Appendix A 

Annual 2002 and 2018 Point Source Emissions in Tons in 

Minnesota(MRPO) Case for 

Minnesota by Stack
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 COUNTY FACID STKID FAC_NAME NOX 

Becker ORIS700127 41 GENERIC UNIT 2

Beltrami ORIS700227 41 GENERIC UNIT 2

Beltrami ORIS7947 41 Solway CT 

Benton ORIS800127 41 GENERIC UNIT 1

Blue_Earth 2701300015 SV001 NSP dba Xcel Energy - Key City 299 22 16 Blue_Earth ORIS900127 41 GENERIC UNIT 54

Blue_Earth 2701300015 SV002 NSP dba Xcel Energy - Key City 307 34 16Blue_Earth 2701300015 SV004 NSP dba Xcel Energy - Key City 3 0

Blue_Earth 2701300015 SV005 NSP dba Xcel Energy - Key City 2 0

Blue_Earth 2701300015 SV006 NSP dba Xcel Energy - Key City 3 0

Blue_Earth 2701300015 SV007 NSP dba Xcel Energy - Key City 2 0

Blue_Earth 2701300078 GN001 Lake Crystal Utilities Commiss 2Brown 2701500008 GN001 Sleepy Eye Public Utility 2 0 Brown 2701500008 42 Sleepy Eye Public Utility 

Brown 2701500010 SV001 New Ulm Public Utilities-Munic 7 1 0

Brown 2701500010 SV002 New Ulm Public Utilities-Munic 3 0

Brown 2701500010 SV003 New Ulm Public Utilities-Munic 45 2 0Brown 2701500014 GN001 Springfield Power Plant 2Carver 2701900010 SV001 Great River Energy - St Bonifa 3 2Carver 2701900010 SV002 Great River Energy - St Bonifa 3 2

Carver 2701900059 SV001 MMPA - Minnesota River Station 

Clay ORIS1978 41 Hawley 

Cook 2703100001 SV001 Minnesota Power - Taconite Har 552 818 2 35 Cook 2703100001 SV001 Minnesota Power - Taconite Har 38Cook 2703100001 SV002 Minnesota Power - Taconite Har 1,090 1,260 4 55 0 Cook 2703100001 SV002 Minnesota Power - Taconite Har 4Cook 2703100001 SV003 Minnesota Power - Taconite Har 665 1,030 3 46 Cook 2703100001 SV003 Minnesota Power - Taconite Har 40

Cottonwood 2703300019 GN001 Mountain Lake Municipal Utilit 3

Dakota 2703700003 SV001 NSP dba Xcel Energy - Black Do 6,460 3,070 18 224 1 Dakota 2703700003 SV001 NSP dba Xcel Energy - Black Do 9,36Dakota 2703700003 SV020 NSP dba Xcel Energy - Black Do 15 217 2 60 0 Dakota 2703700003 SV020 NSP dba Xcel Energy - Black Do 3Dakota 2703700015 SV001 NSP dba Xcel Energy - Inver Hi 25001 Dakota 2703700015 SV001 NSP dba Xcel Energy - Inver Hi Dakota 2703700015 SV002 NSP dba Xcel Energy - Inver Hi 14000 Dakota 2703700015 SV002 NSP dba Xcel Energy - Inver Hi Dakota 2703700015 SV003 NSP dba Xcel Energy - Inver Hi 23001 Dakota 2703700015 SV003 NSP dba Xcel Energy - Inver Hi Dakota 2703700015 SV004 NSP dba Xcel Energy - Inver Hi 21000 Dakota 2703700015 SV004 NSP dba Xcel Energy - Inver Hi Dakota 2703700015 SV005 NSP dba Xcel Energy - Inver Hi 21000 Dakota 2703700015 SV005 NSP dba Xcel Energy - Inver Hi Dakota 2703700015 SV006 NSP dba Xcel Energy - Inver Hi 23000 Dakota 2703700015 SV006 NSP dba Xcel Energy - Inver Hi Douglas 2704100004 GN001 Alexandria Light & Power 2 Douglas 2704100004 GN001 Alexandria Light & Power 

Faribault 2704300045 GN001 Wells Public Utilities 4 0 0 Faribault 2704300045 GN001 Wells Public Utilities 

Faribault 2704300045 GN001 Wells Public Utilities 8 1Fillmore 2704500001 GN001 Spring Valley Utilities 9 1 1Fillmore 2704500001 GN001 Spring Valley Utilities 3 0Fillmore 2704500029 GN001 Preston Public Utilities 5 0 0 Fillmore 2704500029 GN001 Preston Public Utilities 

Fillmore 2704500029 GN001 Preston Public Utilities 3 0Hennepin 2705300015 SV002 Xcel Energy - Riverside Genera 4,412 2,090 10 148 0 Hennepin 2705300015 SV002 Xcel Energy - Riverside Genera 5Hennepin 2705300015 SV003 Xcel Energy - Riverside Genera 8,857 10,811 38 189 0Hennepin 2705301005 ITOTA Woodlake Sanitary Landfill 9211Isanti 2705900014 SV001 Great River Energy - Cambridge 2 1Itasca 2706100004 SV001 Minnesota Power Inc - Boswell 1,957 4,233 9 123 Itasca 2706100004 SV001 Minnesota Power Inc - Boswell 1,53Itasca 2706100004 SV003 Minnesota Power Inc - Boswell 4,911 13,280 26 3 0 Itasca 2706100004 SV003 Minnesota Power Inc - Boswell 92Itasca 2706100004 SV004 Minnesota Power Inc - Boswell 7,656 3,653 4 585 1 Itasca 2706100004 SV004 Minnesota Power Inc - Boswell 4,71

Kanabec 2706500006 GN001 Mora Municipal Utilities 5

Kandiyohi 2706700005 SV002 Willmar Municipal Utilities 1

Kandiyohi 2706700005 SV003 Willmar Municipal Utilities 191 687 1 17 Kandiyohi 2706700005 SV003 Willmar Municipal Utilities 58

Lake 2707500003 SV001 Northshore Mining Co - Silver 1,126 851 5 0 Lake 2707500003 41 Silver Bay Power ORIS10849 3Lake 2707500003 SV002 Northshore Mining Co - Silver 1,821 1,371 8 0 0 Lake 2707500003 42 Silver Bay Power ORIS10849 6Lake 2707500003 SV105 Northshore Mining Co - Silver 6Lake 2707500003 SV114 Northshore Mining Co - Silver 7Lake 2707500003 SV261 Northshore Mining Co - Silver 2

Minnesota Electric Generating Units 2002 Annual Emissions in Tons Minnesota Electric Generating Units 

Page 1 of 3
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 COUNTY FACID STKID FAC_NAME NOX 

Minnesota Electric Generating Units 2002 Annual Emissions in Tons Minnesota Electric Generating Units 

Le_Sueur 2707900008 ITOTA Interstate Power & Light - Mon 2 1Le_Sueur 2707900037 GN001 New Prague Water - Light - Pow 12 1 1 Le_Sueur 2707900037 GN001 New Prague Water - Light - Pow 8Le_Sueur 2707900037 GN001 New Prague Water - Light - Pow 5 1

McLeod 2708500002 SV002 Hutchinson Utilities Commissio 6 0 1 McLeod 2708500002 SV002 Hutchinson Utilities Commissio 

McLeod 2708500002 SV003 Hutchinson Utilities Commissio 12 0 1 McLeod 2708500002 SV003 Hutchinson Utilities Commissio 3

McLeod 2708500002 SV004 Hutchinson Utilities Commissio 

McLeod 2708500002 SV006 Hutchinson Utilities Commissio 6 0 1

McLeod 2708500002 SV007 Hutchinson Utilities Commissio 3McLeod 2708500013 SV001 Glencoe Light & Power Commissi 

McLeod 2708500013 SV003 Glencoe Light & Power Commissi 2 0 0

McLeod 2708500013 SV004 Glencoe Light & Power Commissi 

McLeod 2708500013 SV005 Glencoe Light & Power Commissi 6 0 1 McLeod 2708500013 SV005 Glencoe Light & Power Commissi McLeod 2708500013 SV006 Glencoe Light & Power Commissi 5 0 1 McLeod 2708500013 SV006 Glencoe Light & Power Commissi 2McLeod 2708500013 SV011 Glencoe Light & Power Commissi 5 0 0 McLeod 2708500013 SV011 Glencoe Light & Power Commissi 3McLeod 2708500013 SV012 Glencoe Light & Power Commissi 4 0 0 McLeod 2708500013 SV012 Glencoe Light & Power Commissi 4

McLeod 2708500034 SV002 Hutchinson Utilities Commissio 25001 McLeod 2708500034 SV002 Hutchinson Utilities Commissio 

McLeod 2708500034 SV004 Hutchinson Utilities Commissio 2 0

Martin 2709100007 SV001 Interstate Power & Light - Fox 100000

Martin 2709100007 SV002 Interstate Power & Light - Fox 71 14 5 29 1Martin 2709100007 SV003 Interstate Power & Light - Fox 3 1Martin 2709100009 SV004 Fairmont Power Plant 14102 Martin 2709100009 SV004 Fairmont Power Plant 

Martin 2709100009 SV005 Fairmont Power Plant 14101 Martin 2709100009 SV005 Fairmont Power Plant 

Martin 2709100009 SV007 Fairmont Power Plant 2 0 0

Martin 2709100046 GN001 Truman Public Utilities 

Meeker 2709300001 GN001 Litchfield Public Utilities Co 6 1 Meeker 2709300001 GN001 Litchfield Public Utilities Co 

Mille_Lacs 2709500011 GN001 Princeton Public Utilities Com 10 1 1 Mille_Lacs 2709500011 41 Princeton Public Utilities Com 2

Mower 2709900001 SV001 Austin Utilities - NE Power St 396 2,001 2 6 0 Mower 2709900001 SV001 Austin Utilities - NE Power St 39

Mower 2709900048 SV001 Great River Energy - Pleasant 15007 Mower 2709900048 SV001 Great River Energy - Pleasant 1

Mower 2709900048 SV002 Great River Energy - Pleasant 8004 Mower 2709900048 SV002 Great River Energy - Pleasant 

Mower 2709900048 SV003 Great River Energy - Pleasant 22 5 0 34 Mower 2709900048 SV003 Great River Energy - Pleasant 1

Olmsted 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 Facili 1 0 0Olmsted 2710900011 SV001 Rochester Public Utilities - S 48 69 0 5 Olmsted 2710900011 SV001 Rochester Public Utilities - S 76

Olmsted 2710900011 SV002 Rochester Public Utilities - S 124 185 0 11 Olmsted 2710900011 SV002 Rochester Public Utilities - S 79

Olmsted 2710900011 SV003 Rochester Public Utilities - S 192 873 1 11 Olmsted 2710900011 SV003 Rochester Public Utilities - S 38

Olmsted ORIS54262 43 Saint Marys Hospital Power Pla 

Olmsted 2710900020 SV001 Rochester Public Utility - Cas 10 0 0

Olmsted 2710900020 SV005 Rochester Public Utility - Cas 3 0 0

Olmsted 2710900020 SV006 Rochester Public Utility - Cas 2 0 0

Otter_Tail 2711100002 SV001 Otter Tail Power Co - Hoot Lak 19

Otter_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 1,20

Otter_Tail 2711100002 SV005 Otter Tail Power Co - Hoot Lak 

Ramsey 2712300012 SV001 Xcel Energy - High Bridge Gene 5,612 3,806 16 205 Ramsey 2712300012 SV001 Xcel Energy - High Bridge Gene 6Ramsey 2712300698 ITOTA Zuel Co Inc 4Redwood 2712700038 SV001 Redwood Falls Public Utilities 3 0 0

Redwood ORIS7982 41 South Generation 1Redwood ORIS7982 42 South Generation 1Redwood ORIS7982 43 South Generation 1

Rice 2713100017 ITOTA AAF/McQuay International - Far 4 17

St_Louis 2713700013 SV001 Minnesota Power Inc - Laskin E 2,172 1,605 7 105 St_Louis 2713700013 SV001 Minnesota Power Inc - Laskin E 77St_Louis 2713700027 SV001 Hibbing Public Utilities Commi 132 127 0 4 St_Louis 2713700027 SV001 Hibbing Public Utilities Commi 29St_Louis 2713700027 SV002 Hibbing Public Utilities Commi 55 51 0 2 St_Louis 2713700027 SV002 Hibbing Public Utilities Commi 29St_Louis 2713700027 SV003 Hibbing Public Utilities Commi 96 78 0 3 St_Louis 2713700027 SV003 Hibbing Public Utilities Commi 38St_Louis 2713700028 SV002 Virginia Dept of Public Utilit 62 89 0 4 St_Louis 2713700028 SV002 Virginia Dept of Public Utilit 35

Page 2 of 3
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 COUNTY FACID STKID FAC_NAME NOX 

Minnesota Electric Generating Units 2002 Annual Emissions in Tons Minnesota Electric Generating Units 

St_Louis 2713700028 SV003 Virginia Dept of Public Utilit 257 297 1 9 St_Louis 2713700028 SV003 Virginia Dept of Public Utilit 35

St_Louis 2713700028 SV004 Virginia Dept of Public Utilit 9 1 1 0

Scott 2713900010 SV001 NSP dba Xcel Energy - Blue Lak 6 0 0 Scott 2713900010 41 NSP dba Xcel Energy - Blue Lak 

Scott 2713900010 SV002 NSP dba Xcel Energy - Blue Lak 7 0 0 Scott 2713900010 42 NSP dba Xcel Energy - Blue Lak 

Scott 2713900010 SV003 NSP dba Xcel Energy - Blue Lak 7 0 0Scott 2713900010 SV004 NSP dba Xcel Energy - Blue Lak 7 0 0Sherburne 2714100004 SV001 NSP - Sherburne Generating Pla 14,372 14,762 98 1,306 2 Sherburne 2714100004 SV001 NSP - Sherburne Generating Pla 11,54Sherburne 2714100004 SV002 NSP - Sherburne Generating Pla 11,506 11,924 58 841 1 Sherburne 2714100004 SV002 NSP - Sherburne Generating Pla 8,28Sherburne 2714100004 SV046 NSP - Sherburne Generating Pla 2Sherburne 2714100047 GN001 Elk River Municipal Utilites 3 0 0Stearns 2714500002 GN001 Melrose Public Utilities 3 0 0Steele 2714700002 SV002 Owatonna Public Utilities - Po 9 0 0 0

Swift 2715100006 SV002 Benson Municipal Utilities 1

Waseca 2716100027 GN001 Janesville Municipal Utilities 

Washington 2716300005 SV001 Xcel Energy - Allen S King Gen 11,862 23,241 81 417 Washington 2716300005 SV001 Xcel Energy - Allen S King Gen 2,06Washington 2716300005 SV008 Xcel Energy - Allen S King Gen 9 0 0 0

Washington 2716300087 SV001 LSP Cottage Grove Cogeneration 181151 Washington 2716300087 SV001 LSP Cottage Grove Cogeneration 

Washington 2716300087 SV002 LSP Cottage Grove Cogeneration 6 0 20Washington 2716300087 SV003 LSP Cottage Grove Cogeneration 6 1 20Watonwan 2716500019 GN001 Madelia Light & Power Standby 2 0 Watonwan 2716500019 GN001 Madelia Light & Power Standby 5Wright 2717100016 GN001 Delano Municipal Utilities 5 0 0Wright 2717100020 SV001 Great River Energy - Maple Lak 2 1

Page 3 of 3
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Aitkin 27001SW145 RPU00 Aitkin Area 16 

Aitkin 27001SW150 RPU00 Hickory Grove 12 Anoka 2700300004 BO001 ISD 14 - Stevenson Elementary 2 0 0 2 

Anoka 2700300005 ITOTA Minncast Inc 8 1 

Anoka 2700300010 BO002 Anoka Metro Regional Treatment 2 0 2 

Anoka 2700300019 SI003 Onan Corp 2 0 1 2 0 

Anoka 2700300019 SV423 Onan Corp 3 0 0 3 0 Anoka 2700300020 SV026 United Defense LP 4 1004 

Anoka 2700300020 SV027 United Defense LP 4 1004 

Anoka 2700300031 ITOTA Schwartzman Co Inc 14 Anoka 2700300037 BO002 Unity Hospital 4 0 0 4 

Anoka 2700300073 BO002 Minnesota Correctional - Lino 4 1 0 4 

Anoka 2700300081 ITOTA Honeywell - Commercial Aviatio 2 0 5 1 

Anoka 2700300136 ITOTA Determan Brownie Inc 1 4 1 02

Anoka 2700300156 SI001 Federal Cartridge Co - Anoka 2 0 0 2 

Anoka 2700300156 SI002 Federal Cartridge Co - Anoka 3 0003 Anoka 2700300171 ITOTA Gale's Auto Body Shop 3 1Anoka 2700300182 ITOTA Waldoch Collision Center 3 

Anoka 2700300205 ITOTA Kurt Manufacturing Co - Die Ca 5216 72Anoka 2700300210 ITOTA Lexington Manufacturing Inc 4 

Anoka 2700300222 IOTHE Accent Home & Kitchen Center I 5 

Anoka 2700300224 ITOTA Arrow Cryogenics Inc 0 4 4 1 

Anoka 2777700163 ITOTA Commercial Asphalt Co - Plant 13 3 12 3 17 4 Anoka 2777700284 ITOTA Commercial Asphalt Co - Plant 12 1 13 3 16 1 Anoka 27003SW008 RPU00 Johnson Bros/Lochness 59 

Anoka 27003SW028 RPU00 Waste Disposal Engineering 5 

Anoka 27003SW028 RPU00 Waste Disposal Engineering 1 0 20 0 1 0 

Anoka 27003SW043 RPU00 Oak Grove 6 

Anoka 27003SW043 RPU00 Oak Grove 1 0 23 0 1 0 Anoka 27003SW047 RPU00 East Bethel 74 

Anoka 27003SW094 RPU00 Anoka Municipal Regional 16 Anoka 27003SW094 RPU00 Anoka Municipal Regional 3 1 63 1 3 1 

Anoka 27003XANE RPU00 ANOKA COUNTY-BLAINE ARPT(JAN 10 0 

Anoka 27003XANE RPU00 ANOKA COUNTY-BLAINE ARPT(JAN 2 0 12 6 2 0 

Becker 2700500035 NF001 Sherbrooke Asphalt Inc - Nonme 4 0 0 3 0 Becker 2711100016 SV001 Viking Gas Transmission - Fraz 118 25 3 118 

Becker 2711100016 SV002 Viking Gas Transmission - Fraz 121 25 3 121 

Becker 2711100016 SV003 Viking Gas Transmission - Fraz 114 24 3 114 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Page 1 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Becker 2711100016 SV006 Viking Gas Transmission - Fraz 25 1 0 1 21 1 Becker 2777700304 ITOTA Sherbrooke Asphalt Inc 16 11 3 2 22 15 

Becker 27005POTW1RPS00 Detroit Lakes WWTP 1 4 

Becker 27005SW099 RPU00 Becker County 4 

Becker 27005SW099 RPU00 Becker County 1 0 16 0 1 0 

Beltrami 2700700006 ITOTA Bemidji State University 9 0 2 5 

Beltrami 2700700010 ITOTA North Country Regional Hospita 2Beltrami 2700700019 FS001 Northwood Panelboard Co 12 Beltrami 2700700019 SI001 Northwood Panelboard Co 22 91 22 

Beltrami 2700700019 SI002 Northwood Panelboard Co 22 91 22 

Beltrami 2700700019 SI003 Northwood Panelboard Co 82 4 2 90 4 Beltrami 2700700019 SI007 Northwood Panelboard Co 10 1 2 1 11 1 Beltrami 2700700019 SI008 Northwood Panelboard Co 10 1 2 1 10 1 Beltrami 2700700019 SI012 Northwood Panelboard Co 8 8 20 8 8 Beltrami 2700700019 SI016 Northwood Panelboard Co 20 6 

Beltrami 2700700033 NF001 Gesell Concrete Products - Non 2 0 2

Beltrami 2705700005 FS006 Potlatch - Bemidji 118 Beltrami 2705700005 FS007 Potlatch - Bemidji 136 Beltrami 2705700005 SI001 Potlatch - Bemidji 12 0 1 4 12 0 

Beltrami 2705700005 SI002 Potlatch - Bemidji 12 0 1 4 12 0 

Beltrami 2705700005 SI003 Potlatch - Bemidji 12 0 1 4 12 0 

Beltrami 2705700005 SI004 Potlatch - Bemidji 12 0 1 4 12 0 

Beltrami 2705700005 SI005 Potlatch - Bemidji 10 4 9 10 4 

Beltrami 2705700005 SI006 Potlatch - Bemidji 10 4 9 10 4 

Beltrami 2705700005 SI007 Potlatch - Bemidji 10 4 9 10 4 

Beltrami 2705700005 SI008 Potlatch - Bemidji 2 

Beltrami 2705700005 SI011 Potlatch - Bemidji 4 

Beltrami 2705700005 SI012 Potlatch - Bemidji 2 

Beltrami 2705700005 SI019 Potlatch - Bemidji 7 1 1 7 1 Beltrami 2705700005 SI021 Potlatch - Bemidji 90 19 74 3 99 20 Beltrami 2705700005 SI022 Potlatch - Bemidji 7 11 7 

Beltrami 2705700005 SI023 Potlatch - Bemidji 6 2 6 

Beltrami 2777700059 ITOTA Thorson Inc - Boeing Asphalt P 2200 33Beltrami 2777700150 ITOTA Mark Sand & Gravel Acquisition 1210 23Beltrami 2777700188 ITOTA Thorson Inc - Boeing Asphalt P 11 11 3 2 15 16 Beltrami 2777700307 ITOTA Thorson Inc - Asphalt Plant 1 3711 39Beltrami 27007POTW1RPS00 Bemidji WWTP 1 4 

Beltrami 27007SW031 RPU00 Kummer 39 

Beltrami 27007SW051 RPU00 Eighty-Acre 4 

Page 2 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Beltrami 27007XBJI RPU00 BEMIDJI-BELTRAMI COUNTY 1 0 0 2 0 Beltrami 27007XBJI RPU00 BEMIDJI-BELTRAMI COUNTY 0 2 1 0 

Beltrami 27007XBJI RPU00 BEMIDJI-BELTRAMI COUNTY 0 2 1 0 

Beltrami 27007XBJI RPU00 BEMIDJI-BELTRAMI COUNTY 2 0 2 2 0 Benton 2700900006 SV002 Quebecor World St Cloud 5 12 0 5 

Benton 2700900027 ITOTA Warren Wood Inc 1 0 2 1 

Benton 2700900029 ITOTA X-cel Optical Co 4 

Benton 2700900031 ITOTA Wilkie Sanderson 4 0 Benton 2700900035 IOTHE Bauerly Bros Inc - Nonmetallic 16 Benton 2700900035 NF001 Bauerly Bros Inc - Nonmetallic 83 5 7 75 6 Benton 2700900040 ITOTA Phillips Recycling Systems Inc 5 Benton 2700900041 ITOTA Crystal Cabinet Works Inc - Sa 0 2 0 Benton 2702500038 ITOTA Bauerly Bros Inc - Plant 5 3 1 9 2 4 2 Benton 2777700006 SV001 Bauerly Bros Inc - Plant 2 9 2 9 4 12 2 Benton 2777700007 ITOTA Bauerly Bros Inc - Plant 3 18 28 7 5 25 39 

Benton 2777700008 SV001 Bauerly Bros Inc - Plant 1 8 2 8 3 12 2 Benton 2777700058 ITOTA Thorson Inc - Asphalt Plant 4 18 25 5 3 25 33 

Benton 2777700270 ITOTA Bauerly Bros Inc - Plant 4 10 22 4 3 13 30 

Big_Stone 2701100017 ITOTA Bituminous Paving Inc - Barber 6411 86Big_Stone 2701100026 IOTHE Ortonville Stone Co - Nonmetal 8 Big_Stone 27011SW096 RPU00 Big Stone County 10 

Blue_Earth 2701300006 SV009 ADM - Mankato 8 

Blue_Earth 2701300006 SV018 ADM - Mankato 32 

Blue_Earth 2701300006 SV032 ADM - Mankato 153 143 2 145 136 Blue_Earth 2701300006 SV033 ADM - Mankato 4 0 0 4 Blue_Earth 2701300006 SV034 ADM - Mankato 4 1 0 4 Blue_Earth 2701300007 SI001 Cenex Harvest States Coop - Ma 13 1 0 0 13 

Blue_Earth 2701300007 SI002 Cenex Harvest States Coop - Ma 42 0 6 3 1 42 0 

Blue_Earth 2701300007 SI005 Cenex Harvest States Coop - Ma 3 

Blue_Earth 2701300007 SI009 Cenex Harvest States Coop - Ma 2 0 2 

Blue_Earth 2701300007 SI016 Cenex Harvest States Coop - Ma 3 0 0 3 

Blue_Earth 2701300007 SI019 Cenex Harvest States Coop - Ma 33 2 1 3 28 2 

Blue_Earth 2701300009 ITOTA Minnesota State University - M 17 36 0 1 9 20 Blue_Earth 2701300048 ITOTA River Bend Asphalt Co - Cedar 3100 52Blue_Earth 2701300072 ITOTA Jones Metal Products Inc 0 5 5 1 

Blue_Earth 2701300073 ITOTA Katolight Corp 1 4 3 4 0 

Blue_Earth 2701300075 IOTHE Industrial Fabrication Service 3 

Blue_Earth 2701300077 ITOTA Hiniker Co 0 10 7 1 

Blue_Earth 2701300079 ITOTA City of Mankato - Central Gara 23 1 1 1 13 1 

Page 3 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Blue_Earth 2701300091 NF001 Southern MN Constr Co Inc - No 53 3 5 48 4 Blue_Earth 27013POTW3RPS00 Mankato WWTP 9 21 

Blue_Earth 27013SW064 RPU00 Mankato Public Works 4 

Blue_Earth 27013SW087 RPU00 Blue Earth/Ponderosa 116 

Blue_Earth 27013SW113 RPU00 Hansen 12 

Blue_Earth 27013XMKT RPU00 MANKATO REGIONAL 1063 10Brown 2701500007 SV003 Ochs Brick Co 20 21 3 10 25 31 Brown 2701500009 SI001 Kraft Foods - New Ulm 3000 30Brown 2701500009 SI002 Kraft Foods - New Ulm 3000 30Brown 2701500027 ITOTA Valley Asphalt Products Inc - 7311 93Brown 2701500031 ITOTA Anderson Custom Procesing Inc 4 19 4 

Brown 2701500032 ITOTA Firmenich Inc 0 2 0 

Brown 2701500055 IOTHE MR Paving & Excavating Inc - N 9 Brown 2701500055 NF001 MR Paving & Excavating Inc - N 3 0 0 3 0 Brown 2705301017 NF001 Northern Con-Agg Inc - Nonmeta 7 0 1 6 0 

Brown 27015POTW3RPS00 New Ulm WWTP 4 9 

Brown 27015SW089 RPU00 Brown County 27 

Carlton 2701700002 SI002 Sappi Cloquet LLC 12 32 

Carlton 2701700002 SV002 Sappi Cloquet LLC 149 5 0 1 195 6 

Carlton 2701700002 SV003 Sappi Cloquet LLC 112 79 14 1 1 123 87 Carlton 2701700002 SV004 Sappi Cloquet LLC 72 0 9 5 11 77 0 Carlton 2701700002 SV005 Sappi Cloquet LLC 217 91 27 6 1 239 100 Carlton 2701700002 SV006 Sappi Cloquet LLC 399 6 7 524 8 

Carlton 2701700002 SV010 Sappi Cloquet LLC 13 

Carlton 2701700002 SV029 Sappi Cloquet LLC 223 0 1 4 293 1 

Carlton 2701700002 SV036 Sappi Cloquet LLC 3 7 Carlton 2701700002 SV037 Sappi Cloquet LLC 3 7 Carlton 2701700002 SV038 Sappi Cloquet LLC 2 6 Carlton 2701700004 SI002 MCF - Moose Lake 2 0 0 2 

Carlton 2701700004 SI003 MCF - Moose Lake 2 0 0 2 

Carlton 2701700004 SI015 MCF - Moose Lake 2 0

Carlton 2701700004 SI022 MCF - Moose Lake 2 0

Carlton 2701700006 SI002 USG Interiors Inc - Cloquet 5 Carlton 2701700006 SI003 USG Interiors Inc - Cloquet 10 1 6 0 10 

Carlton 2701700006 SI004 USG Interiors Inc - Cloquet 9 1609 Carlton 2701700006 SI005 USG Interiors Inc - Cloquet 5 Carlton 2701700006 SI006 USG Interiors Inc - Cloquet 2 0 2 

Carlton 2701700006 SI007 USG Interiors Inc - Cloquet 2 0 2 

Carlton 2701700006 SI008 USG Interiors Inc - Cloquet 3 0 3 

Page 4 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Carlton 2701700006 SI014 USG Interiors Inc - Cloquet 2 0 1 3 

Carlton 2701700006 SI017 USG Interiors Inc - Cloquet 1 4 1 

Carlton 2701700006 SI018 USG Interiors Inc - Cloquet 0 4 1 

Carlton 2701700006 SI030 USG Interiors Inc - Cloquet 1 4 1 

Carlton 2701700006 SI040 USG Interiors Inc - Cloquet 2 0 2 2 

Carlton 2701700006 SI044 USG Interiors Inc - Cloquet 1 2 1 

Carlton 2701700006 SI045 USG Interiors Inc - Cloquet 1 2 1 

Carlton 2701700006 SI046 USG Interiors Inc - Cloquet 1 2 1 

Carlton 2701700006 SI047 USG Interiors Inc - Cloquet 1 2 1 

Carlton 2701700006 SI048 USG Interiors Inc - Cloquet 1 2 1 

Carlton 2701700006 SI049 USG Interiors Inc - Cloquet 1 2 1 

Carlton 2701700006 SI050 USG Interiors Inc - Cloquet 1 2 1 

Carlton 2701700006 SI051 USG Interiors Inc - Cloquet 1 2 1 

Carlton 2701700006 SI052 USG Interiors Inc - Cloquet 0 2 0 

Carlton 2701700006 SI060 USG Interiors Inc - Cloquet 1 4 1 

Carlton 2701700006 SI061 USG Interiors Inc - Cloquet 1 4 1 Carlton 2701700006 SI065 USG Interiors Inc - Cloquet 3 0 5 3 

Carlton 2701700006 SI067 USG Interiors Inc - Cloquet 1 0 4 1 

Carlton 2701700006 SI069 USG Interiors Inc - Cloquet 1 4 1 

Carlton 2701700006 SI070 USG Interiors Inc - Cloquet 6 1 6 7 

Carlton 2701700006 SI072 USG Interiors Inc - Cloquet 33 0 4 8 0 35 0 Carlton 2701700006 SI074 USG Interiors Inc - Cloquet 0 4 0 

Carlton 2701700011 SV005 Northern Natural Gas Co - Wren 14 0 0 0 12 0 Carlton 2701700011 SV006 Northern Natural Gas Co - Wren 15 0 0 12 0

Carlton 2701700019 SV002 Northern Natural Gas Co - Carl 78 12 1 78 

Carlton 2701700041 NF001 Lundin Construction Co - Nonme 11 1 1 9 1 

Carlton 2701700042 NF001 Glacier Paving Inc - Nonmetall 6 0 0 5 0 

Carlton 2701700043 IOTHE Ulland Brothers Inc North - No 3 Carlton 2701700043 NF001 Ulland Brothers Inc North - No 37 2 3 33 3 Carlton 2777700281 NF001 Dresel Aggregate Inc - Nonmeta 6 0 0 5 0 Carlton 27017SW102 RPU00 Carlton County #2 19 Carver 2701900001 SI003 Bongards' Creameries 0 14 1 Carver 2701900001 SI004 Bongards' Creameries 0 14 1 Carver 2701900001 SI005 Bongards' Creameries 0 14 1 Carver 2701900001 SI006 Bongards' Creameries 0 14 1 Carver 2701900001 SI007 Bongards' Creameries 49 101 2 2 1 49 101 

Carver 2701900001 SI008 Bongards' Creameries 4 1 3 

Carver 2701900002 ITOTA Wm Mueller & Sons Inc - Carver 1 0 1 1 

Carver 2701900020 BO002 United Sugars Corp - Chaska 2 0 0 2 

Page 5 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Carver 2701900038 ITOTA Mammoth Inc 1 5 0 1 

Carver 2701900041 ITOTA Rosemount Inc - Chanhassen Fac 1 2 0

Carver 2701900044 ITOTA Lake Region Manufacturing Co 12 

Carver 2701900053 IOTHE Wm Mueller & Sons Inc - Nonmet 6Carver 2701900053 NF001 Wm Mueller & Sons Inc - Nonmet 4 0 0 4 0 

Carver 2701900059 SV001 MMPA - Minnesota River Station 12 0 1 10 0 

Carver 27019POTW3RPS00 Norwood Young America WWTP 1 2 

Cass 2702100007 IOTHE Hengel Ready Mix & Constr - No 2Cass 2702100007 NF001 Hengel Ready Mix & Constr - No 2 0 2

Cass 2777700140 ITOTA Anderson Brothers Construction 3200 42Cass 27021SW033 RPU00 Maple 16 

Cass 27021SW169 RPU00 Cass County Longville-Remer 5 

Cass 27021SW179 RPU00 Cass County Walker Hackensack 7 

Cass 27021XPWC RPU00 PINE RIVER REGIONAL 0 0 

Cass 27021XXVG RPU00 LONGVILLE MUNI 0 0 Cass 27021XY49 RPU00 WALKER MUNI 1 0 

Chippewa 2702300009 ITOTA Cargill Inc - Maynard 2 7 1 

Chippewa 2702300034 IOTHE SL - Montevideo Techology Inc 5 

Chippewa 27023POTW1RPS00 Montevideo WWTP 1 3 

Chippewa 27023SW052 RPU00 Chippewa County 43 

Chippewa 27023XMVE RPU00 MONTEVIDEO-CHIPPEWA COUNTY 0 10 0 

Chisago 2702500013 ITOTA Zinpro Corp 3 1 4 

Chisago 2702500037 ITOTA Concrete Pump Repair 0 4 

Chisago 2702500046 BO001 Close Custody Correctional - R 3 0 0 3 

Chisago 2702500046 GN001 Close Custody Correctional - R 2 0 2Chisago 27025POTW2RPS00 Chisago Lakes Joint STC 1 3 

Chisago 27025SW072 RPU00 Pine Lane 5 

Chisago 27025SW072 RPU00 Pine Lane 1 0 18 0 1 0 Clay 2702700001 SV001 American Crystal Sugar - Moorh 109 107 1 3 104 101 Clay 2702700001 SV002 American Crystal Sugar - Moorh 98 82 0 3 93 78 Clay 2702700001 SV003 American Crystal Sugar - Moorh 97 101 0 3 92 96 Clay 2702700001 SV007 American Crystal Sugar - Moorh 12 2 11 0 12 

Clay 2702700001 SV008 American Crystal Sugar - Moorh 11 1 12 0 11 

Clay 2702700001 SV036 American Crystal Sugar - Moorh 0 0 0 1 0 

Clay 2702700008 BO002 Minnesota State University Moo 8 1108 

Clay 2702700014 ITOTA Aggregate Industries Inc - Dil 3401 45Clay 2702700022 SV009 Busch Agricultural Resources - 79 444 1 23 1 79 444 Clay 2702700022 SV010 Busch Agricultural Resources - 9 11

Clay 2702700022 SV011 Busch Agricultural Resources - 9 11

Page 6 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Clay 2702700053 ITOTA American Crystal Sugar - Resea 47 

Clay 2702700063 BO000 ISD 146 - Barnesville High Sch 0 0 1 0 Clay 2702700064 NF001 Aggregate Industries Inc - P-9 19 1 2 17 1 Clay 2702700065 IOTHE Northern Improvement Co - Nonm 7Clay 2702700066 IOTHE Asplin Inc - Nonmetallic 2Clay 2702701006 ITOTA Moorhead WWTP 2 1

Clay 2711900070 IOTHE Strata Corp - Nonmetallic 4 

Clay 27027SW034 RPU00 Clay County 1630 

Clearwater 2702900004 SV001 Great Lakes Gas Transmission - 47 1 1 2 42 1 

Clearwater 2702900004 SV002 Great Lakes Gas Transmission - 385 2 1 5 341 2 

Clearwater 2702900004 SV003 Great Lakes Gas Transmission - 255 2 1 4 225 2 

Cook 2703100002 SI001 Hedstrom Lumber Co Inc - Grand 5015 60Cook 2703100002 SI003 Hedstrom Lumber Co Inc - Grand 6 8Cook 2703100002 SI004 Hedstrom Lumber Co Inc - Grand 2 

Cook 27031SW294 RPU00 Cook County 10 Cottonwood 2703300015 BO002 PM Windom 6 1006 

Cottonwood 2703300025 FS017 Ethanol 2000 LLP 4 Cottonwood 2703300025 SV004 Ethanol 2000 LLP 11 46 3 12 

Cottonwood 2703300025 SV005 Ethanol 2000 LLP 11 47 2 12 

Cottonwood 2703300025 SV012 Ethanol 2000 LLP 16 2 1 0 16 

Cottonwood 2703300025 SV018 Ethanol 2000 LLP 16 2 1 0 16 

Cottonwood 2703300025 SV020 Ethanol 2000 LLP 11 8 2 11 

Cottonwood 27033POTW1RPS00 Windom WWTP 1 3 

Cottonwood 27033SW143 RPU00 Cottonwood County 43 

Crow_Wing 2703500002 SI001 Missota Paper Co 2 0002 

Crow_Wing 2703500002 SI002 Missota Paper Co 6 18 1 6 18 

Crow_Wing 2703500002 SI003 Missota Paper Co 46 123 0 9 44 116 

Crow_Wing 2703500002 SI004 Missota Paper Co 47 120 0 3 0 45 114 Crow_Wing 2703500008 BO000 State of Minnesota Dept of Hum 3 16 0 6 28 

Crow_Wing 2703500008 BO002 State of Minnesota Dept of Hum 5 1005 Crow_Wing 2703500031 SI001 Trus Joist - A Weyerhaeuser Bu 10 9 0 5 11 10 Crow_Wing 2703500031 SI002 Trus Joist - A Weyerhaeuser Bu 55 1 9 56 

Crow_Wing 2703500031 SI003 Trus Joist - A Weyerhaeuser Bu 39 0 11 40 Crow_Wing 2703500031 SI004 Trus Joist - A Weyerhaeuser Bu 11 1 11 

Crow_Wing 2703500031 SI005 Trus Joist - A Weyerhaeuser Bu 2 4 1 2 4 

Crow_Wing 2703500037 ITOTA Lakeland Mold Co 14 13 0 Crow_Wing 2703500043 ITOTA Burlington Northern Railroad - 7 1 0 4 1 Crow_Wing 2703500052 ITOTA Mac Manufacturing Inc 6 1 

Crow_Wing 2713500021 NF001 Roberts Sand & Gravel Inc - No 4 0 0 4 0 

Page 7 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Crow_Wing 2777700002 ITOTA Anderson Brothers Construction 10 6 2 1 13 8 Crow_Wing 2777700087 ITOTA Anderson Brothers Construction 12 8 3 2 17 11 Crow_Wing 27035POTW1RPS00 Brainerd WWTP 4 9 

Crow_Wing 27035SW111 RPU00 Crow Wing County (Old) 12 

Crow_Wing 27035SW111 RPU00 Crow Wing County (Old) 0 7 0 

Crow_Wing 27035SW181 RPU00 Crosby 4 

Crow_Wing 27035SW243 RPU00 Fifty Lakes Modified 2 

Crow_Wing 27035SW376 RPU00 Crow Wing County 74 

Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION 1 0 20

Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION 10 0 

Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION 0 31 0 

Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION 0 

Crow_Wing 27035XBRD RPU00 BRAINERD-CROW WING CO REGION 201 20

Dakota 2703700011 FS018 Flint Hills Resources LP - Pin 17 

Dakota 2703700011 FS019 Flint Hills Resources LP - Pin 15 

Dakota 2703700011 FS022 Flint Hills Resources LP - Pin 10 

Dakota 2703700011 FS024 Flint Hills Resources LP - Pin 12 

Dakota 2703700011 FS027 Flint Hills Resources LP - Pin 14 

Dakota 2703700011 FS029 Flint Hills Resources LP - Pin 9 

Dakota 2703700011 FS031 Flint Hills Resources LP - Pin 14 

Dakota 2703700011 FS032 Flint Hills Resources LP - Pin 10 

Dakota 2703700011 FS035 Flint Hills Resources LP - Pin 6 

Dakota 2703700011 FS036 Flint Hills Resources LP - Pin 7 

Dakota 2703700011 FS039 Flint Hills Resources LP - Pin 10 

Dakota 2703700011 FS041 Flint Hills Resources LP - Pin 21 

Dakota 2703700011 FS042 Flint Hills Resources LP - Pin 0 

Dakota 2703700011 FS045 Flint Hills Resources LP - Pin 35 

Dakota 2703700011 FS048 Flint Hills Resources LP - Pin 58 

Dakota 2703700011 FS052 Flint Hills Resources LP - Pin 10 

Dakota 2703700011 FS054 Flint Hills Resources LP - Pin 12 

Dakota 2703700011 FS055 Flint Hills Resources LP - Pin 21 

Dakota 2703700011 FS058 Flint Hills Resources LP - Pin 10 

Dakota 2703700011 FS060 Flint Hills Resources LP - Pin 34 

Dakota 2703700011 FS062 Flint Hills Resources LP - Pin 12 

Dakota 2703700011 FS063 Flint Hills Resources LP - Pin 8 

Dakota 2703700011 FS064 Flint Hills Resources LP - Pin 2 

Dakota 2703700011 FS069 Flint Hills Resources LP - Pin 9 

Dakota 2703700011 FS070 Flint Hills Resources LP - Pin 16 

Dakota 2703700011 FS075 Flint Hills Resources LP - Pin 32 

Page 8 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Dakota 2703700011 FS077 Flint Hills Resources LP - Pin 17 Dakota 2703700011 FS079 Flint Hills Resources LP - Pin 13 Dakota 2703700011 FS080 Flint Hills Resources LP - Pin 11 Dakota 2703700011 FS083 Flint Hills Resources LP - Pin 41 Dakota 2703700011 FS085 Flint Hills Resources LP - Pin 6 Dakota 2703700011 FS087 Flint Hills Resources LP - Pin 5 Dakota 2703700011 FS088 Flint Hills Resources LP - Pin 6 Dakota 2703700011 FS091 Flint Hills Resources LP - Pin 9 Dakota 2703700011 FS093 Flint Hills Resources LP - Pin 5 Dakota 2703700011 FS095 Flint Hills Resources LP - Pin 9 Dakota 2703700011 FS096 Flint Hills Resources LP - Pin 16 Dakota 2703700011 FS099 Flint Hills Resources LP - Pin 8 Dakota 2703700011 FS102 Flint Hills Resources LP - Pin 5 Dakota 2703700011 FS104 Flint Hills Resources LP - Pin 6 Dakota 2703700011 FS106 Flint Hills Resources LP - Pin 4 Dakota 2703700011 RPU99 Flint Hills Resources LP - Pin 9 Dakota 2703700011 SI013 Flint Hills Resources LP - Pin 25 0 1 22 0 

Dakota 2703700011 SI016 Flint Hills Resources LP - Pin 19 19Dakota 2703700011 SI017 Flint Hills Resources LP - Pin 34 5 34 Dakota 2703700011 SV004 Flint Hills Resources LP - Pin 2085 78 68 109 2085 78 Dakota 2703700011 SV017 Flint Hills Resources LP - Pin 19 19Dakota 2703700011 SV019 Flint Hills Resources LP - Pin 729 2088 48 91 959 2746 Dakota 2703700011 SV042 Flint Hills Resources LP - Pin 279 340

Dakota 2703700011 SV078 Flint Hills Resources LP - Pin 51 3 62 

Dakota 2703700011 SV094 Flint Hills Resources LP - Pin 239 291

Dakota 2703700011 SV104 Flint Hills Resources LP - Pin 1 118 8 1 126 Dakota 2703700011 SV125 Flint Hills Resources LP - Pin 22 8 27 1 23 8 Dakota 2703700014 SV004 Northern Natural Gas Co - Farm 18 3 18 

Dakota 2703700014 SV005 Northern Natural Gas Co - Farm 27 3 0 27 

Dakota 2703700014 SV007 Northern Natural Gas Co - Farm 7001 60Dakota 2703700016 FS001 Gopher Resource Corp 4 

Dakota 2703700016 FS003 Gopher Resource Corp 5 

Dakota 2703700016 FS004 Gopher Resource Corp 5 

Dakota 2703700016 SV003 Gopher Resource Corp 52 1190 1 14 76 2337 Dakota 2703700018 ITOTA McNamara Contracting Inc 3113 51Dakota 2703700027 ITOTA Dakota Bulk Terminal Inc 0 6 0 Dakota 2703700029 ITOTA Pine Bend Paving HMA Plant 1 1 1 1 

Dakota 2703700032 ITOTA Commercial Asphalt Co - Plant 15 1 17 4 20 2 

Dakota 2703700037 GN001 Unisys MACS - Eagan 10 1 1 8 1 

Page 9 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Dakota 2703700038 ITOTA Danner Inc 25 9 5 0 31 10 Dakota 2703700041 ITOTA Commercial Asphalt Co - Plant 16 1 16 4 22 2 

Dakota 2703700043 SV001 Seneca Wastewater Treatment Pl 29 104 1 39 141 Dakota 2703700050 ITOTA CF Industries - Pine Bend Ware 10 Dakota 2703700063 BO000 Minnesota Zoological Garden 2 10 4 18 Dakota 2703700064 SV002 NSP dba Xcel Energy - Wescott 3 0 3 

Dakota 2703700066 SI001 Spectro Alloys Corp 4 Dakota 2703700066 SI006 Spectro Alloys Corp 8 Dakota 2703700066 SI011 Spectro Alloys Corp 24 53 1 41 105 Dakota 2703700070 SV001 Anamax Corp 18 39 1 1 0 18 39 Dakota 2703700078 BO002 Marigold Foods Inc - Farmingto 3 0 0 3 

Dakota 2703700080 ITOTA Regina Medical Center 4 0 0 2

Dakota 2703700093 ITOTA West Group Co - Eagan 9 1 26 1 11 1 

Dakota 2703700094 GN001 Northwest Airlines Inc - Build 5 0 0 4 0 Dakota 2703700103 ITOTA Ecolab - Engineering Center 2 4 32 3 

Dakota 2703700138 SI001 Pine Bend Electric 3 1 0 3 1 

Dakota 2703700138 SI002 Pine Bend Electric 29 5 0 1 24 5 Dakota 2703700138 SI003 Pine Bend Electric 30 5 0 1 25 5 Dakota 2703700138 SI004 Pine Bend Electric 2 0 0 2 1 

Dakota 2703700138 SI005 Pine Bend Electric 2 0 0 2 1 

Dakota 2703700171 ITOTA CenterPoint Energy Minnegasco 3Dakota 2703700196 BO001 Blue Cross/Blue Shield of Minn 2 0 0 2 

Dakota 2703700196 GN001 Blue Cross/Blue Shield of Minn 2Dakota 2703700196 IOTHE Blue Cross/Blue Shield of Minn 2Dakota 2703700205 ITOTA Rosemount Aerospace Inc - Burn 25 1 4 3 14 1 Dakota 2703700208 GN001 Northern Tool & Equipment Co 4 0 0 3 0 Dakota 2703700211 BO002 Dakota Premium Foods LLC - S S 3 0 0 3 Dakota 2703700213 ITOTA Menasha Packaging - Lakeville 5 1 6 

Dakota 2703700218 IOTHE Rupp Industries Inc - Preserve 3 1 

Dakota 2703700242 IOTHE LaMettry's Collision Inc - Bur 3 Dakota 2703700264 ITOTA Bituminous Roadways Inc - 67D 2021 30Dakota 2703700267 NF001 Bury & Carlson Inc - Nonmetall 24 2 2 22 2 Dakota 2703700268 NF001 Cemstone Products - Nonmetalli 4 0 0 4 0 Dakota 2703700269 NF001 Bituminous Roadways Inc - Nonm 2 0 2 

Dakota 2703700272 NF001 McNamara Contracting Inc - Non 14 1 1 13 1 Dakota 2703700280 ITOTA Endres Processing LLC 13 0 18 1 16 0 Dakota 2703700284 ITOTA Skyline Displays Inc - Intl De 0 4 0 

Dakota 2703700285 NF001 Aggregate Industries Inc - Non 4 0 0 4 0 Dakota 2703700290 ITOTA Rayfo Inc - Clayton 0 12 1 0 

Page 10 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Dakota 2703700292 ITOTA Minerals Solutions Inc 2 Dakota 2703700299 ITOTA Travel Tags - Inver Grove Heig 29 63 34 

Dakota 2703700302 ITOTA Cenex Harvest States - Inver G 3 0 1Dakota 2703700316 GN001 Blue Cross/Blue Shield of MN - 3 0 2 

Dakota 2703700324 ITOTA Twin City Container Inc 7 16104 Dakota 2703700325 ITOTA Bernco Inc 2 4 Dakota 2777700081 ITOTA Commercial Asphalt Co - Plant 8 1 7 1 12 1 Dakota 27037POTW1RPS00 Northfield WWTP 3 7 

Dakota 27037POTW3RPS00 Met Council - Rosemount WWTP 1 3 

Dakota 27037POTW4RPS00 Met Council - Hastings WWTP 2 6 

Dakota 27037POTW6RPS00 Met Council - Empire WWTP 13 31 

Dakota 27037SW016 RPU00 Crosby American (amt. that is 21 

Dakota 27037SW050 RPU00 Dakhue 82 

Dakota 27037SW057 RPU00 Freeway 277 

Dakota 27037XLVN RPU00 AIRLAKE AIRPORT 1 6 3 10Dakota 27037XSGS RPU00 SOUTH ST PAUL MUNI-RICHARD E 1 5 2 1 

Dodge 2703900004 ITOTA Energy Economics Inc 1 3 

Dodge 2703900028 SV004 Al-Corn Clean Fuel 11 77 5 12 

Dodge 2703900028 SV005 Al-Corn Clean Fuel 9 1109 Dodge 2703900028 SV010 Al-Corn Clean Fuel 16 3 1 0 16 

Dodge 2703900028 SV012 Al-Corn Clean Fuel 4 0 0 4 

Dodge 2703900028 SV017 Al-Corn Clean Fuel 2Dodge 2777700071 ITOTA Buffalo Bituminous - Plant 1 18 15 4 3 24 21 

Dodge 2777700176 ITOTA Buffalo Bituminous Plant 2 14 18 4 3 19 24 

Dodge 27039POTW5RPS00 Kasson WWTP 1 2 

Dodge 27039SW121 RPU00 Dodge County 20 

Dodge 27039XTOB RPU00 DODGE CENTER 0 0 

Douglas 2704100002 ITOTA Northern Food & Dairy Inc 16 16 0 2 18 17 Douglas 2704100020 ITOTA Midcon Asphalt Co - Alexandria 3100 31Douglas 2704100021 SI001 Pope/Douglas Waste Management 23 11 3 1 31 15 Douglas 2704100021 SI002 Pope/Douglas Waste Management 21 10 3 1 29 14 Douglas 2704100027 ITOTA Alexandria Extrusion Co 0 0 23 0 

Douglas 2704100032 NF001 Central Specialties Inc - Nonm 28 2 2 25 2 Douglas 2704100034 NF001 Morical Bros Inc - Nonmetallic 7 0 1 6 0 Douglas 2704100037 ITOTA TWF Industries Inc - Alexandri 6 1 0 

Douglas 2777700229 SV100 Central Specialties Inc - Plan 7 8 2 3 10 10 Douglas 2777700229 SV200 Central Specialties Inc - Plan 9 1 1 8 1 Douglas 2777700269 SV100 Central Specialties Inc - Plan 9 9 2 3 12 13 Douglas 2777700269 SV200 Central Specialties Inc - Plan 9 1 1 8 1 

Page 11 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Douglas 2777700288 SI001 Central Specialties Inc - Plan 5512 77Douglas 2777700288 SI002 Central Specialties Inc - Plan 8 1 1 7 1 

Douglas 27041POTW2RPS00 Alexandria Lake Area Sanitary 4 9 

Douglas 27041SW029 RPU00 Kluver 30 

Douglas 27041SW141 RPU00 La Grande 8 

Douglas 27041XAXN RPU00 CHANDLER FIELD 1 0 

Douglas 27041XAXN RPU00 CHANDLER FIELD 0 2 1 0 

Faribault 2704300024 BO001 Seneca Foods Corp - Blue Earth 2 0 0 2 

Faribault 2704300041 SI004 Corn Plus 22 

Faribault 2704300041 SI006 Corn Plus 22 

Faribault 2704300041 SV009 Corn Plus 14 109 23 15 

Faribault 2704300041 SV010 Corn Plus 9 72 6 10 

Faribault 2704300041 SV011 Corn Plus 24 3 2 1 24 

Faribault 2704300047 ITOTA Wells Concrete Products Co - P 15 1 

Faribault 27043SW069 RPU00 Faribault County 46 

Faribault 27043XSBU RPU00 BLUE EARTH MUNI 0 1 1 0 

Fillmore 2704500047 NF001 Pederson Brothers Inc - Nonmet 8 0 1 7 1 

Fillmore 2704500049 FS001 Pro-Corn LLC 6 Fillmore 2704500049 SV004 Pro-Corn LLC 11 59 10 12 

Fillmore 2704500049 SV011 Pro-Corn LLC 15 2 1 0 15 

Fillmore 2704500049 SV017 Pro-Corn LLC 13 2 1 0 13 

Fillmore 2704500049 SV018 Pro-Corn LLC 8 7108 Fillmore 2704500049 SV021 Pro-Corn LLC 1 12 0 1 

Fillmore 2704500049 SV022 Pro-Corn LLC 5 0 

Fillmore 27045SW049 RPU00 Ironwood 9 

Fillmore 27045XFKA RPU00 FILLMORE COUNTY 0 

Freeborn 2704700027 ITOTA Progress Casting Group Inc - A 3 0 24 4 

Freeborn 2704700034 SI001 Magellan Pipeline Co LLC - Alb 14 0 16 Freeborn 2704700035 ITOTA Alamco Wood Products Inc 1 4 1 Freeborn 2704700050 ITOTA Albert Lea Medical Center 3Freeborn 2704700051 ITOTA Lou-Rich Inc 1 10 2 2 

Freeborn 2704700055 FS003 Agra Resources Coop dba EXOL 5 

Freeborn 2704700055 FS005 Agra Resources Coop dba EXOL 9 

Freeborn 2704700055 SV007 Agra Resources Coop dba EXOL 30 159 16 1 31 

Freeborn 2704700055 SV010 Agra Resources Coop dba EXOL 18 2 1 1 18 

Freeborn 2704700055 SV016 Agra Resources Coop dba EXOL 22 3 2 1 22 

Freeborn 2704700057 IOTHE Ulland Brothers Inc South - No 4 Freeborn 2704700057 NF001 Ulland Brothers Inc South - No 13 1 1 12 1 

Freeborn 2704700059 SV001 Alliance Pipeline - Albert Lea 49 1 1 5 40 1 

Page 12 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Freeborn 2777700289 ITOTA Ulland Brothers Inc - Plant 3 10 16 4 3 13 22 

Freeborn 27047POTW2RPS00 Albert Lea WWTP 6 14 

Freeborn 27047SW085 RPU00 Albert Lea 98 

Freeborn 27047XAEL RPU00 ALBERT LEA MUNI 1 0 

Freeborn 27047XAEL RPU00 ALBERT LEA MUNI 0 2 1 0 

Freeborn 27047XAEL RPU00 ALBERT LEA MUNI 0 

Goodhue 2704900001 SI009 ADM - Red Wing 2 

Goodhue 2704900001 SI011 ADM - Red Wing 11 

Goodhue 2704900001 SI020 ADM - Red Wing 22 47 1 3 0 22 47 Goodhue 2704900002 BO001 Minnesota Correctional Facilit 2 0 2 

Goodhue 2704900005 SV001 NSP dba Xcel Energy - Red Wing 264 49 2 264 49 

Goodhue 2704900005 SV002 NSP dba Xcel Energy - Red Wing 259 39 3 259 39 

Goodhue 2704900007 SV005 USG Interiors Inc - Red Wing 5 41 63 7 Goodhue 2704900007 SV009 USG Interiors Inc - Red Wing 198 968 0 10 249 1390 

Goodhue 2704900009 ITOTA Minnesota Malting Co 16 4 0 3 18 5 Goodhue 2704900038 SI001 Red Wing Solid Waste Boiler Fa 7 8 17 1 10 10 Goodhue 2704900038 SI002 Red Wing Solid Waste Boiler Fa 7 8 17 1 10 10 Goodhue 2704900062 SV001 Dairy Farmers of America Inc - 8711087Goodhue 2704900062 SV004 Dairy Farmers of America Inc - 1 4 1 Goodhue 2704900067 ITOTA Cannon Equipment Midwest 2 1 4 

Goodhue 2704900077 IOTHE Luhman's Construction Co - Non 2 6 0 

Goodhue 2704900077 NF001 Luhman's Construction Co - Non 7 0 1Goodhue 2704900078 NF001 Holm Bros Construction - Nonme 6 0 0 5 0 Goodhue 27049POTW5RPS00 Red Wing WWTP 3 8 

Goodhue 27049POTW6RPS00 Zumbrota WWTP 1 2 Goodhue 27049SW157 RPU00 Goodhue Cooperative 4 

Goodhue 27049SW174 RPU00 Goodhue County (Red Wing) 27 

Goodhue 27049XSYN RPU00 STANTON AIRFIELD 1 0 0 

Grant 27051XY63 RPU00 ELBOW LAKE MUNI 1 0 

Hennepin 2700300168 BO002 Minneapolis Water Works - Frid 4 0 0 4 

Hennepin 2701900018 ITOTA Automated Building Components 4 

Hennepin 2705300002 SV001 Hennepin County Energy Center 10 2 1 10 

Hennepin 2705300002 SV004 Hennepin County Energy Center 11 1 1 12 

Hennepin 2705300002 SV005 Hennepin County Energy Center 8 1 0 9 

Hennepin 2705300006 SV008 Smith Foundry 1 0 3 1 

Hennepin 2705300010 SI031 Northwest Airlines Inc - Mpls/ 4 1 0 4 Hennepin 2705300010 SI032 Northwest Airlines Inc - Mpls/ 4 1 0 4 Hennepin 2705300010 SI033 Northwest Airlines Inc - Mpls/ 3 0 0 3 Hennepin 2705300010 SI034 Northwest Airlines Inc - Mpls/ 3 3 10 2 4 4 

Page 13 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Hennepin 2705300010 SI046 Northwest Airlines Inc - Mpls/ 2100 21Hennepin 2705300010 SI047 Northwest Airlines Inc - Mpls/ 2100 21Hennepin 2705300011 SV001 NRG Energy Center Minneapolis 78 270 3 7 1 78 270 

Hennepin 2705300011 SV002 NRG Energy Center Minneapolis 115 0 5 2 1 115 0 Hennepin 2705300011 SV003 NRG Energy Center Minneapolis 134 0 6 2 134 0 

Hennepin 2705300011 SV007 NRG Energy Center Minneapolis 59 10 0 49 

Hennepin 2705300013 ITOTA Metropolitan Airports Commissi 41 4 1 13 232

Hennepin 2705300020 SV021 Pechiney Plastic Packaging Inc 0 29 0 Hennepin 2705300025 BO001 Dayton's Plant 2 0 0 2 Hennepin 2705300032 ITOTA Kurt Manufacturing Inc - Quinc 0 3 1 1 

Hennepin 2705300043 SV003 GAF Building Materials Corp 1 1 7 1 

Hennepin 2705300048 SV020 ADM Milling - A Flour Mill 6 1006 

Hennepin 2705300061 SI001 Abbott Northwestern Hospital 12Hennepin 2705300061 SI002 Abbott Northwestern Hospital 2500 28Hennepin 2705300061 SI003 Abbott Northwestern Hospital 6 15 0 1 8 25 

Hennepin 2705300061 SI004 Abbott Northwestern Hospital 13 57 1 3 0 22 97 

Hennepin 2705300075 ITOTA Veterans Agency Medical Center 14 0 1 7 

Hennepin 2705300078 FS002 CS McCrossan Inc - Stationary 2 

Hennepin 2705300078 SI001 CS McCrossan Inc - Stationary 4 2 0 6 Hennepin 2705300079 ITOTA Honeywell Inc - Military Avion 4 5 0 2 

Hennepin 2705300089 BO001 Thermo King Corp - Minneapolis 3 0 0 3 Hennepin 2705300089 GN001 Thermo King Corp - Minneapolis 13 1 1 11 1 

Hennepin 2705300089 IOTHE Thermo King Corp - Minneapolis 2Hennepin 2705300098 ITOTA Hitchcock Industries Inc 5 12 8 6 

Hennepin 2705300099 SI001 Prospect Foundry Inc 3 2 1 5 3 Hennepin 2705300099 SI002 Prospect Foundry Inc 4 

Hennepin 2705300099 SI005 Prospect Foundry Inc 2 1 Hennepin 2705300099 SI007 Prospect Foundry Inc 2 0 3 Hennepin 2705300099 SI009 Prospect Foundry Inc 3 2 1 5 3 Hennepin 2705300112 ITOTA 934th Airlift Wing 11031 60

Hennepin 2705300120 ITOTA Gannett Offset - Minneapolis 2 70 0 3 

Hennepin 2705300121 ITOTA Midwest Asphalt - Eden Prairie 3113 41Hennepin 2705300127 SV019 Owens Corning - Mpls Plant 16 83 3 22 21 114 

Hennepin 2705300149 ITOTA Marshall Field's Distribution 11 6

Hennepin 2705300151 ITOTA Caterpillar Paving Products -B 1 21 1 

Hennepin 2705300154 ITOTA Davis - Frost Inc 1 2 1 2 

Hennepin 2705300167 ITOTA Minneapolis city of - Asphalt 2001 20Hennepin 2705300174 ITOTA Industrial Container Services 4 2 3 2 

Hennepin 2705300180 ITOTA PGI Companies Inc 0 59 0 

Page 14 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Hennepin 2705300201 ITOTA Nilfisk-Advance Inc 4 7 1 7 

Hennepin 2705300235 ITOTA Donaldson Co Inc 4 49 0 6 0 

Hennepin 2705300241 ITOTA General Mills Operations Inc - 10 11 3 2 66Hennepin 2705300251 SV001 Interplastic Corp - Minneapoli 3 2 0 4 

Hennepin 2705300267 ITOTA Intermet Co - Minneapolis Plan 4 4 9 6 

Hennepin 2705300315 ITOTA Bituminous Roadways Inc - Minn 1001 20Hennepin 2705300322 ITOTA Alpha Ceramics Inc 2 1 3 

Hennepin 2705300329 BO000 Methodist Hospital Park Nicoll 16 83 1 1 0 28 142 

Hennepin 2705300343 ITOTA Liberty Carton Co 3 1 0 5 2 

Hennepin 2705300365 ITOTA Gaines & Hanson Printing Co 1 45 0 1 

Hennepin 2705300370 ITOTA Joyner's Die Casting & Plating 6 4 5 7 Hennepin 2705300376 ITOTA Inno-Flex Corp 0 46 0 Hennepin 2705300384 SV001 Banta Catalog Group - Minneapo 1 10 1 

Hennepin 2705300384 SV002 Banta Catalog Group - Minneapo 1 11 1 

Hennepin 2705300384 SV007 Banta Catalog Group - Minneapo 1 74 1 

Hennepin 2705300384 SV009 Banta Catalog Group - Minneapo 1 31 1 

Hennepin 2705300399 BO002 Minnesota Veterans Home - Minn 2 0 0 2 

Hennepin 2705300399 GN001 Minnesota Veterans Home - Minn 4 0 0 3 0 Hennepin 2705300400 SV001 Covanta Hennepin Energy Resour 214 5 3 6 288 7 Hennepin 2705300400 SV002 Covanta Hennepin Energy Resour 217 9 9 7 293 11 Hennepin 2705300403 BO000 Mpls Community & Technical Col 1 2 1 2

Hennepin 2705300406 ITOTA Nordic Press Inc 14 Hennepin 2705300440 ITOTA Progress Casting Group Inc - P 10 3 12 27 0 13 3 Hennepin 2705300445 SV007 Flying Cloud Sanitary Landfill 1 1Hennepin 2705300467 ITOTA Qwest Communications - Minneap 13 0 0 7 0Hennepin 2705300474 ITOTA Qwest Communications Inc - Ply 3 0 2

Hennepin 2705300477 SV003 Ritrama Inc 0 55 0 

Hennepin 2705300477 SV004 Ritrama Inc 0 2 0 

Hennepin 2705300477 SV005 Ritrama Inc 0 6 0 

Hennepin 2705300483 BO002 Dakota Growers Pasta Co - New 6 1006 

Hennepin 2705300484 ITOTA Cypress Semiconductor (Minneso 8 11 1 0 22 0 Hennepin 2705300489 BO002 MCTO - Heywood Garage 2 0 0 2 

Hennepin 2705300498 SI001 Bureau of Engraving Inc 3 0 0 3 

Hennepin 2705300602 ITOTA Rosemount Inc - Eden Prairie F 9130 50Hennepin 2705300648 SV003 Anagram International Inc 2 20 2 Hennepin 2705300649 ITOTA Mentor Urology - Minneapolis 1 16 1 Hennepin 2705300657 ITOTA Ziegler Inc - Bloomington 2451 12Hennepin 2705300663 SI011 University of Minnesota - Camp 2 0 0Hennepin 2705300695 BO001 Wells Fargo Home Mortgage 2 0 2 

Page 15 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Hennepin 2705300695 GN001 Wells Fargo Home Mortgage 2Hennepin 2705300710 ITOTA Phoenix Packaging Inc 2 25 0 2 

Hennepin 2705300778 SV001 Styrotech Inc 5 

Hennepin 2705300778 SV003 Styrotech Inc 5 

Hennepin 2705300790 BO002 NRG Riverside Plant 13 2 1 0 13 

Hennepin 2705300792 ITOTA LeJeune Steel Co 1 6 0 1 

Hennepin 2705300801 ITOTA Honeywell-Plymouth Operations 8 0 Hennepin 2705300805 BO000 General Mills Inc - Minneapoli 0 4 0 6Hennepin 2705300805 BO001 General Mills Inc - Minneapoli 2 0 0 2 Hennepin 2705300810 ITOTA North Memorial Medical Center 11 1 6 

Hennepin 2705300813 GN001 Target Financial Services 5 0 0 4 0 Hennepin 2705300815 GN001 United Health Technology Cente 3 0 2 

Hennepin 2705300827 ITOTA Mail Handling Inc/Bindery Expr 0 13 0 Hennepin 2705300830 ITOTA Smyth Companies Inc - Minneapo 1 39 1 Hennepin 2705300831 ITOTA Shapco Printing Inc 0 43 0 

Hennepin 2705300834 ITOTA GE Osmonics Minnetonka Facilit 1 1 36 3 2 2 Hennepin 2705300847 ITOTA Twin City Die Castings Co - Mp 2 0 2 2 

Hennepin 2705300850 IOTHE North Star International Truck 2Hennepin 2705300854 ITOTA Bystrom Bros Inc 0 13 0 Hennepin 2705300857 IOTHE Star Exhibits Inc - Shingle Cr 2Hennepin 2705300862 ITOTA Process Displays Co 0 48 0 

Hennepin 2705300885 GN001 American Express Financial Cor 12 1 1 10 1 Hennepin 2705300896 ITOTA Holcim US Inc - Minneapolis Te 2Hennepin 2705300904 ITOTA Shriner's Hospital 4 0 2

Hennepin 2705300924 IOTHE LaMettry's Collision Inc - Ede 3 

Hennepin 2705300925 IOTHE LaMettry's Collision Inc - Ric 3Hennepin 2705300930 ITOTA Master Collision Group LLC - B 4 

Hennepin 2705300936 BO001 8100 Building 2 0 0 2 

Hennepin 2705300937 ITOTA Superior Ford Inc 0 3 0 0 Hennepin 2705300938 IOTHE Collision Partners Inc dba Col 2Hennepin 2705300954 IOTHE ABRA Auto Body & Glass - Brook 2Hennepin 2705300956 IOTHE Hopkins Auto Body 4 

Hennepin 2705300964 GN001 City of Minneapolis Traffic Ma 6 1 5 

Hennepin 2705300965 ITOTA 133rd Airlift Wing - MN Air Na 13021 70Hennepin 2705300967 ITOTA Buhler Inc 1 2 3 0 

Hennepin 2705300969 IOTHE Iten Chevrolet-GEO 3 Hennepin 2705300977 ITOTA Jim Lupient Harold Chevrolet 3 

Hennepin 2705300994 BO002 Hennepin Technical College Ede 3 0 0 3 

Hennepin 2705300995 BO001 Hennepin Technical College - B 2 0 0 2 

Page 16 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Hennepin 2705300999 ITOTA Master Collision Group LLC - P 3Hennepin 2705301006 ITOTA MTS Systems Corp 0 4 1 0 

Hennepin 2705301010 IOTHE CS McCrossan Inc - Nonmetallic 17 

Hennepin 2705301011 IOTHE Midwest Asphalt Corp - Nonmeta 11 Hennepin 2705301011 NF001 Midwest Asphalt Corp - Nonmeta 7 0 1 6 0 Hennepin 2705301018 IOTHE Barton Sand & Gravel Co - Nonm 13 Hennepin 2705301018 NF001 Barton Sand & Gravel Co - Nonm 152 10 13 138 11 Hennepin 2705301023 ITOTA Graco Inc - David A Koch Cente 2 9 1 3 

Hennepin 2705301034 NF001 Hassan Sand & Gravel Inc - Non 2Hennepin 2705301050 SV001 University of MN - Twin Cities 75 7 1 1 77 7 Hennepin 2705301050 SV002 University of MN - Twin Cities 53 2 8 5 0 56 2 Hennepin 2705301050 SV005 University of MN - Twin Cities 28 0 3 2 0 30 0 Hennepin 2705301050 SV006 University of MN - Twin Cities 6 2 1 6 2 Hennepin 2705301050 SV007 University of MN - Twin Cities 11 1 1 1 11 1 Hennepin 2705301052 ITOTA Robert B Hill Co 10 Hennepin 2705301053 GN001 Target Stores Corporate Headqu 3 0 0 2 0 Hennepin 2705301055 GN001 Qwest Communications - 12075 5 0 0 4 0 

Hennepin 2705301062 ITOTA Bucks Unpainted Furniture Inc 2 0Hennepin 2705301063 ITOTA Duke's Body Shop 31 Hennepin 2705301078 ITOTA NAPCO International Inc 0 4 0 

Hennepin 2705301120 BO000 Minneapolis Institute of Arts 1 2 1 2

Hennepin 2705301127 ITOTA Retailer Services Corp 1 2 1 

Hennepin 2705301129 GN001 Qwest Communications - GV/Orch 2 0 2 

Hennepin 2777700208 SI004 Intex Corp 13 1 1 12 1 Hennepin 2777700285 ITOTA Commercial Asphalt Co - Plant 24 2 33 8 33 3 

Hennepin 27053POTW1RPS00 Rogers WWTP 1 3 

Hennepin 27053SW058 RPU00 Hopkins 33 

Hennepin 27053SW058 RPU00 Hopkins 0 7 0 

Hennepin 27053SW061 RPU00 Woodlake 40 

Hennepin 27053SW061 RPU00 Woodlake 4 1 74 1 4 1 Hennepin 27053XFCM RPU00 FLYING CLOUD 1 0 0 

Hennepin 27053XFCM RPU00 FLYING CLOUD 3 0 15 7 3 0 Hennepin 27053XFCM RPU00 FLYING CLOUD 0 

Hennepin 27053XMIC RPU00 CRYSTAL 0 

Hennepin 27053XMIC RPU00 CRYSTAL 2 0 11 5 2 0 Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD 1678 163 310 8 2663 259 

Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD 62 9 6 0 104 15 

Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD 3 0 24 9 6 0 

Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD 1 53 1 

Page 17 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD 0 10 0 

Hennepin 27053XMSP RPU00 MINNEAPOLIS-ST PAUL INTL/WOLD 372 32 268 12 372 32 

Houston 2705500020 NF001 Roverud Construction Inc - Non 14 1 1 12 1 

Houston 27055POTW5RPS00 Eitzen WWTP 5 12 

Houston 27055SW126 RPU00 Houston County 16 

Houston 27055XCHU RPU00 HOUSTON COUNTY 0 

Hubbard 2705700002 FS009 Potlatch - Lumbermill - Bemidj 3 Hubbard 2705700002 FS010 Potlatch - Lumbermill - Bemidj 2 Hubbard 2705700002 SV005 Potlatch - Lumbermill - Bemidj 33 4 3 1 36 4 

Hubbard 2705700006 SV001 Lamb Weston/RDO Frozen 32 46 5 3 0 34 49 

Hubbard 27057SW130 RPU00 Pickett 5 

Hubbard 27057SW146 RPU00 Leech Lake 51 

Hubbard 27057XPKD RPU00 PARK RAPIDS MUNI-KONSHOK FIE 0 Hubbard 27057XPKD RPU00 PARK RAPIDS MUNI-KONSHOK FIE 0 1 1 0 

Isanti 2705900001 BO000 Minnesota Extended Treatment O 1 8 3 14 Isanti 2705900001 BO002 Minnesota Extended Treatment O 2 0

Isanti 2705900023 ITOTA Arrow Tank & Engineering Co 2 7 1 3 

Isanti 2705900030 ITOTA RCS Acquisition LLC dba Isanti 0 3 1 Isanti 2705900031 ITOTA Freeport Finishing Inc 6 Isanti 2777700273 NF001 County Line Construction - Non 17 1 1 15 1 

Isanti 27059POTW1RPS00 Cambridge WWTP 1 3 

Isanti 27059SW129 RPU00 Isanti-Chisago County 26 

Isanti 27059XCBG RPU00 CAMBRIDGE MUNI 0 1 1 0 

Itasca 2706100001 SV003 Blandin Paper/Rapids Energy Ce 376 43 70 7 405 43 Itasca 2706100001 SV031 Blandin Paper/Rapids Energy Ce 21 3 2 23 

Itasca 2706100001 SV036 Blandin Paper/Rapids Energy Ce 3 2103 Itasca 2706100001 SV037 Blandin Paper/Rapids Energy Ce 5042150Itasca 2706100010 SV001 Potlatch - Grand Rapids 32 1 1 4 32 1 Itasca 2706100010 SV002 Potlatch - Grand Rapids 32 1 1 4 32 1 Itasca 2706100010 SV003 Potlatch - Grand Rapids 21 0 0 21 Itasca 2706100010 SV004 Potlatch - Grand Rapids 20 0 0 0 20 

Itasca 2706100010 SV005 Potlatch - Grand Rapids 0 6 0 

Itasca 2706100039 ITOTA NorthPrint International Inc - 1 69 1 

Itasca 2706100046 ITOTA Bergquist Co - Big Fork 2 

Itasca 2706100059 ITOTA Grand Rapids WWTP 7 1 

Itasca 2706100063 NF001 Brink Sand & Gravel Inc - Nonm 6 0 0 5 0 

Itasca 2706100065 NF001 Hawkinson Construction Co - No 14 1 1 13 1 Itasca 2706199999 SV999 West Mine projected 1153 387 

Itasca 2706100068 ITOTA Midland Research Center 3

Page 18 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Itasca 2777700024 ITOTA Hawkinson Construction Co Inc 8 8 2 1 11 11 Itasca 2777700112 ITOTA Hawkinson Construction Co Inc 10 9 3 2 13 12 Itasca 2777700290 NF001 Wm J Schwartz & Sons Inc - Non 3 0 2 0 

Itasca 27061SW073 RPU00 Iron Range 8 

Itasca 27061SW135 RPU00 Grand Rapids Area 4 Itasca 27061SW135 RPU00 Grand Rapids Area 1 0 15 0 1 0 Itasca 27061XGPZ RPU00 GRAND RAPIDS/ITASCA CO-GORDO 0 10 0 

Itasca 27061XGPZ RPU00 GRAND RAPIDS/ITASCA CO-GORDO 0 11 0 Itasca 27061XGPZ RPU00 GRAND RAPIDS/ITASCA CO-GORDO 10 1Jackson 2777700029 ITOTA Kruse Paving Inc - A-20 3200 43Jackson 27063SW101 RPU00 Jackson County 13 

Jackson 27063XMJQ RPU00 JACKSON MUNI 1 0 

Kanabec 27065POTW1RPS00 Mora WWTP 1 2 

Kanabec 27065SW017 RPU00 East Central Mixed Mun (kanabe 66 

Kanabec 27065XJMR RPU00 MORA MUNICIPAL 0 1 1 0 

Kandiyohi 2706700004 BO000 Willmar Regional Treatment Cen 5 26 0 0 9 45 

Kandiyohi 2706700004 BO002 Willmar Regional Treatment Cen 19 2 1 1 19 

Kandiyohi 2706700015 SI001 Magellan Pipeline Co LLC - Wil 18 1 21 Kandiyohi 2706700053 IOTHE Duininck Bros Inc - Nonmetalli 11 Kandiyohi 2706700053 NF001 Duininck Bros Inc - Nonmetalli 17 1 1 16 1 Kandiyohi 2706700054 BO001 Ridgewater College 2 0 0 2 

Kandiyohi 2706700055 IOTHE Central-Allied Enterprises - N 2Kandiyohi 2706700058 GN001 Willmar Municipal SW Substatio 2Kandiyohi 2777700018 ITOTA Duininck Bros Inc - Port Plant 18 13 4 2 25 18 

Kandiyohi 2777700019 ITOTA Duininck Bros Inc - Port Plant 13 8 3 2 19 10 Kandiyohi 2777700042 ITOTA Duininck Bros Inc - Port Plant 9 5 2 1 12 7 Kandiyohi 2777700287 ITOTA Duininck Bros Inc - Port Plant 34 21 7 5 47 29 

Kandiyohi 27067POTW2RPS00 Willmar WWTP 5 13 

Kandiyohi 27067SW079 RPU00 Kandiyohi 161 

Kandiyohi 27067XILL RPU00 WILLMAR MUNI-JOHN L RICE FIELD 0 

Kandiyohi 27067XILL RPU00 WILLMAR MUNI-JOHN L RICE FIELD 0 11 0 

Kittson 2706900014 SV001 Great Lakes Gas Transmission - 320 52 2 283 

Kittson 2706900014 SV002 Great Lakes Gas Transmission - 314 56 2 277 

Kittson 2706900014 SV003 Great Lakes Gas Transmission - 185 39 2 164 

Kittson 2706900014 SV004 Great Lakes Gas Transmission - 126 64 3 112 

Kittson 2706900014 SV005 Great Lakes Gas Transmission - 5102 41Kittson 2706900015 SV002 Viking Gas Transmission - Humb 25 15 2 25 

Kittson 2706900015 SV003 Viking Gas Transmission - Humb 26 15 2 26 

Kittson 2706900015 SV004 Viking Gas Transmission - Humb 11 7 1 11 

Page 19 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Kittson 2706900015 SV005 Viking Gas Transmission - Humb 18 12 1 18 

Kittson 27069SW092 RPU00 Mar-Kit (Anderson-Kittson) 29 Kittson 27069SW115 RPU00 Karlstad 2 Kittson 27069XHCO RPU00 HALLOCK MUNI 0 1 1 0 

Koochiching 2707100002 SV170 Boise Cascade Corp - Internati 7 

Koochiching 2707100002 SV173 Boise Cascade Corp - Internati 4 30 5 

Koochiching 2707100002 SV177 Boise Cascade Corp - Internati 7 

Koochiching 2707100002 SV178 Boise Cascade Corp - Internati 7 

Koochiching 2707100002 SV179 Boise Cascade Corp - Internati 7 

Koochiching 2707100002 SV183 Boise Cascade Corp - Internati 70 

Koochiching 2707100002 SV302 Boise Cascade Corp - Internati 7 

Koochiching 2707100002 SV320 Boise Cascade Corp - Internati 343 0 8 451 0 Koochiching 2707100002 SV322 Boise Cascade Corp - Internati 0 3 7 157 1 4 Koochiching 2707100002 SV325 Boise Cascade Corp - Internati 3 

Koochiching 2707100002 SV326 Boise Cascade Corp - Internati 3 

Koochiching 2707100002 SV340 Boise Cascade Corp - Internati 24 0 31 0Koochiching 2707100002 SV420 Boise Cascade Corp - Internati 104 1 12 7 3 104 1 Koochiching 2707100002 SV431 Boise Cascade Corp - Internati 331 48 130 15 0 364 60 Koochiching 2707100002 SV440 Boise Cascade Corp - Internati 25 0 0 5 2 25 0 Koochiching 2707100002 SV450 Boise Cascade Corp - Internati 4 1104 Koochiching 2707100002 SV460 Boise Cascade Corp - Internati 2 1102 Koochiching 2707100002 SV901 Boise Cascade Corp - Internati 12 12 38 1 13 13 Koochiching 2707100015 SI001 International Bildrite Inc 10 1 21 11 

Koochiching 27071POTW1RPS00 North Koochiching WWTP 2 4 

Koochiching 27071SW191 RPU00 Koochiching 58 

Koochiching 27071SW225 RPU00 Northome Modified 6 

Koochiching 27071XINL RPU00 FALLS INTL 0 2 1 1 

Koochiching 27071XINL RPU00 FALLS INTL 1 3 1 0 

Koochiching 27071XINL RPU00 FALLS INTL 1 1 1 

Lac_qui_Parle 2707300002 SV016 Ag Processing Inc - Dawson 2 0 4 2 

Lac_qui_Parle 2707300002 SV017 Ag Processing Inc - Dawson 2 0 4 2 

Lac_qui_Parle 2707300002 SV052 Ag Processing Inc - Dawson 25 17 3 2 1 25 17 Lac_qui_Parle 2707300003 ITOTA Municipal Castings Inc 22 1 1 9 28 1 

Lac_qui_Parle 2707300016 SV002 Associated Milk Producers Inc 5 1 0 5 

Lac_qui_Parle 2707300016 SV006 Associated Milk Producers Inc 3 0 54 3 

Lac_qui_Parle 27073XDXX RPU00 MADISON-LAC QUI PARLE COUNTY 0 

Lake 2707500003 SV261 Northshore Mining Co - Silver 99 9 1 121 11 Lake 2707500003 SV114 Northshore Mining Co - Silver 295 29 38 296 29 Lake 2707500003 SV105 Northshore Mining Co - Silver 307 30 39 294 29 

Page 20 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Lake 2707500003 SV266 Northshore Mining Co - Silver 481 21 Lake 2707500005 FS017 Duluth Missabe & Iron Range Ra 3 Lake 2707500019 SV001 Louisiana-Pacific Corp - Two H 17 0 2 4 0 18 0 Lake 27075POTW1RPS00 Two Harbors WWTP 1 2 

Lake 27075SW140 RPU00 Lake County 34 

Lake 27075XBFW RPU00 SILVER BAY MUNICIPAL 0 

Lake 27075XTWMRPU00 RICHARD B HELGESON 1 0 Lake_of_the_W27077SW171 RPU00 Lake of the Woods 5 

Lake_of_the_W27077XBDE RPU00 BAUDETTE INTL 0 Lake_of_the_W27077XBDE RPU00 BAUDETTE INTL 0 1 0 0 

Le_Sueur 2707900007 ITOTA River Bend Asphalt Co 11 10 3 2 15 13 

Le_Sueur 2707900009 SI001 Unimin Minnesota Corp - Kasota 3 0 4 

Le_Sueur 2707900017 SI016 Le Sueur Inc 6 Le_Sueur 2707900019 SI001 Unimin Minnesota Corp - Le Sue 5 1 5 

Le_Sueur 2707900022 BO002 Seneca Foods Corp - Montgomery 5 1005 Le_Sueur 2707900034 SI001 DAVISCO International Inc - Le 2 0 1 2 

Le_Sueur 2707900043 IOTHE Johnson Aggregates - Nonmetall 7 Le_Sueur 2707900043 NF001 Johnson Aggregates - Nonmetall 6 0 1 6 0 Le_Sueur 2777700275 ITOTA River Bend Asphalt Co - Plant 7 4 1 1 10 5 Le_Sueur 27079SW063 RPU00 MN Sanitation Services 8 

Le_Sueur 27079SW067 RPU00 Tellijohn 55 

Le_Sueur 27079SW067 RPU00 Tellijohn 0 4 0 

Le_Sueur 27079SW091 RPU00 Sun Prairie 7 

Le_Sueur 27079X12Y RPU00 LE SUEUR MUNI 0 

Lyon 2708300001 ITOTA Farmers Coop Elevator Co - Cot 3Lyon 2708300019 ITOTA McLaughlin & Schulz Inc - Plan 5 7 1 1 7 10 Lyon 2708300038 FS003 ADM Corn Processing - Marshall 13 

Lyon 2708300038 FS004 ADM Corn Processing - Marshall 2 

Lyon 2708300038 SV006 ADM Corn Processing - Marshall 8 29 8 10 

Lyon 2708300038 SV009 ADM Corn Processing - Marshall 3 2 4 

Lyon 2708300038 SV010 ADM Corn Processing - Marshall 1 27 1 

Lyon 2708300038 SV011 ADM Corn Processing - Marshall 4 1 17 2 5 1 Lyon 2708300038 SV012 ADM Corn Processing - Marshall 2 1 2 2Lyon 2708300038 SV013 ADM Corn Processing - Marshall 4 4 6 

Lyon 2708300038 SV014 ADM Corn Processing - Marshall 5 7Lyon 2708300038 SV016 ADM Corn Processing - Marshall 234 432 1 0 222 410 Lyon 2708300038 SV019 ADM Corn Processing - Marshall 34 0 4 3 1 34 0 Lyon 2708300038 SV020 ADM Corn Processing - Marshall 30 0 6 3 1 30 0 Lyon 2708300046 ITOTA Koch Materials - Marshall Asph 1 2 6 2 

Page 21 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Lyon 2708300055 NF001 McLaughlin & Schulz Inc - Nonm 3 0 2 0 

Lyon 2708300056 SV001 Northern Border Pipeline - Com 95 3 2 6 79 3 Lyon 2708300057 GN001 Weiner Memorial Medical Center 10 1 1 9 1 

Lyon 27083POTW1RPS00 Marshall WWTP 3 8 

Lyon 27083SW023 RPU00 Lyon County 140 

Lyon 27083XMMLRPU00 MARSHALL MUNI-RYAN FIELD 0 2 1 0 

Lyon 27083XMMLRPU00 MARSHALL MUNI-RYAN FIELD 0 1 0 0 Mahnomen 2708700005 BO000 ISD 432 - Mahnomen Public Scho 1 1 2 1 

Mahnomen 27087SW122 RPU00 Mahnomen County 6 

Mahnomen 27087X3N8 RPU00 MAHNOMEN COUNTYMarshall 2708900012 SV001 Great Lakes Gas Transmission - 143 2 1 4 126 2 Marshall 2708900012 SV002 Great Lakes Gas Transmission - 119 2 1 3 105 2 Marshall 2708900026 NF001 M & J Construction Co - Nonmet 8 0 1 7 1 Marshall 2708900027 NF001 Schenkey Inc - Nonmetallic 13 1 1 12 1 Martin 2709100003 ITOTA Watonwan Farm Service 4 

Martin 2709100043 SV003 Northern Border Pipeline - Com 94 3 2 6 78 3 Martin 2709100057 NF001 W Hodgman & Sons Inc - Nonmeta 6 0 1 6 0 Martin 2777700027 ITOTA W Hodgman & Sons Inc - Plant 9 18 11 4 3 25 15 

Martin 27091POTW5RPS00 Fairmont WWTP 1 4 

Martin 27091SW076 RPU00 Gofer 29 

Martin 27091XFRM RPU00 FAIRMONT MUNI 1 0 0 

Martin 27091XFRM RPU00 FAIRMONT MUNI 0 McLeod 2708500035 BO002 Seneca Foods Corp - Glencoe 6 1006 McLeod 2708500047 SI001 Waste Management - Spruce Ridg 5324 53McLeod 2708500049 SI006 3M - Hutchinson Tape Manufactu 12 

McLeod 2708500049 SV065 3M - Hutchinson Tape Manufactu 56 

McLeod 2708500049 SV156 3M - Hutchinson Tape Manufactu 25 

McLeod 2708500049 SV199 3M - Hutchinson Tape Manufactu 0 7 0 

McLeod 2708500049 SV212 3M - Hutchinson Tape Manufactu 1 35 1 

McLeod 2708500049 SV213 3M - Hutchinson Tape Manufactu 2 15 2 

McLeod 2708500049 SV217 3M - Hutchinson Tape Manufactu 3 0 0 3 

McLeod 2708500049 SV219 3M - Hutchinson Tape Manufactu 4 0 0 4 

McLeod 2708500049 SV221 3M - Hutchinson Tape Manufactu 7300 73McLeod 2708500049 SV222 3M - Hutchinson Tape Manufactu 49 168 1 2 1 49 168 

McLeod 2708500049 SV223 3M - Hutchinson Tape Manufactu 50 169 1 2 1 50 169 

McLeod 2708500049 SV257 3M - Hutchinson Tape Manufactu 2 

McLeod 2708500053 ITOTA Associated Milk Producers Inc 6 4 0 6 5 

McLeod 27085POTW2RPS00 Glencoe WWTP 1 3 

McLeod 27085POTW4RPS00 Hutchinson WWTP 3 8 

Page 22 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Meeker 2709300004 ITOTA Mid-Minnesota Hot Mix Inc - Li 2100 31Meeker 2709300033 ITOTA RIE Coatings 11 0 Meeker 27093POTW2RPS00 Litchfield WWTP 3 6 

Meeker 27093SW070 RPU00 Meeker County ** 26 

Mille_Lacs 2706500025 NF001 Buckley Construction II - Nonm 7 0 1 6 0 Mille_Lacs 2709500003 ITOTA Smith System Manfacturing Co 1 2 1 

Mille_Lacs 2709500004 SV001 Viking Gas Transmission - Mila 142 22 2 142 

Mille_Lacs 2709500004 SV002 Viking Gas Transmission - Mila 143 23 2 143 

Mille_Lacs 2709500004 SV003 Viking Gas Transmission - Mila 141 22 2 141 

Mille_Lacs 2709500004 SV006 Viking Gas Transmission - Mila 23 0 0 1 19 0 Mille_Lacs 2709500006 ITOTA Woodcraft Industries - Foresto 12 1 4 10 70

Mille_Lacs 27095SW082 RPU00 Mille Lacs 3 

Morrison 2709700019 BO000 St Gabriel's Hospital 2 2 6 2 

Morrison 2709700019 BO001 St Gabriel's Hospital 2 0 2 

Morrison 2709700019 GN001 St Gabriel's Hospital 9 1 1 7 1 

Morrison 2709700023 ITOTA Camp Ripley 606103 Morrison 2709700025 SI032 Larson-Glastron Boats Inc 0 16 0 0 

Morrison 2709700026 FS004 Central MN Ethanol Cooperative 3 

Morrison 2709700026 SV008 Central MN Ethanol Cooperative 74 0 53 12 1 79 0 

Morrison 2709700027 IOTHE Little Falls Machine Inc 5 

Morrison 2709700037 NF001 Kingsway Construction Inc - No 3 0 0 3 0 Morrison 2709700038 NF001 Tri-City Paving Inc - Nonmetal 47 3 4 43 3 

Morrison 2709700040 NF001 DLL Excavating - Nonmetallic 3 0 2 0 

Morrison 27097POTW2RPS00 Little Falls WWTP 2 5 

Morrison 27097SW015 RPU00 Greater Morrison 4 

Morrison 27097XLXL RPU00 LITTLE FALLS/MORRISON COUNTY 

Morrison 27097XLXL RPU00 LITTLE FALLS/MORRISON COUNTY 0 21 0 

Mower 2709900002 SV001 Hormel Foods Corp - Austin 55 122 2 3 1 55 122 Mower 2709900011 BO002 Austin Utilities - 4th Ave Pla 2 0 0 2 

Mower 2709900011 GN001 Austin Utilities - 4th Ave Pla 15 3 14 

Mower 2709900050 ITOTA Featherlite Graphics 0 7 

Mower 27099POTW2RPS00 Austin WWTP 5 12 

Mower 27099POTW2RPS00 Austin WWTP 2 5 

Mower 27099SW062 RPU00 Red Rock 80 

Mower 27099XAUM RPU00 AUSTIN MUNIMower 27099XAUM RPU00 AUSTIN MUNI 0 2 1 0 

Murray 2710100020 ITOTA ADM Corn Processing - Dovray 8 

Murray 27101SW104 RPU00 Murray County 14 

Nicollet 2701300093 ITOTA Phenix Manufacturing Co Inc - 2 1 10 3 1 

Page 23 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Nicollet 2710300001 BO000 St Peter Regional Treatment Ce 10 49 0 1 16 85

Nicollet 2710300001 BO002 St Peter Regional Treatment Ce 2 0 0 2 

Nicollet 2710300013 ITOTA Gustavus Adolphus College 6 0 0 4 

Nicollet 2710300023 SI003 DAVISCO International Inc - Ni 1 2 1 

Nicollet 2710300032 ITOTA Hewitt Machine & Manufacturing 0 12 0 0 

Nobles 2710500001 SV001 Swift Pork Co - Worthington 8 1108 

Nobles 2710500001 SV002 Swift Pork Co - Worthington 10 1 1 0 10 

Nobles 2710500018 ITOTA McLaughlin & Schulz Inc - Wort 2300 34Nobles 2710500051 IOTHE Pronk Ready Mix Inc - Nonmetal 4 Nobles 27105POTW1RPS00 Worthington WWTP 2 6 

Nobles 27105SW011 RPU00 Nobles County 65 

Norman 2710700012 SV001 Viking Gas Transmission - Ada 240 27 3 240 

Norman 2710700012 SV002 Viking Gas Transmission - Ada 179 22 2 179 

Norman 2710700012 SV003 Viking Gas Transmission - Ada 462 59 6 462 

Norman 2710700012 SV006 Viking Gas Transmission - Ada 25 0 0 1 21 0 Olmsted 2710900003 ITOTA Rochester Sand & Gravel - Plan 11 33 5 3 15 46 Olmsted 2710900004 ITOTA All American Cooperative - Ste 4 8 2 

Olmsted 2710900006 SV003 IBM - Rochester 3 0 0 3 

Olmsted 2710900006 SV049 IBM - Rochester 12 2 1 12 

Olmsted 2710900010 SV001 Associated Milk Producers Inc 20 44 1 1 0 20 44 

Olmsted 2710900015 ITOTA Shamrock Enterprises - Oronoco 2012 20Olmsted 2710900032 SI005 Quest International 3 0 3 

Olmsted 2710900036 BO002 Seneca Foods Corp - Rochester 7 1107 Olmsted 2710900055 ITOTA Rochester Sand & Gravel - Plan 2611 39Olmsted 2710900084 SI014 Mayo Medical Center - Rocheste 25 1 1 27 

Olmsted 2710900084 SI015 Mayo Medical Center - Rocheste 26 1 1 27 

Olmsted 2710900084 SI016 Mayo Medical Center - Rocheste 95 0 4 0 102 1 

Olmsted 2710900084 SI017 Mayo Medical Center - Rocheste 68 10 3 0 0 74 18 

Olmsted 2710900084 SI028 Mayo Medical Center - Rocheste 7 1 1 7 

Olmsted 2710900084 SI029 Mayo Medical Center - Rocheste 6 1 1 7 

Olmsted 2710900084 SI030 Mayo Medical Center - Rocheste 10 1 1 11 

Olmsted 2710900084 SI032 Mayo Medical Center - Rocheste 2 0 2 0 Olmsted 2710900084 SI039 Mayo Medical Center - Rocheste 7 3 31 11 5 Olmsted 2710900084 SI043 Mayo Medical Center - Rocheste 77 1 0 2 64 1 

Olmsted 2710900084 SI048 Mayo Medical Center - Rocheste 1 6 1 6

Olmsted 2710900095 NF001 Builders Sand & Gravel Co - No 8 0 1 7 1 Olmsted 2710900097 IOTHE Milestone Materials - Nonmetal 10 Olmsted 2710900097 NF001 Milestone Materials - Nonmetal 108 7 9 98 8 Olmsted 2710900098 IOTHE Shamrock Enterprises - Nonmeta 8 

Page 24 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Olmsted 2710900098 NF001 Shamrock Enterprises - Nonmeta 13 1 1 11 1 Olmsted 2777700120 ITOTA Mathy Construction Co - Plant 2 10 1 1 3 14 

Olmsted 2777700228 ITOTA Mathy Construction Co - Plant 7 16 3 2 10 22 

Olmsted 27109POTW1RPS00 Stewartville WWTP 1 2 

Olmsted 27109SW005 RPU00 Olmsted County (Roch/Locoshona 153 

Olmsted 27109SW355 RPU00 Olmsted County-Kalmar 49 

Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL 20 2 5 314Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL 0 2 1 0 

Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL 1 5 2 1 

Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL 0 2 1 0 

Olmsted 27109XRST RPU00 ROCHESTER INTERNATIONAL 7150 71Otter_Tail 2711100021 ITOTA Bongards' Creameries Inc - Per 16 0 22 18 

Otter_Tail 2711100024 ITOTA Dairy Farmers of America Inc - 15 24 12 16 26 

Otter_Tail 2711100036 SI001 Perham Resource Recovery Facil 25 13 3 1 34 17 Otter_Tail 2711100036 SI002 Perham Resource Recovery Facil 19 9 2 1 25 13 

Otter_Tail 2711100048 SI001 Fergus Falls Resource Recovery 22 22 1 6 29 30 

Otter_Tail 2711100048 SI002 Fergus Falls Resource Recovery 32 19 1 5 44 26 Otter_Tail 2711100073 NF001 Aggregate Industries Inc - P-1 5 0 0 4 0 Otter_Tail 2777700063 ITOTA Mark Sand & Gravel Acquisition 22 21 5 3 31 29 

Otter_Tail 2777700306 ITOTA Mark Sand & Gravel Acquisition 3 0 6 1 

Otter_Tail 27111POTW1RPS00 Pelican Rapids WWTP 1 2 

Otter_Tail 27111POTW3RPS00 Fergus Falls WWTP 3 7 

Otter_Tail 27111SW086 RPU00 Battle Lake Area 8 

Otter_Tail 27111SW178 RPU00 Northeast Ottertail 22 

Otter_Tail 27111SW184 RPU00 Fergus Falls 40 

Otter_Tail 27111XFFM RPU00 FERGUS FALLS MUNI-EINAR MICKE 10 0 

Pennington 2711300021 IOTHE Concrete Inc - Nonmetallic 4 Pennington 27113XTVF RPU00 THIEF RIVER FALLS REGIONAL 1 3 1 1 

Pine 2711500011 SV001 Great River Energy - Rock Lake 2 1Pine 2711500023 IOTHE DAKA Corp 2Pine 2711500029 IOTHE Hopkins Sand & Gravel Inc - No 3 

Pine 2711500029 NF001 Hopkins Sand & Gravel Inc - No 2Pine 2711500031 GN001 Grand Casino Hinckley 3 0 0 2 0 Pine 27115SW019 RPU00 Korf Bros. 25 

Pipestone 2711700015 ITOTA Cargill Inc - Pipestone 2 6 1 Pipestone 2711700023 ITOTA ADM Corn Processing - Holland 7 

Pipestone 27117SW120 RPU00 Pipestone County 16 

Polk 2711900001 FS005 American Crystal Sugar - Crook 13 Polk 2711900001 SV001 American Crystal Sugar - Crook 141 123 1 6 134 117 

Page 25 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Polk 2711900001 SV002 American Crystal Sugar - Crook 159 117 1 7 151 111 Polk 2711900001 SV003 American Crystal Sugar - Crook 67 64 1 2 63 60 Polk 2711900001 SV019 American Crystal Sugar - Crook 22 1 0 22 

Polk 2711900001 SV020 American Crystal Sugar - Crook 31 1 0 31 

Polk 2711900002 SV001 American Crystal Sugar - E Gra 330 357 1 19 313 339 Polk 2711900002 SV002 American Crystal Sugar - E Gra 283 328 1 21 268 311 Polk 2711900002 SV003 American Crystal Sugar - E Gra 15 0 2 25 1 15 0 Polk 2711900002 SV004 American Crystal Sugar - E Gra 15 0 2 31 0 15 0 Polk 2711900002 SV005 American Crystal Sugar - E Gra 14 2 38 0 14 

Polk 2711900002 SV016 American Crystal Sugar - E Gra 6 1006 Polk 2711900002 SV017 American Crystal Sugar - E Gra 1101 11Polk 2711900016 SI004 University of Minnesota - Croo 14 19 2 14 19 

Polk 2711900029 SV001 Viking Gas Transmission - Angu 152 23 2 152 

Polk 2711900029 SV002 Viking Gas Transmission - Angu 154 23 3 154 

Polk 2711900029 SV003 Viking Gas Transmission - Angu 458 59 6 458 

Polk 2711900029 SV006 Viking Gas Transmission - Angu 16 0 0 1 14 0 Polk 2711900051 SI001 Polk Cnty Solid Waste Resource 16 17 37 1 22 23 

Polk 2711900051 SI002 Polk Cnty Solid Waste Resource 16 17 37 1 22 23 

Polk 2711900068 IOTHE Donarski Bros Inc - Nonmetalli 5 Polk 2711900068 NF001 Donarski Bros Inc - Nonmetalli 2 0 2 

Polk 2711900069 IOTHE J & S Gravel - Nonmetallic 7 

Polk 2711900069 NF001 J & S Gravel - Nonmetallic 11 1 1 10 1 

Polk 2777700254 ITOTA Northern Paving Inc - Base 5 13 5 3 1 18 7 Polk 27119SW124 RPU00 Polk County 100 

Polk 27119XCKN RPU00 CROOKSTON MUNI KIRKWOOD FLD 0 

Polk 27119XCKN RPU00 CROOKSTON MUNI KIRKWOOD FLD 0 21 0 

Polk 27119XFSE RPU00 FOSSTON MUNI 1 0 Pope 2712100017 IOTHE MHC Inc - Glenwood Pope 27121XGHWRPU00 GLENWOOD MUNI 0 0 

Ramsey 2705300004 ITOTA Schwing America Inc 12 1 26 22 8 21 1 Ramsey 2712300016 SV084 3M - St Paul Main Plant 14 54 1 3 0 14 54 

Ramsey 2712300016 SV085 3M - St Paul Main Plant 18 68 1 3 0 18 68 

Ramsey 2712300016 SV086 3M - St Paul Main Plant 9 29 0 1 0 9 29 

Ramsey 2712300019 SV001 Minnesota Brewing/Gopher State 13 0 2 1 0 13 0 Ramsey 2712300019 SV003 Minnesota Brewing/Gopher State 3 1 2 3 

Ramsey 2712300019 SV010 Minnesota Brewing/Gopher State 11 

Ramsey 2712300020 ITOTA ADM Grain - St Paul - Elevator 4 Ramsey 2712300021 SV026 Silgan Containers Mfg Corp - S 8 

Ramsey 2712300022 SI003 University of St Thomas 2 0 0 2 

Page 26 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Ramsey 2712300024 ITOTA Smyth Companies Inc - St Paul 0 70 0 

Ramsey 2712300030 ITOTA Regions Hospital 6801 34 Ramsey 2712300039 SI001 Ford Motor Co - Twin Cities As 7 1107 Ramsey 2712300039 SV001 Ford Motor Co - Twin Cities As 4 0 0 4 

Ramsey 2712300039 SV002 Ford Motor Co - Twin Cities As 8 1108 

Ramsey 2712300039 SV010 Ford Motor Co - Twin Cities As Ramsey 2712300039 SV020 Ford Motor Co - Twin Cities As 11 238 32 0 11 

Ramsey 2712300039 SV021 Ford Motor Co - Twin Cities As 3 4 0 3 

Ramsey 2712300039 SV022 Ford Motor Co - Twin Cities As 2 10 0 2 

Ramsey 2712300042 BO000 Macalester College 2 10 3 17 Ramsey 2712300045 BO000 College of St Catherine 0 2 1 3Ramsey 2712300045 BO002 College of St Catherine 4 1 0 4 Ramsey 2712300053 SV016 Metropolitan Wastewater Treatm 32 53 1 43 72 Ramsey 2712300053 SV018 Metropolitan Wastewater Treatm 6 1006 Ramsey 2712300053 SV019 Metropolitan Wastewater Treatm 7 1107 Ramsey 2712300053 SV028 Metropolitan Wastewater Treatm 2 0 0 2 0 Ramsey 2712300055 SV002 North Star Steel Minnesota - S 52 18 40 12 65 23 Ramsey 2712300055 SV004 North Star Steel Minnesota - S 79 0 4 2 1 79 0 Ramsey 2712300056 ITOTA Pier Foundry 1 3 1 1 Ramsey 2712300063 SV001 District Energy St Paul Inc-Ha 188 709 1 119 188 709 

Ramsey 2712300063 SV002 District Energy St Paul Inc-Ha 5 1005 

Ramsey 2712300063 SV003 District Energy St Paul Inc-Ha 3 0 0 3 

Ramsey 2712300088 SI002 Versa Iron & Machine 6 Ramsey 2712300093 ITOTA Bethesda Rehabilitation Hospit 3

Ramsey 2712300108 BO002 Hamline University 5 1005 

Ramsey 2712300139 ITOTA St Joseph's Hospital - HealthE 4 1 0 2 0

Ramsey 2712300149 ITOTA T A Schifsky & Sons Inc 1001 20Ramsey 2712300186 ITOTA Midwest Asphalt - Plant 2 - Ne 4 0 12 3 6 1 Ramsey 2712300187 ITOTA Peavey Red Rock Elevator - St 10 

Ramsey 2712300189 ITOTA City of St Paul Asphalt Plant 1 0 1 10Ramsey 2712300213 ITOTA Koch Materials - St Paul Aspha 1 0 2 1 

Ramsey 2712300254 ITOTA HealthEast Midway Health Servi 2Ramsey 2712300273 IOTHE Rihm Motor Co 2Ramsey 2712300280 ITOTA St Paul Community & Technical 3Ramsey 2712300288 SV015 Twin City Concrete Products Co 3 3

Ramsey 2712300333 BO002 Energy Park Utility Co 3 0 0 3 

Ramsey 2712300347 SV001 Commercial Asphalt Co - Plant 3062 41Ramsey 2712300398 ITOTA Ashland Distribution 3 

Ramsey 2712300410 SV003 Waldorf Corp - A Rock-Tenn Co 1 112 1 

Page 27 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Ramsey 2712300410 SV004 Waldorf Corp - A Rock-Tenn Co 1 3 1 

Ramsey 2712300420 ITOTA Qwest Communications - St Paul 7 0 4

Ramsey 2712300433 BO001 Minnesota Historical Society 3 0 0 3 Ramsey 2712300436 BO001 Bethel College and Seminary 2 0 0 2 Ramsey 2712300456 ITOTA Ramsey County Government Cente 2 0Ramsey 2712300458 ITOTA Weyerhaeuser Co - White Bear - 2 2 2 Ramsey 2712300489 GN001 State of MN - Transportation B 3 0 0 3 0 

Ramsey 2712300496 ITOTA HB Fuller Co - Corporate Campu 3010 60Ramsey 2712300498 ITOTA Impressions Inc 68 

Ramsey 2712300601 SI001 Fox Packaging Inc 13 

Ramsey 2712300605 ITOTA Americraft Carton Inc 39 0 

Ramsey 2712300606 ITOTA Tursso Companies Inc 22 

Ramsey 2712300614 IOTHE Quality Manufacturing Inc 5 

Ramsey 2712300622 ITOTA Cardiac Pacemakers Inc 5080 3 Ramsey 2712300624 ITOTA Brown-Wilbert Vault Co - Rosev 4 1 

Ramsey 2712300626 ITOTA Certified Painting Inc 0 12 1 0 

Ramsey 2712300640 ITOTA St John's Hospital (HealthEast 3 1 0 2 1

Ramsey 2712300666 ITOTA Buerkle Buick-Honda Co 2 5 0 1 

Ramsey 2712300680 ITOTA Brenntag Great Lakes LLC 0 3 

Ramsey 2712300690 NF001 Crushers Inc - Nonmetallic 4 0 0 4 0 

Ramsey 2712300694 SI002 3M - Administrative Offices - 17 43 1 1 0 17 43 

Ramsey 2712300694 SI003 3M - Administrative Offices - 61 120 2 2 1 61 120 

Ramsey 2712300694 SI004 3M - Administrative Offices - 134 265 4 4 2 134 265 

Ramsey 2712300694 SI005 3M - Administrative Offices - 149 224 5 5 2 149 224 

Ramsey 2712300694 SI006 3M - Administrative Offices - 5121051Ramsey 2712300702 GN001 MNDOT Metro Div Hq - Waters Ed 2Ramsey 2712300709 IOTHE LaMettry's Collision Inc - Map 2Ramsey 2712300717 ITOTA Metro Council - East Metro Tra 2 1

Ramsey 2712300719 BO001 Meritex Enterprises Inc 2 0 0 2 

Ramsey 2712300721 ITOTA M-Foods Dairy LLC 8 1 1 8 

Ramsey 2712300725 ITOTA Unicircuit - Roseville 17 0 

Ramsey 2712300726 SI004 FM Frattalone Excavating & Gra 6 0 1 5 0 Ramsey 2712300727 ITOTA Signation Sign Group Inc 6 1 Ramsey 2712300729 ITOTA Modernistic Inc - Empire Dr 1 15 1 1 

Ramsey 2712300731 IOTHE Structural Wood Corp 3Ramsey 27123XSTP RPU00 ST PAUL DOWNTOWN HOLMAN FL 0 31 1 

Ramsey 27123XSTP RPU00 ST PAUL DOWNTOWN HOLMAN FL 2 0 13 6 3 0 Ramsey 27123XSTP RPU00 ST PAUL DOWNTOWN HOLMAN FL 1 51 1 Redwood 2712700013 ITOTA Central Bi-Products - North Re 26 5 1 1 81 32 6 

Page 28 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Redwood 2712700023 GN001 Redwood Falls Public Utilities 16 1 1 13 1 

Redwood 2712700041 ITOTA HBOS Manufacturing LP - Schult 4 

Redwood 27127POTW1RPS00 Redwood Falls WWTP 1 3 

Redwood 27127SW083 RPU00 Redwood County 35 

Renville 2712900014 FS001 Southern Minnesota Beet Sugar 18 

Renville 2712900014 SI999 Southern Minnesota Beet Sugar 53 

Renville 2712900014 SV001 Southern Minnesota Beet Sugar 621 586 4 0 0 589 556 

Renville 2712900014 SV002 Southern Minnesota Beet Sugar 31 102 0 5 1 31 102 

Renville 2712900014 SV012 Southern Minnesota Beet Sugar 3 

Renville 2712900014 SV034 Southern Minnesota Beet Sugar 1 1 0 1 1 

Renville 2712900036 FS003 Minnesota Energy 11 

Renville 2712900036 SV005 Minnesota Energy 8 42 3 8 

Renville 2712900036 SV012 Minnesota Energy 10 2 1 11 

Renville 2712900045 NF001 Fairway Construction Inc - Non 3 0 0 3 0 

Renville 2712900046 SV001 Alliance Pipeline - Olivia 23- 72 1 1 6 60 1 

Renville 27129SW090 RPU00 Renville County 51 

Renville 27129X1D6 RPU00 HECTOR MUNI 1 0 

Rice 2713100006 ITOTA St Olaf College 12100 70

Rice 2713100018 BO002 Carleton College 7 1107 Rice 2713100022 SV003 Malt-O-Meal Co - Plant 2 - Nor 9 1109 Rice 2713100022 SV004 Malt-O-Meal Co - Plant 2 - Nor 9 1109 Rice 2713100027 ITOTA Jennie-O Turkey Store - Feed M 3 2 3 

Rice 2713100050 ITOTA Mercury Minnesota Inc 5 

Rice 2713100058 SV001 Northern Natural Gas Co - Fari 6 0 0 5 0 

Rice 2713100059 SI001 Minn Correctional Facility - F 2 15 0 0 4 26

Rice 2713100059 SI002 Minn Correctional Facility - F 9 55 1 0 0 15 95 

Rice 2713100059 SI003 Minn Correctional Facility - F 12 72 1 1 0 20 124 

Rice 2713100060 ITOTA MN Residential Academies for t 2200Rice 2713100062 NF001 Kielmeyer Construction Inc - N 20 1 2 18 1 Rice 2713100063 IOTHE Bituminous Materials Inc - Non 3 Rice 2777700166 ITOTA Bituminous Materials Inc - Pla 7 3 1 1 10 4 Rice 27131POTW2RPS00 Faribault WWTP 6 14 

Rice 27131SW123 RPU00 Rice County 210 

Rice 27131XFBL RPU00 FARIBAULT MUNI 0 2 1 0 

Rock 2713300023 FS001 Agri-Energy LLC 3 Rock 2713300023 SV004 Agri-Energy LLC 15 103 12 0 16 

Rock 2713300023 SV011 Agri-Energy LLC 22 2 1 1 22 

Rock 27133POTW1RPS00 Luverne WWTP 1 3 

Rock 27133SW077 RPU00 Rock County 16 

Page 29 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Roseau 2713500002 SI012 Marvin Windows & Doors 3011 30Roseau 2713500002 SI014 Marvin Windows & Doors 8120 81Roseau 2713500002 SI015 Marvin Windows & Doors 7110 71Roseau 2713500018 ITOTA CB Hockey LLC 2Roseau 27135SW137 RPU00 Salol 44 

Roseau 27135XROX RPU00 ROSEAU MUNI/RUDY BILLBERG FIE 0 

Roseau 27135XROX RPU00 ROSEAU MUNI/RUDY BILLBERG FIE 0 21 0 

Roseau 27135XRRT RPU00 WARROAD INTL-SWEDE CARLSON 

Roseau 27135XRRT RPU00 WARROAD INTL-SWEDE CARLSON 1 0 

Scott 2713900003 FS001 Rahr Malting Co - Shakopee 13 Scott 2713900003 SV003 Rahr Malting Co - Shakopee 13 1 1 0 13 

Scott 2713900003 SV005 Rahr Malting Co - Shakopee 15 18Scott 2713900003 SV006 Rahr Malting Co - Shakopee 7 17 1 0 7 20 

Scott 2713900003 SV007 Rahr Malting Co - Shakopee 16 18Scott 2713900003 SV008 Rahr Malting Co - Shakopee 9 21 1 1 9 25 

Scott 2713900003 SV009 Rahr Malting Co - Shakopee 11 26 1 1 11 31 

Scott 2713900003 SV010 Rahr Malting Co - Shakopee 15 37 2 1 0 15 43 

Scott 2713900005 SI001 Anchor Glass Container Corp - 340 187 5 259 142 Scott 2713900005 SI002 Anchor Glass Container Corp - 434 228 5 331 174 Scott 2713900009 ITOTA Koch Materials Co - Savage 3 1 0 4 

Scott 2713900013 FS001 CertainTeed Corp 5 Scott 2713900013 FS002 CertainTeed Corp 4 Scott 2713900013 SV036 CertainTeed Corp 55 5 3 1 1 55 5 Scott 2713900041 SI030 Prior Lake Aggregates Inc 2 0 0 2 0 

Scott 2713900041 SI031 Prior Lake Aggregates Inc 2 0 0 2 0 

Scott 2713900041 SI033 Prior Lake Aggregates Inc 7 0 1 6 0 

Scott 2713900041 SI034 Prior Lake Aggregates Inc 2 0 0 2 0 

Scott 2713900041 SI040 Prior Lake Aggregates Inc 5 0 0 4 0 

Scott 2713900044 SI039 Superior Minerals Corp - Savag 4 1 0 4 

Scott 2713900073 BO001 St Francis Regional Medical Ce 2 0 2 

Scott 2713900091 IOTHE Bryan Rock Products Inc - Nonm 4 Scott 2713900091 NF001 Bryan Rock Products Inc - Nonm 34 2 3 31 2 Scott 2713900092 NF001 River City Asphalt - Nonmetall 2 0 2 

Scott 2713900097 IOTHE Phillips & Temro Industries In 3 

Scott 2713900098 ITOTA MCES - Blue Lake WWTP 7 14 22 4 

Scott 2713900100 GN001 Lake Marion DG 2Scott 2713900106 ITOTA Bituminous Roadways Inc - E500 3711 49Scott 2777700145 ITOTA Commercial Asphalt Co - Plant 14 1 16 4 19 1 

Scott 2777700179 SV002 Central Soils Remediation 7 7 2 10 9 

Page 30 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Scott 2777700265 ITOTA River City Asphalt 3 40 35 9 1 55 48 

Scott 2777700276 IOTHE Shakopee Gravel Inc - Nonmetal 11 Scott 2777700276 NF001 Shakopee Gravel Inc - Nonmetal 8 0 1 7 1 Scott 27139SW032 RPU00 Louisville 10 

Scott 27139SW032 RPU00 Louisville 2 1 40 1 2 1 

Sherburne 2714100003 SI001 Great River Energy - Elk River 5 15 7 22Sherburne 2714100003 SV003 Great River Energy - Elk River 158 13 16 158 13 Sherburne 2714100003 SV004 Great River Energy - Elk River 196 17 20 196 17 Sherburne 2714100006 ITOTA Minnesota Correctional Facilit 6510 33Sherburne 2714100030 ITOTA Elk River Bituminous Inc 3100 42Sherburne 2714100041 SV003 Waste Management Inc -Elk Rive 6201 62Sherburne 2714100042 IOTHE Elk River Machine Co 4 

Sherburne 2714100058 NF001 ConTeck Enterprises Inc - Nonm 5 0 0 4 0 

Sherburne 2714500148 IOTHE Granite City Ready-Mix - Nonme 4 

Sherburne 2714500148 NF001 Granite City Ready-Mix - Nonme 6 0 1 6 0 

Sherburne 2777700023 ITOTA Commercial Asphalt Co - Plant 16 2 14 2 22 3 Sherburne 27141POTW1RPS00 Elk River WWTP 1 4 

Sherburne 27141POTW2RPS00 Becker WWTP - Municipal 1 3 Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL 1 0 0 2 0 

Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL 0 2 1 0 

Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL 1 6 3 10

Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL 0 

Sherburne 27141XSTC RPU00 ST CLOUD REGIONAL 2 0 2 2 0 

Sibley 2701900029 ITOTA Wm Mueller & Sons Inc - Blakel 9 10 3 2 13 14 Sibley 2707900042 BO002 MG Waldbaum Co - Gaylord 6 1006 Sibley 2713900093 IOTHE Sibley Aggregates - Nonmetalli 6 Sibley 2713900093 NF001 Sibley Aggregates - Nonmetalli 12 1 1 11 1 Sibley 2714300009 ITOTA Dairy Farmers of America Inc - 10 1 16 11 1 

Sibley 2714300014 SV004 Heartland Corn Products 12 88 10 13 

Sibley 2714300014 SV006 Heartland Corn Products 25 3 2 1 25 

Sibley 2714300014 SV010 Heartland Corn Products 13 2 1 0 13 

Sibley 2714300014 SV011 Heartland Corn Products 12 88 10 13 

Sibley 2714300016 BO001 Seneca Foods Corp - Arlington 2 0 0 2 Sibley 27143POTW1RPS00 Arlington WWTP 1 2 

Sibley 27143SW002 RPU00 Sibley County 9 

St_Louis 2713700005 FS002 US Steel Corp - Minntac 29 St_Louis 2713700005 SV001 US Steel Corp - Minntac 14 1 0 0 14 

St_Louis 2713700005 SV002 US Steel Corp - Minntac 17 1 0 0 17 

St_Louis 2713700005 SV003 US Steel Corp - Minntac 7 0007 

Page 31 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

St_Louis 2713700005 SV004 US Steel Corp - Minntac 14 1 0 0 14 

St_Louis 2713700005 SV005 US Steel Corp - Minntac 16 1 0 0 16 

St_Louis 2713700005 SV024 US Steel Corp - Minntac 24 St_Louis 2713700005 SV045 US Steel Corp - Minntac 2 St_Louis 2713700005 SV047 US Steel Corp - Minntac 2 St_Louis 2713700005 SV049 US Steel Corp - Minntac 2 St_Louis 2713700005 SV054 US Steel Corp - Minntac 6 St_Louis 2713700005 SV056 US Steel Corp - Minntac 2 St_Louis 2713700005 SV057 US Steel Corp - Minntac 2 St_Louis 2713700005 SV059 US Steel Corp - Minntac 2 St_Louis 2713700005 SV060 US Steel Corp - Minntac 14 St_Louis 2713700005 SV065 US Steel Corp - Minntac 2 St_Louis 2713700005 SV072 US Steel Corp - Minntac 6 St_Louis 2713700005 SV075 US Steel Corp - Minntac 2 St_Louis 2713700005 SV077 US Steel Corp - Minntac 2 St_Louis 2713700005 SV081 US Steel Corp - Minntac 2 St_Louis 2713700005 SV083 US Steel Corp - Minntac 2 St_Louis 2713700005 SV085 US Steel Corp - Minntac 8 St_Louis 2713700005 SV093 US Steel Corp - Minntac 2 St_Louis 2713700005 SV095 US Steel Corp - Minntac 2 St_Louis 2713700005 SV096 US Steel Corp - Minntac 3 St_Louis 2713700005 SV097 US Steel Corp - Minntac 2 St_Louis 2713700005 SV103 US Steel Corp - Minntac 2194 462 9 393 2195 324 St_Louis 2713700005 SV109 US Steel Corp - Minntac 2 St_Louis 2713700005 SV118 US Steel Corp - Minntac 3601 380 13 304 2521 380 St_Louis 2713700005 SV122 US Steel Corp - Minntac 101 St_Louis 2713700005 SV127 US Steel Corp - Minntac 2879 380 32 51 2015 380 St_Louis 2713700005 SV129 US Steel Corp - Minntac 9 St_Louis 2713700005 SV138 US Steel Corp - Minntac 8 St_Louis 2713700005 SV142 US Steel Corp - Minntac 34 St_Louis 2713700005 SV143 US Steel Corp - Minntac 43 St_Louis 2713700005 SV144 US Steel Corp - Minntac 3652 380 18 79 2557 380 St_Louis 2713700005 SV146 US Steel Corp - Minntac 95 St_Louis 2713700005 SV149 US Steel Corp - Minntac 32 St_Louis 2713700005 SV150 US Steel Corp - Minntac 40 St_Louis 2713700005 SV151 US Steel Corp - Minntac 2525 343 22 59 1767 343 St_Louis 2713700005 SV153 US Steel Corp - Minntac 96 St_Louis 2713700011 FS004 EVTAC Mining - Mine 8 

St_Louis 2713700015 SV001 Minnesota Power Inc - ML Hibba 414 132 52 5 414 132 

Page 32 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

St_Louis 2713700022 SV001 Duluth Steam Cooperative Assoc 250 217 1 10 255 221 

St_Louis 2713700022 SV004 Duluth Steam Cooperative Assoc 72 62 0 3 73 64 

St_Louis 2713700022 SV005 Duluth Steam Cooperative Assoc 7 6 0 7 6 

St_Louis 2713700023 ITOTA AGP Grain Ltd 3 

St_Louis 2713700030 SI001 Hill Wood Products 1 2 1 

St_Louis 2713700030 SI002 Hill Wood Products 0 2 0 

St_Louis 2713700031 SV022 Georgia-Pacific - Duluth Hardb 2 2 4 2 2 

St_Louis 2713700031 SV024 Georgia-Pacific - Duluth Hardb 20 133 0 1 0 20 133 

St_Louis 2713700032 FS001 Northshore Mining Co - Babbitt 3 

St_Louis 2713700032 FS003 Northshore Mining Co - Babbitt 6 St_Louis 2713700032 FS004 Northshore Mining Co - Babbitt 34 St_Louis 2713700032 FS005 Northshore Mining Co - Babbitt 6 St_Louis 2713700032 SI003 Northshore Mining Co - Babbitt 3 21 0 3 21 St_Louis 2713700032 SI004 Northshore Mining Co - Babbitt 1 3 1 3

St_Louis 2713700039 SV001 University of Minnesota - Dulu 2 0 0 2 

St_Louis 2713700039 SV002 University of Minnesota - Dulu 4010 50St_Louis 2713700039 SV003 University of Minnesota - Dulu 9 1 1 1 10 1 St_Louis 2713700042 ITOTA Staver Foundry Co 1 1 2 1 St_Louis 2713700061 FS025 Hibbing Taconite Co 25 St_Louis 2713700061 SV017 Hibbing Taconite Co 4 St_Louis 2713700061 SV024 Hibbing Taconite Co 2066 197 22 61 1797 197 

St_Louis 2713700061 SV028 Hibbing Taconite Co 1933 185 21 61 1682 185 

St_Louis 2713700061 SV029 Hibbing Taconite Co 2202 211 24 66 1916 211 

St_Louis 2713700061 SV033 Hibbing Taconite Co 8 St_Louis 2713700061 SV034 Hibbing Taconite Co 8 St_Louis 2713700061 SV035 Hibbing Taconite Co 9 St_Louis 2713700062 FS007 Ispat Inland Mining Co 18 St_Louis 2713700062 SI001 Ispat Inland Mining Co 19 St_Louis 2713700062 SV017 Ispat Inland Mining Co 3254 155 47 54 3254 155 St_Louis 2713700063 FS007 Keewatin Taconite Operations 6 St_Louis 2713700063 FS015 Keewatin Taconite Operations 6 St_Louis 2713700063 SV020 Keewatin Taconite Operations 2 St_Louis 2713700063 SV026 Keewatin Taconite Operations 64 St_Louis 2713700063 SV030 Keewatin Taconite Operations 6048 704 93 367 6049 464 St_Louis 2713700073 SV010 ME Global Inc 2 1 0 2 1 

St_Louis 2713700073 SV014 ME Global Inc 21 St_Louis 2713700083 SV001 Potlatch - Cook 99 1 23 5 100 1 St_Louis 2713700083 SV002 Potlatch - Cook 12 St_Louis 2713700083 SV003 Potlatch - Cook 34 3 2 10 37 3 

Page 33 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

St_Louis 2713700083 SV004 Potlatch - Cook 34 8 19 3 37 9 St_Louis 2713700083 SV005 Potlatch - Cook 56 8 2 57 8 St_Louis 2713700086 ITOTA Mesabi Bituminous Inc - Schley 1000 20St_Louis 2713700088 BO001 Virginia Regional Medical Cent 2 0 0 2 St_Louis 2713700090 ITOTA Duluth Superior Blacktop Inc 1001 10St_Louis 2713700098 BO000 ISD 2142 - Cherry High School 1 8 0 1 8 

St_Louis 2713700101 BO000 ISD 2142 - Cotton High School 1 7 0 1 7 

St_Louis 2713700108 BO000 College of St Scholastica 3 14 0 5 24 

St_Louis 2713700108 BO002 College of St Scholastica 3 0 0 3 

St_Louis 2713700112 SV024 Western Lake Superior Sanitary 32 

St_Louis 2713700113 FS001 EVTAC Mining - Fairlane Plant 2 St_Louis 2713700113 SV012 EVTAC Mining - Fairlane Plant 2 St_Louis 2713700113 SV013 EVTAC Mining - Fairlane Plant 2 St_Louis 2713700113 SV014 EVTAC Mining - Fairlane Plant 2 St_Louis 2713700113 SV016 EVTAC Mining - Fairlane Plant 6 St_Louis 2713700113 SV025 EVTAC Mining - Fairlane Plant 7 St_Louis 2713700113 SV026 EVTAC Mining - Fairlane Plant 7 St_Louis 2713700113 SV040 EVTAC Mining - Fairlane Plant 3 St_Louis 2713700113 SV042 EVTAC Mining - Fairlane Plant 128 St_Louis 2713700113 SV049 EVTAC Mining - Fairlane Plant 1764 3222 152 13 1764 3222 

St_Louis 2713700113 SV070 EVTAC Mining - Fairlane Plant 3 St_Louis 2713700113 SV046 EVTAC Mining - Fairlane Plant 2626 53 

St_Louis 2713700136 BO000 ISD 2142 - John F Kennedy High 3 13 0 5 23 

St_Louis 2713700141 FS002 Stora Enso DPM & DRPM 4 

St_Louis 2713700141 FS004 Stora Enso DPM & DRPM 2 

St_Louis 2713700141 SI005 Stora Enso DPM & DRPM 2 0 0St_Louis 2713700170 BO000 ISD 2142 - Albrook High School 1 8 0 1 9 

St_Louis 2713700171 BO000 ISD 2142 - Cook High School 1 15 1 1 16 

St_Louis 2713700172 BO000 ISD 2142 - Orr High School 1 8 0 1 8 

St_Louis 2713700243 GN001 Cub Foods - Duluth 2 0 2

St_Louis 2713700245 BO001 Duluth Entertainment & Convent 2 0 2 

St_Louis 2713700245 GN001 Duluth Entertainment & Convent 3 0 2 

St_Louis 2713700256 ITOTA General Electric Int'l Inc - M 0 0 2

St_Louis 2713700283 BO001 ISD 2154 - Eveleth-Gilbert Hig 2 0 2 

St_Louis 2713700289 NF001 Wissota Sand & Gravel Co - Non 2 2St_Louis 2713700290 IOTHE Louis Leustek & Sons Inc - Non 4 St_Louis 2713700290 NF001 Louis Leustek & Sons Inc - Non 7 0 1 7 0 St_Louis 2713700295 NF001 Northland Crushing Inc - Nonme 5 0 0 4 0 St_Louis 2713700298 NF001 Hoover Construction Co - Nonme 4 0 0 4 0 

Page 34 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

St_Louis 2713700299 SI005 NTT of Minnesota Inc - Metalli 3 0 0 3 0 St_Louis 2713700302 SI004 Northland Crushing Inc - Metal 5 0 0 5 0 

St_Louis 2713700318 SV999 Mesabi Nugget 820 344 

St_Louis 2713799999 SV999 East Mine projected 114 24 St_Louis 2777700144 ITOTA Ulland Brothers Inc - Plant 14 7 10 2 2 10 14 

St_Louis 2777700297 ITOTA Ulland Brothers Inc - Plant 34 15 16 4 3 21 22 

St_Louis 27137POTW1RPS00 Hibbing WWTP North Plant 1 2 

St_Louis 27137POTW1RPS00 Hibbing WWTP South Plant 2 5 St_Louis 27137POTW4RPS00 Ely WWTP 1 2 

St_Louis 27137POTW7RPS00 Eveleth WWTP 1 2 St_Louis 27137POTW9RPS00 Virginia WWTP 2 6 

St_Louis 27137SW038 RPU00 Hoyt Lakes 8 

St_Louis 27137SW065 RPU00 WLSSD (Duluth Disposal Co) 147 

St_Louis 27137SW068 RPU00 Northwoods 11 

St_Louis 27137SW097 RPU00 East Mesaba ** 44 

St_Louis 27137SW128 RPU00 Hudson 8 

St_Louis 27137SW161 RPU00 Hibbing 101 

St_Louis 27137SW163 RPU00 Brookston 6 

St_Louis 27137SW164 RPU00 Floodwood Modified 2 

St_Louis 27137SW175 RPU00 Cotton Area Modified 2 

St_Louis 27137SW177 RPU00 Vermillion Modified 9 

St_Louis 27137SW204 RPU00 Orr 2 

St_Louis 27137SW237 RPU00 Cook Area Modified 3 

St_Louis 27137SW262 RPU00 Highway 77 Seasonal 6 

St_Louis 27137SW405 RPU00 St. Louis Co Regional SW 98 

St_Louis 27137XDLH RPU00 DULUTH INTL 18 2 3 28 3 St_Louis 27137XDLH RPU00 DULUTH INTL 0 2 1 0 

St_Louis 27137XDLH RPU00 DULUTH INTL 1 5 2 1 

St_Louis 27137XDLH RPU00 DULUTH INTL 0 4 1 1 

St_Louis 27137XDLH RPU00 DULUTH INTL 6050 60St_Louis 27137XHIB RPU00 CHISHOLM-HIBBING 0 2 1 0 Stearns 2700900011 SI003 International Paper - Sartell 696 764 10 7 703 772 

Stearns 2700900011 SI010 International Paper - Sartell 6 0006 Stearns 2700900011 SI013 International Paper - Sartell 47 103 2 44 98 Stearns 2700900026 SV001 Grede - St Cloud Inc 4 0 1 10 4 1 Stearns 2700900026 SV102 Grede - St Cloud Inc 12 1 

Stearns 2700900026 SV103 Grede - St Cloud Inc 12 1 

Stearns 2705301077 IOTHE Hardrives Inc - Nonmetallic 6 

Stearns 2705301077 NF001 Hardrives Inc - Nonmetallic 11 1 1 10 1 

Page 35 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Stearns 2714500001 SI001 Kraft Foods Inc - Albany 3701 37Stearns 2714500001 SI002 Kraft Foods Inc - Albany 3701 37Stearns 2714500001 SI006 Kraft Foods Inc - Albany 2 

Stearns 2714500003 SV001 Melrose Dairy Proteins LLC 15 16 1 2 0 15 16 

Stearns 2714500003 SV002 Melrose Dairy Proteins LLC 42 7 2 1 0 42 7 Stearns 2714500008 SV001 St Johns University Order of S 32 59 0 19 33 61 Stearns 2714500008 SV002 St Johns University Order of S 31 59 4 31 60 

Stearns 2714500022 ITOTA Hardrives Inc - Plant 701 2 7 2 3 

Stearns 2714500026 SV001 St Cloud State University 22 63 1 3 0 35 109 

Stearns 2714500032 SV001 Associated Milk Producers - Pa 12 7 1 0 12 8 

Stearns 2714500037 ITOTA Veterans Affairs Medical Cente 8 0 0 5 

Stearns 2714500074 BO002 St Cloud Hospital 9 1109 Stearns 2714500074 GN001 St Cloud Hospital 4 0 0 3 0 Stearns 2714500080 SV007 Nahan Printing Inc 1 13 1 Stearns 2714500097 ITOTA Polar Tank Trailer Inc - Co Rd 1 4 12 2 6 Stearns 2714500099 SI001 Granit-Bronz Foundry 2 

Stearns 2714500099 SI002 Granit-Bronz Foundry 2 

Stearns 2714500099 SI003 Granit-Bronz Foundry 2 

Stearns 2714500101 ITOTA Fingerhut Fulfillment 4 2Stearns 2714500104 IOTHE Felling Products Inc 3 

Stearns 2714500107 ITOTA Belgrade Steel Tank Co Inc 2 3 1 03

Stearns 2714500108 ITOTA Merrill/May Inc dba Merrill Co 30 0 

Stearns 2714500113 IOTHE Park Industries 3 Stearns 2714500116 ITOTA DCI Inc 0 3 1 0 

Stearns 2714500121 GN001 TC\American Monorail Inc 5 1 4 

Stearns 2714500121 IOTHE TC\American Monorail Inc 3Stearns 2714500143 BO001 ISD 742 - St Cloud Technical H 2 0 2 

Stearns 2714500144 BO001 ISD 742 - St Cloud Apollo High 2 0 2 

Stearns 2714500149 NF001 Stommes Construction Inc - Non 16 1 1 14 1 Stearns 2714500151 ITOTA Minnesota Sawdust & Shavings C 1 4 1 

Stearns 2714500164 ITOTA Ben's Tool & Iron 4 0 

Stearns 2777700022 ITOTA Hardrives Inc - Plant 41 11 16 3 2 15 23 Stearns 2777700119 ITOTA Hardrives Inc - Plant 51 10 12 2 1 14 17 Stearns 2777700266 ITOTA American Iron 37 3 16 15 201

Stearns 27145POTW2RPS00 Melrose WWTP 3 7 

Stearns 27145POTW4RPS00 Cold Spring WWTP 1 2 

Stearns 27145POTW5RPS00 Holdingford WWTP 1 3 

Stearns 27145POTW7RPS00 Sauk Centre WWTP 1 2 Stearns 27145SW018 RPU00 Bueckers #1 4 

Page 36 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Stearns 27145SW035 RPU00 St. Augusta (Landfill, Inc.) 0 6 0 

Stearns 27145SW116 RPU00 Sauk Centre 12 

Stearns 27145SW172 RPU00 Paynesville 48 Steele 2714700005 ITOTA SPX Service Solutions 1 3 2 

Steele 2714700009 SV001 Northern Natural Gas Co - Owat 5001 40Steele 2714700022 ITOTA ATOFINA Chemicals Inc 6 13 0 8 

Steele 2714700027 ITOTA Jostens Inc - Owatonna 1 3 1 Steele 2714700029 ITOTA Truth Hardware Corp - Owatonna 3 5 0 5 

Steele 2714700039 ITOTA Cybex Inc 0 6 

Steele 2714700040 ITOTA Owatonna Public Utilities - We 7 0 0 4 Steele 2714700044 ITOTA Wenger Corp 1 6 1 0 

Steele 2714700045 ITOTA Blount Inc 0 6 1 1 

Steele 2714700056 IOTHE Owatonna Construction Co - Non 9 Steele 2714700056 NF001 Owatonna Construction Co - Non 13 1 1 12 1 Steele 2714700059 IOTHE Christian Bros Cabinets Inc 5 

Steele 2714700060 ITOTA AAF/McQuay International - Owa 1 8 1 

Steele 2714700061 ITOTA Viracon Inc 2 0 34 0 4 0 

Steele 2777700117 ITOTA Crane Creek Construction - Pla 39 4 6 3 53 5 Steele 2777700277 ITOTA Crane Creek Construction - Por 13131 71Steele 27147POTW3RPS00 Owatonna WWTP 5 12 

Steele 27147SW131 RPU00 Steele County 101 

Steele 27147XOWA RPU00 OWATONNA DEGNER RGNL 0 1 1 0 

Stevens 2714900003 ITOTA University of MN - Morris 6 0 0 3 

Stevens 2714900004 ITOTA Cargill Inc - Alberta 1 3Stevens 2714900012 ITOTA Riley Bros Construction Inc 2021 30Stevens 2714900013 SV017 Diversified Energy Co LLC 16 2 1 0 16 

Stevens 2714900013 SV019 Diversified Energy Co LLC 8 41 9 9 

Stevens 2714900016 ITOTA WestMor Industries LLC - Tank 0 4 1 00

Stevens 2777700261 FS002 Riley Bros Construction Inc - 2 Stevens 2777700261 SI001 Riley Bros Construction Inc - 3311 44Stevens 27149SW066 RPU00 Stevens County 16 

Stevens 27149XMOX RPU00 MORRIS MUNI

Stevens 27149XMOX RPU00 MORRIS MUNI 0 0 

Swift 2715100026 SV005 Chippewa Valley Ethanol Co LLL 11 1 1 0 11 

Swift 2715100026 SV006 Chippewa Valley Ethanol Co LLL 2 0 0 2 0 Swift 2715100026 SV008 Chippewa Valley Ethanol Co LLL 2 0 0 2 0 Swift 2715100026 SV013 Chippewa Valley Ethanol Co LLL 11 1 1 0 11 

Swift 27151SW108 RPU00 Benson 21 

Swift 27151XBBB RPU00 BENSON MUNI 0 0 

Page 37 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Todd 2715300001 ITOTA Land O'Lakes - Browerville 5 27 5 Todd 2715300004 SV001 Viking Gas Transmission - Cush 201 25 3 201 

Todd 2715300004 SV002 Viking Gas Transmission - Cush 203 25 3 203 

Todd 2715300004 SV003 Viking Gas Transmission - Cush 201 25 3 201 

Todd 2715300004 SV006 Viking Gas Transmission - Cush 25 0 0 1 20 0 Todd 2715300023 BO001 Long Prairie Packing Co - Long 3 0 0 3 

Todd 27153SW039 RPU00 Long Prairie 21 

Todd 27153SW078 RPU00 Killian 12 

Todd 27153X14Y RPU00 TODD FIELD 0 0 Traverse 27155XETH RPU00 WHEATON MUNI 0 Wabasha 2715700024 BO002 Lakeside Foods Inc - Plainview 6 1006 

Wabasha 2715700025 ITOTA Engineering Laboratory Design 2Wabasha 27157POTW1RPS00 Lake City WWTP 1 2 

Wabasha 27157POTW4RPS00 Plainview-Elgin Sanitary Distr 1 3 

Wabasha 27157SW154 RPU00 Wabasha County 16 

Wadena 2715900003 ITOTA Wadena Asphalt Inc 21Wadena 2715900022 NF001 Menahga Concrete Products - No 5 0 0 5 0 

Wadena 27159SW007 RPU00 Wadena 28 

Wadena 27159SW114 RPU00 Anderson 3 

Wadena 27159XSAZ RPU00 STAPLES MUNI 1 0 0 

Waseca 2716100013 SV014 Brown Printing Co - Waseca Div 7 258 0 7 

Waseca 2716100030 GN001 ITRON Inc 3 0 0 3 0 Waseca 27161POTW1RPS00 Waseca WWTP 2 4 

Waseca 27161SW100 RPU00 Waseca County 25 

Waseca 27161XACQ RPU00 WASECA MUNI 0 1 0 0 

Washington 2716300001 SV003 Andersen - Main 34 2 1 17 38 2 Washington 2716300002 SI020 3M Cottage Grove Specialty Add 6 2 0 10 3 Washington 2716300003 FS001 Marathon Ashland Petroleum LLC 36 

Washington 2716300003 FS002 Marathon Ashland Petroleum LLC 16 

Washington 2716300003 FS003 Marathon Ashland Petroleum LLC 15 

Washington 2716300003 FS005 Marathon Ashland Petroleum LLC 6 

Washington 2716300003 FS008 Marathon Ashland Petroleum LLC 15 

Washington 2716300003 FS009 Marathon Ashland Petroleum LLC 13 

Washington 2716300003 FS010 Marathon Ashland Petroleum LLC 32 

Washington 2716300003 FS011 Marathon Ashland Petroleum LLC Washington 2716300003 FS014 Marathon Ashland Petroleum LLC Washington 2716300003 FS016 Marathon Ashland Petroleum LLC 10 

Washington 2716300003 FS017 Marathon Ashland Petroleum LLC 33 

Washington 2716300003 FS018 Marathon Ashland Petroleum LLC 4 

Page 38 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Washington 2716300003 FS021 Marathon Ashland Petroleum LLC 6 

Washington 2716300003 FS022 Marathon Ashland Petroleum LLC 8 

Washington 2716300003 FS023 Marathon Ashland Petroleum LLC 13 

Washington 2716300003 FS024 Marathon Ashland Petroleum LLC 23 

Washington 2716300003 FS025 Marathon Ashland Petroleum LLC 23 

Washington 2716300003 FS026 Marathon Ashland Petroleum LLC 12 

Washington 2716300003 FS027 Marathon Ashland Petroleum LLC 2 

Washington 2716300003 FS029 Marathon Ashland Petroleum LLC 3 

Washington 2716300003 FS030 Marathon Ashland Petroleum LLC 6 

Washington 2716300003 FS031 Marathon Ashland Petroleum LLC 32 

Washington 2716300003 FS038 Marathon Ashland Petroleum LLC 2 

Washington 2716300003 FS046 Marathon Ashland Petroleum LLC 2 

Washington 2716300003 FS047 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS048 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS049 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS050 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS051 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS052 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS053 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS054 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS055 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS056 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS057 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS058 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS059 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS060 Marathon Ashland Petroleum LLC 11 

Washington 2716300003 FS063 Marathon Ashland Petroleum LLC 16 

Washington 2716300003 FS064 Marathon Ashland Petroleum LLC 5 

Washington 2716300003 FS065 Marathon Ashland Petroleum LLC 31 

Washington 2716300003 FS066 Marathon Ashland Petroleum LLC 4 

Washington 2716300003 FS068 Marathon Ashland Petroleum LLC 6 

Washington 2716300003 RPU99 Marathon Ashland Petroleum LLC 4 Washington 2716300003 SV001 Marathon Ashland Petroleum LLC 4000 40Washington 2716300003 SV002 Marathon Ashland Petroleum LLC 21 22 1 2 19 30 Washington 2716300003 SV003 Marathon Ashland Petroleum LLC 272 691 323 185 207 179 909 Washington 2716300003 SV004 Marathon Ashland Petroleum LLC 11111 61

Washington 2716300003 SV005 Marathon Ashland Petroleum LLC 41 2 2 3 21 2 

Washington 2716300003 SV006 Marathon Ashland Petroleum LLC 7100 71Washington 2716300003 SV007 Marathon Ashland Petroleum LLC 101 146 1 4 58 200 

Page 39 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Washington 2716300003 SV008 Marathon Ashland Petroleum LLC 6100 61Washington 2716300003 SV009 Marathon Ashland Petroleum LLC 10111 51

Washington 2716300003 SV010 Marathon Ashland Petroleum LLC 16211 82

Washington 2716300003 SV011 Marathon Ashland Petroleum LLC 17111 91

Washington 2716300003 SV012 Marathon Ashland Petroleum LLC 6000 60Washington 2716300003 SV013 Marathon Ashland Petroleum LLC 45 101 1 3 37 138 Washington 2716300003 SV014 Marathon Ashland Petroleum LLC 61 106 1 3 36 146 Washington 2716300003 SV015 Marathon Ashland Petroleum LLC 33 95 0 2 25 130 Washington 2716300003 SV016 Marathon Ashland Petroleum LLC 28 25 1 3 28 25 Washington 2716300003 SV017 Marathon Ashland Petroleum LLC 7101 71Washington 2716300003 SV018 Marathon Ashland Petroleum LLC 9101 91Washington 2716300003 SV019 Marathon Ashland Petroleum LLC 2 0 0 2 0 

Washington 2716300003 SV020 Marathon Ashland Petroleum LLC 2000 20Washington 2716300003 SV021 Marathon Ashland Petroleum LLC 5111 51Washington 2716300003 SV022 Marathon Ashland Petroleum LLC 4101 41Washington 2716300003 SV023 Marathon Ashland Petroleum LLC 3 1 3 3 1 Washington 2716300003 SV050 Marathon Ashland Petroleum LLC 16111 81

Washington 2716300003 SV062 Marathon Ashland Petroleum LLC 3 9 12 3 11 Washington 2716300004 ITOTA Minnesota Correctional Facilit 2 0 6 1 0 Washington 2716300014 ITOTA Tower Asphalt Inc 23 7 3 8 32 9 Washington 2716300025 SI006 3M - Cottage Grove Corporate I 64 0 0 4 0 123 1 

Washington 2716300039 BO001 Lakeview Hospital 2 0 2 

Washington 2716300049 ITOTA Black Diamond Inc 1 9 2 

Washington 2716300050 ITOTA Commercial Asphalt Co - Plant 10 1 12 3 14 1 

Washington 2716300051 ITOTA NRG Energy Resource Recovery - 3Washington 2716300056 ITOTA Ecowater Systems Inc 1 27 2 

Washington 2716300080 SI999 3M - Cottage Grove - Tape Manu 2 

Washington 2716300109 NF001 Custom Crushing & Screening In 5 0 0 4 0 

Washington 2716300112 ITOTA Marathon Ashland Petroleum - C 9 

Washington 2716300127 ITOTA Advance Corp - Cottage Grove 3 6 1 

Washington 27163POTW1RPS00 Met Council - Eagles Point WWT 3 7 

Washington 27163POTW2RPS00 Met Council - St Croix Valley 5 12 

Washington 27163SW001 RPU00 Washington County 5 

Washington 27163SW001 RPU00 Washington County 1 0 20 0 1 0 

Washington 27163X21D RPU00 LAKE ELMO 1063 10Watonwan 27165POTW1RPS00 Madelia WWTP 1 2 

Watonwan 27165POTW2RPS00 St James WWTP 1 3 

Watonwan 27165SW081 RPU00 Watonwan County 52 

Watonwan 27165XJYG RPU00 ST JAMES MUNICIPAL 0 0 

Page 40 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Wilkin 27167SW022 RPU00 Barnesville 4 Winona 2716900007 ITOTA Midwest Metal Products Inc 0 8 1 0 

Winona 2716900010 ITOTA Winona State University 14101 71Winona 2716900013 SV005 Froedtert Malt - Winona 11 14Winona 2716900013 SV006 Froedtert Malt - Winona 10 12Winona 2716900013 SV026 Froedtert Malt - Winona 4 1 0 4 

Winona 2716900018 ITOTA Brom Machine & Foundry Co 0 

Winona 2716900022 ITOTA Winona River & Rail Inc 6 Winona 2716900024 ITOTA United Machine & Foundry 1 0 3 1 0 Winona 2716900036 ITOTA Fusion Coatings Inc - Div of R 1 3 1 2 

Winona 2716900049 ITOTA Modern Transport Terminal 6 Winona 2716900052 ITOTA Community Memorial Hospital 8000 50 Winona 2716900067 ITOTA Technical Die-Casting Inc 4323 54Winona 2716900078 BO001 St Mary's University 2 0 2 

Winona 2716900078 GN001 St Mary's University 2Winona 27169POTW2RPS00 Winona WWTP 5 13 

Winona 27169POTW3RPS00 Whitewater River Pollution Con 1 2 

Winona 27169SW025 RPU00 Winona County 111 

Winona 27169SW026 RPU00 Geisler 2 

Winona 27169XONA RPU00 WINONA MUNI-MAX CONRAD FLD 0 Winona 27169XONA RPU00 WINONA MUNI-MAX CONRAD FLD 1 0 Wright 2717100019 SV001 NSP dba Xcel Energy Monticello 2 1 0021 Wright 2717100024 ITOTA Mid-Minnesota Hot Mix - Annand 7211 93Wright 2717100025 ITOTA Omann Brothers Inc - St Michae 6411 96Wright 2717100030 ITOTA Munson Lakes Nutrition LLC - H 4 0 5 

Wright 2717100061 IOTHE Aero Fiberglass Co 3Wright 2717100077 IOTHE Star West Chev-Olds-GEO 3Wright 2717100080 NF001 Buffalo Bituminous - Nonmetall 3 0 2 0 

Wright 2717100081 NF001 Annandale Rock Products - Nonm 4 0 0 4 0 

Wright 2717100085 SV002 Minnesota Diversified Products 53 

Wright 2717100086 ITOTA Twin City Die Casting Co - Mon 1 0 2 1 Wright 27171POTW1RPS00 St Michael WWTP 1 2 

Wright 27171POTW2RPS00 Monticello WWTP 1 3 

Wright 27171POTW6RPS00 Buffalo WWTP 2 6 Wright 27171POTW9RPS00 Delano WWTP 1 2 Wright 27171SW044 RPU00 Lindenfelser 4 

Wright 27171SW044 RPU00 Lindenfelser 1 0 14 0 1 0 

Wright 27171SW059 RPU00 French Lake 11 

Wright 27171SW060 RPU00 Forest City Road (Yonak) 54 

Page 41 of 42
COUNTY FACID STKID FAC_NAME NOX SO2 VOC PM25 NH3 NOX SO2 

Minnesota Non-Utility Point Sources 2002 Annual Emissions in Tons 2018 Annu

Wright 27171SW060 RPU00 Forest City Road (Yonak) 2 1 33 1 2 1 Wright 27171SW117 RPU00 Lindala 33 Wright 27171X8Y2 RPU00 BUFFALO MUNI 0 2 1 0 

Yellow_Medic 2714500150 IOTHE Martin Marietta Materials - No 12 

Yellow_Medic 2717300008 ITOTA ADM - Prairie Grain Partners - 35 0 1 8 19 

Yellow_Medic 2717300037 ITOTA ADM Corn Processing - Burr 9 

Yellow_Medic 27173SW042 RPU00 Yellow Medicine 20 

Page 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): 

Facility ID County 

Emission 

Unit Unit Name Stack ID 

FIXED 

Stack ID Facility Name 

X 

(LCCm) 

Y 

(LCCm) 

2703100001 Cook EU001/2/3 SV001/2/3 

Minnesota Power – Taconite Harbor 460845.4 851070.0 

2713700063 St_Louis SV016 Keewatin Taconite Operations 298631.2 828978.7 2713700063 St_Louis SV020 Keewatin Taconite Operations 298631.2 828978.7 2713700063 St_Louis SV022 Keewatin Taconite Operations 298631.2 828978.7 2713700063 St_Louis SV024 Keewatin Taconite Operations 298631.2 828978.7 2713700063 St_Louis SV026 Keewatin Taconite Operations 298631.2 828978.7 2706100004 Itasca EU001/2 SV001 SV003 Minnesota Power-Boswell 2713700063 St_Louis EU030 

Grate Kiln - Indurator Waste Gas, Phase II SV030 SV051* Keewatin Taconite Operations 298631.2 828978.7 

2713700063 St_Louis SV032 Keewatin Taconite Operations 298631.2 828978.7 2707100002 Koochiching EU430 Boiler 2 SV431 SV430 

Boise Cascade Corp – International Falls 

2713700022 St_Louis EU003 boiler 3 SV005 SV001 Duluth Steam Cooperative Assoc 2703700011 Dakota EU111 

EU-45002 SRU3 only SV094 SV093 

Flint Hills Resources LP – Pine Bend 

* Changed to SV051 in 2018 only, see text associated with Table 3 below. 
Table 2: Addition of Minnesota Sources to 2018 Model Input File*:

Facility_ID Stack ID Facility Name 

X 

(m)/(lat) 

Y 

(m)/(long) 

Stack Hgt

(m) 

Stack 

Dia. 

(m) 

Exit Temp

(K) 

Exit Vel.

(m/hr) 

Flow Ra

(m3/hr) 

2707500003 SV261 Northshore Mining Co - Silver 435385.5 824585.8 49.4 1.8 333.2 60241 2707500003 SV114 Northshore Mining Co - Silver 435385.5 824585.8 16.9 1.0 435.2 51133 2707500003 SV105 Northshore Mining Co - Silver 435385.5 824585.8 40.8 1.8 333.2 60241 2707500003 SV266-8 Northshore Mining Co - Silver 435385.5 824585.8 49.4 1.8 333.2 45756 2713700113 SV046 Evtac Line 1 332382.3 817667.5 42.7 3.0 327.1 85954 2713700318 SV999 Mesabi Nugget 47.59 -92.19 60.0 4.2 358.0 90000 90002706199999 SV999 West Mine 47.38 -93.22 60.0 4.7 318.2 54900 54902713799999 SV999 East Mine 47.60 -92.14 38.1 1.6 370.4 71316 7131

Facility_ID Stack ID Facility Name 

NO 

(ton/d) 

NO2 

(ton/d) 

PAR 

(ton/d) 

TOL 

(ton/d) 

FORM 

(ton/d) 

NR 

(ton/d) 

PEC 

(ton/d) 

2707500003 SV261 Northshore Mining Co - Silver 0.2452 0.0272 0.0016 0.0002 0.0003 0.0040 0.0002707500003 SV114 Northshore Mining Co - Silver 0.7276 0.0808 0.0047 0.0005 0.0009 0.0123 0.0012707500003 SV105 Northshore Mining Co - Silver 0.7561 0.0840 0.0041 0.0005 0.0008 0.0106 0.0012707500003 SV266-8 Northshore Mining Co - Silver 0.4401 0.0777 2713700113 SV046 Evtac Line 1 7.0404 0.7823 2713700318 SV999 Mesabi Nugget 1.8819 0.2091 2706199999 SV999 West Mine 3.1542 0.3505 2713799999 SV999 East Mine 0.3166 0.0352 

∗ Add all missing Northshore Mining Silver Bay 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 up again in 2004, thus was ad∗ 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 Name 

Pollutant 

ID 

Stack

 ID 

Orig. 

2002 

(tons/day) 

Orig. 

Growth

Factor 

Orig. 

Grown 

2018 

(tons/day) 

Orig. 

Control

Factor 

Orig. 

 Control 

2018 

(tons/day) 

New 

Growth

Factor 

New 

Control

Factor 

C

2

(to

2713700005 US Steel Corp - Minntac SO2 SV103 SUM: 1.224 2713700005 US Steel Corp - Minntac NOX SV118 9.133 1.000 9.133 1.000 9.133 1.000 0.700 2713700005 US Steel Corp - Minntac NOX SV127 6.178 1.000 6.178 1.000 6.178 1.000 0.700 2713700005 US Steel Corp - Minntac NOX SV144 8.907 1.000 8.907 1.000 8.907 1.000 0.700 2713700005 US Steel Corp - Minntac NOX SV151 6.649 1.000 6.649 1.000 6.649 1.000 0.700 2713700061 Hibbing Taconite Co NOX SV024 6.046 1.000 6.046 1.000 6.046 1.000 0.870 2713700061 Hibbing Taconite Co NOX SV028 5.280 1.000 5.280 1.000 5.280 1.000 0.870 2713700061 Hibbing Taconite Co NOX SV029 5.156 1.000 5.156 1.000 5.156 1.000 0.870 2713700062 Ispat Inland Mining Co NOX SV017 SUM: 12.921 2713700062 Ispat Inland Mining Co SO2 SV017 SUM: 0.616 2713700063 

Keewatin Taconite Operations NOX SV051 SUM: 17.614 

2713700063 

Keewatin Taconite Operations SO2 SV051 SUM: 2.049 

2713700113 

EVTAC Mining - Fairlane Plant NOX SV049 SUM: 4.754 

2713700113 

EVTAC Mining - Fairlane Plant SO2 SV049 SUM: 8.849 

∗ US Steel Minntac new SO2 control added for MACT required installation of wet scrubber; ∗ US Steel Minntac new NOX control proposed as BART involving burning wood and natural gas on lines 4 an

lines 6 and 7 after 2002, a 20% reduction from fuel blending (although not specifically proposed as BART th

inventories; and a 10% reduction from installation of lo-NOX burners in pre-heat zone in lines 4 through 7; 

∗ Hibbing Taconite control proposed as BART reflecting fuel use efficiency projects completed in 2005 and 20∗ Keewatin Taconite Operations stack SV051 with associated new wet scrubbers CE110 and CE111 connected 

multiclones CE030 and CE031 as of October 1, 2005, to comply with MACT. SV051 replaces previous stac

∗ 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 SO2 NOX NH3 PM25 PM10 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 SO2 NOX NH3 PM25 PM10 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 Directors 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 5th generation Mesoscale Model version 3.6.3 

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

LADCO/Midwest RPO is the Red Hat version 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 PREGRID, 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 model 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 completed 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 meteorological analyses data such as NCEP or Eta 

to an intermediate data format that the REGRIDDER processor can 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 definition 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 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 

observation data. Since these 

observations are incorporated 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 levels and interpolates 

that data to the vertical grid defined by 

the user. The vertical grid is defined in 

terms of sigmas, where 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 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 was only used for the 36 km annual simulation of 2002. This option is no 

longer recommended since it has been shown 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 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 use 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, diffusion using perturbation temperature, 3D 

coriolis force, and upper radiative boundary 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 minute 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 hours at the end of the simulation that are 

not used. 

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

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

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

summer and 2003 annual simulations will be modeled using 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 MM5 will be analyzed 

qualitatively by comparing 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 Data 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 quantify 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 will compare model predictions to Techniques Data 

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

The MM5 model outputs approximately 15 meter predictions while observations are 

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

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

second time step) as opposed to the values 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 will 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 better 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 converting 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 will 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 correspondence between the mean prediction 

and the mean observation, with lower numbers 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 tends to inflate RMSE, 

particularly when extreme values are present. 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 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 

interpreted as a measure of potential accuracy 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 the 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 diagnosed 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 are calculated inside the CMAQ 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 takes the PBL height output 

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

diffusivity coefficient for CAMx4, REMSAD, and CMAQ are 0.1 m2

/s. The CMAQ 

coefficient is established (and modified from the default of 1.0 m2

/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 m2/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 meteorological processing programs for each 

model not only translate data, but also diagnose 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 layer 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 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 processes, 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 National RPO Grid, Presented at the Ad-Hoc 

Meteorological Modeling Meeting, 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 Conservation 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. Alapaty, 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 Directors 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 Consortium (LADCO) and the Midwest Regional 

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

LADCO/Midwest RPO is the Red Hat Linux operating 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 completed by Matthew Johnson at Iowa 

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

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

The 36 km non-summer portion of the annual 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 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. 

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 meteorological 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 the source of sea surface temperature. 

The ETA analysis files with the extension “.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 observation data. Since these observations 

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

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

and without RAWINS objective analysis demonstrated 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 top of the MM5 simulation 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, particularly 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 simulations 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 use linear interpolation. Other important 

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

advection using potential temperature, diffusion using perturbation temperature, 3D 

coriolis force, and upper radiative boundary 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 minute 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 quantitatively 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 index of agreement. 

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

Canada surface hourly observations (NCAR dataset ds472.0). 

The MM5 model outputs predictions approximately 15 meters above the surface 

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

adjustments to the MM5 estimates to approximate 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 estimated 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/products/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 correspondence between the mean prediction 

and the mean observation, with lower numbers 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 tends to inflate RMSE, 

particularly when extreme values are present. 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 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 

interpreted as a measure of potential accuracy 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 the 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 performance in the Great Lakes region is the existence of 

a cool diurnal temperature bias in the winter 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 adequately 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 plots 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 similar to the Great Lakes except where noted. 

The Ohio Valley has slightly worse performance 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 southern Plaines/Texas region 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 field performance compared to the other 

Eastern regions. 

Figure 3. Daily humidity bias and gross error (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 direction 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 simulation 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 Midwest region. The humidity error is highest 

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

the winter and slight warm bias in the summer. Temperature 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 problems 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 error (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 direction 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 totaled for each 36 km grid cell in the 

Eastern United States by month. The National Weather Service 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 overwriting 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-observation time series plots in Appendix 

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

sub-region of interest. A summary of performance 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 over-predicting night 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 between the previous 

annual simulation and the current annual simulation with mosaic difference plots. 

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

particular in the winter. Wind speed performance improved and wind direction 

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

same in both simulations. The difference in model performance is largely due to the 

inclusion of surface nudging in the previous 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 necessarily 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 domain. 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 degraded 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 16th, 18th, 24th, and 27th and degrade IOA. 

Humidity root mean square 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

 6/11 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 6/30

 7/ 1

g/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. The 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

 6/25

 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 National RPO Grid, Presented at the Ad-Hoc 

Meteorological Modeling Meeting, 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 Conservation 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. Alapaty, 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 Analysis 
January 2002
February 2002
March 2002
April 2002
May 2002
June 2002
July 2002
August 2002
September 2002
October 2002
November 2002
December 2002
January 2003
February 2003
March 2003
April 2003
May 2003
June 2003
July 2003
August 2003
September 2003
October 2003
November 2003
December 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

1/11

 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

 1/31

m/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

 1/17

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

 1/31

m/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

 1/31

deg

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

 1/11

 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

 1/31

deg

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

 1/25

1/26

1/27

1/28

1/29

 1/30

 1/31

K

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

 1/25

1/26

1/27

1/28

1/29

 1/30

 1/31

K

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

 1/25

1/26

1/27

1/28

1/29

 1/30

 1/31

g/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

 1/25

1/26

1/27

1/28

1/29

 1/30

 1/31

g/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

 6/11

 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

 6/30

m/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

 6/16

 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

 6/30

m/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

 6/13

 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

 6/30

deg

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

 6/10

 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

 6/30

deg

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

 6/30

K

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

 6/30

K

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

 6/30

g/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

 6/30

g/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

 7/10

 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

 7/26

7/27

 7/28

 7/29

 7/30

 7/31

m/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

 7/10

 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

 7/26

7/27

 7/28

 7/29

 7/30

 7/31

m/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

 7/10

 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

 7/26

7/27

 7/28

 7/29

 7/30

 7/31

deg

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

 7/10

 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

 7/26

7/27

 7/28

 7/29

 7/30

 7/31

deg

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

 7/26

 7/27

 7/28

 7/29

7/30

7/31

g/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

 7/25

7/26

7/27

7/28

7/29

 7/30

 7/31

g/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

 7/25

7/26

7/27

7/28

7/29

 7/30

 7/31

K

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

 7/25

7/26

7/27

7/28

7/29

 7/30

 7/31

K

ObsTemp 36 km Pred_T 12 km Pred_T

Ohio Valley July 2002
APPENDIX C 

 Performance for Summers 2001, 2002, and 2003 
JUNE 2001
June 2001
JULY 2001
July 2001
AUGUST 2001
August 2001
JUNE 2002
June 2002
JULY 2002
July 2002
AUGUST 2002
August 2002
JUNE 2003
June 2003
JULY 2003
July 2003
AUGUST 2003
August 2003
Upper Midwest High Ozone Da

• 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 
= - - 2"

,.5

,..

• .5

3..0 o.>j1

Tempel"atul"e RMSE: Dine,""nee' ohioval GI"id

Chang!! In D4IIily SlatJsJcal Perform4llnce: 36 1m

(nt$l~IvC'viLIul:s IndlCllll: curren' slJnulllilion Iml)rOV1:m~r1l1

~2.S

-<l.S

~2.0

...

K ~3.0 o.y31":i~=--o_::-""-...=--o....:::,'"""_::-""""-:::::;--;=-=.....,=-d JlIlIUlllY 1.2002 0:00:00

tAln.-I..sat('1.IS~tAD. l."at(II~)

Tempe,"atul"e .RMS]~ Diflel"enee· eeno"apS G'"id

~2.S

K ~3.0 o.y31'=,..-,=--.:_::--.:...=--o....=,:--.:_=-,"'.:-,-,.... =-,a;;:',.-,"';;:'..J..-,=-=

JlInulllY 1.2002 0:00:00

tAln. -0.8 at(lnl. tAD. 1.8al(3..'tI)

..- /1411 Mar.Alr'l JUI ""OI$iI Mp OCt

JlInulllY 1.2002 0:00:00

tAln.-ljJat('1.ISl.tAD.I.Aat(lI,3)

Tempe,-atu,"e RMSE: Dif"fel"enee - eeno"apN GI-id

~..s

2"

,.5

-- ,..

• .5

...

-<l.S

~2.0

~2.S

-

...

-

-

f---'---------,__

--.-"I------l

- ,..

,.5

• .5

- 3..0 o.>j1

Tempe.....ture R MSIi: Dille'"enee - gl Grid

(nt$l~~:~~~~~7r~~%~~a~~::~~:11nour;:~~~el~::':m~nl]

-

-2.0

-- -

-<l.S

~2.S

2"

...

K ~3.0 o.y31'=,..-,=--.:_::--.:...=--o....=,;-._=-,"'.:-,--;::""-;=-""",,..J..-,=-=

-- JlInulllY 1.2002 0:00:00

tAln.·ljJat('1.ISl.tAD.1.7al(lI,3)

JlInulllY 1.2002 0:00:00

••tln_ ••.7 4IItl.... tS\ ••f4l)(w 1..3 alf1....sl

TempemtUl"e RMSE DHle,"ence' NE: GI"id

Chang!! In D4IIily SlatJsJcal Perform4llnce; 36 1m

(nt$l~IvC'viLIul:s IndiCllll: curren' sltnuillilion iml)rOV1:m~nl]

2"

,.5 - ,..

• .5

... - -<l.S

- =- 2"

,..

,.5

• .5

~2.S

-<l.S

-2.0

...

3..0 o.>j1r===I.r._;;;;;;:========I==

Chang!! In D4IIily SlatjsJcal Perform4llnce; ~i6 1m

(nt$l~IvC'viLIul:s IndlclIill: curn:n' shnuillilion iml)rOV1:m~rll]

Tempel"atul"e RMSE: Din·el"enee· idAllantie G'"id

K ~3.0 o.y31'=:"o=-._;;;;-....=--o....,;;;-"'_;;-,:;::~,.....,.... ""-.o;,....,,..-J.......,.....,r;;;!

JlInulllY 1.2002 0:00:00

Mln.·I":; alfI2.11.\ M4l)(w 1."\alf1..131

JUI 1iIIp OCt

JlInulllY 1.2002 0:00:00

tAln.-lhatl:?'.'1lM.xw 12aJfl2:.II'

Chang!! In D4IIily SlatJsJul Perform4llnce; 36 1m

(nt$l~IvC'viLIul:s IndlCllll: curren' shnuillilion iml)rOV1:m~nl]

Tempel"atul"e RMSE: Din-el"enee· S]~ <,'"id

• .5

...

~2.0

2"

-<l.S

,..

~2.s

,.5

... -- ,.5

2"

3..0 o.>j1r=::JI••r::==========t=::l1llll

TempeJ"atuy"e RMSIi: DifTe,"enee - Iowa GI-id

Change In D4IIIly SlatJsJul Perform4llnce; ~i6 1m

(nt$l~IvC'viLIuts IndlClIilt curn:n' shnuillilion iml)rOV1:m~rll]

• .5

,..

~2.S

-2.0

-<l.S

...

-

=

- -

•

,

'.5

.. - 2"

..5

3..0 o..t 1

-1.5

Tempe,-atUl"" RMSE: Oille,"ence - l'acilkNW G'"id

Change In Dally Slatjstcal Perform4llnce: 3S km

(1II:gil.tlvC' vlllun Indlcillll: cu:rn:nt siJnulilitlon ImprOV1:m~nU

-1.0

-<l.S

..z.0

...

K -J.O 0fIJ ~l,:;;;;;-;""._".",""'M"";;,~_,,,-:,,"~;-; .... ;-"''',..-J..,,;;;!

Jllnuary 12(I()20:OO:OO

Mln--o..8411I(?..s)..Max- l.oat(IO.3)

Change In Dally Slatjstcal Perform4llnce: 3S km

(nl:gil.tlvC'vllllln Indlcillll: cu:rn:nt siJnulilitlon imprOV1:m~nn

2"

Temp","atUl"" RMS:E Din·el-enc,,· or1h G'"id

Jllnuary 12(1()2 0:00:00

••Un- ·1.'14111(.... '8). M.ax- 1..3 U(ZoS)

'.5

,..

• .5

-<l.S

-1.0

-1.5

...

..z.0

...

Jllnuary 12002 0:00:00

Mln- ·1.0 4111 (S.2).. Max- 1.1 at (120)

Chang!! In Daily Slatjstcal Perform4llnce: 3S km

(nl:gil.tlvC'vllllll:s indlcillll: curn:nt siJnulilitlon ImprOV1:m~nn

'.5

2"

,..

• .5

-<l.S

-1.0

-1.5

..z.0

...

...

,..

-1.0

3..0 o..t 1

-<l.S

,.5

2"

Tempel-atuy"e RMSE DilTel"enee - Dessel"tSW Gd(

Change In 04ll11y Slatjstcal Perform4llnce: 3S 1m

(m:gil.tlvC'vlllul:s Indlcillll: curn:nt siJnulilitlon imprOV1:m~nn

-1.5

• .5

...

..z.0
---

-

-- -

0-0

0.'

:2..0 ~1

1.'

0.0

'.3

,.0

-1.7

Humidity RMSE Din'e,"ence' ohioval G,"id

-1-0

Chan!J1! In Oillily Slatlslcal Performillnce: 36 1m

(nl:$IlLtlvC'viLIuI:J IndlciIIll: curren' JlJnuhllion lmprO't':mC"nl]

.1.0

g!'M~.O o.t[ 31..,_=--;:::-::_=-""...~~_!!!,~_::-,"'",....,.... =-=,...".,....-=,...,-= JllnulIIY 1.2002 0:00:00

Min- -0.7 ill' (3..8). Wax.. O.lil;ll (8J!1:1)

-1.3

-1.7

0.'

- Humidity RMSI~DHle,"ence-ceOl"apS G,"id

-0.3

- 0.0

'.3

0-0

1."

,.0

.1.0

g!'M~.O o.t[31"'_=---;;:,""""_=-....~~....!!!,~_=-,"'.;-, ..... =.a:;:',....,"';;:'..J...,=-=

JllnulIIY 1.2002 0:00:00

Min- -od'll ill' (....l. Wax_ 0.$;11 (8.31)

... .... JllnulllY 1.2002 0:00:00

Min.. -<1-2 at (R.:Jl. tAD" o.5.at (8)1)

. -_.

-- - -- -- - - ,

- N' _ --.AIr'I

-

=JUI a "" "'. Do<

:2..0 Do>

1.'

'.3

,.0

0.'

0-0

0.0

-0_,

.1.0

-1-0

-1.7

Humidity RMSE:Diffe,"ence - ceOl"apN Grid

..z.O o.t[3 ....

--

-

-

,.0

0-0

'.3

0.'

0.0

1."

-0.3

-1.7

Humidity RMS.~: Diffe,"ence - gl Grid

(nt$l~~:~~~~~7r~~%~~a~~::~~:11nour;:~~~el~::':m~nl]

-I.J

.1.0

g!'K~.o o.t 31"'_=---;;:,""""_=-....=--o....:::,:-._=-",'!!!.!",'"'.... =.a:;:',....,"';;:'..J...,=-= JlIlIUlIIY 1.2002 0:00:00

Min.. -o..B at (:i.fll. tAD" 0.5 .Ill (8..$)

Humidity RMSE: Diffe,"ence - Iowa G,"id Humidity RMSE Din·e,"ence· SE G,"id Humidity RMSE Din'e,"ence· idAtiantic G,"id Humidity RMSE Difle,"ence' NE G,"id

Change In D4IIIly SlatJsJcal PerformJlnce; ~i6 1m

(nl:srlLtlvC'viLIul:I IndlclIh: curn:n' Ihnulillion impcrOV1:rnc-rll]

Chan!J1! In Oillily Slatblul Performillnce; 36 1m

(nl:$llLtlvC'viLIul:J lndlull: curren' Jltnuhllion imprO't':mC"nl]

Chan!J1! In DilIlly Slatjslcal Performillnce; ~i6 1m

(nl:$IlLtlvC'viLIuI:J IndlciIIll: curn:n' Jltnuhllion imprO't':mC"nl]

Chan!J1! In Oillily Slatlslcal Performillnce; 36 1m

(nl:$llLtlvC'viLIuI:J IndiciIIll: curren' Jltnuhllion imprO't':mC"nl]

2..0 o.>j 1 r=====--=--=--=--=--===::J_==::::::j==::::j

..z.O o.rt31

1

;;;;-;;;;-",,~~"~~;~..,...c;;l,=j g.!Kg '- ,...., • Mar """ Jill P Cltl

JlIlIUlIIY 1.2002 0:00:00

Min.. -(\-'1 at (R..'J~ tAD" 0.8 at ($..2)

-

•

--

•

'.3

0.0

0.'

,.0

0-0

-1.7

-1.3

:2..0 1 r=====--=--=--=--=-==:;;;;;:===::::::j===l 1.'

-0.3

.1.0

g!lI~'O o.t[31"_=--;;;;-;:_......;;-;;;......,,..,._..,!!!,,~.... =-;-....,""'..,.....,...-;;;;!

JlIlIUlllY 1.2002 0:00:00

••tln_ -0.11 ilIt IS.9'\. Max.. 12 ;111"1.1 Sl

-

•

•

- --

.-

2..0 Do>'

1."

'.3

,.0

0.'

0-0

0.0

-0.3

.1.0

-1.3

-1.7

g!lI~'O o.t[31"_;;;;-'N;;;''' -........._;n-A_...''.'!!.!'','-;.... =-;;o;,,.-,"'''',.....,=-r;;;! JlIlIUlllY 1.2002 0:00:00

Min.. -<1..3 al '11.18\ M.x- O.fi al (':1.141

.... JlIlIUlllY 1.2002 0:00:00

tAln.. -0..8 at Ian'\. tAD" 0.8.at (8.28 \

. --

_I

- ,

- ND •iWIIr • .... -'" 5IIp OWICI .... Do<

:2..0 Do>

1.'

'.3

,.0

0.'

0-0

0.0

-0.3

.1.0

-1.3

-1.7

..z.O o.t[3 ....

- --

-

0.'

0.0

'.3

0-0

,.0

1."

-1.7

-I.J

-0.:1

.1.0

Humidity RMSE Dil'fe,"ence - Desse,1SW G,"id Humidity RMSE:DilTe,"ence- SW Grid Humidity RMSE Din'er"ence - NOJ1h Grid Humidity RMSE OilTerence - l'acilicNW Gl"id

Change In Dally Slatjstcal Perform.ance: 3S km (n1:gil.tlvC'vllluI:J IndlcilllI:: current JiJnuhlUon imprOvt:mc-nl]Chang!!: In Dally Slatj51cal Performillnce: 36 1m

(lII:gidlvlC'vllluI:J IndlciIIll: cu:rn:n' JiJnulilition imprO't':mC"nl]

Chang!!: In Dally Slatj51cal Performillnce: 36 1m

(nl:gidlvlC'vllllll:J IndlciIIll: curn:n' JiJnulilitlon imprO't':mC"nl]

Chang!!: In Dally Slatj51cal Performillnce: 36 1m

(1Il:gidlvlC' vlllul:J IndlciIIll: cu:rn:n' JiJnulilition imprO't':mC"nl]

g,Ilt-:'O 0eq31':;;;;-;;;;-._........_;n-A_....':;;;.;','-;.... =...;0;,,.-,"".....,,0;;1

January 12(1()2 0:00:00

Mln- ·l.s at (8..2"1).. Max- O~1 at(12.16)

,.0

--- 0-0

0.0

0.7

,.3

-1.3

-0.7

-1.0

'.0

-

·1.7

g.r;:'O 0fI/31':;;;;;-;....._......;;-;;;_;;-:;;_;;-,;;;";-;.... =.....,,,"'..,.....,...-;;;;!

January 1..20020:00:00

tAln... -0." at (S28~ tAD- O.8.at (7.19)

•

-

-

,.3

-1.3

0.'

-1.0

0.0

-0.7

0-0

-0.3

·1.7

0.0

0.7

'.0

0-0

,.3

-0.7

-1.3

-1.0

·1.7

g,r;:'O o.t 31':;;;;;-;....._..........;;;....;;,~_....,;;;";-;....=.....,,,...-"..,,;;;!

Jl1nuary 12002 0:00:00 Min... -o.s at(S.I). Mn- OOS a.1 (11.1il1)

- -1.3

-0.7

-1.0

0-0

-0.:1

0.'

,.3

,.0

0.0

·1.7
Wind Spd RMSE Difference - gl G,"id

Change In Dally Stallslcal PertOmlillnce: 36 tim

(n1:glllm vQlUI:~ Indlcah: CU-fn:nt ~i1nul"Uon tmprOV":rnC'nl]

Wind Spd R MS.Ii: Diffe,...nce - cenrapN a ,"id

Change In Dally Stallslcal Perlormillnce: 36lrim

(m:gllliv'C' v>t!uI:'li Indlcr.lh: cu.rn:nt 'lihnul..Uon lmprO'V":rnC'nl]

Wind Spd R MSE Dilference - cenrapS a rid

Chang! In Dally Statlsleal Performanc!: 38lm

(nl:gllllvt' vQlUl::O indlca!i: curn:nt :ohnulaUon tmprOV":rnC'nl]

Wind Spd RMSE Diflerence - ohioval (jrid

Chang! In Dally Statbtcal Performanc!: 38 km

(nl:$I'lIllvevl:lllli:s Indlca'l: curn:nt shnull;ltion impr~ml:nt)

L..o o.y 1 1..0 Ooloyl I" .."

0'"

0.7

0"

0" -- 0..

-0.'

-O.S

-0.7

-0.'

0.7

0"

0..

-0.'

-O.S

-0.7

-0.'

1-"----_._---1--1

-

-

0.7

0"

0..

-0.'

-O.S

-0.7

-0.'

_._---------I~-I

-._--.--------.--+-.--1

- -

..,. 0'"

0.7

0"

0..

0..

0"

00.:(:

-0.'

-O.S

-0.7

-0.' -- Januflry 1.20020;00;00 t.Un.. 00.2 .lIIH.rt.ltI\t.t.,. o..:s at 12..511 Jl;l:nuary 1.2002 0;00;00 t.nn.. 00~' at (.... 18). ••(.IJ(wo 0"" at (ZoS)

Wind Spd RMSE Diflel"ence - NE arid

Change In Dally Statbtcal Performanc!: 38 km

(nl:$I'lIllvil:'vl:lllli:s indlcall: curn:nt liitnuliilion impr~ml:nt)

0.7

..,.

0..

0..

-0.'

-0.'

-0.7

-O.S

-

- --

Wind Spd RMSE Difle,"ence - MidAtl3ntic G,"id

Change In Dally Statlsleal Performance: 36lm

(nl:glllivl: V>IJUI::O indlcah: curn:nt :ohnulaUon tmprOV":mC'nl]

1..0 Ooloyl

0..

0.7

0"

-0.'

-O.S

-0.'

-0.7

-

Wind Spd RMSE Difle,"ence - S.Ii: Grid

Change In Dally St..tJ,lcal Performance: 36lrim

(m:gll.tivl: v>IJuI::o indlca!1: curn:nt :ohnulaUon tmprOV":rnC'nl]

1..0 o.y 1

0..

0.7

-O.S

-0.7

-0.'

."

-0.'

- - -----------'"""1------1 --

-

-0.'

0"

-O.S

0.7

-0.7

1..0 o.y 1

-0.'

0..

Wind Spd RMS.Ii: Difference - Iowa a,"id

Change In Dally St..,tblcal Performance: 36 tim (n1:gll.tNl: villul:~ indlcah: cu.rn:nt ~hnul"Uon tmprOV":rnC'nl]

J"nuflry 1.20020;00;00 Uln.. ..-u;; lOt (it 111\ U.-v. n s ",I? s\

January 1.20020;00;00 tAln.. 00.3 at (4..e). t,t.lJ(wo O..l at (1l..l8) January 1.20020;00;00 lJlln.. .n~:>.t'7:7\ u~ n~",J/II_lHI'January 1.2002 0;00;00 Uln... .n <1 :lit I? 4\ ...."'..._ n I :lit III ~\

Wind Spd RMSE Difference - SW (,rid Wind Spd R MSE Din"e,'ence - Dessel"tSW G,'id Wind Spd RMSE Dill'e,"ence- No,'th (j"id Wind Spd RMSE Dilference -l'acilicNW G,"id

1-0 O-Y'

Change In Dally Slatlucal Performance: 38lm (ni:grallv\:' Yldui:'li indlc..li: cl.Irn:n' 'liiJnulflUon lmpr~ml:'l1llChang! In Dally Stallsleal Performanc!: 38lm

(ni:glLtlv\:' vl:llui:'li indlca!i: curn:nt 'liiJnulaUon tmpr~mll'nl]

1.0 Ooloy' 1..0 Ooloy'

Chang! In Dally Stallueal Performanc!: 38lm

(ni:glLtlv\:' vl:llui:'li indlca!i: curn:nt 'liiJnulaUon tmpr~ml:'nl]

Change In Dally St.atlstcal Performanc!: 38 km

(ni:$I'lIllv\:' VI:llIlI::O Indica": l:urn:nt siJnull;ltion improwmll'nt)

1..0 0-,'

-0.7

0..

0.7

0..

0..

-0.'

-0.7

-o.s

-

0..

0.7

0"

-O.S

-0.'

-0.7

-0.' -0.'

-O.S

-0.'

0.7

-

0"

-

-

-0.'

0"

0..

-O.S

-0.'

-0.7

0.7

mJ;I,O o.y31':;;;"....._........._;-;;...;;;-:;;"", ... =,..;;.....",;;,r.=..K!

Jralll.lflry 1.2002:0;00;00

Mln- -0.'1 .II (:tI1) M.IX- O..:i aJ (Z,s)

-0.' • -0.'

mJ;I,O o.y31':;;;;"....._;;;-......._;;;-"..."",r.;.,"...=,..,,""'....-o;;!

January 1.2002:0;00;00 Mln- -o.s at (S,2;S). M.mr- 0..3 at (ZoS)

-0.'

mJ;I,O o.y31':;;;"....._........._;-;;......:;;,."";... =,..;;.....,,,;;.,r.=..K!

January 1.2002:0;00;00 Mln- -o.s at (""18). M.mr- 0..3 at (ZoS)

-0.,

J,lnuary 1.2002:0;00;00 Min" -0.6 ;It (VI).. Max- 02 at (7.1 I)
Wind Dk Gross E:'"l"Or"nin"er-ence - gl GI"id

(nt$l~~:~~~~~ 7r~~%~~a~~::~~:11nour;:~~~el~::':m~nl]

Wind Dir" Gr"oss Err-or" Dine,"ence - cem"apN G ,"id

(ntsrlLt~:~~~~~ 7r~~~~~a:~~~~~:11nour:~~~~~~:':mC"nll

Wind Dil" Gr"oss En"OJ" J)iffe,"ence - ceOl"apS GI"id

(nt$l~~:~~~~~ 7r~~%~~a~~::~~:11nour;:~~~el~::':rnC"nl]

Wind Dil" Gr"oss l~rmr" Diffe,"ence - ohioval G ,"id

Chang!! In Dally Slati!5tc:a1 Performance: 36 t:m

(nt$l~IvC'vilJul:llndlull: curren' IlJnuhllion lmprO't':rnC"nl]

3..0 o.>j1 3..0 ~1 3..0 o.>j1 3..0 o.>j1

,..

,.5

• .5

-<l.S

...

-2.5

-2.0

...

-- -2.0

-2.5

dC;;~Uo.y31,"=~=--=,....,...=-""" -....:;:,:-._=-~.""., -':.... :!!!"-,a:;:',.-,"';;:'..J..-,=-=

JllnulllY 1.2002 0:00:00

Min- ...1-2.il1 (~.s'l. Max.. 1.1 OIl' (:$,1'1)

... - - 2"

,.5

- ,.5 - •

,..

.5 - ,.. - - • .5

... - ... -<l.S • -<l.S

-

=- ,..

2"

,.5

• .5

-2.5

-2.0

-<l.S

...

dC;;~Uo.y31,=,....,=--=,....,...=-"""....:;:,:-._=-,,,,",.,-,.... =-,a:;:',.-,"';;:'..J..-,=-=

JlInulllY 1.2002 0:00:00

Min- -2-8 .ill Q."U. Max_ ~1;11 ("1..1 B)

Wind Dk Gr"oss l~n"or"nifTe'"ence - Iowa G,"id Wind n;,- G,"OSS Er,"or" DifTe,"ence· SE GI"id Wind J)ir" Gr"oss EI"m," DilTer"ence - MidAUantic GI"id Wind Dil" Gmss ErTOJ" J)iffel"ence - Nl~ G,"id

Change In D4IIIly SlatJsJcal PerformJlllce; ~i6 1m

(nl:srlLtlvC'viLIul:I IndlclIh: curn:nJ Ihnulillion impcrOV1:rnc-rll]

3.0 o.>j 1r=====--==--.;;;;;;;:=====t=::llllll

2"

,.5

,..

• .5

...

-<l.S

-2.0

-2.5

JlllIUlIIY 1.2002 0:00:00

••Un_ -4il.,2 .ill (~."'). Wax_ I.AI;l1 if"J!S)

Chan!)!! In 04llily SlatbJcal Perform.llllce; 36 1m

(nl:$llLtlvC'viLIuI:J lndluh: curren' Iltnuhllion imprO't':rnC"nl]

ChangE! In Dally Slatj5tcal Performance; ~i6 t:m Chang!! In Dally Slati!5tc:a1 Performance; 36 t:m

(nt$l~IvC'vilJul:llndICllll: curn:n' Iltnullllion imprO't':rnC"nl] (nt$l~IvC'vilJul:llndicllll: curren' Iltnullllion imprO't':rnC"nl]

U U

u u

-0.5 -0.5

-2.0 -2.0

-2.5 -2.5

JllnulllY 1.2002 0:00:00 JllnulllY 1.2002 0:00:00 tAln• ...,.1 atla.22'ltAD. 3.0 ..t(>1.20' Mln.-2:.3atII0.8\).4.o:.-II.1 atlV'Il

Wind Di," Gmss En"or" ))ilTe,"ence· ))essel"tSW G,"id Wind DiI" Gr"OSS HrmdJilTerence - SW GI"id Wind J)iI" Gr"oss lkmr" Oifference - OJ"th Gr-id Wind Oi," G'"OSSEITOJ" Dine,"ence" P3cificNW G,"id

Chan!ll! In Dally Slatjstcal Perform.ance: 3S km (n1:gil.tlvC'vllluI:J IndlcilllI:: current JiJnuhlUon imprOvt:mc-nl]Chang!!: In Dally Slatj5tcal Performanc!!:: 3S t:m

(lII:gidlvlC'vlllul:llndlciIIll: cu:rn:n' liJnulillUon imprO't':mC"nl]

Chang!!: In Dally Slatj5tcal Performanc!!:: 3S t:m

(nl:gidlvlC'vllllll:J Indlcillll: curn:n' liJnuhlUon imprO't':mC"nl]

Chang!!: In Dally Slatj5tcal Performanc!!:: 3S t:m

(1Il:gidlvlC' vlllul:J Indlcillll: cu:rn:n' liJnuhlUon imprO't':mC"nl]

'.5

-<l.S

,..

• .5

...

-1.0

-,.5

..z.O

...

...

January 1.2002 0:00:00

tAln- -8.1 at C9.30~ tAifl)l- 1.2.at (S. 12) Jl1nuary J2002 0:00:00Mln- -e-s aI (<\.18).. Max- '1.'1 at (12.24) Jl1nuary J2002 0:00:00••iln- -5.0 at (3.14). Max- 3.1 U(2A) Jl1nuary J2002 0:00:00Mln- -8.5 at (1.18). Max- IO.R at (2.'1)
Meteorological Model Performance 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 ................................................................................................................. 49 

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 BACKGROUND

Projects pursing PM2.5, 8-hour ozone, and regional 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 decisions has created the need to 

implement sophisticated models developed for regional scale multi-pollutant environments 

encompassing diverse climatological regimes. Computational limitations have historically 

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

resolution. Exponential growth in computational efficiency has partially 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 continental scale simulations. 

Annual continental scale air quality simulations require the implementation of a triumvirate 

modeling system composed of meteorological, emissions, and air quality models. 

Meteorological modeling is the first component addressed as meteorological data supports both 

the emissions and air quality models. In preparation for regulatory requirements involving 

regional haze, PM2.5, and ozone, the Iowa Department 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 meteorological simulation and 

provides performance evaluation results. 

1.2 MODEL SELECTION

Due to scientific progression, historical application, 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 first documented by Anthes and Warner (1978), the 

MM5 modeling system maintains its status as a state-of-the-science1

 model through 

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

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

modeling system is routinely employed in operational forecasting frameworks as well as research 

applications spanning meteorological 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 continental scale annual simulations: 1996, 2001, and 

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

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

modeling for the Visibility Improvement – State and Tribal Association of the Southeast 

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

exhaustive as both public and private 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. Each component contains highly configurable control files; together they 

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

ultimately results visualization. The inherit complexity of the MM5 modeling system 

complicates the development of a sound model configuration suitable for regional scale annual 

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

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

configurations remains with most applications. The first step 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 collaborative project lead by 

Kirk Baker with the Lake Michigan Air Directors Consortium (LADCO) and Matthew Johnson 

(IDNR). Wyat Appel and Mike Abraczinskas with 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 surface parameterizations, and snow models. Two 

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

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

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

Following the sensitivity study, the IDNR completed a 2002 annual simulation. This simulation 

utilized surface moisture and temperature nudging. 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 unrelated to the cold bias, utilization of surface 

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

practice has lead to the generation of super-adiabatic lapse rates near the 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 Meteorological Systems, 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 yielded 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 OVERVIEW

Version 3.6.3 of the MM5 modeling system was utilized in the second1 (and final) 2002 IDNR 

annual meteorological simulation. The 3.6.3 release represented the most current version 

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

modeling system code modifications capable of altering results were rendered. 

3.1.1 TERRAIN 

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

elevation, the dominant landuse category, and vegetative and soil data were assigned using the 2-

minute 24-category USGS data. The horizontal grid structure consists of a 36 km domain 

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

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

projection centered at 90o W longitude, 40o N latitude, with true latitudes of 33 and 45o N. The 

horizontal extent of the RPO domain was engineered according to the bounds of the Eta 212 

grid. Domain development involved the implementation 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-centric upwind and downwind flows. Both grid 

structures are described in Table 3.1 and depicted in Figure 3.1. 

Table 3.1. Grid data, referencing MM5 terminology 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 processor prepares archived gridded meteorological data for use within MM5 

through conversion to an intermediate data format readable 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 deleted 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 data was included in PREGRID to supply timevariant sea-surface temperature (SST) data, as the Eta surface files supply only a time-invariant 

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

the temporally variable NCEP SST data was found to lead to unrealistic diurnal temperature 

profiles over the Great Lakes and near shorelines. 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 Lake Erie, and most shorelines) reach 20o F. 

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

observed through use of the NCEP data are unrealistic. 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 provided 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 NCEP 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 period. 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 July 4 – July 9 period, no questionable diurnal 

variability or artificial warming trends are present. 

Figure 3.2. Lake temperature variability across 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

Date

Sea Surface Temperature

(K)

Figure 3.4 Observed SST temperature data for buoy 45007. 

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

45007. 

8
Figure 3.6. Constant SST data derived from Eta 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 observational data into the gridded first-guess fields. Following 

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

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

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

in both the four-dimensional data assimilation (FDDA) and Pleim-Xiu soil moisture nudging 

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

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

(Baker, 2002). 

3.1.4 INTERPF 

The IDNR 2002MM5v363 simulation uses a 34 vertical layer structure defined through the 

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

coordination with Dennis McNally to parallel the 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 coordinates. Sigma levels are defined according to Eq. 

3.1, where ps equals the surface pressure, and pt 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 variables assigned the following 

values: ps = 1000 mb; pt = 100 mb; Ts = 275 K; A = 50 K. R and g represent the gas and 

gravitational constants of 287 J/(kg K) and 9.8 m/s2

, respectively. 

ts

t

pp

pp

−

− σ = (3.1) 

( ) − + pppp tts =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 configuration used in the IDNR 2002MM5v363 

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

the cumulative efforts of several sensitivity studies, in combination with guidance from the AdHoc Meteorological Modeling community. In comparison with the original IDNR 2002 

simulation, the cessation of continuous soil field techniques within the PX LSM is one of the 

most notable modifications.1 With the PX LSM no longer restricted to sequential operation, the 

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

integrated through five days (versus 5-day blocks arranged in quarterly sequential simulations in 

the original run). This temporal structure allows 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 period 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 photochemical model spin-up period, the simulation 

started at 12/16/2001 12Z. The completion date 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 photochemical 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 derivative 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 temperatures; 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: Sea surface temperatures remained 

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

temperatures. Snow cover effects were considered. 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, respectively. A nudging coefficient of 1.5 x 10-5 was 

established for the mixing ratios at both 36 and 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 continental scale annual simulation. 

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

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

options incorporated include: moist vertical diffusion in clouds, temperature advection 

using potential temperature, diffusion using 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, and IFGROW. ISMRD was set 

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

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

takes vegetative growth into account based on vegetative fraction data from the 

TERRAIN file. 

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

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

strengths. While the terminology is questionable, the nesting technique employed is commonly 

referred to as “a two-way nested run without feedback”. 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 condition updates at every timestep. 

3.3 COMPUTATIONAL SUMMARY

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

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

machine a dual 2.0 GHz processor. Each machine1

 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 SCSI-IDE RAID array. In summation, 41 wall-clock days were 

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

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

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

Approximately 100 wall-clock hours was required 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 BACKGROUND

No rigid guidelines exist for systematically and objectively evaluating the quality of 

meteorological simulations. However, sound comprehensive philosophies exist. A seven point 

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

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

and a scientific evaluation (Emery and Tai, 2001). The scientific evaluation requires rigorous 

examinations of model formulation and algorithm development, methods beyond the scope of 

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

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

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

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

4.2 METHODS

Climatic variability, complex mesoscale meteorological phenomena, and scientific unknowns 

contribute to meteorological modeling 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 metrics are calculated is a subjective decision, 

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

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

modeling domain into small subdomains renders sample sizes unrepresentative. The logical 

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

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

technique was applied, resulting in the twelve rectangular1

 subdomains pictured in Figure 4.1. 

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

tandem with surface statistics. Upper air features 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 introduces a level of complexity exceeding the 

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

in combination with a relatively sparse observing network gathering only 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 between 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 efficiency and simplify the review of soundings, a 

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

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

briefly summarized. While technically a surface feature, the precipitation evaluation indirectly 

enhances the upper air review given the three dimensional nature 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 performance evaluation. Areas of overlap are shaded 

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

4.3 STATISTICAL MEASURES

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

standard statistical measures has 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 strict criteria establishing acceptable model 

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

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

15
Table 4.1. List of statistical measures commonly discussed in meteorological model evaluations. 

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

particular metric. Conversely, D or H indicates that the value is available 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 meteorological 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 metric is provided by Baker et al. (2004): 

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

mean observation, with lower numbers 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 tends to inflate RMSE, particularly 

when extreme values are present. 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 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 interpreted as a measure of 

potential accuracy 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. 

16
Index of Agreement is a relative measure of the degree of which predictions are errorfree. The denominator accounts for the 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.” 

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

the Techniques 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 direction, temperature and humidity were generated 

using the Metstat program and MS Excel post-processing macro developed by Environ. Time 

series of modeled and observed conditions were also prepared via Metstat. As continental-scale 

domain averaged statistical measures are susceptible to error cancellation, metrics were 

calculated over the twelve subdomains illustrated in Fig. 4.1. 

The volume of data associated with the annual simulation can quickly overwhelm standard time 

series displays or similar attempts at numerical 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 horizontally by date. This allows for an annual graphical display of 

daily averaged metrics in a single plot, simplifying the identification of error trends or pervasive 

biases. Even with this method of simplification, 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 provided graphically in the form of a “Bakergram”. 

The Bakergram, developed by Kirk Baker, allows for 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 bias, 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 (for example, see Figure 

5.2) with temperature and mixing ratio metrics plotted. 

5.1 GREAT LAKES

In previous sensitivity studies, the Pleim-Chang/Pleim-Xiu PBL/LSM configuration was found to 

improve wind vector performance versus the use of alterative PBL parameterizations. Consistent 

with this discovery, the wind vector performance in the GL region is encouraging. Wind speed 

metrics are generally favorable, and no clear trends in error or bias are evident (see Figure 5.1). A 

notable caveat, daily metrics may hide inconsistencies occurring within the diurnal profile.1

 

Turning to the wind direction evaluation, again results are satisfactory, with one exception found, an 

increase in the summertime gross error. 

In the Great Lakes region, the problems of greatest concern lie in the wintertime cold temperature 

biases, the warm summertime biases, and the summertime positive moisture biases (See Figure 5.2). 

Examining the temperature biases from a diurnal2 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 mixing ratio (humidity) evaluation (see Figure 5.2), 

although the gross error metrics are generally within the statistical 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 NORTHERN AND SOUTHERN CENRAP

In general, the statistical evaluation for the CenrapN subdomain (Figures 5.3 - 5.4) yields results 

similar to the Great Lakes region. A notable exception being the nearly consistently negatively 

biased wind speeds. Examining the wind speed bias in greater detail (through diurnal profiles), this 

fault is predominantly influenced by the underprediction in the daily peak wind speeds. Keeping 

these errors in perspective, the 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 trivial, CenrapN does differ from the GL subdomain 

during May, where several surface moisture underpredictions occur. 

 

1

 Diurnal metrics are examined in Chapter 6. 

2

 Ibid. 
Turning to the Southern CENRAP 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 biased, but more pronounced in this region. The 

mixing ratio biases reveal excess moisture, although a drier than observed fall was predicted. 

Examining temperature performance, late winter/early spring temperatures yielded positively biased 

trends, in contrast to the pervasive cold winter biases found in the CenrapN and GL regions. 

Examination of the diurnal profiles revealed the biases were attributable to warm nighttime lows. 

5.3 OHIO VALLEY

Once again, the wind speeds are generally too low, however, the associated error is well within the 

acceptable guidelines. Wind directions errors are also generally small, but an increase in error is 

found in the summer months. Mixing ratios are consistently too moist, except 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 IOWA 

Within the Iowa subdomain wind vector performance is favorable, with wind speed bias and error 

measures predominantly meeting the statistical goals. Wind directions exhibit greater errors in the 

late summer/early fall timeframe versus the CenrapN and GL subdomains, but are not cause for 

severe alarm (see Figure 5.9). As is common, cold winter and warm summer biases are present 

(Figure 5.10). In terms of the moisture bias, the Iowa domain exhibits greater springtime negative 

moisture bias versus CenrapN, otherwise similar performance is shown (this result is not 

unexpected, given the superposition of the Iowa subdomain over CenrapN). 

5.5 EASTERN REGIONS

A detailed discussion of model performance for all areas is beyond the scope of this document. 

Alternatively, summary remarks are provided. Over 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 the MidAtlantic and SE regions. Examining the NE 

region, wind speed, and wind direction errors approach the upper extreme of acceptable 

performance. Again, the moisture bias is positively biased, with errors maximized over the summer 

months. Given moisture carrying capacity is a non-linear function of temperature, the relatively 

small mixing ratio gross errors occurring in the wintertime of regions with colder climates should 

not be interpreted as superior model performance. The daily averaged statistical results are 

provided in Appendix B for each of the individual Eastern subregions. 

5.6 WESTERN REGIONS

The daily averaged statistical results for the western subdomains are also provided in Appendix B. 

The complex topography found in the Western United States clearly introduces a degree of 

modeling difficulty not found in other regions. Performance metrics are discouraging when viewed 

initially, however, the appropriateness of the statistical measures are questionable as model 

resolution is not designed to capture the topographically induced near-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 mixing ratio metrics for the Great Lakes (GL) 

subdomain. 

21
Figure 5.3. Daily averaged wind speed/direction metrics for the CenrapN subdomain. 

22
Figure 5.4. Daily averaged temperature and mixing ratio metrics for the CenrapN subdomain. 

23
Figure 5.5. Daily averaged wind speed/direction metrics for the CenrapS subdomain 

24
Figure 5.6. Daily averaged temperature and mixing ratio metrics for the CenrapS subdomain. 

25
Figure 5.7. Daily averaged wind speed/direction metrics for the OhioVal subdomain. 

26
Figure 5.8. Daily averaged temperature and mixing ratio metrics for the OhioVal subdomain. 

27
Figure 5.9. Daily averaged wind speed/direction metrics for the Iowa subdomain. 

28
Figure 5.10. Daily averaged temperature and mixing ratio metrics for the Iowa subdomain. 

29
6. TWELVE KILOMETER EVALUATION 

6.1 DAILY AVERAGED STATISTICS

Generalizing the impacts of the 12 km domain upon the Great Lakes region, in terms of daily 

averaged metrics, a decrease in simulation accuracy 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 direction1

 and temperature errors. The wintertime temperature cold bias (found at 

36 km resolution) is thus even more pronounced in the 12 km domain. Mixing ratio statistics 

were generally uninfluenced by domain resolution. These results are depicted in Figures 6.1 – 

6.2, where the Bakergram concept is maintained, however, 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 the 12 km data. As the comparison only involves 

gross and root mean square error metrics, negative values indicate an improvement in model 

performance at 12 km resolution. This methodology is maintained for Figures 6.1 - 6.8.

The CenrapN regions shows only minor variations in the temperature fields, with the greatest 

change concentrated to the cooler months, with slight performance disbenefits. Wind direction 

metrics produced a drastically different trend, as nearly all days showed poorer performance. 

Figures 6.3 – 6.4 provide a graphical depiction of the 12 km domain impacts upon the daily 

averaged metrics for this subdomain. 

Over the Ohio Valley, only minor differences were calculated between the 12 and 36 km daily 

averaged statistical results, in general. A slight improvement in the mixing ratio fields was 

computed. As in CenrapN, wind direction gross errors encountered widespread performance 

degradation during the winter and early spring months. Keeping 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 fall, 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 temperature error, particularly in the winter. 

Fortunately, gross error degradation remained below 0.5 K. The results are depicted in Figures 

6.7 - 6.8. 

Due to the spatial extent of the 12 km domain, 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 statistical performance for selected wind metrics 

in relation to the 36 km grid for the Great Lakes (GL) subdomain. 

31
Figure 6.2. Twelve km domain daily averaged statistical 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 statistical performance for selected wind metrics 

in relation to the 36 km grid for the CenrapN subdomain. 

33
Figure 6.4. Twelve km domain daily averaged statistical 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 statistical performance for selected wind metrics 

in relation to the 36 km grid for the OhioVal subdomain. 

35
Figure 6.6. Twelve km domain daily averaged statistical 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 statistical performance for selected wind metrics 

in relation to the 36 km grid for the Iowa subdomain. 

37
Figure 6.8. Twelve km domain daily averaged statistical performance for selected temperature 

and mixing ratio metrics in relation to the 36 km grid for the Iowa subdomain. 

38
6.2 HOURLY STATISTICS

Additional comparisons between the 36 and 12 km simulations are provided below through 

review of hourly timeseries. Modeled (both 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 with 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 (Figures 6.9 - 6.10) leads to a general conclusion 

that improvement occurs in the daytime wind speed 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 domain, 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 during 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

1/12

 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

 1/31

m/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

 1/17

 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

 1/31

m/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

 1/31

deg

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

 1/31

deg

36km Bias WD 12km Bias WD

bias shifted 

Great Lakes Domain

Figure 6.9. Twelve and 36 km hourly wind vector statistics 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

 6/16

 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

 6/30

m/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

 6/16

 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

 6/30

m/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

 6/13

 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

 6/30

deg

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

 6/19

 6/20

 6/21

 6/22

 6/23

 6/24

 6/25

 6/26

 6/27

 6/28

 6/29 6/30

deg

ObsWndDir 36 km PredWnd 12 km PredWndDir

Figure 6.10. Twelve and 36 km hourly wind vector statistics 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

 1/15

 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

1/31

K

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

 1/25

 1/26

1/27

 1/28

 1/29

 1/30

1/31

K

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

 1/25

 1/26

 1/27

1/28

 1/29

 1/30

 1/31

g/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

 1/25

 1/26

 1/27

1/28

 1/29

 1/30

 1/31

g/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

 1/11

 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

 1/31

g/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

 1/25

 1/26

 1/27

1/28

 1/29

 1/30

 1/31

g/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

6/12

 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

6/30

K

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

6/30

K

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

6/24

 6/25

6/26

6/27

6/28

6/29

6/30

g/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

6/30

g/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 SOUNDINGS

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 precipitation fields, PBL depths, and vertical profiles of temperature, 

moisture, and wind vectors. As readily available 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 completed by Kirk Baker (Baker et al., 2004) and is 

summarized below. In combination with the precipitation evaluation, the most efficient method 

available for an upper air analysis is to focus upon radiosonde observations. To aid in the review 

of upper air feature, the IDNR created the RAOBPLOT software tool that efficiently displays 

modeled versus observed radiosonde upper air measurements. With twice-daily soundings 

available from approximately 70 observing stations, 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 data available, in combination 

with only inefficient subjective methods for evaluations 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 many sites in the Central U.S. was 

conducted, with no terminal deficiencies discovered. 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 tendency 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 estimated PBL depths, the mixed 

layer commonly appears shallower than observed. While error is never desired, in terms of 

modeling air quality (in a conservative sense) a shallow PBL is preferred versus excessive depth. 

A sample of the observed versus modeled sounding produced by RAOBPLOT is provided in 

Figure 7.1. 

7.2 PRECIPITATION

Kirk Baker with the Lake Michigan Air Directors Consortium was provided a complete copy of 

the 36 km meteorological dataset 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 precipitation events produce an excess of 

precipitation. Rainfall patterns also exhibit greater spatial coverage than observed. Additional 

detail, including graphical representation of predicted and observed rainfall, is available in Baker 

et al., 2004. 

43
Figure 7.1. Sample ROABPLOT observed versus 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 temperature 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 PBLDEPTHS

Additional upper air analyses included a limited comparison of the 36 and 12 km predicted PBL 

heights. Figure 7.2 provides an example comparison. As expected, the degree of agreement 

between the 36 and 12 km results exceeds variability. Areas in Western Illinois and Eastern 

Texas (among others) do display deviations. In Eastern Texas, MM5 predicts a precipitation 

event (which is weakly supported by observations, see Figures 7.3 - 7.4). The reduction in PBL 

heights in Western Illinois would appear to be precipitation driven as well, but no convective or 

non-convective rainfall was predicted by MM5 during this time. The observed radar 

reflectivities also suggests no precipitation occurred during this time. In summary, the 12 km 

grid yields improved feature detail yet the accuracy of such fields, across a continental scale 

annual simulation, is difficult to assess within a reasonable timeframe.

Figure 7.2. PBL heights predicted by MM5 for 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 Central U.S. through the Ohio River valley, the surface statistical 

evaluation reveals a dominant wintertime cold bias, with cool conditions typically present in the 

evening hours, while overly aggressive nighttime 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. exhibit low statistical error and provide 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 all fields. As discussed, this result is not 

completely unexpected given complex Western topography. Regions within the Eastern U.S. 

demonstrate prediction skill above Western regions, yet statistical accuracy falls below that 

found in the Midwest. 

Expanding the evaluation into upper air features reveals no fundamental 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 discovered. In subjective terms, such error is 

acceptable as perfect model performance is unattainable. A similar conclusion is reached for the 

precipitation shortfalls discussed 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 daytime wind speed predictions, however, nighttime 

predictions suffer. Overall, wind speed error showed little variability between the 36 and 12 km 

domains. Beyond the statistical evaluation, additional field detail is resolved by the 12 km 

domain as expected. As in the upper air analysis for the 36 km grid, no fundamental flaws were 

identified in review of 12 km upper air features. 

In summary, the statistical evaluation yields results predominantly within acceptable guidelines 

for the principal regions of interest (the States near and within LADCO and the northern twothirds of CENRAP). Concurrently, no major simulation 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 modeling studies focused within the central United 

States. 

48
9. REFERENCES 

Anthes, R. A. and T. T. Warner, 1978: The Development of Mesoscale Models Suitable for Air

Pollution and Other Mesometeorological Studies. Monthly Weather Review, 106, 1045-

1078. 

Baker, K., 2002: MM5 Sensitivity for the National RPO Grid, Presented at the Ad-Hoc 

Meteorological Modeling Meeting, Des Plaines, IL, 2002. 

Baker, Kirk, Matthew Johnson and Steven King, 2004: Meteorological Modeling Performance 

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

http://ladco.org/tech/photo/docs/mm5_protocol_dec2004.pdf

Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/hydrology model 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-hydrostatic Version of the Penn State/NCAR Mesoscale Model: 

Validation Tests and Simulation of an Atlantic Cyclone and Cold Front. Monthly Weather 

Review, 121, 1493-1513. 

Emery, C., and Edward Tai, 2001: Meteorological Modeling and Performance Evaluation of the 

September 13-20, 1999 Ozone Episode. Prepared 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 31st 2003. 

Olerud, Donald, K. Alapaty, and N. Wheeler, 2000: Meteorological Modeling of 1996 for the 

United States with MM5. Prepared for Normal Possiel, Client Representative, U.S. 

EPA/OAQPS. EPA Task Order Number CAA689805 

Olerud, Don and Aaron Sims, 2004: Draft Protocol for Annual MM5 Modeling in Support of 

VISTAS (Visibility Improvement – State and Tribal Association) (DRAFT), Prepared for 

Mr. Mike Abraczinskas, VISTAS Technical Analysis Workgroup. 

Pleim, J. E., and J. S. Chang, 1992: A non-local closure model for vertical mixing in the 

convective boundary layer. Atmospheric Environment, 26A, 965-981. 

Stauffer, D. R., and N. L. Seaman, 1990: Use of Four Dimensional 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: Use of Four Dimensional 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 

Photochemical 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 model. Part I: Application in a 

mesoscale meteorology model. Journal of Applied Meteorology, 40, 192-209. 
APPENDIX A 

Temporal structure example for the 2002 annual simulation. 

Start BLOCK End BLOCK Start (Z) End (Z) Filename M

Start (Z) 

12/28/2001 12:00 1/2/2002 12:00 12/28/2001 12:00 12/28/2001 12:00 MMOUT_DOMAIN1_00 

12/28/2001 13:00 12/29/2001 12:00 MMOUT_DOMAIN1_01 12/29/2001 012/29/2001 13:00 12/30/2001 12:00 MMOUT_DOMAIN1_02 12/29/2001 112/30/2001 13:00 12/31/2001 12:00 MMOUT_DOMAIN1_03 12/30/2001 1

12/31/2001 13:00 1/1/2002 12:00 MMOUT_DOMAIN1_04 12/31/2001 1

1/1/2002 13:00 1/2/2002 12:00 MMOUT_DOMAIN1_05 1/1/2002 13

1/1/2002 12:00 1/6/2002 12:00 1/1/2002 12:00 1/1/2002 12:00 MMOUT_DOMAIN1_00 

1/1/2002 13:00 1/2/2002 12:00 MMOUT_DOMAIN1_01 1/2/2002 0:

1/2/2002 13:00 1/3/2002 12:00 MMOUT_DOMAIN1_02 1/2/2002 13

1/3/2002 13:00 1/4/2002 12:00 MMOUT_DOMAIN1_03 1/3/2002 131/4/2002 13:00 1/5/2002 12:00 MMOUT_DOMAIN1_04 1/4/2002 131/5/2002 13:00 1/6/2002 12:00 MMOUT_DOMAIN1_05 1/5/2002 13

1/5/2002 12:00 1/10/2002 12:00 1/5/2002 12:00 1/5/2002 12:00 MMOUT_DOMAIN1_00 

1/5/2002 13:00 1/6/2002 12:00 MMOUT_DOMAIN1_01 1/6/2002 0:

1/6/2002 13:00 1/7/2002 12:00 MMOUT_DOMAIN1_02 1/6/2002 13

1/7/2002 13:00 1/8/2002 12:00 MMOUT_DOMAIN1_03 1/7/2002 131/8/2002 13:00 1/9/2002 12:00 MMOUT_DOMAIN1_04 1/8/2002 131/9/2002 13:00 1/10/2002 12:00 MMOUT_DOMAIN1_05 1/9/2002 13
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

 1/31

m/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

 1/31

m/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

1/31

deg

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

1/31

deg

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

K

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

K

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

g/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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

g/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

 6/30

m/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

6/30

m/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

 6/30

deg

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

6/30

deg

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

 6/10

 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

6/30

K

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

6/20

 6/21

 6/22

6/23

 6/24

 6/25

6/26

 6/27

6/28

6/29

 6/30

K

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

 6/20

6/21

6/22

 6/23

6/24

6/25

 6/26

6/27

 6/28

 6/29

6/30

g/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

6/20

 6/21

 6/22

6/23

 6/24

 6/25

6/26

 6/27

6/28

6/29

 6/30

g/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

 1/31

m/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

 1/31

m/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

 1/31

deg

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

 1/31

deg

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

K

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

K

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

g/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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

g/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

6/30

m/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

6/30

m/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

6/30

deg

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

6/30

deg

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

 6/20

6/21

6/22

 6/23

6/24

6/25

 6/26

6/27

 6/28

 6/29

6/30

K

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

 6/20

6/21

6/22

 6/23

6/24

6/25

 6/26

6/27

 6/28

 6/29

6/30

K

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

 6/20

6/21

6/22

 6/23

6/24

6/25

 6/26

6/27

 6/28

 6/29

6/30

g/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

 6/20

6/21

6/22

 6/23

6/24

6/25

 6/26

6/27

 6/28

 6/29

6/30

g/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

 1/31

m/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

 1/31

m/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

1/31

deg

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

1/31

deg

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

K

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

K

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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

g/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

 1/20

1/21

1/22

 1/23

1/24

1/25

 1/26

1/27

 1/28

 1/29

1/30

 1/31

g/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

 6/30

m/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

6/30

m/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

 6/30

deg

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

6/30

deg

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

6/20

 6/21

 6/22

6/23

 6/24

 6/25

6/26

 6/27

6/28

6/29

 6/30

K

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

6/20

 6/21

 6/22

6/23

 6/24

 6/25

6/26

 6/27

6/28

6/29

 6/30

K

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

6/20

 6/21

 6/22

6/23

 6/24

 6/25

6/26

 6/27

6/28

6/29

 6/30

g/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

6/20

 6/21

 6/22

6/23

 6/24

 6/25

6/26

 6/27

6/28

6/29

 6/30

g/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 Directors 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 conceptual 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 projection centered at (-97, 40) and true 

latitudes at 33 and 45. The 36 km photochemical modeling domain consists of 97 

cells in the X direction and 90 cells in the Y direction covering the central and eastern 

United States with 36 km grid cells (Figure 2.1; Table 2.1). The 2-way nested 12 km 

photochemical domain covers most of the 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 States 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 sensitivity study was conducted to compare winter 

and summer episode averaged PM2.5 concentrations between a Continental U.S. 

domain and Central/Eastern U.S. domain using clean boundary conditions released 

with the CMAQ model. The episode average 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 (left) and Winter (right) episodes 

 

Meteorological Inputs

Meteorological input data for the photochemical modeling runs are processed using 

the National Center for Atmospheric Research (NCAR) 5th generation 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 scheme, Rapid Radiative Transfer Model, Pleim-Chang 

planetary boundary layer (PBL), and the Pleim-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 vertical 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 diffusivity 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 inside the PBL. The minimum vertical 

diffusivity coefficient is 0.1 m2

/s. A landuse-weighted vertical diffusivity coefficient 

(maximum of 1.0 m2

/s in a completely urban grid cell) is assigned to all grid cells up 

to approximately 150 meters above ground (model layer 3). This is done to better 

represent the greater mechanical mixing overnight in urban areas. An additional 

adjustment to vertical diffusivity coefficients creates a transitional 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/s2

) 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 photochemical activity. PBL heights at a land cell 

are typically over 1000 meters and the adjacent 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 diffusivity coefficients (m/s2

) 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 adjustments 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) observations (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 millibars. Figure 2.7 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 layer collapsing in the lower boundary 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 processes, 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 requirements of regional and daily emissions 

processing. In addition to extensive quality assurance and control capabilities, EMS2003 also performs basic emissions processes such as chemical speciation, spatial 

allocation, temporal allocation, and control of area, point, and mobile source 

emissions (Janssen, 1998; Wilkinson et al, 1994). Outputs from EMS-2003 include a 

coordinate-based elevated point source file and gridded emissions estimates for lowpoint, area, mobile, and biogenics sources. Anthropogenic emission estimates are 

made for a weekday, Saturday, and Sunday for each month. The biogenic emissions 

are day-specific. Volatile organic 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 Consolidated Emissions 

Reporting Rule (CERR) submittals, other RPOs, and the 2002 National Emission 

Inventory (EPA, 2006). Continuous emissions monitoring data were used to develop 

temporal profiles for electrical generating units. These new profiles account for 

month of year and day of week variations and are unit specific. 

On-road emissions are estimated using MOBILE6.2 emission factors and VMT from 

the 2002 NEI. The MOBILE6 inputs 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 other States. Updated on-road temporal 

data is based on an analysis of traffic count data in Michigan, Wisconsin, and 

Minnesota. Default temporal tables are modified to represent a more complex 

distribution of vehicle 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 Iowa and Minnesota. Contractor supplied 

emissions estimates are used for commercial 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 estimated with EMS-2003 using the BEIS3 model (Guenther 

et al, 2000). The BELD3 land use dataset is input to the biogenic model for fractional 

land-use and vegetative speciation information (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 likelihood that ammonia emissions from these 

sources would make it out of domestic dwellings in the case of humans, cats, and 

dogs and forested areas in the case of deer. Ammonia emissions are removed from 

other RPO’s point source inventory to eliminate double-counting confined animal 

operations with CMU model estimates. Updated monthly and diurnal profiles were 

developed using the new process based ammonia model. The new profile represents 

beef, hogs, and dairy. Hog farms are assumed to represent poultry since the new 

process based ammonia model did not have a fully functional poultry housing model. 

Currently, there are no anthropogenic Mexican emissions in the emissions input files. 

Canadian emissions are based on a 2000 inventory 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 improved speciation profiles for certain 

emission categories. Details of this project are available in “Improving Modeling 

Inventory Data: Speciation Profiles – February 17, 2005” and available by request. 

The development of the future year and even the base year 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 varies by season and land 

use category and are taken from EPA’s AERMET 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 landuse information. The BELD3 was chosen 

because it incorporates the USGS data with 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 USGS (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 available 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 response to drought stress (ENVIRON, 2005). 

Snow cover is also input to CAMx4 for the 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 characteristics 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 angle, 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 albedo 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 and 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 the discrete ordinate approach is more 

accurate. 

A sensitivity application with CMAQ using TOMS derived photolysis rates and rates 

based on seasonal average ozone column showed differences in ozone up to 3 ppb 

and differences in sulfate ion up to 1.5 ug/m3

. These differences suggest day specific 

ozone column data from satellites should be used rather than seasonal averages and 

that accurate photolysis rates are important 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 rather 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 7th day of the episode; ozone 

modeling episodes will be spun up with 11 days. The monitors used in model 

performance evaluation are far enough away 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/m3

 by the seventh day of the episode. PM2.5 elemental carbon, PM2.5 soil, and 

coarse mass have less than 1 ng/m3

 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) application 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/m3

 at IMPROVE and EPA STN monitor 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 simulation used for regional modeling, the monthly 

averaged concentrations are used to support photochemical modeling applications. 

Quality Assurance of Model Inputs 

The model input files are checked for reasonableness 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 extensive QA done in the emission 

inventory compilation process, EMS emissions 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 landuse 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 directly viewed in PAVE and the spatial 

representation is checked. The ozone column, albedo, and turbidity data are kept in 

ASCII files. Each file is checked to ensure the data looks spatially reasonable and 

that bad data did not get included in the file. 

The emissions inputs are extensively checked for appropriateness. The steps taken in 

manipulating EMS-2003 output files to CAMx4 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 and 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 each species is totaled by State and over 

the entire modeling domain and compared 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 performance of the air quality model input 

meteorological data at several monitor locations. 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 (Nobel 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; Nenes 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 weeks 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 standard will be using a horizontal grid resolution of 12 

km. The modeling to support the 8-hr Ozone 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 with 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 simulations to assess changes in ozone, visibility, and PM2.5 due 

to control strategies and future growth. The 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 Protection 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 reactions 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 reactions 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 important 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 based on the equations developed by Wesley 

(ENVIRON, 2005; Wesley, 1989). The dry deposition equation is modified to adjust 

for special properties of certain chemical species 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 processes (near-field deposition) not in the deposition model. 

A field study at a Colorado alpine tundra location showed 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 computational 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 interaction 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 recommends that modeling to support the 

annual PM2.5 NAAQS be applied at a 12 km horizontal 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 and 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 simulations. 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-grid treatment include any source near the 

Great Lakes with NOx emissions greater than 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 the fine grid appropriately captures the small scale physical and 

chemical processes. 

Probing Tools (Source Apportionment) 

Probing tools are valuable from a scientific and regulatory perspective for oneatmosphere modeling. Use of source apportionment 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 elevated point sources into particular regions 

rather than placement based on coordinates 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 example 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 assesses 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, including the standard 

OSAT option (ENVRION, 2005). 

When ozone is formed under VOC limited 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 anthropogenic NOx sources. APCA is designed to 

provide more control strategy relevant information and recognizes that there are 

source categories such as biogenics that 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 average 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 distributions 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 tracked for each receptor location. 

Table 2.4 Complete list of source regions tracked for ozone contribution 

Canada Illinois Chicago non-attainment (NA) Counties 

Northeast States (MANE-VU) Detroit NA Counties 

Central/Western States (CENRAP+ WRAP) 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 emissions 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 contribution 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 contributions from biogenic and anthropogenic sources 

are part of the standard CAMx output 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 the 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 Committee 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 contribution are listed in Table 2.5 and shown 

graphically in Figure 2.11. The contribution from the lateral 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+ WRAP) 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 sectors are tracked for contribution to particulate matter: 

onroad mobile, offroad mobile, area, electrical 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 useful 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 models. Model performance is 

typically evaluated on an operational basis 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 (NOX), 

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 assesses how appropriately the modeling system responds to 

emissions adjustments. Since the modeled attainment demonstration includes 

modeling current and future year emissions it is important to have confidence that 

the model will predict concentrations appropriately when emissions change (US EPA, 

2007). This type of evaluation includes modeling two different 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 estimated ozone for the summers of 2002 and 

2005 is useful for a diagnostic assessment because high quality emission inventories 

were developed for each year and a large NOX emissions reduction occurred between 

these years due in part to NOX SIP Call compliance. Modeling two full summer 

seasons provides an opportunity to make another diagnostic evaluation which 

assesses model performance for high ozone 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 are designed to support the development of 

regional control strategies for PM2.5 and Regional 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). Additionally, the model will be used to 

assess improvements in PM2.5 concentrations and visibility as a result of changes in 

emissions. These prominent end-uses of the modeling applications make 

comprehensive evaluations important. Clearly, reliance on model performance for 

PM2.5 total mass would be misleading since it is likely that the model and ambient 

data could estimate the same total mass but very different chemical composition. 

This scenario would compromise the development and interpretation of potential 

regulatory control strategies (Baker, 2004d). 

The species to be compared to monitor concentrations 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 estimates: 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 modeled 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 based on matching 

predictions and observations in time and space. 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 scatter 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 visibility 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 (US EPA, 2007). Since these modeling applications will 

support PM2.5/Haze rules, model performance 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 nitrate and positive and 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 output. These factors may also be used to 

reduce modeled estimates of organic material to organic carbon. 

Performance metrics used to describe model performance for PM2.5 species include 

mean bias, gross error, fractional 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 (μg/m3

). 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 for the PM2.5 NAAQS and Regional Haze rule 

require relative reduction factors to be applied to actual concentrations and not 

transformed concentrations. No minimum value 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 fractional 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 compare performance of the same specie if 

concentrations are very large in some seasons and very small in others. The 

fractional metrics are best when close 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 monitors in the 12 km modeling domain are 

included in the analysis. Model performance evaluation plots and metrics are based 

on matching predictions and observations in time and space. EPA has suggested 

several statistical metrics to describe 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-normal 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=observation; N=number of days; M=number of monitors 

These metrics have traditionally been calculated when the observation value exceeds 

a certain minimum value, often 60 ppb for 1-hour ozone evaluation (Hogrefe et al, 

2003). The MNBE and MNGE will be estimated 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 ambient concentrations 

(US EPA, 2007). The 20 and 40 ppb minimum thresholds are included in the 

evaluation to get a better idea about how well 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. The episode average metrics are estimated from the daily 

metrics. 

3.3 Deposition 

Wet deposition is measured at several monitoring networks and is also output by the 

photochemical model. The National Trends Network (NTN) and the Atmospheric 

Integrated Research Monitoring Network (AIRMoN) make up the National 

Atmospheric Deposition Program (NADP). NTN 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/km2

 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 km2

 ) * ( 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 mm3

 ) * precipitation in mm * ( 1 mm2 / 

0.000000000001 km2

) * ( 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 

Kirk Baker, LADCO 

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 recommended by the IMPROVE steering 

committee (IMPROVE, 2006). The new and old equations produce very similar 

estimates of light extinction in the upper Midwest. The new equation will be 

emphasized for the SIP modeling demonstration 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 scattering of sulfate and 

nitrate based on climatologically averaged relative humidity. 

 

βext = 2.2*fSRH*[small sulfate] + 2.4*fS(RH)*[small nitrate] + 4.8*fLRH*[large 

sulfate] + 5.1*fL(RH)*[large nitrate]+ 2.8*[small OCM] + 6.1*[large OCM] + 10*EC 

+ 1*SOIL + 0.6*CM + 1.7*fSS(RH)*SS + βrayleigh

Βext 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 

βrayleigh Light scattering due to Rayleigh 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]) * [Total 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 NO2 component to the light extinction equation is not 

included since it is not measured at Class I areas in the upper Midwest. The visibility 

equation is expressed as an extinction coefficient (βext) and is converted to deciviews 

using the equation below. 

Deciview = 10ln(βext/ βrayleigh) 

The reasonable progress test to determine the relationship between current and 

future year visibility is expressed in deciview 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[(βext)future / (βext)base] 

- or - 

Change in Deciview = Deciviewbase - Deciviewfuture

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 procedures 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 IMPROVE light extinction equation. 

2. The mean deciviews for the 20% days with 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 extinction coefficient for the days during the modeling 

period (2002) with the 20% best and 20% worst visibility will be calculated. 

4. The mean predicted extinction coefficient 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 particulate 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 identified 20% best and 20% worst days of monitored data. 

Light extinction is converted to deciviews 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 worst and best days (one from each of 

the 5 years) are averaged together for 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 the U.S. EPA modeling guidance document (US EPA, 2007). 

The major steps of this attainment test are outlined below: 

1. Chemically speciated IMPROVE and 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 components sulfate, nitrate, 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 factors for sulfate, nitrate, elemental 

carbon, organic carbon, and crustal material. 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 concentration is the average of the 4 

future year quarterly average PM 2.5 concentrations. 

9. Compare value to annual NAAQS standard of 15 ug/m3

. If value is ≤ 15 

ug/m3 then the test is passed. 

Organic carbon mass is estimated using a mass balance approach (EPA, 2006). The 

organic carbon spatial fields are only used to supply a minimum value for OCM when 

OCM estimated by mass balance is less than OC*1.4*0.7. A spatial field of the 

degree of sulfate neutralization is developed 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 Demonstrations 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 the 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 near 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 additional review to identify areas that might 

exceed the 8-hr ozone or annual PM2.5 NAAQS if monitors were present (US EPA, 

2007). This analysis uses interpolated spatial fields of ambient concentrations and 

photochemical model estimated concentrations 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 that 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 future year 8-hr ozone 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 “Guidance on the Use of Models 

and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, 

PM2.5, and Regional Haze“ (US 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 and 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 samples are used as the basis of the 

chemically speciated data used for seasonal spatial fields. 

2. Estimate the observed 98th percentile value for each year of the 5 year 

baseline period. Additionally, the next highest concentration in each quarter is 

identified. This results in data for each year and site which contains one 

quarter that equals the 98th percentile and 3 quarters which are less than or 

equal to the 98th 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 reduction factors are estimated at each 

monitor for each quarter. 

5. The component specific RRFs are multiplied by the observed values to 

estimate future year concentrations. 

6. The quarterly components are summed to estimate the quarterly future year 

98th percentile value. 

7. The 3 consecutive future year 98th percentiles are averaged together to 

estimate 3 different future year design values. The 3 future year design 

values are averaged to estimate a single 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 less 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 are less than 5 days in the RRF calculation 

then that quarter is not used for the estimation 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 the Midwest Regional Planning Organization and cooperating 

organizations have to opportunity to acquire a turn-key modeling system. This will 

include all the model inputs, scripts, and support documents to perform model 

simulations. States participate in an extensive sensitivity projects and preliminary 

strategy rounds which are designed in part 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 rather 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 distribution of media (i.e. 

hard drives or DLT tapes) via the mail system. 

Where very large datasets need to be transferred USB/firewire drives 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 problems, 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 technology 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/photo/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/photo/photochemical.html

Baker, K. Ozone Source Apportionment Results for Receptors in Non-Attainment Counties in 

the Great Lakes Region. 2007. AWMA Annual 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.; Field, 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 episodes in Kaohsiung, Taiwan, Atmospheric 

Environment, 37(13), 1811-1823. 

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. 

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

FLAG. 2000. Federal Land Manager’s Air Quality Related Values Workgroup (FLAG) Phase I 

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 description of the Fifth Generation Penn State/NCAR 

Mesoscale Model (MM5), NCAR Tech. Note, NCAR TN-398-STR, 138 pp., 1994. 
2002 Basecase Modeling Protocol: Technical Details 

Kirk Baker, LADCO 

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; carbon 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.; Lee, 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. 

Janssen, M.; Hua., C. Emissions Modeling System-95 User's Guide. 1998. 

http://www.ladco.org/emis/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 Performance 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. 

Kinnee, E., C. Geron, and T. Pierce (1997) United States land use inventory for estimating 

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. 

NASA. Goddard Space Flight Center. http://jwocky.gsfc.nasa.gov/ (accessed January 23, 

2006). 

National Weather Service. Climate Prediction Center. See 

http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/cdus/palmer_drought/wpdanote.

shtml (accessed October 1, 2005) 

Nenes A, Pandis SN, Pilinis C. 1998. ISORROPIA: A new thermodynamic equilibrium model for 

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. 
2002 Basecase Modeling Protocol: Technical Details 

Kirk Baker, LADCO 

Page 30 of 31 

Morris, R., Koo, B., Lau, S., McNalley, D., Tesche, T., Tonnesen, G., Wang, Z., Chien, C. Initial 

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) 

Influence of Population Density and Temporal Variations in Emissions on the Air Quality 

Benefits of NOx Emission Trading, Environmental Science & Technology, 36, 3465-3473. 

Pinder RW, Strader R, Davidson CI, Adams PJ. 2004. A temporally and spatially resolved 

ammonia emission inventory for dairy cows in the United States. Atmospheric Environment 

38, 3747-3756. 

Pinker, R.T. and I. Laszlo (1992) Modeling surface solar irradiance for satellite applications on 

a global scale. J. Appl. Meteor., 31, 194-211. 

Strader, R, Peckney, NJ, Pinder, RW, Adams, PJ, Goebes, M, Ayers, J, Davidson, CI. 2005. 

The CMU Ammonia Emission Inventory. Available at http://www.cmu.edu/ammonia

Tanaka, P. L., D. T. Allen, E. C. McDonald-Buller, S. Chang, Y. Kimura, C. B. Mullins, G. 

Yarwood, and J. D. Neece (2003) Development of a chlorine mechanism for use in the CAMx 

regional photochemical model, J. Geophys. Res., 108(D4), 4145. 

Timin, B., Jang, C., Dolwick, P., Possiel, N., Braverman, T., 2002. Operational Evaluation and 

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. 

Turpin, B.J., and Lim, H.-J., 2001: Contributions to PM2.5 mass concentrations: revisiting 

common assumptions for estimating organic mass. Aerosol. Sci. Technol., 35, 602-610. 

Wilkinson, J.; Loomis, C.; Emigh, R.; McNalley, D.; Tesche, T., 1994. Technical Formulation 

Document: SARMAP/LMOS Emissions Modeling System (EMS-95). Final Report prepared for 

Lake Michigan Air Directors Consortium (Des Plaines, IL) and Valley Air Pollution Study Agency 

(Sacramento, CA). 

U. S. Environmental Protection Agency, Guidance for Tracking Progress Under the Regional 

Haze Rule, EPA-454/B-03-004, Office of Air Quality Planning and Standards, Research Triangle 

Park, NC, 2003a. 

U. S. Environmental Protection Agency, Guidance for Estimating Natural Visibility Conditions 

Under the Regional Haze Program, EPA-454/B-03-005, Office of Air Quality Planning and 

Standards, Research Triangle Park, NC, 2003b. 

U.S. Environmental Protection Agency, User’s Guide for the AERMOD Meteorological 

Preprocessor (AERMET), EPA-454/B-03-002, Office of Air Quality Planning and Standards, 

Research Triangle Park, NC, 2004. 

U. S. Environmental Protection Agency, Developing Spatially Interpolated Surfaces and 

Estimating Uncertainty, EPA-454/R-04-004, Office of Air and Radiation, Office of Air Quality 

Planning and Standards, Research Triangle Park, NC, 2004 (2004b). 

U. S. Environmental Protection Agency. See 

http://www.epa.gov/ttn/chief/net/2002inventory.html (accessed March 2, 2006). 

U. S. Environmental Protection Agency. See http://www.epa.gov/ttn/chief/emch/biogenic/

(accessed August 9, 2006). 2006b. 
2002 Basecase Modeling Protocol: Technical Details 

Kirk Baker, LADCO 

Page 31 of 31 

U. S. Environmental Protection Agency, Guidance on the Use of Models and Other Analyses for 

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 2002
Gregorian/Julian Calendar for 2002 

Quarter 1 ? Quarter 3 ?

January February July August

Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun M

12345 12 123456 1 2 3 4 5 32 33 182 183 184 185 186 187 

6 7 8 9 10 11 12 3 4 5 6 7 8 9 7 8 9 10 11 12 13 4 6 7 8 9 10 11 12 34 35 36 37 38 39 40 188 189 190 191 192 193 194 216 

13 14 15 16 17 18 19 10 11 12 13 14 15 16 14 15 16 17 18 19 20 11 13 15 16 16 17 18 19 41 42 43 44 45 46 47 195 196 197 198 199 200 201 223 20 21 22 23 24 25 26 17 18 19 20 21 22 23 21 22 23 24 25 26 27 18 20 21 22 23 24 25 26 48 49 50 51 52 53 54 202 203 204 205 206 207 208 230 27 28 29 30 31 24 25 29 27 28 28 29 30 31 25 27 28 29 30 31 55 56 57 58 59 209 210 211 212 237 

Quarter 2 ? 

March April September OctoberSun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun M

12 123456 1234567 

60 61 91 92 93 94 95 96 244 245 246 247 248 249 250 

3 4 5 6 7 8 9 7 8 9 10 11 12 13 8 9 10 11 12 13 14 6 

62 63 64 65 66 67 68 97 98 99 100 101 102 103 251 252 253 254 255 256 257 279 10 11 12 13 14 15 16 14 15 16 17 18 19 20 15 16 17 18 19 20 21 13 69 70 71 72 73 74 75 104 105 106 107 108 109 110 258 259 260 261 262 263 264 286 17 18 19 20 21 22 23 21 22 23 24 25 26 27 22 23 24 25 26 27 28 20 76 77 78 79 80 81 82 111 112 113 114 115 116 117 265 266 267 268 269 270 271 293 24 25 26 27 28 29 30 28 29 30 30 31 27 

83 84 85 86 87 88 89 118 119 120 272 273 300 

3190

May June November Decemb

Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun M

1234 1 12 1

121 122 123 124 152 305 306 335 

5 6 7 8 9 10 11 2 3 4 5 6 7 8 3 4 5 6 7 8 9 8 

125 126 127 128 129 130 131 153 154 155 156 157 158 159 307 308 309 310 311 312 313 342 

12 13 14 15 16 17 18 9 10 11 12 13 14 15 10 11 12 13 14 15 16 15 

132 133 134 135 136 137 138 160 161 162 163 164 165 166 314 315 316 317 318 319 320 349 

19 20 21 22 23 24 25 16 17 18 19 20 21 22 17 18 19 20 21 22 23 22 

139 140 141 142 143 144 145 167 168 169 170 171 172 173 321 322 323 324 325 326 327 356 

26 27 28 29 30 31 23 24 25 26 27 28 29 24 25 26 27 28 29 30 29 

146 147 148 149 150 151 174 175 176 177 178 179 180 328 329 330 331 332 333 334 363 

30

181
Appendix G 

Minnesota(MRPO) Air Quality Model 

Performance Evaluation Plots
Figures 1 - 12 Fractional Bias and Error for 36km Grid at 20% Worst Days at BOWA and VOYA 

SO4 fractional bias from run 20% Worst Days at 

BOWA & VOYA (36km)

-200-150-100

-50

0

50

100150200

0 1 2 3 4 5 6 7 8 9 10

Average Concentration [ug/m3]

Fractional Bias [%] (+) Goal (-) Goal (+) Criteria (-) Criteria

FracBias

SO4 fractional error from run 20% Wor

BOWA & VOYA (36km)

0

50

100150200

0 1 2 3 4 5 6 7 8 9 

Average Concentration [ug/m3]

Fractional Error [%]

NO3 fractional bias from run 20% Worst Days at 

BOWA & VOYA (36km)

-200-150-100

-50

0

50

100150200

0 1 2 3 4 5 6 7 8 9 10

Average Concentration [ug/m3]

Fractional Bias [%] (+) Goal (-) Goal (+) Criteria (-) Criteria

FracBias

NO3 fractional error from run 20% Wor

BOWA & VOYA (36km)

0

50

100150200

0 1 2 3 4 5 6 7 8 9 

Average Concentration [ug/m3]

Fractional Error [%]
OC fractional bias from run 20% Worst Days at 

BOWA & VOYA (36km)

-200-150-100

-50

0

50

100150200

0 1 2 3 4 5 6 7 8 9 10

Average Concentration [ug/m3]

Fractional Bias [%] (+) Goal (-) Goal (+) Criteria (-) Criteria

FracBias

OC fractional error from run 20% Wors

BOWA & VOYA (36km)

0

50

100150200

0 1 2 3 4 5 6 7 8 9 

Average Concentration [ug/m3]

Fractional Error [%]

EC fractional bias from run 20% Worst Days at 

BOWA & VOYA (36km) 

-200-150-100

-50

0

50

100150200

0 1 2 3 4 5 6 7 8 9 10

Average Concentration [ug/m3]

Fractional Bias [%] (+) Goal (-) Goal (+) Criteria (-) Criteria

FracBias

EC fractional error from run 20% Wors

BOWA & VOYA (36km)

0

50

100150200

0 1 2 3 4 5 6 7 8 9 

Average Concentration [ug/m3]

Fractional Error [%]
CRUSTAL fractional bias from 20% Worst Days at BOWA & 

VOYA (36km)

-200-150-100

-50

0

50

100150200

0 1 2 3 4 5 6 7 8 9 10

Average Concentration [ug/m3]

Fractional Bias [%] (+) Goal (-) Goal (+) Criteria (-) Criteria

FracBias

CRUSTAL fractional error from 20% Worst 

& VOYA (36km)

0

50

100150200

0 1 2 3 4 5 6 7 8 9 

Average Concentration [ug/m3]

Fractional Error [%]

CM fractional bias from 20% Worst Days at BOWA & VOYA 

(36km)

-200-150-100

-50

0

50

100150200

0 1 2 3 4 5 6 7 8 9 10

Average Concentration [ug/m3]

Fractional Bias [%] (+) Goal (-) Goal (+) Criteria (-) Criteria

FracBias

CM fractional error from 20% Worst Days at B

(36km)

0

50

100150200

0 1 2 3 4 5 6 7 8 9 

Average Concentration [ug/m3]

Fractional Error [%]
Figure 13. Time Series 36km - Sulfate at Boundary Waters Canoe Area:

BOWAV: 412.572 895.199

0

1

2

3

4

5

Date

SO4 [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

2

4

6

8

10

12

14

Date

SO4 [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

2

4

6

8

10

12

14

Date

SO4 [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

2

4

6

8

10

Date

SO4 [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

Date

SO4 [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

2

4

6

8

10

12

14

Date

SO4 [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

2

4

6

8

10

12

14

Date

SO4 [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

2

4

6

8

10

Date

SO4 [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

Date

NO3 [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

1

2

3

4

5

Date

NO3 [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

0.2

0.4

0.6

0.8

1

Date

NO3 [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

2

4

6

8

10

Date

NO3 [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

Date

NO3 [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

1

2

3

4

5

Date

NO3 [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

0.2

0.4

0.6

0.8

1

Date

NO3 [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

2

4

6

8

10

Date

NO3 [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

Date

EC [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

0.2

0.4

0.6

0.8

1

Date

EC [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

0.2

0.4

0.6

0.8

1

Date

EC [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

0.2

0.4

0.6

0.8

1

Date

EC [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

Date

EC [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

0.2

0.4

0.6

0.8

1

Date

EC [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

0.2

0.4

0.6

0.8

1

Date

EC [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

0.2

0.4

0.6

0.8

1

Date

EC [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

Date

OC [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

2

4

6

8

10

Date

OC [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

5

10

15

20

Date

OC [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

1

2

3

4

5

Date

OC [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

Date

OC [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

2

4

6

8

10

Date

OC [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

5

10

15

20

Date

OC [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

1

2

3

4

5

Date

OC [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

Date

CRUSTAL [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

0.2

0.4

0.6

0.8

1

Date

CRUSTAL [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

1

2

3

4

5

Date

CRUSTAL [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

0.2

0.4

0.6

0.8

1

Date

CRUSTAL [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

Date

CRUSTAL [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

0.2

0.4

0.6

0.8

1

Date

CRUSTAL [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

1

2

3

4

5

Date

CRUSTAL [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

0.2

0.4

0.6

0.8

1

Date

CRUSTAL [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

Date

CM [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

2

4

6

8

10

Date

CM [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

5

10

15

20

Date

CM [ug/m3]

Minimum to Maximum Range Observed Predicted

BOWAV: 412.572 895.199

0

2

4

6

8

10

Date

CM [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

Date

CM [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

2

4

6

8

10

Date

CM [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

5

10

15

20

Date

CM [ug/m3]

Minimum to Maximum Range Observed Predicted

VOYA2: 310.340 942.037

0

2

4

6

8

10

Date

CM [ug/m3]

Minimum to Maximum Range Observed Predicted
Appendix H 

Particulate Source Appportionment Results for 

CENRAP and MRPO Cases
CENRAP BaseF Boundary Waters: Extinction Contribution by Specie and Sector for Each Region on the 20 Per

Figures H.1 and H.3: 2018 Figures H.2 and H.4: 2018 

0 2 4 6 8 10 12 14 16 18

BC

Canada

West

East

Wisconsin

Texas

South Dakota

Oklahoma

North Dakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

SO4 NO3 POA EC SOIL CM

Total Extinction: 51 Mm-1Total SO4+NO3: 43 Mm-1

-2.5 -2 -1.5 -1 -0.5 0 0

BC

Canada

West

East

Wisconsin

Texas

South Dakota

Oklahoma

North Dakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

SO4 NO3 POA EC 

0 2 4 6 8 10 12 14 16 18

BC

Canada

West

East

Wisconsin

Texas

South Dakota

Oklahoma

North Dakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

EGU+nonEGU onroad nonroad other biogenic BC

Total Extinction: 51 Mm-1

-2.5 -2 -1.5 -1 -0.5 0 0

BC

Canada

West

East

Wisconsin

Texas

South Dakota

Oklahoma

North Dakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

EGU+nonEGU onroad nonroad 
CENRAP BaseF Voyageurs: Extinction Contribution by Specie and Sector for Each Region on the 20 Percent W

Figures H.5 and H.7: 2018 Figures H.6 and H.8: 2018 

0 2 4 6 8 10 12 14 16 18

BC

Canada

West

East

Wisconsin

Texas

South Dakota

Oklahoma

North Dakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

SO4 NO3 POA EC SOIL CM

Total Extinction: 47 Mm-1Total SO4+NO3: 38 Mm-1

-2.5 -2 -1.5 -1 -0.5 0 0.

BC

Canada

West

East

Wisconsin

Texas

South Dakota

Oklahoma

North Dakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

SO4 NO3 POA EC 

0 2 4 6 8 10 12 14 16 18

BC

Canada

West

East

Wisconsin

Texas

South Dakota

Oklahoma

North Dakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

EGU+nonEGU onroad nonroad other biogenic BC

Total Extinction: 47 Mm-1

-2.5 -2 -1.5 -1 -0.5 0 0.5 

BC

Canada

West

East

Wisconsin

Texas

South Dakota

Oklahoma

North Dakota

Nebraska

Missouri

Minnesota

Michigan

Louisiana

Kansas

Iowa

Indiana

Illinois

Arkansas

Extinction (Mm-1)

EGU+nonEGU onroad nonroad o
MRPO BaseM Boundary Waters: Extinction Contribution by Specie and Sector for Each Region on the 20 Perc

Figures H.9 and H.11: 2018 Figures H.10 and H.12: 2018 

0 1 2 3 4 5 6 7 8 9 10

BC

Canada

WRAP+SCNRP

Southeast

Northeast

Wisconsin

Missouri

Minnesota

Michigan

Iowa

Indiana

Illinois

Extinction (Mm-1)

PSO4 PNO3 PNH4 POC PEC FPRM FCRS

Total Extinction: 41 Mm-1Total SO4+NO3: 34 Mm -1

-2.5 -2 -1.5 -1 -0.5 0 0.

BC

Canada

WRAP+SCNRP

Southeast

Northeast

Wisconsin

Missouri

Minnesota

Michigan

Iowa

Indiana

Illinois

Extinction (Mm-1)

PSO4 PNO3 PNH4 POC PE

0 1 2 3 4 5 6 7 8 9 10

BC

Canada

WRAP+SCNRP

Southeast

Northeast

Wisconsin

Missouri

Minnesota

Michigan

Iowa

Indiana

Illinois

Extinction (Mm-1)

EGU nonEGU onroad nonrd_mar other_area biog_nh3 BC

Total Extinction: 41 Mm-1

-2.5 -2 -1.5 -1 -0.5 0 0.

BC

Canada

WRAP+SCNRP

Southeast

Northeast

Wisconsin

Missouri

Minnesota

Michigan

Iowa

Indiana

Illinois

Extinction (Mm-1)

EGU nonEGU onroad nonrd_mar ot
MRPO BaseM Voyageurs: Extinction Contribution by Specie and Sector for Each Region on the 20 Percent Wo

Figures H.13 and H.15: 2018 Figures H.14 and H.16: 201

0 1 2 3 4 5 6 7 8 9 10

BC

Canada

WRAP+SCNRP

Northeast

Southeast

Wisconsin

Missouri

Minnesota

Michigan

Iowa

Indiana

Illinois

Extinction (Mm-1)

PSO4 PNO3 PNH4 POC PEC FPRM FCRS

Total Extinction: 38 Mm-1Total SO4+ NO3:

31 Mm-1

-2.5 -2 -1.5 -1 -0.5 0 0.

BC

Canada

WRAP+SCNRP

Southeast

Northeast

Wisconsin

Missouri

Minnesota

Michigan

Iowa

Indiana

Illinois

Extinction (Mm-1)

PSO4 PNO3 PNH4 POC PE

0 1 2 3 4 5 6 7 8 9 10

BC

Canada

WRAP+SCNRP

Southeast

Northeast

Wisconsin

Missouri

Minnesota

Michigan

Iowa

Indiana

Illinois

Extinction (Mm-1)

EGU nonEGU onroad nonrd_mar other_area biog_nh3 BC

Total Extinction: 38 Mm-1

-2.5 -2 -1.5 -1 -0.5 0 0.

BC

Canada

WRAP+SCNRP

Southeast

Northeast

Wisconsin

Missouri

Minnesota

Michigan

Iowa

Indiana

Illinois

Extinction (Mm-1)

EGU nonEGU onroad nonrd_mar ot