010 SV002 New Ulm Public Utilities-Munic30Brown 2701500010 SV003 New Ulm Public Utilities-Munic 452 0Brown 2701500014 GN001 Springfield Power Plant2Carver 2701900010 SV001 Great River Energy - St Bonifa3 2Carver 2701900010 SV002 Great River Energy - St Bonifa3 2Carver 2701900059 SV001 MMPA - Minnesota River Station 20Clay ORIS1978 41 Hawley5Cook 2703100001 SV001 Minnesota Power - Taconite Har 552 818 2 35 Cook 2703100001 SV001 Minnesota Power - Taconite Har 381 669 6 58 5Cook 2703100001 SV002 Minnesota Power - Taconite Har 1,090 1,260 4 55 0 Cook 2703100001 SV002 Minnesota Power - Taconite Har 412 681 5 51 5Cook 2703100001 SV003 Minnesota Power - Taconite Har 665 1,030 3 46 Cook 2703100001 SV003 Minnesota Power - Taconite Har 406 673 5 51 5Cottonwood 2703300019 GN001 Mountain Lake Municipal Utilit 33Dakota 2703700003 SV001 NSP dba Xcel Energy - Black Do 6,460 3,070 18 224 1 Dakota 2703700003 SV001 NSP dba Xcel Energy - Black Do 9,365 7,530 23 221 19Dakota 2703700003 SV020 NSP dba Xcel Energy - Black Do 15 217 2 60 0 Dakota 2703700003 SV020 NSP dba Xcel Energy - Black Do 343 0 11Dakota 2703700015 SV001 NSP dba Xcel Energy - Inver Hi 25001Dakota 2703700015 SV001 NSP dba Xcel Energy - Inver Hi90Dakota 2703700015 SV002 NSP dba Xcel Energy - Inver Hi 14000Dakota 2703700015 SV002 NSP dba Xcel Energy - Inver Hi90Dakota 2703700015 SV003 NSP dba Xcel Energy - Inver Hi 23001Dakota 2703700015 SV003 NSP dba Xcel Energy - Inver Hi90Dakota 2703700015 SV004 NSP dba Xcel Energy - Inver Hi 21000Dakota 2703700015 SV004 NSP dba Xcel Energy - Inver Hi90Dakota 2703700015 SV005 NSP dba Xcel Energy - Inver Hi 21000Dakota 2703700015 SV005 NSP dba Xcel Energy - Inver Hi90Dakota 2703700015 SV006 NSP dba Xcel Energy - Inver Hi 23000Dakota 2703700015 SV006 NSP dba Xcel Energy - Inver Hi90Douglas 2704100004 GN001 Alexandria Light & Power2Douglas 2704100004 GN001 Alexandria Light & Power3Faribault 2704300045 GN001 Wells Public Utilities4 0 0Faribault 2704300045 GN001 Wells Public Utilities4Faribault 2704300045 GN001 Wells Public Utilities81Fillmore 2704500001 GN001 Spring Valley Utilities9 1 1Fillmore 2704500001 GN001 Spring Valley Utilities30Fillmore 2704500029 GN001 Preston Public Utilities5 0 0Fillmore 2704500029 GN001 Preston Public Utilities18Fillmore 2704500029 GN001 Preston Public Utilities30Hennepin 2705300015 SV002 Xcel Energy - Riverside Genera 4,412 2,090 10 148 0 Hennepin 2705300015 SV002 Xcel Energy - Riverside Genera 5910 1 35Hennepin 2705300015 SV003 Xcel Energy - Riverside Genera 8,857 10,811 38 189 0Hennepin 2705301005 ITOTA Woodlake Sanitary Landfill9211Isanti 2705900014 SV001 Great River Energy - Cambridge2 1Itasca 2706100004 SV001 Minnesota Power Inc - Boswell 1,957 4,233 9 123 Itasca 2706100004 SV001 Minnesota Power Inc - Boswell 1,530 3,582 11 70 9Itasca 2706100004 SV003 Minnesota Power Inc - Boswell 4,911 13,280 26 3 0 Itasca 2706100004 SV003 Minnesota Power Inc - Boswell 922 1,188 24 573 21Itasca 2706100004 SV004 Minnesota Power Inc - Boswell 7,656 3,653 4 585 1 Itasca 2706100004 SV004 Minnesota Power Inc - Boswell 4,711 3,215 34 542 29Kanabec 2706500006 GN001 Mora Municipal Utilities500Kandiyohi 2706700005 SV002 Willmar Municipal Utilities112 230050Kandiyohi 2706700005 SV003 Willmar Municipal Utilities191 687 1 17 Kandiyohi 2706700005 SV003 Willmar Municipal Utilities587 1,460 2 44 2Lake 2707500003 SV001 Northshore Mining Co - Silver 1,126 851 5 0 Lake 2707500003 41 Silver Bay Power ORIS10849318 806 3 17 2Lake 2707500003 SV002 Northshore Mining Co - Silver 1,821 1,371 8 0 0 Lake 2707500003 42 Silver Bay Power ORIS10849613 1,544 5 32 5Lake 2707500003 SV105 Northshore Mining Co - Silver6Lake 2707500003 SV114 Northshore Mining Co - Silver7Lake 2707500003 SV261 Northshore Mining Co - Silver22002 Annual Emissions in Tons2018 Annual Emissions in TonsMinnesota Electric Generating UnitsMinnesota Electric Generating UnitsPage 1 of 3COUNTY FACID STKID FAC_NAMENOX SO2 VOC PM25 NH3 COUNTY FACID STKID FAC_NAMENOX SO2 VOC PM25 NH32002 Annual Emissions in Tons2018 Annual Emissions in TonsMinnesota Electric Generating UnitsMinnesota Electric Generating UnitsLe_Sueur 2707900008 ITOTA Interstate Power & Light - Mon2 1Le_Sueur 2707900037 GN001 New Prague Water - Light - Pow 12 1 1Le_Sueur 2707900037 GN001 New Prague Water - Light - Pow 830Le_Sueur 2707900037 GN001 New Prague Water - Light - Pow51McLeod 2708500002 SV002 Hutchinson Utilities Commissio6 01 McLeod 2708500002 SV002 Hutchinson Utilities Commissio 17McLeod 2708500002 SV003 Hutchinson Utilities Commissio 12 01 McLeod 2708500002 SV003 Hutchinson Utilities Commissio 33McLeod 2708500002 SV004 Hutchinson Utilities Commissio 19McLeod 2708500002 SV006 Hutchinson Utilities Commissio6 01McLeod 2708500002 SV007 Hutchinson Utilities Commissio 38McLeod 2708500013 SV001 Glencoe Light & Power Commissi 3McLeod 2708500013 SV003 Glencoe Light & Power Commissi 2 00McLeod 2708500013 SV004 Glencoe Light & Power Commissi 8McLeod 2708500013 SV005 Glencoe Light & Power Commissi 6 01 McLeod 2708500013 SV005 Glencoe Light & Power Commissi 8McLeod 2708500013 SV006 Glencoe Light & Power Commissi 5 01 McLeod 2708500013 SV006 Glencoe Light & Power Commissi 26McLeod 2708500013 SV011 Glencoe Light & Power Commissi 5 00 McLeod 2708500013 SV011 Glencoe Light & Power Commissi 35McLeod 2708500013 SV012 Glencoe Light & Power Commissi 4 00 McLeod 2708500013 SV012 Glencoe Light & Power Commissi 450McLeod 2708500034 SV002 Hutchinson Utilities Commissio 25001McLeod 2708500034 SV002 Hutchinson Utilities Commissio2McLeod 2708500034 SV004 Hutchinson Utilities Commissio20Martin 2709100007 SV001 Interstate Power & Light - Fox100000Martin 2709100007 SV002 Interstate Power & Light - Fox71 14 5 29 1Martin 2709100007 SV003 Interstate Power & Light - Fox3 1Martin 2709100009 SV004 Fairmont Power Plant14102Martin 2709100009 SV004 Fairmont Power Plant5Martin 2709100009 SV005 Fairmont Power Plant14101Martin 2709100009 SV005 Fairmont Power Plant5Martin 2709100009 SV007 Fairmont Power Plant20 0Martin 2709100046 GN001 Truman Public Utilities13Meeker 2709300001 GN001 Litchfield Public Utilities Co61Meeker 2709300001 GN001 Litchfield Public Utilities Co16Mille_Lacs 2709500011 GN001 Princeton Public Utilities Com10 1 1Mille_Lacs 2709500011 41 Princeton Public Utilities Com23Mower 2709900001 SV001 Austin Utilities - NE Power St396 2,001 2 6 0 Mower 2709900001 SV001 Austin Utilities - NE Power St397 839 2 14 2Mower 2709900048 SV001 Great River Energy - Pleasant15007Mower 2709900048 SV001 Great River Energy - Pleasant1001Mower 2709900048 SV002 Great River Energy - Pleasant8004Mower 2709900048 SV002 Great River Energy - Pleasant801Mower 2709900048 SV003 Great River Energy - Pleasant22 5 0 34 Mower 2709900048 SV003 Great River Energy - Pleasant1101Olmsted 2710900005 SV001 Olmsted Waste-to-Energy Facili 55 33 7 5Olmsted 2710900005 SV002 Olmsted Waste-to-Energy Facili 57 34 8 5Olmsted 2710900005 SV004 Olmsted Waste-to-Energy Facili10 0Olmsted 2710900011 SV001 Rochester Public Utilities - S48 69 0 5 Olmsted 2710900011 SV001 Rochester Public Utilities - S764 1,087 2 20 2Olmsted 2710900011 SV002 Rochester Public Utilities - S124 185 0 11 Olmsted 2710900011 SV002 Rochester Public Utilities - S795 1,133 2 20 2Olmsted 2710900011 SV003 Rochester Public Utilities - S192 873 1 11 Olmsted 2710900011 SV003 Rochester Public Utilities - S387 1,128 51 572 306Olmsted ORIS54262 43 Saint Marys Hospital Power Pla2Olmsted 2710900020 SV001 Rochester Public Utility - Cas10 00Olmsted 2710900020 SV005 Rochester Public Utility - Cas3 00Olmsted 2710900020 SV006 Rochester Public Utility - Cas2 00Otter_Tail 2711100002 SV001 Otter Tail Power Co - Hoot Lak 192 210 0 14 1Otter_Tail 2711100002 SV002 Otter Tail Power Co - Hoot Lak 1,794 2,821 9 72 Otter_Tail 2711100002 SV002 Otter Tail Power Co - Hoot Lak 1,203 3,850 11 103 10Otter_Tail 2711100002 SV005 Otter Tail Power Co - Hoot Lak0Ramsey 2712300012 SV001 Xcel Energy - High Bridge Gene 5,612 3,806 16 205 Ramsey 2712300012 SV001 Xcel Energy - High Bridge Gene 6911 1 41Ramsey 2712300698 ITOTA Zuel Co Inc4Redwood 2712700038 SV001 Redwood Falls Public Utilities3 0 0Redwood ORIS7982 41 South Generation10Redwood ORIS7982 42 South Generation10Redwood ORIS7982 43 South Generation11Rice 2713100017 ITOTA AAF/McQuay International - Far 417St_Louis 2713700013 SV001 Minnesota Power Inc - Laskin E 2,172 1,605 7 105 St_Louis 2713700013 SV001 Minnesota Power Inc - Laskin E 771 1,814 10 181 8St_Louis 2713700027 SV001 Hibbing Public Utilities Commi 132 127 0 4 St_Louis 2713700027 SV001 Hibbing Public Utilities Commi 290 595 1 18 1St_Louis 2713700027 SV002 Hibbing Public Utilities Commi 55 51 0 2 St_Louis 2713700027 SV002 Hibbing Public Utilities Commi 290 595 1 18 1St_Louis 2713700027 SV003 Hibbing Public Utilities Commi 96 78 0 3 St_Louis 2713700027 SV003 Hibbing Public Utilities Commi 388 794 1 24 1St_Louis 2713700028 SV002 Virginia Dept of Public Utilit62 89 0 4 St_Louis 2713700028 SV002 Virginia Dept of Public Utilit356 730 1 17 1Page 2 of 3COUNTY FACID STKID FAC_NAMENOX SO2 VOC PM25 NH3 COUNTY FACID STKID FAC_NAMENOX SO2 VOC PM25 NH32002 Annual Emissions in Tons2018 Annual Emissions in TonsMinnesota Electric Generating UnitsMinnesota Electric Generating UnitsSt_Louis 2713700028 SV003 Virginia Dept of Public Utilit257 297 1 9 St_Louis 2713700028 SV003 Virginia Dept of Public Utilit356 730 1 17 1St_Louis 2713700028 SV004 Virginia Dept of Public Utilit91 1 0Scott 2713900010 SV001 NSP dba Xcel Energy - Blue Lak 6 00 Scott 2713900010 41 NSP dba Xcel Energy - Blue Lak 1301Scott 2713900010 SV002 NSP dba Xcel Energy - Blue Lak 7 00 Scott 2713900010 42 NSP dba Xcel Energy - Blue Lak 1301Scott 2713900010 SV003 NSP dba Xcel Energy - Blue Lak 7 00Scott 2713900010 SV004 NSP dba Xcel Energy - Blue Lak 7 00Sherburne 2714100004 SV001 NSP - Sherburne Generating Pla 14,372 14,762 98 1,306 2 Sherburne 2714100004 SV001 NSP - Sherburne Generating Pla 11,548 9,658 102 10,116 88Sherburne 2714100004 SV002 NSP - Sherburne Generating Pla 11,506 11,924 58 841 1 Sherburne 2714100004 SV002 NSP - Sherburne Generating Pla 8,284 5,940 65 809 56Sherburne 2714100004 SV046 NSP - Sherburne Generating Pla2Sherburne 2714100047 GN001 Elk River Municipal Utilites3 0 0Stearns 2714500002 GN001 Melrose Public Utilities3 0 0Steele 2714700002 SV002 Owatonna Public Utilities - Po90 0 0Swift 2715100006 SV002 Benson Municipal Utilities1Waseca 2716100027 GN001 Janesville Municipal Utilities5Washington 2716300005 SV001 Xcel Energy - Allen S King Gen 11,862 23,241 81 417 Washington 2716300005 SV001 Xcel Energy - Allen S King Gen 2,067 2,481 71 451 34Washington 2716300005 SV008 Xcel Energy - Allen S King Gen90 0 0Washington 2716300087 SV001 LSP Cottage Grove Cogeneration 181151Washington 2716300087 SV001 LSP Cottage Grove Cogeneration 71 0 4Washington 2716300087 SV002 LSP Cottage Grove Cogeneration 60 20Washington 2716300087 SV003 LSP Cottage Grove Cogeneration 61 20Watonwan 2716500019 GN001 Madelia Light & Power Standby20Watonwan 2716500019 GN001 Madelia Light & Power Standby 520Wright 2717100016 GN001 Delano Municipal Utilities5 0 0Wright 2717100020 SV001 Great River Energy - Maple Lak2 1Page 3 of 3COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Aitkin 27001SW145 RPU00 Aitkin Area1616Aitkin 27001SW150 RPU00 Hickory Grove1212Anoka 2700300004 BO001 ISD 14 - Stevenson Elementary20 020 0Anoka 2700300005 ITOTA Minncast Inc8 110 2Anoka 2700300010 BO002 Anoka Metro Regional Treatment2020Anoka 2700300019 SI003 Onan Corp2 0 12 0 1Anoka 2700300019 SV423 Onan Corp3 0 03 0 0Anoka 2700300020 SV026 United Defense LP41004 100Anoka 2700300020 SV027 United Defense LP41004 100Anoka 2700300031 ITOTA Schwartzman Co Inc147Anoka 2700300037 BO002 Unity Hospital40 040 0Anoka 2700300073 BO002 Minnesota Correctional - Lino41 041 0Anoka 2700300081 ITOTA Honeywell - Commercial Aviatio2 0 513Anoka 2700300136 ITOTA Determan Brownie Inc1 4 10272Anoka 2700300156 SI001 Federal Cartridge Co - Anoka20 020 0Anoka 2700300156 SI002 Federal Cartridge Co - Anoka30003 000Anoka 2700300171 ITOTA Gale's Auto Body Shop3 1Anoka 2700300182 ITOTA Waldoch Collision Center32Anoka 2700300205 ITOTA Kurt Manufacturing Co - Die Ca5216 7227Anoka 2700300210 ITOTA Lexington Manufacturing Inc46Anoka 2700300222 IOTHE Accent Home & Kitchen Center I53Anoka 2700300224 ITOTA Arrow Cryogenics Inc04 416 7Anoka 2777700163 ITOTA Commercial Asphalt Co - Plant13 3 12 317 4 17 4Anoka 2777700284 ITOTA Commercial Asphalt Co - Plant12 1 13 316 1 18 4Anoka 27003SW008 RPU00 Johnson Bros/Lochness5959Anoka 27003SW028 RPU00 Waste Disposal Engineering55Anoka 27003SW028 RPU00 Waste Disposal Engineering1 0 20 01 0 20 0Anoka 27003SW043 RPU00 Oak Grove66Anoka 27003SW043 RPU00 Oak Grove1 0 23 01 0 23 0Anoka 27003SW047 RPU00 East Bethel7474Anoka 27003SW094 RPU00 Anoka Municipal Regional1616Anoka 27003SW094 RPU00 Anoka Municipal Regional3 1 63 13 1 63 1Anoka 27003XANE RPU00 ANOKA COUNTY-BLAINE ARPT(JAN10 0 21Anoka 27003XANE RPU00 ANOKA COUNTY-BLAINE ARPT(JAN2 0 12 6 2 0 13 6Becker 2700500035 NF001 Sherbrooke Asphalt Inc - Nonme4 0 03 0 0Becker 2711100016 SV001 Viking Gas Transmission - Fraz11825 311825 3Becker 2711100016 SV002 Viking Gas Transmission - Fraz12125 312125 3Becker 2711100016 SV003 Viking Gas Transmission - Fraz11424 311424 3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsPage 1 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsBecker 2711100016 SV006 Viking Gas Transmission - Fraz25 1 0 1 21 1 0 1Becker 2777700304 ITOTA Sherbrooke Asphalt Inc16 11 3 222 15 4 3Becker 27005POTW1RPS00 Detroit Lakes WWTP1414Becker 27005SW099 RPU00 Becker County44Becker 27005SW099 RPU00 Becker County1 0 16 01 0 16 0Beltrami 2700700006 ITOTA Bemidji State University90 251Beltrami 2700700010 ITOTA North Country Regional Hospita2Beltrami 2700700019 FS001 Northwood Panelboard Co1216Beltrami 2700700019 SI001 Northwood Panelboard Co22912242Beltrami 2700700019 SI002 Northwood Panelboard Co22912242Beltrami 2700700019 SI003 Northwood Panelboard Co82 4290 42Beltrami 2700700019 SI007 Northwood Panelboard Co10 1 2 1 11 1 2 1Beltrami 2700700019 SI008 Northwood Panelboard Co10 1 2 1 10 1 2 1Beltrami 2700700019 SI012 Northwood Panelboard Co8 8208 821Beltrami 2700700019 SI016 Northwood Panelboard Co20 610 6Beltrami 2700700033 NF001 Gesell Concrete Products - Non202Beltrami 2705700005 FS006 Potlatch - Bemidji118160Beltrami 2705700005 FS007 Potlatch - Bemidji136184Beltrami 2705700005 SI001 Potlatch - Bemidji12 0 1 4 12 0 0 4Beltrami 2705700005 SI002 Potlatch - Bemidji12 0 1 4 12 0 0 4Beltrami 2705700005 SI003 Potlatch - Bemidji12 0 1 4 12 0 0 4Beltrami 2705700005 SI004 Potlatch - Bemidji12 0 1 4 12 0 0 4Beltrami 2705700005 SI005 Potlatch - Bemidji10 4910 49Beltrami 2705700005 SI006 Potlatch - Bemidji10 4910 49Beltrami 2705700005 SI007 Potlatch - Bemidji10 4910 49Beltrami 2705700005 SI008 Potlatch - Bemidji24Beltrami 2705700005 SI011 Potlatch - Bemidji46Beltrami 2705700005 SI012 Potlatch - Bemidji24Beltrami 2705700005 SI019 Potlatch - Bemidji7 1 17 1 1Beltrami 2705700005 SI021 Potlatch - Bemidji90 19 74 399 20 81 4Beltrami 2705700005 SI022 Potlatch - Bemidji711711Beltrami 2705700005 SI023 Potlatch - Bemidji6262Beltrami 2777700059 ITOTA Thorson Inc - Boeing Asphalt P2200 3310Beltrami 2777700150 ITOTA Mark Sand & Gravel Acquisition1210 2311Beltrami 2777700188 ITOTA Thorson Inc - Boeing Asphalt P11 11 3 215 16 3 2Beltrami 2777700307 ITOTA Thorson Inc - Asphalt Plant 13711 3911Beltrami 27007POTW1RPS00 Bemidji WWTP1414Beltrami 27007SW031 RPU00 Kummer3939Beltrami 27007SW051 RPU00 Eighty-Acre44Page 2 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsBeltrami 27007XBJI RPU00 BEMIDJI-BELTRAMI COUNTY1 0 02 0 1Beltrami 27007XBJI RPU00 BEMIDJI-BELTRAMI COUNTY02 103 1Beltrami 27007XBJI RPU00 BEMIDJI-BELTRAMI COUNTY02 103 1Beltrami 27007XBJI RPU00 BEMIDJI-BELTRAMI COUNTY2 0 22 0 2Benton 2700900006 SV002 Quebecor World St Cloud512 0516 0Benton 2700900027 ITOTA Warren Wood Inc10 210 3Benton 2700900029 ITOTA X-cel Optical Co42Benton 2700900031 ITOTA Wilkie Sanderson4 02 0Benton 2700900035 IOTHE Bauerly Bros Inc - Nonmetallic169Benton 2700900035 NF001 Bauerly Bros Inc - Nonmetallic83 5 775 6 7Benton 2700900040 ITOTA Phillips Recycling Systems Inc53Benton 2700900041 ITOTA Crystal Cabinet Works Inc - Sa0202Benton 2702500038 ITOTA Bauerly Bros Inc - Plant 53 1 9 2 4 2 12 3Benton 2777700006 SV001 Bauerly Bros Inc - Plant 29 2 9 4 12 2 12 5Benton 2777700007 ITOTA Bauerly Bros Inc - Plant 318 28 7 525 39 10 6Benton 2777700008 SV001 Bauerly Bros Inc - Plant 18 2 8 3 12 2 12 5Benton 2777700058 ITOTA Thorson Inc - Asphalt Plant 418 25 5 325 33 7 4Benton 2777700270 ITOTA Bauerly Bros Inc - Plant 410 22 4 313 30 6 4Big_Stone 2701100017 ITOTA Bituminous Paving Inc - Barber6411 8611Big_Stone 2701100026 IOTHE Ortonville Stone Co - Nonmetal84Big_Stone 27011SW096 RPU00 Big Stone County1010Blue_Earth 2701300006 SV009 ADM - Mankato89Blue_Earth 2701300006 SV018 ADM - Mankato3239Blue_Earth 2701300006 SV032 ADM - Mankato153 143 2145 136 2Blue_Earth 2701300006 SV033 ADM - Mankato40 040 0Blue_Earth 2701300006 SV034 ADM - Mankato41 041 0Blue_Earth 2701300007 SI001 Cenex Harvest States Coop - Ma131 0 0 131 0 0Blue_Earth 2701300007 SI002 Cenex Harvest States Coop - Ma42 0 6 3 1 42 0 6 3 1Blue_Earth 2701300007 SI005 Cenex Harvest States Coop - Ma34Blue_Earth 2701300007 SI009 Cenex Harvest States Coop - Ma2020Blue_Earth 2701300007 SI016 Cenex Harvest States Coop - Ma30 030 0Blue_Earth 2701300007 SI019 Cenex Harvest States Coop - Ma33 2 1 3 28 2 1 3Blue_Earth 2701300009 ITOTA Minnesota State University - M17 36 0 19 201Blue_Earth 2701300048 ITOTA River Bend Asphalt Co - Cedar3100 5210Blue_Earth 2701300072 ITOTA Jones Metal Products Inc05 518 9Blue_Earth 2701300073 ITOTA Katolight Corp14 34 0 10 8Blue_Earth 2701300075 IOTHE Industrial Fabrication Service32Blue_Earth 2701300077 ITOTA Hiniker Co010 7118 12Blue_Earth 2701300079 ITOTA City of Mankato - Central Gara23 1 1 1 13 1 1 0Page 3 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsBlue_Earth 2701300091 NF001 Southern MN Constr Co Inc - No53 3 548 4 4Blue_Earth 27013POTW3RPS00 Mankato WWTP921921Blue_Earth 27013SW064 RPU00 Mankato Public Works44Blue_Earth 27013SW087 RPU00 Blue Earth/Ponderosa116116Blue_Earth 27013SW113 RPU00 Hansen1212Blue_Earth 27013XMKT RPU00 MANKATO REGIONAL1063 1063Brown 2701500007 SV003 Ochs Brick Co20 21 3 1025 31 4 14Brown 2701500009 SI001 Kraft Foods - New Ulm3000 3000Brown 2701500009 SI002 Kraft Foods - New Ulm3000 3000Brown 2701500027 ITOTA Valley Asphalt Products Inc -7311 9311Brown 2701500031 ITOTA Anderson Custom Procesing Inc419421Brown 2701500032 ITOTA Firmenich Inc0202Brown 2701500055 IOTHE MR Paving & Excavating Inc - N95Brown 2701500055 NF001 MR Paving & Excavating Inc - N3 0 03 0 0Brown 2705301017 NF001 Northern Con-Agg Inc - Nonmeta7 0 16 0 1Brown 27015POTW3RPS00 New Ulm WWTP4949Brown 27015SW089 RPU00 Brown County2727Carlton 2701700002 SI002 Sappi Cloquet LLC12321541Carlton 2701700002 SV002 Sappi Cloquet LLC149 5 01 195 6 01Carlton 2701700002 SV003 Sappi Cloquet LLC112 79 14 1 1 123 87 16 1 1Carlton 2701700002 SV004 Sappi Cloquet LLC72 0 9 5 11 77 0 9 5 12Carlton 2701700002 SV005 Sappi Cloquet LLC217 91 27 6 1 239 100 29 6 1Carlton 2701700002 SV006 Sappi Cloquet LLC399 6 7524 8 9Carlton 2701700002 SV010 Sappi Cloquet LLC1317Carlton 2701700002 SV029 Sappi Cloquet LLC223 0 14 293 1 25Carlton 2701700002 SV036 Sappi Cloquet LLC3739Carlton 2701700002 SV037 Sappi Cloquet LLC3739Carlton 2701700002 SV038 Sappi Cloquet LLC2638Carlton 2701700004 SI002 MCF - Moose Lake20 020 0Carlton 2701700004 SI003 MCF - Moose Lake20 020 0Carlton 2701700004 SI015 MCF - Moose Lake20Carlton 2701700004 SI022 MCF - Moose Lake20Carlton 2701700006 SI002 USG Interiors Inc - Cloquet58Carlton 2701700006 SI003 USG Interiors Inc - Cloquet101 6 0 101 9 0Carlton 2701700006 SI004 USG Interiors Inc - Cloquet91609 180Carlton 2701700006 SI005 USG Interiors Inc - Cloquet58Carlton 2701700006 SI006 USG Interiors Inc - Cloquet2020Carlton 2701700006 SI007 USG Interiors Inc - Cloquet2020Carlton 2701700006 SI008 USG Interiors Inc - Cloquet3030Page 4 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsCarlton 2701700006 SI014 USG Interiors Inc - Cloquet20 130 1Carlton 2701700006 SI017 USG Interiors Inc - Cloquet1410 6Carlton 2701700006 SI018 USG Interiors Inc - Cloquet0415Carlton 2701700006 SI030 USG Interiors Inc - Cloquet1416Carlton 2701700006 SI040 USG Interiors Inc - Cloquet20 220 3Carlton 2701700006 SI044 USG Interiors Inc - Cloquet1212Carlton 2701700006 SI045 USG Interiors Inc - Cloquet1212Carlton 2701700006 SI046 USG Interiors Inc - Cloquet1213Carlton 2701700006 SI047 USG Interiors Inc - Cloquet1213Carlton 2701700006 SI048 USG Interiors Inc - Cloquet1213Carlton 2701700006 SI049 USG Interiors Inc - Cloquet1213Carlton 2701700006 SI050 USG Interiors Inc - Cloquet1213Carlton 2701700006 SI051 USG Interiors Inc - Cloquet1213Carlton 2701700006 SI052 USG Interiors Inc - Cloquet0202Carlton 2701700006 SI060 USG Interiors Inc - Cloquet1416Carlton 2701700006 SI061 USG Interiors Inc - Cloquet1416Carlton 2701700006 SI065 USG Interiors Inc - Cloquet30 530 7Carlton 2701700006 SI067 USG Interiors Inc - Cloquet10 410 6Carlton 2701700006 SI069 USG Interiors Inc - Cloquet1416Carlton 2701700006 SI070 USG Interiors Inc - Cloquet61 671 8Carlton 2701700006 SI072 USG Interiors Inc - Cloquet33 0 4 8 0 35 0 5 11 0Carlton 2701700006 SI074 USG Interiors Inc - Cloquet0406Carlton 2701700011 SV005 Northern Natural Gas Co - Wren14 0 0 0 12 0 0Carlton 2701700011 SV006 Northern Natural Gas Co - Wren15 0 012 0Carlton 2701700019 SV002 Northern Natural Gas Co - Carl7812 17812 1Carlton 2701700041 NF001 Lundin Construction Co - Nonme11 1 19 1 1Carlton 2701700042 NF001 Glacier Paving Inc - Nonmetall6 0 05 0 0Carlton 2701700043 IOTHE Ulland Brothers Inc North - No32Carlton 2701700043 NF001 Ulland Brothers Inc North - No37 2 333 3 3Carlton 2777700281 NF001 Dresel Aggregate Inc - Nonmeta6 0 05 0 0Carlton 27017SW102 RPU00 Carlton County #21919Carver 2701900001 SI003 Bongards' Creameries014116Carver 2701900001 SI004 Bongards' Creameries014116Carver 2701900001 SI005 Bongards' Creameries014116Carver 2701900001 SI006 Bongards' Creameries014116Carver 2701900001 SI007 Bongards' Creameries49 101 2 2 1 49 101 2 2 1Carver 2701900001 SI008 Bongards' Creameries4130Carver 2701900002 ITOTA Wm Mueller & Sons Inc - Carver10 110 1Carver 2701900020 BO002 United Sugars Corp - Chaska20 020 0Page 5 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsCarver 2701900038 ITOTA Mammoth Inc15 018 0Carver 2701900041 ITOTA Rosemount Inc - Chanhassen Fac12 0Carver 2701900044 ITOTA Lake Region Manufacturing Co126Carver 2701900053 IOTHE Wm Mueller & Sons Inc - Nonmet6Carver 2701900053 NF001 Wm Mueller & Sons Inc - Nonmet4 0 04 0 0Carver 2701900059 SV001 MMPA - Minnesota River Station12 0110 01Carver 27019POTW3RPS00 Norwood Young America WWTP1212Cass 2702100007 IOTHE Hengel Ready Mix & Constr - No2Cass 2702100007 NF001 Hengel Ready Mix & Constr - No202Cass 2777700140 ITOTA Anderson Brothers Construction3200 4210Cass 27021SW033 RPU00 Maple1616Cass 27021SW169 RPU00 Cass County Longville-Remer55Cass 27021SW179 RPU00 Cass County Walker Hackensack77Cass 27021XPWC RPU00 PINE RIVER REGIONAL0 00 0Cass 27021XXVG RPU00 LONGVILLE MUNI0 00 0Cass 27021XY49 RPU00 WALKER MUNI1 01 0Chippewa 2702300009 ITOTA Cargill Inc - Maynard2714Chippewa 2702300034 IOTHE SL - Montevideo Techology Inc53Chippewa 27023POTW1RPS00 Montevideo WWTP1313Chippewa 27023SW052 RPU00 Chippewa County4343Chippewa 27023XMVE RPU00 MONTEVIDEO-CHIPPEWA COUNTY0 10 0 10Chisago 2702500013 ITOTA Zinpro Corp3141Chisago 2702500037 ITOTA Concrete Pump Repair043Chisago 2702500046 BO001 Close Custody Correctional - R30 030 0Chisago 2702500046 GN001 Close Custody Correctional - R202Chisago 27025POTW2RPS00 Chisago Lakes Joint STC1313Chisago 27025SW072 RPU00 Pine Lane55Chisago 27025SW072 RPU00 Pine Lane1 0 18 01 0 18 0Clay 2702700001 SV001 American Crystal Sugar - Moorh109 107 1 3104 101 0 3Clay 2702700001 SV002 American Crystal Sugar - Moorh98 82 0 393 78 0 3Clay 2702700001 SV003 American Crystal Sugar - Moorh97 101 0 392 96 0 3Clay 2702700001 SV007 American Crystal Sugar - Moorh122 11 0 122 13 0Clay 2702700001 SV008 American Crystal Sugar - Moorh111 12 0 111 14 0Clay 2702700001 SV036 American Crystal Sugar - Moorh0 001 00Clay 2702700008 BO002 Minnesota State University Moo81108 110Clay 2702700014 ITOTA Aggregate Industries Inc - Dil3401 4511Clay 2702700022 SV009 Busch Agricultural Resources -79 444 1 23 1 79 444 1 23 1Clay 2702700022 SV010 Busch Agricultural Resources -911Clay 2702700022 SV011 Busch Agricultural Resources -911Page 6 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsClay 2702700053 ITOTA American Crystal Sugar - Resea4756Clay 2702700063 BO000 ISD 146 - Barnesville High Sch00 100 1Clay 2702700064 NF001 Aggregate Industries Inc - P-919 1 217 1 1Clay 2702700065 IOTHE Northern Improvement Co - Nonm7Clay 2702700066 IOTHE Asplin Inc - Nonmetallic2Clay 2702701006 ITOTA Moorhead WWTP21Clay 2711900070 IOTHE Strata Corp - Nonmetallic42Clay 27027SW034 RPU00 Clay County16301630Clearwater 2702900004 SV001 Great Lakes Gas Transmission -47 1 1 2 42 1 0 2Clearwater 2702900004 SV002 Great Lakes Gas Transmission -385 2 1 5 341 2 1 5Clearwater 2702900004 SV003 Great Lakes Gas Transmission -255 2 1 4 225 2 1 4Cook 2703100002 SI001 Hedstrom Lumber Co Inc - Grand5015 6015Cook 2703100002 SI003 Hedstrom Lumber Co Inc - Grand68Cook 2703100002 SI004 Hedstrom Lumber Co Inc - Grand24Cook 27031SW294 RPU00 Cook County1010Cottonwood 2703300015 BO002 PM Windom61006 100Cottonwood 2703300025 FS017 Ethanol 2000 LLP45Cottonwood 2703300025 SV004 Ethanol 2000 LLP1146 31249 3Cottonwood 2703300025 SV005 Ethanol 2000 LLP1147 21251 2Cottonwood 2703300025 SV012 Ethanol 2000 LLP162 1 0 162 1 0Cottonwood 2703300025 SV018 Ethanol 2000 LLP162 1 0 162 1 0Cottonwood 2703300025 SV020 Ethanol 2000 LLP118 2118 2Cottonwood 27033POTW1RPS00 Windom WWTP1313Cottonwood 27033SW143 RPU00 Cottonwood County4343Crow_Wing 2703500002 SI001 Missota Paper Co20002 000Crow_Wing 2703500002 SI002 Missota Paper Co6 1816 181Crow_Wing 2703500002 SI003 Missota Paper Co46 123 0 944 116 0 9Crow_Wing 2703500002 SI004 Missota Paper Co47 120 0 3 0 45 114 0 3 0Crow_Wing 2703500008 BO000 State of Minnesota Dept of Hum3 1606 28 0 0Crow_Wing 2703500008 BO002 State of Minnesota Dept of Hum51005 100Crow_Wing 2703500031 SI001 Trus Joist - A Weyerhaeuser Bu10 9 0 5 11 10 0 6Crow_Wing 2703500031 SI002 Trus Joist - A Weyerhaeuser Bu551 9561 9Crow_Wing 2703500031 SI003 Trus Joist - A Weyerhaeuser Bu390 11400 11Crow_Wing 2703500031 SI004 Trus Joist - A Weyerhaeuser Bu111111Crow_Wing 2703500031 SI005 Trus Joist - A Weyerhaeuser Bu2 412 41Crow_Wing 2703500037 ITOTA Lakeland Mold Co14 130 18 17Crow_Wing 2703500043 ITOTA Burlington Northern Railroad -7 1 04 1 0Crow_Wing 2703500052 ITOTA Mac Manufacturing Inc6 13 1Crow_Wing 2713500021 NF001 Roberts Sand & Gravel Inc - No4 0 04 0 0Page 7 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsCrow_Wing 2777700002 ITOTA Anderson Brothers Construction10 6 2 1 13 8 3 2Crow_Wing 2777700087 ITOTA Anderson Brothers Construction12 8 3 2 17 11 4 3Crow_Wing 27035POTW1RPS00 Brainerd WWTP4949Crow_Wing 27035SW111 RPU00 Crow Wing County (Old)1212Crow_Wing 27035SW111 RPU00 Crow Wing County (Old)0707Crow_Wing 27035SW181 RPU00 Crosby44Crow_Wing 27035SW243 RPU00 Fifty Lakes Modified22Crow_Wing 27035SW376 RPU00 Crow Wing County7474Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION1 0200Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION10 0 21Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION0 31 0 31Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION0 0Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION201201Dakota 2703700011 FS018 Flint Hills Resources LP - Pin1723Dakota 2703700011 FS019 Flint Hills Resources LP - Pin1520Dakota 2703700011 FS022 Flint Hills Resources LP - Pin1013Dakota 2703700011 FS024 Flint Hills Resources LP - Pin1216Dakota 2703700011 FS027 Flint Hills Resources LP - Pin1418Dakota 2703700011 FS029 Flint Hills Resources LP - Pin911Dakota 2703700011 FS031 Flint Hills Resources LP - Pin1418Dakota 2703700011 FS032 Flint Hills Resources LP - Pin1014Dakota 2703700011 FS035 Flint Hills Resources LP - Pin68Dakota 2703700011 FS036 Flint Hills Resources LP - Pin79Dakota 2703700011 FS039 Flint Hills Resources LP - Pin1013Dakota 2703700011 FS041 Flint Hills Resources LP - Pin2128Dakota 2703700011 FS042 Flint Hills Resources LP - Pin046Dakota 2703700011 FS045 Flint Hills Resources LP - Pin3577Dakota 2703700011 FS048 Flint Hills Resources LP - Pin5813Dakota 2703700011 FS052 Flint Hills Resources LP - Pin1016Dakota 2703700011 FS054 Flint Hills Resources LP - Pin1228Dakota 2703700011 FS055 Flint Hills Resources LP - Pin2113Dakota 2703700011 FS058 Flint Hills Resources LP - Pin1044Dakota 2703700011 FS060 Flint Hills Resources LP - Pin3415Dakota 2703700011 FS062 Flint Hills Resources LP - Pin1210Dakota 2703700011 FS063 Flint Hills Resources LP - Pin82Dakota 2703700011 FS064 Flint Hills Resources LP - Pin213Dakota 2703700011 FS069 Flint Hills Resources LP - Pin920Dakota 2703700011 FS070 Flint Hills Resources LP - Pin1642Dakota 2703700011 FS075 Flint Hills Resources LP - Pin3222Page 8 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsDakota 2703700011 FS077 Flint Hills Resources LP - Pin1717Dakota 2703700011 FS079 Flint Hills Resources LP - Pin1315Dakota 2703700011 FS080 Flint Hills Resources LP - Pin1154Dakota 2703700011 FS083 Flint Hills Resources LP - Pin418Dakota 2703700011 FS085 Flint Hills Resources LP - Pin67Dakota 2703700011 FS087 Flint Hills Resources LP - Pin58Dakota 2703700011 FS088 Flint Hills Resources LP - Pin612Dakota 2703700011 FS091 Flint Hills Resources LP - Pin96Dakota 2703700011 FS093 Flint Hills Resources LP - Pin512Dakota 2703700011 FS095 Flint Hills Resources LP - Pin921Dakota 2703700011 FS096 Flint Hills Resources LP - Pin1610Dakota 2703700011 FS099 Flint Hills Resources LP - Pin86Dakota 2703700011 FS102 Flint Hills Resources LP - Pin58Dakota 2703700011 FS104 Flint Hills Resources LP - Pin66Dakota 2703700011 FS106 Flint Hills Resources LP - Pin42Dakota 2703700011 RPU99 Flint Hills Resources LP - Pin99Dakota 2703700011 SI013 Flint Hills Resources LP - Pin25 0 122 0 1Dakota 2703700011 SI016 Flint Hills Resources LP - Pin1919Dakota 2703700011 SI017 Flint Hills Resources LP - Pin345345Dakota 2703700011 SV004 Flint Hills Resources LP - Pin2085 78 68 1092085 78 68 109Dakota 2703700011 SV017 Flint Hills Resources LP - Pin1919Dakota 2703700011 SV019 Flint Hills Resources LP - Pin729 2088 48 91959 2746 36 119Dakota 2703700011 SV042 Flint Hills Resources LP - Pin279340Dakota 2703700011 SV078 Flint Hills Resources LP - Pin51 362 3Dakota 2703700011 SV094 Flint Hills Resources LP - Pin239291Dakota 2703700011 SV104 Flint Hills Resources LP - Pin1 118 81 126 9Dakota 2703700011 SV125 Flint Hills Resources LP - Pin22 8 27 123 8 29 1Dakota 2703700014 SV004 Northern Natural Gas Co - Farm183183Dakota 2703700014 SV005 Northern Natural Gas Co - Farm273 0273 0Dakota 2703700014 SV007 Northern Natural Gas Co - Farm7001 6001Dakota 2703700016 FS001 Gopher Resource Corp47Dakota 2703700016 FS003 Gopher Resource Corp59Dakota 2703700016 FS004 Gopher Resource Corp59Dakota 2703700016 SV003 Gopher Resource Corp52 1190 1 1476 2337 2 28Dakota 2703700018 ITOTA McNamara Contracting Inc3113 5114Dakota 2703700027 ITOTA Dakota Bulk Terminal Inc0603Dakota 2703700029 ITOTA Pine Bend Paving HMA Plant11 111 1Dakota 2703700032 ITOTA Commercial Asphalt Co - Plant15 1 17 420 2 23 6Dakota 2703700037 GN001 Unisys MACS - Eagan10 1 18 1 1Page 9 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsDakota 2703700038 ITOTA Danner Inc25 9 5 0 31 10 6 0Dakota 2703700041 ITOTA Commercial Asphalt Co - Plant16 1 16 422 2 22 5Dakota 2703700043 SV001 Seneca Wastewater Treatment Pl29 104 139 141 1Dakota 2703700050 ITOTA CF Industries - Pine Bend Ware106Dakota 2703700063 BO000 Minnesota Zoological Garden2 104 180Dakota 2703700064 SV002 NSP dba Xcel Energy - Wescott3030Dakota 2703700066 SI001 Spectro Alloys Corp48Dakota 2703700066 SI006 Spectro Alloys Corp816Dakota 2703700066 SI011 Spectro Alloys Corp24 53141 1051Dakota 2703700070 SV001 Anamax Corp18 39 1 1 0 18 39 1 1 0Dakota 2703700078 BO002 Marigold Foods Inc - Farmingto30 030 0Dakota 2703700080 ITOTA Regina Medical Center40 02Dakota 2703700093 ITOTA West Group Co - Eagan9 1 26 111 1 31 1Dakota 2703700094 GN001 Northwest Airlines Inc - Build5 0 04 0 0Dakota 2703700103 ITOTA Ecolab - Engineering Center24 3236 45Dakota 2703700138 SI001 Pine Bend Electric3 103 11Dakota 2703700138 SI002 Pine Bend Electric29 5 0 1 24 5 0 1Dakota 2703700138 SI003 Pine Bend Electric30 5 0 1 25 5 0 1Dakota 2703700138 SI004 Pine Bend Electric2 002 10Dakota 2703700138 SI005 Pine Bend Electric2 002 10Dakota 2703700171 ITOTA CenterPoint Energy Minnegasco3Dakota 2703700196 BO001 Blue Cross/Blue Shield of Minn20 020 0Dakota 2703700196 GN001 Blue Cross/Blue Shield of Minn2Dakota 2703700196 IOTHE Blue Cross/Blue Shield of Minn2Dakota 2703700205 ITOTA Rosemount Aerospace Inc - Burn25 1 4 3 14 1 2 1Dakota 2703700208 GN001 Northern Tool & Equipment Co4 0 03 0 0Dakota 2703700211 BO002 Dakota Premium Foods LLC - S S30 030 0Dakota 2703700213 ITOTA Menasha Packaging - Lakeville5161Dakota 2703700218 IOTHE Rupp Industries Inc - Preserve311 1Dakota 2703700242 IOTHE LaMettry's Collision Inc - Bur32Dakota 2703700264 ITOTA Bituminous Roadways Inc - 67D2021 3022Dakota 2703700267 NF001 Bury & Carlson Inc - Nonmetall24 2 222 2 2Dakota 2703700268 NF001 Cemstone Products - Nonmetalli4 0 04 0 0Dakota 2703700269 NF001 Bituminous Roadways Inc - Nonm2020Dakota 2703700272 NF001 McNamara Contracting Inc - Non14 1 113 1 1Dakota 2703700280 ITOTA Endres Processing LLC13 0 18 116 0 22 2Dakota 2703700284 ITOTA Skyline Displays Inc - Intl De0402Dakota 2703700285 NF001 Aggregate Industries Inc - Non4 0 04 0 0Dakota 2703700290 ITOTA Rayfo Inc - Clayton012 1021 1Page 10 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsDakota 2703700292 ITOTA Minerals Solutions Inc23Dakota 2703700299 ITOTA Travel Tags - Inver Grove Heig29633475Dakota 2703700302 ITOTA Cenex Harvest States - Inver G3 0 1Dakota 2703700316 GN001 Blue Cross/Blue Shield of MN -3020Dakota 2703700324 ITOTA Twin City Container Inc716104 910Dakota 2703700325 ITOTA Bernco Inc2 41 2Dakota 2777700081 ITOTA Commercial Asphalt Co - Plant8 1 7 1 12 1 9 2Dakota 27037POTW1RPS00 Northfield WWTP3737Dakota 27037POTW3RPS00 Met Council - Rosemount WWTP1313Dakota 27037POTW4RPS00 Met Council - Hastings WWTP2626Dakota 27037POTW6RPS00 Met Council - Empire WWTP13311331Dakota 27037SW016 RPU00 Crosby American (amt. that is2121Dakota 27037SW050 RPU00 Dakhue8282Dakota 27037SW057 RPU00 Freeway277277Dakota 27037XLVN RPU00 AIRLAKE AIRPORT16 31063Dakota 27037XSGS RPU00 SOUTH ST PAUL MUNI-RICHARD E 15 215 2Dodge 2703900004 ITOTA Energy Economics Inc1 31 2Dodge 2703900028 SV004 Al-Corn Clean Fuel1177 51284 6Dodge 2703900028 SV005 Al-Corn Clean Fuel91109 110Dodge 2703900028 SV010 Al-Corn Clean Fuel163 1 0 163 1 0Dodge 2703900028 SV012 Al-Corn Clean Fuel40 041 0Dodge 2703900028 SV017 Al-Corn Clean Fuel2Dodge 2777700071 ITOTA Buffalo Bituminous - Plant 118 15 4 324 21 6 4Dodge 2777700176 ITOTA Buffalo Bituminous Plant 214 18 4 319 24 6 4Dodge 27039POTW5RPS00 Kasson WWTP1212Dodge 27039SW121 RPU00 Dodge County2020Dodge 27039XTOB RPU00 DODGE CENTER0 00 0Douglas 2704100002 ITOTA Northern Food & Dairy Inc16 16 0 218 17 0 2Douglas 2704100020 ITOTA Midcon Asphalt Co - Alexandria3100 3100Douglas 2704100021 SI001 Pope/Douglas Waste Management23 11 3 131 15 4 1Douglas 2704100021 SI002 Pope/Douglas Waste Management21 10 3 129 14 4 1Douglas 2704100027 ITOTA Alexandria Extrusion Co00 2300 30Douglas 2704100032 NF001 Central Specialties Inc - Nonm28 2 225 2 2Douglas 2704100034 NF001 Morical Bros Inc - Nonmetallic7 0 16 0 1Douglas 2704100037 ITOTA TWF Industries Inc - Alexandri6 1010 1Douglas 2777700229 SV100 Central Specialties Inc - Plan7 8 2 3 10 10 3 4Douglas 2777700229 SV200 Central Specialties Inc - Plan9 1 18 1 1Douglas 2777700269 SV100 Central Specialties Inc - Plan9 9 2 3 12 13 3 5Douglas 2777700269 SV200 Central Specialties Inc - Plan9 1 18 1 1Page 11 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsDouglas 2777700288 SI001 Central Specialties Inc - Plan5512 7723Douglas 2777700288 SI002 Central Specialties Inc - Plan8 1 17 1 1Douglas 27041POTW2RPS00 Alexandria Lake Area Sanitary4949Douglas 27041SW029 RPU00 Kluver3030Douglas 27041SW141 RPU00 La Grande88Douglas 27041XAXN RPU00 CHANDLER FIELD1 01 0Douglas 27041XAXN RPU00 CHANDLER FIELD02 102 1Faribault 2704300024 BO001 Seneca Foods Corp - Blue Earth20 020 0Faribault 2704300041 SI004 Corn Plus2224Faribault 2704300041 SI006 Corn Plus2224Faribault 2704300041 SV009 Corn Plus14109 2315118 25Faribault 2704300041 SV010 Corn Plus972 61079 6Faribault 2704300041 SV011 Corn Plus243 2 1 243 2 1Faribault 2704300047 ITOTA Wells Concrete Products Co - P15 121 2Faribault 27043SW069 RPU00 Faribault County4646Faribault 27043XSBU RPU00 BLUE EARTH MUNI01 101 1Fillmore 2704500047 NF001 Pederson Brothers Inc - Nonmet8 0 17 1 1Fillmore 2704500049 FS001 Pro-Corn LLC68Fillmore 2704500049 SV004 Pro-Corn LLC1159 101264 11Fillmore 2704500049 SV011 Pro-Corn LLC152 1 0 152 1 0Fillmore 2704500049 SV017 Pro-Corn LLC132 1 0 132 1 0Fillmore 2704500049 SV018 Pro-Corn LLC87108 810Fillmore 2704500049 SV021 Pro-Corn LLC112 0112 0Fillmore 2704500049 SV022 Pro-Corn LLC5 05 0Fillmore 27045SW049 RPU00 Ironwood99Fillmore 27045XFKA RPU00 FILLMORE COUNTY00 0Freeborn 2704700027 ITOTA Progress Casting Group Inc - A30 2440 30Freeborn 2704700034 SI001 Magellan Pipeline Co LLC - Alb140161Freeborn 2704700035 ITOTA Alamco Wood Products Inc1417Freeborn 2704700050 ITOTA Albert Lea Medical Center3Freeborn 2704700051 ITOTA Lou-Rich Inc110 2217 4Freeborn 2704700055 FS003 Agra Resources Coop dba EXOL57Freeborn 2704700055 FS005 Agra Resources Coop dba EXOL913Freeborn 2704700055 SV007 Agra Resources Coop dba EXOL30159 16 1 31190 17 1Freeborn 2704700055 SV010 Agra Resources Coop dba EXOL182 1 1 182 1 1Freeborn 2704700055 SV016 Agra Resources Coop dba EXOL223 2 1 223 2 1Freeborn 2704700057 IOTHE Ulland Brothers Inc South - No42Freeborn 2704700057 NF001 Ulland Brothers Inc South - No13 1 112 1 1Freeborn 2704700059 SV001 Alliance Pipeline - Albert Lea49 1 1 5 40 1 1 5Page 12 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsFreeborn 2777700289 ITOTA Ulland Brothers Inc - Plant 310 16 4 313 22 6 4Freeborn 27047POTW2RPS00 Albert Lea WWTP614614Freeborn 27047SW085 RPU00 Albert Lea9898Freeborn 27047XAEL RPU00 ALBERT LEA MUNI1 01 0Freeborn 27047XAEL RPU00 ALBERT LEA MUNI02 102 1Freeborn 27047XAEL RPU00 ALBERT LEA MUNI00Goodhue 2704900001 SI009 ADM - Red Wing23Goodhue 2704900001 SI011 ADM - Red Wing1113Goodhue 2704900001 SI020 ADM - Red Wing22 47 1 3 0 22 47 1 3 0Goodhue 2704900002 BO001 Minnesota Correctional Facilit2020Goodhue 2704900005 SV001 NSP dba Xcel Energy - Red Wing264 492264 492Goodhue 2704900005 SV002 NSP dba Xcel Energy - Red Wing259 393259 393Goodhue 2704900007 SV005 USG Interiors Inc - Red Wing5 41 637 59 91Goodhue 2704900007 SV009 USG Interiors Inc - Red Wing198 968 0 10249 1390 0 14Goodhue 2704900009 ITOTA Minnesota Malting Co16 4 0 3 18 5 0 3Goodhue 2704900038 SI001 Red Wing Solid Waste Boiler Fa7 8 17 110 10 23 1Goodhue 2704900038 SI002 Red Wing Solid Waste Boiler Fa7 8 17 110 10 23 1Goodhue 2704900062 SV001 Dairy Farmers of America Inc -8711087110Goodhue 2704900062 SV004 Dairy Farmers of America Inc -1415Goodhue 2704900067 ITOTA Cannon Equipment Midwest2142Goodhue 2704900077 IOTHE Luhman's Construction Co - Non26 0 1Goodhue 2704900077 NF001 Luhman's Construction Co - Non7 0 1Goodhue 2704900078 NF001 Holm Bros Construction - Nonme6 0 05 0 0Goodhue 27049POTW5RPS00 Red Wing WWTP3838Goodhue 27049POTW6RPS00 Zumbrota WWTP1212Goodhue 27049SW157 RPU00 Goodhue Cooperative44Goodhue 27049SW174 RPU00 Goodhue County (Red Wing)2727Goodhue 27049XSYN RPU00 STANTON AIRFIELD1 001 0Grant 27051XY63 RPU00 ELBOW LAKE MUNI1 01 0Hennepin 2700300168 BO002 Minneapolis Water Works - Frid40 040 0Hennepin 2701900018 ITOTA Automated Building Components42Hennepin 2705300002 SV001 Hennepin County Energy Center102 1102 1Hennepin 2705300002 SV004 Hennepin County Energy Center111 1121 1Hennepin 2705300002 SV005 Hennepin County Energy Center81 091 1Hennepin 2705300006 SV008 Smith Foundry10 310 5Hennepin 2705300010 SI031 Northwest Airlines Inc - Mpls/41 041 0Hennepin 2705300010 SI032 Northwest Airlines Inc - Mpls/41 041 0Hennepin 2705300010 SI033 Northwest Airlines Inc - Mpls/30 030 0Hennepin 2705300010 SI034 Northwest Airlines Inc - Mpls/3 3 10 24 4 14 3Page 13 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsHennepin 2705300010 SI046 Northwest Airlines Inc - Mpls/2100 2100Hennepin 2705300010 SI047 Northwest Airlines Inc - Mpls/2100 2100Hennepin 2705300011 SV001 NRG Energy Center Minneapolis78 270 3 7 1 78 270 3 7 1Hennepin 2705300011 SV002 NRG Energy Center Minneapolis115 0 5 2 1 115 0 5 2 1Hennepin 2705300011 SV003 NRG Energy Center Minneapolis134 0 62 134 0 62Hennepin 2705300011 SV007 NRG Energy Center Minneapolis5910 0498 0Hennepin 2705300013 ITOTA Metropolitan Airports Commissi41 4 1 1323217Hennepin 2705300020 SV021 Pechiney Plastic Packaging Inc029053Hennepin 2705300025 BO001 Dayton's Plant20 020 0Hennepin 2705300032 ITOTA Kurt Manufacturing Inc - Quinc03 115 1Hennepin 2705300043 SV003 GAF Building Materials Corp11 711 10Hennepin 2705300048 SV020 ADM Milling - A Flour Mill61006 100Hennepin 2705300061 SI001 Abbott Northwestern Hospital1200Hennepin 2705300061 SI002 Abbott Northwestern Hospital2500 2800Hennepin 2705300061 SI003 Abbott Northwestern Hospital6 15 0 18 25 1 1Hennepin 2705300061 SI004 Abbott Northwestern Hospital13 57 1 3 0 22 97 1 4 0Hennepin 2705300075 ITOTA Veterans Agency Medical Center140 170 0Hennepin 2705300078 FS002 CS McCrossan Inc - Stationary22Hennepin 2705300078 SI001 CS McCrossan Inc - Stationary42 063 0Hennepin 2705300079 ITOTA Honeywell Inc - Military Avion45 023 0Hennepin 2705300089 BO001 Thermo King Corp - Minneapolis30 030 0Hennepin 2705300089 GN001 Thermo King Corp - Minneapolis13 1 111 1 1Hennepin 2705300089 IOTHE Thermo King Corp - Minneapolis2Hennepin 2705300098 ITOTA Hitchcock Industries Inc512 8615 10Hennepin 2705300099 SI001 Prospect Foundry Inc3 215 31Hennepin 2705300099 SI002 Prospect Foundry Inc48Hennepin 2705300099 SI005 Prospect Foundry Inc2 13 1 0Hennepin 2705300099 SI007 Prospect Foundry Inc2030Hennepin 2705300099 SI009 Prospect Foundry Inc3 215 31Hennepin 2705300112 ITOTA 934th Airlift Wing11031 6020Hennepin 2705300120 ITOTA Gannett Offset - Minneapolis270 0383 0Hennepin 2705300121 ITOTA Midwest Asphalt - Eden Prairie3113 4114Hennepin 2705300127 SV019 Owens Corning - Mpls Plant16 83 3 2221 114 5 30Hennepin 2705300149 ITOTA Marshall Field's Distribution116Hennepin 2705300151 ITOTA Caterpillar Paving Products -B121137Hennepin 2705300154 ITOTA Davis - Frost Inc12 123 1Hennepin 2705300167 ITOTA Minneapolis city of - Asphalt2001 2001Hennepin 2705300174 ITOTA Industrial Container Services42 321 2Hennepin 2705300180 ITOTA PGI Companies Inc059070Page 14 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsHennepin 2705300201 ITOTA Nilfisk-Advance Inc47 1712 2Hennepin 2705300235 ITOTA Donaldson Co Inc449 06 0 85 0Hennepin 2705300241 ITOTA General Mills Operations Inc -10 11 3 26621Hennepin 2705300251 SV001 Interplastic Corp - Minneapoli32 044 0Hennepin 2705300267 ITOTA Intermet Co - Minneapolis Plan44 966 12Hennepin 2705300315 ITOTA Bituminous Roadways Inc - Minn1001 2001Hennepin 2705300322 ITOTA Alpha Ceramics Inc2131Hennepin 2705300329 BO000 Methodist Hospital Park Nicoll16 83 1 1 0 28 142 1 2 0Hennepin 2705300343 ITOTA Liberty Carton Co3 105 21Hennepin 2705300365 ITOTA Gaines & Hanson Printing Co145 0154 0Hennepin 2705300370 ITOTA Joyner's Die Casting & Plating6 4 57 5 7Hennepin 2705300376 ITOTA Inno-Flex Corp046055Hennepin 2705300384 SV001 Banta Catalog Group - Minneapo110112Hennepin 2705300384 SV002 Banta Catalog Group - Minneapo111113Hennepin 2705300384 SV007 Banta Catalog Group - Minneapo174188Hennepin 2705300384 SV009 Banta Catalog Group - Minneapo131137Hennepin 2705300399 BO002 Minnesota Veterans Home - Minn20 020 0Hennepin 2705300399 GN001 Minnesota Veterans Home - Minn4 0 03 0 0Hennepin 2705300400 SV001 Covanta Hennepin Energy Resour214 5 3 6 288 7 4 9Hennepin 2705300400 SV002 Covanta Hennepin Energy Resour217 9 9 7 293 11 12 9Hennepin 2705300403 BO000 Mpls Community & Technical Col1 21 2Hennepin 2705300406 ITOTA Nordic Press Inc1417Hennepin 2705300440 ITOTA Progress Casting Group Inc - P10 3 12 27 0 13 3 16 34 0Hennepin 2705300445 SV007 Flying Cloud Sanitary Landfill11Hennepin 2705300467 ITOTA Qwest Communications - Minneap13 0 07 0Hennepin 2705300474 ITOTA Qwest Communications Inc - Ply3 02Hennepin 2705300477 SV003 Ritrama Inc0550114Hennepin 2705300477 SV004 Ritrama Inc0204Hennepin 2705300477 SV005 Ritrama Inc06013Hennepin 2705300483 BO002 Dakota Growers Pasta Co - New61006 100Hennepin 2705300484 ITOTA Cypress Semiconductor (Minneso811 1 0 22 0 30 2 0Hennepin 2705300489 BO002 MCTO - Heywood Garage20 020 0Hennepin 2705300498 SI001 Bureau of Engraving Inc30 030 0Hennepin 2705300602 ITOTA Rosemount Inc - Eden Prairie F9130 5020Hennepin 2705300648 SV003 Anagram International Inc220235Hennepin 2705300649 ITOTA Mentor Urology - Minneapolis116129Hennepin 2705300657 ITOTA Ziegler Inc - Bloomington2451 1230Hennepin 2705300663 SI011 University of Minnesota - Camp2 0 0Hennepin 2705300695 BO001 Wells Fargo Home Mortgage2020Page 15 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsHennepin 2705300695 GN001 Wells Fargo Home Mortgage2Hennepin 2705300710 ITOTA Phoenix Packaging Inc225 0230 0Hennepin 2705300778 SV001 Styrotech Inc510Hennepin 2705300778 SV003 Styrotech Inc510Hennepin 2705300790 BO002 NRG Riverside Plant132 1 0 132 1 0Hennepin 2705300792 ITOTA LeJeune Steel Co16 0110 1Hennepin 2705300801 ITOTA Honeywell-Plymouth Operations8 023 0Hennepin 2705300805 BO000 General Mills Inc - Minneapoli0 40 6Hennepin 2705300805 BO001 General Mills Inc - Minneapoli20 020 0Hennepin 2705300810 ITOTA North Memorial Medical Center11160Hennepin 2705300813 GN001 Target Financial Services5 0 04 0 0Hennepin 2705300815 GN001 United Health Technology Cente3020Hennepin 2705300827 ITOTA Mail Handling Inc/Bindery Expr013016Hennepin 2705300830 ITOTA Smyth Companies Inc - Minneapo139146Hennepin 2705300831 ITOTA Shapco Printing Inc043051Hennepin 2705300834 ITOTA GE Osmonics Minnetonka Facilit1 1 36 32 2 63 6Hennepin 2705300847 ITOTA Twin City Die Castings Co - Mp20 220 3Hennepin 2705300850 IOTHE North Star International Truck2Hennepin 2705300854 ITOTA Bystrom Bros Inc013024Hennepin 2705300857 IOTHE Star Exhibits Inc - Shingle Cr2Hennepin 2705300862 ITOTA Process Displays Co048057Hennepin 2705300885 GN001 American Express Financial Cor12 1 110 1 1Hennepin 2705300896 ITOTA Holcim US Inc - Minneapolis Te2Hennepin 2705300904 ITOTA Shriner's Hospital402Hennepin 2705300924 IOTHE LaMettry's Collision Inc - Ede32Hennepin 2705300925 IOTHE LaMettry's Collision Inc - Ric3Hennepin 2705300930 ITOTA Master Collision Group LLC - B42Hennepin 2705300936 BO001 8100 Building20 020 0Hennepin 2705300937 ITOTA Superior Ford Inc03 002 0Hennepin 2705300938 IOTHE Collision Partners Inc dba Col2Hennepin 2705300954 IOTHE ABRA Auto Body & Glass - Brook2Hennepin 2705300956 IOTHE Hopkins Auto Body42Hennepin 2705300964 GN001 City of Minneapolis Traffic Ma6151Hennepin 2705300965 ITOTA 133rd Airlift Wing - MN Air Na13021 7010Hennepin 2705300967 ITOTA Buhler Inc12 301 2Hennepin 2705300969 IOTHE Iten Chevrolet-GEO32Hennepin 2705300977 ITOTA Jim Lupient Harold Chevrolet32Hennepin 2705300994 BO002 Hennepin Technical College Ede30 030 0Hennepin 2705300995 BO001 Hennepin Technical College - B20 020 0Page 16 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsHennepin 2705300999 ITOTA Master Collision Group LLC - P3Hennepin 2705301006 ITOTA MTS Systems Corp04 102 1Hennepin 2705301010 IOTHE CS McCrossan Inc - Nonmetallic179Hennepin 2705301011 IOTHE Midwest Asphalt Corp - Nonmeta116Hennepin 2705301011 NF001 Midwest Asphalt Corp - Nonmeta7 0 16 0 1Hennepin 2705301018 IOTHE Barton Sand & Gravel Co - Nonm137Hennepin 2705301018 NF001 Barton Sand & Gravel Co - Nonm152 10 13138 11 13Hennepin 2705301023 ITOTA Graco Inc - David A Koch Cente29 1316 2Hennepin 2705301034 NF001 Hassan Sand & Gravel Inc - Non2Hennepin 2705301050 SV001 University of MN - Twin Cities75 7 1 1 77 7 1 1Hennepin 2705301050 SV002 University of MN - Twin Cities53 2 8 5 0 56 2 9 5 0Hennepin 2705301050 SV005 University of MN - Twin Cities28 0 3 2 0 30 0 3 2 0Hennepin 2705301050 SV006 University of MN - Twin Cities6 2 16 2 1Hennepin 2705301050 SV007 University of MN - Twin Cities11 1 1 1 11 1 1 1Hennepin 2705301052 ITOTA Robert B Hill Co105Hennepin 2705301053 GN001 Target Stores Corporate Headqu3 0 02 0 0Hennepin 2705301055 GN001 Qwest Communications - 120755 0 04 0 0Hennepin 2705301062 ITOTA Bucks Unpainted Furniture Inc2 0Hennepin 2705301063 ITOTA Duke's Body Shop3117Hennepin 2705301078 ITOTA NAPCO International Inc04 02 0Hennepin 2705301120 BO000 Minneapolis Institute of Arts1 21 2Hennepin 2705301127 ITOTA Retailer Services Corp1213Hennepin 2705301129 GN001 Qwest Communications - GV/Orch2020Hennepin 2777700208 SI004 Intex Corp13 1 112 1 1Hennepin 2777700285 ITOTA Commercial Asphalt Co - Plant24 2 33 833 3 45 11Hennepin 27053POTW1RPS00 Rogers WWTP1313Hennepin 27053SW058 RPU00 Hopkins3333Hennepin 27053SW058 RPU00 Hopkins0707Hennepin 27053SW061 RPU00 Woodlake4040Hennepin 27053SW061 RPU00 Woodlake4 1 74 14 1 74 1Hennepin 27053XFCM RPU00 FLYING CLOUD1 001 0Hennepin 27053XFCM RPU00 FLYING CLOUD3 0 15 73 0 16 8Hennepin 27053XFCM RPU00 FLYING CLOUD00 0Hennepin 27053XMIC RPU00 CRYSTAL00Hennepin 27053XMIC RPU00 CRYSTAL2 0 11 52 0 12 6Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD1678 163 310 82663 259 492 13Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD62 9 6 0 104 15 10 1Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD3 0 24 9 6 0 41 14Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD1 53 1 63Page 17 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsHennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD0 10 0 20Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD372 32 268 12372 32 268 12Houston 2705500020 NF001 Roverud Construction Inc - Non14 1 112 1 1Houston 27055POTW5RPS00 Eitzen WWTP512512Houston 27055SW126 RPU00 Houston County1616Houston 27055XCHU RPU00 HOUSTON COUNTY00Hubbard 2705700002 FS009 Potlatch - Lumbermill - Bemidj34Hubbard 2705700002 FS010 Potlatch - Lumbermill - Bemidj23Hubbard 2705700002 SV005 Potlatch - Lumbermill - Bemidj33 4 3 1 36 4 3 1Hubbard 2705700006 SV001 Lamb Weston/RDO Frozen32 46 5 3 0 34 49 6 3 0Hubbard 27057SW130 RPU00 Pickett55Hubbard 27057SW146 RPU00 Leech Lake5151Hubbard 27057XPKD RPU00 PARK RAPIDS MUNI-KONSHOK FIE01 0Hubbard 27057XPKD RPU00 PARK RAPIDS MUNI-KONSHOK FIE 01 101 1Isanti 2705900001 BO000 Minnesota Extended Treatment O1 83 140Isanti 2705900001 BO002 Minnesota Extended Treatment O20Isanti 2705900023 ITOTA Arrow Tank & Engineering Co27 1312 1Isanti 2705900030 ITOTA RCS Acquisition LLC dba Isanti0313Isanti 2705900031 ITOTA Freeport Finishing Inc68Isanti 2777700273 NF001 County Line Construction - Non17 1 115 1 1Isanti 27059POTW1RPS00 Cambridge WWTP1313Isanti 27059SW129 RPU00 Isanti-Chisago County2626Isanti 27059XCBG RPU00 CAMBRIDGE MUNI01 101 1Itasca 2706100001 SV003 Blandin Paper/Rapids Energy Ce376 43 70 7405 43 77 8Itasca 2706100001 SV031 Blandin Paper/Rapids Energy Ce213 2233 2Itasca 2706100001 SV036 Blandin Paper/Rapids Energy Ce32103 210Itasca 2706100001 SV037 Blandin Paper/Rapids Energy Ce5042150421Itasca 2706100010 SV001 Potlatch - Grand Rapids32 1 1 4 32 1 1 4Itasca 2706100010 SV002 Potlatch - Grand Rapids32 1 1 4 32 1 1 4Itasca 2706100010 SV003 Potlatch - Grand Rapids210 0 210 0Itasca 2706100010 SV004 Potlatch - Grand Rapids200 0 0 200 0 0Itasca 2706100010 SV005 Potlatch - Grand Rapids0606Itasca 2706100039 ITOTA NorthPrint International Inc -169183Itasca 2706100046 ITOTA Bergquist Co - Big Fork210Itasca 2706100059 ITOTA Grand Rapids WWTP7 14 1Itasca 2706100063 NF001 Brink Sand & Gravel Inc - Nonm6 0 05 0 0Itasca 2706100065 NF001 Hawkinson Construction Co - No14 1 113 1 1Itasca 2706199999 SV999 West Mine projected1153 387619Itasca 2706100068 ITOTA Midland Research Center3Page 18 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsItasca 2777700024 ITOTA Hawkinson Construction Co Inc8 8 2 1 11 11 3 2Itasca 2777700112 ITOTA Hawkinson Construction Co Inc10 9 3 2 13 12 4 2Itasca 2777700290 NF001 Wm J Schwartz & Sons Inc - Non302 0 0Itasca 27061SW073 RPU00 Iron Range88Itasca 27061SW135 RPU00 Grand Rapids Area44Itasca 27061SW135 RPU00 Grand Rapids Area1 0 15 01 0 15 0Itasca 27061XGPZ RPU00 GRAND RAPIDS/ITASCA CO-GORDO0 10 0 21Itasca 27061XGPZ RPU00 GRAND RAPIDS/ITASCA CO-GORDO0 11 0 21Itasca 27061XGPZ RPU00 GRAND RAPIDS/ITASCA CO-GORDO10 10Jackson 2777700029 ITOTA Kruse Paving Inc - A-203200 4310Jackson 27063SW101 RPU00 Jackson County1313Jackson 27063XMJQ RPU00 JACKSON MUNI1 01 0Kanabec 27065POTW1RPS00 Mora WWTP1212Kanabec 27065SW017 RPU00 East Central Mixed Mun (kanabe6666Kanabec 27065XJMR RPU00 MORA MUNICIPAL01 101 1Kandiyohi 2706700004 BO000 Willmar Regional Treatment Cen5 26 0 09 45 0 1Kandiyohi 2706700004 BO002 Willmar Regional Treatment Cen192 1 1 192 1 1Kandiyohi 2706700015 SI001 Magellan Pipeline Co LLC - Wil181211Kandiyohi 2706700053 IOTHE Duininck Bros Inc - Nonmetalli116Kandiyohi 2706700053 NF001 Duininck Bros Inc - Nonmetalli17 1 116 1 1Kandiyohi 2706700054 BO001 Ridgewater College20 020 0Kandiyohi 2706700055 IOTHE Central-Allied Enterprises - N2Kandiyohi 2706700058 GN001 Willmar Municipal SW Substatio2Kandiyohi 2777700018 ITOTA Duininck Bros Inc - Port Plant18 13 4 225 18 5 3Kandiyohi 2777700019 ITOTA Duininck Bros Inc - Port Plant13 8 3 2 19 10 4 2Kandiyohi 2777700042 ITOTA Duininck Bros Inc - Port Plant9 5 2 1 12 7 2 1Kandiyohi 2777700287 ITOTA Duininck Bros Inc - Port Plant34 21 7 547 29 10 6Kandiyohi 27067POTW2RPS00 Willmar WWTP513513Kandiyohi 27067SW079 RPU00 Kandiyohi161161Kandiyohi 27067XILL RPU00 WILLMAR MUNI-JOHN L RICE FIELD0 0Kandiyohi 27067XILL RPU00 WILLMAR MUNI-JOHN L RICE FIELD0 11 0 11Kittson 2706900014 SV001 Great Lakes Gas Transmission -32052 228349 2Kittson 2706900014 SV002 Great Lakes Gas Transmission -31456 227752 3Kittson 2706900014 SV003 Great Lakes Gas Transmission -18539 216437 2Kittson 2706900014 SV004 Great Lakes Gas Transmission -12664 311259 3Kittson 2706900014 SV005 Great Lakes Gas Transmission -5102 4102Kittson 2706900015 SV002 Viking Gas Transmission - Humb2515 22515 2Kittson 2706900015 SV003 Viking Gas Transmission - Humb2615 22615 2Kittson 2706900015 SV004 Viking Gas Transmission - Humb117 1117 1Page 19 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsKittson 2706900015 SV005 Viking Gas Transmission - Humb1812 11812 1Kittson 27069SW092 RPU00 Mar-Kit (Anderson-Kittson)2929Kittson 27069SW115 RPU00 Karlstad22Kittson 27069XHCO RPU00 HALLOCK MUNI01 102 1Koochiching 2707100002 SV170 Boise Cascade Corp - Internati710Koochiching 2707100002 SV173 Boise Cascade Corp - Internati4 305 39Koochiching 2707100002 SV177 Boise Cascade Corp - Internati710Koochiching 2707100002 SV178 Boise Cascade Corp - Internati710Koochiching 2707100002 SV179 Boise Cascade Corp - Internati710Koochiching 2707100002 SV183 Boise Cascade Corp - Internati7092Koochiching 2707100002 SV302 Boise Cascade Corp - Internati710Koochiching 2707100002 SV320 Boise Cascade Corp - Internati343 0 8451 0 10Koochiching 2707100002 SV322 Boise Cascade Corp - Internati0 3 7157 1 4 10206Koochiching 2707100002 SV325 Boise Cascade Corp - Internati34Koochiching 2707100002 SV326 Boise Cascade Corp - Internati34Koochiching 2707100002 SV340 Boise Cascade Corp - Internati24 031 0Koochiching 2707100002 SV420 Boise Cascade Corp - Internati104 1 12 7 3 104 1 12 7 3Koochiching 2707100002 SV431 Boise Cascade Corp - Internati331 48 130 15 0 364 60 135 17 0Koochiching 2707100002 SV440 Boise Cascade Corp - Internati25 0 0 5 2 25 0 0 5 2Koochiching 2707100002 SV450 Boise Cascade Corp - Internati41104 110Koochiching 2707100002 SV460 Boise Cascade Corp - Internati21102 110Koochiching 2707100002 SV901 Boise Cascade Corp - Internati12 12 38 113 13 41 1Koochiching 2707100015 SI001 International Bildrite Inc101 21111 31Koochiching 27071POTW1RPS00 North Koochiching WWTP2424Koochiching 27071SW191 RPU00 Koochiching5858Koochiching 27071SW225 RPU00 Northome Modified66Koochiching 27071XINL RPU00 FALLS INTL02 110Koochiching 27071XINL RPU00 FALLS INTL13 104 1Koochiching 27071XINL RPU00 FALLS INTL1113 1Lac_qui_Parle 2707300002 SV016 Ag Processing Inc - Dawson20 420 5Lac_qui_Parle 2707300002 SV017 Ag Processing Inc - Dawson20 420 5Lac_qui_Parle 2707300002 SV052 Ag Processing Inc - Dawson25 17 3 2 1 25 17 3 2 1Lac_qui_Parle 2707300003 ITOTA Municipal Castings Inc22 1 1 9 28 1 2 11Lac_qui_Parle 2707300016 SV002 Associated Milk Producers Inc51 051 0Lac_qui_Parle 2707300016 SV006 Associated Milk Producers Inc30 5430 62Lac_qui_Parle 27073XDXX RPU00 MADISON-LAC QUI PARLE COUNTY0 0Lake 2707500003 SV261 Northshore Mining Co - Silver99 91121 11 3 5Lake 2707500003 SV114 Northshore Mining Co - Silver295 2938296 29 7 38Lake 2707500003 SV105 Northshore Mining Co - Silver307 3039294 29 5 39Page 20 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsLake 2707500003 SV266 Northshore Mining Co - Silver481 2137Lake 2707500005 FS017 Duluth Missabe & Iron Range Ra34Lake 2707500019 SV001 Louisiana-Pacific Corp - Two H17 0 2 4 0 18 0 2 4 0Lake 27075POTW1RPS00 Two Harbors WWTP1212Lake 27075SW140 RPU00 Lake County3434Lake 27075XBFW RPU00 SILVER BAY MUNICIPAL00Lake 27075XTWMRPU00 RICHARD B HELGESON1 01 0Lake_of_the_W27077SW171 RPU00 Lake of the Woods55Lake_of_the_W27077XBDE RPU00 BAUDETTE INTL00Lake_of_the_W27077XBDE RPU00 BAUDETTE INTL01 001 0Le_Sueur 2707900007 ITOTA River Bend Asphalt Co11 10 3 215 13 4 3Le_Sueur 2707900009 SI001 Unimin Minnesota Corp - Kasota3040Le_Sueur 2707900017 SI016 Le Sueur Inc611Le_Sueur 2707900019 SI001 Unimin Minnesota Corp - Le Sue5151Le_Sueur 2707900022 BO002 Seneca Foods Corp - Montgomery51005 100Le_Sueur 2707900034 SI001 DAVISCO International Inc - Le20 120 1Le_Sueur 2707900043 IOTHE Johnson Aggregates - Nonmetall74Le_Sueur 2707900043 NF001 Johnson Aggregates - Nonmetall6 0 16 0 0Le_Sueur 2777700275 ITOTA River Bend Asphalt Co - Plant7 4 1 1 10 5 2 1Le_Sueur 27079SW063 RPU00 MN Sanitation Services88Le_Sueur 27079SW067 RPU00 Tellijohn5555Le_Sueur 27079SW067 RPU00 Tellijohn0404Le_Sueur 27079SW091 RPU00 Sun Prairie77Le_Sueur 27079X12Y RPU00 LE SUEUR MUNI00Lyon 2708300001 ITOTA Farmers Coop Elevator Co - Cot3Lyon 2708300019 ITOTA McLaughlin & Schulz Inc - Plan5 7 1 1 7 10 1 1Lyon 2708300038 FS003 ADM Corn Processing - Marshall1317Lyon 2708300038 FS004 ADM Corn Processing - Marshall22Lyon 2708300038 SV006 ADM Corn Processing - Marshall8 29 810 35 9Lyon 2708300038 SV009 ADM Corn Processing - Marshall3242Lyon 2708300038 SV010 ADM Corn Processing - Marshall1 271 36Lyon 2708300038 SV011 ADM Corn Processing - Marshall4 1 17 25 1 22 3Lyon 2708300038 SV012 ADM Corn Processing - Marshall2 12 2Lyon 2708300038 SV013 ADM Corn Processing - Marshall4465Lyon 2708300038 SV014 ADM Corn Processing - Marshall57Lyon 2708300038 SV016 ADM Corn Processing - Marshall234 432 1 0222 410 1 0Lyon 2708300038 SV019 ADM Corn Processing - Marshall34 0 4 3 1 34 0 4 3 1Lyon 2708300038 SV020 ADM Corn Processing - Marshall30 0 6 3 1 30 0 6 3 1Lyon 2708300046 ITOTA Koch Materials - Marshall Asph12 622 8Page 21 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsLyon 2708300055 NF001 McLaughlin & Schulz Inc - Nonm302 0 0Lyon 2708300056 SV001 Northern Border Pipeline - Com95 3 2 6 79 3 1 6Lyon 2708300057 GN001 Weiner Memorial Medical Center10 1 19 1 1Lyon 27083POTW1RPS00 Marshall WWTP3838Lyon 27083SW023 RPU00 Lyon County140140Lyon 27083XMMLRPU00 MARSHALL MUNI-RYAN FIELD02 104 1Lyon 27083XMMLRPU00 MARSHALL MUNI-RYAN FIELD01 001 1Mahnomen 2708700005 BO000 ISD 432 - Mahnomen Public Scho11 211 2Mahnomen 27087SW122 RPU00 Mahnomen County66Mahnomen 27087X3N8 RPU00 MAHNOMEN COUNTYMarshall 2708900012 SV001 Great Lakes Gas Transmission -143 2 1 4 126 2 1 4Marshall 2708900012 SV002 Great Lakes Gas Transmission -119 2 1 3 105 2 1 3Marshall 2708900026 NF001 M & J Construction Co - Nonmet8 0 17 1 1Marshall 2708900027 NF001 Schenkey Inc - Nonmetallic13 1 112 1 1Martin 2709100003 ITOTA Watonwan Farm Service42Martin 2709100043 SV003 Northern Border Pipeline - Com94 3 2 6 78 3 1 6Martin 2709100057 NF001 W Hodgman & Sons Inc - Nonmeta6 0 16 0 0Martin 2777700027 ITOTA W Hodgman & Sons Inc - Plant 918 11 4 325 15 5 3Martin 27091POTW5RPS00 Fairmont WWTP1414Martin 27091SW076 RPU00 Gofer2929Martin 27091XFRM RPU00 FAIRMONT MUNI1 002 1Martin 27091XFRM RPU00 FAIRMONT MUNI00McLeod 2708500035 BO002 Seneca Foods Corp - Glencoe61006 100McLeod 2708500047 SI001 Waste Management - Spruce Ridg5324 5324McLeod 2708500049 SI006 3M - Hutchinson Tape Manufactu1224McLeod 2708500049 SV065 3M - Hutchinson Tape Manufactu56115McLeod 2708500049 SV156 3M - Hutchinson Tape Manufactu2525McLeod 2708500049 SV199 3M - Hutchinson Tape Manufactu07013McLeod 2708500049 SV212 3M - Hutchinson Tape Manufactu135135McLeod 2708500049 SV213 3M - Hutchinson Tape Manufactu215215McLeod 2708500049 SV217 3M - Hutchinson Tape Manufactu30 030 0McLeod 2708500049 SV219 3M - Hutchinson Tape Manufactu40 040 0McLeod 2708500049 SV221 3M - Hutchinson Tape Manufactu7300 7300McLeod 2708500049 SV222 3M - Hutchinson Tape Manufactu49 168 1 2 1 49 168 1 2 1McLeod 2708500049 SV223 3M - Hutchinson Tape Manufactu50 169 1 2 1 50 169 1 2 1McLeod 2708500049 SV257 3M - Hutchinson Tape Manufactu22McLeod 2708500053 ITOTA Associated Milk Producers Inc6 406 50McLeod 27085POTW2RPS00 Glencoe WWTP1313McLeod 27085POTW4RPS00 Hutchinson WWTP3838Page 22 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsMeeker 2709300004 ITOTA Mid-Minnesota Hot Mix Inc - Li2100 3100Meeker 2709300033 ITOTA RIE Coatings11 018 0Meeker 27093POTW2RPS00 Litchfield WWTP3636Meeker 27093SW070 RPU00 Meeker County **2626Mille_Lacs 2706500025 NF001 Buckley Construction II - Nonm7 0 16 0 1Mille_Lacs 2709500003 ITOTA Smith System Manfacturing Co1214Mille_Lacs 2709500004 SV001 Viking Gas Transmission - Mila14222 214222 2Mille_Lacs 2709500004 SV002 Viking Gas Transmission - Mila14323 214323 2Mille_Lacs 2709500004 SV003 Viking Gas Transmission - Mila14122 214122 2Mille_Lacs 2709500004 SV006 Viking Gas Transmission - Mila23 0 0 1 19 0 0 1Mille_Lacs 2709500006 ITOTA Woodcraft Industries - Foresto12 1 4 107028Mille_Lacs 27095SW082 RPU00 Mille Lacs33Morrison 2709700019 BO000 St Gabriel's Hospital22 622 6Morrison 2709700019 BO001 St Gabriel's Hospital2020Morrison 2709700019 GN001 St Gabriel's Hospital9 1 17 1 1Morrison 2709700023 ITOTA Camp Ripley606103 310Morrison 2709700025 SI032 Larson-Glastron Boats Inc016 0030 0Morrison 2709700026 FS004 Central MN Ethanol Cooperative35Morrison 2709700026 SV008 Central MN Ethanol Cooperative74 0 53 12 1 79 0 57 13 1Morrison 2709700027 IOTHE Little Falls Machine Inc53Morrison 2709700037 NF001 Kingsway Construction Inc - No3 0 03 0 0Morrison 2709700038 NF001 Tri-City Paving Inc - Nonmetal47 3 443 3 4Morrison 2709700040 NF001 DLL Excavating - Nonmetallic302 0 0Morrison 27097POTW2RPS00 Little Falls WWTP2525Morrison 27097SW015 RPU00 Greater Morrison44Morrison 27097XLXL RPU00 LITTLE FALLS/MORRISON COUNTY0Morrison 27097XLXL RPU00 LITTLE FALLS/MORRISON COUNTY0 21 0 21Mower 2709900002 SV001 Hormel Foods Corp - Austin55 122 2 3 1 55 122 2 3 1Mower 2709900011 BO002 Austin Utilities - 4th Ave Pla20 020 0Mower 2709900011 GN001 Austin Utilities - 4th Ave Pla153142Mower 2709900050 ITOTA Featherlite Graphics074Mower 27099POTW2RPS00 Austin WWTP512512Mower 27099POTW2RPS00 Austin WWTP2525Mower 27099SW062 RPU00 Red Rock8080Mower 27099XAUM RPU00 AUSTIN MUNIMower 27099XAUM RPU00 AUSTIN MUNI02 102 1Murray 2710100020 ITOTA ADM Corn Processing - Dovray84Murray 27101SW104 RPU00 Murray County1414Nicollet 2701300093 ITOTA Phenix Manufacturing Co Inc -2 1103 115Page 23 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsNicollet 2710300001 BO000 St Peter Regional Treatment Ce10 49 0 116 85110Nicollet 2710300001 BO002 St Peter Regional Treatment Ce20 020 0Nicollet 2710300013 ITOTA Gustavus Adolphus College60 040Nicollet 2710300023 SI003 DAVISCO International Inc - Ni1213Nicollet 2710300032 ITOTA Hewitt Machine & Manufacturing012 0021 1Nobles 2710500001 SV001 Swift Pork Co - Worthington81108 110Nobles 2710500001 SV002 Swift Pork Co - Worthington101 1 0 101 1 0Nobles 2710500018 ITOTA McLaughlin & Schulz Inc - Wort2300 3400Nobles 2710500051 IOTHE Pronk Ready Mix Inc - Nonmetal42Nobles 27105POTW1RPS00 Worthington WWTP2626Nobles 27105SW011 RPU00 Nobles County6565Norman 2710700012 SV001 Viking Gas Transmission - Ada24027 324027 3Norman 2710700012 SV002 Viking Gas Transmission - Ada17922 217922 2Norman 2710700012 SV003 Viking Gas Transmission - Ada46259 646259 6Norman 2710700012 SV006 Viking Gas Transmission - Ada25 0 0 1 21 0 0 1Olmsted 2710900003 ITOTA Rochester Sand & Gravel - Plan11 33 5 315 46 7 4Olmsted 2710900004 ITOTA All American Cooperative - Ste4824Olmsted 2710900006 SV003 IBM - Rochester30 030 0Olmsted 2710900006 SV049 IBM - Rochester122 1122 1Olmsted 2710900010 SV001 Associated Milk Producers Inc20 44 1 1 0 20 44 1 1 0Olmsted 2710900015 ITOTA Shamrock Enterprises - Oronoco2012 2012Olmsted 2710900032 SI005 Quest International3030 0Olmsted 2710900036 BO002 Seneca Foods Corp - Rochester71107 110Olmsted 2710900055 ITOTA Rochester Sand & Gravel - Plan2611 3911Olmsted 2710900084 SI014 Mayo Medical Center - Rocheste251 1271 1Olmsted 2710900084 SI015 Mayo Medical Center - Rocheste261 1271 1Olmsted 2710900084 SI016 Mayo Medical Center - Rocheste95 0 4 0 102 1 5 1 0Olmsted 2710900084 SI017 Mayo Medical Center - Rocheste68 10 3 0 0 74 18 3 0 0Olmsted 2710900084 SI028 Mayo Medical Center - Rocheste71 171 1Olmsted 2710900084 SI029 Mayo Medical Center - Rocheste61 171 1Olmsted 2710900084 SI030 Mayo Medical Center - Rocheste101 1112 1Olmsted 2710900084 SI032 Mayo Medical Center - Rocheste202 00Olmsted 2710900084 SI039 Mayo Medical Center - Rocheste7 3 3111 5 47Olmsted 2710900084 SI043 Mayo Medical Center - Rocheste77 1 0 2 64 1 0 2Olmsted 2710900084 SI048 Mayo Medical Center - Rocheste1 61 6Olmsted 2710900095 NF001 Builders Sand & Gravel Co - No8 0 17 1 1Olmsted 2710900097 IOTHE Milestone Materials - Nonmetal105Olmsted 2710900097 NF001 Milestone Materials - Nonmetal108 7 998 8 9Olmsted 2710900098 IOTHE Shamrock Enterprises - Nonmeta84Page 24 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsOlmsted 2710900098 NF001 Shamrock Enterprises - Nonmeta13 1 111 1 1Olmsted 2777700120 ITOTA Mathy Construction Co - Plant2 10 1 13 14 1 2Olmsted 2777700228 ITOTA Mathy Construction Co - Plant7 16 3 210 22 4 3Olmsted 27109POTW1RPS00 Stewartville WWTP1212Olmsted 27109SW005 RPU00 Olmsted County (Roch/Locoshona153153Olmsted 27109SW355 RPU00 Olmsted County-Kalmar4949Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL20 2 531470Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL02 103 1Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL15 215 2Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL02 103 1Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL7150 7150Otter_Tail 2711100021 ITOTA Bongards' Creameries Inc - Per160 22180 24Otter_Tail 2711100024 ITOTA Dairy Farmers of America Inc -15 241216 2613Otter_Tail 2711100036 SI001 Perham Resource Recovery Facil25 13 3 134 17 5 1Otter_Tail 2711100036 SI002 Perham Resource Recovery Facil19 9 2 1 25 13 3 1Otter_Tail 2711100048 SI001 Fergus Falls Resource Recovery22 22 1 629 30 1 8Otter_Tail 2711100048 SI002 Fergus Falls Resource Recovery32 19 1 544 26 1 6Otter_Tail 2711100073 NF001 Aggregate Industries Inc - P-15 0 04 0 0Otter_Tail 2777700063 ITOTA Mark Sand & Gravel Acquisition22 21 5 331 29 7 4Otter_Tail 2777700306 ITOTA Mark Sand & Gravel Acquisition3 0613Otter_Tail 27111POTW1RPS00 Pelican Rapids WWTP1212Otter_Tail 27111POTW3RPS00 Fergus Falls WWTP3737Otter_Tail 27111SW086 RPU00 Battle Lake Area88Otter_Tail 27111SW178 RPU00 Northeast Ottertail2222Otter_Tail 27111SW184 RPU00 Fergus Falls4040Otter_Tail 27111XFFM RPU00 FERGUS FALLS MUNI-EINAR MICKE10 0 21Pennington 2711300021 IOTHE Concrete Inc - Nonmetallic42Pennington 27113XTVF RPU00 THIEF RIVER FALLS REGIONAL13 113 1Pine 2711500011 SV001 Great River Energy - Rock Lake2 1Pine 2711500023 IOTHE DAKA Corp2Pine 2711500029 IOTHE Hopkins Sand & Gravel Inc - No32Pine 2711500029 NF001 Hopkins Sand & Gravel Inc - No2Pine 2711500031 GN001 Grand Casino Hinckley3 0 02 0 0Pine 27115SW019 RPU00 Korf Bros.2525Pipestone 2711700015 ITOTA Cargill Inc - Pipestone2613Pipestone 2711700023 ITOTA ADM Corn Processing - Holland74Pipestone 27117SW120 RPU00 Pipestone County1616Polk 2711900001 FS005 American Crystal Sugar - Crook1314Polk 2711900001 SV001 American Crystal Sugar - Crook141 123 1 6134 117 2 6Page 25 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsPolk 2711900001 SV002 American Crystal Sugar - Crook159 117 1 7151 111 1 7Polk 2711900001 SV003 American Crystal Sugar - Crook67 64 1 263 60 1 2Polk 2711900001 SV019 American Crystal Sugar - Crook2210 2210Polk 2711900001 SV020 American Crystal Sugar - Crook3110 3110Polk 2711900002 SV001 American Crystal Sugar - E Gra330 357 1 19313 339 1 18Polk 2711900002 SV002 American Crystal Sugar - E Gra283 328 1 21268 311 1 20Polk 2711900002 SV003 American Crystal Sugar - E Gra15 0 2 25 1 15 0 2 25 1Polk 2711900002 SV004 American Crystal Sugar - E Gra15 0 2 31 0 15 0 2 31 0Polk 2711900002 SV005 American Crystal Sugar - E Gra142 38 0 142 38 0Polk 2711900002 SV016 American Crystal Sugar - E Gra61006 100Polk 2711900002 SV017 American Crystal Sugar - E Gra1101 1101Polk 2711900016 SI004 University of Minnesota - Croo14 19214 192Polk 2711900029 SV001 Viking Gas Transmission - Angu15223 215223 2Polk 2711900029 SV002 Viking Gas Transmission - Angu15423 315423 3Polk 2711900029 SV003 Viking Gas Transmission - Angu45859 645859 6Polk 2711900029 SV006 Viking Gas Transmission - Angu16 0 0 1 14 0 0 1Polk 2711900051 SI001 Polk Cnty Solid Waste Resource16 17 37 122 23 50 2Polk 2711900051 SI002 Polk Cnty Solid Waste Resource16 17 37 122 23 50 2Polk 2711900068 IOTHE Donarski Bros Inc - Nonmetalli53Polk 2711900068 NF001 Donarski Bros Inc - Nonmetalli2020Polk 2711900069 IOTHE J & S Gravel - Nonmetallic74Polk 2711900069 NF001 J & S Gravel - Nonmetallic11 1 110 1 1Polk 2777700254 ITOTA Northern Paving Inc - Base 513 5 3 1 18 7 3 2Polk 27119SW124 RPU00 Polk County100100Polk 27119XCKN RPU00 CROOKSTON MUNI KIRKWOOD FLD0 10Polk 27119XCKN RPU00 CROOKSTON MUNI KIRKWOOD FLD0 21 0 21Polk 27119XFSE RPU00 FOSSTON MUNI1 01 0Pope 2712100017 IOTHE MHC Inc - Glenwood3Pope 27121XGHWRPU00 GLENWOOD MUNI0 00 0Ramsey 2705300004 ITOTA Schwing America Inc12 1 26 22 8 21 1 46 24 8Ramsey 2712300016 SV084 3M - St Paul Main Plant14 54 1 3 0 14 54 1 3 0Ramsey 2712300016 SV085 3M - St Paul Main Plant18 68 1 3 0 18 68 1 3 0Ramsey 2712300016 SV086 3M - St Paul Main Plant9 29 0 1 0 9 29 0 1 0Ramsey 2712300019 SV001 Minnesota Brewing/Gopher State13 0 2 1 0 13 0 2 1 0Ramsey 2712300019 SV003 Minnesota Brewing/Gopher State31 232 2Ramsey 2712300019 SV010 Minnesota Brewing/Gopher State1112Ramsey 2712300020 ITOTA ADM Grain - St Paul - Elevator43Ramsey 2712300021 SV026 Silgan Containers Mfg Corp - S817Ramsey 2712300022 SI003 University of St Thomas20 020 0Page 26 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsRamsey 2712300024 ITOTA Smyth Companies Inc - St Paul070084Ramsey 2712300030 ITOTA Regions Hospital6801 34 0Ramsey 2712300039 SI001 Ford Motor Co - Twin Cities As71107 110Ramsey 2712300039 SV001 Ford Motor Co - Twin Cities As40 040 0Ramsey 2712300039 SV002 Ford Motor Co - Twin Cities As81108 110Ramsey 2712300039 SV010 Ford Motor Co - Twin Cities As3Ramsey 2712300039 SV020 Ford Motor Co - Twin Cities As11238 32 0 11489 64 0Ramsey 2712300039 SV021 Ford Motor Co - Twin Cities As34 038 0Ramsey 2712300039 SV022 Ford Motor Co - Twin Cities As210 0220 0Ramsey 2712300042 BO000 Macalester College2 103 170Ramsey 2712300045 BO000 College of St Catherine0 21 3Ramsey 2712300045 BO002 College of St Catherine41 041 0Ramsey 2712300053 SV016 Metropolitan Wastewater Treatm32 53 143 72 1Ramsey 2712300053 SV018 Metropolitan Wastewater Treatm61006 100Ramsey 2712300053 SV019 Metropolitan Wastewater Treatm71107 110Ramsey 2712300053 SV028 Metropolitan Wastewater Treatm2 0 02 0 0Ramsey 2712300055 SV002 North Star Steel Minnesota - S52 18 40 1265 23 51 16Ramsey 2712300055 SV004 North Star Steel Minnesota - S79 0 4 2 1 79 0 4 2 1Ramsey 2712300056 ITOTA Pier Foundry1 3 11 4 1Ramsey 2712300063 SV001 District Energy St Paul Inc-Ha188 709 1 119188 709 1 119Ramsey 2712300063 SV002 District Energy St Paul Inc-Ha51005 100Ramsey 2712300063 SV003 District Energy St Paul Inc-Ha30 030 0Ramsey 2712300088 SI002 Versa Iron & Machine612Ramsey 2712300093 ITOTA Bethesda Rehabilitation Hospit3Ramsey 2712300108 BO002 Hamline University51005 100Ramsey 2712300139 ITOTA St Joseph's Hospital - HealthE4 102 0Ramsey 2712300149 ITOTA T A Schifsky & Sons Inc1001 2012Ramsey 2712300186 ITOTA Midwest Asphalt - Plant 2 - Ne4 0 12 36 1 16 4Ramsey 2712300187 ITOTA Peavey Red Rock Elevator - St105Ramsey 2712300189 ITOTA City of St Paul Asphalt Plant10 11001Ramsey 2712300213 ITOTA Koch Materials - St Paul Aspha10 210 2Ramsey 2712300254 ITOTA HealthEast Midway Health Servi2Ramsey 2712300273 IOTHE Rihm Motor Co2Ramsey 2712300280 ITOTA St Paul Community & Technical3Ramsey 2712300288 SV015 Twin City Concrete Products Co33Ramsey 2712300333 BO002 Energy Park Utility Co30 030 0Ramsey 2712300347 SV001 Commercial Asphalt Co - Plant3062 4183Ramsey 2712300398 ITOTA Ashland Distribution32Ramsey 2712300410 SV003 Waldorf Corp - A Rock-Tenn Co11121143Page 27 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsRamsey 2712300410 SV004 Waldorf Corp - A Rock-Tenn Co1314Ramsey 2712300420 ITOTA Qwest Communications - St Paul7 04Ramsey 2712300433 BO001 Minnesota Historical Society30 030 0Ramsey 2712300436 BO001 Bethel College and Seminary20 020 0Ramsey 2712300456 ITOTA Ramsey County Government Cente2 0Ramsey 2712300458 ITOTA Weyerhaeuser Co - White Bear -2223Ramsey 2712300489 GN001 State of MN - Transportation B3 0 03 0 0Ramsey 2712300496 ITOTA HB Fuller Co - Corporate Campu3010 6010Ramsey 2712300498 ITOTA Impressions Inc6881Ramsey 2712300601 SI001 Fox Packaging Inc1322 0Ramsey 2712300605 ITOTA Americraft Carton Inc39046Ramsey 2712300606 ITOTA Tursso Companies Inc2227Ramsey 2712300614 IOTHE Quality Manufacturing Inc53Ramsey 2712300622 ITOTA Cardiac Pacemakers Inc5080 3 40Ramsey 2712300624 ITOTA Brown-Wilbert Vault Co - Rosev4 16 1Ramsey 2712300626 ITOTA Certified Painting Inc012 1022 2Ramsey 2712300640 ITOTA St John's Hospital (HealthEast3 1 02 1Ramsey 2712300666 ITOTA Buerkle Buick-Honda Co25 013 0Ramsey 2712300680 ITOTA Brenntag Great Lakes LLC032Ramsey 2712300690 NF001 Crushers Inc - Nonmetallic4 0 04 0 0Ramsey 2712300694 SI002 3M - Administrative Offices -17 43 1 1 0 17 43 1 1 0Ramsey 2712300694 SI003 3M - Administrative Offices -61 120 2 2 1 61 120 2 2 1Ramsey 2712300694 SI004 3M - Administrative Offices -134 265 4 4 2 134 265 4 4 2Ramsey 2712300694 SI005 3M - Administrative Offices -149 224 5 5 2 149 224 5 5 2Ramsey 2712300694 SI006 3M - Administrative Offices -5121051210Ramsey 2712300702 GN001 MNDOT Metro Div Hq - Waters Ed2Ramsey 2712300709 IOTHE LaMettry's Collision Inc - Map2Ramsey 2712300717 ITOTA Metro Council - East Metro Tra21Ramsey 2712300719 BO001 Meritex Enterprises Inc20 020 0Ramsey 2712300721 ITOTA M-Foods Dairy LLC81 181 1Ramsey 2712300725 ITOTA Unicircuit - Roseville17045 0Ramsey 2712300726 SI004 FM Frattalone Excavating & Gra6 0 15 0 1Ramsey 2712300727 ITOTA Signation Sign Group Inc6 13 0Ramsey 2712300729 ITOTA Modernistic Inc - Empire Dr115 1118 1Ramsey 2712300731 IOTHE Structural Wood Corp3Ramsey 27123XSTP RPU00 ST PAUL DOWNTOWN HOLMAN FL0 31 1 62Ramsey 27123XSTP RPU00 ST PAUL DOWNTOWN HOLMAN FL2 0 13 6 3 0 14 6Ramsey 27123XSTP RPU00 ST PAUL DOWNTOWN HOLMAN FL1 51 1 72Redwood 2712700013 ITOTA Central Bi-Products - North Re26 5 1 1 81 32 6 1 1 99Page 28 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsRedwood 2712700023 GN001 Redwood Falls Public Utilities16 1 113 1 1Redwood 2712700041 ITOTA HBOS Manufacturing LP - Schult45Redwood 27127POTW1RPS00 Redwood Falls WWTP1313Redwood 27127SW083 RPU00 Redwood County3535Renville 2712900014 FS001 Southern Minnesota Beet Sugar1820Renville 2712900014 SI999 Southern Minnesota Beet Sugar5363Renville 2712900014 SV001 Southern Minnesota Beet Sugar621 586 4 0 0 589 556 4 0 0Renville 2712900014 SV002 Southern Minnesota Beet Sugar31 102 0 5 1 31 102 0 5 1Renville 2712900014 SV012 Southern Minnesota Beet Sugar34Renville 2712900014 SV034 Southern Minnesota Beet Sugar1 101 10Renville 2712900036 FS003 Minnesota Energy1115Renville 2712900036 SV005 Minnesota Energy842 3846 3Renville 2712900036 SV012 Minnesota Energy102 1112 1Renville 2712900045 NF001 Fairway Construction Inc - Non3 0 03 0 0Renville 2712900046 SV001 Alliance Pipeline - Olivia 23-72 1 1 6 60 1 1 6Renville 27129SW090 RPU00 Renville County5151Renville 27129X1D6 RPU00 HECTOR MUNI1 01 0Rice 2713100006 ITOTA St Olaf College12100 7000Rice 2713100018 BO002 Carleton College71107 110Rice 2713100022 SV003 Malt-O-Meal Co - Plant 2 - Nor91109 110Rice 2713100022 SV004 Malt-O-Meal Co - Plant 2 - Nor91109 110Rice 2713100027 ITOTA Jennie-O Turkey Store - Feed M3232Rice 2713100050 ITOTA Mercury Minnesota Inc58Rice 2713100058 SV001 Northern Natural Gas Co - Fari6 005 00Rice 2713100059 SI001 Minn Correctional Facility - F2 15 0 04 26000Rice 2713100059 SI002 Minn Correctional Facility - F9 55 1 0 0 15 95 1 1 0Rice 2713100059 SI003 Minn Correctional Facility - F12 72 1 1 0 20 124 1 1 0Rice 2713100060 ITOTA MN Residential Academies for t2200Rice 2713100062 NF001 Kielmeyer Construction Inc - N20 1 218 1 2Rice 2713100063 IOTHE Bituminous Materials Inc - Non32Rice 2777700166 ITOTA Bituminous Materials Inc - Pla7 3 1 1 10 4 1 1Rice 27131POTW2RPS00 Faribault WWTP614614Rice 27131SW123 RPU00 Rice County210210Rice 27131XFBL RPU00 FARIBAULT MUNI02 102 1Rock 2713300023 FS001 Agri-Energy LLC34Rock 2713300023 SV004 Agri-Energy LLC15103 12 0 16111 13 0Rock 2713300023 SV011 Agri-Energy LLC222 1 1 222 1 1Rock 27133POTW1RPS00 Luverne WWTP1313Rock 27133SW077 RPU00 Rock County1616Page 29 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsRoseau 2713500002 SI012 Marvin Windows & Doors3011 3011Roseau 2713500002 SI014 Marvin Windows & Doors8120 8120Roseau 2713500002 SI015 Marvin Windows & Doors7110 7110Roseau 2713500018 ITOTA CB Hockey LLC2Roseau 27135SW137 RPU00 Salol4444Roseau 27135XROX RPU00 ROSEAU MUNI/RUDY BILLBERG FIE0 10Roseau 27135XROX RPU00 ROSEAU MUNI/RUDY BILLBERG FIE0 21 0 21Roseau 27135XRRT RPU00 WARROAD INTL-SWEDE CARLSON0Roseau 27135XRRT RPU00 WARROAD INTL-SWEDE CARLSON1 01 0Scott 2713900003 FS001 Rahr Malting Co - Shakopee1318Scott 2713900003 SV003 Rahr Malting Co - Shakopee131 1 0 131 1 0Scott 2713900003 SV005 Rahr Malting Co - Shakopee1518Scott 2713900003 SV006 Rahr Malting Co - Shakopee7 17 1 07 20 1 1Scott 2713900003 SV007 Rahr Malting Co - Shakopee1618Scott 2713900003 SV008 Rahr Malting Co - Shakopee9 21 1 19 25 1 1Scott 2713900003 SV009 Rahr Malting Co - Shakopee11 26 1 111 31 1 1Scott 2713900003 SV010 Rahr Malting Co - Shakopee15 37 2 1 0 15 43 2 1 0Scott 2713900005 SI001 Anchor Glass Container Corp -340 187 5259 142 4Scott 2713900005 SI002 Anchor Glass Container Corp -434 228 5331 174 4Scott 2713900009 ITOTA Koch Materials Co - Savage31 041 0Scott 2713900013 FS001 CertainTeed Corp56Scott 2713900013 FS002 CertainTeed Corp45Scott 2713900013 SV036 CertainTeed Corp55 5 3 1 1 55 5 3 1 1Scott 2713900041 SI030 Prior Lake Aggregates Inc2 0 02 0 0Scott 2713900041 SI031 Prior Lake Aggregates Inc2 0 02 0 0Scott 2713900041 SI033 Prior Lake Aggregates Inc7 0 16 0 1Scott 2713900041 SI034 Prior Lake Aggregates Inc2 0 02 0 0Scott 2713900041 SI040 Prior Lake Aggregates Inc5 0 04 0 0Scott 2713900044 SI039 Superior Minerals Corp - Savag41 041 0Scott 2713900073 BO001 St Francis Regional Medical Ce2020Scott 2713900091 IOTHE Bryan Rock Products Inc - Nonm42Scott 2713900091 NF001 Bryan Rock Products Inc - Nonm34 2 331 2 3Scott 2713900092 NF001 River City Asphalt - Nonmetall2020Scott 2713900097 IOTHE Phillips & Temro Industries In32Scott 2713900098 ITOTA MCES - Blue Lake WWTP714 2248 12Scott 2713900100 GN001 Lake Marion DG2Scott 2713900106 ITOTA Bituminous Roadways Inc - E5003711 4922Scott 2777700145 ITOTA Commercial Asphalt Co - Plant14 1 16 419 1 22 5Scott 2777700179 SV002 Central Soils Remediation7 7 210 9 3Page 30 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsScott 2777700265 ITOTA River City Asphalt 340 35 9 155 48 13 1Scott 2777700276 IOTHE Shakopee Gravel Inc - Nonmetal116Scott 2777700276 NF001 Shakopee Gravel Inc - Nonmetal8 0 17 1 1Scott 27139SW032 RPU00 Louisville1010Scott 27139SW032 RPU00 Louisville2 1 40 12 1 40 1Sherburne 2714100003 SI001 Great River Energy - Elk River5 157 22Sherburne 2714100003 SV003 Great River Energy - Elk River158 1316158 1316Sherburne 2714100003 SV004 Great River Energy - Elk River196 1720196 1720Sherburne 2714100006 ITOTA Minnesota Correctional Facilit6510 330Sherburne 2714100030 ITOTA Elk River Bituminous Inc3100 4211Sherburne 2714100041 SV003 Waste Management Inc -Elk Rive6201 6201Sherburne 2714100042 IOTHE Elk River Machine Co42Sherburne 2714100058 NF001 ConTeck Enterprises Inc - Nonm5 0 04 0 0Sherburne 2714500148 IOTHE Granite City Ready-Mix - Nonme42Sherburne 2714500148 NF001 Granite City Ready-Mix - Nonme6 0 16 0 0Sherburne 2777700023 ITOTA Commercial Asphalt Co - Plant16 2 14 222 3 19 3Sherburne 27141POTW1RPS00 Elk River WWTP1414Sherburne 27141POTW2RPS00 Becker WWTP - Municipal1313Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL1 0 02 0 1Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL02 104 1Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL16 31063Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL00Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL2 0 22 0 2Sibley 2701900029 ITOTA Wm Mueller & Sons Inc - Blakel9 10 3 213 14 4 3Sibley 2707900042 BO002 MG Waldbaum Co - Gaylord61006 100Sibley 2713900093 IOTHE Sibley Aggregates - Nonmetalli63Sibley 2713900093 NF001 Sibley Aggregates - Nonmetalli12 1 111 1 1Sibley 2714300009 ITOTA Dairy Farmers of America Inc -10 11611 117Sibley 2714300014 SV004 Heartland Corn Products1288 101396 10Sibley 2714300014 SV006 Heartland Corn Products253 2 1 253 2 1Sibley 2714300014 SV010 Heartland Corn Products132 1 0 132 1 0Sibley 2714300014 SV011 Heartland Corn Products1288 101396 10Sibley 2714300016 BO001 Seneca Foods Corp - Arlington20 020 0Sibley 27143POTW1RPS00 Arlington WWTP1212Sibley 27143SW002 RPU00 Sibley County99St_Louis 2713700005 FS002 US Steel Corp - Minntac2940St_Louis 2713700005 SV001 US Steel Corp - Minntac141 0 0 141 0 0St_Louis 2713700005 SV002 US Steel Corp - Minntac171 0 0 171 0 0St_Louis 2713700005 SV003 US Steel Corp - Minntac70007 000Page 31 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsSt_Louis 2713700005 SV004 US Steel Corp - Minntac141 0 0 141 0 0St_Louis 2713700005 SV005 US Steel Corp - Minntac161 0 0 161 0 0St_Louis 2713700005 SV024 US Steel Corp - Minntac2432St_Louis 2713700005 SV045 US Steel Corp - Minntac22St_Louis 2713700005 SV047 US Steel Corp - Minntac23St_Louis 2713700005 SV049 US Steel Corp - Minntac22St_Louis 2713700005 SV054 US Steel Corp - Minntac68St_Louis 2713700005 SV056 US Steel Corp - Minntac22St_Louis 2713700005 SV057 US Steel Corp - Minntac22St_Louis 2713700005 SV059 US Steel Corp - Minntac22St_Louis 2713700005 SV060 US Steel Corp - Minntac1419St_Louis 2713700005 SV065 US Steel Corp - Minntac23St_Louis 2713700005 SV072 US Steel Corp - Minntac67St_Louis 2713700005 SV075 US Steel Corp - Minntac22St_Louis 2713700005 SV077 US Steel Corp - Minntac22St_Louis 2713700005 SV081 US Steel Corp - Minntac22St_Louis 2713700005 SV083 US Steel Corp - Minntac22St_Louis 2713700005 SV085 US Steel Corp - Minntac810St_Louis 2713700005 SV093 US Steel Corp - Minntac23St_Louis 2713700005 SV095 US Steel Corp - Minntac23St_Louis 2713700005 SV096 US Steel Corp - Minntac34St_Louis 2713700005 SV097 US Steel Corp - Minntac23St_Louis 2713700005 SV103 US Steel Corp - Minntac2194 462 9 3932195 324 9 393St_Louis 2713700005 SV109 US Steel Corp - Minntac23St_Louis 2713700005 SV118 US Steel Corp - Minntac3601 380 13 3042521 380 13 412St_Louis 2713700005 SV122 US Steel Corp - Minntac101138St_Louis 2713700005 SV127 US Steel Corp - Minntac2879 380 32 512015 380 32 51St_Louis 2713700005 SV129 US Steel Corp - Minntac913St_Louis 2713700005 SV138 US Steel Corp - Minntac811St_Louis 2713700005 SV142 US Steel Corp - Minntac3446St_Louis 2713700005 SV143 US Steel Corp - Minntac4358St_Louis 2713700005 SV144 US Steel Corp - Minntac3652 380 18 792557 380 18 79St_Louis 2713700005 SV146 US Steel Corp - Minntac95129St_Louis 2713700005 SV149 US Steel Corp - Minntac3244St_Louis 2713700005 SV150 US Steel Corp - Minntac4055St_Louis 2713700005 SV151 US Steel Corp - Minntac2525 343 22 591767 343 22 59St_Louis 2713700005 SV153 US Steel Corp - Minntac96130St_Louis 2713700011 FS004 EVTAC Mining - Mine810St_Louis 2713700015 SV001 Minnesota Power Inc - ML Hibba414 132 52 5414 132 52 5Page 32 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsSt_Louis 2713700022 SV001 Duluth Steam Cooperative Assoc250 217 1 10255 221 1 10St_Louis 2713700022 SV004 Duluth Steam Cooperative Assoc72 62 0 373 64 0 3St_Louis 2713700022 SV005 Duluth Steam Cooperative Assoc7 607 60St_Louis 2713700023 ITOTA AGP Grain Ltd32St_Louis 2713700030 SI001 Hill Wood Products1212St_Louis 2713700030 SI002 Hill Wood Products0202St_Louis 2713700031 SV022 Georgia-Pacific - Duluth Hardb2 242 24St_Louis 2713700031 SV024 Georgia-Pacific - Duluth Hardb20 133 0 1 0 20 133 0 1 0St_Louis 2713700032 FS001 Northshore Mining Co - Babbitt34St_Louis 2713700032 FS003 Northshore Mining Co - Babbitt68St_Louis 2713700032 FS004 Northshore Mining Co - Babbitt3446St_Louis 2713700032 FS005 Northshore Mining Co - Babbitt68St_Louis 2713700032 SI003 Northshore Mining Co - Babbitt3 2103 210St_Louis 2713700032 SI004 Northshore Mining Co - Babbitt1 31 3St_Louis 2713700039 SV001 University of Minnesota - Dulu20 020 0St_Louis 2713700039 SV002 University of Minnesota - Dulu4010 5010St_Louis 2713700039 SV003 University of Minnesota - Dulu9 1 1 1 10 1 1 1St_Louis 2713700042 ITOTA Staver Foundry Co11 211 2St_Louis 2713700061 FS025 Hibbing Taconite Co2534St_Louis 2713700061 SV017 Hibbing Taconite Co45St_Louis 2713700061 SV024 Hibbing Taconite Co2066 197 22 611797 197 22 83St_Louis 2713700061 SV028 Hibbing Taconite Co1933 185 21 611682 185 21 82St_Louis 2713700061 SV029 Hibbing Taconite Co2202 211 24 661916 211 24 89St_Louis 2713700061 SV033 Hibbing Taconite Co811St_Louis 2713700061 SV034 Hibbing Taconite Co811St_Louis 2713700061 SV035 Hibbing Taconite Co912St_Louis 2713700062 FS007 Ispat Inland Mining Co1824St_Louis 2713700062 SI001 Ispat Inland Mining Co1925St_Louis 2713700062 SV017 Ispat Inland Mining Co3254 155 47 543254 155 64 74St_Louis 2713700063 FS007 Keewatin Taconite Operations69St_Louis 2713700063 FS015 Keewatin Taconite Operations69St_Louis 2713700063 SV020 Keewatin Taconite Operations22St_Louis 2713700063 SV026 Keewatin Taconite Operations6487St_Louis 2713700063 SV030 Keewatin Taconite Operations6048 704 93 3676049 464 126 498St_Louis 2713700073 SV010 ME Global Inc2 102 11St_Louis 2713700073 SV014 ME Global Inc21 0St_Louis 2713700083 SV001 Potlatch - Cook99 1 23 5100 1 23 5St_Louis 2713700083 SV002 Potlatch - Cook1212St_Louis 2713700083 SV003 Potlatch - Cook34 3 2 1037 3 2 11Page 33 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsSt_Louis 2713700083 SV004 Potlatch - Cook34 8 19 337 9 20 4St_Louis 2713700083 SV005 Potlatch - Cook56 8257 82St_Louis 2713700086 ITOTA Mesabi Bituminous Inc - Schley1000 2001St_Louis 2713700088 BO001 Virginia Regional Medical Cent20 020 0St_Louis 2713700090 ITOTA Duluth Superior Blacktop Inc1001 1001St_Louis 2713700098 BO000 ISD 2142 - Cherry High School1 801 80St_Louis 2713700101 BO000 ISD 2142 - Cotton High School1 701 70St_Louis 2713700108 BO000 College of St Scholastica3 1405 24 0 0St_Louis 2713700108 BO002 College of St Scholastica30 030 0St_Louis 2713700112 SV024 Western Lake Superior Sanitary3243St_Louis 2713700113 FS001 EVTAC Mining - Fairlane Plant23St_Louis 2713700113 SV012 EVTAC Mining - Fairlane Plant22St_Louis 2713700113 SV013 EVTAC Mining - Fairlane Plant22St_Louis 2713700113 SV014 EVTAC Mining - Fairlane Plant22St_Louis 2713700113 SV016 EVTAC Mining - Fairlane Plant68St_Louis 2713700113 SV025 EVTAC Mining - Fairlane Plant79St_Louis 2713700113 SV026 EVTAC Mining - Fairlane Plant710St_Louis 2713700113 SV040 EVTAC Mining - Fairlane Plant33St_Louis 2713700113 SV042 EVTAC Mining - Fairlane Plant128173St_Louis 2713700113 SV049 EVTAC Mining - Fairlane Plant1764 3222 152 131764 3222 171 18St_Louis 2713700113 SV070 EVTAC Mining - Fairlane Plant34St_Louis 2713700113 SV046 EVTAC Mining - Fairlane Plant2626 5350St_Louis 2713700136 BO000 ISD 2142 - John F Kennedy High3 1305 230St_Louis 2713700141 FS002 Stora Enso DPM & DRPM46St_Louis 2713700141 FS004 Stora Enso DPM & DRPM23St_Louis 2713700141 SI005 Stora Enso DPM & DRPM2 0 0St_Louis 2713700170 BO000 ISD 2142 - Albrook High School1 801 90St_Louis 2713700171 BO000 ISD 2142 - Cook High School1 1511 161St_Louis 2713700172 BO000 ISD 2142 - Orr High School1 801 80St_Louis 2713700243 GN001 Cub Foods - Duluth202St_Louis 2713700245 BO001 Duluth Entertainment & Convent2020St_Louis 2713700245 GN001 Duluth Entertainment & Convent3020St_Louis 2713700256 ITOTA General Electric Int'l Inc - M00 2St_Louis 2713700283 BO001 ISD 2154 - Eveleth-Gilbert Hig2020St_Louis 2713700289 NF001 Wissota Sand & Gravel Co - Non22St_Louis 2713700290 IOTHE Louis Leustek & Sons Inc - Non42St_Louis 2713700290 NF001 Louis Leustek & Sons Inc - Non7 0 17 0 1St_Louis 2713700295 NF001 Northland Crushing Inc - Nonme5 0 04 0 0St_Louis 2713700298 NF001 Hoover Construction Co - Nonme4 0 04 0 0Page 34 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsSt_Louis 2713700299 SI005 NTT of Minnesota Inc - Metalli3 0 03 0 0St_Louis 2713700302 SI004 Northland Crushing Inc - Metal5 0 05 0 0St_Louis 2713700318 SV999 Mesabi Nugget820 344474St_Louis 2713799999 SV999 East Mine projected114 24348St_Louis 2777700144 ITOTA Ulland Brothers Inc - Plant 147 10 2 210 14 3 3St_Louis 2777700297 ITOTA Ulland Brothers Inc - Plant 3415 16 4 321 22 6 4St_Louis 27137POTW1RPS00 Hibbing WWTP North Plant1212St_Louis 27137POTW1RPS00 Hibbing WWTP South Plant2525St_Louis 27137POTW4RPS00 Ely WWTP1212St_Louis 27137POTW7RPS00 Eveleth WWTP1212St_Louis 27137POTW9RPS00 Virginia WWTP2626St_Louis 27137SW038 RPU00 Hoyt Lakes88St_Louis 27137SW065 RPU00 WLSSD (Duluth Disposal Co)147147St_Louis 27137SW068 RPU00 Northwoods1111St_Louis 27137SW097 RPU00 East Mesaba **4444St_Louis 27137SW128 RPU00 Hudson88St_Louis 27137SW161 RPU00 Hibbing101101St_Louis 27137SW163 RPU00 Brookston66St_Louis 27137SW164 RPU00 Floodwood Modified22St_Louis 27137SW175 RPU00 Cotton Area Modified22St_Louis 27137SW177 RPU00 Vermillion Modified99St_Louis 27137SW204 RPU00 Orr22St_Louis 27137SW237 RPU00 Cook Area Modified33St_Louis 27137SW262 RPU00 Highway 77 Seasonal66St_Louis 27137SW405 RPU00 St. Louis Co Regional SW9898St_Louis 27137XDLH RPU00 DULUTH INTL18 2 328 3 5St_Louis 27137XDLH RPU00 DULUTH INTL02 103 1St_Louis 27137XDLH RPU00 DULUTH INTL15 215 2St_Louis 27137XDLH RPU00 DULUTH INTL04 116 2St_Louis 27137XDLH RPU00 DULUTH INTL6050 6050St_Louis 27137XHIB RPU00 CHISHOLM-HIBBING02 103 1Stearns 2700900011 SI003 International Paper - Sartell696 764 10 7703 772 17 7Stearns 2700900011 SI010 International Paper - Sartell60006 000Stearns 2700900011 SI013 International Paper - Sartell47 103244 981Stearns 2700900026 SV001 Grede - St Cloud Inc4 0 1 104 1 2 16Stearns 2700900026 SV102 Grede - St Cloud Inc12 120 2Stearns 2700900026 SV103 Grede - St Cloud Inc12 120 1Stearns 2705301077 IOTHE Hardrives Inc - Nonmetallic63Stearns 2705301077 NF001 Hardrives Inc - Nonmetallic11 1 110 1 1Page 35 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsStearns 2714500001 SI001 Kraft Foods Inc - Albany3701 3701Stearns 2714500001 SI002 Kraft Foods Inc - Albany3701 3701Stearns 2714500001 SI006 Kraft Foods Inc - Albany22Stearns 2714500003 SV001 Melrose Dairy Proteins LLC15 16 1 2 0 15 16 1 2 0Stearns 2714500003 SV002 Melrose Dairy Proteins LLC42 7 2 1 0 42 7 2 1 0Stearns 2714500008 SV001 St Johns University Order of S32 59 0 1933 61 0 20Stearns 2714500008 SV002 St Johns University Order of S31 59431 604Stearns 2714500022 ITOTA Hardrives Inc - Plant 70127 239 2Stearns 2714500026 SV001 St Cloud State University22 63 1 3 0 35 109 2 6 0Stearns 2714500032 SV001 Associated Milk Producers - Pa12 7 1 0 12 8 1 0Stearns 2714500037 ITOTA Veterans Affairs Medical Cente8 0050Stearns 2714500074 BO002 St Cloud Hospital91109 110Stearns 2714500074 GN001 St Cloud Hospital4 0 03 0 0Stearns 2714500080 SV007 Nahan Printing Inc113123Stearns 2714500097 ITOTA Polar Tank Trailer Inc - Co Rd1 4 122 6 20Stearns 2714500099 SI001 Granit-Bronz Foundry25Stearns 2714500099 SI002 Granit-Bronz Foundry25Stearns 2714500099 SI003 Granit-Bronz Foundry25Stearns 2714500101 ITOTA Fingerhut Fulfillment42Stearns 2714500104 IOTHE Felling Products Inc32Stearns 2714500107 ITOTA Belgrade Steel Tank Co Inc2 3 10351Stearns 2714500108 ITOTA Merrill/May Inc dba Merrill Co30035Stearns 2714500113 IOTHE Park Industries32Stearns 2714500116 ITOTA DCI Inc03 104 3Stearns 2714500121 GN001 TC\American Monorail Inc5141Stearns 2714500121 IOTHE TC\American Monorail Inc3Stearns 2714500143 BO001 ISD 742 - St Cloud Technical H2020Stearns 2714500144 BO001 ISD 742 - St Cloud Apollo High2020Stearns 2714500149 NF001 Stommes Construction Inc - Non16 1 114 1 1Stearns 2714500151 ITOTA Minnesota Sawdust & Shavings C1415Stearns 2714500164 ITOTA Ben's Tool & Iron4 05 1Stearns 2777700022 ITOTA Hardrives Inc - Plant 4111 16 3 215 23 4 3Stearns 2777700119 ITOTA Hardrives Inc - Plant 5110 12 2 114 17 3 2Stearns 2777700266 ITOTA American Iron37 3 16 1520198Stearns 27145POTW2RPS00 Melrose WWTP3737Stearns 27145POTW4RPS00 Cold Spring WWTP1212Stearns 27145POTW5RPS00 Holdingford WWTP1313Stearns 27145POTW7RPS00 Sauk Centre WWTP1212Stearns 27145SW018 RPU00 Bueckers #144Page 36 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsStearns 27145SW035 RPU00 St. Augusta (Landfill, Inc.)0606Stearns 27145SW116 RPU00 Sauk Centre1212Stearns 27145SW172 RPU00 Paynesville4848Steele 2714700005 ITOTA SPX Service Solutions1326Steele 2714700009 SV001 Northern Natural Gas Co - Owat5001 4001Steele 2714700022 ITOTA ATOFINA Chemicals Inc613 0818 0Steele 2714700027 ITOTA Jostens Inc - Owatonna1314Steele 2714700029 ITOTA Truth Hardware Corp - Owatonna35 059 0Steele 2714700039 ITOTA Cybex Inc0 63Steele 2714700040 ITOTA Owatonna Public Utilities - We70 040Steele 2714700044 ITOTA Wenger Corp16 103 0Steele 2714700045 ITOTA Blount Inc06 1111 2Steele 2714700056 IOTHE Owatonna Construction Co - Non95Steele 2714700056 NF001 Owatonna Construction Co - Non13 1 112 1 1Steele 2714700059 IOTHE Christian Bros Cabinets Inc53Steele 2714700060 ITOTA AAF/McQuay International - Owa18114Steele 2714700061 ITOTA Viracon Inc2 0 34 04 0 59 1Steele 2777700117 ITOTA Crane Creek Construction - Pla39 4 6 3 53 5 8 5Steele 2777700277 ITOTA Crane Creek Construction - Por13131 7111Steele 27147POTW3RPS00 Owatonna WWTP512512Steele 27147SW131 RPU00 Steele County101101Steele 27147XOWARPU00 OWATONNA DEGNER RGNL01 102 1Stevens 2714900003 ITOTA University of MN - Morris60 030Stevens 2714900004 ITOTA Cargill Inc - Alberta13Stevens 2714900012 ITOTA Riley Bros Construction Inc2021 3022Stevens 2714900013 SV017 Diversified Energy Co LLC162 1 0 162 1 0Stevens 2714900013 SV019 Diversified Energy Co LLC841 9944 10Stevens 2714900016 ITOTA WestMor Industries LLC - Tank0 4 10061Stevens 2777700261 FS002 Riley Bros Construction Inc -23Stevens 2777700261 SI001 Riley Bros Construction Inc -3311 4411Stevens 27149SW066 RPU00 Stevens County1616Stevens 27149XMOXRPU00 MORRIS MUNIStevens 27149XMOXRPU00 MORRIS MUNI0 00 0Swift 2715100026 SV005 Chippewa Valley Ethanol Co LLL111 1 0 111 1 0Swift 2715100026 SV006 Chippewa Valley Ethanol Co LLL2 0 02 0 0Swift 2715100026 SV008 Chippewa Valley Ethanol Co LLL2 0 02 0 0Swift 2715100026 SV013 Chippewa Valley Ethanol Co LLL111 1 0 111 1 0Swift 27151SW108 RPU00 Benson2121Swift 27151XBBB RPU00 BENSON MUNI0 00 0Page 37 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsTodd 2715300001 ITOTA Land O'Lakes - Browerville527529Todd 2715300004 SV001 Viking Gas Transmission - Cush20125 320125 3Todd 2715300004 SV002 Viking Gas Transmission - Cush20325 320325 3Todd 2715300004 SV003 Viking Gas Transmission - Cush20125 320125 3Todd 2715300004 SV006 Viking Gas Transmission - Cush25 0 0 1 20 0 0 1Todd 2715300023 BO001 Long Prairie Packing Co - Long30 030 0Todd 27153SW039 RPU00 Long Prairie2121Todd 27153SW078 RPU00 Killian1212Todd 27153X14Y RPU00 TODD FIELD0 01 0Traverse 27155XETH RPU00 WHEATON MUNI00 0Wabasha 2715700024 BO002 Lakeside Foods Inc - Plainview61006 100Wabasha 2715700025 ITOTA Engineering Laboratory Design2Wabasha 27157POTW1RPS00 Lake City WWTP1212Wabasha 27157POTW4RPS00 Plainview-Elgin Sanitary Distr1313Wabasha 27157SW154 RPU00 Wabasha County1616Wadena 2715900003 ITOTA Wadena Asphalt Inc2100Wadena 2715900022 NF001 Menahga Concrete Products - No5 0 05 0 0Wadena 27159SW007 RPU00 Wadena2828Wadena 27159SW114 RPU00 Anderson33Wadena 27159XSAZ RPU00 STAPLES MUNI1 001 0Waseca 2716100013 SV014 Brown Printing Co - Waseca Div72580 73070Waseca 2716100030 GN001 ITRON Inc3 0 03 0 0Waseca 27161POTW1RPS00 Waseca WWTP2424Waseca 27161SW100 RPU00 Waseca County2525Waseca 27161XACQ RPU00 WASECA MUNI01 001 1Washington 2716300001 SV003 Andersen - Main34 2 1 1738 2 1 18Washington 2716300002 SI020 3M Cottage Grove Specialty Add6 2 010 3 1Washington 2716300003 FS001 Marathon Ashland Petroleum LLC3647Washington 2716300003 FS002 Marathon Ashland Petroleum LLC1621Washington 2716300003 FS003 Marathon Ashland Petroleum LLC1519Washington 2716300003 FS005 Marathon Ashland Petroleum LLC68Washington 2716300003 FS008 Marathon Ashland Petroleum LLC1520Washington 2716300003 FS009 Marathon Ashland Petroleum LLC1317Washington 2716300003 FS010 Marathon Ashland Petroleum LLC3242Washington 2716300003 FS011 Marathon Ashland Petroleum LLC2Washington 2716300003 FS014 Marathon Ashland Petroleum LLC2Washington 2716300003 FS016 Marathon Ashland Petroleum LLC1013Washington 2716300003 FS017 Marathon Ashland Petroleum LLC3344Washington 2716300003 FS018 Marathon Ashland Petroleum LLC45Page 38 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsWashington 2716300003 FS021 Marathon Ashland Petroleum LLC68Washington 2716300003 FS022 Marathon Ashland Petroleum LLC811Washington 2716300003 FS023 Marathon Ashland Petroleum LLC1317Washington 2716300003 FS024 Marathon Ashland Petroleum LLC2330Washington 2716300003 FS025 Marathon Ashland Petroleum LLC2330Washington 2716300003 FS026 Marathon Ashland Petroleum LLC1216Washington 2716300003 FS027 Marathon Ashland Petroleum LLC22Washington 2716300003 FS029 Marathon Ashland Petroleum LLC35Washington 2716300003 FS030 Marathon Ashland Petroleum LLC67Washington 2716300003 FS031 Marathon Ashland Petroleum LLC3242Washington 2716300003 FS038 Marathon Ashland Petroleum LLC22Washington 2716300003 FS046 Marathon Ashland Petroleum LLC22Washington 2716300003 FS047 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS048 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS049 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS050 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS051 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS052 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS053 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS054 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS055 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS056 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS057 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS058 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS059 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS060 Marathon Ashland Petroleum LLC1114Washington 2716300003 FS063 Marathon Ashland Petroleum LLC1620Washington 2716300003 FS064 Marathon Ashland Petroleum LLC56Washington 2716300003 FS065 Marathon Ashland Petroleum LLC3141Washington 2716300003 FS066 Marathon Ashland Petroleum LLC45Washington 2716300003 FS068 Marathon Ashland Petroleum LLC68Washington 2716300003 RPU99 Marathon Ashland Petroleum LLC44Washington 2716300003 SV001 Marathon Ashland Petroleum LLC4000 4000Washington 2716300003 SV002 Marathon Ashland Petroleum LLC21 22 1 219 30 1 2Washington 2716300003 SV003 Marathon Ashland Petroleum LLC 272 691 323 185 207 179 909 242 243 272Washington 2716300003 SV004 Marathon Ashland Petroleum LLC11111 6111Washington 2716300003 SV005 Marathon Ashland Petroleum LLC41 2 2 3 21 2 2 3Washington 2716300003 SV006 Marathon Ashland Petroleum LLC7100 7100Washington 2716300003 SV007 Marathon Ashland Petroleum LLC 101 146 1 458 200 1 5Page 39 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsWashington 2716300003 SV008 Marathon Ashland Petroleum LLC6100 6100Washington 2716300003 SV009 Marathon Ashland Petroleum LLC10111 5111Washington 2716300003 SV010 Marathon Ashland Petroleum LLC16211 8211Washington 2716300003 SV011 Marathon Ashland Petroleum LLC17111 9111Washington 2716300003 SV012 Marathon Ashland Petroleum LLC6000 6000Washington 2716300003 SV013 Marathon Ashland Petroleum LLC45 101 1 337 138 1 4Washington 2716300003 SV014 Marathon Ashland Petroleum LLC61 106 1 336 146 1 3Washington 2716300003 SV015 Marathon Ashland Petroleum LLC33 95 0 225 130 0 3Washington 2716300003 SV016 Marathon Ashland Petroleum LLC28 25 1 328 25 1 3Washington 2716300003 SV017 Marathon Ashland Petroleum LLC7101 7101Washington 2716300003 SV018 Marathon Ashland Petroleum LLC9101 9101Washington 2716300003 SV019 Marathon Ashland Petroleum LLC2 002 00Washington 2716300003 SV020 Marathon Ashland Petroleum LLC2000 2000Washington 2716300003 SV021 Marathon Ashland Petroleum LLC5111 5111Washington 2716300003 SV022 Marathon Ashland Petroleum LLC4101 4101Washington 2716300003 SV023 Marathon Ashland Petroleum LLC3 133 13Washington 2716300003 SV050 Marathon Ashland Petroleum LLC16111 8111Washington 2716300003 SV062 Marathon Ashland Petroleum LLC3 9 123 11 15Washington 2716300004 ITOTA Minnesota Correctional Facilit2 0 61 0 3Washington 2716300014 ITOTA Tower Asphalt Inc23 7 3 8 32 9 4 12Washington 2716300025 SI006 3M - Cottage Grove Corporate I64 0 0 4 0 123 1 0 8 0Washington 2716300039 BO001 Lakeview Hospital2020Washington 2716300049 ITOTA Black Diamond Inc19212Washington 2716300050 ITOTA Commercial Asphalt Co - Plant10 1 12 314 1 16 4Washington 2716300051 ITOTA NRG Energy Resource Recovery -3Washington 2716300056 ITOTA Ecowater Systems Inc127247 0Washington 2716300080 SI999 3M - Cottage Grove - Tape Manu23Washington 2716300109 NF001 Custom Crushing & Screening In5 0 04 0 0Washington 2716300112 ITOTA Marathon Ashland Petroleum - C99Washington 2716300127 ITOTA Advance Corp - Cottage Grove3613Washington 27163POTW1RPS00 Met Council - Eagles Point WWT3737Washington 27163POTW2RPS00 Met Council - St Croix Valley512512Washington 27163SW001 RPU00 Washington County55Washington 27163SW001 RPU00 Washington County1 0 20 01 0 20 0Washington 27163X21D RPU00 LAKE ELMO1063 1073Watonwan 27165POTW1RPS00 Madelia WWTP1212Watonwan 27165POTW2RPS00 St James WWTP1313Watonwan 27165SW081 RPU00 Watonwan County5252Watonwan 27165XJYG RPU00 ST JAMES MUNICIPAL0 00 0Page 40 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsWilkin 27167SW022 RPU00 Barnesville44Winona 2716900007 ITOTA Midwest Metal Products Inc08 1010 1Winona 2716900010 ITOTA Winona State University14101 7100Winona 2716900013 SV005 Froedtert Malt - Winona1114Winona 2716900013 SV006 Froedtert Malt - Winona1012Winona 2716900013 SV026 Froedtert Malt - Winona41 041 0Winona 2716900018 ITOTA Brom Machine & Foundry Co00Winona 2716900022 ITOTA Winona River & Rail Inc63Winona 2716900024 ITOTA United Machine & Foundry1 0 31 0 4Winona 2716900036 ITOTA Fusion Coatings Inc - Div of R13 126 2Winona 2716900049 ITOTA Modern Transport Terminal63Winona 2716900052 ITOTA Community Memorial Hospital8000 50 0Winona 2716900067 ITOTA Technical Die-Casting Inc4323 5434Winona 2716900078 BO001 St Mary's University2020Winona 2716900078 GN001 St Mary's University2Winona 27169POTW2RPS00 Winona WWTP513513Winona 27169POTW3RPS00 Whitewater River Pollution Con1212Winona 27169SW025 RPU00 Winona County111111Winona 27169SW026 RPU00 Geisler22Winona 27169XONA RPU00 WINONA MUNI-MAX CONRAD FLD00Winona 27169XONA RPU00 WINONA MUNI-MAX CONRAD FLD1 01 0Wright 2717100019 SV001 NSP dba Xcel Energy Monticello2 10021 00Wright 2717100024 ITOTA Mid-Minnesota Hot Mix - Annand7211 9311Wright 2717100025 ITOTA Omann Brothers Inc - St Michae6411 9611Wright 2717100030 ITOTA Munson Lakes Nutrition LLC - H4050Wright 2717100061 IOTHE Aero Fiberglass Co3Wright 2717100077 IOTHE Star West Chev-Olds-GEO3Wright 2717100080 NF001 Buffalo Bituminous - Nonmetall302 0 0Wright 2717100081 NF001 Annandale Rock Products - Nonm4 0 04 0 0Wright 2717100085 SV002 Minnesota Diversified Products53110Wright 2717100086 ITOTA Twin City Die Casting Co - Mon10 210 2Wright 27171POTW1RPS00 St Michael WWTP1212Wright 27171POTW2RPS00 Monticello WWTP1313Wright 27171POTW6RPS00 Buffalo WWTP2626Wright 27171POTW9RPS00 Delano WWTP1212Wright 27171SW044 RPU00 Lindenfelser44Wright 27171SW044 RPU00 Lindenfelser1 0 14 01 0 14 0Wright 27171SW059 RPU00 French Lake1111Wright 27171SW060 RPU00 Forest City Road (Yonak)5454Page 41 of 42COUNTY FACID STKID FAC_NAMENOXSO2VOCPM25NH3NOXSO2VOCPM25NH3Minnesota Non-Utility Point Sources2002 Annual Emissions in Tons2018 Annual Emissions in TonsWright 27171SW060 RPU00 Forest City Road (Yonak)2 1 33 12 1 33 1Wright 27171SW117 RPU00 Lindala3333Wright 27171X8Y2 RPU00 BUFFALO MUNI02 102 1Yellow_Medic2714500150 IOTHE Martin Marietta Materials - No127Yellow_Medic2717300008 ITOTA ADM - Prairie Grain Partners -35 0 1 8 19 1 4Yellow_Medic2717300037 ITOTA ADM Corn Processing - Burr95Yellow_Medic27173SW042 RPU00 Yellow Medicine2020Page 42 of 42

# Appendix B

Post Emissions Modeling File Changes for

Base Year 2002 and Future Year 2018 Estimates for

Minnesota.Table 1: Source Location and Stack Parameter Fixes (in 2002 and 2018):FIXEDFacility ID CountyEmissionUnit Unit Name Stack IDFIXEDStack IDFacility NameX(LCCm)Y(LCCm)StackHeight(m)Dia.(m)ExitTemp(K)Exit Vel.(m/hr)2703100001 Cook EU001/2/3SV001/2/3Minnesota Power – TaconiteHarbor460845.4851070.02713700063 St_LouisSV016Keewatin Taconite Operations298631.2828978.72713700063 St_LouisSV020Keewatin Taconite Operations298631.2828978.72713700063 St_LouisSV022Keewatin Taconite Operations298631.2828978.72713700063 St_LouisSV024Keewatin Taconite Operations298631.2828978.72713700063 St_LouisSV026Keewatin Taconite Operations298631.2828978.72706100004 Itasca EU001/2SV001SV003Minnesota Power-Boswell7002917556147.822713700063 St_Louis EU030Grate Kiln -Indurator WasteGas, Phase IISV030SV051*Keewatin Taconite Operations298631.2828978.748.7685.7932954697.192713700063 St_LouisSV032Keewatin Taconite Operations298631.2828978.72707100002 Koochiching EU430 Boiler 2SV431SV430Boise Cascade Corp –International Falls35.542.2947875753.882713700022 St_Louis EU003 boiler 3SV005SV001Duluth Steam Cooperative Assoc73.153.9651126344.022703700011 Dakota EU111EU-45002 SRU3only SV094SV093Flint Hills Resources LP – PineBend60.961.4554755359.74* Changed to SV051 in 2018 only, see text associated with Table 3 below.Table 2: Addition of MinnesotaSources to 2018 Model Input File*:Facility_ID Stack ID Facility NameX(m)/(lat)Y(m)/(long)Stack Hgt(m)StackDia.(m)Exit Temp(K)Exit Vel.(m/hr)Flow Rate(m3/hr)2707500003 SV261 Northshore Mining Co - Silver 435385.5 824585.8 49.4 1.8 333.2 602412707500003 SV114 Northshore Mining Co - Silver 435385.5 824585.8 16.9 1.0 435.2 511332707500003 SV105 Northshore Mining Co - Silver 435385.5 824585.8 40.8 1.8 333.2 602412707500003 SV266-8 Northshore Mining Co - Silver 435385.5 824585.8 49.4 1.8 333.2 457562713700113 SV046 Evtac Line 1332382.3 817667.5 42.7 3.0 327.1 859542713700318 SV999 Mesabi Nugget47.59 -92.19 60.0 4.2 358.0 90000 900002706199999 SV999 West Mine47.38 -93.22 60.0 4.7 318.2 54900 549002713799999 SV999 East Mine47.60 -92.14 38.1 1.6 370.4 71316 71316Facility_ID Stack ID Facility NameNO(ton/d)NO2(ton/d)PAR(ton/d)TOL(ton/d)FORM(ton/d)NR(ton/d)PEC(ton/d)POA(ton/d)PSO4(ton/d)CCRS(ton/d)2707500003 SV261 Northshore Mining Co - Silver 0.2452 0.0272 0.0016 0.0002 0.0003 0.0040 0.0000 0.0001 0.0002 0.00282707500003 SV114 Northshore Mining Co - Silver 0.7276 0.0808 0.0047 0.0005 0.0009 0.0123 0.0015 0.0065 0.0145 0.18922707500003 SV105 Northshore Mining Co - Silver 0.7561 0.0840 0.0041 0.0005 0.0008 0.0106 0.0016 0.0068 0.0151 0.19652707500003 SV266-8 Northshore Mining Co - Silver 0.4401 0.07772713700113 SV046 Evtac Line 17.0404 0.78232713700318 SV999 Mesabi Nugget1.8819 0.20910.24552706199999 SV999 West Mine3.1542 0.35050.00072713799999 SV999 East Mine0.3166 0.03520.0315∗Add all missing Northshore Mining SilverBay sources to 2018 non-utility file;∗Add Northshore Mining Silver Bay Furnace 5;∗EVTAC Mining – Fairlane Plant Line 1 did not operate in 2002 or 2003. It started upagain in 2004, thus was added to 2018;∗Add Mesabi Nugget;∗Add two mines to reflect growth,West Mine (like Minnesota Steel)and East Mine (like Polymet).Table 3: Subtract out Growth and Revise Control at Existing Taconite Facilities in 2018 Model Input Files*.Facility ID Facility NamePollutantIDStackIDOrig.2002(tons/day)Orig.GrowthFactorOrig.Grown2018(tons/day)Orig.ControlFactorOrig.Control2018(tons/day)NewGrowthFactorNewControlFactorNewControl2018(tons/day)Differencenew - oldPercentChange2018Cutfactor2713700005 US Steel Corp - Minntac SO2 SV103SUM:1.224 0.852 -0.371 -30 0.6972713700005 US Steel Corp - Minntac NOX SV118 9.133 1.000 9.133 1.000 9.133 1.000 0.700 6.393 -2.740 -30 0.7002713700005 US Steel Corp - Minntac NOX SV127 6.178 1.000 6.178 1.000 6.178 1.000 0.700 4.325 -1.853 -30 0.7002713700005 US Steel Corp - Minntac NOX SV144 8.907 1.000 8.907 1.000 8.907 1.000 0.700 6.235 -2.672 -30 0.7002713700005 US Steel Corp - Minntac NOX SV151 6.649 1.000 6.649 1.000 6.649 1.000 0.700 4.654 -1.995 -30 0.7002713700061 Hibbing Taconite Co NOX SV024 6.046 1.000 6.046 1.000 6.046 1.000 0.870 5.260 -0.786 -13 0.8702713700061 Hibbing Taconite Co NOX SV028 5.280 1.000 5.280 1.000 5.280 1.000 0.870 4.594 -0.686 -13 0.8702713700061 Hibbing Taconite Co NOX SV029 5.156 1.000 5.156 1.000 5.156 1.000 0.870 4.486 -0.670 -13 0.8702713700062 Ispat Inland Mining Co NOX SV017SUM:12.921 9.522 -3.399 -26 0.7372713700062 Ispat Inland Mining Co SO2 SV017SUM:0.616 0.454 -0.162 -26 0.7372713700063Keewatin TaconiteOperations NOX SV051SUM:17.614 12.980 -4.634 -26 0.7372713700063Keewatin TaconiteOperations SO2 SV051SUM:2.049 0.997 -1.053 -51 0.4862713700113EVTAC Mining - FairlanePlant NOXSV049SUM:4.7544.302 -0.452 -10 0.9052713700113EVTAC Mining - FairlanePlantSO2 SV049SUM:8.8497.856 -0.992 -11 0.888∗US Steel Minntac new SO2control added for MACT required installation of wet scrubber;∗US Steel Minntac new NOXcontrol proposed as BART involving burning wood and natural gas on lines 4 and 5 and Coal/NG onlines 6 and 7 after 2002, a 20% reduction fromfuel blending (although not specifically proposed as BARTthat appears in recentinventories; and a 10% reductionfrom installation of lo-NOXburners in pre-heat zone in lines 4 through 7;∗Hibbing Taconite control proposed as BART reflecting fueluse efficiency projectscompleted in 2005 and 2006;∗Keewatin Taconite Operations stack SV051 with associated new wet scrubbersCE110 and CE111 connected to two existingmulticlones CE030 and CE031 as of October 1, 2005, to comply withMACT. SV051 replaces previous stacks SV030 and SV031;∗Keewatin Taconite Operations primary fuel natural gas, but after 2005 started also burning coal;∗EVTAC Mining line 2.

# Appendix C

Annual 2005 and 2018 Emissions in Tons in

MRPO 2005 Case by Source and Category for

Minnesota and Surrounding States.

# 2005 (baseM) Emissions

SrcGroup SO 2 

## NO X 

## NH 3 

## PM 25 

## PM 10 

## VOC

Point 129,000 158,000 1,510 3,030 36,500 36,500

Area 15,700 57,600 188,000 17,800 67,500 117,000

On-road 2,450 146,000 6,270 1,600 2,630 92,100 Mobile

Non-road 9,460 102,000 76 4,900 5,330 116,000

Biogenics 0 34,700 0 0 0 602,000

Minnesota TOTAL: 157,000 498,000 196,000 27,300 112,000 964,000 M

I

N

N

E

S

O

T

A

(no biogenics) TOTAL: 157,000 464,000 196,000 27,300 112,000 362,000

Point 178,000 113,000 3,310 10,100 23,800 36,000

Area 5,660 9,420 267,000 8,440 35,600 72,200

On-road 1,760 92,900 3,380 1,150 1,770 73,800 Mobile

Non-road 7,810 86,200 59 4,820 5,300 58,600

Biogenics 0 38,700 0 0 0 224,000

Iowa TOTAL: 193,000 340,000 274,000 24,500 66,500 465,000 I

O

W

A

(no biogenics) TOTAL: 193,000 302,000 274,000 24,500 66,500 241,000

Point 154,000 88,300 378 2,690 3,310 649

Area 5,220 15,400 78,700 1,040 1,430 64,000

On-road 522 19,200 754 289 433 12,200 Mobile

Non-road 7,490 82,900 68 322 586 19,200

Biogenics 0 24,200 0 0 0 132,000

North Dakota TOTAL: 167,000 230,000 79,900 4,340 5,760 228,000 N D

O A

R K

T O

H T

A

(no biogenics) TOTAL: 167,000 206,000 79,900 4,340 5,760 96,000

Point 13,200 20,300 70 1,000 1,220 1,240

Area 9,200 6,280 115,000 1,620 4,330 28,600

On-road 665 23,700 935 366 546 14,100 Mobile

Non-road 6,740 65,500 46 343 596 18,200

Biogenics 0 26,500 0 0 0 212,000

South Dakota TOTAL: 29,800 142,000 116,000 3,330 6,690 274,000 S D

O A

U K

T O

H T

A

(no biogenics) TOTAL: 29,800 116,000 116,000 3,330 6,690 62,100

Point 242,000 108,000 865 118 11,500 28,100

Area 8,860 22,800 116,000 11,900 14,200 110,000

On-road 2,640 149,000 5,770 1,780 2,790 58,600 Mobile

Non-road 6,940 68,500 63 3,630 3,990 116,000

Biogenics 0 25,800 0 0 0 527,000

Wisconsin TOTAL: 260,000 374,000 123,000 17,400 32,500 840,000 W

I

S

C

O

N

S

I

N

(no biogenics) TOTAL: 260,000 348,000 123,000 17,400 32,500 313,000

# 2018 (baseM) Emissions

SrcGroup SO 2 

## NO X 

## NH 3 

## PM 25 

## PM 10 

## VOC

Point 75,800 102,000 800 16,800 36,900 37,600

Area 16,500 63,600 219,000 13,900 43,900 128,000

On-road 624 47,800 7,760 1,020 2,170 43,100 Mobile

Non-road 1,870 64,500 87 2,470 2,720 77,900

Biogenics 0 34,700 0 0 0 602,000

Minnesota TOTAL: 94,800 313,000 228,000 34,200 85,700 889,000 M

I

N

N

E

S

O

T

A

(no biogenics) TOTAL: 94,800 278,000 228,000 34,200 85,700 287,000

Point 153,000 93,600 5,010 12,000 22,600 50,100

Area 6,480 10,600 368,000 7,100 27,800 84,900

On-road 377 37,200 4,360 631 1,320 37,800 Mobile

Non-road 641 49,200 73 2,530 2,830 36,100

Biogenics 0 38,700 0 0 0 224,000

Iowa TOTAL: 160,000 229,000 377,000 22,300 54,600 433,000 I

O

W

A

(no biogenics) TOTAL: 160,000 191,000 377,000 22,300 54,600 209,000

Point 139,000 98,000 376 5,900 8,670 3,850

Area 5,080 20,300 108,000 1,570 10,200 75,000

On-road 78 6,080 877 136 281 5,890 Mobile

Non-road 187 58,300 87 2,220 2,350 13,000

Biogenics 0 24,200 0 0 0 132,000

North Dakota TOTAL: 144,000 207,000 109,000 9,830 21,500 230,000 N D

O A

R K

T O

H T

A

(no biogenics) TOTAL: 144,000 183,000 109,000 9,830 21,500 97,700

Point 6,250 10,900 279 543 1,600 2,260

Area 10,500 7,050 158,000 2,230 14,900 33,400

On-road 102 7,870 1,150 179 371 7,130 Mobile

Non-road 87 43,100 63 1,840 1,950 11,500

Biogenics 0 26,500 0 0 0 212,000

South Dakota TOTAL: 16,900 95,400 159,000 4,790 18,800 266,000 S D

O A

U K

T O

H T

A

(no biogenics) TOTAL: 16,900 68,900 159,000 4,790 18,800 54,300

Point 177,000 83,500 1,370 7,300 17,800 33,100

Area 7,980 23,700 108,000 13,200 14,900 111,000

On-road 583 45,400 6,510 1,020 2,090 23,300 Mobile

Non-road 1,540 39,800 70 1,830 2,060 73,500

Biogenics 0 25,800 0 0 0 527,000

Wisconsin TOTAL: 187,000 218,000 116,000 23,400 36,900 768,000 W

I

S

C

O

N

S

I

N

(no biogenics) TOTAL: 187,000 192,000 116,000 23,400 36,900 241,000

# Appendix D

Meteorological Modeling

Protocol and Performance Evaluation

# Meteorological Modeling Protocol

For Application to PM2.5/Haze/Ozone Modeling Projects

Kirk Baker

December 12, 2004

Lake Michigan Air Di rectors Consortium

Midwest Regional Planning Organization

Des Plaines, Illinois

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 2 of 9 

# INTRODUCTION

The purpose of this document is to outline the configuration and application of MM5

to support photochemical and emissions modeling projects. All information provided

in this document is relevant to NCAR’s 5 th 

# generation Mesoscale Model version 3.6.3

Dudhia, 1993 and Grell et al, 1994). Th e computing platform supported by

LADCO/Midwest RPO is the Red Hat versio n 7.X Linux operating system and the

Portland Group Fortran compiler. MM5 consists of the Mesoscale model MM5 and a

suite of pre-processors including PR EGRID, REGRIDDER, RAWINS, LITTLE R,

INTERPF, INTERPX, and TERRAIN.

The model parameterizations and physics options outlined in this document are

based on a series of sensitivity runs that indicate an optimal configuration for the

Upper Midwest (Johnson, 2003). The mode l configuration and parameterizations

outlined in this document describe recent MM5 applications. Evolving science in

meteorological modeling and photochemical modeling necessitate that this document

change to reflect the state of the science.

The annual 2002 36 km MM5 simulation was co mpleted by Matthew Johnson at Iowa

DNR. The 36/12 km 2-way nested simulation for the Summer of 2002 was conducted

jointly by Steven King at Illinois EPA and Kirk Baker at LADCO.

TERRAIN

The TERRAIN processor defines the

horizontal grid of the MM5

application. The 24 category USGS

10 minute (~19 km) data is used

for the National RPO 36-km

domain, and 5 minute (~9 km)

data for 12-km domains. The

National RPO grid is a Lambert

conic projection centered at

coordinates –97, 40 with first and

second true latitudes at 33 and 45

degrees (See Figure).

The 36 km grid contains 165 x 129

km grid cells and the 12 km has

193 x 199 grid cells (See Table 1).

The 12 km grid is two-way nested within the mother grid to allow fine grid feedback

into the coarse grid. Additional options are set to allow generation of data to support

the Pleim-Xu land surface module. Variables LSMDATA and IEXTRA are both set

equal to TRUE.

Domain

ID Grid X Cells

(EastWest) Y Cells

(NorthSouth) Cell Size

(km) Mother

Domain

ID Lower Left

X,Y of Nest

1 National RPO 165 129 36 1 1, 1

2 Upper Midwest 193 199 12 1 66, 30

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 3 of 9 

# PREGRID

The PREGRID processor converts meteorologic al analyses data such as NCEP or Eta

to an intermediate data format that the REGRIDDER processor ca n utilize. Eta/AWIP

3D and SF analyses data (ds609.2) will be used to initialize the REGRID processor.

Snow cover will be estimated from water equivalent snow depth. The input analyses

will be processed 3 hourly (10,800 seconds). The AWIP grib definiti on tables will be

used to map Eta data into MM5. The ETA skin temperature is used as the source of

sea surface temperature. The Eta analysis files with the extension “.tm12” are not

used since they are the "cold st art" global analysis files.

REGRIDDER

The REGRIDDER processor takes the data extracted from analyses fields and

interpolates the data to user specified pressure levels and to the user specified

horizontal grid.

LITTLE R

The RAWINS and LITTLE R processors perform objective analysis on the output from

REGRIDDER using surface and upper air

observation data. Since these

observations are incorp orated into the Eta

analysis fields this step is considered

redundant. Sensitivity tests where Eta 3

hourly analysis was used to initialize with

and without RAWINS objective analysis

showed no difference in model

performance (Baker, 2002).

Even though this step is redundant,

LITTLE R is applied to enable surface

nudging of soil moisture and temperature

in the Pleim-Xu land surface module.

NCEP ADP surface (ds 464.0) and upper

air (ds 353.1 and ds 353.4) data are the

appropriate data to input into LITTLE R

and/or RAWINS.

INTERPF

The INTERPF processor takes the

REGRIDDER/LITTLE R output that is at

standard pressure leve ls and interpolates

that data to the vert ical grid defined by

the user. The vertical grid is defined in

terms of sigmas, wher e 1 is the surface

and 0 is the top of the model atmosphere.

The top of the MM5 simulation is 100

millibars, which is approximately 15

kilometers above ground level. k(MM5) sigma press.(mb) height(m) depth(m)

34 0.000 10000 14662 1841

33 0.050 14500 12822 1466

32 0.100 19000 11356 1228

31 0.150 23500 10127 1062

30 0.200 28000 9066 939

29 0.250 32500 8127 843

28 0.300 37000 7284 767

27 0.350 41500 6517 704

26 0.400 46000 5812 652

25 0.450 50500 5160 607

24 0.500 55000 4553 569

23 0.550 59500 3984 536

22 0.600 64000 3448 506

21 0.650 68500 2942 480

20 0.700 73000 2462 367

19 0.740 76600 2095 266

18 0.770 79300 1828 259

17 0.800 82000 1569 169

16 0.820 83800 1400 166

15 0.840 85600 1235 163

14 0.860 87400 1071 160

13 0.880 89200 911 158

12 0.900 91000 753 78

11 0.910 91900 675 77

10 0.920 92800 598 77

9 0.930 93700 521 76

8 0.940 94600 445 76

7 0.950 95500 369 75

6 0.960 96400 294 74

5 0.970 97300 220 74

4 0.980 98200 146 37

3 0.985 98650 109 37

2 0.990 99100 73 36

1 0.995 99550 36 36

0 1.000 100000 0 --SURF--

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 4 of 9 

# The vertical atmosphere is resolved to 34 layers, with thinner layers in the planetary

boundary layer. This is to capture the important diurnal variations in the boundary

layer while also having layers in the upper troposphere to try and resolve convective

activity. Output from the INTERPF processor is ready for input into MM5.

INTERPX

The INTERPX processor is used to extract th e soil temperature and soil moisture data

from MM5 output files and overwrite the so il temperature and mois ture fields on the

MMINPUT file for the next 5 day simulation block. This allows soil moisture and

temperature to be carried over to subsequent modeling simulations.

For example, to simulate 20 days in 4 bloc ks of 5 days, the first block of 5 days

would use the standard MMINPUT to run MM5, and the subseq uent 3 blocks of 5 days

would take the MM5 output and extract soil temperature and moisture data for the

next 5 day block.

This option was only used for the 36 km annu al simulation of 2002. This option is no

longer recommended since it has been sh own to introduce a cold bias for the

temperature field, particularly in the winter months (Olerud, 2003).

MM5

The output from INTERPF, LITTLE R, and TERRAIN processors were used to run MM5.

These files must be in the “./MM5/Run” directory and have the generic filenames

given directly out of these processors. 3D analysis nudging for the wind field,

temperatures, and moisture were applied above the boundary layer only. Analysis

nudging was not performed on the rotational wind field. In addition, the observation

nudging flag was turned off. This type of nudging is appropriate usually when you

have a very dense set of observation data from a field study, which this application

lacked. The default nudging weighting factors were used for all simulations: 2.5 x 10

-

4 

# for wind fields and temperatures and 1.0 x 10 -5 

# for moisture fields.

Table 3

Configuration 36km and 12km Domains

Explicit Moisture Mixed Phase (Reisner I)

Cumulus Kain-Fritsch 2

PBL Pleim-Chang (ACM)

Radiation RRTM

Multi-Layer Soil Model Pleim-Xu

Shallow convection No

4-D Data Assimilation Analysis nudging on above PBL



# Moist Physics Table No

Table 3 outlines the model configuration used for MM5 modeling up to the date of

this document. All simulations use the mixed phase moisture rather than simple ice

so that all four phases of water will be explicitly output by MM5. This is important

since the photochemical model is app lied for an annual basis and correctly

characterizing the phase of water is import ant for several physiochemical processes.

Atmospheric radiation is calculated every 15 minutes in the model. Vertical moisture

and temperature advection are set to us e linear interpolation. Other important

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 5 of 9 

# variables switched to ON include: moist vertical diffusion in clouds, temperature

advection using potential temperature, diff usion using perturbation temperature, 3D

coriolis force, and upper radiative boundar y condition. Sea surface temperature and

snow cover are set to vary with time.

The Pleim-Xu land surface module requires that 3 additional variables be set in the

MM5 deck: ISMRD, NUDGE, and IFGROW. ISMRD is set to use soil moisture fields

from the Eta analyses. NUDGE is set to nudge soil moisture data to the analyses

fields. IFGROW is set to option 2, which takes vegetative growth into account based

on vegetative fraction data from the TERRAIN file.

MODEL EXECUTION

MM5 was executed in 5 day blocks (7200 minu te simulation) with a 90 second time

step. Model results are output every 60 minutes and the model output files are

written out (i.e. split) every 24 hours to accommodate post-processing utilities. The

start and end times are 12Z. Only 4 days from each block will be used for input to a

photochemical model: 5Z to 5Z, which means the first 17 hours of the MM5

simulation are ramp-up and there are 5 hour s at the end of the simulation that are

not used.

The 2002 annual simulation was initiate d at 12Z December 16, 2001 and was run

through 12Z January 1, 2003. The 5 day bloc ks are evenly divided between these 2

dates. Alternatively, the following table illustrates how each month from the 2001

summer and 2003 annual simula tions will be modeled us ing 5 day blocks. This

standardized approach to simulating each month reduces post-processing burden.

MM5 Run start date start time end date end time total

days total

hours ramp

up

hours extra

hours

1 MM/01/YY 12Z MM/06/YY 12Z 5 120 17 5

2 MM/05/YY 12Z MM/10/YY 12Z 5 120 17 5

3 MM/09/YY 12Z MM/14/YY 12Z 5 120 17 5

4 MM/13/YY 12Z MM/18/YY 12Z 5 120 17 5

5 MM/17/YY 12Z MM/22/YY 12Z 5 120 17 5

6 MM/21/YY 12Z MM/26/YY 12Z 5 120 17 5

7 MM/25/YY 12Z MM/30/YY 12Z 5 120 17 5

8 MM/29/YY 12Z NEXTMM/02/YY 12Z 5 120 17 5

PCM Run start date start time end date end time total

days total

hours

1 MM/02/YY 5Z MM/06/YY 5Z 4 97

2 MM/06/YY 5Z MM/10/YY 5Z 4 97

3 MM/10/YY 5Z MM/14/YY 5Z 4 97

4 MM/14/YY 5Z MM/18/YY 5Z 4 97

5 MM/18/YY 5Z MM/22/YY 5Z 4 97

6 MM/22/YY 5Z MM/26/YY 5Z 4 97

7 MM/26/YY 5Z MM/30/YY 5Z 4 97

8 MM/30/YY 5Z NEXTMM/01/YY 5Z 4 97

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 6 of 9 

# MODEL PERFORMANCE

The performance of MM 5 will be analyzed

qualitatively by comp aring output surface

fields of temperature, winds, convective

activity, and cloud cover to 12 hourly

UNISYS surface weather maps and satellite

cloud cover images. Vertical sounding plots

of temperature, humidity, wind speed, and

wind direction will be analyzed for select

upper air stations in the Midwest. Vertical

sounds plots will show model predictions

against Forecast Systems Laboratory (FSL) /

National Climatic Da ta Center (NCDC)

Radiosonde data archive (RAOBS) upper air

data at 0Z and 12Z.

Model performance will be assessed quantitatively with the METSTAT tool from

Environ (Emery et al, 2001) . The metrics used to qu antify model performance

include mean observation, mean prediction , bias error, gross error, root mean

square error (including systematic and unsystematic components), and index of

agreement. These metrics wi ll compare model predicti ons to Techniques Data

Laboratory U.S. and Canada surface hour ly observations (NCAR dataset ds472.0).

The MM5 model outputs approximately 15 meter predictions while observations are

at 10 meters. METSTAT applies micro-me teorological adjustments to the MM5

estimates to approximate 10-m values. MM5 outputs near-instantaneous values (90

second time step) as opposed to the valu es with longer averaging times taken at

monitor stations, so that should be kept in consideration when interpreting model

performance metrics and making qualitative comparisons to satellite maps.

Model performance metrics wi ll be applied to sub-regions of the Upper Midwest,

meaning the metrics are hourly spatial averages of multiple monitor locations. This

will be done to gain a be tter understanding of MM5 performance for 2 geographically

and meteorologically diverse regions: Great Lakes and Ohio Valley. All metrics are

calculated for all sites within the specified model performance region for an hourly

and daily time period (0Z to 23Z). Mean wind direction is estimated by averaging the

U and V wind vector components and conver ting those averages to an average wind

direction in compass degrees.

MPE Region Great Lakes Ohio Valley

X Coordinate SW (km) 200 450

X Coordinate NE (km) 1500 1600

Y Coordinate SW (km) 100 -472

Y Coordinate NE (km) 1200 300

~ NX 36km cells 36 32

~ NY 36km cells 31 22

Surface Met Stations (jan 2000) 273 179

Surface Met Stations (july 2001) 283 210

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 7 of 9 

# Additional analysis of rainfall will be done on a seasonal basis. Rainfall totals in each

grid cell by season will be compared to the corresponding seasonal totals at

observation sites.

Annual simulations present a challenge in terms of adequately assessing model

performance so photochemical modelers wi ll know the strengths and weaknesses of

the meteorological inputs. A report will be compiled with the following elements:

select vertical sounding plots at Upper Midwest sounding stations, daily metrics

output by METSTAT, rainfall analysis results, and any qualitative comparisons

between model output and UNISYS plots.

PERFORMANCE METRICS

The bias error (bias) is the degree of correspon dence between the mean prediction

and the mean observation, with lower nu mbers indicative of better performance.

Values less than 0 indicate under-prediction. The gross error , or mean absolute

error, is the mean of the absolute value of the residuals from a fitted statistical

model. Lower numbers indicate better model performance.

Root Mean Square Error (RMSE) is a good overall measure of model performance.

The weighting of ( prediction-observation ) by its square tend s to inflate RMSE,

particularly when extreme values are presen t. With respect to a good model the root

mean square error should approach zero. RMSE can be divided into a systematic and

unsystematic component by least-squares re gression. Since differe nces described by

systematic RMSE can be described by a linear function, they should be relatively

easy to dampen by a new parameterization of the model. Unsystematic RMSE can be

interpreted as a measure of potential accu racy or noise level (Emery et al, 2001).

With respect to a good model the systematic difference should approach zero while

the unsystematic difference approaches RMSE.

Index of Agreement is a relative measure of the degree of which predictions are

error-free. The denominator accounts for th e model's deviation from the mean of the

observations as well as to the observations deviation from their mean. It does not

provide information regarding systematic and unsystematic errors. The index of

agreement approaches one when model performance is best.

POST PROCESSING FOR PHOTOCHEMICAL MODELS

The meteorological fields output by MM5 are prepared for use by the photochemical

models with processing utilities. These programs translate certain meteorological

parameters from the MM5 grid to the photochemical grid. Additionally, these

processors must estimate parameters that are not explicitly output by MM5. Cloud

cover is not output by MM5 and must be di agnosed based on moisture ratios. Vertical

mixing is based on vertical diffusivity coefficients. This is a key variable not output by

MM5 using the configuration outlined in this protocol.

The vertical diffusivities ar e calculated inside the CM AQ model based on the PBL

height output by MM5. CAMx4 and REMSAD have vertical diffusivities based on the

O’Brien 1970 vertical diffusivity algorithm. This scheme ta kes the PBL height output

by MM5 and creates a well-mixed atmosphere inside the PBL. Th e minimum vertical

diffusivity coefficient for CAMx4, REMSAD, and CMAQ are 0.1 m 2 

# /s. The CMAQ

coefficient is established (and mo dified from the default of 1.0 m 2 

# /s) in the model

code. A processing utility was applied to the vertical diffusivity coefficient input files

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 8 of 9 k(MM5) sigma p(mb) depth(m) k(PCM) depth(m)

34 0.000 100 1841 16 5597

33 0.050 145 1466

32 0.100 190 1228

31 0.150 235 1062

30 0.200 280 939 15 2549

29 0.250 325 843

28 0.300 370 767

27 0.350 415 704 14 2533

26 0.400 460 652

25 0.450 505 607

24 0.500 550 569

23 0.550 595 536 13 1522

22 0.600 640 506

21 0.650 685 480

20 0.700 730 367 12 634

19 0.740 766 266

18 0.770 793 259 11 428

17 0.800 820 169

16 0.820 838 166 10 329

15 0.840 856 163

14 0.860 874 160 9 318

13 0.880 892 158

12 0.900 910 78 8 155

11 0.910 919 77

10 0.920 928 77 7 153

9 0.930 937 76

8 0.940 946 76 6 151

7 0.950 955 75

6 0.960 964 74 5 148

5 0.970 973 74

4 0.980 982 37 4 37

3 0.985 987 37 3 37

2 0.990 991 36 2 36

1 0.995 996 36 1 36

--SURF-- 1 1000 0 --SURF-- --SURF-- 

# for CAMx4 and REMSAD. A minimum vertical diffusivity coefficient of 1.0 m 2 

# /s is

assigned to all grid cells with an urban land use fraction up to 350 meters above

ground (model layer 5). This is done to better represent the greater vertical mixing

overnight in urban areas. Since the meteor ological processing programs for each

model not only translate data, but also di agnose certain key parameters, this step

must be scrutinized to achieve optimal model results.

The vertical resolution used in MM5

consists of 34 sigma layers that

represent the terrain following

atmosphere up to 100 millibars. The

table below displays each vertical

layer in terms of sigma level,

pressure (millibars), height above

ground level (meters) and layer

thickness (meters). The relationship

to the layer structure used in the

photochemical models is also shown.

The photochemical model layer

structure avoids la yer collapsing in

the lower boundary layer to better

resolve the mixing depth. A

compromise in the upper

troposphere is met by employing

layer collapsing to reduce

computational effort and still

maintain some upper troposphere

resolution for long-range transport.

It is difficult to establish an optimal

vertical grid resolution for ozone and

PM applications, so it should meet

certain scientific criteria outlined in

the WRAP modeling protocol

(Tonnesen et al, 2001). The laye r structure chosen for a mo deling application should

be capable of adequately resolving the di urnal variations in the boundary layer

growth and mixing, long-range transport proc esses, wind shear, as well as transport

to and from the free troposphere.

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 9 of 9 

# REFERENCES

Baker, K. MM5 Sensitivity for the Nation al RPO Grid, Presented at the Ad-Hoc

Meteorological Modeling Meet ing, Des Plaines, IL, 2002.

Dudhia, J., A nonhydrostatic version of the Penn State/NCAR mesoscale model:

Validation tests and simulation of an Atlantic cyclone and cold front, Mon. Wea. Rev.,

121, 1493-1513, 1993.

Emery, C., Tai, E., Yarwood, G., Enhanced Meteorological Modeling and Performance

Evaluation for Two Texas Ozone Episodes, Final report submitted to Texas Natural

Resources Conservati on Commission, 2001.

Grell, G. A.; J. Dudhia , and D. R. Stauffer, A description of the Fifth Generation Penn

State/NCAR Mesoscale Model (MM5), NCAR Tech. Note, NCAR TN-398-STR, 138 pp.,

1994.

Johnson, M., Meteorological Modeling Protocol: IDNR 2002 Annual MM5 Application,

2003.

Olerud, D., K. Alapat y, and N. Wheeler, Meteorological modeling of 1996 for the

United States with MM5 , Final report submitted to OAQPS, U. S. EPA, 2000.

Olerud, D., VISTAS Meteorological Modeling, Presented at the Ad-Hoc Meteorological

Modeling Meeting, Des Plaines, IL, 2003.

Tonnesen, G., Wang, Z., Lents. J., Gemmill , D., Morris, R., Modeling Protocol and

Quality Assurance Plan for the WRAP Visibility Modeling Project, 2001.

# Meteorological Modeling Performance Summary

For Application to PM2.5/Haze/Ozone Modeling Projects

Kirk Baker, LADCO

Matthew Johnson, Iowa DNR

Steven King, Illinois EPA

Wusheng Ji, Wisconsin DNR

February 18, 2005

Lake Michigan Air Di rectors Consortium

Midwest Regional Planning Organization

Des Plaines, Illinois

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 2 of 15 

# INTRODUCTION

The purpose of this document is to outline the model configuration and application of

the Fifth-Generation NCAR / Penn State Mesoscale Model (MM5) v3.6.3 to support

photochemical and emissions modeling projects (Dudhia, 1993 and Grell et al, 1994)

at Lake Michigan Air Directors Consor tium (LADCO) and th e Midwest Regional

Planning Organization (Midwest RPO). The computing platform supported by

LADCO/Midwest RPO is the Red Hat Linux op erating system and the Portland Group

Fortran compiler. MM5 consists of the Mesoscale model MM5 and a suite of preprocessors including PREGRID, REGRIDDER, RAWINS, LITTLE R, INTERPF, INTERPX,

and TERRAIN.

The model parameterizations and physics options outlined in this document were

chosen based on the results of a series of sensitivity runs. The performance of the

sensitivity tests provided a clear indication of an optimal configuration for the Upper

Midwest (Johnson, 2003). The model configuration and parameterizations outlined in

this document describe recent MM5 applications.

The annual 2002 36 km MM5 simulation was co mpleted by Matthew Johnson at Iowa

DNR. The 36/12 km 2-way nested simula tion for the summers of 2001, 2002, and

2003 were conducted jointly by Steven King at Illinois EP A and Kirk Baker at LADCO.

The 36 km non-summer portion of the a nnual 2003 simulation was conducted by

Wusheng Ji at Wisconsin DNR.

TERRAIN

Figure 1

The TERRAIN processor defines the

horizontal grid of the MM5 application.

The 24 category USGS 10 minute (~19

km) data is used for the National RPO

36-km domain, and 5 minute (~9 km)

data for 12-km domains. The National

RPO grid is a Lambert conic projection

centered at coordinates –97, 40 with

first and second true latitudes at 33

and 45 degrees (See Figure 1).

The 36 km grid contains 165 x 129

grid cells and the 12 km has 193 x 199

grid cells. The 12 km grid is two-way

nested within the mother grid to allow fi ne grid feedback into the coarse grid.

Additional options are set to allow generation of data to support the Pleim-Xu land

surface module. Variables LSMDATA and IEXTRA are both set equal to TRUE.

Table 1

Domain

ID Grid X Cells

(East-West) Y Cells (NorthSouth) Cell Size

(km) Mother

Domain ID Lower Left

X,Y of Nest

1 National RPO 165 129 36 1 1, 1

2 Upper Midwest 193 199 12 1 66, 30

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 3 of 15 

# PREGRID

The PREGRID processor converts meteorologic al analyses data such as NCEP or ETA

to an intermediate data format that the REGRIDDER processor can utilize. For

PREGRID, the following options were set:

• ETA/AWIP 3D and SF analyses data (ds609. 2) is used to initialize the REGRID

processor.

• Snow cover is estimated from water equivalent snow depth.

• The input analyses data is processed 3 hourly (10,800 seconds).

• The AWIP grib definition tables are used to map ETA data into MM5.

• The ETA skin temperature is used as th e source of sea surface temperature.

The ETA analysis files with the extensio n “.tm12” are not used since they are

the "cold start" global analysis files.

REGRIDDER

The REGRIDDER processor takes the data extracted from analyses fields and

interpolates the data to user specified pressure levels and to the user specified

horizontal grid.

LITTLE R

The RAWINS and LITTLE R processors perform objective analysis on the output from

REGRIDDER using surface and upper air obse rvation data. Since these observations

are incorporated into the ETA analysis fiel ds this step is considered redundant.

Results of sensitivity tests where ETA 3-hourly analysis was utilized to initialize with

and without RAWINS objective analysis demons trated little or no difference in model

performance (Baker, 2002).

Even though this step is redundant, LITTLE R is applied to enable surface nudging of

soil moisture and temperature in the Pleim-Xu land surface module. NCEP ADP

surface (ds 464.0) and upper air (ds 353.1 and ds 353.4) data are the appropriate

data to input into LITTLE R and/or RAWINS.

INTERPF

The INTERPF processor takes the REGRIDDER/LITTLE R output that is at standard

pressure levels and interpolates that data to the vertical grid defined by the user

(Table 2). The vertical grid is defined in terms of sigmas, where 1 is the surface and

0 is the top of the model atmosphere. The to p of the MM5 simulati on is 100 millibars,

which is approximately 15 kilometers above ground level.

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 4 of 15 

# The vertical atmosphere was resolved to

34 layers, with thinner layers in the

planETAry boundary layer. The layer

configuration was selected to capture the

important diurnal variations in the

boundary layer while also having layers

in the upper troposphere to try and

resolve convective activity. Output from

the INTERPF processor is ready for input

into MM5.

INTERPX

The INTERPX processor is used to

extract the soil temperature and soil

moisture data from MM5 output files and

overwrite the soil temperature and

moisture fields on the MMINPUT file for

the next 5 day simulation block. This

allows soil moisture and temperature to

be carried over to subsequent modeling

simulations.

For example, to simulate 20 days in 4

blocks of 5 days, the first block of 5 days

would use the standard MMINPUT to run

MM5, and the subsequent 3 blocks of 5

days would take the MM5 output and

extract soil temperature and moisture

data for the next 5 day block. This

option has been shown to introduce a

cold bias for the temperature field, particul arly in the winter months (Olerud, 2003).

INTERPX was not used for any of the MM5 simulations.

MM5

The output from INTERPF, LITTLE R, and TERRAIN processors were used to run MM5.

These files must be in the “./MM5/Run” directory and have the generic filenames

given directly out of these processors. 3D analysis nudging for the wind field,

temperatures, and moisture were applied above the boundary layer only. Analysis

nudging was not performed on the rotational wind field. In addition, the observation

nudging flag was turned off. This type of nudging is appropriate when there is a very

dense set of observation data from a field study, which this application lacked. The

default nudging weighting factors were used for all simulations: 2.5 x 10 -4 

# for wind

fields and temperatures and 1.0 x 10 -5 

# for moisture fields. Table 2

k(MM5) sigma press.(mb) height(m) depth(m)

34 0.000 10000 14662 1841

33 0.050 14500 12822 1466

32 0.100 19000 11356 1228

31 0.150 23500 10127 1062

30 0.200 28000 9066 939

29 0.250 32500 8127 843

28 0.300 37000 7284 767

27 0.350 41500 6517 704

26 0.400 46000 5812 652

25 0.450 50500 5160 607

24 0.500 55000 4553 569

23 0.550 59500 3984 536

22 0.600 64000 3448 506

21 0.650 68500 2942 480

20 0.700 73000 2462 367

19 0.740 76600 2095 266

18 0.770 79300 1828 259

17 0.800 82000 1569 169

16 0.820 83800 1400 166

15 0.840 85600 1235 163

14 0.860 87400 1071 160

13 0.880 89200 911 158

12 0.900 91000 753 78

11 0.910 91900 675 77

10 0.920 92800 598 77

9 0.930 93700 521 76

8 0.940 94600 445 76

7 0.950 95500 369 75

6 0.960 96400 294 74

5 0.970 97300 220 74

4 0.980 98200 146 37

3 0.985 98650 109 37

2 0.990 99100 73 36

1 0.995 99550 36 36

0 1.000 100000 0 --SURF--

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 5 of 15



# Table 3

Configuration 36km and 12km Domains

Explicit Moisture Mixed Phase (Reisner I)

Cumulus Kain-Fritsch 2

PBL Pleim-Chang (ACM)

Radiation RRTM

Multi-Layer Soil Model Pleim-Xu

Shallow convection No

4-D Data Assimilation Analysis nudging on above PBL



# Moist Physics Table No

Table 3 outlines the model configuration used for MM5 modeling up to the date of

this document. All simulation s use the mixed phase moisture scheme so that all four

phases of water will be explicitly output by MM5. This is important since the

photochemical model is applied for an annual basis and correctly characterizing the

phase of water is important for several physiochemical processes.

Atmospheric radiation is calculated every 15 minutes in the model. Vertical moisture

and temperature advection are set to us e linear interpolation. Other important

variables switched to ON include: moist vertical diffusion in clouds, temperature

advection using potential temperature, diff usion using perturbation temperature, 3D

coriolis force, and upper radiative boundar y condition. Sea surface temperature and

snow cover are set to vary with time.

The Pleim-Xu land surface module requires that 3 additional variables be set in the

MM5 deck: ISMRD, NUDGE, and IFGROW. ISMRD is set to use soil moisture fields

from the ETA analyses. NUDGE is set to nudge soil moisture data to the analyses

fields. IFGROW is set to option 2, which takes vegETAtive growth into account based

on vegETAtive fraction data from the TERRAIN file.

MODEL EXECUTION

MM5 was executed in 5 day blocks (7200 minu te simulation) with a 90 second time

step. Model results are output every 60 minutes and the model output files are

written out (i.e. split) every 24 hours to accommodate post-processing utilities. The

2002 annual simulation was initiated at 12Z December 16, 2001 and was run

through 12Z January 1, 2003.

MODEL PERFORMANCE EVALUATION

Model performance was assessed quantitative ly with the METSTAT tool from Environ

(Emery et al, 2001). The metrics used to quantify model performance include mean

observation, mean prediction , bias error, gross error, root mean square error

(including systematic and unsystematic components), and in dex of agreement.

These metrics compare model predictions to Techniques Data Laboratory U.S. and

Canada surface hourly observ ations (NCAR dataset ds472.0).

The MM5 model outputs predictions appr oximately 15 meters above the surface

while observations are at 10 meters. METSTAT applies micro-meteorological

adjustments to the MM5 estimates to a pproximate 10-m values. MM5 also outputs

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 6 of 15 

# near-instantaneous values (90 second time step) as opposed to the values with

longer averaging times taken at monitor stations. This should be considered when

interpreting model performance metrics and making qualitative comparisons to

satellite maps.

Figure 2

Model performance metrics were applied

to a sub-region of the Upper Midwest

(Figure 2), meaning the metrics are

hourly spatial averages of multiple

monitor locations. This will be done to

gain a better understanding of MM5

performance in the Great Lakes region. All

metrics are calculated within the specified

model performance region for an hourly

and daily time period (0Z to 23Z). Mean

wind direction is es timated by averaging

the U and V wind vector components and

converting those averages to compass

degrees.

Additional analysis of rainfall is done on a monthly basis. Rainfall observation

analysis data is available from the National Weather Service Climate Prediction

Center on an hourly basis for the Continental United States

( http://www.cpc.ncep.noaa.gov/produ cts/precip/realtime/retro.html ). The rainfall

analysis resolution is 0.25 degree longitude by 0.25 degree latitude and extends

from 140W to 60W and 20N to 60N.

PERFORMANCE METRICS

The bias error (bias) is the degree of correspon dence between the mean prediction

and the mean observation, with lower nu mbers indicative of better performance.

Values less than 0 indicate under-prediction. The gross error , or mean absolute

error, is the mean of the absolute value of the residuals from a fitted statistical

model. Lower numbers indicate better model performance.

Root Mean Square Error (RMSE) is a good overall measure of model performance.

The weighting of ( prediction-observation ) by its square tend s to inflate RMSE,

particularly when extreme values are presen t. With respect to a good model the root

mean square error should approach zero. RMSE can be divided into a systematic and

unsystematic component by least-squares re gression. Since differe nces described by

systematic RMSE can be described by a linear function, they should be relatively

easy to dampen by a new parameterization of the model. Unsystematic RMSE can be

interpreted as a measure of potential accu racy or noise level (Emery et al, 2001).

With respect to a good model the systematic difference should approach zero while

the unsystematic difference approaches RMSE.

Index of Agreement is a relative measure of the degree of which predictions are

error-free. The denominator accounts for th e model's deviation from the mean of the

observations as well as to the observations deviation from their mean. It does not

provide information regarding systematic and unsystematic errors. The index of

agreement approaches one when model performance is best.

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 7 of 15 

# MODEL PERFORMANCE: ANNUAL 2002 36 km

The biggest issue with the pe rformance in the Great Lakes region is the existence of

a cool diurnal temperature bias in the wint er and warm temperature bias over-night

during the summer. The wind fields are good , although it should be noted that some

diurnal errors in wind speed may not be ad equately represented in the daily metric

plots. The model appears to be too wet in the late spring and summer months in the

Eastern United States. Mosaic tile plot s showing daily bias and gross error for

humidity, temperature, wind speed, and wind direction are shown in the Figures

below.

Performance in the other regions is simila r to the Great Lakes except where noted.

The Ohio Valley has slightly worse perfor mance for day-time wind speed and wind

direction. The central Plaines (CenrapN and CenrapS) have low peak wind speeds

and a warm temperature bias in the sout hern Plaines/Texas re gion due to overpredicted night-times lows.

The Mid-Atlantic region has performance similar to the Great Lakes region. The

southeast region is very wet compared to observations during the entire year. The

northeast region shows degraded wind fi eld performance compared to the other

Eastern regions.

Figure 3. Daily humidity bias and gross e rror (day 1 top row, last day of month

bottom row)

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 8 of 15 

# Figure 4. Daily temperature bias and gross error (day 1 top row, last day of month

bottom row)

Figure 5. Daily wind directio n bias and gross error (day 1 top row, last day of month

bottom row)

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 9 of 15 

# Figure 6. Daily wind speed bias and gross error (day 1 top row, last day of month

bottom row)

MODEL PERFORMANCE: Annual 2003 36 km

Model performance for the 2003 36 km simu lation is very similar to the 2002 36 km

annual simulation. Similar diurnal and seasonal issues are seen for both annual

simulations in the Great Lakes/Upper Midw est region. The humidity error is highest

in the summer and tends to be over-predict ed. There is a cool temperature bias in

the winter and slight warm bias in the summer. Temperat ure errors are highest in

the winter months.

The wind fields are good; wind direction errors are highest in the summer and no

seasonal trends are seen in the bias. Daily wind speed bias and error appear good,

although similar diurnal performance proble ms are seen that are similar to the 2002

annual simulation. Rainfall is over-predicted in terms of magnitude in the summer

months. The spatial patterns of rainfall tend to look good and performance for

rainfall is very good in the winter months.

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 10 of 15 

# Figure 7. Daily humidity bias and gross e rror (day 1 top row, last day of month

bottom row)

Figure 8. Daily temperature bias and gross error (day 1 top row, last day of month

bottom row)

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 11 of 15 

# Figure 9. Daily wind directio n bias and gross error (day 1 top row, last day of month

bottom row)

Figure 10. Daily wind speed bias and gross error (day 1 top row, last day of month

bottom row)

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 12 of 15 

# MODEL PERFORMANCE: RAINFALL (2002 and 2003)

Convective and non-convective rainfall was to taled for each 36 km grid cell in the

Eastern United States by mont h. The National Weather Serv ice rainfall analysis field

was mapped to the same grid projection and domain and totaled by month. These

plots are shown side by side in Appendix A.

The modeled and observed rainfall totals show good agreement spatially and in

terms of magnitude in the winter, fall, and early spring months of 2002 and 2003.

There are large over-predictions of rainfall in the late spring and summer months.

These over-predictions are seen spatially and in magnitude over the entire domain,

particularly in the Southeast United States . These over-predictions are likely due to

excessive convective rainfall being predicted in MM5. The summer month overpredictions are seen in 2002 and 2003 and vary in magnitude.

MODEL PERFORMANCE: 36 km v 12 km (2002 annual simulation)

The 12 km nest was 2-way nested and the feedback option was turned off, thus

preventing the 12 km data from overwritin g the 36km fields. This allows for a true

evaluation of 36 v. 12 km performance. The boundary conditions are updated for the

12 km grid at every time-step in this method of 2-way nesting in MM5. This provides

an advantage over the hourly updates to the 12 km grid if running MM5 in 1-way

nesting mode.

Model performance comparing the 36 and 12 km performance in the Great Lakes

region is shown in the form of mosaic difference plots in Appendix B. In the Great

Lakes region, a winter-time performance degradation is seen in the 12 km run

compared to the 36 km run for the wind field and temperature. The temperatures

have a cold bias at 36 km and get even colder at 12 km.

Daytime wind speed bias improves at 12 km, but nighttime bias degrades. The

mixing ratio performance improves slightly in the Ohio Valley region at 12 km.

Overall, the 12 km grid results in little if any benefit in terms of statistical model

performance.

MODEL PERFORMANCE: Summers of 2001, 2002, and 2003

The model performance for the summers of 2001, 2002, and 2003 focusing on the

Great Lakes Region is shown in prediction-o bservation time series plots in Appendix

C. The predictions and observ ations are averages over many observation sites in the

sub-region of interest. A summary of pe rformance in the Upper Midwest is given

below.

Summer of 2001:

• MM5 performs well for temperatures and wind field

• Not quite capturing temperature peaks during August 2001 ozone episode

• Overall moisture and rainfall slightly over-predicted, but spatial patterns look

good

Summer of 2002:

• Capturing temperate peaks ok, but ov er-predicting nigh t time minimums

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 13 of 15 

# • Over-predictions of moisture and rainfall in the Ohio Valley; large overpredictions of rainfall in the South

• Wind fields look ok; diurnal wind speed day time peaks not always captured

during the ozone episodes

Summer of 2003:

• Wind field ok; diurnal wind speed patterns not quite captured in August ozone

episode

• Temperatures ok; not getting night time minimums consistently

• Rainfall ok in Upper Midwest; over-predictions in the South

MODEL PERFORMANCE: New 2002 v. Old 2002 Simulation

An annual MM5 simulation for 2002 was previously completed using an older version

of MM5, simple ice microphysics, surface nudging, and continuous soil moisture

(INTERPX). The figures in Appendix D show the differences be tween the previous

annual simulation and the cu rrent annual simulation with mosaic difference plots.

Temperatures in general show a slight degradation in the new simulation, in

particular in the winter. Wind speed pe rformance improved and wind direction

improved in the Central and Western United States. Humidity results are almost the

same in both simulations. Th e difference in model perfor mance is largely due to the

inclusion of surface nudging in the prev ious annual 2002 simulation. That practice

improves surface performance statistics but tends to create unusual meteorological

artifacts like super-adiabatic lapse rates.

MODEL PERFORMANCE: Other Notes

The index of agreement hourly metrics for humidity are interesting in that periods of

poor index of agreement do not necessari ly match up to poor error. Since the

metrics are averaged over a sub-domain, the hourly index of agreement tends to

degrade when fronts move across the doma in. This causes very different model

predictions and observations on the front and back side of the frontal passage.

This lack of agreement results in a degrad ed index of agreement metric even though

the error and actual model performance is not poor. This should be acknowledged

when interpreting index of agreement over a spatial region; degraded IOA may

actually show a frontal passage and not truly reflect poor performance.

The time series plots below show RMSE and IOA for June 2002. Frontal passages

pass through the area on the 16 th 

# , 18 th 

# , 24 th 

# , and 27 th 

# and degrade IOA.

Humidity root mean squa re error (top) and index of agreement (bottom)

RMSE Humidity

0 0.5

1 1.5

2 2.5

3 3.5

4

6/

2 6/ 3

6/ 4

6/ 5

6/

6 6/ 7

6/ 8

6/

9 6/10

6/1

1 6 /12

6 /13

6/1

4 6/

15

6 /16

6/17

6/1

8 6 /19

6 /20

6/21 6/

22

6 /23

6/24

6/25 6 /26

6 /27

6/28 6 /29

6 /30

7 / 1g/kg RMSEHum RMSESHum RMSEUHum

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 14 of 15 

# CONCLUSION & DISCUSSION

This document contains an overview of MM5 performance. Th e results shown are

intended to provide a context for future meteorological modeling applications, to

identify deficiencies in the MM5 output that may result in poor photochemical model

performance, and to increase confidence in the photochemical modeling applications

that use these meteorological modeling estimates. IOA Humidity

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

6 / 2 6

/ 3

6/ 4

6/ 5

6 / 6

6/ 7

6/ 8

6 / 9 6 / 10

6 /11

6/12

6 / 13

6 /14

6/15

6/16

6 / 17

6 /18

6/19

6/20

6 / 21

6/22

6/23

6 / 24

6 /2 5 6/26

6/27

6 / 28

6 /29

6/30 7 / 1 IOAHum

# Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO

Page 15 of 15 

# REFERENCES

Baker, K. MM5 Sensitivity for the Nation al RPO Grid, Presented at the Ad-Hoc

Meteorological Modeling Meet ing, Des Plaines, IL, 2002.

Dudhia, J., A nonhydrostatic version of the Penn State/NCAR mesoscale model:

Validation tests and simulation of an Atlantic cyclone and cold front, Mon. Wea. Rev.,

121, 1493-1513, 1993.

Emery, C., Tai, E., Yarwood, G., Enhanced Meteorological Modeling and Performance

Evaluation for Two Texas Ozone Episodes, Final report submitted to Texas Natural

Resources Conservati on Commission, 2001.

Grell, G. A.; J. Dudhia , and D. R. Stauffer, A description of the Fifth Generation Penn

State/NCAR Mesoscale Model (MM5), NCAR Tech. Note, NCAR TN-398-STR, 138 pp.,

1994.

Johnson, M., Meteorological Modeling Protocol: IDNR 2002 Annual MM5 Application,

2003.

Olerud, D., K. Alapat y, and N. Wheeler, Meteorological modeling of 1996 for the

United States with MM5 , Final report submitted to OAQPS, U. S. EPA, 2000.

Olerud, D., VISTAS Meteorological Modeling, Presented at the Ad-Hoc Meteorological

Modeling Meeting, Des Plaines, IL, 2003.

Tonnesen, G., Wang, Z., Lents. J., Gemmill , D., Morris, R., Modeling Protocol and

Quality Assurance Plan for the WRAP Visibility Modeling Project, 2001.

Yarwood, G. Improvements to CAMx Wet Depostion, Presented at the Spring

Modeling Meeting, Des Plaines, IL, 2004.

# APPENDIX A

Rainfall AnalysisJanuary 2002February 2002March 2002April 2002May 2002June 2002July 2002August 2002September 2002October 2002November 2002December 2002January 2003February 2003March 2003April 2003May 2003June 2003July 2003August 2003September 2003October 2003November 2003December 2003

# APPENDIX B

12 v 36 km Performance

# Observed/Predicted Windspeed

2 3 4 5 6 7 8

1/

1 1/ 2

1

/

3

1/ 4 1/

5

1

/

6 1/

7

1/

8

1/

9 1/10 1/1

1

1

/1

2

1

/1

3 1/14 1/1

5

1

/1

6 1/17 1/1

8

1/1

9

1

/2

0 1/21 1/2

2

1

/2

3 1/24 1/25 1/2

6 1/

2 7 1/28 1/2

9

1

/3

0 1/31m/s ObsWndSpd 36km Predicted 12km Predict ed 

# Bias Windspeed

-1.5

-1 -0.5 0 0.5

1 1.5

1/

1

1

/ 2

1

/

3

1/ 4 1/

5

1/

6

1

/

7

1/ 8 1/ 9

1

/1

0

1

/1

1 1/12 1/1 3

1

/1

4

1

/1

5 1/16 1/17 1/1

8

1

/1

9 1/20 1/21 1/2

2

1

/2

3

1

/2

4 1/25 1/26 1/2

7

1

/2

8 1/

2 9 1/30 1/3 1m/s 36km Bias 12km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

1/

1

1

/ 2

1

/ 3

1/ 4 1/

5

1/

6

1/

7

1/

8

1/ 9 1/10 1/1 1

1

/1

2 1/

1 3 1/

1 4 1/15 1/1

6

1/1

7

1

/1

8 1/19 1/20 1/21 1/2 2

1

/2

3 1/

2 4 1/

2 5 1/26 1/2

7

1/2

8

1

/2

9 1/30 1/31deg ObsWndDir 36km Predicted 12km Predict ed 

# Bias Wind Direction

-30

0 30

1/

1

1

/ 2

1

/ 3

1/ 4 1/

5

1/

6

1/

7

1/

8

1/ 9 1/10 1/1 1

1

/1

2 1/

1 3 1/

1 4 1/15 1/1

6

1/1

7

1

/1

8 1/19 1/20 1/21 1/2 2

1

/2

3 1/

2 4 1/

2 5 1/26 1/2

7

1/2

8

1

/2

9 1/30 1/31deg 36km Bias WD 12km Bias WD 

# Great Lakes Jan 2002

# Bias Temperature

-5 -4 -3 -2 -1 0 1 2

1/

1

1/ 2 1/ 3 1/ 4 1/

5

1/

6 1/

7 1/

8

1/

9 1/

10

1/

11

1/12 1/13 1/14 1/15 1/

16

1/

17

1/

18

1/19 1/20 1/21 1/22 1/

23

1/

24

1/2 5

1/

26

1/

27

1/

28

1/

29

1/30 1/31K 36 km Bias T 12 km Bias T



# Observed/Predicted Temperature

255 260 265 270 275 280

1/

1

1/ 2 1/ 3 1/ 4 1/

5

1/

6 1/

7 1/

8

1/

9 1/

10

1/

11

1/12 1/13 1/14 1/15 1/

16

1/

17

1/

18

1/19 1/20 1/21 1/22 1/

23

1/

24

1/2 5

1/

26

1/

27

1/

28

1/

29

1/30 1/31K ObsTemp 36 km Pred_T 12 km Pred_T 

# Predicted/Observed Humidity

0 1 2 3 4 5 6

1/

1

1/ 2 1/ 3 1/ 4 1/

5

1/

6 1/

7 1/

8

1/

9 1/

10

1/

11

1/12 1/13 1/14 1/15 1/

16

1/

17

1/

18

1/19 1/20 1/21 1/22 1/

23

1/

24

1/2 5

1/

26

1/

27

1/

28

1/

29

1/30 1/31g/kg ObsHum 36km Pred q 12 k Pred q 

# Bias Humidity

-0.4 -0.2

0 0.2 0.4 0.6 0.8

1 1.2

1/

1

1/ 2 1/ 3 1/ 4 1/

5

1/

6 1/

7 1/

8

1/

9 1/

10

1/

11

1/12 1/13 1/14 1/15 1/

16

1/

17

1/

18

1/19 1/20 1/21 1/22 1/

23

1/

24

1/2 5

1/

26

1/

27

1/

28

1/

29

1/30 1/31g/kg 36 km Bias q 12 km Bias q 

# Great Lakes Jan 2002

# Observed/Predicted Windspeed

2 4 6 8

6/

1

6/ 2 6

/ 3

6/

4

6/ 5 6

/ 6

6/ 7 6

/ 8

6/

9 6

/10

6/1 1 6/12 6/1 3 6/14 6

/ 1 5 6/1 6 6/17 6

/ 1 8 6/19 6

/ 2 0 6/2 1

6 /22

6

/ 2 3 6/24 6

/ 2 5 6/26

6 /27

6

/ 2 8 6/29 6

/30m/s ObsWndSpd 36km Predict ed 12km Predict ed



# Bias Windspeed

-2 -1 0 1 2

6/

1

6/ 2 6

/ 3

6/

4

6/ 5 6

/ 6

6/ 7 6

/ 8

6/

9 6

/10

6/1 1 6/12 6/1 3 6/14 6

/ 1 5 6/1 6 6/17 6

/ 1 8 6/19 6

/ 2 0 6/2 1

6 /22

6

/ 2 3 6/24 6

/ 2 5 6/26

6 /27

6

/ 2 8 6/29 6

/30m/s 36 km Bias 12 km Bias



# Bias Wind Direction

-30

0 30

6/

1

6/ 2 6

/ 3

6/

4

6/ 5 6

/ 6

6/ 7 6

/ 8

6/

9 6

/10

6/1 1 6/12 6/1 3 6/14 6

/ 1 5 6/1 6 6/17 6

/ 1 8 6/19 6

/ 2 0 6/2 1

6 /22

6

/ 2 3 6/24 6

/ 2 5 6/26

6 /27

6

/ 2 8 6/29 6

/30deg 36km Bias 12km Bias 

# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

6/

1

6/ 2 6

/ 3

6

/ 4

6

/ 5

6

/ 6

6/

7

6

/ 8

6/

9 6

/ 1 0 6/11 6

/12

6/13 6

/ 1 4 6/15 6

/ 1 6 6/17 6/1 8 6/19 6

/ 2 0

6 /21

6/2 2 6/23 6/2 4

6 /25

6/2 6

6 /27

6/2 8 6

/ 2 9 6/3 0deg ObsWndDir 36 km PredWnd 12 km PredWndDir 

# Great Lakes June 2002

# Observed/Predicted Temperature

270 275 280 285 290 295 300 305 310

6/

1

6/ 2 6/

3

6/ 4 6/

5

6/ 6 6/

7

6/

8

6/

9 6/10 6/

11

6/12 6/13 6/

14

6/15 6/

16

6/17 6/

18

6/

19

6/20 6/

21

6/22 6/

23

6/

24

6/

25

6/26 6/

27

6/28 6/

29

6/30K ObsTemp 36 km 12 km 

# Bias Temperature

-3 -2 -1 0 1 2 3

6/

1

6/ 2 6/

3

6/ 4 6/

5

6/ 6 6/

7

6/

8

6/

9 6/10 6/

11

6/12 6/13 6/

14

6/15 6/

16

6/17 6/

18

6/

19

6/20 6/

21

6/22 6/

23

6/

24

6/

25

6/26 6/

27

6/28 6/

29

6/30K 36 km Bias 12km Bias 

# Predicted/Observed Humidity

0 2 4 6 8 10 12 14 16 18

6/

1

6/ 2 6/

3

6/ 4 6/

5

6/ 6 6/

7

6/

8

6/

9 6/10 6/

11

6/12 6/13 6/

14

6/15 6/

16

6/17 6/

18

6/

19

6/20 6/

21

6/22 6/

23

6/

24

6/

25

6/26 6/

27

6/28 6/

29

6/30g/kg ObsHum 36 km Predicted 12k m Predict ed 

# Bias Humidity

-2 -1 0 1 2 3 4

6/

1

6/ 2 6/

3

6/ 4 6/

5

6/ 6 6/

7

6/

8

6/

9 6/10 6/

11

6/12 6/13 6/

14

6/15 6/

16

6/17 6/

18

6/

19

6/20 6/

21

6/22 6/

23

6/

24

6/

25

6/26 6/

27

6/28 6/

29

6/30g/kg 36 km bias 12 km Bias 

# Great Lakes June 2002

# Observed/Predicted Windspeed

0 2 4 6

7/

1

7/ 2 7

/ 3

7

/ 4

7

/ 5

7

/ 6

7/

7

7

/ 8

7/

9

7

/ 1 0 7/11 7

/12

7/13 7

/ 1 4 7/15 7

/ 1 6 7/17 7/1 8 7/19 7

/ 2 0

7 /21

7/2 2 7/23 7/2 4

7 /25

7/2 6

7 /27

7/2 8 7

/ 2 9 7/3 0 7

/31m/s ObsWndSpd 36 km winds 12 km winds



# Bias Windspeed

-2 -1 0 1

7/

1

7/ 2 7

/ 3

7

/ 4

7

/ 5

7

/ 6

7/

7

7

/ 8

7/

9

7

/ 1 0 7/11 7

/12

7/13 7

/ 1 4 7/15 7

/ 1 6 7/17 7/1 8 7/19 7

/ 2 0

7 /21

7/2 2 7/23 7/2 4

7 /25

7/2 6

7 /27

7/2 8 7

/ 2 9 7/3 0 7

/31m/s 36 km Bias 12 km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

7/

1

7/ 2 7

/ 3

7

/ 4

7

/ 5

7

/ 6

7/

7

7

/ 8

7/

9

7

/ 1 0 7/11 7

/12

7/13 7

/ 1 4 7/15 7

/ 1 6 7/17 7/1 8 7/19 7

/ 2 0

7 /21

7/2 2 7/23 7/2 4

7 /25

7/2 6

7 /27

7/2 8 7

/ 2 9 7/3 0 7

/31deg ObsWndDir 36 km PredWnd 12 km PredWndDir



# Bias Wind Direction

-30

0 30

7/

1

7/ 2 7

/ 3

7

/ 4

7

/ 5

7

/ 6

7/

7

7

/ 8

7/

9

7

/ 1 0 7/11 7

/12

7/13 7

/ 1 4 7/15 7

/ 1 6 7/17 7/1 8 7/19 7

/ 2 0

7 /21

7/2 2 7/23 7/2 4

7 /25

7/2 6

7 /27

7/2 8 7

/ 2 9 7/3 0 7

/31deg 36km Bias 12km Bias 

# Ohio Valley July 2002

# Predicted/Observed Humidity

10 11 12 13 14 15 16 17 18

7/ 1 7/ 2

7/

3

7/

4

7/ 5 7/ 6 7/

7

7/

8

7/ 9

7/10 7/11 7/

12

7/

13

7/

14

7/

15

7/16 7/17 7/18 7/

19

7/

20

7/

21

7/

22

7/23 7/24 7/

25

7/2 6 7/27 7/28 7/29 7/

30

7/

31g/kg ObsHum 36km Pred q 12 k Pred q 

# Bias Humidity

-3 -2 -1 0 1 2 3 4

7/

1

7/ 2 7/ 3 7/ 4 7/

5

7/

6 7/

7 7/

8

7/

9 7/

10

7/

11

7/12 7/13 7/14 7/15 7/

16

7/

17

7/

18

7/19 7/20 7/21 7/22 7/

23

7/

24

7/2 5

7/

26

7/

27

7/

28

7/

29

7/30 7/31g/kg 36 km Bias q 12 km Bias q



# Bias Temperature

-3 -2 -1 0 1 2 3

7/

1

7/ 2 7/ 3 7/ 4 7/

5

7/

6 7/

7 7/

8

7/

9 7/

10

7/

11

7/12 7/13 7/14 7/15 7/

16

7/

17

7/

18

7/19 7/20 7/21 7/22 7/

23

7/

24

7/2 5

7/

26

7/

27

7/

28

7/

29

7/30 7/31K 36 km Bias T 12 km Bias T



# Observed/Predicted Temperature

280 285 290 295 300 305 310

7/

1

7/ 2 7/ 3 7/ 4 7/

5

7/

6 7/

7 7/

8

7/

9 7/

10

7/

11

7/12 7/13 7/14 7/15 7/

16

7/

17

7/

18

7/19 7/20 7/21 7/22 7/

23

7/

24

7/2 5

7/

26

7/

27

7/

28

7/

29

7/30 7/31K ObsTemp 36 km Pred_T 12 km Pred_T 

# Ohio Valley July 2002

# APPENDIX C

Performance for Summers 2001, 2002, and 2003JUNE 2001June 2001JULY 2001July 2001AUGUST 2001August 2001JUNE 2002June 2002JULY 2002July 2002AUGUST 2002August 2002JUNE 2003June 2003JULY 2003July 2003AUGUST 2003August 2003Upper Midwest High Ozone Days• 2001:– June 13-14, 18-19, 25-30– July 16-20; July 31 – Aug 2– Aug 5-8• 2002:– June 19-25– July 7-8, 14-17– Aug 1-4, 9-13• 2003:– June 22-26– July 1-3– Aug 20-21

# APPENDIX D

Performance Comparison: Old v. New Annual 2002 Simulation

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# Meteorological Model Performa nce Evaluation of an

Annual 2002 MM5 (version 3.6.3) Simulation

Matthew T. Johnson

Iowa Department of Natural Resources

2004 - 2007

v2.0.3

# Table of Contents

1. Introduction ................................................................................................................ . 3

1.1 Background....................................................................................................... 3

1.2 Model Selection ................................................................................................ 3

2. Sensitivity Projects...................................................................................................... 4

3. Modeling System Configuration ................................................................................. 5

3.1 Overview........................................................................................................... 5

3.1.1 Terrain......................................................................................................... 5

3.1.2 PREGRID/REGRIDDER ........................................................................... 5

3.1.3 LIITLE_R ................................................................................................. 10

3.1.4 INTERPF .................................................................................................. 10

3.2 MM5 ............................................................................................................... 12

3.3 Computational Summary ................................................................................ 13

4. Model Performance Evaluation................................................................................. 14

4.1 Background..................................................................................................... 14

4.2 Methods........................................................................................................... 14

4.3 Statistical Measures ........................................................................................ 15

5. Surface Evaluation (36 km)....................................................................................... 18

5.1 Great Lakes ..................................................................................................... 18

5.2 Northern and Southern Cenrap ....................................................................... 18

5.3 Ohio Valley..................................................................................................... 19

5.4 Iowa................................................................................................................. 19

5.5 Eastern Regions .............................................................................................. 19

5.6 Western Regions ............................................................................................. 19

6. Twelve Kilometer Evaluation ................................................................................... 30

6.1 Daily Averaged Statistics................................................................................ 30

6.2 Hourly Statistics.............................................................................................. 39

7. Upper Air Evaluation ................................................................................................ 43

7.1 Soundings........................................................................................................ 43

7.2 Precipitation .................................................................................................... 43

7.3 PBL Depths..................................................................................................... 45

8. Conclusions ............................................................................................................... 48

9. References ................................................................................................................. 4 9

Appendix A....................................................................................................................... 51

Appendix B ....................................................................................................................... 52

Eastern Subdomains................................................................................................... 52

Western Subdomains ................................................................................................. 58

Appendix C ....................................................................................................................... 66

CenrapN: January 2002 ............................................................................................ 66

CenrapN: June 2002 ................................................................................................. 68

OhioVal: January 2002............................................................................................. 70

OhioVal: June 2002 .................................................................................................. 72

Iowa: January 2002................................................................................................... 74

Iowa: June 2002........................................................................................................ 76

2

# 1. INTRODUCTION

1.1 B ACKGROUND



# Projects pursing PM2.5, 8-hour ozone, and regiona l haze are generating modeling requirements

at spatial and temporal scales only recently confronted within the regulatory air quality

community. The scope of recent legislative and executive decisi ons has created the need to

implement sophisticated models developed fo r regional scale multi-pollutant environments

encompassing diverse climatological regimes. Computational limitations have historically

bound the modeler’s ability to investigate br oad and complex scenarios with sufficient

resolution. Exponential growth in computational efficiency has par tially minimized this hurdle.

As scientific theory and model complexity evolve, computational innovations remain moderately

offset. Currently, a balance has been achieved which permits the development of large modeling

databases such as annual c ontinental scale simulations.

Annual continental scale air quality simu lations require the implementation of a triumvirate

modeling system composed of meteorolog ical, emissions, and air quality models.

Meteorological modeling is the first component a ddressed as meteorological data supports both

the emissions and air quality models. In pr eparation for regulator y requirements involving

regional haze, PM2.5, and ozone, the Iowa Depart ment of Natural Resources (IDNR) developed

a continental scale annual meteorological dataset designed for use in air quality applications.

This document details the methods employed to create the annual meteor ological simulation and

provides performance evaluation results.



# 1.2 M ODEL 

# S ELECTION



# Due to scientific progression, historical applica tion, community support, and availability, the

Fifth Generation Penn State University/National Center for Atmospheric Research Mesoscale

Model (MM5) was selected for the development of an annual meteorological dataset. Originally

formulated in the 1970s at Penn State and fi rst documented by Anthes and Warner (1978), the

MM5 modeling system maintains its status as a state-of-the-science 1 

# model through

enhancements provided by a broad user community ( e.g. Chen and Dudhia, 2001; Dudhia, 1993;

Stauffer and Seaman, 1990; Stauffer and Se aman, 1991; Xiu and Pleim, 2000). The MM5

modeling system is routinely employed in operati onal forecasting frameworks as well as research

applications spanning meteorologi cal disciplines from synoptic to mesoscale. Utilization of

MM5 within air quality applications is also a conventional practice. The MM5 modeling system

was recently selected to generate three c ontinental scale annual simulations: 1996, 2001, and

2002. The 1996 and 2001 simulations were conducte d through EPA contracts (Olerud et al.,

2000; McNally, 2003). The 2002 simulation was conducted in support of regional haze

modeling for the Visibility Improvement – Stat e and Tribal Association of the Southeast

(VISTAS) regional planning organization (RPO) (Olerud and Sims, 2004). This list is not

exhaustive as both public and pr ivate organizations continue to pursue annual meteorological

modeling episodes.

Additional information regarding MM5 is available at: http://www.mmm.ucar.edu/mm5/

1 

# True during project implementation. MM5 is no longer regularly updated as the focus has shifted to WRF.

# 2. SENSITIVITY PROJECTS

The MM5 modeling system consists of several pre-processors, the core prognostic model, and

post-processing tools. E ach component contains hi ghly configurable control files; together they

control the aspects of grid structure, first-guess fields, model physics, temporal operation, and

ultimately results visualization. The inheri t complexity of the MM5 modeling system

complicates the development of a sound model c onfiguration suitable fo r regional scale annual

episode air quality applications. Although the comp lete matrix of configuration options reduces

in size as inappropriate options are eliminated, a large matr ix of potentially acceptable model

configurations remains with mo st applications. The first st ep in developing the annual MM5

dataset was therefore completion of a series of sensitivities studies designed to identify the

configuration yielding optimum results.

The first sensitivity study project began in 2002 and involved a collaborat ive pr oject lead by

Kirk Baker with the Lake Mi chigan Air Directors Consorti um (LADCO) and Matthew Johnson

(IDNR). Wyat Appel and Mike Abraczinskas w ith the North Carolina Division of Air Quality

participated through the generation of a summary analysis for select sensitivity runs. The project

was conducted in coordination with sensitivity work performed by Dennis McNally (with Alpine

Geophysics). Components evaluated included, for example, PBL schemes, microphysical

schemes, convective parameterizations, land su rface parameterizations, and snow models. Two

one-month long episodes were selected for evaluation, January and July of 2001. The

performance evaluation of each se nsitivity run included, but was not limited to, temperatures,

wind vectors, cloud cover, precipitation, and mixing ratios.

Following the sensitivity study, the IDNR comple ted a 2002 annual simulation. This simu lation

utilized surface moisture and temperature nudgi ng. Within implementation of the Pleim-Xiu

(PX) land surface model (LSM), soil moisture and soil temperatures were modeled in continuum

from one 5-day episode block to the next. The model performance evaluation revealed an

extreme cold bias over the Central U.S. While unr elated to the cold bias, utilization of surface

nudging techniques was abandoned following discussi on with the modeling community, as this

practice has lead to the generation of super-adia batic lapse rates near th e surface. The optimum

IDNR/LADCO configuration was thus modified accordingly and this annual simulation was

deemed unsuitable for use in air quality modeling projects.

In a similar timeframe, VISTAS contracted with Baron Advanced Meteor ological System s, LLC

(BAMS) for the development of an annual MM5 dataset (Olerud and Sims, 2004). The work of

VISTAS (through Olerud and Sims, 2004) also included a series of sensitivity studies.

Independent results from the VISTAS project yiel ded findings similar to the conclusions reached

by IDNR and LADCO. The compilation of all project results subsequently produced the

configuration utilized by the IDNR in development of an annual metrological dataset suitable for

regional scale air quality modeling.

4

# 3. MODELING SYSTEM CONFIGURATION

3.1 O VERVIEW



# Version 3.6.3 of the MM5 modeling sy stem was utilized in the second 1 

# (and final) 2002 IDNR

annual meteorological simulation. The 3.6.3 rel ease represented the mo st current version

available at the time of project inception. Other than the necessary configuration parameters, no

modeling system code modifications capab le of altering results were rendered.

3.1.1 TERRAIN

The terrain processor is used to define grid structure and assign va rious surface features. Terrain

elevation, the dominant landuse cate gory, and vegetative and soil da ta were assigned using the 2-

m inute 24-category USGS data. The horizontal gr id structure consists of a 36 km domain

conforming to the RPO meteorol ogical grid specifications. A nested 12 km grid was also

included. The RPO 36 km meteorological doma in consists of a Lambert Conic Conformal

projection centered at 90o W longit ude, 40o N latitude, with true la titudes of 33 and 45o N. The

horizontal extent of the RPO domain was engi neered according to th e bounds of the Eta 212

grid. Domain development involved the impl ementation of TERRAIN through a series of

sensitivity runs designed to extract the largest domain which remains within the borders of the

Eta 212 grid. The 12 km grid was designed to achieve a balance between computational

resources while maximizing coverage of Iowa-cen tric upwind and downwind flows. Both grid

structures are described in Tabl e 3.1 and depicted in Figure 3.1.

Table 3.1. Grid data, referencing MM5 terminol ogy specifications refer to dot points.

Grid Resolution

(km) NX NY Nest

Location

(x,y) Southwest

Coordinate

(km offset)

1 36 165 129 1,1 (-2952, -2304)

2 12 193 199 66,30 (-612, -1260)

3.1.2 PREGRID/REGRIDDER

The PREGRID proces sor prepares archived gri dded me teorological data for use within MM5

through conversion to an inte rmediate data format readab le by MM5. REGRIDDER invokes a

horizontal interpolation scheme to translate data to the MM5 domain. The 3-hour Eta analysis

and surface fields (ds609.2) were used to supply initial and boundary conditions to MM5. As the

Eta analysis fields obtained from NCAR are a compressed (tar) file, the data were first

uncompressed prior to use within PREGRID. The tar files also include the undesirable 12 hourly

cold start files. All cold start files (*.tm12) were dele ted prior to running PREGRID.

1 

# The first simulation was deemed unsuitable for use in air quality modeling projects and has been deleted.



# 5

# Figure 3.1. Twelve and 36 km domains utilized in the IDNR 2002 MM5v363 annual simulation.

In the first IDNR 2002 MM5 simulation, NCEP da ta was included in PREGRID to supply timevariant sea-s urface temperature (SST) data, as th e Eta surface files supply only a time-invariant

SST approximation known as skin-temperature. U pon further examination of SST data sources,

the temporally variable NCEP SST data was f ound to lead to unrealisti c diurnal temperature

profiles over the Great Lakes and near shorel ines. Figure 3.2 shows the NCEP-based Great

Lakes SSTs for July 4, 2002, at 12 and 18Z. Over this 6-hour span, temperature fluctuations

over many areas of the Great Lakes (particularly La ke Erie, and most shorelines) reach 20o F.

While some variability is expe cted along shorelines and other shallow areas, the magnitudes

observed through use of the NCEP data are unr ealistic. Observed SST data from buoy 45007

(located in the southern end of Lake Michigan yet far removed from the shoreline, see Figure

3.3) for the period July 4 – July 9 are provi ded in Figure 3.4. The maximum temperature

variation throughout July 4 at this site was less than 3o F. Figure 3.5 depicts the 5-day SST

timeseries produced using the NCEP SST data within REGRIDDER for the 36 km grid cell

corresponding to the location of buoy 45007. The NC EP data yields a diurnal temperature range

of approximately 7o F in this cell on July 4. The NCEP data also generates unrealistic diurnal

profiles with a net upward trend in SST over this five-day peri od. In contrast, the observed data

show less variability and a downward trend in SST. Utilization of the Eta skin-temperature data

produces the constant SST boundary conditions shown in Figure 3.6. The corresponding Eta

6

# skin-temperature for the location of buoy 45007 is ~294 K. While this yields warmer surface

temperatures than observed throughout the Ju ly 4 – July 9 period, no questionable diurnal

variability or artificial warming trends are present.

Figure 3.2. Lake temperature variability ac ross a 6 hour span, from 12Z 7/4/2002 to 18Z

7/4/2002, using the NCEP SST data.

Figure 3.3. Great Lake buoy locations.

7

## Observed SST Data

Buoy 45007 - Lake Michigan

289 290 291 292 293 294

07/04/02 07/05/02 07/06/02 07/07/02 07/08/02 07/09/02

DateSea Surface Temperature(K)



# Figure 3.4 Observed SST temperature data for buoy 45007.

Figure 3.5 NCEP derived SST profile for the gr id cell corresponding to the location of buoy

45007.

8

# Figure 3.6. Constant SST data derived from Et a skin-temperatures for the period 12Z 7/4/2002

through12Z 7/9/2002.

9

# 3.1.3 LIITLE_R

LITTLE_R was originally designed to improve the REGRIDDER output by using objective

analysis techniques to blend obs ervational data into the gridde d first-guess fields. Following

trad itional practices, the NWS upper air (ds353.4) and surface (ds464.0) datasets supply the

observations. As the Eta fields already cont ain these NWS datasets, the implementation of

LITTLE_R is viewed as partially redundant. Howe ver, LITTLE_R also generates the files used

in both the four-dimensional da ta assimilation (FDDA) and Pl eim-Xiu soil moisture nudging

schemes and therefore must be invoked. The implementation of LITTLE_R does not negatively

affect model performance when th e Eta surface and analysis data provide the first-guess fields

(Baker, 2002).

3.1.4 INTERPF

The IDNR 2002MM5v363 simulation uses a 34 vert ical layer structur e defined through the

INTERPF preprocessor. The layer interfaces, pr ovided in Table 3.2, were designed through

coordination with Dennis McNally to parallel th e vertical structure in use by EPA. INTERPF

interpolates the pressure level data developed in the previous preprocessors to MM5’s native

vertical system - terrain following sigma coordina tes. Sigma levels are defined according to Eq.

3.1, where p

s 

# equals the surface pressure, and p

t 

# equals the pressure at model top. The model top

was defined at 100 mb, or approximately 14,662 meters above ground level. Approximate sigma

heights are calculated using Eqs. 3.1 – 3.3, with the user-defined variab les assigned the following

values: p

s 

# = 1000 mb; p

t 

# = 100 mb; T

s 

# = 275 K; A = 50 K. R and g represent the gas and

gravitational constants of 287 J/(kg K) and 9.8 m/s 2 

# , respectively.

ts t



# pp pp

− −

= σ 

# (3.1)



# ( )

tts 

# pppp +

− ⋅ = σ (3.2)



# ⎥ ⎥

⎦ ⎤

⎢ ⎢

⎣ ⎡

⎟ ⎟

⎠ ⎞

⎜ ⎜

⎝ ⎛

⋅ + ⎟ ⎟

⎠ ⎞

⎜ ⎜

⎝ ⎛

⋅ ⋅

−=

s s

s 

# p p

g TR

p p

g AR

z ln ln

2 2



# (3.3)

10

# Table 3.2. Details of the 34-layer vertical structure.

Level Sigma Height (m) p (mb) Depth (m)

34 0.000 14662 100 1841

33 0.050 12822 145 1466

32 0.100 11356 190 1228

31 0.150 10127 235 1062

30 0.200 9066 280 939

29 0.250 8127 325 843

28 0.300 7284 370 767

27 0.350 6517 415 704

26 0.400 5812 460 652

25 0.450 5160 505 607

24 0.500 4553 550 569

23 0.550 3984 595 536

22 0.600 3448 640 506

21 0.650 2942 685 480

20 0.700 2462 730 367

19 0.740 2095 766 266

18 0.770 1828 793 259

17 0.800 1569 820 169

16 0.820 1400 838 166

15 0.840 1235 856 163

14 0.860 1071 874 160

13 0.880 911 892 158

12 0.900 753 910 78

11 0.910 675 919 77

10 0.920 598 928 77

9 0.930 521 937 76

8 0.940 445 946 76

7 0.950 369 955 75

6 0.960 294 964 74

5 0.970 220 973 74

4 0.980 146 982 37

3 0.985 109 987 37

2 0.990 73 991 36

1 0.995 36 996 36

0 1.000 0 1000 0



# 11

# 3.2 MM5

An overview of the physics parameterization c onfiguration used in the IDNR 2002MM5v363

simulation is provided in Table 3.3. As previous ly discussed, the configuration emerges from

the cumulative efforts of several sensitivity studi es, in combination with guidance from the AdHoc Meteorological Modeling co mmunity. In comparison with the original IDNR 2002

simulation, the cessation of contin uous soil field techniques with in the PX LSM is one of the

most notable modifications. 1 

# With the PX LSM no longer restri cted to sequential operation, the

annual simulation was generated from 95 indepe ndent simulations initialized at 12Z and

integrated through five days (ver sus 5-day blocks arranged in quart erly sequential simulations in

the original run). This temporal structure allo ws maximum air quality modeling flexibility as

photochemical simulations can be initialized using midnight local time or midnight GMT

without the need to split any given 24-hour pe riod across multiple MM5 simulation blocks.

While this methodology does increase the number of runs required to complete an annual

simulation (versus initialization at 00Z with a 5.5 day run time), the increased computational

requirements are not prohibitive. An example of the temporal structure is provided in Appendix

A. To allow for approximately a two week ph otochemical model spin-up period, the simulation

started at 12/16/2001 12Z. The completion da te occurred at 12Z on 1/1/2003. A 90 second

timestep was used with output written every hour. The output files were split every 24 hours to

simplify the post-processing (and photo chemical pre-processing) stages.

Table 3.3 Description of the options selected within the IDNR 2002 annual MM5v363 run.



# Option Configuration Details

Microphysics Mixed-Phase (Reisner I)

Cumulus Scheme Kain-Fritsch 2

PBL Asymmetric Convective Model * 

# Required by Pleim-Xiu LSM

Radiation RRTM Calculated every 15 minutes

Land Surface Model Pleim-Xiu No continuous soil fields

Shallow Convection Not enabled

SST Data source Eta Skin-Temperature

Snow Cover Effects Considered IFSNOW=1

Timestep 90 seconds (PX uses an internal 40s timestep)

* 

# The Asymmetric Convective Model (ACM) is also referred to as the Pleim-Chang PBL. The

ACM parameterization is a deri vative of the Blackadar scheme (Pleim and Chang, 1992).

1 

# While discussion of the complete list of configuration variability between the original and 2020MM5v363

simulations is beyond the scope of this document, additional key updates include: the abandonment of NCEP SST

data in favor of Eta-Skin temperatur es; the addition of the 12 km domain; use of a more recent modeling system

release; and a new temporal structure.



# 12

# Additional configuration details include the following: S ea surface temperatures remained

constant during the simulation as Eta skin temperatures were used as surrogate sea surface

temperatures. Snow cover effects were cons idered. Analysis nudging of the temperature,

mixing ratio, and wind fields was applied above the PBL. At the surface only the wind field was

nudged. The default nudging strengths of 2.5 x 10 -4 

# and 1.0 x 10 -4 

# were used for the temperature

and wind fields at 36 and 12 km, respec tively. A nudging coefficient of 1.5 x 10 -5 

# was

established for the mixing ratios at both 36 a nd 12 km. The rotational wind field was not

nudged, nor were observational nudging techniques applied. Optimal observational nudging

methods require a station density not available across a continen tal scale annual simulation.

Referencing Baker et al. (2004) th e following details are provided:

Vertical m oisture and temperature advection are set to use linear interpolation. Other

options incorporated include: moist verti cal diffusion in clouds, tem perature advection

using potential temperature, diffusion usi ng perturbation temperature, and an upper

radiative boundary condition. The Pleim-Xiu land surface module requires the addition

of three variables in the MM5 deck: ISMRD, NUDGE, a nd IFGROW. ISMRD was set

to use soil moisture fields from the ETA an alyses. NUDGE was assigned to adjust the

soil moisture data to the analyses fields. Finally, IFGROW was se t to option 2, which

takes vegetative growth into account base d on vegetative fraction data from the

TERRAIN file.

The configuration of the 12 km grid pictured in Figure 3.1 closely resemb les the 36 km grid

methodology. The explicit exceptions include a decrease in the wind and temperature nudging

strengths. While the terminology is questionabl e, the nesting technique employed is commonly

referred to as “a two-way nested run without f eedback”. In this method, the 12 km model

solution is not feed back to the master domain, but the grids are run simultaneously to allow the

fine grid to receive boundary c ondition updates at every timestep.



# 3.3 C OMPUTATIONAL 

# S UMMARY



# Seven dual CPU Linux workstations were acquired to complete the annual simulation. Six

machines were equipped with dual 3.06 GHz Inte l Pentium Xeon processors, with the final

machine a dual 2.0 GHz processor. Each machine 1 

# was equipped with 2 Gb of RAM, and Ultra

320 SCSI local hard drives for model I/O. Upon completion of each run, output data was

transferred via NFS to a SC SI-IDE RAID array. In summation, 41 wall-clock days were

required to complete the annual simulation. Th is represents each machine computing two

independent simulations simultaneously (essentially each CPU was tasked with one simulation at

any given time). Open MP wa s not an available option due to the implementation of PX.

Approximately 100 wall-clock hours was require d for a 3.06 GHz machine to complete two

simulations running simultaneously. Storage requirements reached 1.1 terabytes, with the 36 km

simulation occupying 400 Gb and the 12 km data using 700 Gb.

1 

# The 2.0 GHz machine had only 1 Gb of onboard RAM.



# 13

# 4. MODEL PERFORMANCE EVALUATION

4.1 B ACKGROUND



# No rigid guidelines exist for systematical ly and objectively eval uating the quality of

meteorological simulations. However, sound comp rehensive philosophies exist. A seven point

approach outlined by Tesche (1994) provides th e framework for a thorough model performance

evaluation. The framework can be classified in to two components: an operational evaluation

and a scientific evaluation (Emery and Tai, 20 01). The scientific ev aluation requires rigorous

examinations of model formulation and algorit hm development, methods beyond the scope of

most modeling projects. Historic al development and applications of MM5 within the scientific

community (including air quality and prognos tic projects published through peer-reviewed

journal articles) must then serve to support th e scientific evaluation. Thus the performance

evaluation of the IDNR 2002MM5v363 annual simu lation will focus upon operational criteria.



# 4.2 M ETHODS



# Climatic variability, complex mesoscale meteorological phenom ena, and scientific unknowns

contribute to meteorological m odeling difficulties and force modelers to take a subjective

approach to model performance. Objective statistical measures which offer a quantitative model

assessment exist, but implementation of the metrics is subjective to a degree. For example,

defining the area over which domain averaged me trics are calculated is a subjective decision,

buffered only through guidelines. In general, metrics averag ed over large meteorological

modeling domain are avoided, as error cancellation dilutes relevance. Conversely, splitting the

modeling domain into small subdomains renders sample sizes unrepresen tative. The logical

approach falls well within the bounds of the extremes, leaving optimum subdomain definition

open to interpretation. As one means of addres sing the issue, a subjective grid decomposition

technique was applied, resulti ng in the twelve rectangular 1 

# subdomains pictured in Figure 4.1.

Model performance measures must also minimally include a review of upper air features in

tandem with surface s tatistics. Upper air featur es are key variables in terms of air quality

modeling given the importance of fields such as three dimension wind flows and PBL depths.

Evaluation of the upper atmosphere also intr oduces a level of complexity exceeding the

difficulty associated with asse ssing surface features. The sheer volume of upper air model data,

in combination with a relatively sparse observing network gathering on ly twice daily soundings,

creates problems in terms of scale. A limited set of data analysis tools also restricts the review

process. In an attempt to achieve a balance betw een available resources and the level of detailed

review, the upper air evaluation includes review of PBL features and focuses upon observed

versus modeled soundings. To improve the effi ciency and simplify the review of soundings, a

new software tool was developed in-house: RAOB PLOT. In the final aspect of the upper air

evaluation, an independent revi ew of precipitation prediction, conducted by Kirk Baker, is

briefly summarized. While technica lly a surface feature, the precipitation ev aluation indirectly

enhances the upper air review given the three dimensional na ture of precipitation events.

1 

# Processing requirements necessitated that subdomains be simple rectangles defined only through a southwest and

northeast grid coordinate.



# 14

# PacificNW



# SW North

DesertSW

CenrapS CenrapN GL

SE NE

MidAtlantic

OhioVal Iowa

Figure 4.1. Decomposition of the continental scale MM5 domain into simple rectangular

subregions designed for targeted model perfor mance evaluation. Areas of overlap are shaded

differently and outlines have been added to highlight individual subdomain boundaries.



# 4.3 S TATISTICAL 

# M EASURES



# Within the statistical degrees of freedom available to the meteorological modeler, a subset of

standard statistical measures ha s emerged, outlined in Table 4.1. These metrics are calculated

based upon data contained within a given subdomain (See Figure 4.1). Metrics are calculated

using hourly and daily averages. While no st rict criteria establis hing acceptable model

performance exist, the genera l guidelines established by Emery and Tai (2001) provide a

community adopted frame of reference. A summ ary of the guidelines is provided in Table 4.2.

15

# Table 4.1. List of statistical me asures commonly discussed in mete orological model evaluations.

The DH designation represents that both daily an d hourly averaged values are calculated for a

particular metric. Conversely, D or H indicates that the value is ava ilable only on a daily or

hourly average, respectively.

Statistical Measure Wind Speed Wind

Direction Temperature Humidity

Obs. vs Predicted Timeline DH DH DH DH

Bias DH DH DH DH

Gross Error D D D D

Total RMSE DH DH DH

Systematic RMSE DH DH DH

Unsystematic RMSE DH DH DH

Index of Agreement DH DH DH

Table 4.2. Guidelines for meteorologi cal model performance. Source: Meteorological

Modeling and Performance Evaluation of the September 13-20, 1999 Ozone Episode (Emery

and Tai, 2001). Data pertain to daily averaged values.

Wind Speed Wind Direction Temperature Humidity

RMSE ≤ 2 m/s

Mean Bias ≤ ± 0.5 m/s

IOA ≥ 0.6 Gross Error ≤ 30 deg

Mean Bias ≤± 10 deg Gross Error ≤ 2 K

Mean Bias ≤ ± 0.5 K

IOA ≥ 0.8 Gross Error ≤ 2 g/kg

Mean Bias ≤ ± 1 g/kg

IOA ≥ 0.6



# An overview of the significance for each me tric is provide d by Baker et al. (2004):

“ Bias error (bias) is the degree of corresponden ce between the mean prediction and the

m ean observation, with lower numbers indicative of better performance. Values less than

0 indicate under-prediction. The gross error , or mean absolute erro r, is the mean of the

absolute value of the residuals from a fitted statistical model. Lower numbers indicate

better model performance.

Root Mean Square Error (RMSE) is a good overall measure of m odel performance. The

weighting of (prediction-observation) by its s quare tends to inflate RMSE, particularly

when extreme values are present. With re spect to a good model th e root mean square

error should approach zero. RMSE can be divided into a systematic and unsystematic

component by least-squares regression. Since differences described by systematic RMSE

can be described by a linear function, they should be relatively easy to dampen by a new

parameterization of the model. Unsystematic RMSE can be interpre ted as a measure of

potential accuracy or noise level (Emery et al., 2001). With resp ect to a good model the

systematic difference should approach zero wh ile the unsystematic difference approaches

RMSE.

16

# Index of Agreement is a relative measure of the degr ee of which pred ictions are errorfree. The denominator accounts for the mo del's deviation from the mean of the

observations as well as to the observations de viation from their mean. It does not provide

information regarding systematic and unsys tematic errors. The index of agreement

approaches one when model performance is best.”

The basis of the statistical analysis if formed through a comparison of the modeled fields with

the Techniq ues Data Laboratory U.S. and Canada surface hourly observations (ds472.0). Hourly

and daily averaged bias, error, RMSE (total , systematic, and unsystematic), and index of

agreement metrics for wind speed, wind directi on, temperature and humidity were generated

using the Metstat program and MS Excel post- processing macro devel oped by Environ. Time

series of modeled and observed conditions were al so prepared via Metstat. As continental-scale

domain averaged statistical measures are su sceptible to e rror cancellation, metrics were

calculated over the twelve subdomains illustrated in Fig. 4.1.

The volume of data associated with the annual simu lation can quickly overwhelm standard time

series displays or similar attempts at numeri cal data presentation. As a solution Kirk Baker

developed and ingenious method of data display. PAVE is used to plot daily metrics, aligned

vertically by month, and horizonta lly by date. This allows for an annual graphical display of

daily averaged metrics in a singl e plot, simplifying the identificati on of error trends or pervasive

biases. Even with this method of simplificatio n, a detailed discussion of all twelve subdomains

becomes excessive. The statistical analysis therefore focuses upon those regions encompassing

the CENRAP and Midwest RPO states, primarily the regions: CenrapN, CenrapS, GL (Great

Lakes), OhioVal (Ohio Valley), and Iowa.

17

# 5. SURFACE EVALUATION (36 KM)

The daily averaged metrics described below are provi ded graphically in th e form of a “Bakergram”.

The Bakergram, developed by Kirk Baker, allows fo r the meaningful depiction of an annual set of

daily averaged statistical values in a single plot . For example, Figure 5.1 consists of a compilation

of four Bakergrams, one each for the wind speed bi as, wind speed error, wind direction bias, and

wind direction error. Focusing on the wind speed bias Bakergram in Figure 5.1 (top left), 365 daily

averaged metrics are provided. Twelve columns are provided, which each column containing a

monthly dataset. The individual days are provided in rows, with the first of the month displayed at

the top, with days descending from top to bottom. The concept is repeated (f or example, see Figure

5.2) with temperature and mixi ng ratio metrics plotted.



# 5.1 G REAT 

# L AKES



# In previous sensitivity studies, the Pleim-Cha ng/Pleim-Xiu PBL/LSM conf iguration was found to

improve wind vector performance versus the use of alterative PBL parameterizations. Consistent

with this discovery, the wind vect or performance in the GL region is encouraging. Wind speed

metrics are generally favorable, and no clear trends in erro r or bias are evident (see Figure 5.1). A

notable caveat, daily metrics may hide inconsis tencies occurring within the diurnal profile. 1



# Turning to the wind direction evaluation, again re sults are satisfactory, with one exception found, an

increase in the summertime gross error.

In the Great Lakes region, the problems of greates t concern lie in the wintertime cold temperature

biases, the warm summertime biases, and the summe rtime positive moisture biases (See Figure 5.2).

Examining the temperature biases from a diurnal 2 

# perspective, the warm bias is predominantly

caused by nighttime temperatures remaining warmer than observed. The cold wintertime

temperature bias is often traced to underpredicted high temperatures, evening temperatures falling

too rapidly, and nighttime lows often colder than observed. Caution should be exercised when

generalizing the wintertime bias trends though, as exceptions are more abundant than with the

summertime warm biases. Turning to the mixi ng ratio (humidity) evaluation (see Figure 5.2),

although the gross error metrics ar e generally within the statisti cal guidelines, the summertime

positive bias is a concerning trend. Only on rare occasions do negative biases occur. The likely

culprit is MM5’s tendency to overpredict precipitation.



# 5.2 N ORTHERN AND 

# S OUTHERN 

# C ENRAP



# In general, the statistical evaluation for the Ce nrapN subdomain (Figures 5.3 - 5.4) yields results

similar to the Great Lakes regi on. A notable exception being the nearly consistently negatively

biased wind speeds. Examining the wind speed bias in greater detail (thr ough diurnal profiles), this

fault is predominantly influenced by the underpredi ction in the daily peak wind speeds. Keeping

these errors in perspective, th e magnitude of the underprediction typically remains below 1 m/s.

Examining the mixing ratio performance, the most serious issue remains the abundance of

summertime surface moisture. While arguable trivia l, CenrapN does differ from the GL subdomain

during May, where several surface mo isture underpredictions occur.

1 

# Diurnal metrics are examined in Chapter 6.

2 

# Ibid.

# Turning to the Southern CENR AP subdomain (Figures 5.5 - 5.6 ), wind direction performance

remains encouraging, similar to the performance for the CenrapN and GL regions. As found in

CenrapN, wind speeds are generally negatively bi ased, but more pronounced in this region. The

mixing ratio biases reveal excess moisture, alt hough a drier than observed fall was predicted.

Examining temperature performance, late winter/ear ly spring temperatures yielded positively biased

trends, in contrast to the perv asive cold winter bias es found in the CenrapN and GL regions.

Examination of the diurnal profiles revealed the biases were attributable to warm nighttime lows.



# 5.3 O HIO 

# V ALLEY



# Once again, the wind speeds are generally too low, however, the associated er ror is well within the

acceptable guidelines. Wind directions errors are al so generally small, but an increase in error is

found in the summer months. Mixing ratios are cons istently too moist, ex cept in the mid-October

timeframe. As in the Great Lakes regions, a cold winter bias is found, while summer temperatures

remain too warm (predominantly over the nighttime hours). The results are depicted in Figures 5.7

- 5.8.



# 5.4 I OWA



# Within the Iowa subdomain wind vector performan ce is favorable, with wind speed bias and error

measures predominantly meeting the statistical goals. Wind directions exhibi t greater errors in the

late summer/early fall timeframe versus the Ce nrapN and GL subdomains, but are not cause for

severe alarm (see Figure 5.9). As is common, cold winter and warm summ er biases are present

(Figure 5.10). In terms of the moisture bias, the Iowa domain e xhibits greater springtime negative

moisture bias versus CenrapN, otherwise sim ilar performance is shown (this result is not

unexpected, given the superposition of the Iowa subdomain over CenrapN).



# 5.5 E ASTERN 

# R EGIONS



# A detailed discussion of model performance for a ll areas is beyond the scope of this document.

Alternatively, summary remarks are provided. Ov er the MidAtlantic, no serious abnormalities are

found beyond the errors identified previously in Central U.S. subdomains. As is common to MM5,

a positive moisture bias exists, affecting both th e MidAtlantic and SE regi ons. Examining the NE

region, wind speed, and wind direction errors approach the upper extreme of acceptable

performance. Again, the moisture bias is positiv ely biased, with errors maximized over the summer

months. Given moisture carrying capacity is a non-linear function of temp erature, the relatively

small mixing ratio gross errors oc curring in the wintertime of regions with colder climates should

not be interpreted as superior model performan ce. The daily averaged statistical results are

provided in Appendix B for each of the individual Eastern subregions.



# 5.6 W ESTERN 

# R EGIONS



# The daily averaged statistical results for the west ern subdomains are also pr ovided in Appendix B.

The complex topography found in the Western Un ited States clearly introduces a degree of

modeling difficulty not found in other regions. Pe rformance metrics are di scouraging when viewed

initially, however, the appropriateness of the st atistical measures are questionable as model

resolution is not designed to captu re the topographically induced nea r-field flows affecting many of

the local observations.

19

# Figure 5.1. Daily averaged wind speed/direction metrics for the Great Lakes (GL) subdomain.

# Figure 5.2. Daily averaged temperature and mixi ng ratio metrics for th e Great Lakes (GL)

subdomain.

21

# Figure 5.3. Daily averaged wind speed/dir ection metrics for the CenrapN subdomain.

22

# Figure 5.4. Daily averaged temperature and mi xing ratio metrics for the CenrapN subdomain.

23

# Figure 5.5. Daily averaged wind speed/dir ection metrics for the CenrapS subdomain

24

# Figure 5.6. Daily averaged temperature and mi xing ratio metrics for the CenrapS subdomain.

25

# Figure 5.7. Daily averaged wind speed/dir ection metrics for the OhioVal subdomain.

26

# Figure 5.8. Daily averaged temperature and mi xing ratio metrics for the OhioVal subdomain.

27

# Figure 5.9. Daily averaged wind speed/dir ection metrics for the Iowa subdomain.

28

# Figure 5.10. Daily averaged temperature and mi xing ratio metrics for the Iowa subdomain.

29

# 6. TWELVE KILOMETER EVALUATION

6.1 D AILY 

# A VERAGED 

# S TATISTICS



# Generalizing the impacts of the 12 km domain upon the Great Lakes region, in terms of daily

averaged metrics, a decrease in simulation accu racy during the winter months is found, while

only negligible changes occur across the remainder of the year. This trend is prevalent for wind

speed, wind direction 1 

# and temperature errors. The wintertime temperature cold bias (found at

36 km resolution) is thus even more pronounced in the 12 km domain. Mixing rati o statistics

were generally uninfluenced by domain resolution. These result s are depicted in Figures 6.1 –

6.2, where the Bakergram concept is maintained, how ever, the results are presented in terms of

the differences between the 36 and 12 km results. The plots were generated by subtracting the

36 km daily averaged statistical values from th e 12 km data. As the comparison only involves

gross and root mean square error metrics, nega tive values indicate an improvement in model

performance at 12 km resolution. This me thodology is maintained for Figures 6.1 - 6.8.

The CenrapN regions shows only mi nor variations in the temp erat ure fields, with the greatest

change concentrated to the cooler months, with slight performance disbenefits. Wind direction

metrics produced a drastically di fferent trend, as nearly all days showed poorer performance.

Figures 6.3 – 6.4 provide a graphi cal depiction of the 12 km domain impacts upon the daily

averaged metrics for this subdomain.

Over the Ohio Valley, only minor differences we re calculated between the 12 and 36 km daily

averaged statistical results, in general. A sl ight improvement in the mixing ratio fields was

computed. As in CenrapN, wi nd direction gross errors encount ered widespread performance

degradation during the winter and early spring months. Keep ing the increasing errors in

perspective, additional error remained below 3.5 degrees. See Figures 6.5 - 6.6.

In terms of daily averaged statistical measures , the Iowa subdomain receives few benefits from

increased resolution. Wind speeds generally exhibit slightly greater error in the winter, spring,

and f all, while demonstrating little variability during the summer. Consistent with nearby

subdomains, wind direction performance suffers. While mixing ratios impacts were negligible,

most months exhibited days with increased temp erature error, particularly in the winter.

Fortunately, gross error degradation remained belo w 0.5 K. The results are depicted in Figures

6.7 - 6.8.

Due to the spatial extent of the 12 km do ma in, neither the CenrapS domain, nor any other

subdomain, is eligible for comparison.

1 

# With additional errors occurring into the early spring months.

# Figure 6.1. Twelve km domain daily averaged statis tical performance for selected wind metrics

in relation to the 36 km grid fo r the Great Lakes (GL) subdomain.

31

# Figure 6.2. Twelve km domain daily averaged statis tical performance for selected temperature

and mixing ratio metrics in relation to the 36 km grid for the Great Lakes (GL) subdomain.

32

# Figure 6.3. Twelve km domain daily averaged statis tical performance for selected wind metrics

in relation to the 36 km gr id for the CenrapN subdomain.

33

# Figure 6.4. Twelve km domain daily averaged statis tical performance for selected temperature

and mixing ratio metrics in relation to the 36 km grid for the CenrapN subdomain.

34

# Figure 6.5. Twelve km domain daily averaged statis tical performance for selected wind metrics

in relation to the 36 km gr id for the OhioVal subdomain.

35

# Figure 6.6. Twelve km domain daily averaged statis tical performance for selected temperature

and mixing ratio metrics in relation to the 36 km grid for the OhioVal subdomain.

36

# Figure 6.7. Twelve km domain daily averaged statis tical performance for selected wind metrics

in relation to the 36 km gr id for the Iowa subdomain.

37

# Figure 6.8. Twelve km domain daily averaged statis tical performance for selected temperature

and mixing ratio metrics in relation to the 36 km grid for the Iowa subdomain.

38

# 6.2 H OURLY 

# S TATISTICS



# Additional comparisons between the 36 and 12 km simulations are provided below through

review of hourly timeseries. Modeled (bot h 36 and 12 km) versus observed conditions are

plotted below, with the associated bias also depicted. The hourly time series evaluation

eliminates the statistical smoothing associated w ith the daily averaging periods. These charts

also serve as the diurnal profile data source referenced in previous chapters, however, the

discussion below will primarily focus upon differences between the 12 and 36 km simulations.

Assessing the timeseries from a winter (January) and summer (June) monthly subset of the

annual simulation for the Great Lakes region (Figur es 6.9 - 6.10) leads to a general conclusion

that improvement occurs in the daytime wind sp eed biases with implementation of the 12 km

grid, while nighttime disbenefits are observed. At 12 km resolution, the wintertime cold bias is

even more pronounced versus the 36 km domai n, as nighttime low temperatures dip further

below observed values (Figure 6.11). The ultimate cause for the low temperature bias is

unknown, but this is not an uncommon feature of MM5 simulations (Ad-Hoc Meteorological

Modelers Meeting group discussion, 2007). For the GL region, no significant differences are

found in either temperature or humidity duri ng the summer month of June (Figure 6.12).

Appendix C provides additional January and June hourly 12 versus 36 km statistical charts for

the Great Lakes, OhioVal, CenrapN, and Iowa subdomains.

Observed/Predicted Windspeed

2 3 4 5 6 7 8

1/ 1

1 / 2

1 / 3

1/

4

1/ 5

1 / 6

1/

7

1/ 8

1 / 9 1/10 1/11

1 / 1 2 1/13 1/14

1 / 1 5

1/1

6

1/17 1/18

1 / 1 9

1/20 1/21

1 / 2 2

1/23 1/24

1 / 2 5

1/2

6

1/27 1/28

1 / 2 9

1/30 1/31m/s ObsWndSpd 36km Predicted 12km Predict ed



## Bias Windspeed

-1.5

-1 -0.5

0 0.5

1 1.5

1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9

1 /10

1/11 1/12 1/13 1/14 1/15 1/16 1/1

7 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31m/s 36km Bias 12km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

1/ 1 1

/ 2

1/ 3 1

/ 4

1/ 5 1

/ 6

1 / 7

1/ 8 1/ 9

1/10 1/11 1/12

1

/ 13

1

/14

1/15

1 /16

1/17

1

/1

8

1

/ 19

1

/2

0 1/21

1

/ 22

1/23

1

/2

4

1

/25

1

/2

6

1 /27

1/28

1

/2

9

1

/ 30

1

/3

1deg ObsWndDir 36km Predict ed 12km Predict ed



# Bias Wind Direction

-30

0 30

1/

1

1/ 2 1/

3

1

/ 4

1/

5 1

/ 6 1/

7

1/

8

1/

9 1/10

1

/1

1 1/1

2

1

/1

3 1/14 1/1

5 1/16 1/1

7

1

/1

8

1

/1

9 1/

2 0 1/2 1

1

/2

2

1/2

3

1

/2

4 1/25 1/

2 6 1/27

1

/2

8 1/

2 9

1

/3

0 1/31deg 36km Bias WD 12km Bias WD 

# bias shifted 

# Great Lakes Domain

Figure 6.9. Twelve and 36 km hourly wind vector stat istics for the Great Lakes subdomain for

January, 2002.

39

# Observed/Predicted Windspeed

2 4 6 8

6/ 1 6/ 2 6/ 3 6/ 4 6

/ 5

6/

6

6/ 7 6

/ 8

6/

9

6 /10

6/11 6

/ 12

6 /13

6/14 6/15 6

/1

6 6

/1

7 6

/18

6

/ 19

6

/2

0 6

/21

6

/22

6

/23

6/24 6

/25

6

/26

6 /27

6/28 6

/ 29

6

/3

0m/s ObsWndSpd 36km Predict ed 12km Predict ed



# Bias Windspeed

-2 -1 0 1 2

6/ 1 6/ 2 6/ 3 6/ 4 6

/ 5

6/

6

6/ 7 6

/ 8

6

/ 9

6/10 6

/11

6

/12

6/13 6

/14

6

/15

6/1

6 6/1

7 6/18 6/

19

6/2

0 6/21 6/22 6/23 6

/24

6/25 6/26 6/27 6

/28

6/

29

6/3

0m/s 36 km Bias 12 km Bias



# Bias Wind Direction

-30

0 30

6/ 1 6/ 2 6/ 3 6/ 4 6/ 5 6

/ 6

6/ 7 6/ 8 6/ 9

6

/10

6

/11

6

/12

6

/1

3 6

/14

6

/15

6

/16

6 /17

6/18 6

/19

6

/20

6 /21

6/22 6/23 6

/ 24

6 /25

6/26 6/27 6

/ 28

6

/2

9 6

/3

0deg 36km Bias 12km Bias 

# Great Lakes Domain

Observed/Predicted Wind Direction

0 60 120 180 240 300 360

6/

1

6/ 2 6

/ 3

6/ 4 6/ 5 6/ 6 6/

7

6/ 8 6

/ 9

6

/10

6/11 6/12 6/13 6

/14

6/15 6

/16

6/17 6/

18

6/1

9 6

/20

6/21 6/

22

6/2

3 6

/24

6/25 6/26 6/2

7 6

/28

6/29 6/30deg ObsWndDir 36 km PredWnd 12 km PredWndDir



# Figure 6.10. Twelve and 36 km hourly wind vector st atistics for the Great Lakes subdomain for

June, 2002.

40

### Bias Temperature

-5 -4 -3 -2 -1 0 1 2

1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9 1/10 1/ 11 1/12 1/ 13

1/14 1/

1 5 1/16 1/ 17

1/18 1/ 19

1/20 1/ 21 1/ 22 1/ 23

1/24 1/

2 5 1/26 1/ 27 1/ 28

1/29 1/30 1/ 31K 36 km Bias T 12 km Bias T 

### Observed/Predicted Temperature

255 260 265 270 275 280

1/ 1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9 1/

10 1/11 1/ 1 2 1/13 1/

14

1/15 1/

16 1/17 1/

18 1/19 1/

20 1/21 1/22 1/23 1/

24

1/25 1/

26 1/27 1/

28

1/

29 1/ 30 1/31K ObsTemp 36 km Pred_T 12 km Pred_T



### Predicted/Observed Humidity

0 1 2 3 4 5 6

1/

1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9 1/

10

1/

1 1 1/ 12 1/13 1/

14

1/

15 1/ 16

1/17 1/18 1/19 1/

20

1/

2 1 1/22 1/23 1/24 1/

25 1/ 26

1/27 1/28 1/

29 1/ 30

1/

3 1g/kg ObsHum 36km Pred q 12 k Pred q



### Bias Humidity

-0.4 -0.2

0 0.2 0.4 0.6 0.8

1 1.2

1/

1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9 1/

10

1/

1 1 1/ 12 1/13 1/

14

1/

15 1/ 16

1/17 1/18 1/19 1/

20

1/

2 1 1/22 1/23 1/24 1/

25 1/ 26

1/27 1/28 1/

29 1/ 30

1/

3 1g/kg 36 km Bias q 12 km Bias q 

### Predicted/Observed Humidity

0 1 2 3 4 5 6

1/

1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9 1/

10

1/

1 1 1/ 12 1/13 1/

14

1/

15 1/ 16

1/17 1/18 1/19 1/

20

1/

2 1 1/22 1/23 1/24 1/

25 1/ 26

1/27 1/28 1/

29 1/ 30

1/

3 1g/kg ObsHum 36km Pred q 12 k Pred q



### Bias Humidity

-0.4 -0.2

0 0.2 0.4 0.6 0.8

1 1.2

1/

1 1/ 2 1/ 3 1/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9 1/

10

1/

1 1 1/ 12 1/13 1/

14

1/

15 1/ 16

1/17 1/18 1/19 1/

20

1/

2 1 1/22 1/23 1/24 1/

25 1/ 26

1/27 1/28 1/

29 1/ 30

1/

3 1g/kg 36 km Bias q 12 km Bias q 

# 12km even colder 

# Great Lakes Domain

Figure 6.11. Twelve and 36 km hourly temperature and moisture statistics for the Great Lakes

subdomain for January, 2002.

41

### Observed/Predicted Temperature

270 275 280 285 290 295 300 305 310

6/ 1 6/ 2 6/ 3 6/ 4 6/ 5 6/ 6 6 / 7 6/ 8 6/ 9

6 / 10 6/ 11 6/ 1 2

6 / 13 6/ 14 6/ 15 6/16 6/17 6/18 6/19 6/20 6/21 6/22 6/23 6/ 24

6/25 6/26 6/ 27 6/ 28 6/29 6/30K ObsTemp 36 km 12 km



### Bias Temperature

-3 -2 -1 0 1 2 3

6/ 1 6/ 2 6/ 3 6/ 4 6/ 5 6/ 6 6

/ 7 6/ 8 6/ 9

6 / 10 6/ 11 6/ 12

6 / 13 6/ 14 6/ 15 6/16 6/17 6/18 6/ 1 9 6/20 6/21 6/22 6/23 6/ 24

6/25 6/26 6/ 27 6/ 28 6/ 2 9 6/30K 36 km Bias 12km Bias 

# Great Lakes Domain

Predicted/Observed Humidity

0 2 4 6 8 10 12 14 16 18

6/ 1 6/ 2 6/ 3 6/ 4 6 / 5 6/ 6 6/ 7 6/ 8 6 / 9

6/10 6/11 6/12 6/13 6/14 6/15 6/16 6 / 17 6/ 18

6/19 6/20 6 / 21 6/ 22 6/ 23 6/ 2 4

6 / 25 6/ 26 6/ 27 6/ 2 8 6/29 6/30g/kg ObsHum 36 km Predicted 12k m Predict ed



### Bias Humidity

-2 -1 0 1 2 3 4

6/ 1 6/ 2 6/ 3 6/ 4 6/ 5 6/ 6 6 / 7 6/ 8 6/ 9

6 / 10 6/ 11 6/ 1 2

6 / 13 6/ 14 6/ 15 6/16 6/17 6/18 6/19 6/20 6/21 6/22 6/23 6/ 24

6/25 6/26 6/ 27 6/ 28 6/29 6/30g/kg 36 km bias 12 km Bias 

# no significant changes



# Figure 6.12. Twelve and 36 km hourly temperature and moisture statistics comparison for the

Great Lakes subdomain for June, 2002.

42

# 7. UPPER AIR EVALUATION

7.1 S OUNDINGS



# A comprehensive assessment of model performance cannot be completed through the evaluation

of surface statistical measures alone. A rigorous evaluation requires the examination of

additional features such as precip itation fields, PBL depths, and ve rtical profiles of temperature,

moisture, and wind vectors. As readily availabl e tools have not been identified which yield

objective measures of such parameters, evaluations are typically subjective. A precipitation

evaluation of the 36 km dataset has been comple ted by Kirk Baker (Baker et al., 2004) and is

summarized below. In combination with the pr ecipitation evaluation, the most efficient method

available for an upper air analysis is to focus upo n radiosonde observations. To aid in the review

of upper air feature, the IDNR created the RAOBPL OT software tool that efficiently displays

modeled versus observed radiosonde upper air measurements. With twice-daily soundings

available from approximately 70 observing sta tions, roughly 51,100 modeled versus observed

soundings are available for examination from the 36 km annual simulation alone. Clearly a

complete examination is resource prohibitive. The volume of da ta available, in combination

with only inefficient subjective methods for evalua tions highlights a current deficiency in annual

scale regional modeling applications. While inelegant, the immediately practicable solution

requires a targeted review of specific data.

A brief review of the modeled versus observed sounding for ma ny sites in the Central U.S. was

conducted, with no terminal deficiencies disc overed. A more focused evaluation upon the

Davenport, Iowa, station was completed over the simulated summer months, with the following

conclusions reached: Upper level wind vectors are well simulated. The temperature fields

below approximately 900 mb yielded a tendenc y toward underprediction at 0Z, while the

moisture fields were generally overstated during the same region and time. At 12Z, temperatures

were generally underpredicted below 900 mb. In terms of es timated PBL depths, the mixed

layer commonly appears shallower than observed. While error is never desired, in terms of

modeling air quality (in a conserva tive sense) a shallow PBL is pr eferred versus excessive depth.

A sample of the observed versus modeled s ounding produced by RAOBPL OT is provided in

Figure 7.1.



# 7.2 P RECIPITATION



# Kirk Baker with the Lake Michigan Air Direct ors Consortium was provide d a complete copy of

the 36 km meteorological datase t and subsequently completed a model performance evaluation

examining precipitation fields. In summary, both rainfall totals and precipitation spatial

coverage are generally well simulated in the fall, winter, and springtime periods. As is common

with many MM5 simulations, summertime pr ecipitation events produce an excess of

precipitation. Rainfall patterns also exhibit grea ter spatial coverage th an observed. Additional

detail, including graphical repres entation of predicted and observed rainfall, is available in Baker

et al., 2004.

43

# Figure 7.1. Sample ROABPLOT observed versu s predicted (36 km domain) sounding for

Davenport Iowa, on July 13, 2007, at 0Z. Wind speed and directions are accurately simulated

throughout the depth of the sounding. The temp erature profile performance is more than

adequate. As is common, a positive moisture bias exists at ( and above the surface), while the

estimated PBL depth remains too shallow.

44

# 7.3 PBL D EPTHS



# Additional upper air analyses included a limited comparison of the 36 and 12 km predicted PBL

heights. Figure 7.2 provides an example comparis on. As expected, the degree of agreement

between the 36 and 12 km results exceeds variabili ty. Areas in Western Illinois and Eastern

Texas (among others) do display deviations. In Eastern Texas, MM5 predicts a precipitation

event (which is weakly supporte d by observations, see Figures 7.3 - 7.4). The re duction in PBL

heights in Western Illinois would appear to be precipitation driven as well, but no convective or

non-convective rainfall was predicted by MM 5 during this time. The observed radar

reflectivities also suggests no pr ecipitation occurred during this ti me. In summary, the 12 km

grid yields improved feature deta il yet the accuracy of such fields, across a continental scale

annual simulation, is difficult to a ssess within a reas onable timeframe.

Figure 7.2. PBL heights predicted by MM5 fo r June 23, 2002, at 19Z. for the 12 and 36 km

modeling domains.

45

# Figure 7.3. Observed conditions on June 24, 0Z.

46

# Figure 7.3. Radar reflectivity on June 23, 2002 at 19Z.

47

# 8. CONCLUSIONS

In the northern half of the Cent ral U.S. through the Ohio River valley, the surface statistical

evaluation reveals a domi nant wint ertime cold bias, with cool c onditions typically present in the

evening hours, while overly aggressive night time lows and weak high temperatures also

contribute to the cold bias. The summer months exhibit a warm bias, attributable to the

overprediction of nighttime temperatures. Wind speed and direction predictions over the central

and northern Central U.S. exhibi t low statistical error and provi de for an increase in model

confidence. Continuing the evaluation into the Western U.S. yields a reduction in model

confidence, as error measures increase across al l fields. As discusse d, this result is not

completely unexpected given complex Western t opography. Regions within the Eastern U.S.

demonstrate prediction skill above Western regions, yet statistic al accuracy falls below that

found in the Midwest.

Expanding the evaluation into upper air features reveals no fundam ental flaws jeopardizing the

adequacy of the simulation in terms of air quality modeling. A tendency to slightly underpredict

summertime PBL depths over Eastern Iowa was disc overed. In subjective terms, such error is

acceptable as perfect model performa nce is unattainable. A simila r conclusion is reached for the

precipitation shortfalls di scussed by Baker et al., 2004.

Within the Central U.S, increasing the horizontal resolution from 36 to 12 km yielded no benefits

from a surface-feature statistical evaluation perspective. Within the Great Lakes subdomain, the

12 km simulation appears to improve daytim e wind speed predictions, however, nighttime

predictions suffer. Overall, wind speed error sh owed little variability between the 36 and 12 km

domains. Beyond the statistical evaluation, add itional field detail is resolved by the 12 km

domain as expected. As in the upper air analys is for the 36 km grid, no fundamental flaws were

identified in review of 12 km upper air features.

In summary, the statistical evaluation yields re sults predo minantly within acceptable guidelines

for the principal regions of interest (the Stat es near and within LADCO and the northern twothirds of CENRAP). Concurrently, no major si mulation deficiencies were revealed during the

upper air review. The 36 and 12 km Iowa DNR 2002MM5v363 datasets are thus judged

acceptable for use in regional scale air quality m odeling studies focused within the central United

States.

48

# 9. REFERENCES

Anthes, R. A. and T. T. Warner, 1978: The Deve lopment of Mesoscale Models Suitable for Air

Pollution and Other Mesometeorological Studies. Monthly Weather Review

, 106 , 1045-

1078.

Baker, K., 2002: MM5 Sensitivity for the Na tional RPO Grid, Presented at the Ad-Hoc

Meteorological Modeling Meet ing, Des Plaines, IL, 2002.

Baker, Kirk, Matthew Johnson and Steven Ki ng, 2004: Meteorological Modeling Pe rformance

Summary For Application to PM2.5/Haze/Ozone Modeling Projects.

http://ladco.org/tech/pho to/docs/mm5_protocol_dec2004.pdf

Chen, F., and J. Dudhia, 2001: Coupling an ad vanced land-surface/hyd rology mo del with the

Penn State/NCAR MM5 modeling system. Part I: Model implementation and sensitivity.

Monthly Weather Review

, 129 , 569-585.

Dudhia, J., 1993: A Non-hydros tatic Version of the Penn State/NCAR Mesoscale Model:

Validation Te sts and Simulation of an Atlantic Cyclone and Cold Front . Monthly Weather

Review

, 121 , 1493-1513.

Emery, C., and Edward Tai, 2001: Meteorological Modeling and Performa nce Evaluation of the

September 13-20, 1999 Ozone Episode. Prepar ed for the Texas Near Non-Attainment

Areas through the Alamo Area Council of Governments, by ENVIRON International

Corp, Novato, CA.

McNally, Dennis, 2003: Annual Application of MM5 for Calendar Year 2001. Prepared for Pat

Dolwick, USEPA, March 31 st 

# 2003.

Olerud, Donald, K. Alapaty, and N. Wheeler, 2000: Meteorological Modeling of 1996 for the

United States with MM5. Prepared for Norm al Possiel, Client Representative, U.S.

EPA/OAQPS. EPA Task Order Number CAA689805

Olerud, Don and Aaron Sim s, 2004: Draft Protocol for Annua l MM5 Modeling in Support of

VISTAS (V isibility Improvement – State and Tr ibal Association) (DRAFT), Prepared for

Mr. Mike Abraczinskas, VISTAS Technical Analysis Workgroup.

Pleim, J. E., and J. S. Chang, 1992: A non-lo cal closure model for vertical m ixing in the

convective boundary layer. Atmospheric Environment

, 26A , 965-981.

Stauffer, D. R., and N. L. Se am an, 1990: Use of Four Dimens ional Data Assimilation in a

Limited-Area Mesoscale Model. Part I: Experiments with Synoptic Data. Monthly

Weather Review

, 118 , 1250-1277.

49

# 50 Stauffer, D. R., and N. L. Seaman. 1991: Us e of Four Dime nsional Data Assimilation in a

Limited-Area Mesoscale Model. Part II: Effects of Data Assimilation within the Planetary

Boundary Layer. Monthly Weather Review

, 119 , 734-754

Tesche, T. W. 1994. “Evaluation Procedures for Regional Emissions, Meteorological, and

Photochem ical Models.” Presented at the 86th Annual Meeting of the Air and Waste

Management Association, 14-18 June, Denver, CO.

Xiu, A., and J. E. Pleim, 2000: Development of a land surface m odel. Part I: Application in a

mesoscale meteorology model. Journal of Applied Meteorology

, 40 , 192-209.51APPENDIX ATemporal structure examplefor the 2002 annual simulation.Start BLOCKEnd BLOCKStart (Z)End (Z)FilenameMetstat UsableStart (Z)End (Z)12/28/2001 12:001/2/2002 12:0012/28/2001 12:0012/28/2001 12:00MMOUT_DOMAIN1_0012/28/2001 13:0012/29/2001 12:00MMOUT_DOMAIN1_0112/29/2001 0:0012/29/2001 12:0012/29/2001 13:0012/30/2001 12:00MMOUT_DOMAIN1_0212/29/2001 13:0012/30/2001 12:0012/30/2001 13:0012/31/2001 12:00MMOUT_DOMAIN1_0312/30/2001 13:0012/31/2001 12:0012/31/200113:001/1/200212:00MMOUT_DOMAIN1_0412/31/2001 13:001/1/2002 12:001/1/2002 13:001/2/2002 12:00MMOUT_DOMAIN1_051/1/2002 13:001/1/2002 23:001/1/2002 12:001/6/2002 12:001/1/2002 12:001/1/2002 12:00MMOUT_DOMAIN1_001/1/2002 13:001/2/2002 12:00MMOUT_DOMAIN1_011/2/2002 0:001/2/2002 12:001/2/2002 13:001/3/2002 12:00MMOUT_DOMAIN1_021/2/2002 13:001/3/2002 12:001/3/2002 13:001/4/2002 12:00MMOUT_DOMAIN1_031/3/2002 13:001/4/2002 12:001/4/2002 13:001/5/2002 12:00MMOUT_DOMAIN1_041/4/2002 13:001/5/2002 12:001/5/2002 13:001/6/2002 12:00MMOUT_DOMAIN1_051/5/2002 13:001/5/2002 23:001/5/2002 12:001/10/2002 12:001/5/2002 12:001/5/2002 12:00MMOUT_DOMAIN1_001/5/2002 13:001/6/2002 12:00MMOUT_DOMAIN1_011/6/2002 0:001/6/2002 12:001/6/2002 13:001/7/2002 12:00MMOUT_DOMAIN1_021/6/2002 13:001/7/2002 12:001/7/2002 13:001/8/2002 12:00MMOUT_DOMAIN1_031/7/2002 13:001/8/2002 12:001/8/2002 13:001/9/2002 12:00MMOUT_DOMAIN1_041/8/2002 13:001/9/2002 12:001/9/200213:001/10/200212:00MMOUT_DOMAIN1_051/9/2002 13:001/9/2002 23:00

# APPENDIX B

Daily averaged metrics from the 36 km simulation.



# Eastern Subdomains

52

# 53

# 54

# 55

# 56

# 57

# Western Subdomains

58

# 59

# 60

# 61

# 62

# 63

# 64

# 65

# APPENDIX C

Hourly statistical results for both the 36 and 12 km grids.



# CenrapN: January 2002

Observed/Predicted Windspeed

0 2 4 6 8

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/15 1/

16

1/17 1/18 1/

19

1/20 1/

21

1/

22

1/23 1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31m/s ObsWndSpd 36 km winds 12 km winds



# Bias Windspeed

-2.5 -1.5 -0.5 0.5 1.5

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/20 1/

21

1/

22

1/23 1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31m/s 36km Bias 12km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

1/ 1 1/ 2 1/ 3

1/ 4 1/

5

1/ 6 1/

7

1/ 8 1/

9

1/

10

1/11 1/

12

1/13 1/

14

1/15 1/16 1/

17

1/

18

1/19 1/

20

1/21 1/

22

1/23 1/

24

1/25 1/26 1/

27

1/28 1/

29

1/30 1/

31deg ObsWndDir 36km Predict ed 12km Predict ed



# Bias Wind Direction

-30

0 30

1/ 1 1/ 2

1/ 3 1/

4

1/ 5 1/

6

1/ 7 1/

8

1/ 9 1/

10

1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1 /18

1/

19

1/

20

1/21 1/

22

1/23 1/

24

1/25 1/

26

1/27 1/

28

1/29 1/30 1/

31deg 36km Bias WD 12km Bias WD



# 66

# Observed/Predicted Temperature

240 245 250 255 260 265 270 275 280 285 290

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31K ObsTemp 36 km Pred_T 12 km Pred_T



# Bias Temperature

-5 -4 -3 -2 -1 0 1 2 3

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31K 36 km Bias T 12 km Bias T



# Predicted/Observed Humidity

0 1 2 3 4 5 6

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31g/kg ObsHum 36km Pred q 12 k Pred q



# Bias Humidity

-0.4 -0.2

0 0.2 0.4 0.6 0.8

1

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31g/kg 36 km Bias q 12 km Bias q



# 67

# CenrapN: June 2002

Observed/Predicted Windspeed

0 5 10

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/

15

6/16 6/

17

6/

18

6/19 6/

20

6/21 6/22 6/

23

6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30m/s ObsWndSpd 36 km winds 12 km winds



# Bias Windspeed

-2 -1 0 1

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/20 6/

21

6/

22

6/23 6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30m/s 36 km Bias 12 km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/

15

6/16 6/

17

6/

18

6/19 6/

20

6/21 6/22 6/

23

6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30deg ObsWndDir 36 km PredWnd 12 km PredWndDir



# Bias Wind Direction

-30

0 30

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/20 6/

21

6/

22

6/23 6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30deg 36km Bias 12km Bias



# 68

# Observed/Predicted Temperature

270 275 280 285 290 295 300 305 310

6/

1

6/

2

6/

3

6/

4

6/

5

6/

6

6/

7 6/

8

6/

9 6/1

0 6/1 1

6/

1 2 6/1

3

6/

1 4 6/15 6/

1 6 6/17 6/18 6/

1 9 6/2

0

6/

2 1 6/

2 2 6/2

3

6/

2 4 6/

2 5 6/2

6

6/

2 7 6/2

8 6/2

9

6/

3 0K ObsTemp 36 km Pred_T 12 km Pred_T



# Bias Temperature

-2 -1 0 1 2 3 4

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/15 6

/ 16

6/17 6/18 6/19 6/

2 0 6/21 6/22 6/

2 3 6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30K 36 km Bias T 12 km Bias T



# Predicted/Observed Humidity

0 5 10 15 20

6/

1

6/

2

6/

3

6/

4

6/

5

6/

6

6/

7 6/

8

6/

9 6/1

0 6/1 1

6/

1 2 6/1

3

6/

1 4 6/15 6/

1 6 6/17 6/18 6/

1 9 6/2

0

6/

2 1 6/

2 2 6/2

3

6/

2 4 6/

2 5 6/2

6

6/

2 7 6/2

8 6/2

9

6/

3 0g/kg ObsHum 36km Pred q 12 k Pred q



# Bias Humidity

-2 -1 0 1 2 3

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/15 6

/ 16

6/17 6/18 6/19 6/

2 0 6/21 6/22 6/

2 3 6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30g/kg 36 km Bias q 12 km Bias q



# 69

# OhioVal: January 2002

Observed/Predicted Windspeed

0 2 4 6 8

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/15 1/

16

1/17 1/18 1/

19

1/20 1/

21

1/

22

1/23 1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31m/s ObsWndSpd 36 km winds 12 km winds



# Bias Windspeed

-2 -1 0 1

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/20 1/

21

1/

22

1/23 1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31m/s 36 km Bias 12 km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/15 1/

16

1/17 1/18 1/

19

1/20 1/

21

1/

22

1/23 1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31deg ObsWndDir 36 km PredWnd 12 km PredWndDir



# Bias Wind Direction

-30

0 30

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/20 1/

21

1/

22

1/23 1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31deg 36km Bias 12km Bias



# 70

# Observed/Predicted Temperature

245 250 255 260 265 270 275 280 285 290 295

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31K ObsTemp 36 km Pred_T 12 km Pred_T



# Bias Temperature

-5 -4 -3 -2 -1 0 1 2 3

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31K 36 km Bias T 12 km Bias T



# Predicted/Observed Humidity

0 1 2 3 4 5 6 7 8 9 10

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31g/kg ObsHum 36km Pred q 12 k Pred q



# Bias Humidity

-1 -0.5

0 0.5

1 1.5

2

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31g/kg 36 km Bias q 12 km Bias q



# 71

# OhioVal: June 2002

Observed/Predicted Windspeed

0 2 4 6 8

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/15 6/

16

6/17 6/18 6/

19

6/20 6/

21

6/

22

6/23 6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30m/s ObsWndSpd 36 km winds 12 km winds



# Bias Windspeed

-1.5

-1 -0.5

0 0.5

1

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/20 6/

21

6/

22

6/23 6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30m/s 36 km Bias 12 km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/15 6/

16

6/17 6/18 6/

19

6/20 6/

21

6/

22

6/23 6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30deg ObsWndDir 36 km PredWnd 12 km PredWndDir



# Bias Wind Direction

-30

0 30

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/20 6/

21

6/

22

6/23 6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30deg 36km Bias 12km Bias



# 72

# Observed/Predicted Temperature

275 280 285 290 295 300 305 310

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/2 0

6/

21

6/

22

6/2

3

6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30K ObsTemp 36 km Pred_T 12 km Pred_T



# Bias Temperature

-2 -1.5

-1 -0.5

0 0.5

1 1.5

2 2.5

3

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/2 0

6/

21

6/

22

6/2

3

6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30K 36 km Bias T 12 km Bias T



# Predicted/Observed Humidity

0 2 4 6 8 10 12 14 16 18 20

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/2 0

6/

21

6/

22

6/2

3

6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30g/kg ObsHum 36km Pred q 12 k Pred q



# Bias Humidity

-2 -1.5

-1 -0.5

0 0.5

1 1.5

2 2.5

3 3.5

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/2 0

6/

21

6/

22

6/2

3

6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30g/kg 36 km Bias q 12 km Bias q



# 73

# Iowa: January 2002

Observed/Predicted Windspeed

0 5 10 15

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/15 1/

16

1/17 1/18 1/

19

1/20 1/

21

1/

22

1/23 1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31m/s ObsWndSpd 36 km winds 12 km winds



# Bias Windspeed

-3.5 -2.5 -1.5 -0.5 0.5 1.5

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/20 1/

21

1/

22

1/23 1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31m/s 36km Bias 12km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

1/ 1 1/ 2 1/ 3

1/ 4 1/

5

1/ 6 1/

7

1/ 8 1/

9

1/

10

1/11 1/

12

1/13 1/

14

1/15 1/16 1/

17

1/

18

1/19 1/

20

1/21 1/

22

1/23 1/

24

1/25 1/26 1/

27

1/28 1/

29

1/30 1/

31deg ObsWndDir 36km Predict ed 12km Predict ed



# Bias Wind Direction

-30

0 30

1/ 1 1/ 2

1/ 3 1/

4

1/ 5 1/

6

1/ 7 1/

8

1/ 9 1/

10

1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1 /18

1/

19

1/

20

1/21 1/

22

1/23 1/

24

1/25 1/

26

1/27 1/

28

1/29 1/30 1/

31deg 36km Bias WD 12km Bias WD



# 74

# Observed/Predicted Temperature

230 240 250 260 270 280 290 300

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31K ObsTemp 36 km Pred_T 12 km Pred_T



# Bias Temperature

-5 -4 -3 -2 -1 0 1 2 3 4

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31K 36 km Bias T 12 km Bias T



# Predicted/Observed Humidity

0 1 2 3 4 5 6

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31g/kg ObsHum 36km Pred q 12 k Pred q



# Bias Humidity

-0.6 -0.4 -0.2

0 0.2 0.4 0.6 0.8

1 1.2 1.4

1/

1

1/ 2 1/

3

1/

4

1/ 5 1/

6

1/ 7 1/ 8 1/

9 1/10 1/11 1/

12

1/13 1/

14

1/

15

1 /16

1/

17

1/

18

1/

19

1/2 0

1/

21

1/

22

1/2

3

1/

24

1/

25

1/26 1/

27

1/28 1/29 1/

30

1/31g/kg 36 km Bias q 12 km Bias q



# 75

# Iowa: June 2002

Observed/Predicted Windspeed

0 5 10 15

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/

15

6/16 6/

17

6/

18

6/19 6/

20

6/21 6/22 6/

23

6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30m/s ObsWndSpd 36 km winds 12 km winds



# Bias Windspeed

-4 -2 0 2

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/20 6/

21

6/

22

6/23 6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30m/s 36 km Bias 12 km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/

15

6/16 6/

17

6/

18

6/19 6/

20

6/21 6/22 6/

23

6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30deg ObsWndDir 36 km PredWnd 12 km PredWndDir



# Bias Wind Direction

-30

0 30

6/

1

6/ 2 6/

3

6/

4

6/ 5 6/

6

6/ 7 6/ 8 6/

9 6/10 6/11 6/

12

6/13 6/

14

6/

15

6 /16

6/

17

6/

18

6/

19

6/20 6/

21

6/

22

6/23 6/

24

6/

25

6/26 6/

27

6/28 6/29 6/

30deg 36km Bias 12km Bias



# 76

# Observed/Predicted Temperature

270 275 280 285 290 295 300 305 310

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/15 6

/ 16

6/17 6/18 6/19 6/

2 0 6/21 6/22 6/

2 3 6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30K ObsTemp 36 km Pred_T 12 km Pred_T



# Bias Temperature

-3 -2 -1 0 1 2 3 4

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/15 6

/ 16

6/17 6/18 6/19 6/

2 0 6/21 6/22 6/

2 3 6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30K 36 km Bias T 12 km Bias T



# Predicted/Observed Humidity

0 5 10 15 20

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/15 6

/ 16

6/17 6/18 6/19 6/

2 0 6/21 6/22 6/

2 3 6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30g/kg ObsHum 36km Pred q 12 k Pred q



# Bias Humidity

-4 -3 -2 -1 0 1 2 3 4

6/ 1 6/ 2

6/ 3 6/ 4 6/

5

6/ 6 6/

7

6/

8

6/ 9 6/

10

6/

11

6/12 6/

13

6/14 6/15 6

/ 16

6/17 6/18 6/19 6/

2 0 6/21 6/22 6/

2 3 6/24 6/25 6/

26

6/27 6/

28

6/

29

6/30g/kg 36 km Bias q 12 km Bias q



# 77

# Appendix E

MRPO Air Quality Modeling

Protocol

# Modeling Protocol:

2002 Basecase Technical Details

Kirk Baker

October 15, 2007

Lake Michigan Air Di rectors Consortium

Midwest Regional Planning Organization

Rosemont, Illinois

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 2 of 31 

# 1. INTRODUCTION

The purpose of this addendum is to provide technical details related to the

photochemical transport modeling done to support State Implementation Plans

(SIPs) for ozone, particulate matter less than 2.5 microns (PM2.5), and regional

haze. Documents that relate to a concep tual description of ozone, PM2.5, and

regional haze in the Upper Midwest are available on the organization website:

www.ladco.org

.

Modeling Platform

The computing platforms are Intel-based PCs running variations of the Linux

operating system. The Portland Group (PGI) Fortran compiler is used to create all

executables.

2. METHODOLOGY

Grid Projection and Domains

All models are applied with a Lambert proj ection centered at (-97, 40) and true

latitudes at 33 and 45. The 36 km photoche mical modeling domain consists of 97

cells in the X direction and 90 cells in the Y direction covering th e central and eastern

United States with 36 km grid cells (Figur e 2.1; Table 2.1). The 2-way nested 12 km

photochemical domain covers most of th e upper Midwest region . A 2-way nested 4

km photochemical domain is situated over the lower portion of Lake Michigan and

over Detroit-Toledo-Cleveland.

Figure 2.1 Modeling Domains: Meteorological (left), photochemical (right)

The 36 km meteorological modeling domain covers the entire continental United

States (Figure 2.1; Table 2. 1). The 12 km meteorological domain covers most of the

central and eastern United St ates and the 4 km domain covers the lower portion of

the Great Lakes.

CAMx4 is applied with the vertical atmosphere resolved with 16 layers up to

approximately 15 kilometers above ground level.

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 3 of 31 

# Table 2.1 Modeling Domains

Grid Cell Size XY Origin (km) NX, NY

Emissions 36 km (-2628., -1980.) 147, 111

Meteorological 4 km (576., 108.) 214, 142

Meteorological 12 km (-648., -1260.) 193, 199

Meteorological 36 km (-2952., -2304.) 165, 129

Photochemical 36 km (-900., -1620.) 97, 90

Photochemical (chimil) 4 km (680., 176.) 56, 83

Photochemical (detcle) 4 km (1040., 176.) 74, 56

Photochemical/Emissions 12 km (-48., -552.) 131,131



# The photochemical model is not being applied to the entire 36 km Continental U.S.

domain to maximize resources. A sensitiv ity study was conducted to compare winter

and summer episode averaged PM2.5 concen trations between a Continental U.S.

domain and Central/Eastern U.S. domain using clean boundary conditions released

with the CMAQ model. The episode averag e differences in PM2.5 were less than 1

ug/m3 in the Midwest RPO States and neighboring States (Figure 2.2).

Figure 2.2 Continental Domain – Central/Eastern U.S. Domain Episode Average

PM2.5 Difference Plots for Summer (lef t) and Winter (right) episodes

Meteorological Inputs

Meteorological input data for the photochemical modeling runs are processed using

the National Center for Atmospheric Rese arch (NCAR) 5th ge neration Mesoscale

Model (MM5) version 3.6.1 (Dudhia, 1993; Grell et al, 1994). Important MM5

parameterizations and physics options include mixed phase (Reisner 1) microphysics,

Kain-Fritsch 2 cumulus sch eme, Rapid Radiative Tran sfer Model, Pleim-Chang

planetary boundary layer (PBL), and the Pl eim-Xiu land surface module. Analysis

nudging for temperature and moisture is only applied above the boundary layer.

Analysis nudging of the wind field is applied above and below the boundary layer.

These parameters and options are selected as an optimal configuration for the

central United States based on multiple MM5 simulations using a variety of physics

and configuration options (Johnson, 2003; Baker 2004a).

The meteorological fields output by MM5 are prepared for use by the photochemical

model with processing utilities. These programs translate certain meteorological

parameters from the MM5 grid to the photochemical grid. Additionally, these

processors estimate parameters such as ve rtical diffusivity coefficients that are not

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 4 of 31 

# explicitly output by MM5. The MM5CAMx version 4.4 utility is used to translate MM5

output to CAMx input. The vertical diffusi vity coefficients are based on the O’Brien

1970 vertical diffusivity algorithm. This scheme takes the PBL height output by MM5

and creates a well-mixed atmosphere in side the PBL. The minimum vertical

diffusivity coefficient is 0.1 m 2 

# /s. A landuse-weighted vertical diffusivity coefficient

(maximum of 1.0 m 2 

# /s in a completely urban grid cell) is assigned to all grid cells up

to approximately 150 meters above ground (m odel layer 3). This is done to better

represent the greater mechanical mixing ov ernight in urban area s. An additional

adjustment to vertical diffusivi ty coefficients creates a tran sitional gradient in values

from shore to large water bodies. Figure 2.4 shows maximum vertical diffusivity

coefficients and PBL height for a typical model episode day.

Figure 2.4 Peak Kv (m/s 2 

# ) values and peak PBL (m) values

The gradient from land to lake vertical diffusivity coefficients extends over an order

of magnitude during mid-day peak photochemi cal activity. PBL heig hts at a land cell

are typically over 1000 meters and the adjace nt cell over one of the Great Lakes is

30 meters. Air over the Great Lakes is typically stable and has low mixing, but the

model does not have any transition from land to lake. An adjustment scheme is

employed when cells having greater than 75% water have a vertical diffusivity

coefficient equal to the average of the 5 x 5 group of cells centered on that particular

grid cell.

Figure 2.5. Vertical diffu sivity coeffi cients (m/s 2 

# ) using standard MM5 output (left)

and land-lake gradient adjustment (right)

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 5 of 31 

# The land-lake vertical diffusivity adjustment s are shown for an episode day in Figure

2.5. These adjustments result in minimal change to model performance (Figure 2.6)

and a reduction in extreme NOX disbenefit response in grid cells near the lake-shore.

Figure 2.6. Peak 8-hr O3 (ppb) observat ions (left), modeled with standard Kv

(middle), and modeled with land-lake gradient adjusted Kv (right)

The vertical resolution used in MM5 consists of 34 sigma layers that represent the

terrain following atmosphere up to 100 millib ars. Figure 2.7 displays each vertical

layer in terms of sigma level, pressure (millibars), height above ground level

(meters) and layer thickness (meters). The re lationship to the layer structure used in

the photochemical models is also shown. The photochemical model layer structure

avoids layer collapsing in the lower bo undary layer to better resolve the mixing

depth.

Figure 2.7 Vertical Layer Structure

k(MM5) sigma p(mb) depth(m) k(PCM) depth(m)

34 0.000 100 1841 16 5597

33 0.050 145 1466

32 0.100 190 1228

31 0.150 235 1062

30 0.200 280 939 15 2549

29 0.250 325 843

28 0.300 370 767

27 0.350 415 704 14 2533

26 0.400 460 652

25 0.450 505 607

24 0.500 550 569

23 0.550 595 536 13 1522

22 0.600 640 506

21 0.650 685 480

20 0.700 730 367 12 634

19 0.740 766 266

18 0.770 793 259 11 428

17 0.800 820 169

16 0.820 838 166 10 329

15 0.840 856 163

14 0.860 874 160 9 318

13 0.880 892 158

12 0.900 910 78 8 155

11 0.910 919 77

10 0.920 928 77 7 153

9 0.930 937 76

8 0.940 946 76 6 151

7 0.950 955 75

6 0.960 964 74 5 148

5 0.970 973 74

4 0.980 982 37 4 37

3 0.985 987 37 3 37

2 0.990 991 36 2 36

1 0.995 996 36 1 36

--SURF-- 1 1000 0 --SURF-- --SURF--

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 6 of 31 

# A compromise in the upper troposphere is met by employing layer collapsing to

reduce computational effort and still maintain some upper troposphere resolution for

long-range transport. The layer structure chosen for a modeling application should

be capable of adequately resolving the diurnal variations in the boundary layer

growth and mixing, long-range transport proc esses, wind shear, as well as transport

to and from the free troposphere.

Emissions Inputs

Emissions data is processed using EMS-2003. The EMS-2003 model is selected for its

ability to efficiently process the large requ irements of regional and daily emissions

processing. In addition to extensive quality assurance and control capabilities, EMS2003 also performs basic emissions processe s such as chemical speciation, spatial

allocation, temporal allocation, and cont rol of area, point, and mobile source

emissions (Janssen, 1998; Wilkinson et al, 1994). Outputs from EMS-2003 include a

coordinate-based elevated point source f ile and gridded emissi ons estimates for lowpoint, area, mobile, and biogenics source s. Anthropogenic emission estimates are

made for a weekday, Saturday, and Sunday for each month. The biogenic emissions

are day-specific. Volatile or ganic compounds are speciated to the Carbon Bond IV

(CB4) chemical speciation profile (Carter, 1996).

Table 2.2 CAMx Emissions Species

SPECIE DESCRIPTION

ALD2 Aldehydes

ETH Ethylene

FORM Formaldehyde

ISOP Isoprene

OLE Olefins - Anthropogenic

OLE2 Olefins - Biogenic (OVOC)

PAR Paraffins

TOL Toluene

XYL Xylene

NH3 Ammonia

CO Carbon monoxide

NO2 Nitrogen dioxide

NO Nitrogen oxide

SULF Sulfur

SO2 Sulfur dioxide

PEC Primary PM-fine elemental carbon

PNO3 Primary PM-fine nitrate

POA Primary PM-fine organic aerosol

PSO4 Primary PM-fine sulfate

CCRS Primary PM-coarse crustal

FCRS Primary PM-fine crustal

CPRM Primary PM-coarse "other"

FPRM Primary PM-fine "other"



# The point and area source inventories are based on the State Co nsolidated Emissions

Reporting Rule (CERR) submittals, othe r RPOs, and the 2002 National Emission

Inventory (EPA, 2006). Continuous emissions monitoring data were used to develop

temporal profiles for electrical generati ng units. These new profiles account for

month of year and day of week variations and are unit specific.

On-road emissions are estimated using MO BILE6.2 emission factors and VMT from

the 2002 NEI. The MOBILE6 inpu ts were supplied by the MRPO States, Iowa, and

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 7 of 31 

# Minnesota and from the 2002 NEI for all ot her States. Updated on-road temporal

data is based on an analysis of traffi c count data in Michigan, Wisconsin, and

Minnesota. Default temporal tables are modified to represent a more complex

distribution of vehi cle miles traveled for the weekend.

Off-road emissions are estimated with the NONROAD2004 and NMIM models using

data from the State CERR submittals, EPA’s 2002 NEI, and local data for agricultural

equipment for the MRPO States plus Io wa and Minnesota. Contractor supplied

emissions estimates are used for commerc ial marine and locomotive non-road

categories. NMIM was run with fuel parameter inputs consistent with the on-road

emissions modeling. These emissions do not include permeation effects.

Biogenic emissions are es timated with EMS-2003 using the BEIS3 model (Guenther

et al, 2000). The BELD3 land use dataset is in put to the biogenic model for fractional

land-use and vegetative speciation info rmation (US EPA, 2006b; Kinnee et al. 1997;

Kinnee et al. in press ). Other inputs to the biogenic emissions model include hourly

satellite photosynthetically activated radiation (PAR) and 15 m (above ground level)

temperature data output from MM5 (Pinker and Laszlo, 1992).

Ammonia emissions are based on the July 2004 version (v3.6) of Carnegie Mellon

University’s (CMU) ammonia model using 2002 census of agriculture data (Strader et

al. 2005; Pinder et al., 2004; Goebes et al., 2003). CMU ammonia emissions

estimates are not used from the following categories: humans, dogs, cats, and deer.

These omissions are based on the low likel ihood that ammonia emissions from these

sources would make it out of domestic dw ellings in the case of humans, cats, and

dogs and forested areas in the case of deer. Ammonia emission s are removed from

other RPO’s point source inventory to eliminate double-counting confined animal

operations with CMU model estimates. Up dated monthly and diurnal profiles were

developed using the new process based ammo nia model. The new profile represents

beef, hogs, and dairy. Hog farms are assu med to represent poultry since the new

process based ammonia model did not have a fully functional poultry housing model.

Currently, there are no anthropogenic Mexica n emissions in the em issions input files.

Canadian emissions are based on a 2000 in ventory made available by Environment

Canada to the Environmental Protection Agency.

The speciation profiles used by EMS are obtained from the latest version of EPA’s

SPECIATE database. MRPO contracted impr oved speciation profiles for certain

emission categories. Details of this pro ject are available in “Improving Modeling

Inventory Data: Speciation Profiles – Febr uary 17, 2005” and available by request.

The development of the future year and even the base ye ar emissions are continually

being updated. The best place to find the most recent explanation of the base and

future year scenarios is at the LADCO website (LADCO, 2006).

Landuse

The photochemical model uses 11 land use categories to describe the surface. The

land use file is based on BELD3 1 km data (US EPA, 2006b; Kinnee et al. 1997;

Kinnee et al. in press ). The 1 km data was aggregated to the appropriate grid

resolution for photochemical modeling. Surface roughness va ries by season and land

use category and are taken from EPA’s AE RMET User’s Guide (EPA, 2004; ENVIRON,

2005).

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 8 of 31 

# Table 2.3 Landuse categories

Category Landuse

1 Urban

2 Agricultural

3 Rangeland

4 Deciduous forest

5 Coniferous forest

6 Mixed forest

7 Water

8 Mixed agriculture/forest

9 Non-forested wetlands

10 Mixed agriculture/range

11 Rocky with low shrubs



# USGS data was previously used for land use information. The BELD3 was chosen

because it incorporates the USGS data wi th other sources of information such as

satellite data. A spatial comparison of the agriculture (category 2) landuse fractions

are shown below.

Figure 2.8 BELD3 (left) and US GS (right) agriculture landuse

Drought Stress and Snow Cover

The Palmer Drought Severity Index (PDSI) is an indicator of unusual excess or

deficient moisture. The PDSI is calculated for 350 climatic divisions in the United

States and Puerto Rico. PDSI data is availa ble for each week of a calendar year and

is obtained from the National Weather Service Climate Prediction Center (National

Weather Service, 2005). The dry deposition calculations for non-water landuse

categories are impacted by vegetative re sponse to drought stress (ENVIRON, 2005).

Snow cover is also input to CAMx4 for th e deposition scheme. Three-hourly snow

cover data for each grid cell is extracted from MM5 output files. If snow exists in a

grid cell, the deposition char acteristics of the landuse are switched from “winter” to

“winter with snow.” This switch has an impact on surface resistances for dry

deposition, surface roughness , and chemistry due to the ultraviolet albedo being

changed to the maximum class (ENVIRON, 2005).

Photolysis Rates

Many chemical reactions in the atmosphere are started by the photolysis of certain

trace gases. Photochemical models require these rates be input to accurately

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 9 of 31 

# estimate these reactions. CAMx4 is applied with day specific photolysis rate look-up

tables.

The Tropospheric Ultraviolet-Visible (TUV) radiation model is used to calculate

photolysis rates based on solar zenith angl e, height above ground, ultraviolet albedo

of the ground, atmospheric turbidity, and total ozone column density. The TUV

generates rates for each day as a function of 11 heights, 10 solar zenith angles, 5

ozone column values, 5 al bedo values, and 3 turbidity values (ENVIRON, 2005;

NCAR, 2006).

The ozone column data is derived from daily TOMS satellite observations (NASA,

2006). The albedo data varies by month an d is based on over 10 years of TOMS

satellite reflectivity observations. Actinic flux is estimated using the discrete ordinate

algorithm. The two-stream delta-Eddington method is also available in the TUV

model, but was not selected because th e discrete ordinate approach is more

accurate.

A sensitivity application with CMAQ using TOMS derived photolysis rates and rates

based on seasonal average oz one column showed differenc es in ozone up to 3 ppb

and differences in sulfate ion up to 1.5 ug/m 3 

# . These differences suggest day specific

ozone column data from satell ites should be used rather than seasonal averages and

that accurate photolysis rates are impo rtant for ozone and particulate matter

applications.

For those days that do not have TOMS ozone column data, the data from the

previous day is used instead. This option is more realistic than defaulting to a

seasonal average, which may create a rath er large discontinuity between the missing

day and adjoining simulation days.

Initial and Boundary Conditions

Boundary conditions represent pollution inflow into the model from the lateral edges

of the grid and initial conditions provide an estimation of pollution that already

exists. In the past a spin-up period of two to three days was used to eliminate initial

condition effects for ozone modeling.

CAMx4 source apportionment runs show ozone attributed to initial concentrations

does not exceed 5 ppb anywhere in the domain by the 7 th 

# day of the episode; ozone

modeling episodes will be spun up with 11 days. The monitors used in model

performance evaluation are far enough aw ay from the boundaries that boundary

influence is considered minimal.

CAMx4 particulate source apportionment (PSAT) runs show PM2.5 sulfate ion, nitrate

ion, and ammonium ion contributions from initial concentrations fall below 0.05

μg/m 3 

# by the seventh day of the episode. PM2.5 elemental carbon, PM2.5 soil, and

coarse mass have less than 1 ng/m 3 

# contribution from initial concentrations on the

first day of the model episode everywhere in the modeling domain. Since gas phase

chemistry is coupled with particulate formation, the annual simulations have two

weeks of spin-up to minimize initial condition influence.

The initial and boundary conditions are based on monthly averaged species output

from an annual (calendar year 2002) applic ation of the GEOS-CHEM global chemical

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 10 of 31 

# transport model (Jacob et al, 2005; Bey et al, 2001). Boundary conditions vary by

month and in the horizontal and vertical direction. Where an initial or boundary

concentration is not specified for a pollutant the model will default to a near-zero

concentration.

A study applying CMAQ with monthly averaged and 3-hr GEOS-CHEM initial and

boundary conditions showed almost no change in model performance for any PM2.5

species. The error for total PM2.5 and each of the chemical species differed by less

than 0.04 ug/m 3 

# at IMPROVE and EPA STN monito r sites (Morris et al, 2004b).

Considering the need to model multiple annual simulations and potential issues

related with inconsistencies between in-flows and out-flows between the GEOS-CHEM

meteorology and the MM5 si mulation used for regional modeling, the monthly

averaged concentrations are used to suppo rt photochemical modeling applications.

Quality Assurance of Model Inputs

The model input files are checked for reas onableness to ensure they accurately

represent the underlying data used to create the files. The checks described in this

document are steps that are in addition to the extensiv e QA done in the emission

inventory compilation process, EMS emission s modeling, and MM5 modeling process.

The landuse files are converted to a CAMx4 output file format and directly viewed in

PAVE over a political map. An example of the water landus e category is shown in the

figure in this section.

Figure 2.9 Water landuse

The initial and boundary conditions processor outputs an ASCII file showing the

specie concentration at each vertical layer. This is visualized in EXCEL to make sure

the data is correctly mapped in the vertical direction. The initial and boundary

concentration files themselves are also di rectly viewed in PAVE and the spatial

representation is checked. The ozone column, albedo, and turbidity data are kept in

ASCII files. Each file is ch ecked to ensure the data l ooks spatially reasonable and

that bad data did not get included in the file.

The emissions inputs are exte nsively checked for appropriat eness. The steps taken in

manipulating EMS-2003 output files to CAMx 4 input files and the quality assurance of

those files are detailed in “Emissions Processing and QA” (Baker, 2004b). Each

emission file is checked for spatial an d temporal agreement with EMS-2003 and for

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 11 of 31 

# reasonableness. Additionally, the mass for ea ch species is totaled by State and over

the entire modeling domain and co mpared to EMS-2003 QA reports.

The MM5 output used to support the photochemical modeling is extensively

evaluated from a meteorological perspective. An additional layer of quality assurance

is done by evaluating model perfor mance of the air qu ality model input

meteorological data at several monitor lo cations. This is done for temperature,

relative humidity, wind speed, and wind direction.

Photochemical model simulations also provide a level of quality assurance since

deficiencies in emissions and meteorological inputs will be apparent in the

photochemical model performance.

Photochemical Model Configuration

The Comprehensive Air Quality Model with Extensions (CAMx) version 4.30 uses

state of the science routines to model particulate matter formation and removal

processes over a large modeling domain (N obel et al. 2002; Tanaka et al. 2003;

Chen et al. 2003; Morris, Mansell, Tai, 2004). The model is applied with ISORROPIA

inorganic chemistry, SOAP organic chemistry, regional acid deposition model (RADM)

aqueous phase chemistry, and an updated carbon-bond IV (CB4) gas phase

chemistry module (ENVIRON, 2005; Nene s et al, 1998; Carter, 1996). CAMx4 is

applied using the PPM horizontal transport scheme and an implicit vertical transport

scheme with the fast CMC chemistry solver (ENVIRON, 2005).

The photochemical model is initiated at midnight Eastern Standard Time and run for

24 hours for each episode day. The summer 2002 simulation is initiated on June 2

and run through August 31. The annual simulation is run separately by calendar

quarter and is initiated 2 w eeks prior to each quarter: December 17 (2001), March

15, June 15, and September 15. The base and future year scenarios submitted as

support for the annual PM2.5 st andard will be using a horizo ntal grid resolution of 12

km. The modeling to support the 8-hr Oz one NAAQS will be at 12 km horizontal

resolution over the entire upper Midwest and 2-way nested grids over the lower

portion of Lake Michigan and over the Detroit-Toledo-Cleveland region.

CAMx4 models PM particles in the fine and coarse size fraction. There is no

mechanism in the model to transfer mass between these 2 size sections. The particle

density and diameter does not change from specie specific input values during a

model simulation for either particle size bin.

Future year simulations will be applied wi th the same model configuration as for the

base case simulation. All inputs except for emissions will be the same in the future

year and base year simulati ons to assess changes in ozon e, visibility, and PM2.5 due

to control strategies and future growth. Th e terms base case and base line emissions

inventories are one in the same, both referring to day specific biogenics and monthly

weekday, Saturday, Sunday anthropogenic emissions.

Gas Phase Chemistry

CB4 was originally developed for application to high NOx conditions, such as those

that exist in urban areas (Tonnesen et al, 2001). RADM and SAPRC were developed

specifically for low NOx conditions, such as those that exist in rural areas. The United

States Environmental Protec tion Agency ran CMAQ with CB4 and RADM gas-phase

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 12 of 31 

# chemistry and found the ozone predictions to be very comparable. However, the run

times associated with RADM were twice as long as those with CB4 (Timin, 2002).

SAPRC chemistry also typically has run times much longer than CB4, usually at least

twice as long.

Starting in version 4.20, CAMx4 contains 17 new inorganic reactions that improve

the science in the model without being inconsistent with the evaluation of CB4

against smog chamber data. The new reac tions have little impact on predicted

PM2.5, but increase ozone concentrations regionally. This regional increase in ozone

improves model performance in the Midwest United States and is due to reactions

that recycle NOX. These reacti ons include the photolysis of organic nitrates and nitric

acid and are included in other mechanisms including SAPRC99 and CBM-Z

(ENVIRON, 2005; Carter, 2000; Zaveri and Peters, 1999).

Deposition

Deposition processes are an important factor in pollution and visibility estimation.

Wet and dry removal play an even more impo rtant role in regional modeling as the

spatial and temporal scope of application increase. The wet deposition routine in

CAMx4 has been upgraded to improve cloud and rainfall estimation (Kemball-Cook et

al, 2004). The dry deposition routine is base d on the equations developed by Wesley

(ENVIRON, 2005; Wesley, 1989). The dry depositi on equation is modified to adjust

for special properties of certain chemical sp ecies such as nitric acid (very sticky) and

ammonia (very reactive, fairly sticky, and shows a high degree of near-field

deposition).

The ammonia RSCALE factor in the chemistry parameters input file to CAMx4 is set

to 0.0, which is the same as nitric acid to account for the chemical characteristics of

ammonia and physical processe s (near-field deposi tion) not in the deposition model.

A field study at a Colorado alpine tundra location show ed that ammonia and nitric

acid deposition velocities were very similar: both 1.3 ± 0.6 cm/s (Rattray et al.,

2001). The photochemical landuse model annual mean ammonia deposition velocity

for all sites is 3.0 cm/s and the annual mean estimated nitric acid deposition velocity

is 2.5 cm/s. The modeled ammonia and nitric acid deposition velocities agree within

the uncertainty provided for in the Colorado alpine tundra field study.

Nesting

Nested grids are useful to keep comput ational and data management resources

acceptable while addressing important model application issues such as complex

terrain, land-sea or land-lake breezes, and spatial emission gradients. They may also

be useful to keep large point source plumes in smaller grid cells in lieu of having

explicit sub-grid scale plume treatments.

CAMx4 allows for the inclusion of a fine grid within the coarse grid in a 2-way nesting

mode. The 2-way nesting mode allows for in teraction between the larger coarse grid

with the smaller fine grid. This improves pollutant transport around the boundaries of

the fine grid since a parcel of air may move from the fine grid, out to the coarse grid,

and back into the fine grid depending on the shifting wind fields. This re-circulation is

impossible in 1-way nesting applications.

Several modeling applications have shown minimal benefit to PM2.5 model

performance from the inclusion of a nested 12 km grid (Baker, 2004b; Morris, Koo et

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 13 of 31 

# al, 2004). The EPA modeling guidance re commends that modeling to support the

annual PM2.5 NAAQS be applied at a 12 km ho rizontal grid resolution so that grid

resolution will be used to support the SIP. A 2-way nested 4 km grid will be applied

over the lower portion of Lake Michigan an d over the Cleveland- Toledo-Detroit area

to better resolve the complex interaction between high density urban emissions and

land-Lake meteorology.

Plume-in-Grid

The GREASD sub-grid plume treatment option is being applied in CAMx4 for the

summer season 12 km ozone si mulations. This option is selected to improve the

model treatment of large NOx plumes being released near Lake Michigan and Lake

Erie. Sources included for the plume-in-gri d treatment include any source near the

Great Lakes with NOx emissions greater th an 12 tons per day for any day of the

summer in 2002 and 6 tons per day in future year scenarios.

At high grid resolutions of 4 km or finer, sub-grid scale treatment of plumes should

not be applied since th e fine grid appropriately captur es the small scale physical and

chemical processes.

Probing Tools (Source Apportionment)

Probing tools are valuable from a scienti fic and regulatory perspective for oneatmosphere modeling. Use of source apport ionment is more desirable for regulatory

applications than the use of the “zero-out” approach to determine geographic and

emissions sector culpability for annual modeling simulations. Zeroing out emissions

for large regions such as entire States fundamentally changes the atmospheric

chemistry and makes interpretation of the results difficult.

An option in CAMx is employed to force el evated point sources into particular regions

rather than placement based on coordinate s and the 12 km geographic region map.

This ensures that elevated emissions are placed in the appropriate geographic region

and not incorrectly grouped with another region when a grid cell contains the

boundary for more than one region. A good ex ample of this is the Ohio River Valley

where many large stationary point sources exist along State boundaries and could be

grouped into the wrong region based on the 12 km grid cell source region map. This

option improves the confidence in the source apportionment results for stationary

point sources.

Ozone

CAMx4 contains a variety of ozone source apportionment tools, which includes the

standard ozone source apportionment tool (OSAT). The anthropogenic pre-cursor

culpability assessment (APCA) tool as sesses regional and emission sector

contribution to ozone formation and provides information that is most policy

relevant. The APCA tool is chosen over the other options, in cluding the standard

OSAT option (ENVRION, 2005).

When ozone is formed under VOC limi ted conditions due to biogenic VOC

+anthropogenic NOx then OSAT attributes it to the biogenic VOC sources. When

ozone is formed under NOx-limited conditions due to biogenic VOC + anthropogenic

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 14 of 31 

# NOx then OSAT attributes it to the anthropo genic NOx sources. APCA is designed to

provide more control strategy relevant information and recognizes that there are

source categories such as biogenics th at can not be controlled so the model

attributes ozone to biogenics when it is due to the interaction of biogenic

VOC+biogenic NOx. In the case where ozone formed to biogenic VOC +

anthropogenic NOx under VOC-limited conditions, OSAT attributes it to biogenic VOC,

but APCA redirects the attribution to anthropogenic NOx. In NOx-limited conditions

both OSAT and APCA attribute the ozone to anthropogenic NOx. There is a similar

situation with biogenic NOx + anthropogenic VOC but this rarely happens in the

eastern United States (ENVIRON, 2005).

The source apportionment data is the aver age contribution over all modeled hours

where predicted ozone at the monitor is greater than a threshold concentration

value. Two different thresholds are used to examine different di stributions of high

modeled 8-hour ozone: 75 and 85 ppb (Baker, 2007). The geographic regions

tracked for ozone contribution are listed in Table 2.4 and shown graphically in Figure

2.10 over the 12 km modeling domain. The contribution from the lateral and top

boundaries of the model is also tr acked for each receptor location.

Table 2.4 Complete list of source re gions tracked for ozone contribution

Canada Illinois Chicago non-attainment (NA) Counties



# Northeast States (MANE-VU) Detroit NA Counties

Central/Western States (CENRAP+ WR AP) Indiana Chicago NA Counties

Ohio Cleveland NA Counties

Michigan Milwaukee NA Counties

Indiana Southeast States (VISTAS)

Illinois Minnesota+Iowa

Wisconsin Missouri

Kentucky West Virginia



# Figure 2.10 Source regions tracked in the 12 km grid domain

Six emissions source sectors are tracked for contribution to ozone: onroad mobile,

offroad mobile, area, electrical generating units, non-electrical generating units, and

biogenics. Offroad mobile em issions include sources such as construction equipment,

locomotives, commercial marine vessels, and airports. Two distinct groups of

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 15 of 31 

# stationary point sources are tracked for cont ribution to ozone: electrical generating

units and non-electrical generating units.

Particulate Matter and Visibility

The Particulate Source Apportionment Tool (PSAT) tracks contributions of PM2.5

sulfate ion, nitrate ion, ammonium ion, elemental carbon, and primary emissions of

organic aerosol, soil, and coarse mass. Secondary organic aerosol tracking is also

part of the tool but not employed for this study due to resource constraints.

Secondary organic aerosol co ntributions from biogenic and anthropogenic sources

are part of the standard CAMx outp ut and included in the analysis.

Source apportionment results will be estimated on an annual average basis and on a

daily 24-hr basis to be relevant to th e annual and 24-hr PM2.5 NAAQS. The 24-hr

average source apportionment results for the 20% worst and 20% best days at the

Class I area receptors will be converted to light extinction then averaged together

using the latest IMPROVE Steering Commi ttee recommended equation (IMPROVE,

2006). Contributions from initial conditions are quantified to determine an optimal

amount of spin-up time required to minimize the impacts from initial concentrations.

The geographic regions tracked for contribu tion are listed in Table 2.5 and shown

graphically in Figure 2.11. The contribution from the la teral and top boundaries of

the model is also tracked for each receptor location.

Figure 2.11 Source regions tracked by PSAT

Table 2.5 Complete list of source regions tracked for contribution

Canada Illinois Chicago non-attainment (NA) Counties



# Northeast States (MANE-VU) Detroit NA Counties

Central/Western States (CENRAP+ WR AP) Indiana Chicago NA Counties

Ohio Cleveland NA Counties

Michigan Milwaukee NA Counties

Indiana Southeast States (VISTAS)

Illinois Minnesota

Wisconsin Minneapolis-St. Paul [visibility only]

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 16 of 31 Kentucky West Virginia

Iowa North Dakota [visibility only]

Missouri



# Seven emissions source sector s are tracked for contribution to particulate matter:

onroad mobile, offroad mobile, area, elec trical generating units, non-electrical

generating units, agricultural ammonia, and biogenics.

Probing Tools (Other)

Currently, none of the PM models include process analysis for inorganic, secondary

organic aerosol, or aqueous phase chemistry. A limited amount of information

regarding nitric acid formation is available as process analysis implementation is

limited to gas phase chemistry reactions. Process analysis will not be emphasized

until further development makes it us eful beyond gas phase chemistry.

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 17 of 31 

# 3. Model Performance Evaluation

State Implementation Plans will include modeling the impacts of emission control

scenarios with 3-D Eulerian photochemical transport mo dels. Model performance is

typically evaluated on an operational ba sis and rarely to support a diagnostic

(dynamic) assessment. Operational evaluations for ozone modeling purposes include

matching model estimates with observation data for ozone, nitrogen oxides (NO X 

# ),

and total volatile organic compounds (VOC). Operational evaluations for PM2.5 and

visibility modeling purposes include matching model estimates with observation data

for chemically speciated PM2.5 and important pre-cursor species including sulfur

dioxide, nitric acid, and ammonia.

A diagnostic evaluation asse sses how appropriately the modeling system responds to

emissions adjustments. Since the modeled attainment demonstration includes

modeling current and future ye ar emissions it is importan t to have confidence that

the model will predict concentrations appr opriately when emissions change (US EPA,

2007). This type of evaluati on includes modeling two di fferent ozone episodes that

are separated by enough years that large emissions differences exist. The diagnostic

evaluation is an important assessment to make in addition to an operational

evaluation because it is directly linked to the end use of the model, which is

modeling the change in ozone concentrations after emissions adjustments.

A comparison between observed and esti mated ozone for the summers of 2002 and

2005 is useful for a diagnostic assessment be cause high quality emission inventories

were developed for each year and a large NO X 

# emissions reduction occurred between

these years due in part to NO X 

# SIP Call compliance. Modeling two full summer

seasons provides an opportunity to make another diagnostic evaluation which

assesses model performance for high ozon e by day of the week (Baker, 2007b).

Emissions change substantially from weekday to weekend and having two full

summers provides enough days with high ozone on each day of the week to make

this type of evaluation useful.

The photochemical modeling applications ar e designed to support the development of

regional control strategies for PM2.5 and Re gional Haze. EPA guidance states that an

attainment test for either standard will require the use of chemically speciated PM

relative reduction factors (US EPA, 2007). Ad ditionally, the model will be used to

assess improvements in PM2.5 concentrations and visibility as a result of changes in

emissions. These prominen t end-uses of the modeling applications make

comprehensive evaluations important. Clea rly, reliance on model performance for

PM2.5 total mass would be misl eading since it is likely that the model and ambient

data could estimate the same total mass bu t very different chemical composition.

This scenario would compromi se the development and interpretation of potential

regulatory control strategies (Baker, 2004d).

The species to be compared to monitor co ncentrations include ozone, total VOC,

NOX, SO2, NH3, HNO3, and speciated PM2. 5 (see Table 3.1). Initially, scatter-plots

of point-to-point relationships for all monitors in the domain for all episode days will

be used for analysis for PM. This will allow for identification of gross model over or

under-prediction by specie. Gas and aerosol data are taken from a variety of monitor

networks for comparison to modeled esti mates: IMPROVE, EPA Speciation Trends

(STN), AIRS, and PAMS. The data is obtained directly from the VIEWS website and

from the AFS database; a comparison of the monitor species to model species is

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 18 of 31 

# shown below. PM2.5 ammonium ion is only measured at EPA Speciation Trends

locations so the model performance for this chemical specie is dominated by, but not

limited to, urban measurement locations.

Table 3.1 Species mapping between mode led and observed species (observed

species from the VIEWS website)

IMPROVE STN CAMx4 species

Sulfate aerosol SO4f SO4f PSO4



# Nitrate aerosol NO3f NO3f PNO3



# Ammonium

aerosol NH4f PNH4



# Organic aerosol OCf*FACTOR

FACTOR =

1.6 rural

2.1 urban OCf*FACTOR

FACTOR =

1.6 rural

2.1 urban SOA1+SOA2+

SOA3+SOA4+

SOA5+POA



# Elemental carbon ECf ECf PEC



# Soil/Crustal SOILf SOIL = 2.2*ALf +

2.49*SIf+1.63*CAf+

2.42*FEf+1.94*TIf FCRS



# PM2.5 other MF-RCFM MF-(RCFM) FPRM



# Coarse mass CM_calculated CPRM+CCRS



# PM2.5 MF MF PSO4+PNO3+PNH4+POA+

SOA1+SOA2+SOA3+SOA4+

SOA5+PEC+NA+PCL+

FPRM+FCRS



# Re-constructed

fine mass RCFM RCFM = SO4f+NO3f+

NH4f+OCf*FACTOR+

ECf+(SOIL) 1.375*PSO4+1.29*PNO3+

POA+SOA1+SOA2+SOA3+

SOA4+SOA5+PEC+NA+

PCL+FPRM+FCRS



# Re-constructed

bext aerosol_bext fRH*[4.125*PSO4+

3.87*PNO3]+4*(SOA1+SOA2+

SOA3+SOA4+SOA5+POA)+

10*PEC+NA+PCL+FPRM+FCRS+

0.6*(CPRM+CCRS)



# Model performance evaluation plots and metrics will be ba sed on matching

predictions and observations in time and sp ace. There will not be any averaging over

multiple-cell regions to match with an observation value. Qualitative evaluation will

be done largely through graphical comparison of predictions and observations using

spatial plots, time series plots, and sca tter plots. The US EPA modeling guidance

recommends against using any bright-line evaluation of performance metrics to

determine whether the modeling is satisfactory (US EPA, 2007).

3.1 Particulate Matter and Regional Haze

The components of the visibi lity equation match up very closely to the prominent

chemical forms of PM2.5: nitrate ion, sulfate ion, ammonium ion, organic carbon,

elemental carbon, and soil (U S EPA, 2007). Since these modeling applications will

support PM2.5/Haze rules, model performanc e will be most rigorous for each of

these PM2.5 species and coarse mass.

One of the problems related to PM model performance evaluation involves matching

inconsistent monitor methodologies and model specie definition. Additionally,

speciated measurements rarely add up to measurements of total fine mass. This

unexplained fraction is usually attributed to the retention of water on the weighed

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 19 of 31 

# samples (Timin, 2002). Other problems with comparing speciation samples and FRM

measurements include volatilization of ni trate and positive an d negative organic

carbon artifacts (Timin, 2002).

Organic material is typically estimated from organic carbon using a 1.4 factor, which

is based on the assumption that carbon accounts for 70% of the organic mass.

Recent literature recommends a factor of 1.6 ± 0.2 for urban aerosol and 2.1 ± 0.2

for non-urban areas that would see more aged aerosol (Turpin and Lim, 2001;

IMPROVE, 2006). These factors are applied to the observation data based on landuse

type before being compared to model outp ut. These factors may also be used to

reduce modeled estimates of organi c material to or ganic carbon.

Performance metrics used to describe mode l performance for PM2.5 species include

mean bias, gross error, frac tional bias, and fractional error (Table 3.2) (US EPA,

2007; Boylan et al, 2006). The bias and error metrics are used to describe

performance in terms of the measured concentration units ( μ g/m 3 

# ). Even though the

distribution of PM2.5 is log- normal, the data is not transformed for this analysis. The

model attainment tests outlined by EPA fo r the PM2.5 NAAQS and Regional Haze rule

require relative reduction factors to be applied to actual concentrations and not

transformed concentrations. No minimum valu e is used to eliminate data points for

the purposes of this analysis.

Table 3.2. Model Performance Metrics.

Mean Bias

Gross Error

Fractional Bias

Fractional Gross Error



# *P=model prediction; O=observation; N= number of days; M=number of monitors

Fractional bias and fraction al error metrics are useful for comparison of model

performance between species that tend to have large concentrations and those with

small concentrations. It also helps comp are performance of the same specie if

concentrations are very large in some seasons and very small in others. The

fractional metrics are best when clos e to 0 and worst when close to 2.

3.2 Ozone

Hourly running 8-hour averaged surface ozone observations from EPA’s AIRS

database are matched to hourly running 8-hour averaged layer 1 (30 m height)

model estimates for evaluation. Only moni tors in the 12 km modeling domain are

included in the analysis. Model performanc e evaluation plots and metrics are based

on matching predictions and observations in time and space. EPA has suggested

several statistical metrics to describ e model performance and include mean

normalized bias error (MNBE) and mean normalized gross error (MNGE) (see Table

3.3) (US EPA, 2007). 

# ∑∑

= = 

## + −

×

× = N

i M

j j

i j

i j

i j

i



## OP OP

MN

1 1 

## 2

1 

# ∑∑

= = 

## ⎟ ⎟

⎠ ⎞

⎜ ⎜

⎝ ⎛

+ −

×

× = N

i M

j j

i j

i j

i j

i



## OP OP

MN

1 1 

## 2

1 

# ∑∑

= = 

## −

× = N

i M

j j

i j

i 

## OP

MN

1 1 

## )(

1



# ∑∑

= = 

## −

× = N

i M

j j

i j

i 

## OP

MN

1 1 

## ||

1

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 20 of 31 

# This modeling system is used to support regulatory applications, so the model

performance analysis reflects this end-use of the modeling results. It is well known

that ozone data tends to follow a log-norm al distribution and for the purposes of

scientific evaluations the data is often log- transformed before evaluation (Hogrefe et

al, 2003). Observations and predictions used in the attainment test may not be

transformed, so the data used for model performance evaluation will likewise not be

transformed.

Table 3.3 Model Performance Metric Definitions.

Metric Equation

Mean Normalized Bias Error (MNBE)

Mean Normalized Gross Error (MNGE)

*P=model prediction; O=obse rvation; N=number of days; M=number of monitors

These metrics have traditionally been calc ulated when the obse rvation value exceeds

a certain minimum value, ofte n 60 ppb for 1-hour ozone evaluation (Hogrefe et al,

2003). The MNBE and MNGE will be estima ted using 3 different minimum 8-hour

ozone thresholds: 20, 40, and 60 ppb. The 60 ppb minimum threshold level excludes

prediction-observation pairs that are not of direct regulatory importance since the 8-

hour ozone attainment test only applies to days with high am bient concentrations

(US EPA, 2007). The 20 and 40 ppb minimu m thresholds are included in the

evaluation to get a better idea about how we ll the model is performing at predicting

diurnal formation and removal processes and for days between high ozone episodes.

The metrics are estimated for all stations in the 12 km modeling domain for each day

of the summer episode. Th e episode average metrics are estimated from the daily

metrics.

3.3 Deposition

Wet deposition is measured at several monito ring networks and is also output by the

photochemical model. The National Tren ds Network (NTN) and the Atmospheric

Integrated Research Monitoring Netw ork (AIRMoN) make up the National

Atmospheric Deposition Program (NADP). NT N sites collect weekly measurements of

wet deposition fluxes of sulfate and nitrate anions and the ammonium cation. NADP

network stations measure wet deposition as mass per volume (mg/L) and the model

outputs mass per area (g/ha or mole/ha). CAMx4 wet deposition output is matched

to NTN/NADP measurement data in units of kg/km 2 

# according to the details outlined

below.

The calculations used to convert CAMx wet deposition output to compare to

NTN/NADP network data:

SPECIE_WD (g/ha) * ( 1 ha / 2.5 acres ) * ( 1 acre / 0.0040469 km 2 

# ) * ( 1 kg /

1000 g)

The calculations used to convert NTN/NADP data to compare with CAMx output data: 

# ∑∑

= = 

# ⎟ ⎟

⎠ ⎞

⎜ ⎜

⎝ ⎛

−

× = N

i M

j j

i j

i j



# O OP

MN

1 1 

# 1



# ∑∑

= = 

# −

× = N

i M

j j

i j

i j

i



# O OP

MN

1 1 

# 1

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 21 of 31 

# SPECIES (mg/L) * ( 1 L / 1,000,000 mm 3 

# ) * precipitation in mm * ( 1 mm 2 

# /

0.000000000001 km 2 

# ) * ( 1 g / 1000 mg) * ( 1 kg / 1000 g )

The table below outlines the matching of observed species to CAMx output species.

Table 3.4 Observed and Modeled Wet Deposition

NADP/NTN CAMx4

Sulfate SO4 PSO4_WD + SULF_WD

Nitrate NO3 PNO3_WD + HNO3_WD

Ammonium NH4 PNH4_WD + NH3_WD

Crustal Ca + Cl + Mg +K + Na FCRS_WD + FPRM_WD

# 2002 Basecase Modeling Protocol: Technical Details

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Page 22 of 31 

# 4. Attainment Tests

Visibility

Visibility may be estimated by two similar methods that relate light extinction to

ambient PM2.5 concentrations (FLAG, 2000; US EPA, 2007). Visibility will be

estimated using the new equation re commended by the IMPROVE steering

committee (IMPROVE, 2006). The new and ol d equations produce very similar

estimates of light extinction in the upper Midwest. The new equation will be

emphasized for the SIP modeling demonstrat ion due to its more up to date science.

The equation shown below relates PM2.5 specie concentrations to light extinction.

Additional factors of f(RH) are included that change the light scatt ering of sulfate and

nitrate based on climatologically averaged relative humidity.

β

ext 

# = 2.2*f S 

# RH*[small sulfate] + 2.4*f S 

# (RH)*[small nitrate] + 4.8*f L 

# RH*[large

sulfate] + 5.1*f L 

# (RH)*[large nitrate]+ 2.8*[small OCM] + 6.1*[large OCM] + 10*EC

+ 1*SOIL + 0.6*CM + 1.7*f SS 

# (RH)*SS + β rayleigh



# Β ext Estimated extinction coefficient (Mm-1)

Sulfate Sulfate associated with ammonium (SO4*1.375)

Nitrate Nitrate associated with ammonium (NO3*1.29)

OCM Organic carbon Mass

EC Elemental carbon

SOIL Inorganic primary PM2.5 (soil, crustal, other)

CM Coarse fraction particulate matter

SS Sea salt

β rayleigh 

# Light scattering due to Raylei gh scattering (site specific)

fRH Relative humidity adjustment factor

The apportionment of sulfate, nitrate, and organic carbon mass into small and large

size fractions is shown below using ‘X ’ as a placeholder for these species.

Large X = ([Total X] / [20 ug/m3]) * [T otal X], where [Total X] < 20 ug/m3

Large X = [Total X], where [Total X] ≥ 20 ug/m3

Small X = [Total X] – [Large X]

The fRH values are long-term averages that are site and month specific (US EPA,

2003a; US EPA 2003b; FLAG, 2000). The light scattering due to Rayleigh is site

specific (IMPROVE, 2006). The NO 2 

# component to the light extinction equation is not

included since it is not measured at Class I areas in the upper Mi dwest. The visibility

equation is expressed as an extinction coefficient ( β

ext 

# ) and is converted to deciviews

using the equation below.

Deciview = 10ln( β

ext 

# / β rayleigh 

# )

The reasonable progress test to determin e the relationship between current and

future year visibility is expressed in deci view units. The changes in deciview between

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 23 of 31 

# the current and future year strategy is the reasonable progress test and is shown

below.

Change in Deciview = 10ln[( β

ext 

# ) future 

# / ( β

ext 

# ) base 

# ]

- or -

Change in Deciview = Deciview base 

# - Deciview future



# Visibility will be estimated for key Class I area in the Midwest for the base year and

various future year scenarios . The changes in visibility between the base line and

future year will be assessed using proced ures in U.S. EPA’s modeling guidance

document (US EPA, 2007).

1. The visibility in deciviews will be ranked from high to low at each Class I area

for the calendar years 2000-2004 using the monthly and site specific fRH

values and the more recent IMPR OVE light extinction equation.

2. The mean deciviews for the 20% days wi th the best and the 20% days with

the worst visibility is estimated for each Class I area for each year of the

2000-04 baseline period.

3. The mean observed extincti on coefficient for the da ys during the modeling

period (2002) with the 20% best and 20% worst visibility wi ll be calculated.

4. The mean predicted extinction coeffici ent for the corresponding 20% best and

20% worst days of the modeling period of the base case and future year

strategy will be calculated using monthly site specific fRH values.

5. The relative reduction factor for the 20% best and 20% worst group of days

for each site for each of the particulat e matter species in the light extinction

equation are estimated.

6. The relative reduction factors are multiplied by daily measured PM data during

the 2000-04 baseline to estimate future daily values of these species.

7. These future daily PM estimates are used to estimate light extinction for each

of the previously identifi ed 20% best and 20% worst days of monitored data.

Light extinction is converted to decivi ews and the mean value for the best and

worst days for each year of the baseline period is estimated.

8. The 5 mean deciview values for the wors t and best days (one from each of

the 5 years) are averaged together fo r a mean value for the best and worst

days.

9. The future year mean deciview values in step 8 are compared to the observed

values from step 2. The differences are compared to established goals for

reasonable progress to determine if reasonable progress is demonstrated.

Annual PM2.5 Standard

Progress in meeting the annual PM2.5 standard will be assessed by application of the

procedures outlined by th e U.S. EPA modeling guidance document (US EPA, 2007).

The major steps of this attainme nt test are outlined below:

1. Chemically speciated IMPROVE an d STN PM2.5 data from 2000-2004 is

spatially interpolated to match the grid domain and resolution used for the

photochemical modeling. Spatial fields are developed for each PM2.5 chemical

species for each season using the SAS statistical software package PROC

KRIG function (EPA, 2004b).

2. The estimated fractional composition of each species by quarter is multiplied

by the 5 year weighted average 2000-2004 FRM quarterly mean

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 24 of 31 

# concentrations at each FRM monitor, resulting in estimated quarterly mean

ambient concentrations of PM2.5 comp onents sulfate, ni trate, ammonium,

elemental carbon, organic carbon, particle bound water, and crustal material.

3. Estimate the modeled quarterly mean concentration for each chemical

component of PM2.5 in the base year and future scenarios.

4. Calculate quarterly relative reduction fa ctors for sulfate, nitrate, elemental

carbon, organic carbon, and crustal ma terial. The RRF is the ratio of the

future year to the base year.

5. Quarterly specific RRFs are multiplied by the quarterly average species

concentration from step 2 to estimate future case quarterly average

concentrations for each of the PM2.5 species.

6. Calculate the quarterly average future scenario concentrations for ammonium

and particle bound water using estimated ambient concentrations of sulfate,

nitrate, and degree of sulfate neutralization. Particle bound water is estimated

with an empirical equation.

7. Sum the quarterly future species concentrations to estimate the future

quarterly average PM2.5 concentration.

8. The annual average future scenario co ncentration is the average of the 4

future year quarterly average PM 2.5 concentrations.

9. Compare value to annual NAAQS standard of 15 ug/m 3 

# . If value is ≤ 15

ug/m 3 

# then the test is passed.

Organic carbon mass is estimated using a mass balance approach (EPA, 2006). The

organic carbon spatial fields are only us ed to supply a minimum value for OCM when

OCM estimated by mass balance is less th an OC*1.4*0.7. A spatial field of the

degree of sulfate neutralization is develo ped to estimate PM2.5 ammonium. Particle

bound water is estimated using an empirical equation with spatially interpolated

PM2.5 sulfate ion, FRM equivalent PM2.5 nitrate ion, and FRM equivalent PM2.5

ammonium ion (EPA, 2006).

Ozone

Progress in meeting the 8-hour ozone standard will be assessed in part using the

modeled attainment test outlined by the U. S. EPA’s “Guidance on the Use of Models

and Other Analyses in Attainment Demonstr ations for the 8-hour Ozone, PM2.5, and

Regional Haze” (US EPA, 2007). The attainment test is only applicable to monitors

with design values ≥ 75 ppb. The major steps of the attainment test are described

below:

1. Calculate the 8-hour ozone design value at each monitor location; the design

value used in the attainment test is the average of 3 consecutive 3 year

averaged design values: 2000-2002, 2001-2003, and 2002-2004.

2. Apply the photochemical model to a current year and future year to estimate

a monitor specific relative reduction factor.

3. Calculate the future year design value by multiplying the monitor-specific

observed design value by the monitor-specific relative reduction factor.

4. If the future year design value is ≤ 84 ppb then the test is passed at that

monitor location.

The highest 8 hour daily maximum predicted in the 3x3 (or 7x7 for 4 km modeling)

group of cells surrounding and including the cell in which the monitor is located will

be used in the attainment test. The attainment test will be applied to all days during

the summer of 2002 that meet the meet th e inclusion criteria for the relative

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 25 of 31 

# reduction factor calculation (US EPA, 2007). An episode day must have a peak 8-hr

ozone model prediction > 85 ppb at a specific monitor or ne ar the monitor (definition

of near mentioned above) to be included in the attainment test. If there are less than

10 days of estimated peak 8-hr ozone at a monitor then the threshold for inclusion to

the relative reduction factor is decreased until the number of days equals 10 or the

threshold goes below 70 ppb (US EPA, 2007). If there are less than 4 days in the

relative reduction factor calculation then the attainment test is not applied for that

monitor.

Unmonitored Area Analysis

An un-monitored area analysis is an additi onal review to identify areas that might

exceed the 8-hr ozone or annual PM2.5 N AAQS if monitors were present (US EPA,

2007). This analysis uses interpolated spatial fields of ambient concentrations and

photochemical model estimated concentratio ns to develop “model adjusted spatial

fields of observations” (US EPA, 2007). The model adjusted spatial fields are

developed for the base year. Future year concentrations are estimated by applying

RRFs to the base year model adjusted spatial field.

8-hr Ozone NAAQS

1. Ambient 8-hr ozone design values are interpolated to create the ambient

spatial field. The design values are the average of the 2000-2002, 2001-

2003, and 2002-2004 8-hr ozone design values.

2. The ambient spatial field is adjusted using gridded ozone seasonal average

base year model output gradients.

3. Gridded RRFs are applied to the adjusted spatial field developed in step 2.

4. If any grid cell exceeds 84 ppb then that grid cell is predicted to exceed the

8-hr ozone NAAQS in the future scenario.

Annual PM2.5 NAAQS

1. Quarterly PM2.5 chemical species are interpolated to create the ambient

spatial fields.

2. The ambient spatial field is adjusted using gridded ozone seasonal average

base year model output gradients.

3. Quarterly gridded RRFs for each PM2.5 species are applied to the adjusted

spatial field developed in step 2.

4. If any grid cell exceeds 15 ug/m3 then th at grid cell is predicted to exceed

the annual PM2.5 NAAQS in the future scenario.

US EPA intends to provide software that incorporates monitor observation data and

CAMx output to generate the gridded fu ture year 8-hr ozon e and annual PM2.5

estimates (US EPA, 2007). This software will be used to apply the un-monitored area

analysis.

24-hr PM2.5 Standard

Progress in meeting the new 24-hr PM2.5 standard will be assessed by application of

the procedures outlined by the U.S. EPA document “Guida nce on the Use of Models

and Other Analyses for Demonstrating Atta inment of Air Quality Goals for Ozone,

PM2.5, and Regional Haze“ (U S EPA, 2007). The major steps of this attainment test

are outlined below:

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 26 of 31 

# 1. Chemically speciated IMPROVE an d STN PM2.5 data from 2000-2004 is

spatially interpolated to match the grid domain and resolution used for the

photochemical modeling. Spatial fields are developed for each PM2.5 chemical

species for each season using the SAS statistical software package PROC

KRIG function (EPA, 2004b). Rather than interpolating seasonal averages, the

top 15% of reconstructed PM2.5 mass sa mples are used as the basis of the

chemically speciated data used for seasonal spatial fields.

2. Estimate the observed 98 th 

# percentile value for each year of the 5 year

baseline period. Additionally , the next highest concentr ation in each quarter is

identified. This results in data for each year and site which contains one

quarter that equals the 98 th 

# percentile and 3 quarters which are less than or

equal to the 98 th 

# percentile.

3. The quarterly maximum daily concentration is multiplied by the fractional

composition of PM2.5 species based on the spatial fields.

4. PM2.5 component specific relative redu ction factors are estimated at each

monitor for each quarter.

5. The component specific RRF s are multiplied by the observed values to

estimate future year concentrations.

6. The quarterly components are summed to estimate the quarterly future year

98 th 

# percentile value.

7. The 3 consecutive future year 98 th 

# percentiles are averaged together to

estimate 3 different future year desi gn values. The 3 future year design

values are averaged to estimate a si ngle 5-year weighted average 24-hour

design value.

8. If this 5 year weighted average 24-hour design value is less than 35 ug/m3

then the test is passed.

The relative reduction factor is only estimated for days with 24-hour average

modeled PM2.5 greater than 35 ug/m3. If le ss than 10 days in a quarter meet this

criteria, then the threshold is lowered until the number of days equals 10 or the

threshold goes below 20 ug/m3. If there ar e less than 5 days in the RRF calculation

then that quarter is not used for the estimati on of the future year design value. If no

quarter has more than 5 days included in the RRF calculation then the attainment

test is not applied for that monitor.

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 27 of 31 

# 5.0 Other Issues

Technology Transfer and Modeling Capacity Building

States that are part of th e Midwest Regional Planning Organization and cooperating

organizations have to opportunity to acqu ire a turn-key modeling system. This will

include all the model inputs, scripts, an d support documents to perform model

simulations. States participate in an extensive sensitivity projects and preliminary

strategy rounds which are designed in pa rt to allow States to develop modeling

expertise in-house.

The model input data will be available on an FTP site. The drawback is that transfer

times will be long since the files are ra ther large, but the benefit is that as

improvements and updates to input files, model code, and processing utilities

become available they will immediately be available to everyone. This approach

greatly reduces the resource burden involved with data distribu tion of media (i.e.

hard drives or DLT tapes) via the mail system.

Where very large datasets need to be transf erred USB/firewire driv es will be sent via

the mail system. A general figure where USB drives will be used for transfer instead

of FTP would be 50+ gigabytes of data.

States and cooperating organizations will also participate in regular conference calls

and face to face meetings to discuss pr oblems, progress, and outline cooperative

work objectives.

Ultimately, States that are inclined will be able to use the model inputs developed by

the Midwest Regional Planning Organization as the basis for local emphasis modeling

projects.

Data Management and Storage

The file storage requirements for annual modeling are large and data backup is an

important consideration. Important files including raw emissions and meteorological

files will be stored redundantly on multiple hard drives. Additionally, all the model

inputs will have a redundant copy at each member State as they will be using them

for model simulations as part of the tec hnology transfer and capacity building.

# 2002 Basecase Modeling Protocol: Technical Details

Kirk Baker, LADCO

Page 28 of 31 

# 6. References

Baker, K. Meteorological Modeling Protocol For Application to PM2.5/Haze/Ozone Modeling

Projects, 2004a. http://www.ladco.org/tech/photo/photochemical.html

Baker, K. Processing and Quality Assurance of Emissions Files for Photochemical Model Input,

2004b. http://www.ladco.org/tech/p hoto/photochemical.html

Baker, K. Fine Grid v. Coarse Grid results, presented at the National Regional Planning

Organization Modeling Meeting, Denver, CO, 2004b.

Baker, K. Application of Multiple One-Atmosphere Air Quality Models Emphasizing PM2.5

Performance Evaluation, presented at the Air & Waste Management Association 2004 Annual

Meeting, Indianapolis, IN, 2004d. http://www.ladco.org/tech/p hoto/photochemical.html

Baker, K. Ozone Source Apportionment Results for Receptors in Non-Attainment Counties in

the Great Lakes Region. 2007. AWMA A nnual Conference. Pittsburgh, PA.

Baker, K., Kenski, D. Diagnostic and Operational Evaluation of 2002 and 2005 Estimated 8-hr

Ozone to Support Model Attainment Demonstrations. Presented at the 2007 CMAS Conference.

Chapel Hill, NC. 2007b.

Bey, I.; Jacob, D; Yantosca, R.; Logan, J.; Fiel d, B.; Fiore, A.;Li, Q. ; Liu, H.; Mickley, L.;

Schultz, M. Global modeling of tropospheric chemistry with assimilated meteorology: Model

description and evaluation, J. Geophys. Res., 106, 23,073-23,096, 2001.

Boylan, J. W., Odman, M. T., Wilkinson, J. G., Russell, A. G.: Integrated assessment modeling

of atmospheric pollutants in the southern Appalachian Mountains: Part II. fine particulate

matter and visibility. Journal of Air & Waste Management Association . 56; 12-22: 2006.

Carter, W.P.L. (1996) Condensed Atmospheric Photooxidation Mechanisms for Isoprene.

Atmos. Environ., 30, 4275-4290.

Carter, W.P.L. 2000. Programs and Files Implementing the SAPRC-99 Mechanism and its

Associates Emissions Processing Procedures for Models-3 and Other Regional Models. January

31, 2000. http://pah.cert.ucr.edu/~carter/SAPRC99.htm

Chen, K. S., Y. T. Ho, C. H. Lai and Y.-M. Chou (2003) Photochemical modeling and analysis of

meteorological parameters during ozone ep isodes in Kaohsiung, Taiwan, Atmospheric

Environment, 37(13), 1811-1823.

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and simulation of an Atlantic cyclone and co ld front, Mon. Wea. Rev., 121, 1493-1513, 1993.

ENVIRON International Corporation. 2005. User's Guide Comprehensive Air Quality Model

with Extensions (CAMx4) Version 4.20. ENVIRON International Corporation, Novato,

California. www.camx.com

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Report. http://www2.nature.nps. gov/ard/flagfiee/AL.doc

Gery, M.W.; Whitten, G.Z.; Killus, J.P.; Dodge, M.C. A photochemical kinetics mechanism for

urban and regional scale computer modeling. Journal of Geophysical Research, 1989, 94,

12925–12956.

Grell, G. A.; Dudhia, J.; Stauffer, D. A descrip tion of the Fifth Generation Penn State/NCAR

Mesoscale Model (MM5), NCAR Tech. Note, NCAR TN-398-STR, 138 pp., 1994.

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Page 29 of 31 Goebes MD, Strader R, Davidson CI. 2003. An ammonia emission inventory for fertilizer

application in the United States. Atmospheric Environment 37, 2539-2550

Guenther A., C. Geron, T. Pierce, B. Lamb, P. Harley, and R. Fall (2000) Natural emissions of

non-methane volatile organic compounds; carb on monoxide, and oxides of nitrogen from

North America, Atmos. Environ., 34: 2205-2230.

Hogrefe, C., G. Sistla, E. Zalewsky, W. Hao, and J.-Y. Ku (2003) An assessment of the

emissions inventory processing systems EMS-2001 and SMOKE in grid-based air quality

models, J. Air & Waste Management Assoc., 53(9): 1121-1129.

IMPROVE Steering Committee. Revised IMPROVE Algorithm for Estimating Light Extinction

from Particle Speciation Data.

http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm

Accessed August 22, 2006.

Jang, M.; Czoschke, N.M.; L ee, S.; Kamens, R.M. Heterogeneous atmospheric aerosol

production by acid-catalyzed particle-phase reactions. Science, 298, 814-817, 2002.

Jacob, D.; Park, R.; Logan, J., Documentation and Evaluation Of The Geos-Chem Simulation

for 2002 Provided to the Vistas Group. June 24, 2005. djacob@fas.harvard.edu

.

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http://www.ladco.org/em is/guide/ems95.html

Johnson, M. Meteorological Modeling Protocol: IDNR 2002 Annual MM5 Application, 2003.

Kemball-Cook, S., Emery, C, Yarwood, G, Working Draft Report: Improvements to the MM5-

CAMx Interface for Wet Deposition and Perfor mance Evaluation for 2002 Annual Simulations,

2004.

Kinnee, E., C. Geron, and T. Pierce (2004) Development of a 1-km resolved vegetation

database for surface exchange modeling in North America, Ecological Applications, in press.

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biogenic ozone precursor emissions. Ecological Applications, 7(1), 46-58.

LADCO. http://www.ladco.org/tech/photo/photochemical.html (Protocols/Documents section)

and http://www.ladco.org/mrpo.html (Emissions section of RPO Projects) Accessed August 22,

2006.

NCAR. National Center for Atmospheric Research. http://cprm.acd.ucar.edu/Models/TUV/

Accessed February 26, 2006.

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2006).

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http://www.cpc.ncep.noaa.gov/pr oducts/analysis_monitoring/c dus/palmer_drought/wpdanote.

shtml (accessed October 1, 2005)

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multiphase multicomponent inorganic aerosols. Aquat.Geoch. 4: 123-152.

Morris, R.E., G. Mansell, and E. Tai. (2004) Air Quality Modeling Analysis for the Denver Early

Action Ozone Compact. Prepared for Denver Regional Air Quality Council, Denver, CO.

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Phase II 2002 CMAQ Operational Model Performance Evaluation. Presented at the VISTAS

TAWG Meeting, September 2004 (2004b) http://pah.cert.ucr.edu/vistas/vistas2/docs.shtml

Nobel, C. E., E. C. McDonald-Buller, Y. Kimura, K .E. Lumbley, and D. T. Allen (2002)

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a global scale. J. Appl. Meteor., 31, 194-211.

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The CMU Ammonia Emission Inventory. Available at http://www.cmu.edu/ammonia

Tanaka, P. L., D. T. Allen, E. C. McDonald-Bul ler, S. Chang, Y. Kimura, C. B. Mullins, G.

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regional photochemical model, J. Geophys. Res., 108(D4), 4145.

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Comparison of CMAQ and REMSAD- An Annual Simulation. 1st Annual CMAS Conference,

Research Triangle Park, NC.

Tonnesen, G.; Wang, Z.; Lents. J.; Gemmill, D. ; Morris, R. Modeling Protocol and Quality

Assurance Plan for the WRAP Visibility Modeling Project, 2001.

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common assumptions for estimating organic mass. Aerosol. Sci. Technol., 35, 602-610.

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Preprocessor (AERMET), EPA-454/B-03-002, Office of Air Quality Planning and Standards,

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U. S. Environmental Protection Agency. See

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(accessed August 9, 2006). 2006b.

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Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. April

2007. EPA -454/B-07-002.

Wesely, M.L. 1989. Parameterization of Surface Resistances to Gaseous Dry Deposition in

Regional-Scale Numerical Models. Atmos. Environ.

, 23 , 1293-1304.

Zaveri, R.A. and L. K Peters. 1999. A new lumped structure photochemical mechanism for

large-scale applications. J. Geophys. Res ., 104, 30: 387-30,415.

# Appendix F

Julian and Gregorian Calendar

Cross-Reference for the Year 2002Gregorian/Julian Calendar for 2002Quarter 1?Quarter 3?JanuaryFebruaryJulyAugustSun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat12345 12 123456 1231 2 3 4 5 32 33 182 183 184 185 186 187 213 214 2156 7 8 9 10 11 12 3 4 5 6 7 8 9 7 8 9 10 11 12 13 4 5 6 7 8 9 106 7 8 9 10 11 12 34 35 36 37 38 39 40 188 189 190 191 192 193 194 216 217 218 219 220 221 22213 14 15 16 17 18 19 10 11 12 13 14 15 16 14 15 16 17 18 19 20 11 12 13 14 15 16 1713 15 16 16 17 18 19 41 42 43 44 45 46 47 195 196 197 198 199 200 201 223 224 225 226 227 228 22920 21 22 23 24 25 26 17 18 19 20 21 22 23 21 22 23 24 25 26 27 18 19 20 21 22 23 2420 21 22 23 24 25 26 48 49 50 51 52 53 54 202 203 204 205 206 207 208 230 231 232 233 234 235 23627 28 29 30 31 24 25 29 27 28 28 29 30 31 25 26 27 28 29 30 3127 28 29 30 31 55 56 57 58 59 209 210 211 212 237 238 239 240 241 242 243Quarter 2?Quarter 4?MarchAprilSeptemberOctoberSun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat12 123456 1234567 1234560 61 91 92 93 94 95 96 244 245 246 247 248 249 250 274 275 276 277 2783 4 5 6 7 8 9 7 8 9 10 11 12 13 8 9 10 11 12 13 14 6 7 8 9 10 11 1262 63 64 65 66 67 68 97 98 99 100 101 102 103 251 252 253 254 255 256 257 279 280 281 282 283 284 28510 11 12 13 14 15 16 14 15 16 17 18 19 20 15 16 17 18 19 20 21 13 14 15 16 17 18 1969 70 71 72 73 74 75 104 105 106 107 108 109 110 258 259 260 261 262 263 264 286 287 288 289 290 291 29217 18 19 20 21 22 23 21 22 23 24 25 26 27 22 23 24 25 26 27 28 20 21 22 23 24 25 2676 77 78 79 80 81 82 111 112 113 114 115 116 117 265 266 267 268 269 270 271 293 294 295 296 297 298 29924 25 26 27 28 29 30 28 29 3030 3127 28 29 30 3183 84 85 86 87 88 89 118 119 120272 273300 301 302 303 3043190MayJuneNovemberDecemberSun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat1234 1 12 1234567121 122 123 124 152 305 306 335 336 337 338 339 340 3415 6 7 8 9 10 11 2 3 4 5 6 7 8 3 4 5 6 7 8 9 8 9 10 11 12 13 14125 126 127 128 129 130 131 153 154 155 156 157 158 159 307 308 309 310 311 312 313 342 343 344 345 346 347 34812 13 14 15 16 17 18 9 10 11 12 13 14 15 10 11 12 13 14 15 16 15 16 17 18 19 20 21132 133 134 135 136 137 138 160 161 162 163 164 165 166 314 315 316 317 318 319 320 349 350 351 352 353 354 35519 20 21 22 23 24 25 16 17 18 19 20 21 22 17 18 19 20 21 22 23 22 23 24 25 26 27 28139 140 141 142 143 144 145 167 168 169 170 171 172 173 321 322 323 324 325 326 327 356 357 358 359 360 361 36226 27 28 29 30 31 23 24 25 26 27 28 29 24 25 26 27 28 29 30 29 30 31146 147 148 149 150 151 174 175 176 177 178 179 180 328 329 330 331 332 333 334 363 364 36530181

# Appendix G

Minnesota (MRPO) 

# Air Quality Model

Performance Evaluation PlotsFigures 1 - 12 Fractional Bias and Error for36km Grid at 20% WorstDays at BOWA and VOYASO4 fractional bias from run 20% Worst Days atBOWA & VOYA(36km)-200-150-100-500501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Bias [%](+) Goal(-) Goal(+) Criteria(-) CriteriaFracBiasSO4 fractional error from run 20% Worst Days atBOWA & VOYA(36km)0501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Error [%]GoalCriteriaFracErrNO3 fractional bias from run 20% Worst Days atBOWA & VOYA(36km)-200-150-100-500501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Bias [%](+) Goal(-) Goal(+) Criteria(-) CriteriaFracBiasNO3 fractional error from run 20% Worst Days atBOWA & VOYA(36km)0501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Error [%]GoalCriteriaFracErrOC fractional bias from run 20% Worst Days atBOWA & VOYA(36km)-200-150-100-500501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Bias [%](+) Goal(-) Goal(+) Criteria(-) CriteriaFracBiasOC fractional error from run 20% Worst Days atBOWA & VOYA(36km)0501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Error [%]GoalCriteriaFracErrEC fractional bias from run 20% Worst Days atBOWA & VOYA(36km)-200-150-100-500501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Bias [%](+) Goal(-) Goal(+) Criteria(-) CriteriaFracBiasEC fractional error from run 20% Worst Days atBOWA & VOYA(36km)0501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Error [%]GoalCriteriaFracErrCRUSTAL fractional bias from 20% Worst Days at BOWA &VOYA(36km)-200-150-100-500501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Bias [%](+) Goal(-) Goal(+) Criteria(-) CriteriaFracBiasCRUSTAL fractional error from 20% Worst Days at BOWA& VOYA(36km)0501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Error [%]GoalCriteriaFracErrCM fractional bias from 20% Worst Days at BOWA & VOYA(36km)-200-150-100-500501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Bias [%](+) Goal(-) Goal(+) Criteria(-) CriteriaFracBiasCM fractional error from 20% Worst Days at BOWA & VOYA(36km)0501001502000 1 2 3 4 5 6 7 8 9 10Average Concentration [ug/m3]

Fractional Error [%]GoalCriteriaFracErr

# Figure 13. Time Series 36km - Sulfate at Boundary Waters Canoe Area:

BOWAV: 412.572 895.199

0 1 2 3 4 5



## DateSO4 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 2 4 6 8 10 12 14



## DateSO4 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 2 4 6 8 10 12 14



## DateSO4 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 2 4 6 8 10



## DateSO4 [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 14. Time Series 36km - Sulfate at Voyageurs

VOYA2: 310.340 942.037

0 1 2 3 4 5



## DateSO4 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 2 4 6 8 10 12 14



## DateSO4 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 2 4 6 8 10 12 14



## DateSO4 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 2 4 6 8 10



## DateSO4 [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 15. Time Series 36km - Nitrate at Boundary Waters Canoe Area:

BOWAV: 412.572 895.199

0 1 2 3 4 5



## DateNO3 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 1 2 3 4 5



## DateNO3 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 0.2 0.4 0.6 0.8 1



## DateNO3 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 2 4 6 8 10



## DateNO3 [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 16. Time Series 36km - Nitrate at Voyageurs:

VOYA2: 310.340 942.037

0 1 2 3 4 5



## DateNO3 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 1 2 3 4 5



## DateNO3 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 0.2 0.4 0.6 0.8 1



## DateNO3 [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 2 4 6 8 10



## DateNO3 [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 17. Time Series 36km – Elemental Carbon at Boundary Waters:

BOWAV: 412.572 895.199

0 0.2 0.4 0.6 0.8 1



## DateEC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 0.2 0.4 0.6 0.8 1



## DateEC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 0.2 0.4 0.6 0.8 1



## DateEC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 0.2 0.4 0.6 0.8 1



## DateEC [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 18. Time Series 36km – Elemental Carbon at Voyageurs:

VOYA2: 310.340 942.037

0 0.2 0.4 0.6 0.8 1



## DateEC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 0.2 0.4 0.6 0.8 1



## DateEC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 0.2 0.4 0.6 0.8 1



## DateEC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 0.2 0.4 0.6 0.8 1



## DateEC [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 19. Time Sseries 36km – Organic Carbon at Boundary Waters:

BOWAV: 412.572 895.199

0 2 4 6 8 10



## DateOC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 2 4 6 8 10



## DateOC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 5 10 15 20



## DateOC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 1 2 3 4 5



## DateOC [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 20. Time Series 36km – Organic Carbon at Voyageurs:

VOYA2: 310.340 942.037

0 2 4 6 8 10



## DateOC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 2 4 6 8 10



## DateOC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 5 10 15 20



## DateOC [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 1 2 3 4 5



## DateOC [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 21. Time Series 36km – Crustal/Soil at Boundary Waters:

BOWAV: 412.572 895.199

0 0.2 0.4 0.6 0.8 1



## DateCRUSTAL [ug/m3]



### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 0.2 0.4 0.6 0.8 1



## DateCRUSTAL [ug/m3]



### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 1 2 3 4 5



## DateCRUSTAL [ug/m3]



### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 0.2 0.4 0.6 0.8 1



## DateCRUSTAL [ug/m3]



### Minimum to Maximum Range Observed Predicted

# Figure 22. Time Series 36km – Crustal/Soil at Voyageurs:

VOYA2: 310.340 942.037

0 0.2 0.4 0.6 0.8 1



## DateCRUSTAL [ug/m3]



### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 0.2 0.4 0.6 0.8 1



## DateCRUSTAL [ug/m3]



### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 1 2 3 4 5



## DateCRUSTAL [ug/m3]



### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 0.2 0.4 0.6 0.8 1



## DateCRUSTAL [ug/m3]



### Minimum to Maximum Range Observed Predicted

# Figure 23. Time Series 36km – Coarse Mass at Boundary Waters:

BOWAV: 412.572 895.199

0 2 4 6 8 10



## DateCM [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 2 4 6 8 10



## DateCM [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 5 10 15 20



## DateCM [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## BOWAV: 412.572 895.199

0 2 4 6 8 10



## DateCM [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Figure 24. Time Series 36km – Coarse Mass at Voyageurs:

VOYA2: 310.340 942.037

0 2 4 6 8 10



## DateCM [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 2 4 6 8 10



## DateCM [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 5 10 15 20



## DateCM [ug/m3] 

### Minimum to Maximum Range Observed Predicted



## VOYA2: 310.340 942.037

0 2 4 6 8 10



## DateCM [ug/m3] 

### Minimum to Maximum Range Observed Predicted

# Appendix H

Particulate Source Appportionment Results for

CENRAP and MRPO CasesCENRAP BaseF Boundary Waters:Extinction Contribution by Specie and Sector for Each Region on the 20 Percent Worst DaysFigures H.1 and H.3: 2018Figures H.2 and H.4: 2018 minus 20020 2 4 6 8 10 12 14 16 18BCCanadaWestEastWisconsinTexasSouth DakotaOklahomaNorth DakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)SO4NO3POAECSOILCMTotal Extinction:51 Mm-1Total SO4+NO3:43 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWestEastWisconsinTexasSouth DakotaOklahomaNorth DakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)SO4NO3POAECSOILCMTotal Extinction Diff:-11 Mm-1Total SO4+NO3 Diff:-10 Mm-10 2 4 6 8 10 12 14 16 18BCCanadaWestEastWisconsinTexasSouth DakotaOklahomaNorth DakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)EGU+nonEGUonroadnonroadotherbiogenicBCTotal Extinction:51 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWestEastWisconsinTexasSouth DakotaOklahomaNorth DakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)EGU+nonEGUonroadnonroadotherbiogenicBCTotal Extinction Diff:-11 Mm-1CENRAP BaseF Voyageurs:Extinction Contribution by Specie and Sector for Each Region on the 20 Percent Worst DaysFigures H.5 and H.7: 2018Figures H.6 and H.8: 2018 minus 20020 2 4 6 8 10 12 14 16 18BCCanadaWestEastWisconsinTexasSouth DakotaOklahomaNorth DakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)SO4NO3POAECSOILCMTotal Extinction:47 Mm-1Total SO4+NO3:38 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWestEastWisconsinTexasSouth DakotaOklahomaNorth DakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)SO4NO3POAECSOILCMTotal Extinction Diff:-7 Mm-1Total SO4+NO3 Diff:-6 Mm-10 2 4 6 8 10 12 14 16 18BCCanadaWestEastWisconsinTexasSouth DakotaOklahomaNorth DakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)EGU+nonEGUonroadnonroadotherbiogenicBCTotal Extinction:47 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWestEastWisconsinTexasSouth DakotaOklahomaNorth DakotaNebraskaMissouriMinnesotaMichiganLouisianaKansasIowaIndianaIllinoisArkansasExtinction (Mm-1)EGU+nonEGUonroadnonroadotherbiogenicBCTotal Extinction Diff:-7 Mm-1MRPO BaseM Boundary Waters:Extinction Contribution by Specie and Sectorfor Each Region on the 20 Percent Worst DaysFigures H.9 and H.11: 2018Figures H.10 and H.12: 2018 minus 20050 1 2 3 4 5 6 7 8 9 10BCCanadaWRAP+SCNRPSoutheastNortheastWisconsinMissouriMinnesotaMichiganIowaIndianaIllinoisExtinction (Mm-1)PSO4PNO3PNH4POCPECFPRMFCRSTotal Extinction:41 Mm-1Total SO4+NO3:34 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWRAP+SCNRPSoutheastNortheastWisconsinMissouriMinnesotaMichiganIowaIndianaIllinoisExtinction (Mm-1)PSO4PNO3PNH4POCPECFPRMFCRSTotal Extinction Diff:-12 Mm-1Total SO4+NO3 Diff:-12 Mm-10 1 2 3 4 5 6 7 8 9 10BCCanadaWRAP+SCNRPSoutheastNortheastWisconsinMissouriMinnesotaMichiganIowaIndianaIllinoisExtinction (Mm-1)EGUnonEGUonroadnonrd_marother_areabiog_nh3BCTotal Extinction:41 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWRAP+SCNRPSoutheastNortheastWisconsinMissouriMinnesotaMichiganIowaIndianaIllinoisExtinction (Mm-1)EGUnonEGUonroadnonrd_marother_areabiog_nh3ICBCTotal Extinction Diff:-12 Mm-1MRPO BaseM Voyageurs:Extinction Contribution by Specie and Sectorfor Each Region on the 20 Percent Worst DaysFigures H.13 and H.15: 2018 Figures H.14 and H.16: 2018 minus 20050 1 2 3 4 5 6 7 8 9 10BCCanadaWRAP+SCNRPNortheastSoutheastWisconsinMissouriMinnesotaMichiganIowaIndianaIllinoisExtinction (Mm-1)PSO4PNO3PNH4POCPECFPRMFCRSTotal Extinction:38 Mm-1Total SO4+ NO3:31 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWRAP+SCNRPSoutheastNortheastWisconsinMissouriMinnesotaMichiganIowaIndianaIllinoisExtinction (Mm-1)PSO4PNO3PNH4POCPECFPRMFCRSTotal Extinction Diff:-12 Mm-1Total SO4+NO3 Diff:- 12 Mm-10 1 2 3 4 5 6 7 8 9 10BCCanadaWRAP+SCNRPSoutheastNortheastWisconsinMissouriMinnesotaMichiganIowaIndianaIllinoisExtinction (Mm-1)EGUnonEGUonroadnonrd_marother_areabiog_nh3BCTotal Extinction:38 Mm-1-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5BCCanadaWRAP+SCNRPSoutheastNortheastWisconsinMissouriMinnesotaMichiganIowaIndianaIllinoisExtinction (Mm-1)EGUnonEGUonroadnonrd_marother_areabiog_nh3ICBCTotal Extinction Diff:-12 Mm-1